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Special Issue
Integrating Phylogenies into Community Ecology1
The organisms that live together in a community do so both because they are present in the larger
regional pool and because they have characteristics that permit their existence at that locality and
their coexistence with other species in the community. Neither a species’ regional presence nor its
characters can be fully understood without taking the species’ history into account. That history is
contingent on chance events and on deterministic interactions with other species in historical
communities. As this historical approach gains favor in ecology, and as our understanding of the tree
of life expands, ecologists and systematists are increasingly working together. However, this new
partnership often requires synthesizing ideas across disciplines. Our goal with this Special Issue is to
explore the practical interchange of concepts between evolutionary biology and community ecology,
highlighting studies that both use phylogenetic information and consider the community context of
individual organisms, and that represent a range of disciplines, from microbiology and parasitology
to ornithology.
Several common threads weave through the papers. The first concerns the importance and
definition of the local community itself. As one steps back and takes a historical and biogeographic
view of a species, averaging over variation in local community composition across its range, local
interspecific interactions appear less influential for the species’ evolution. Ricklefs, in considering the
causes of variation in the emergent property of community species richness, goes so far as to say that
‘‘ecologists [must] abandon the idea of the local community.’’ At the same time, however, there is
abundant evidence that inter-individual interactions do influence which particular taxa co-occur
(e.g., Webb et al.), alter their ranges, and under certain circumstances, lead to evolutionary adaptation that reduces negative interspecific interaction. Separating the effects of local processes on
regional patterns and regional processes on local patterns will always be hard. However, with large
and numerous samples we can come to understand variation in community structure over wide
areas. Lovette and Hochachka draw on the vast Breeding Bird Survey data set to examine both local
and regional composition in warbler communities, and Kembel and Hubbell examine phylogenetic
community structure of trees at varying scales within the 50-ha BCI Forest Dynamics Plot.
As well as drawing spatial boundaries around communities, we are forced to define their
taxonomic bounds. Cavender-Bares et al. demonstrate how increasing the ‘‘phylogenetic scale’’ of
communities influences our understanding of their phylogenetic structure. Brooks et al. and Weiblen
et al. address the complex question of the phylogenetic structure of compound communities, with
platyhelminth parasites of anurans, and insect herbivores of plants, respectively.
The second key thread dealt with by many authors is, ‘‘what exactly are ecological characters, and
how do they evolve?’’ For ecologists, it is obvious to ask how an organism’s niche has evolved and to
treat it as a character to be reconstructed on a phylogeny. However, systematists often argue that
because the habitats and realized niches that we can observe are influenced by interspecific interactions and community composition, they are not actually evolvable entities. Instead we should
decompose overall niches into directly heritable, morphological, and physiological characters. For
example, Agrawal and Fishbein show how defensive syndromes involve combinations of numerous
characters of plants, and Fine et al. show that defensive traits evolve in a trade-off with growth.
Different components of the overall niche may also be subject to different ecological interactions: an
organism might occupy a habitat that conforms to a niche on one environmental axis, while
competition within a habitat might lead to resource partitioning on another axis. Silvertown et al.
1
Reprints of this 166-page Special Issue are available for $20.00 each. Prepayment is required. Order
reprints from the Ecological Society of America, Attention: Reprint Department, 1707 H Street, N.W., Suite
400, Washington, DC 20006. Costs for this Special Issue were defrayed by NSF grant DEB-0408432 to C. O.
Webb and J. B. Losos. NCEAS funded a workshop on ‘‘Phylogenies and Community Ecology’’ in 2002,
which a number of authors in this Special Issue attended.
S1
and Ackerly et al. attempt to detect which characters diverged earlier during the course of plant
diversification. Knouft et al. use the new methods of GIS-based niche modeling to examine the
evolution of multidimensional niches in a clade of lizards, by associating specimens’ collection
locations with layers of environmental variation in space.
Inferring evolutionary process from the pattern of character evolution we observe requires models
of character change under known mechanisms. Unbounded continuous characters evolving under
Brownian drift (slow relative to speciation rate) will tend to show conservatism (closely related
species tend to be similar). Stabilizing selection will tend to increase that conservatism, while,
conversely, a reduced number of potential character states, a long time between speciation events,
and divergent selection will tend to increase convergence. Using various methods, the authors found
generally more conservatism in ecological characters (e.g., Ackerly et al., Silvertown et al.) than
convergence (e.g., Knouft et al.), with some studies finding no clear relationship of traits with
relatedness (e.g., Agrawal and Fishbein). Overall, the results are consistent with the action of
divergent selection in some systems, overlaid on a null expectation of some level of phylogenetic
conservatism in all systems.
The third major thread in these papers is closely linked to the second: ‘‘what is the phylogenetic
relatedness of co-occurring taxa in communities, and what does this tell us about community
assembly?’’ Authors used a number of methods to combine community lists with phylogenies to
answer this question. Cavender-Bares et al. and Lovette and Hochachka correlated taxon cooccurrence rates (across many samples) with phylogenetic distance (e.g., with Mantel tests). HornerDevine and Bohannan, Kembel and Hubbell, Weiblen et al., Silvertown et al., and Webb et al. tested
the observed distribution of intra-sample, inter-taxon phylogenetic distances against null models of
community assembly (so raising many of the perennial questions of community null models). Several
authors pointed out that methods using inter-taxon phylogenetic distance rather than ancestral state
reconstruction are less prone to bias introduced by the sampling of taxa that are very widely
distributed on the tree of life. Several authors found that taxa in their communities were more closely
related than expected, indicating a common role of habitat choice and evolutionarily conserved
characters (e.g., Horner-Devine and Bohannan, Weiblen et al.). Cavender-Bares et al. and Kembel
and Hubbell found cases where taxa were less closely related than expected. Taking the results of
trait evolution and community phylogenetic structure together, the importance of community
interactions does appear to be diminished, and a long-term regional view of taxa more justified.
However, Lovette and Hochachka found both conservatism of habitat specialization in warblers at
regional scales and evidence for competitive ‘‘repulsion’’ among close relatives at local sites. Because
different combinations of trait evolution pattern and ecological interaction (competition vs. habitat
choice) can give similar community phylogenetic structure, trait data, community data, and
phylogenies are all needed for a full understanding of the evolution and assembly of communities.
The discussion of the nature of communities and niche evolution is decades old; note that a
similarly titled Special Feature appeared in this journal ten years ago. However, the vast number of
species that have been sequenced, and for which phylogenies have been generated, means that
ecologists can now often infer the phylogenetic relationships of their taxa from publications and
databases without further systematics work. We hope that this Special Issue will inspire readers to
take advantage of these opportunities, to phrase their questions in a more evolutionary way, and as
Westoby anticipates, to participate in the new Natural History.
—CAMPBELL O. WEBB
Guest Editor
Harvard University
—JONATHAN B. LOSOS
Guest Editor
Washington University
—ANURAG A. AGRAWAL
Special Features Editor
Cornell University
Key words: character evolution; community structure; competition; habitat filtering; multidimensional
niche; null models.
Ó 2006 by the Ecological Society of America
Ecology, 87(7) Supplement, 2006, pp. S3–S13
Ó 2006 by the Ecological Society of America
EVOLUTIONARY DIVERSIFICATION AND THE ORIGIN
OF THE DIVERSITY–ENVIRONMENT RELATIONSHIP
ROBERT E. RICKLEFS1
Department of Biology, University of Missouri–St. Louis, 8001 Natural Bridge Road, St. Louis, Missouri 63121-4499 USA
Abstract. Global patterns in species richness have resisted explanation since they first
caught the attention of ecologists in the 1960s. The failure of ecology to fully integrate the
diversity issue into its core of accepted wisdom derives from an inappropriate concept of
community and the rejection of history and region as formative contexts for ecological
systems. Traditionally, ecologists have held that the pervasive relationship between species
richness and conditions of the physical environment reflects the influence of environment on
the ability of populations to coexist locally. However, many ecologists now recognize that this
relationship can also develop historically from the evolutionary diversification of lineages
within and between ecological zones. To assess the relative roles of local ecological constraint
vs. regional and historical unfolding of diversity–environment relationships, we must abandon
localized concepts of the community and adopt historical (particularly phylogenetic) and
geographic methods to evaluate the evolution of diversity within large regions and its influence
on diversity at local scales. This integrated perspective opens new research directions for
ecologists to explore the formation of species, adaptive diversification, and the adjustment of
ecological distributions of species on regional scales.
Key words: adaptation; community; diversity gradient; history; local determinism; phylogeny;
saturation; speciation; species packing; species richness.
which the relative contributions of local and regional
processes can be weighed, and sketch out some
implications of diversity patterns for our concepts of
ecological systems.
All biological systems have general properties, which
are governed by the pervasive influences of thermodynamics, evolutionary adaptation, and other ubiquitous
processes. They also have special properties, which
reflect the unique history and present-day circumstances
of every species and location. These special properties
form the foundation of systematics and biogeography.
Ecologists traditionally have been concerned with
general properties of systems arising from universal
processes with deterministic outcomes. Ideas about the
distributions of organisms, population regulation, population interactions, community succession, and ecosystem energetics have stemmed from such thinking
(Kingsland 1985, McIntosh 1985, Brown et al. 2004).
Global patterns of species richness occupy an uncertain
position between the special and the general. Early
treatments regarded such patterns as the special outcome of history and, therefore, outside the realm of
ecology (Wallace 1878, Matthew 1915, Willis 1922,
Fischer 1960). Tropical diversity was thought to reflect
the greater age, area, and climatic stability of equatorial,
compared to temperate and boreal, environments, which
allowed ample time and opportunity for the evolution of
diverse forms of life (Dobzhansky 1950, Pianka 1966).
INTRODUCTION
The number of species at all spatial scales varies
widely over the surface of the earth. Ecologists have
sought explanations for patterns of diversity in the
varied expression of ecological processes under different
local physical conditions of the environment. However,
patterns in diversity also can be explained plausibly as
the outcome of large-scale processes that control the
production and extinction of species within regions,
which in turn influence the number of species in local
assemblages. These alternatives are not mutually exclusive, and ecologists are beginning to join features of
both into a unified theory for the origin and maintenance of patterns of diversity. The task is more
difficult than it would seem, because it requires the
integration of dissimilar properties of biological systems:
deterministic properties that are molded by local
ecological processes, and the outcomes of evolutionary
and biogeographic processes that depend on unique
features of the history and physiography of regions
(Ricklefs 2004, 2005a). Because local determinism has
been so prominent in ecological thinking, I first consider
how local determinism has achieved its current status,
then briefly review some of the conflicting data that
weaken this central ecological paradigm, suggest ways in
Manuscript received 17 January 2005; revised 22 August
2005; accepted 7 September 2005. Corresponding Editor (ad
hoc): C. O. Webb. For reprints of this Special Issue, see
footnote 1, p. S1.
1
E-mail: [email protected]
THE RISE
OF
LOCAL DETERMINISM
With the development of community ecology as a mature discipline in the 1960s, ecologists began to regard
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ROBERT E. RICKLEFS
diversity as a general feature of biological systems
regulated locally by processes with deterministic outcomes (MacArthur 1965, 1972). Moreover, because local
population interactions achieve equilibrium within tens
of generations, they were thought to be fast enough to
override more ponderous regional and evolutionary
processes (Ricklefs 1989). These considerations led to a
theoretical construct wherein population interactions
limited membership in a community to species that are
ecologically compatible (MacArthur 1968, May 1975,
Case 1990, Morton et al. 1996). Accordingly, differences
in the number of species between communities reflected
the different outcomes of species interactions under
particular environmental conditions. This implied that
diversity would be correlated with variation in the
physical environment, which has been borne out, to
greater or lesser degree, by empirical studies (e.g.,
Mittelbach et al. 2001, Hawkins et al. 2003b).
Ironically, the rise of local determinism in community
ecology occurred almost simultaneously with two other
developments that contradicted its basic tenets. The first
was the acceptance by most ecologists of an ‘‘open’’
community structure (Gleason 1926), which is to say
that communities lack boundaries and that locally
coexisting species have more or less independent
distributions over spatial and ecological gradients within
regions (Whittaker 1967). The second was the colonization–extinction steady state in MacArthur and Wilson’s
equilibrium theory of island biogeography (MacArthur
and Wilson 1967), in which local (island) diversity is
responsive to an external driver (colonization).
Referring to the number of species on islands of
different size close to the source of colonization,
MacArthur and Wilson (1963) introduced the idea of
saturation. In their meaning of the word, saturation was
not a local property, but reflected the influence of the
size of an area on sampling properties and rates of
extinction of populations. Abbott and Grant (1976) also
used this concept of saturation to represent diversity
within a particular continental source area for colonists,
against which the diversity of islands could be measured.
These empirical concepts were transferred to local
communities through theoretical analyses of limiting
similarity among species (e.g., MacArthur and Levins
1967), which led to the idea of species packing and its
corollary that diversity (species coexistence) was constrained by the capacity of the local environment to
support interacting species. During the following decade, ecologists conducted comparative studies of communities largely in the context of species packing and the
partitioning of niche space, presuming that environment
constrained diversity (e.g., Pianka 1973, Cody 1974).
In fact, community theory sets no upper ‘‘saturated’’
limit to diversity in a particular environment. Referring
to a community of competing species, MacArthur (1970)
emphasized that ‘‘. . . in a constant environment there is
almost no limit to the number of species which can
improve the fit and hence be packed in . . . .’’ However,
Ecology Special Issue
the flexible filling of niche space was too complicated to
be handled by theory. Eventually, local community
ecology was insulated from such external drivers as
colonization and regional species production. Many
ecologists accepted local saturation, and they reconciled
differences between local and regional diversity by
differential turnover of species between habitats (beta
diversity; Cody 1975). Thus, while recognizing the
influence of large-scale processes on regional diversity,
most ecologists still regarded patterns of local diversity
as the consequence of ecological sorting of species
available within a region (MacArthur 1965, Diamond
1975, Zobel 1997, Weiher and Keddy 1999).
NEW CONCEPTS
OF
ECOLOGICAL COMMUNITIES
Recent excitement over Hubbell’s (2001) neutral
theory of communities suggests that many ecologists
were willing, after struggling for decades without
resolving the diversity problem, to entertain theories
that discount ecological interactions and niche specialization completely. Hubbell’s theory is historical and
geographical (geohistorical). Diversity depends solely on
the area of suitable habitat within a region (that is,
proportional to the number of individuals in the
metacommunity) and the rate of species production,
assuming that systems have had sufficient time to attain
equilibrium. Species extinction is purely stochastic,
depending only on the size of a population. The theory
includes ecology only in the sense that the total number
of individuals within the regional metacommunity is
fixed. Thus, all individuals compete on a homogeneous
ecological landscape, but on equal footing irrespective of
their identity. As species richness within the metacommunity increases, average population size decreases, as
does the time to extinction. This balances species
production to arrive at the equilibrium diversity.
Although a purely neutral theory does not withstand
scrutiny on a number of counts (Chave 2004), particularly because neutral drift is too slow to account for the
development of ecological patterns (Ricklefs 2006a), a
purely local, ecological theory of diversity also cannot
account for certain empirical patterns. These include
correlations between local and regional diversity (Cornell and Lawton 1992, Srivastava 1999), which contradict the idea of local saturation of species richness, and
incomplete convergence in diversity between areas of
similar environment in different regions with independently evolved biotas (e.g., Latham and Ricklefs 1993a,
Qian and Ricklefs 2000).
A geohistorical, evolutionary alternative to both local
determinism and neutral theory is the generation of
gradients of species richness through diversification
within ancestral ecological zones of origin combined
with occasional adaptive shifts associated with invasion
of new ecological zones (Farrell et al. 1992, Latham and
Ricklefs 1993b, Wiens and Donoghue 2004). The idea
presumes that species are best adapted to the conditions
in the ecological zone of origin of their lineage;
July 2006
THE DIVERSITY–ENVIRONMENTAL RELATIONSHIP
transitions to other ecological zones require evolutionary change. This evolutionary model establishes
gradients of diversity, producing greater species richness
in environments that are older, more widespread, or less
stressful. The idea originated with biogeographers (e.g.,
Darlington 1957, Axelrod 1966), but was slow to be
adopted by ecologists. Terborgh (1973) was the first to
articulate a comprehensive theory of community diversity that explicitly included historical and geographic
influences on local species richness. Applied, for example, to the species richness of forest trees, the latitudinal
gradient could be explained by the origination and
diversification of most flowering plant lineages in the
extensive tropics of the early Tertiary (Burnham and
Johnson 2004, Davis et al. 2005), followed by diversification of some lineages across adaptive barriers into
high-latitude frost zones (Latham and Ricklefs 1993b).
This process is shown diagrammatically in Fig. 1.
A second evolutionary alternative to local determination is that rates of evolutionary proliferation of
species (speciation minus extinction) are higher in
regions or ecological zones of high diversity (Farrell
and Mitter 1993, Jablonski 1993, Cardillo 1999). This
mechanism could be called a general process if certain
ecological conditions, such as temperature, promoted or
retarded proliferation (Rohde 1992, Allen et al. 2002), or
a special process if proliferation depended on unique
physiography or geographic configurations of regions or
if extinction depended on unique history. For example,
Qian and Ricklefs (2000) speculated that the high
diversity of plants in temperate eastern Asia could be
related, in part, to the complex physiography of the
region, which is both mountainous and features land
masses (China mainland, Korean peninsula, and Japan)
that have alternately been connected and separated as
sea levels have risen and fallen during the Tertiary.
Molecular phylogenetic analysis of plant clades distributed in eastern Asia and North America supports both
an older age (Asian taxa paraphyletic to North
American taxa) and more rapid diversification in eastern
Asia as underlying causes of diversity differences (Xiang
et al. 2004). Extinction also can play a role (Vermeij
1987, Jablonski 1991). For example, Latham and
Ricklefs (1993a) and Svenning (2003) concluded from
comparisons of fossil and modern taxa that the
impoverished tree flora of Europe resulted from differential extinction of species within predominantly tropical and subtropical groups caused by cooling climates
and glaciation during the late Tertiary.
THE INFLUENCE
OF
LOCAL
AND
REGIONAL PROCESSES
If one accepts the premise that local diversity
represents a balance between the constraining influence
of local population interactions and the augmenting
influence of regional species production, then one can
hope to estimate only the relative balance of these
factors in determining patterns of species richness.
Moreover, general and special processes often make
S5
FIG. 1. Evolutionary diversification of a clade within its
ecological zone of origin, with occasional adaptive shifts (stars)
to different ecological zones. The adaptive shifts might be
difficult and occur infrequently, potentially establishing a
gradient in contemporary diversity that favors the ecological
zone of origin. Modified from Ricklefs (2005a); see also Wiens
and Donoghue (2004).
the same predictions, which cannot then be used to
distinguish between them. For example, if species
richness were determined locally by ecological constraints on species packing, the phylogenetic history of
the species in regions having different diversity might
also be consistent with a diversification constraint.
A simple classification of the mechanisms that
influence diversity is presented in Table 1. These are
divided into local and regional/historical processes, and
the latter are further subdivided into the influences of
the ecological zone of origin, of environment on the rate
of diversification, and of diversity itself in promoting or
retarding further diversification.
Community saturation
Taken to its extreme, local determinism predicts that
communities are saturated with species and that species
richness is directly related to factors in the physical
environment that determine the number of species that
can coexist locally (Table 1: Panel A). Community
saturation implies an upper limit to the number of
species in a local assemblage, regardless of the diversity
within the surrounding region (Cody 1966). In principle,
this hypothesis can be confirmed in comparisons of
species assemblages in the same habitats between areas
having different regional diversity, i.e., the principle of
community convergence (e.g., Orians and Paine 1983,
Schluter and Ricklefs 1993), particularly when local
diversity levels off with increasing regional diversity.
Terborgh and Faaborg (1980) were the first to apply a
saturation test explicitly, with a positive result. Most
subsequent studies have failed to find evidence for a
hard upper limit to species number, but Srivastava
(1999) called attention to the shortcomings of such
analyses, including inconsistent definitions of local and
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ROBERT E. RICKLEFS
Ecology Special Issue
TABLE 1. A classification of influences on large-scale patterns in local diversity.
Mechanism
A) Local determinism
a) Limiting similarity and saturation
b) Diversity increases resistance
to invasion
B) Regional/historical processes
a) Ecological zone of origin
i) Age and area
ii) Adaptive diversification
b) Net rate of diversification
i) Physiography and history
promote speciation
ii) Climate change and catastrophes
cause extinction
iii) Diversity promotes or retards
diversification
Comments
Explanations based on local factors require explicit models of how the physical
environment influences coexistence.
This extreme form of local determinism predicts community convergence, which is
rejected in many comparative studies.
Ecological compression and release demonstrate the influence of population
interactions on local community membership.
These models predict diversity within larger regions, but also provide external
drivers for local species richness.
Phylogenetic analysis permits the reconstruction of ancestral ecological positions
within the environmental landscape.
Phylogenetic analysis provides an estimate of relative age; area effects are apparent
in contemporary biotas, but the relative roles of species production and
extinction should be distinguished.
Zones of origin can be identified by phylogenetic analysis, and these should
support the highest species richness regardless of the depth of the clade stem.
Differences in rates of diversification can be seen in lineage-through-time plots and
inferred, to some degree, from genetic distances between sister taxa.
Assuming allopatric speciation, regional analyses of geographic heterogeneity at
appropriate scales would be informative, as would studies of incipient species
formation (e.g., genetic differentiation of populations)
This can be judged primarily through analysis of fossil material and the geological
record, hence applications are limited to groups with good fossil records.
Diversity might be self-accelerating or self-limiting. The pattern is accessible
through analysis of lineages through time and analysis of biological and
physical aspects of niche structure in contemporary biotas.
regional scales and lack of independence between
regions. Ricklefs (2000) emphasized the balance between
local and regional factors, pointing out that increased
‘‘regional’’ diversity among West Indian island avifaunas was accommodated equally by increases in local
species richness and turnover of species between
habitats. Consistent with this ecological flexibility,
introduced species apparently have not caused extinctions on islands through competitive exclusion (Sax et
al. 2002; but see Gurevitch and Padilla (2004)), and
establishment of new colonists in the avifauna of the
Lesser Antilles appears to be independent of local
diversity (Table 1, [Panel B, row b(iii)]; Ricklefs and
Bermingham 2001). Over longer periods observable in
the marine fossil record of the Paleozoic Era, for
example, diversity does appear to have constrained
diversification (e.g., Foote 2000).
Local constraint
Local constraint predicts that local species richness
should be strongly correlated with environmental conditions, irrespective of the mechanisms regulating
coexistence. Under local determinism, diversity patterns
must ultimately be related to physical aspects of the
environment (e.g., Ricklefs 1977, Currie et al. 2004).
Many studies have demonstrated strong correlations
between local species richness, using various definitions
of the scale of ‘‘local’’ (Rahbek and Graves 2001, Willis
and Whittaker 2002), and climate (Currie 1991, O’Brien
1998, Hawkins et al. 2003b) or other variables, such as
habitat structure (Cody 1974) and productivity (Rosenzweig 1995, Mittelbach et al. 2001). Coefficients of
determination commonly fall in the range of 60–70%, or
more (Hawkins et al. 2003a). However, such correlations
might also be predicted by evolutionary theories of
species richness patterns, where diversity reflects either
environmental history combined with evolutionary
inertia, or the influence of environment on net species
proliferation.
Unique history and geography
Local constraints predict convergence of local community properties in similar environments regardless of
the evolutionary and geographic history of a region,
within which species richness might vary widely.
Conversely, differences in species richness in the same
habitat between regions suggest that special factors
influence local assemblages. Such differences have been
noted frequently in community comparisons. Among
the most conspicuous of these diversity anomalies in
plants, for example, are the difference in species richness
between mangrove communities in the Indo-West
Pacific and the Atlantic/Caribbean regions (Duke
1992, Ricklefs and Latham 1993), the depauperate tree
flora of Europe compared to eastern Asia (Latham and
Ricklefs 1993a), and the greater diversity within disjunct
genera of angiosperms in eastern Asia compared to
eastern North America (Qian and Ricklefs 1999). Such
diversity anomalies have been explained by differences
between regions in (a) the frequency of adaptive shifts of
new lineages to the stressful mangrove environment, (b)
extinction in the case of the European tree flora, and (c)
a combination of species production, species invasion
from the tropics, and late-Tertiary extinction in the case
July 2006
THE DIVERSITY–ENVIRONMENTAL RELATIONSHIP
of the disjunct temperate floras of Asia and North
America. Although special explanations for diversity
anomalies make good sense, they are suggested by the
data themselves rather than emerging from the use of
data to test contrasting predictions. Special explanations
are difficult to place in an experimental or hypothesistesting framework (Francis and Currie 1998), and the
anomalies themselves might reflect unmeasured differences in local environments between regions (Pianka
1975, Morton 1993, Ricklefs et al. 2004).
Can diversity anomalies be used other than to provide
anecdotal support for the idea of special influences on
diversity? Some special geographic circumstances are
repeated themes in many regions of the world. Among
terrestrial environments, for example, mountainous
areas coincide with diversity hotspots (e.g., Barthlott et
al. 1996, Orme et al. 2005), providing an opportunity to
test the consistency of some special relationships. Even
where these relationships are unique, geographic/historical models suggest mechanisms, such as enhancement of
allopatric speciation or restriction of evolutionary
diversification, which can be assessed in multiple,
independently evolving lineages. Thus, although empirical observations might not provide statistical support
for a particular historical scenario, one might obtain
statistically valid appraisals of mechanisms postulated to
be at work within a unique geohistorical framework.
Speciation rate
The role of speciation rate in creating patterns of
diversity (Table 1, [Panel B, row b]) has received
considerable attention, particularly with respect to
identifying the influence of ‘‘key innovations’’ on
diversification (Slowinski and Guyer 1993, Heard and
Hauser 1995, Bennett and Owens 2002). Among the
more successful of these tests have been the association
of rapid diversification in insects with the switch to
herbivorous diets and the association of rapid diversification in plants with the evolution of certain defenses
against herbivory (Farrell and Mitter 1994). The
physiography of particular regions, such as eastern
Asia, might promote species production. General
characteristics of the environment, such as the benign
nature of tropical climates, could also influence speciation rate (Dobzhansky 1950, Schemske 2002). Several
authors have suggested that thermal energy can accelerate speciation (e.g., Rohde 1992, Allen et al. 2002).
Attempts to test hypotheses on the rate of speciation
have focused on comparisons of sister (same-age) clades
in tropical and temperate regions (Farrell and Mitter
1993, Cardillo 1999, Cardillo et al. 2005, Ricklefs 2005b,
2006b), but these have been relatively inconclusive
because suitable data are difficult to assemble. Such
comparisons depend on well-supported phylogenies.
Because these are rapidly increasing in number, testing
the effect of various environmental or regional factors
on rates of species production will become more
commonplace and will show whether and how speci-
S7
ation rate and the ages of lineages influence diversity
within regions (e.g., Xiang et al. 2004).
Ecological zone of origin
Every lineage of organism has an ecological zone of
origin (Table 1 [Panel B, row a]), which, under some
circumstances, can be identified by tracing character
(i.e., ecological zone) evolution on a phylogenetic tree
(e.g., Schluter et al. 1997). This is similar to inferring the
geographic area of origin, which often can be ambiguous
(Ronquist 1997, Brown and Lomolino 1998:Chapter 12,
Sanmartin et al. 2001). When an entire clade is restricted
to a particular ecological zone, one can infer that it
originated within that zone, even though this parsimonious conclusion might be incorrect (Davis et al. 2005).
When a clade is distributed over several ecological
zones, identifying the origin in the absence of fossil
evidence depends on the paraphyletic distribution of
zones over the clade.
Diversification across adaptive barriers
Some of the strongest diversity gradients are clearly
associated with particular environmental stressors (Table 1 [Panel B, row a(ii)]), including high salt and
substrate anoxia in the case of mangrove vegetation and
freezing in the case of temperate vegetation. The roughly
20 present-day lineages of mangrove trees and shrubs
were independently derived from terrestrial lineages of
plants over a period of more than 60 million years
(Ricklefs and Latham 1993, Ellison et al. 1999). This
suggests that the evolutionary transition from terrestrial
to mangrove environments is difficult and that this
adaptive barrier is crossed infrequently. The same is
likely true of evolutionary transitions from mesic
tropical to temperate habitats (Terborgh 1973). Phylogenetic analysis reveals that the lineages of trees in north
temperate latitudes are mostly nested within clades
having deep tropical origins (Ricklefs 2005a). Indeed,
more than half the families of flowering plants are
restricted to tropical latitudes and evidently have not
been able to cross the adaptive barrier into temperate
latitudes (Ricklefs and Renner 1994). During most of
the early evolution and diversification of the flowering
plants, the planet’s environments were predominately
tropical (Behrensmeyer et al. 1992, Graham 1999).
Models of the establishment of regional patterns of
species richness, based on origins vs. rates of proliferation, can be distinguished by phylogenetic analysis
(compare Figs. 1 and 2). Whether either of these
evolutionary models were to shape gradients in local
diversity, in contrast to merely reflecting the gradient in
numbers of contemporary coexisting species permitted
by ecological interactions, is more difficult to resolve.
That is, all species have an evolutionary history,
including relationships to other lineages, and one can
reconstruct phylogenies for the members of a local
community regardless of the mechanisms that control
local diversity. A role for adaptive barriers can be
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ROBERT E. RICKLEFS
Ecology Special Issue
FIG. 2. When species proliferate more rapidly in a novel environment than in the ecological zone of origin, diversity need not
coincide with the ancestral environment. In this example, the zone of origin can be identified through phylogenetic analysis, because
species within the ancestral zone are paraphyletic with respect to species in the zone of highest diversity.
inferred from the partitioning of evolutionary lineages
between ecological zones (Webb 2000, Webb et al.
2002). If species can cross between these zones easily,
ecological zone itself would be labile, varying at a
shallow depth within a phylogeny, as in the case of oak
species across a moisture gradient in Florida (CavenderBares et al. 2004) or evolutionary transitions to chaparral environments in western North America (Ackerly
2004). However, if these adaptive shifts were difficult, as
in the case of entering the mangrove environment,
ecological zone would appear to be a conservative trait.
For example, the four genera and perhaps 17 species of
mangrove Rhizophoraceae represent a single lineage
that entered the mangrove environment early in the
evolution of the family and diversified there without any
lineages crossing back to the terrestrial ecological zone
(Schwarzbach and Ricklefs 2000).
PROSPECTS
Ecological regions of high diversity and ecological
regions of origin likely coincide, although rates of lineage proliferation also appear to differ between regions
and might produce a contradictory pattern (Fig. 2). In
general, ecological and evolutionary models for the
origin and maintenance of diversity gradients cannot be
distinguished by examining patterns of diversity. Because of this, ecologists must expand their inquiries to
include evolutionary hypotheses (Webb et al. 2002,
Ackerly 2004), and they must develop mechanistic,
testable, ecological models that connect diversity to the
physical environment (Currie et al. 2004).
Special explanations for species richness require that
special processes have strong enough influence to leave a
distinctive imprint on ecological pattern. Evolution and
adaptation are slow compared to local ecological
dynamics. However, when the interactions between
species play out within regions rather than local
communities, general processes can adjust ecological
and geographic distributions of species so that all
populations come into demographic balance within the
region (Ricklefs 2004). Ironically, this deterministic
outcome is independent of special aspects of regions
and lineages, including the number of species that have
been generated within a region. As more species are
added, ecological distributions are compressed, beta
diversity (species turnover with respect to ecology and
distance) increases, and equilibrium is maintained
(Terborgh 1973). This concept requires that ecologists
consider populations as regional ecological entities made
integral by the movements of individuals (Lennon et al.
1997, Ricklefs 2004, Case et al. 2005, Holt et al. 2005).
Evolutionary and geographic history—special components of ecological systems—can be revealed through
phylogenetic analysis, among other approaches, which
provides insight into the development of diversity
patterns and unique aspects of biological communities
in different regions. Speciation and extinction also
reflect the intimate connections between ecology, geography, and evolution. Although many areas of ecological inquiry are independent of evolution and history,
interactions between species played out within a large
regional context bring ecological and evolutionary
processes onto a continuum of scale that intimately
links local ecology, history, and geography. Within this
regional context, ecologists can characterize species
distributions, including sampling on a variety of scales
that comprise local individual movements, population
interactions, habitat heterogeneity, and regional pro-
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THE DIVERSITY–ENVIRONMENTAL RELATIONSHIP
cesses of population subdivision and species formation
(Rahbek 1997, Rahbek and Graves 2001). Islands,
particularly archipelagoes, will continue to be important
laboratories for studying mechanisms of species origination (Grant 1998, Mayr and Diamond 2001) and the
response of ecological distributions to the pressure of
regional diversity (Cox and Ricklefs 1977). Analyses of
the niche structure of assemblages, particularly by
defining the multivariate dimensionality of niche axes
from ecological, morphological, and life history data
(Ricklefs and Miles 1994), will assess the contribution of
niche space to diversification and the complementary
contribution of niche diversification to the evolutionary
development of species assemblages. Most of these
techniques are familiar to ecologists; new directions for
the future will arise from their application in a novel
framework.
Within this framework, I can make several specific
recommendations. Of course, this list reflects my own
perspective and would be broadened considerably by
others. First, it is important to understand that the scale
of analysis must be appropriate to the scale of the
process in both time and space. Thus, if one accepts the
premise that regional and historical processes can
influence the development of local assemblages of
species, then analyses of patterns of local diversity must
include regional distribution and both environmental
and evolutionary history. These might be accommodated, in part, in the following ways.
1) It is essential that ecologists abandon the idea of the
local community. Interactions occur between populations over large regions, and we should therefore
characterize the distributions of species on the geographic and ecological gradients over which they interact
(Case et al. 2005). What ecologists have traditionally
regarded as a ‘‘community’’ comprises the populations
that co-occur together at a particular point in these
regional continua of conditions. Whittaker (1967) and
others used this approach in gradient analysis of
vegetation. Sampling is conducted in plots along
environmental gradients, and species populations can
be characterized by their mean position, dispersion, and
modal abundance along these gradients. When gradients
are made comparable between regions, differences in the
overlap and partitioning of species along these gradients
can be compared as an approach to understanding how
population responses accommodate variation in region
diversity (e.g., Nekola and White 1999, Qian et al. 2005).
Although this will be straightforward in principle, it may
be difficult in practice because ecologists typically
consider too few large-scale gradients within regions to
separate hundreds or thousands of species. Ordination
and canonical correspondence analysis typically explain
a modest proportion of the variance in species distributions, leaving the rest to biological complexities in the
environment and chance, both of which have traditionally been ignored by ecologists (cf. Condit et al. 2002).
S9
2) The history of distribution of clades over environmental gradients involves adaptive changes and may
constrain the diversity of species. Accordingly, it is
important to examine patterns of species richness across
environmental gradients in a phylogenetic framework.
The general context for this is presented in Figs. 1 and 2.
Ecologists should focus on strong barriers (e.g., frost
tolerance, salinity gradients) as well as less imposing
temperature and moisture gradients within regions
(Ackerly 2004, Cavender-Bares et al. 2004). Such studies
should seek factors that influence multiple lineages and
that presumably impose the strongest constraints. These
analyses of habitat shifts should be based on wellsupported phylogenies, preferably in which branch
points can be dated by fossils or calibrations developed
for other lineages (e.g., Kishino et al. 2001, Renner and
Meyer 2001) to relate adaptive shifts to climate or
geographic change, and they must also use proper
estimates of ancestral environments (Schluter et al.
1997). Although this approach is primarily descriptive
rather than hypothesis testing, it can provide support for
the prediction from adaptive constraint that ancestral
lineages might change abruptly at a deep level across
strong environmental gradients (Westoby 2006).
3) Building upon the analysis of population distributions within regions and phylogenetic analysis across
environmental gradients, it seems reasonable to examine
local assemblages and distributions of species across
particularly important gradients of diversity. Among
these, one that has been important in development of
ideas about species richness patterns is the difference in
tree species diversity between tropical and temperate
environments. The transitions between these ends of an
environmental continuum for broad-leaved trees are
located in areas (eastern Mexico, southern China) for
which few floristic data exist, but where we might expect
the greatest information concerning diversity gradients.
Although altitudinal gradients do not have the same
seasonal dimension as latitudinal gradients, they are also
informative to the extent that species turn over in
parallel fashion.
4) Several studies have examined the depth of the
clades that comprise a community as a way to assess the
relationship between the ages of lineages and their
diversity. Simply put, if the net rate of diversification
(speciation minus extinction) is homogeneous, older
lineages will leave more descendants. That is, the
logarithm of the number of species is a function of the
net rate of diversification and time. Ricklefs and Schluter
(1993) showed that the clades that comprised avian
assemblages in a tropical locality in Panama were
roughly twice as old as the clades that made up an
assemblage in Illinois, based on field data in Karr (Karr
1971) and lineage ages from Sibley and Ahlquist (1990).
The implication is that time is an important factor in
both regional and local diversity. Hawkins (2005) has
provided a more comprehensive analysis that shows the
same pattern in the birds of Australia, and Ricklefs
S10
ROBERT E. RICKLEFS
(2005a) confirmed the conventional botanical wisdom
(Judd et al. 1994) that temperate trees belong to clades
that are nested within deeper clades of tropical lineages.
This topology is illustrated in Fig. 1, where the species in
the more diverse ecological region belong to clades that
have diversified within that region for longer periods.
5) A second facet of the historical dimension is the rate
of diversification of species. All other things being equal,
diversity increases as a function of the rate of
diversification and time (Ricklefs 2006b). Thus, faster
diversification leads to more species over a given period.
Although few studies have made this comparison to
date, and these have been inconclusive, the increasing
availability of phylogenetic reconstructions will change
this prospect rapidly. Appropriate comparisons include
sister clades that occur in different regions or environments (Farrell and Mitter 1993, Cardillo 1999), which,
by definition, are of equal age, or samples of clades from
different regions or environments for which age and
number of taxa are known (e.g., Cardillo et al. 2005,
Ricklefs 2005b). Another approach is the lineagethrough-time plot (Nee et al. 1992, Harvey et al. 1994),
whose slope can be used, with well-sampled phylogenetic
reconstructions of large clades, to estimate both
speciation and extinction rates (e.g., Ricklefs 2006a).
Another index to the rate of species proliferation is the
genetic divergence between sister species, which in
general decreases as the rate of speciation increases
and extinction decreases. In all aspects of analyses that
address species properties, results depend on the way in
which species are defined, which should be as uniform as
possible across comparisons.
6) Speciation as a process contributing to regional
diversity can be examined phylogeographically by searching for incipient speciation among geographically separated populations. Incipient species might be recognized
by large genetic differences between populations (Avise
2000), but, in any event, these can be compared easily
among regions in terms of the number and geographic
distribution of populations or subpopulations at a
particular phylogenetic depth (e.g., Brumfield and Capparella 1996, Bates et al. 1999, Weir and Schluter 2004).
This type of analysis could identify regions of rapid
population differentiation that might lead to high rates of
species production. Diversity is built up locally only when
geographically isolated, evolutionarily independent populations achieve secondary sympatry. Thus, comparison
of sister taxa in allopatry and sympatry (assuming an
allopatric model of species formation), would permit an
analysis of the time required for the local accumulation of
species and whether the ecological differentiation that
permits coexistence evolves in allopatry, or primarily by
divergent selection after the initiation of secondary
sympatry. Island groups, with their discrete geographic
organization, provide ideal opportunities for exploring
these issues (Grant 1998, Ricklefs and Bermingham
2001).
Ecology Special Issue
7) Finally, extinction can play an important role in the
establishment of patterns of diversity. Where an
adequate fossil record exists, one has a direct estimate
of diversity, at some resolution, through time. The
extinction of many clades of animals at the Cretaceous–
Tertiary boundary, as well as the disappearance of many
plants from Europe during the late-Tertiary period of
climate cooling (Sauer 1988, Latham and Ricklefs
1993b, Svenning 2003), provide instructive examples of
the importance of this factor. A large literature in
paleontology addresses rate of extinction and its
relationship to diversification, environmental changes,
catastrophic events, and changes in the configurations of
landmasses and ocean basins (Jablonski 1989, 1991,
1993, Vermeij 1991, Jackson et al. 1993). Unfortunately,
the fossil record is rarely resolved to the taxonomic level
of species and hardly exists for many groups. This
should not prevent those of us interested in living forms
from examining what is known of the fossil history of a
group. At some level of resolution, this will provide at
least an envelope around possible historical scenarios.
Where fossil data are not available, information can be
extracted from models of lineage diversification (e.g.,
Magallón and Sanderson 2001, Ricklefs 2006a) and
extinction inferred, for example, from gaps in the
distribution of species through archipelagoes (Ricklefs
and Cox 1972, Ricklefs and Bermingham 1999).
The better we develop the space and time contexts of
ecological systems, the more we shall appreciate the
variety of factors that have influenced their composition
and diversity. We should view populations as evolved
entities, with unique histories and adaptations, that are
distributed geographically according to their tolerance
of ecological conditions and interactions with other
populations, and, within the limitations of dispersal,
over regional landscapes. Inevitable consequences of
these processes are the co-occurrence of a unique
assemblage of species at any particular point and
geographic patterns in the number of species observed
over many such points over the surface of the earth.
ACKNOWLEDGMENTS
I am grateful to Cam Webb for discussion, encouragement,
and helpful comments on the manuscript. The National
Geographic Society, Smithsonian Institution, National Science
Foundation, and the University of Missouri Board of Curators
have supported research related to this paper.
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Ecology, 87(7) Supplement, 2006, pp. S14–S28
Ó 2006 by the Ecological Society of America
SIMULTANEOUS EFFECTS OF PHYLOGENETIC NICHE CONSERVATISM
AND COMPETITION ON AVIAN COMMUNITY STRUCTURE
IRBY J. LOVETTE1
WESLEY M. HOCHACHKA
AND
Laboratory of Ornithology, Cornell University, 159 Sapsucker Woods Road, Ithaca, New York 14850 USA
Abstract. We currently have only a partial understanding of how phylogenetic relationships relate to patterns of community structure, in part because, for most groups of organisms,
we do not know the extent to which ecological similarity results from common ancestry.
Associations between phylogenetic relatedness and local community structure are particularly
interesting for groups in which many species that span a gradient of phylogenetic divergence
occur in potential sympatry. We explored the relationship between evolutionary relatedness
and current species co-occurrence among the North American wood-warblers (Aves:
Parulidae), a group of songbirds known both for its species diversity and for exhibiting
high levels of sympatry at breeding sites. Species co-occurrences were derived from North
American Breeding Bird Survey transects comprising 160 000 census points distributed across
North America. The nested point-within-transect structure of this survey provides an unusual
opportunity to remove larger-scale geographical effects on local community composition and
thereby consider patterns of co-occurrence only among regionally sympatric pairs of species.
We indexed evolutionary relatedness among all pairs of taxa by genetic distances based on
long mitochondrial DNA protein-coding sequences. Most regionally sympatric taxon pairs
rarely co-occur at local sites, and the most closely related never exhibit high local cooccurrences, as predicted if past or present competitive effects are strongest for these recently
separated lineages. Quantile regression shows that, for a subset of taxa, local co-occurrence
does increase with time since common ancestry, and that this apparent relaxation of
competitive exclusion is strongest for distantly related species that have differentiated in
fundamental ecological and behavioral traits, such as terrestrial vs. arboreal foraging.
Comparisons against a null model of species co-occurrence further demonstrate that these
patterns occur against a background of phylogenetic niche conservatism: across all
phylogenetic distances, sympatric species co-occurred at higher rates than expected by
chance, a pattern that might stem from a tendency by these species to show conservatism in
their selection of similar general habitat types. Considered in concert, these analyses suggest
the simultaneous mediation of local community structure by the ecological similarity of closely
related species and by trait divergence among a subset of more distant lineages.
Key words: community structure; competition; conservation; niche phylogeny; Parulidae; phylogenetic;
wood-warbler.
INTRODUCTION
The connection between phylogenetic similarity and
community composition is an important but largely
unresolved issue in evolutionary ecology. Because closely
related species will usually share many ecological traits
via common ancestry, evolutionary relationships are
likely to be associated with patterns of community
assembly (Darwin 1859, Lack 1971, Ricklefs and Schluter
1993), but these phylogenetic effects might influence
species coexistence in several ways. The general observation that closely related organisms tend to occupy similar
habitats and often use similar environmental resources
long predates the founding of ecology as a science, and
Manuscript received 28 January 2005; revised 6 September
2005; accepted 7 September 2005; final version received 5
October 2005. Corresponding Editor (ad hoc): J. B. Losos. For
reprints of this Special Issue, see footnote 1, p. S1.
1
E-mail: [email protected]
the extent to which this ecological stasis results from the
inheritance of niche-related traits from a common
ancestor is termed ‘‘phylogenetic niche conservation’’
(e.g., Wiens and Graham 2005). Research on phylogenetic niche conservatism has been recently invigorated by
the growing availability of robust phylogenetic hypotheses based on data independent from ecological traits,
but few generalizable trends are apparent across
studies. The present literature suggests that phylogenetic niche conservatism is detectable in some but not
all communities of organisms. Furthermore, its magnitude differs depending on the ecological traits under
comparison and additional factors, such as the degree
of ecological interaction among related species (Losos
1996, McPeek and Miller 1996, Peterson et al. 1999,
Prinzing et al. 2001, Webb et al. 2002, Ackerly 2003,
Losos et al. 2003, Anderson et al. 2004, Cavender-Bares
et al. 2004, 2006, Wiens and Graham 2005, Kembel and
Hubbel 2006, Knouft et al. 2006, Weiblen et al. 2006).
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WARBLER PHYLOGENY AND COMMUNITY STRUCTURE
Competition among related species is likely one of the
principal forces causing the erosion of phylogenetic
niche conservation: for species that interact ecologically,
the initial ecological similarity that results from common
ancestry will likely increase competition, potentially
leading over time either to competitive exclusion or
ecological differentiation (Webb et al. 2002). This
process is most evident in adaptive radiations, in which
groups of sympatric species have diversified extensively
with concomitant differentiation in traits related to their
use of ecological resources (Losos and Miles 2002). In
such situations, divergent selection that results from
competition could overwhelm any underlying retention
of niche conservatism, as found by Losos et al. (2003)
for a highly diverse community of Cuban Anolis lizards,
within which phylogenetic relationships explained only a
small amount of the among-species ecological variation.
This is perhaps an extreme example of a general trend:
most groups of closely related and ecologically interacting species will share some traits by common ancestry
and differ in other traits, owing to divergent selection
and other processes of evolutionary differentiation.
An interesting intermediate situation involves groups
in which the species composition of local communities
varies substantially in time and space, as these taxa are
individually involved in many potential interactions. In
the absence of more stable associations between
particular species that result in tight coevolutionary
responses, species in highly variable communities might
be more likely to assort themselves along relatively few
axes of dissimilarity and therefore retain a larger
signature of phylogenetic niche conservation. In addition, groups of organisms with high diversity, geographic distributions that have been labile through
evolutionary time, and high individual dispersal are
particularly appropriate models for exploring how
phylogenetic relationships relate to the balance between
niche conservation and ecological divergence, because
these attributes will diminish the degree of biogeographic determinism in community composition by
bringing together many pairs of taxa that would
otherwise be allopatric. The diverse North American
Parulidae fit these criteria, as nearly all wood-warbler
species have persisted as independent lineages for at least
several million years (see Plate 1; Lovette and Bermingham 1999, 2002). During their tenures in North
America, the continental landscape has been transformed many times by glacial and climate cycles and
consequent broad-scale shifts in habitat distributions
(Graham 1999, Klicka and Zink 1999, Johnson and
Cicero 2004, Weir and Schluter 2004, Lovette 2005).
Nearly all of the approximately 6.0 3 108 individual
North American wood-warblers (Rich et al. 2004) also
migrate annually, and many studies of marked individuals (summarized in Poole and Gill [2004]) have shown
that natal philopatry is low, at least at the fine local scale
of intensively monitored study plots. Resulting from this
natal dispersal and movements of adults among sites in
S15
different years (Holmes et al. 1996, Cilimburg et al.
2002), wood-warblers redistribute themselves across
local habitats annually. This combination of large
population sizes, repeated historical displacement of
geographic ranges, and high individual dispersal means
that most wood-warbler individuals breed at locations
where other wood-warbler species are present.
As documented by Robert MacArthur in his classic
study of five sympatric ‘‘spruce woods’ warblers’’
(MacArthur 1958), at the local scale co-occurring
songbird species show differentiation in their spatial
foraging niches and other traits (e.g., Holmes et al. 1979,
Morse 1989, Suhonen et al. 1994). In a MacArthurian
framework, behavioral and ecological differentiation
reduce interspecific competition and permit related
species to co-occur. However, whether local niche
differentiation explains broad spatial patterns of species
co-occurrence is controversial because the species
composition of wood-warbler communities varies across
sites and years (Wiens 1989).
A principal reason why the effect of evolutionary
relatedness on community structure remains largely
unexplored is that some fundamental tests of the
relationship require information from different spatial
scales (Anderson et al. 2004, Cavender-Bares et al.
2006): the competitive ecological interactions that can
drive local patterns of community structure occur at a
fine local scale best studied at points or in small study
plots, yet these interactions must simultaneously be
integrated across the much broader geographic distributions of potentially co-occurring taxa. Most previous
studies of phylogenetic patterns in community structure
have concentrated on interactions at one of these scales
(e.g., Webb 2000, Silvertown et al. 2001, Losos et al.
2003, Gillespie 2004, Kembel and Hubbell 2006);
however, unique avian censuses organized by the North
American Breeding Bird Survey (BBS) program (Sauer
et al. 2003) allow us to synthesize complementary
information at both levels. Distinguishing between these
alternatives requires separating the biogeographic or
other historical factors that constrain the pool of
candidate species from the ecological factors that cause
competitive exclusion or facilitate co-occurrence
(Ricklefs 1987, 2006). Underlying historical factors
unrelated to direct competition include the fact that
lineages that have speciated in allopatry might remain
separated by the same physiographic features that
caused their initial isolation and hence never come into
ecological contact. Even pairs of species that do occur in
sympatry will usually have geographic distributions that
do not perfectly overlap. Current ecological interactions
are thus limited to a part of each species’ overall range.
The fact that each species has a unique geographic
distribution that varies in overlap with those of other
species is a general challenge in comparing ecological cooccurrences across many different pairs of species, as an
absence of species’ overlap at a given point could result
from being outside one species’ geographic range, or
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IRBY J. LOVETTE AND WESLEY M. HOCHACHKA
Ecology Special Issue
PLATE 1. The Prairie Warbler (Dendroica discolor) is one of 42 common species of North American wood-warblers that
frequently breed in sympatry with closely related species. Photo credit: Marie P. Read.
more interestingly from ecological processes, such as
differential habitat selection or competitive exclusion
(Stone et al. 1996, Ackerly et al. 2006, Silvertown et al.
2006). The spatial structure of the BBS (i.e., many
census points replicated along short transects) is
particularly useful for our purposes because it allows
us to remove the confounding effects of these larger
scale differences in total geographic range from our
measures of local co-occurrence.
Here, we use BBS census information in combination
with DNA-based genetic distances to assess whether
phylogenetic relatedness among regionally sympatric
wood-warblers is positively or negatively correlated with
co-occurrence at the same points in space, the birds’
local breeding sites. A positive relationship (i.e., related
species co-occurring at higher than expected rates)
would suggest that local community composition is
mediated in part by phylogenetic niche conservatism of
habitat or habitat-linked aspects of niches, whereas a
negative relationship would suggest that past or present
competition among closely related species limits their
local ecological co-occurrence. In further analyses, we
explore whether these patterns are influenced by a
fundamental attribute of warbler behavior that leads to
strong spatial niche segregation, the species-specific
tendency to forage terrestrially or arboreally. We also
compare patterns of co-occurrence at the local spatial
scale defined by census points, with the regional-scale
patterns defined by entire census transects. Unlike most
previous studies of phylogenetic effects on community
structure, the structure of our data sets requires that we
compare matrices of genetic and ecological distances;
our comparisons therefore do not require ancestral state
reconstructions and thereby minimize the importance of
the underlying assumption that we have identified the
correct phylogenetic tree topology. Instead, we use a
null-model approach to identify and explore associations between phylogenetic relatedness and overlap in
species’ use of local breeding sites.
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WARBLER PHYLOGENY AND COMMUNITY STRUCTURE
S17
FIG. 1. Locations of Breeding Bird Survey (BBS) transects. Colors indicate the total number of wood-warbler species on each
transect. Inset histograms depict the distributions of species richness on the .4000 transects and at the .162 000 census points
within transects.
METHODS
Breeding bird survey co-occurrences
Breeding Bird Survey (BBS) censuses are run during
the breeding season by experienced volunteers who
conduct standardized 3-minute counts of all birds heard
or seen at 50 equally spaced locations along a 39.4-km
(25-mile) transect (Sauer et al. 2003). Most transects are
recensused annually, and data spanning 1997–2003 are
available for the species detected at each census point, as
well as the summed observations for each transect. The
resulting 162 800 census points (Fig. 1) provide a robust
representation of all but the most geographically
restricted or peripheral North American wood-warblers.
Here we define the wood-warblers detected at a single
census point as co-occurring in local sympatry, as these
individuals are certainly within auditory detection
distance of one another and are likely to occupy at
least partially overlapping territories. To derive a
measure of local sympatry, we considered only BBS
transects on which both members of each pair of species
were detected, as this physical proximity indicates that
both species are members of the pool of taxa from which
we drew the local community at each census point. For
each point observation of species A, we determined the
probability that wood-warbler species B was detected at
that same census point, generating an index of dissimilarity in co-occurrence that spans 0 (complete cooccurrence) to 1 (no co-occurrence). To account for
differences in prevalence among species, the matrix of
co-occurrences also included the reciprocal calculation
for each species pair (i.e., probability of detecting species
A at points where species B was found). These values
provide a continent-wide assessment of species cooccurrences measured only at the fine spatial scale most
relevant to current ecological interactions among
S18
IRBY J. LOVETTE AND WESLEY M. HOCHACHKA
species. Scaling these values such that higher numbers
indicate lower co-occurrence facilitates comparisons
with the corresponding pairwise genetic distances, in
which higher values indicate more ancient common
ancestry.
We have contrasted these patterns of local cooccurrence with patterns of regional co-occurrence, in
order to determine whether patterns found in local cooccurrence are the result of phenomena that were not
happening at larger scales. Regional co-occurrence was
defined as existing when both members of a species pair
were reported at least once on an individual 50-point
transect in a given year, regardless of whether the two
species were reported at the same point. Taken across all
BBS transects, regional co-occurrence was given as a
value ranging from 0 (species B is always reported on a
transect that also reported species A) to 1 (species B is
never reported on a transect that has species A).
We base both local and regional co-occurrence indices
on reported detections of warbler species, and each
detection is a combined result of the presence of a bird
species at a site and the human detection of the individual
bird(s). Because the detection data result from both a
biological process and a methodological filter (detection
by observers), it is important to know whether the
patterns found reflect the biological process and not a
methodological artifact. To our knowledge, there has
been no comprehensive attempt to estimate the detection
probabilities of all of the warbler species examined in this
study. While we cannot calculate these detection
probabilities in a fully rigorous manner (e.g., MacKenzie
et al. 2002) with the data at hand, we can calculate indices
of detection probability that allow us to explore some
potential biases. For each species of wood-warbler, we
randomly selected a single point on each BBS transect
that had reported the species on at least two points in at
least one year; the actual median number of points at
which a species was reported ranging from 2 (for one
species only) to 11 across all species. The transect had to
be censused for at least five years, and transects censused
for longer had one or two years of data randomly
discarded to leave exactly five years of available data.
Using a standardized number of years’ data avoided
inducing variation in the detection index due to differences among species in the number of years that transects
were censused. The randomly selected census point
within the transect had to have reported the target
species in at least two separate years. Our rationale is that
we are purposefully selecting transects in regions in
which a given species was likely to present, as well as
specific census points at which habitat was suitable and
the species potentially present every year. The detection
index for each species was the proportion of point-years,
summed across all selected points, on which that species
was detected. The values of the detection indices used
here are the median values from 1000 iterations of the
process of randomly selecting a single census point per
transect. In calculating these indices of detection, we
Ecology Special Issue
assume that among-year detection probability contains
information about within-year detection probability, the
latter being the detection probability of interest.
Values of the detection index spanned 0.43–0.62 for
different species, with a median of 0.52 and a mean value
of 0.53. Thus, values of the index were roughly
symmetrically distributed, and most species detection
rates were at intermediate values: the interquartile range
spanned 0.50–0.56. While our index of detection
probability is biased high relative to true detection
probability, due to the use of data for which each species
had a minimum probability of 0.4 of being detected at
each individual site, this bias should be roughly equal
for all species.
Spatial foraging niche characters
Co-occurring songbirds usually vary in traits that
include foraging height, foraging substrate, and nest
location (e.g., MacArthur 1958, Lack 1971, Robinson
and Holmes 1982, Morse 1989, Richman and Price
1992, Martin 1998), suggesting that within-site niche
partitioning is pervasive in these communities. There is
also strong evidence that some closely related woodwarblers compete antagonistically in sympatry (Martin
and Martin 2001). MacArthur’s 1958 paper spawned a
large number of subsequent studies of wood-warbler
niche segregation in traits such as foraging behavior,
habitat strata, nest locations, and prey types, but
methodological differences among studies in different
local communities make it difficult to integrate these
measures into a common index of niche differentiation.
Instead, we coded a fundamental and unambiguously
categorizing attribute of the foraging niche of woodwarblers, designating each species as either terrestrial
(mean foraging height ,1 m; 10 species) or arboreal
(mean height .1 m; 33 species) based on quantitative
foraging studies conducted during the breeding season
(Poole and Gill 2004). Each pair of taxa could therefore
comprise species that forage primarily in similar
(terrestrial–terrestrial and arboreal–arboreal pairs) or
separated (terrestrial–arboreal) strata. This categorization allowed us to determine whether species pairs that
are predicted to interact more intensively (because they
occur in the same stratum; MacArthur 1958, Morse
1989:293, Wiens 1989) show a different association
between phylogenetic relationship and co-occurrence
than do species pairs with a higher degree of spatial
separation.
Taxon sampling
We included 43 Parulidae species that breed within the
BBS region in the continental United States and Canada,
excluding only six rare or geographically peripheral
species that were recorded on few (10) BBS transects;
all included species were recorded on 41 transects.
Excluded species were Colima Warbler (Vermivora
crissalis), Tropical Parula (Parula pitiayumi), Goldencheeked Warbler (Dendroica chrysoparia), Kirtland’s
July 2006
WARBLER PHYLOGENY AND COMMUNITY STRUCTURE
Warbler (D. kirtlandii), Red-faced Warbler (Cardellina
rubrifrons), and Painted Redstart (Myioborus pictus). We
excluded two additional species, the Yellow-breasted
Chat (Icteria virens) and the Olive Warbler (Peucedramus taeniatus), because, although these morphologically
aberrant lineages have traditionally been placed in the
Parulidae, more recent phylogenetic analyses (Sibley and
Ahlquist 1990, Klicka et al. 2000, Lovette and Bermingham 2002) have shown unambiguously that they fall
outside the Parulidae and that they are more closely
related to species in other families.
Genetic distances
As indices of phylogenetic similarity, we generated
two matrices of genetic distances among the 43 woodwarbler species based on the complete DNA sequences
of the mitochondrially encoded cytochrome oxidase I,
cytochrome oxidase II, NADH dehydrogenase II,
ATPase 6, and ATPase 8 genes (4116 nucleotides/taxon;
GenBank accession numbers AY650182–AY650224).
Noncoding spacer regions and tRNA sequences situated
between these coding genes were sequenced, but they
were excluded from distance calculations owing to their
different underlying pattern of molecular evolution.
These sequences are substantially longer than the
mitochondrial alignments typically generated for species-level avian phylogenetics; by basing our distance
metric on this robust mtDNA data set, we minimize the
stochastic error in our estimates of pairwise distances.
Laboratory methods used to generate these sequences
have been described elsewhere (e.g., Lovette 2004).
We calculated the first matrix of pairwise distances
using the maximum likelihood (ML) method implemented in PAUP*4.0 (Swofford 2002) under the general
time reversal plus gamma plus invariant sites (GTR-gþI)
model. Because these ML distances could be biased if
mitochondrial divergence is not constant across lineages,
as would be the case if a subset of the taxa included here
had a higher or lower mutation rate than the remaining
taxa, we also calculated pairwise distances using the
summed branch lengths connecting each pair of termini
in an ultrametric tree. We derived this ultrametric tree
from an analysis in which the Bayesian topology
described below was imported into PAUP* as a
constraint tree, the heuristic search algorithm was set
to produce a clocklike (ultrametric) topology, and a
maximum-likelihood analysis was conducted using
mean GTR-gþI parameters derived from the Bayesian
Markov chain Monte Carlo (MCMC) analysis. Hereafter, we use the term ‘‘ML distances’’ to refer to the
nonultrametric distance matrix, and ‘‘ultrametric distances’’ to refer to the ML distance matrix derived from
the ultrametric branch lengths in the topology with an
enforced molecular clock. For all statistical analyses
involving genetic distance matrices, we report the results
from the ML distances followed by the ultrametric
distances (hence these summaries each have two
successive P values, etc.). As the results from the two
S19
alternative distance metrics were highly congruent (see
Results and Discussion), the figures printed here illustrate
only the ML distance-based comparisons; the Appendix
depicts the alternative figures based on the ultrametric
distance matrix.
We reconstructed phylogenetic relationships among
the 43 species using the same sequences employed for the
genetic distance calculations. We generated these phylogenetic analyses via the Bayesian MCMC approach
implemented in MrBayes 3.0b4 (Huelsenbeck and
Ronquist 2001) under the GTR-gþI model of sequence
evolution. The search was run for 5 3 106 generations
and sampled every 2500 generations; the initial 1000
samples were discarded as burn-in. An important
assumption in our use of mtDNA-based distances as a
measure of species relationships is that the mtDNA gene
tree reflects the overall organismal ‘‘species tree’’ for
these taxa. This assumption would be violated if past
hybridization resulted in interspecific mitochondrial
transfer (introgression), as has been documented in a
few avian groups in which closely allied species breed in
sympatry (e.g., Galápagos finches; Sato et al. 1999), or
between species with active hybrid zones. In the paruline
warblers several lines of evidence—including long,
species-specific mtDNA lineages (Lovette and Bermingham 1999, 2002) and the very high congruence between
the mtDNA gene tree and independent trees from DNA
sequences from six unlinked nuclear loci (I. J. Lovette,
unpublished data)—suggest that the mitochondrial tree is
not highly biased by past introgression. The relative
constancy of mitochondrial substitution rates is also
apparent in this phylogram, in which there are no
conspicuously long or short terminal branches or clades.
Quantile regression
Conventional regression analyses describe changes in
mean response, which might not be biologically meaningful information under some circumstances, including
our examination of the relationships between phylogenetic and ecological distances. While the potential for
high ecological overlap could change with greater
phylogenetic distance between warbler species, this
potential might not be realized for all pairs of species.
Thus, on average, ecological overlap could vary little
with changes in phylogenetic distance, even if some
proportion of species pairs that are more distantly
related do have greater ecological overlap; i.e., a
regression through the mean could show no relationship
between phylogenetic distance and ecological overlap,
but a regression through data from the most highly
overlapping species (e.g., the 95th percentile of highest
overlap at each value of phylogenetic distance) would
exhibit a relationship between phylogenetic distance and
ecological overlap. Thus, we needed to analyze our data
using statistical techniques that could provide us
regression lines through percentiles (quantiles) of our
choosing, if we wanted to detect the patterns that we
expected.
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IRBY J. LOVETTE AND WESLEY M. HOCHACHKA
We did not simply select the data points from the
quantiles of our choosing and run conventional regression lines through these. This approach would have
required us to arbitrarily divide the continuous variation
in phylogenetic distance into discrete intervals, in which
to determine the appropriate value of co-occurrence for
each quantile used. Such arbitrariness would have meant
that regression lines, and thus biological conclusions,
would be dependent on the rules used to divide data into
groups with differing ranges of phylogenetic distance. In
order to avoid these complications in interpretation of
results, we used a statistically justifiable and repeatable
analytical technique, linear quantile regression (Koenker
and Bassett 1978, Cade and Noon 2003), to quantify the
relationship between phylogenetic distance and quantiles of the distribution of co-occurrence. Instead of
describing a single line through the mean, as is done in
conventional linear regression, a linear quantile regression describes linear changes in the shape of the entire
distribution of the response variable at all values of a
predictor variable, allowing a user to request production
of regression lines through one or several arbitrarily
chosen quantiles of the response variable’s distribution.
Regression equations for any arbitrary quantiles of this
distribution can be calculated.
In our analyses, we examined changes in median and
5% quantiles of our co-occurrence measures as a
function of changes in phylogenetic distance. Remember
that our indices of co-occurrence were defined so that
high numbers correspond to lower co-occurrence; thus a
5% quantile denotes the 95th percentile of high cooccurrence. Regressions through the median inform us
as to whether pervasive changes in co-occurrence were
seen along the gradient of phylogenetic distances. A
median-quantile regression line and a conventional
linear regression line would be essentially identical for
data with normally distributed errors. Slopes from the
5% quantiles, in contrast, indicate whether the extent of
co-occurrence varied only for a small proportion of
species pairs with greater than median co-occurrence.
The 5% quantile reflects variation in the relationship
between co-occurrence and phylogenetic distance when
only a small proportion of species pairs have actually
realized the potential for greater co-occurrence with
changing phylogenetic distance. Note that, as mentioned
above, changes in the 5% quantile reflect changes in the
shape of the entire distribution of co-occurrence, and
should not be interpreted to mean that only the 5% of
most highly co-occurring species varied in co-occurrence
with changes in phylogenetic distance. We do not
present results for 95% quantiles, as the 95% quantile
of co-occurrence did not vary with changes in phylogenetic distance. In all cases the 5% of species pairs with
the lowest co-occurrence (i.e., the 95% quantile) abutted
the hard boundary of complete non-co-occurrence at
point or transect (i.e., had a co-occurrence of zero and
hence an ecological distance of 1.0).
Ecology Special Issue
Randomizations and statistical analyses
Two attributes of the data require the use of
randomization tests for exploring the association
between ecological co-occurrence and phylogenetic
distance. First, the underlying comparisons between
pairs of species are not independent: owing to differences in abundance, each pair of species was represented
twice in calculations of ecological co-occurrence (A with
B, and B with A), and a given species was also represented in all pairwise associations with co-occurring
species. Each species was similarly represented multiple
times in the matrix of pairwise genetic distances. As a
result, a nonzero slope for the relationship between
phylogenetic distance and co-occurrence could be generated by chance alone. This nonindependence would
also cause true Type I error rates to differ from the
nominal rates calculated by more conventional statistical tests, another problem that is dealt with by
randomization tests.
For our examination of local (within-BBS-transect)
patterns, we generated null distributions against which
to compare the observed relationships between phylogenetic and ecological co-occurrence by randomly
shuffling species identities among points within each
BBS transect. The algorithm used in the randomizations
was to randomly reorder the values within the species
name column in the input data table, with the reordering
done separately and independently for data from each
BBS transect. The input data table for this randomization had one line for each species and census point on
a BBS transect on which that species was observed, with
each BBS transect being represented by only one year of
data. The result of the randomization was to maintain
the same number of warbler species being recorded at
each point on a transect, maintain the number of points
at which each species was reported, and maintain the
same species composition on a transect as was present in
the actual data. The randomization procedure was
designed to break up local ecological associations
between species, but still (1) maintain the identities
and relative abundances of all species within each
transect and (2) maintain information on the proportion
of points within a transect that were suitable for any
species of wood-warbler, for example, ensuring that
points with no reported warblers continued to have no
warbler species after the randomization. This randomization procedure is similar to a Mantel’s test in which
cells in a matrix of similarity values are randomly
rearranged to produce null distributions. Our procedure
differed from that in a Mantel’s test by (1) constraining
the randomization as explained and (2) employing a
different test statistic, in our case slopes from quantile
regression.
In our analyses of among-transect associations
between phylogenetic and regional co-occurrence, we
used a similar randomization procedure. Both the
number of warbler species per transect and the total
number of times each species was found in the data table
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WARBLER PHYLOGENY AND COMMUNITY STRUCTURE
remained identical to the values in the real data. Only
the associations between species pairs were broken up by
the randomization procedure. Mechanistically, the
randomization was done on an input data table that
contained a column identifying each transect uniquely,
and another column containing species’ names. Each
transect appeared 43 times in the file, one time for each
warbler’s name. A third column in the input data was a
Boolean variable that indicated whether each species in
the real data was reported on that specific transect. Each
transect was represented by a single year’s data in the
input data file. The randomization was accomplished by
randomly reordering the warbler species’ names relative
to the rest of the information in the input file; this
reordering was done separately for species that were
listed as having been seen, and those listed as having not
been seen on a specific transect in the real data. These
two separate reorderings were needed to keep the
number of transects on which a species was present in
the randomized data at the same value as in the real
data. Although the transect-level results are interesting
in comparison to the within-transect results that include
only species pairs with demonstrated regional sympatry,
it is important to note that the transect-level randomization brings together species that never occur together
in nature, because they have nonoverlapping geographic
distributions.
The second reason for using randomization tests was
to incorporate replication across multiple years for each
transect, as occurrences may differ among years for
biological reasons (e.g., demographic productivity in the
previous year) and as sampling artifacts. We accounted
for this interannual variation by generating multiple
data sets, each of which contained a single, randomly
selected year’s data for each transect. We combined the
two levels of randomization by (1) randomly selecting
one year for each transect and (2) creating a null data set
by randomizing species’ distributions for that transectyear as we have described. This two-stage randomization was repeated 1000 times each for the withintransect analyses and the among-transect analyses, and
we separately performed the quantile regression analyses
for each iteration on the true and randomized data.
We investigated whether the patterns found in our
analyses were substantially influenced by single species
through analyses that tested individually for the
influence of each species on the overall results. In these
influence analyses, all species pairs containing data from
a target species were removed from the data set, and
quantile regressions were calculated for each of the 1000
randomly sampled years of true data, as just described.
We used the differences between regression slopes with
and without the target species as our metric of influence
of this species on the overall results. We treated each of
the 43 species in turn as the target species and compared
their influences by examining median changes in
regression slopes, from the 1000 data sets, when that
species’ data were excluded.
S21
We calculated probabilities from our regressions of
the relationships between phylogeny and ecological
overlap as the proportion of randomizations in which
the regression slope from the true data was greater than
the slope from the randomized data. Counts of steeper
positive and negative slopes were tallied separately, and
the proportion of steeper slopes in one direction gave a
one-tailed probability. The two-tailed probabilities that
we report in the text were calculated by doubling these
one-tailed probabilities. The confidence intervals depicted in the figures are the range of the central 95% of
all regressions. Plotted results from quantile regression
analyses are not derived from median regression
coefficients, but are lines that connect a series of
predicted values, with the median predicted values
representing the regression line and the range of the
central 95% of predicted values representing the 95%
confidence limits at each point. As a result of plotting
lines connecting point estimates, it is possible for plotted
lines to show deviations from perfect linearity even
though linear quantile regressions were used.
We used the SAS statistical software to generate
random data sets for analysis and the quantreg library
(Quantreg package, 2004, version 3.35, by R. Koenker
[available online])2 for the R statistical software (R
Development Core Team 2004) to conduct all quantile
regression analyses. Conclusions did not change qualitatively between analyses based on 500 and 1000
randomization iterations, indicating that 1000 iterations
were sufficient to describe variability in null associations
among taxa.
RESULTS
AND
DISCUSSION
Patterns of association within local areas (BBS transects)
Three features are immediately obvious in comparisons of ecological dissimilarity with phylogenetic
distance (Fig. 2a). First, although pairwise genetic
distances varied within 1–18%, the majority of pairwise
distances were clustered at intermediate levels of
divergence owing to several periods of rapid cladogenesis during the diversification of this group (Lovette
and Bermingham 1999, 2002). This temporal clustering
of nodes is visible in the phylogenetic reconstruction for
these taxa (Fig. 3). Second, there is a notable lack of
taxa that are both phylogenetically similar and have
high local co-occurrence. This is seen in the empty lower
left quadrant in Fig. 2a. Third, across the full gradient of
genetic divergence, most species pairs detected on the
same Breeding Bird Survey (BBS) transect were rarely
detected at the same transect points, leading to the
majority of pairwise comparisons of ecological dissimilarity being clustered near the upper boundary in Fig.
2a. A smaller number of species pairs co-occur at higher
frequency, with a median (of 1000 random subsamples
of BBS data) of only 0.085% of the 1184 pairwise
2
hhttp://cran.r-project.org/doc/packages/quantreg.pdfi
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IRBY J. LOVETTE AND WESLEY M. HOCHACHKA
Ecology Special Issue
FIG. 2. Ecological co-occurrence of wood-warbler species pairs across a gradient of phylogenetic divergence (maximumlikelihood distances). In each panel, the background of plotted points depicts the same random sample of real census transect-years
that is representative of patterns observed across all 1000 such iterations. (a) Distribution of pairwise associations between
phylogeny and ecological co-occurrence among taxa that forage at similar (green) or different (black) habitat strata. (b) Median
(thick lines) and lower fifth quantile (thin lines) relationships between phylogenetic distance and ecological co-occurrence for
species pairs that forage in different habitat strata. Regression through the true data (red) and randomized null-model data (blue)
are both plotted. For each of the four lines, the median from the 1000 iterations is plotted with the inner 95% of predicted values
forming the confidence intervals (dashed lines). (c) Median and lower fifth quantile regressions for species pairs foraging in the same
habitat stratum. (d) Corresponding quantile regressions for pairs of taxa foraging in different strata, but with data from Ovenbirds
(Seiurus aurocapilla) excluded from the analyses. In panels (b) and (d), regression lines are only plotted across the range on the xaxis of actual data points used to derive the regressions. In these panels, the background points represent the relevant subset of data
from the full distribution in panel (a).
comparisons involving taxa with .50% detected cooccurrence. On a historical note, the five ‘‘spruce-woods
warblers’’ featured in Robert MacArthur’s 1958 study
all have median (of 1000 random subsamples of BBS
data) pairwise dissimilarity values .0.83, suggesting
that these species are not usually detected in local
sympatry.
Our interpretation of the conspicuous absence of
species pairs in the lower left quadrant of Fig. 2a is that
closely related taxa effectively exclude each other at a
local scale, even when they occur in geographic
proximity. Some wood-warbler species pairs separated
by greater phylogenetic distances had high likelihoods of
local co-occurrence, although across all phylogenetic
distances the majority of species pairs had relatively high
local ecological dissimilarity. We contrasted these
observed patterns with a null model of randomized
species occurrences. When all pairs of taxa are
considered, the median co-occurrence does not change
significantly with phylogenetic distance relative to the
null distribution (P ¼ 0.192 for differences in slopes
compared to the null model, based on maximumlikelihood distances; P ¼ 0.164, based on ultrametric
distances). However, maximal co-occurrence significantly increases as ML distance increases (P ¼ 0.03
from the fifth-quantile regression slope), although the
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WARBLER PHYLOGENY AND COMMUNITY STRUCTURE
S23
FIG 3. Phylogenetic relationships among the 43 wood-warbler taxa included in this study based on Bayesian Markov chain
Monte Carlo analysis of long mitochondrial DNA sequences (see Methods). Thick branches indicate species (or groups of species)
coded as terrestrial foragers; the remaining taxa are arboreal foragers. The tree is rooted to various non-wood-warbler outgroup
taxa, as in Lovette and Bermingham (2002).
pattern only approached statistical significance when
ultrametric distances were used (P ¼ 0.066). These
results suggest that a subset of the pairs of more
distantly related species is capable of higher local
association than expected by chance.
Further analyses suggest that the increased cooccurrence of some more distantly related species pairs
requires divergence in foraging niche in order to reduce
potential competition. Adding each species pair’s foraging strata information (in the form of a categorical
variable with two categories) to the quantile regression
analyses revealed a nonrandom relationship at the
lower (fifth-quantile) boundary for the mixed-strata
pairs (P ¼ 0.026 for ML distances, P ¼ 0.000, for ultrametric distances), for which fifth-quantile co-occurrence
increased with greater phylogenetic distance (Fig. 2b),
but not for same-stratum pairs, for which the slopes of
the real and randomized regressions do not differ
significantly (P ¼ 0.526 for ML distances, P ¼ 0.736 for
ultrametric distances) (Fig. 2c). Results from analyses
using ultrametric distances even suggested the possibility of higher co-occurrrence with greater phylogenetic
distance from the median regression for mixed-substrate
pairs (P ¼ 0.070), although this pattern was not
reflected in the median regression when ML distances
were used (P ¼ 0.236). No hint of nonrandom
S24
IRBY J. LOVETTE AND WESLEY M. HOCHACHKA
associations were found from the median regressions
from same-substrate pairs (P ¼ 0.556 for ML distances,
P ¼ 0.478 for ultrametric distances). Thus, in spite of
already having a greater average genetic distance
associated with their differing foraging substrates
(compare the ranges of genetic distances in Fig. 2b,
c), only the mixed-stata pairs showed changes in cooccurrence with phylogenetic distance. This result
suggests that differences in or associated with foraging
substrate are a necessary but not sufficient precondition
for co-occurrence to vary with phylogenetic distance.
To explore which taxa were driving the fifth-quantile
relationship between phylogeny and ecological overlap
(Fig. 2b), we repeated these analyses and serially
removed comparisons involving each of the 43 taxa.
These influence analyses were only conducted on the
fifth-quantile regressions for mixed-strata pairs, as this
was the only case in which a clear effect of phylogenetic
distance on ecological co-occurrence was detected.
Median changes in the fifth-quantile slope, with removal
of data from a single species, almost all clustered
between 0.5 and 0.5 for the analyses based on ML
phylogenetic distances, and between 0.2 and 0.08 for
analyses based on ultrametric distances. The only
exceptions were for Ovenbirds, in whose absence the
median increase in slope was 2.9 (2.7 with ultrametric
distances) (Fig. 2d) and Palm Warblers (Dendroica
palmarum), for which the median change in slope was
1.6 1.2 for ultrametric distances). Only the increase in
regression slope in the absence of Ovenbirds is relevant
to explaining the increased ecological association with
increasing phylogenetic distance, and for analyses based
on ML phylogenetic distances only for Ovenbirds was
the inner 95% of changes above zero, with none of the
1000 data sets showing a lower slope when Ovenbirds
were excluded from the quantile regressions. With
ultrametric phylogenetic distances, the slope of the 5%
quantile line was always higher in the absence of data
from Ovenbirds and always lower in the absence of data
from Palm Warblers. The effect of Ovenbirds is not
surprising because of the Ovenbird’s placement as the
earliest branch within the entire Parulidae (Lovette and
Bermingham 2002; I. J. Lovette, unpublished data)
(Fig. 3). If ecological differentiation is associated with
time, this relatively ancient lineage is expected to exhibit
the highest ecological differentiation (and concomitant
spatial association) relative to the more derived species
within the radiation.
A nonrandom increase in the potential for ecological
co-occurrence of distantly related species was one of two
patterns evident in our data. We also find evidence of
consistent phylogenetic niche conservatism in the
ubiquitous vertical offset between the actual and null
quantile regression lines (Fig. 2b–d). This phenomenon
is present in all quantile regressions and is particularly
evident in the lower fifth quantiles. The consistently
lower position of the regression lines based on the real
data, relative to the lines from the corresponding null
Ecology Special Issue
models, indicates that, regardless of phylogenetic
distance, similarly related sympatric species choose a
larger number of similar habitats than expected by
chance. This pattern is consistent with a growing body
of evidence showing that phylogenetic relationships
sometimes explain substantial variation in how related
species are distributed across habitat types, bioclimatic
regions, and other niche-related environmental variables
(e.g., Ricklefs and Latham 1992, Peterson et al. 1999).
The historical legacy in ecological niches is not surprising, given that phylogenetic effects are usually apparent
in organismal traits with a genetic basis, from molecular
pathways to complex behaviors (Harvey and Pagel 1991,
Ackerly 2003). In a different context, niche conservation
is also apparent in the distribution of arboreal vs.
terrestrial foraging among these 43 wood-warbler
species (Fig. 3): evolutionary switches among these
strata are uncommon, and no pairs of very closely
related species have diverged across this level of spatial
segregation.
Are patterns driven by differences in species’
detection probabilities?
The data presented in Fig. 2 are based on detections
of individual birds, which require both that a given
species is actually present and also that this species has
been detected by the censusing observers. Actual
presence, and hence ecological co-occurrence, is thereby
systematically underestimated by the inevitable missed
detections. As a result, the percentages of co-occurrence
presented here are not direct quantifications of
ecological co-occurrence, but instead comparable indices of relative co-occurrence. Nevertheless, examination of patterns in our index of detection probability
(see Methods) leads us to believe that the general
patterns of ecological co-occurrence that we have
summarized would be essentially impossible to create
as a result of among-species variation in detection
probability alone.
We expect that relatively low ecological co-occurrence
for the majority of species pairs is a biologically real
phenomenon, because creation of this pattern through
variation in detection probability would require at least
one member of the majority of species pairs to have an
anomalously low detection probability. As long as the
distribution of species’ detection probabilities has a
mode at some intermediate value (as our index of
detection probability does; see Methods), the observed
distribution of ecological co-occurrence would be
extremely unlikely to be produced as a result of
interspecific variation in detection rates. The products
of detection indices for all observed species pairs are, as
expected, distributed with a peak at intermediate values,
which indicates that the generally low local co-occurrence of species pairs (Fig. 2a) is a biologically real
phenomenon.
For reasons similar to those we have discussed, increasing ecological co-occurrence with increasing phy-
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WARBLER PHYLOGENY AND COMMUNITY STRUCTURE
S25
FIG. 4. Regional co-occurrence of wood-warbler species pairs across a gradient of phylogenetic divergence (maximumlikelihood distances). In each panel the background of plotted points depicts the same random sample of real census transect-years
that is representative of patterns observed across all 1000 such iterations. (a) Distribution of pairwise associations between
phylogeny and regional co-occurrence among taxa that forage at similar (green) or different (black) habitat strata. (b) Median
(thick lines) and lower fifth quantile (thin lines) relationships between phylogenetic distance and regional co-occurrence for all
species pairs combined. Regressions through the true data (red) and randomized null-model data (blue) are both plotted. For each
of the four lines, the median from the 1000 iterations is plotted with the inner 95% of predicted values forming the confidence
intervals (dashed lines).
logenetic distance (Fig. 2b) also appears highly unlikely
to be an artifact of systematic variation in detection
probabilities among species as a function of their
phylogenetic distance. For variation in detection probabilities to have caused this pattern, we would need to
find detection probabilities of some proportion of
species pairs to increase with increasing phylogenetic
distance. However, we found no evidence for this, and in
fact a significant tendency in the opposite direction using
a fifth-quantile regression of minimum detection index
(within each of the 903 species pairs) against phylogenetic distance. In this fifth-quantile regression, confidence intervals around the estimated slope did not
come close to overlapping zero (slope ¼ 0.45, 95%
confidence limits of 0.50 to 0.41). Thus, we believe
that the increasing ecological co-occurrence with greater
phylogenetic distance is a biological phenomenon and
not an artifact of limitations of data collection.
Interspecific variation in detection rates could only
have caused the significant influence of Ovenbirds on the
results (Fig. 2b, d) if increasing genetic distance from
Ovenbirds were correspondingly correlated with increasing detection probability (e.g., by louder and more
persistent vocalization) for arboreally foraging woodwarblers. While there was an increase in detection
probability of arboreally foraging species paired with
Ovenbirds as their phylogenetic distance from Ovenbirds increased, the result was not statistically significant
(r2 ¼ 0.05, P ¼ 0.19, N ¼ 33, slope ¼ 0.76). Even were this
increase in detection probability real, it still would
translate to an average change in the detection index of
only 8% across the full range of phylogenetic distances
between Ovenbirds and arboreally foraging warblers
found on the same transects. In contrast, the presence of
species pairs with Ovenbirds leads to a roughly 135%
change in ecological co-occurrence at the fifth quantile
(Fig. 2d). This contrast of effects suggests that, while
systematic variation in detectability contributed slightly
to the closer measured ecological co-occurrence of
arboreally foraging warblers more distantly related to
Ovenbirds, the effect of this measurement artifact was
trivial.
Finally, the lower position of the real vs. null
regression lines that we interpret as indicating higher
than expected niche conservatism could only be an
artifact of varying detection probabilities if the actual
species pairs were composed of pairings in which both
members had higher detection probabilities than was
typical of randomly selected pairs of warbler species.
The median product of the detection probabilities of the
actual species pairs was 0.281, and products ranged
from 0.198 to 0.375 in the 903 observed species pairs.
From 1000 iterations of an equal number of random
species pairings, the median product was 0.281 (95%
confidence limits 0.279–0.282). There is no evidence to
suggest that phylogenetic niche conservatism is a
sampling artifact.
Contrasting local and regional relationships between
phylogenetic distance and spatial co-occurrence
At the broader spatial scale involving comparisons
among transects, there was a greater range of variation
S26
IRBY J. LOVETTE AND WESLEY M. HOCHACHKA
in species co-occurrence (Fig. 4a) than was found for
local, ecological co-occurrence (Fig. 2a). However, the
higher values of co-occurrence may largely be accounted
for by the higher probability of detection of a given
species on a transect as a whole, relative to a single point
on that transect. Nevertheless, we found a predominance
of species pairs with little or no overlap in occurrence on
transects, as we would expect given that at maximum 23
of the 46 North American wood-warblers ever occur on
the same transect, and most transects support substantially fewer species (Fig. 1). This real geographical
separation among many species pairs drives much of the
pattern in the transect-level analyses and demonstrates
the value of the point-based comparisons we describe, in
which we are able to restrict analyses to only those
transects where both members of a given pair of species
occur.
At the transect level, the fact that many pairs of
warbler species have largely to fully nonoverlapping
ranges is evidenced in the y-axis offsets between actual
median regional overlap and the overlap expected by
chance (Fig. 4b). Here, the median regional overlap was
lower (rather than higher, as in the point-based analyses)
than expected by chance, as indicated by the complete
displacement of the median-quantile regression line
above the null-model regression line. The opposite
pattern was found in the fifth-quantile regressions, for
which the observed level of regional co-occurrence was
higher, for the 5% of most overlapping species, than that
expected by chance alone, regardless of phylogenetic
distances. This fifth-quantile result is likely a function of
some species pairs having very similar habitat associations, and thus ranges.
The final pattern to emerge from analyses of regional
association is that variation in regional overlap with
phylogenetic distance is different from that expected by
chance alone, when analyses were based on ML
distances. If warbler species were randomly distributed
across North America, the randomization suggests that
there would be a slight decrease in regional cooccurrence with increasing phylogenetic distance (as
indicated by the positive slope of the blue medianquantile regression line in Fig. 4b). What we actually
found for the real data is no relationship between
regional co-occurrence and phylogenetic distance (as
represented by the flat red median-quantile in Fig. 4b), a
statistically significant difference from the null slope (P
¼ 0.000). For analyses based on ultrametric distances,
the slope of the line through the real data did not differ
from zero, although in this case the result was not a
departure from the null regression line (P ¼ 0.110), the
slope of which also did not differ from zero. This
comparison suggests that there is only a weak signature
of phylogeny on the overall geographic ranges of these
North American warbler species. Previous studies of
other taxa have sometimes reported stronger (and
usually positive) correlations between phylogenetic
distances and the overlap of species’ geographic ranges
Ecology Special Issue
(e.g., Barraclough and Vogler 2000), but the lack of such
an association in our analyses is the result predicted for
groups of organisms—such as these warblers—in which
species’ ranges must have been shifted repeatedly by
large-scale climate cycles, thereby scrambling most
geographic patterns associated with their initial speciation events (Losos and Glor 2003).
In summary, when comparing point (Fig. 2) and
transect (Fig. 4) scales, there are clear differences in both
the general magnitude of species’ co-occurrence, and in
the relationship between phylogenetic distance and
species co-occurrence. Broadly speaking, these differences reinforce the notion that the factors that are
determining local ecological co-occurrence are different
than those biogeographic influences that have created
the varying distributional ranges of North America’s
parulid warblers.
CONCLUSIONS
Phylogenetic niche conservation has most often been
observed in allopatric species that occupy similar niches
despite a period of evolutionary isolation (e.g., Peterson
et al. 1999); in such cases, niche conservation may even
enhance the probability of continued isolation if it
lowers the likelihood that one or both populations
evolve ecologically to occupy the previously unsuitable
habitat that separated them (Wiens 2004). Stabilizing
selection on niche attributes is one potential cause of
phylogenetic niche conservation (Harvey and Pagel
1991, Ackerly 2003), but stabilizing selection leading to
phylogenetic niche conservatism may be more likely in
allopatry because it is less likely to be opposed by
divergent selection among closely related sympatric
taxa. The analyses here suggest that niche conservation
is evident in a large group of species, even with
substantial sympatry. In the wood-warblers, divergent
selection stemming from interspecific competition has
not been sufficient to completely erase the phylogenetic
legacy of habitat specialization.
The majority of our comparisons address only the
component of ecological differentiation that is associated with species’ spatial co-occurrences, or their lack
thereof. For example, these comparisons are much more
likely to detect patterns related to fundamental differences in species’ selection of habitats than they are to the
more subtle ecological differences that likely allow coexisting species to partition ecological resources within
habitats. The contrasting quantile regressions for pairs
of warbler species that forage in similar vs. mixed
vegetation strata (Fig. 2b, c) demonstrate that behavioral differentiation contributes to patterns of local
community composition, but we were unable to further
address this level of ecological niche differentiation.
Another important limitation of our approach is that
both present and past competition might cause species
to be spatially segregated, and our analyses do not
distinguish between the effects of ongoing species
interactions and differentiation in site selection caused
July 2006
WARBLER PHYLOGENY AND COMMUNITY STRUCTURE
by competition in the past. Similarly, at an even broader
spatial scale, it is possible that competitive interactions
feed back to influence species’ ranges, particularly if
species with highly similar ecological niches tend to
exclude one another at this broad scale. The influence of
competition on the regional species pool has been
termed the Narcissus Effect in the ecological literature
(Colwell and Winkler 1984), but has received less
attention in a phylogenetic context. Because the
comparisons presented here are most biologically
informative at the site-within-transect level, we are
unable to provide a robust test for any patterns at the
broader regional level.
Considered in concert, our results suggest that woodwarbler community assembly is constrained by ecological similarity among closely related species, with
subsequent local co-occurrence of more distantly related
lineages mediated in part by divergence in ecology and
behavior. Considerable evolutionary time appears to be
required for high levels of spatial overlap. This
interpretation challenges the classic depiction of the
North American wood-warbler adaptive radiation as
one in which many species have quickly evolved
substantial behavioral differences that allow extensive
coexistence (Lack 1971, Morse 1989, Price et al. 2000).
Phylogenetic niche conservatism (Ricklefs and Latham
1992, Peterson et al. 1999, Webb et al. 2002, Losos et al.
2003) appears to play a continuing role in structuring
even these highly spatially and temporally dynamic
communities, and the relatively few pairs of taxa that
consistently co-occur with high frequency are both
distantly related and ecologically differentiated. At the
most general level, this new phylogenetic perspective on
the community ecology of the parulid warblers reinforces the idea that for most groups of sympatric species,
the relationship between phylogeny and community
structure likely involves simultaneous stasis and differentiation along many ecological niche axes.
ACKNOWLEDGMENTS
We thank D. Rabosky, J. Losos, T. Price, R. Ricklefs,
C. Webb, and two anonymous reviewers for insightful suggestions and comments, C. Cooper for bringing quantile regressions to our attention, and I. Fiorentino and L. Stenzler for
laboratory assistance. We gratefully acknowledge the contributions of the thousands of volunteer BBS participants and the
BBS staff at the USGS Patuxent Wildlife Research Center and
Canadian Wildlife Service who have collectively assembled a
uniquely comprehensive resource for studies of avian abundance and distribution. This research was supported by the
National Science Foundation with logistic support during the
initial data analyses to WMH from the Max Planck Institute
for Ornithology, Vogelwarte Radolfzell.
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APPENDIX
Alternative versions of Figs. 2 and 4, based on ultrametric genetic distances (Ecological Archives E087-109-A1).
Ecology, 87(7) Supplement, 2006, pp. S29–S38
Ó 2006 by the Ecological Society of America
PHYLOGENETIC ANALYSIS OF THE EVOLUTION OF THE NICHE
IN LIZARDS OF THE ANOLIS SAGREI GROUP
JASON H. KNOUFT,1,4 JONATHAN B. LOSOS,2 RICHARD E. GLOR,3
1
AND
JASON J. KOLBE2
Department of Ecology and Evolutionary Biology and University of Colorado Museum, UCB 265, University of Colorado,
Boulder, Colorado 80309 USA
2
Department of Biology, Campus Box 1137, Washington University in St. Louis, St. Louis, Missouri 63130 USA
3
Center for Population Biology, University of California, Davis, California 95616 USA
Abstract. Recent advances in ecological niche modeling (ENM) algorithms, in
conjunction with increasing availability of geographic information system (GIS) data, allow
species’ niches to be predicted over broad geographic areas using environmental characteristics
associated with point localities for a given species. Consequently, the examination of how
niches evolve is now possible using a regionally inclusive multivariate approach to characterize
the environmental requirements of a species. Initial work that uses this approach has suggested
that niche evolution is characterized by conservatism: the more closely related species are, the
more similar are their niches. We applied a phylogenetic approach to examine niche evolution
during the radiation of Cuban trunk-ground anoles (Anolis sagrei group), which has produced
15 species in Cuba. We modeled the niche of 11 species within this group using the WhyWhere
ENM algorithm and examined the evolution of the niche using a phylogeny based on ;1500
base pairs of mitochondrial DNA.
No general relationship exists between phylogenetic similarity and niche similarity.
Examination of species pairs indicates some examples in which closely related species display
niche conservatism and some in which they exhibit highly divergent niches. In addition, some
distantly related species exhibit significant niche similarity. Comparisons also revealed a
specialist–generalist sister species pair in which the niche of one species is nested within, and
much narrower than, the niche of another closely related species.
Key words: anole; Anolis; Cuba; ecological niche modeling; fundamental niche; niche conservatism;
niche evolution; phylogenetics.
INTRODUCTION
The fundamental niche, which encompasses the
theoretical range of conditions a species can occupy
(Hutchinson 1957), provides a conceptual framework to
predict the potential geographic distribution of a species
(MacArthur 1972, Soberón and Peterson 2005). Species
traits, whether morphological, physiological, or behavioral, are often obviously linked to the niche and
generally susceptible to the processes of evolution.
Consequently, the shaping of niche characteristics can
be viewed as an evolutionary phenomenon. Because the
fundamental niche provides details about the potential
distribution of species, and the niche is determined by
the processes of evolution, understanding evolutionary
patterns of niche diversification can reveal valuable
insights into factors related to the diversity and
distribution of species.
Studies of the niche have historically involved detailed
analyses of local habitat requirements of an organism
(Chase and Leibold 2003). Recently, the availability of
Manuscript received 25 January 2005; revised 16 August
2005; accepted 24 August 2005. Corresponding Editor (ad hoc):
C. O. Webb. For reprints of this Special Issue, see footnote 1, p.
S1.
4
E-mail: [email protected]
global climate and land cover Geographic Information
System (GIS) and remote-sensing data has provided
environmental information at a regional scale. These
data, when integrated into ecological niche modeling
(ENM) algorithms, have provided a powerful opportunity to characterize the habitat requirements of a
variety of species and assess patterns of niche differentiation in a comparative framework. The majority of
recent niche modeling efforts have focused on predicting
species’ distributions, species’ response to climate
change, and potential distributions of invasive species
(Guisan and Zimmerman 2000, Peterson 2001, 2003,
Peterson and Vieglais 2001, Oberhauser and Peterson
2003, Peterson and Robins 2003, Illoldi-Rangel et al.
2004, Peterson et al. 2004). While these studies have
provided novel insights into aspects of broad-scale
ecological niche characteristics, recent research has
begun to realize the potential to examine the results of
niche modeling efforts in an evolutionary context
(Peterson et al. 1999, Rice et al. 2003, Graham et al.
2004).
Two approaches have been taken to integrating
information on evolutionary relationships into niche
modeling studies. On one hand, some studies have
focused on recent evolutionary events by comparing
pairs of closely related taxa (either subspecies or sister
S29
S30
JASON H. KNOUFT ET AL.
FIG. 1. Ultrametric phylogeny for the Anolis sagrei group
derived from Bayesian phylogenetic analysis of ;1500 base
pairs of mtDNA. The support values and simplified tree
presented here were generated by culling taxa from a larger tree
that included 306 unique sequences obtained from 315
individuals representing 11 of 15 A. sagrei group species.
Numbers above nodes represent posterior probabilities obtained from Bayesian analysis. Boldface numbers below nodes
represent bootstrap values obtained from 200 bootstrapped
data sets analyzed via maximum parsimony.
species [Peterson et al. 1999, Peterson and Holt 2003]).
Results from these studies suggest that conservatism,
where taxa that are more closely related possess
characteristics that are more similar (Harvey and Pagel
1991, Lord et al. 1995, Webb et al. 2002), is a frequent
occurrence and characterizes evolutionary patterns of
niche diversification. Alternatively, other studies have
included deeper evolutionary divergence by using
phylogenetic methods to reconstruct the niche of
ancestral taxa (Rice et al. 2003, Graham et al. 2004).
Results from these studies suggest that niche conservatism might not be a consistent characteristic during
diversification. Both of these approaches have limitations: the former approach has limited scope and only
applies to evolutionary events in the recent past, whereas
the accuracy of the latter approach is questionable
because a number of studies have demonstrated that
phylogenetically derived estimates of ancestral states will
in many cases have low accuracy and extremely high
uncertainty (Schluter et al. 1997, Losos 1999, Martins
1999, Oakley and Cunningham 2000, Webster and
Purvis 2002).
We propose an alternative approach, similar to one
applied by Rice et al. (2003), that makes full use of
phylogenetic information and thus permits inferences
beyond comparison of closely related taxa, while
Ecology Special Issue
avoiding the pitfalls of ancestor reconstruction. Specifically, following the logic of Harvey and Pagel’s (1991)
‘‘non-directional approach,’’ we examine the extent to
which niche similarity among extant species is a function
of phylogenetic similarity. Like the widely used independent-contrasts method (Felsenstein 1985), this approach allows the integration of phylogenetic
information into comparative analyses without requiring inference concerning the characteristics of hypothetical ancestral taxa.
Caribbean lizards of the genus Anolis are a particularly good group for such studies (see Plate 1). Anoles
are abundant and diverse on Caribbean islands, with as
many as 57 species on a single island (Cuba) and up to
11 species occurring sympatrically, and their ecology has
been extensively studied (reviewed in Losos [1994] and
Roughgarden [1995]). Moreover, recent research has
established a firm phylogenetic framework for the
Caribbean anole radiation (Poe 2004, Nicholson et al.
2005). Our focus in this study is on the A. sagrei species
group on Cuba. This clade contains 15 species, all but
one of which use similar microhabitats, occurring on
tree trunks and other broad surfaces low to the ground,
and using the ground extensively for foraging and
intraspecific interactions (the ‘‘trunk ground ecomorph’’
niche of Williams [1983]). Several species occur widely
throughout Cuba, whereas others have restricted distributions; as many as four occur sympatrically (Losos
et al. 2003). Recent phylogenetic work (Glor 2004) has
provided the phylogenetic framework for the investigation of the evolution of the niche and community
composition.
In this study, we examine the evolution of niche
characteristics in species within the A. sagrei group on
Cuba. We characterize the broad-scale environmental
components of the niche using ENM algorithms and
GIS data. We then incorporate the results of these
analyses with information on the phylogenetic relationships within the A. sagrei group to examine patterns of
niche diversification within this island group.
METHODS
Phylogenetic relationships among members
of the Anolis sagrei group
We obtained an mtDNA phylogeny for the A. sagrei
species group from Glor (2004). This tree was reconstructed from 306 unique sequences obtained from 315
individuals representing 11 of 16 A. sagrei group species
and including extensive intraspecific sampling within
most widespread species (Glor 2004). We treat A. allogus
as two separate species, A. allogus (east) and A. allogus
(west), because they are highly divergent genetically and
do not form a clade (Glor 2004) (Fig. 1); thus the
discrepancy between the 15 recognized species and the
16 species referred to in our analyses. Maximumparsimony analysis implemented by PAUP* 4.0b10
(Swofford 2002) and Bayesian analysis implemented in
MrBayes 3.0 (Huelsenbeck and Ronquist 2001) yielded
July 2006
NICHE EVOLUTION IN ANOLIS LIZARDS
S31
TABLE 1. Results of niche modeling predictions for species in the Anolis sagrei group.
Species
A.
A.
A.
A.
A.
A.
A.
A.
A.
A.
A.
N ahli
allogus (east)
allogus (west)
bremeri
homolechis
jubar
mestrei
ophiolepis
quadriocellifer
rubribarbus
sagrei
GARP external accuracy
mean (range)
16
65
23
25
146
63
35
43
24
21
124
0.748
0.542
0.500
0.545
0.962
0.594
0.597
0.814
0.735
0.547
0.953
(0.500–0.999)
(0.466–0.972)
(0.500–0.500)
(0.500–0.952)
(0.951–0.972)
(0.500–0.984)
(0.500–0.993)
(0.500–0.973)
(0.500–0.992)
(0.500–0.971)
(0.937–0.968)
WhyWhere
External accuracy
0.490
0.758
0.945
0.845
0.595
0.736
0.958
0.756
0.981
0.971
0.627
Z statistic
31.15***
23.19***
29.26***
30.40***
18.50***
22.00***
30.13***
19.16***
30.73***
30.23***
17.80***
***P , 0.001 for all of the WhyWhere predictions.
Number of localities for each species.
congruent and well-supported topologies (Fig. 1).
Phylogenetic analysis of a nuclear DNA fragment (the
third intron of the rhodopsin-encoding gene) for a
subset of the taxa included in the mtDNA phylogeny
also yields a topology that is concordant with all of the
nodes presented in Fig. 1 (Glor 2004).
We then converted the Bayesian mtDNA tree into an
ultrametric form using Sanderson’s (2002) penalizedlikelihood approach, as implemented by the program r8s
(Sanderson 2003). A smoothing value for the penalizedlikelihood analysis was determined via cross-validation.
Following conversion of the tree into an ultrametric
format, taxa within monophyletic groups were pruned
until a single representative of each species remained
(Fig. 1). In the case of A. allogus, we retained two
individuals representing the genetically distinct eastern
and western populations. This pruned tree was then used
to derive patristic distances among all pairwise taxonomic comparisons.
Niche characteristics of members
of the Anolis sagrei group
Ecological niche modeling provides the ability to
estimate the niche of a species based on known species
localities and environmental parameters characterized in
GIS data sets. This estimation is then used to predict the
potential geographic distribution of a species based on
the same GIS data (Peterson 2001). These predictions
are based on broad-scale environmental data and do not
account for microhabitat characteristics. Consequently,
microhabitat partitioning among sympatric anoles,
which has been an important component in the
community structure and evolutionary diversification
of anoles (e.g., Williams 1983, Losos et al. 2003), is not
considered in the ENM algorithms. Similar broad-scale
ENM analyses have suggested a high predictive ability
for geographic distribution with the ENM algorithms
(Peterson 2001, 2003, Peterson and Vieglais 2001,
Oberhauser and Peterson 2003, Peterson and Robins
2003, Illoldi-Rangel et al. 2004, Peterson et al. 2004). In
other words, the broad-scale ENM approach is useful
for predicting whether a species will occur in a particular
region, but is not insightful regarding microhabitat
differences among co-occurring species.
We compiled locality information for 11 species in the
A. sagrei group from natural history museum collection
records (American Museum of Natural History; California Academy of Sciences; Field Museum; Museum of
Comparative Zoology, Harvard University; Museum of
Vertebrate Zoology, University of California-Berkeley;
Smithsonian National Museum of Natural History; University of Kansas Natural History Museum) and published data (Schwartz and Henderson 1991, Rodrı́guezSchettino 1999) (Table 1). The five species for which
phylogenetic data are not available are known from an
extremely limited number of individuals or localities
(Rodrı́guez-Schettino 1999).
We predicted the niche of each species in the A. sagrei
group using the WhyWhere niche modeling program
(Stockwell 2006; software available online).5 The WhyWhere algorithm (newly available in June 2004) affords
greater predictive ability and decreased computational
time compared to the commonly used GIS-based
Genetic Algorithm for Rule-Set Prediction (GARP)
ENM application (Stockwell and Peters 1999). To
achieve results, WhyWhere converts environmental data
layers into multicolor images and applies a data-mining
approach to image processing methods to sort through
large amounts of data to determine the variables that
best predict species occurrences. Testing of the predictive ability of each model is performed by calculating the
accuracy of the model based on species presence data
and randomly generated pseudo-absence points within a
specified geographic region (similar to GARP) (Stockwell and Peters 1999), in this case Cuba.
We used the WhyWhere ENM algorithm, 184
terrestrial environmental data layers (see Stockwell
[2006] for information on data layers), and georeferenced species occurrence data to predict the niche of
each species in the A. sagrei group. These ENM
5
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JASON H. KNOUFT ET AL.
Ecology Special Issue
FIG. 2. Potential distributions of species in the Anolis sagrei group based on WhyWhere and Genetic Algorithm for Rule-Set
Prediction (GARP) ecological niche modeling predictions. Points represent locality data used in model development and testing for
each species. Species designations are as follows: A, A. ahli; B, A. allogus (east); C, A. allogus (west); D, A. bremeri; E, A.
homolechis; F, A. jubar; G, A. mestrei; H, A. ophiolepis; I, A. quadriocellifer; J, A. rubribarbus; K, A. sagrei.
predictions reflect the potential geographic distribution
of each species on Cuba based on the ENM algorithms
and the GIS data used to construct each prediction.
Each prediction was calculated based on a 0.18
resolution per grid cell, a 0.5 occurrence cut for each
prediction, and a 1.28 Z score termination condition.
For each species, 50% of the locality data were used for
model training (internal accuracy), while 50% were held
back for testing of model accuracy (external accuracy).
We determined accuracy of each prediction using the
same protocol as GARP (Stockwell and Peters 1999).
Although WhyWhere has been demonstrated to
generate predictions with higher accuracy than the
frequently used GARP ENM (Stockwell 2006), we also
modeled the niche of each species using GARP for
qualitative assessment of the WhyWhere application.
Both GARP and WhyWhere were developed by the
same person/research group, and both applications
calculate model accuracy in a similar manner (Stockwell
and Peters 1999, Stockwell 2006). Thus, the accuracy of
predictions generated by the two ENM algorithms are
qualitatively comparable.
Using the Anolis locality data and the GARP ENM
application, we again predicted the niche of each species
in the A. sagrei group. GIS data sets were at a 0.18
resolution and included layers describing topography
(elevation, slope, aspect, flow accumulation, and flow
direction) and climate (annual means of total, minimum,
and maximum temperature, precipitation, solar radiation, vapor pressure, and wet days). We developed 100
July 2006
NICHE EVOLUTION IN ANOLIS LIZARDS
S33
FIG. 2. Continued.
niche models for each species based on locality data. The
GARP algorithm was run for 1000 iterations or until a
convergence limit of 0.1 was achieved for each species.
During model development, 50% of the localities were
used for model training, while 50% of the localities were
held back to test model accuracy. Using the best-subset
selection criteria (Anderson et al. 2003), we chose 20
models that had an omission error of ,5% based on the
localities used to test each model. From these 20 ‘‘best’’
models, we then selected the 10 models that exhibited an
intrinsic commission index closest to the mean intrinsic
commission index for the 20 models (Anderson et al.
2003; similar to Oberhauser and Peterson [2003]). We
then imported these models into GIS software (DIVAGIS [Hijmans et al. 2001]), identified the areas of
predicted occurrence that were present in all 10 models,
and used this area as our prediction for each species. The
use of areas that only occur in the predictions of all 10
models is a conservative approach; however, this
methodological choice still resulted in predictions that
encompassed all of the locality data for each species.
We investigated the relationship between niche
similarity and phylogenetic relatedness in a geographic
context by examining the frequency that known localities from a particular species fall within the ENM
predicted geographic distribution of another species
(similar to Peterson et al. [1999]). Using a binomial
probability distribution, we determined if the number of
times that the occurrence data points of one species
overlapped the predicted distribution of another species
was nonrandom. For the binomial probability calculation, the null expectation was that the percentage of
actual occurrence data points that fell within the
predicted range of the other species would correspond
to the proportion of Cuba lying within the predicted
distribution range. For each species pair, we conducted
reciprocal tests for each species. In sister species pairs,
greater than expected overlap by both species indicates
niche conservatism, and less than expected overlap in
both species indicates niche divergence. Also, in sister
species pairs, greater than expected overlap by one
species and less than expected overlap by the other could
suggest a case of niche specialization. In more distantly
S34
JASON H. KNOUFT ET AL.
Ecology Special Issue
TABLE 2. Pairwise comparisons of percentage of locality points of one Anolis species that fall within the predicted range of a
second Anolis species.
Species 2
Species 1
A. ahli
A. allogus (east)
A. allogus (west)
A. bremeri
A. homolechis
A. jubar
A. mestrei
A. ophiolepis
A. quadriocellifer
A. rubribarbus
A. sagrei
A. ahli
4.6,
4.3,
0.0,
6.5,
0.0,
0.0,
11.6,
0.0,
25.0,
10.2,
0.0
93.8
93.8
68.8
0.0
93.8
93.8
0.0
0.0
25.0
A. allogus (east)
8.7,
4.5,
59.4,
60.3,
0.0,
41.9,
54.2,
40.0,
39.8,
A. allogus (west)
6.2
10.8
95.4
69.2
0.0
73.8
0.0
56.9
86.2
100, 91.3
47.8, 95.7
6.3, 0.0
97.1, 95.7
58.1, 82.6
91.7, 4.3
0.0, 0.0
51.7, 95.7
A. bremeri
39.9,
14.3,
97.1,
44.2,
91.7,
0.0,
44.9,
91.3
12.0
100
100
26.1
0.0
100
A. homolechis
50.8,
94.3,
90.7,
66.7,
35.0,
70.3,
39.1
35.5
62.3
5.8
18.1
76.1
A. jubar
8.6,
30.2,
37.5,
40.0,
30.5,
0.0
42.9
0.0
12.7
57.1
Notes, Percentages in boldface represent cases in which a smaller than expected number of locality points of one species fall
within the predicted range of the second species. Percentages in italics represent cases in which a greater than expected number of
locality points of one species fall within the predicted range of the second species. In each cell, the value on the left represents the
percentage of locality points from Species 1 that fall in the predicted range of Species 2. The value on the right represents the
percentage of locality points from Species 2 that fall in the predicted range of Species 1.
related species pairs, greater than expected overlap could
suggest either niche convergence or suggest that the
species have both retained the ancestral condition (i.e.,
stasis).
In addition to the geographic approach to niche
overlap using species locality data and the predictions
generated by the ENM algorithms, we also examined
overlap of the ‘‘environmental envelopes’’ of species
pairs using GIS-derived environmental data extracted
from localities for each species. We generated the
environmental envelope for each species based on data
extracted from WorldClim Global Climate GIS data sets
(30-second resolution; WorldClim interpolated global
terrestrial climate surfaces, version 1.3; data and
software available online)6 (Hijmans et al. 2004). The
WorldClim data sets consisted of 19 bioclimatic
variables including annual mean temperature, mean
diurnal temperature range, isothermality, temperature
seasonality, maximum temperature of warmest month,
minimum temperature of coldest month, temperature
annual range, mean temperature of wettest quarter,
mean temperature of driest quarter, mean temperature
of warmest quarter, mean temperature of coldest
quarter, annual precipitation, precipitation of wettest
month, precipitation of driest month, precipitation
seasonality, precipitation of wettest quarter, precipitation of driest quarter, precipitation of warmest quarter,
and precipitation of coldest quarter, with all temperatures reported in degrees Celsius and all precipitation
amounts reported in millimeters. Environmental data
for each species was compiled by importing species
locality points into DIVA-GIS (Hijmans et al. 2001) to
generate longitude–latitude layers for each species.
Environmental data at each locality were then extracted
from each GIS data set to provide 19 climatic measures
6
hhttp://biogeo.berkeley.edui
at each species locality. All climatic data were log10transformed to standardize data for statistical analyses.
A principal-components analysis (PCA) was performed on the correlation matrix of transformed
environmental data to generate an environmental
envelope for each species. To generate the environmental envelope, the first two axes of the PCA for each
species were plotted in x–y space using ArcGIS (version
9.0). A minimum convex polygon (MCP) was then
calculated around the points for each species using the
Hawth’s Tools extension in ArcGIS (available online).7
The area for each species MCP was then calculated in
ArcGIS. The percentage niche overlap for each species
pair was calculated as the ratio of the sum of the overlap
areas in each species’ MCP to the total area of each
species’ MCP, with the resulting quotient multiplied by
100 to yield a percentage.
While the geographic approach applied to the ENM
predictions and species locality data is useful for
assessing similarities between individual species pairs,
the environmental-envelope method allows for the
examination of the relationship between niche similarity
and phylogenetic similarity among all members of the
clade. The environmental envelope could not be used to
assess differences between individual species pairs,
because we do not know the breadth of the environmental envelope for all of Cuba. Consequently, we could
not apply a binomial probability calculation to the
overlap between species pairs. We calculated the
correlation between niche similarity (i.e., percentage
envelope overlap) and phylogenetic similarity (patristic
distance between taxa) among all species pairs using a
Mantel test. A significant negative correlation indicates
niche conservatism between closely related species and
niche divergence between distantly related species.
7
hhttp://www.spatialecology.com/htoolsi
July 2006
NICHE EVOLUTION IN ANOLIS LIZARDS
S35
little overlap, and most of the cases of the greatest
overlap are among distantly related species pairs.
TABLE 2. Extended.
DISCUSSION
A. mestrei
48.8,
91.7,
0.0,
42.4,
82.9
11.4
0.0
97.1
A. ophiolepis
A. quadriocellifer
A. rubribarbus
41.7, 7.0
25.0, 14.0
66.9, 88.4
0.0, 0.0
7.6, 75.0
11.8, 50.0
RESULTS
Niche characteristics of members
of the Anolis sagrei group
The modeled niche of each species served as an
accurate predictor of the species’ distribution in all cases
(Table 1, Fig. 2). The GARP application provided 10
‘‘best’’ models. We used the average accuracy of these
models for comparisons with WhyWhere model accuracy. Prediction accuracy was higher with the WhyWhere algorithm than with GARP for 7 of 11 species
(Table 1), thus we used the WhyWhere predictions to
assess individual species pair overlap.
When locality data from one species were compared
to the predicted distribution of a sister species using the
WhyWhere predictions, significant cases of niche conservatism are recovered (Table 2). Additionally, several
cases occurred in which distantly related species had
niches that are more similar than would be expected by
chance, whether resulting either from convergence or
stasis (Table 2). There are also cases in which species
pairs exhibit less overlap than expected by chance;
however, whether this limited overlap is due to selectiondriven divergence or random diversification is unclear.
In all of these previous scenarios, both members of the
sister species pair exhibited the same reciprocal relationship. In addition, in one case, A. quadriocellifer and A.
bremeri, the relationship was not reciprocal: although a
significant percentage of locality data from A. bremeri
does not fall within the A. quadriocellifer prediction, a
significant percentage of locality data from A. quadriocellifer does fall with in the A. bremeri prediction. We
regard this as indicating that A. quadriocellifer resides in
a specialized component of the A. bremeri niche (Fig. 3).
The first and second principal components explained
42.4% and 25.7% of the overall variance in the PCA,
respectively (Table 3). The Mantel test examination of
the relationship between percentage of environmental
envelope overlap and phylogenetic similarity indicates
no consistent pattern in the evolution of the species
niche among species in the A. sagrei group (r ¼ 0.10, P ¼
0.26) (Fig. 4). Indeed, the most closely related taxa show
A common finding related to trait evolution is that
conservatism is the expected pattern during species
diversification (Webb et al. 2002). In terms of niche
evolution, this conservatism has been hypothesized to
result from active, stabilizing selection (Lord et al. 1995),
or from fixation of ancestral traits that limit the
potential range of outcomes during niche evolution
(Westoby et al. 1995; see review in Webb et al. [2002]).
Initial ENM work examining niche overlap in species
pairs separated by a geographic barrier supported the
prediction that niche conservatism characterizes evolutionary diversification (Peterson 2001). However, more
recent ENM work has suggested that patterns of niche
evolution beyond sister taxa can be inconsistent and not
conserved (Rice et al. 2003). Our results from the
environmental-envelope overlap analysis are congruent
with these more recent findings. No evidence of
generalized niche conservatism exists for the A. sagrei
group in Cuba. Overall, no relationship was found
between phylogenetic and niche similarity. This results
because some closely related species have greatly
divergent niches, whereas some distantly related species
are quite similar in their niches.
A closer look at patterns of niche similarity using the
ENM predictions and species locality data allows a more
detailed level of inference. All combinations of phylogenetic relatedness and degree of niche overlap are seen
in the data (Table 2). Examples of closely related taxa
with significantly high niche overlap (e.g., A. bremeri–A.
ophiolepis, A. bremeri–A. sagrei, A. mestrei–A. ophiolepis) indicate niche conservatism. Additionally, high
levels of niche differentiation are seen in close relatives
that exhibit significantly little niche overlap (e.g., A.
allogus (east)–A. allogus (west), A. homolechis–A. jubar).
FIG. 3. (A) Predicted distribution of A. quadriocellifer with
actual localities (points) of A. bremeri. (B) Predicted distribution of A. bremeri with actual localities (points) of A.
quadriocellifer.
S36
JASON H. KNOUFT ET AL.
TABLE 3. PC1 and PC2 loadings from principal-components
analysis of environmental variables for species in the Anolis
sagrei group.
Environmental variable
PC1
loadings
PC2
loadings
Elevation
Mean annual temperature
Mean diurnal temperature
Isothermality
Temperature seasonality
Maximum temperature, warmest month
Minimum temperature, coldest month
Temperature annual range
Mean temperature, wettest quarter
Mean temperature, driest quarter
Mean temperature, warmest quarter
Mean temperature, coldest quarter
Annual precipitation
Precipitation, wettest month
Precipitation, driest month
Precipitation seasonality
Precipitation wettest quarter
Precipitation, driest quarter
Precipitation, warmest quarter
Precipitation, coldest quarter
0.738
0.921
0.006
0.023
0.099
0.867
0.801
0.020
0.803
0.837
0.891
0.892
0.794
0.709
0.501
0.301
0.684
0.565
0.520
0.461
0.061
0.145
0.731
0.061
0.562
0.148
0.537
0.864
0.337
0.427
0.038
0.330
0.027
0.045
0.718
0.835
0.216
0.705
0.644
0.796
Many cases of low niche overlap among distantly related
taxa are also apparent (e.g., A. allogus (east)–A. bremeri,
A. mestrei–A. rubribarbus). In these cases, examination
of species’ niches in a phylogenetic context makes
evolutionary interpretation, either conservatism or
divergence, obvious. By contrast, the evolutionary
explanation for similar niches among distantly related
taxa is not so clear-cut: long-term conservatism or
convergence are both possibilities.
Although the natural history of many members of the
A. sagrei group is poorly studied, there appear to be at
least four axes along which the members of this group
partition habitat: heliothermy (sun vs. shade-loving),
forest type (xeric vs. mesic), habitat openness (woodland
vs. open habitat), and substrate type (trunks vs. rocks)
(Ruibal 1961, Ruibal and Williams 1961, Schwartz and
Henderson 1991, Rodrı́guez-Schettino 1999, Losos et al.
2003). Only one of these axes (i.e., forest type) involves a
geographic scale of niche partitioning that is appropriate
for the methods discussed here. A previous study (Glor
2004) suggested that divergence along this axis, and with
respect to substrate type, has occurred repeatedly,
perhaps accounting for the observed lack of conservatism. The two other axes (heliothermy and habitat
openness) meanwhile, appear more conserved in the
sense that they characterize large deeply divergent clades
(Glor 2004). Consequently, further study may reveal a
greater degree of niche conservatism, particularly along
these axes, than we discuss.
Our approach differs from previous niche modeling
exercises (Rice et al. 2003, Graham et al. 2004) in which
phylogenetic methods have been used to infer the niche
of hypothetical ancestral taxa. Because in many cases
ancestral reconstructions probably have low accuracy
(Schluter et al. 1997, Losos 1999, Martins 1999, Oakley
Ecology Special Issue
and Cunningham 2000, Webster and Purvis 2002), we
have avoided this approach by focusing only on the
niches of extant species in the context of their
phylogenetic similarity. The trade-off of this ‘‘nondirectional’’ (sensu Harvey and Pagel 1991) approach,
however, is that we are less able to make statements
about the direction in which evolution has occurred. In
particular, we have a number of cases in which nonsister
taxa, and indeed sometimes taxa that are only distantly
related, have significantly similar niches. Two processes
could account for such cases. On the one hand, two
species might have retained the ancestral niche through
the course of time; in other words, their conserved niche
similarity would be an example of evolutionary stasis.
Alternatively, the two species might have independently
derived the same niche through convergent evolution.
Without inferring ancestral niches, these two possibilities are difficult to distinguish.
One other interesting situation occurred between the
sister taxa A. quadriocellifer and A. bremeri, in which a
significant proportion of A. quadriocellifer localities fall
within the A. bremeri prediction, whereas the reciprocal
pattern is not exhibited. We interpret this to indicate that
the A. quadriocellifer niche is more specialized than, and
nested within, the A. bremeri niche. Again, however, the
direction in which this evolutionary change occurred,
whether from generalist to specialist or vice versa, is
difficult to discern. Nevertheless, the sister taxon to the A.
quadriocellifer–A. bremeri species pair (Fig. 1) is A. sagrei,
a species that has a broad niche similar to that of A.
bremeri (Table 2). Consequently, the parsimonious conclusion is that a broader niche is ancestral and the narrower niche of A. quadriocellifer is derived in this species.
Despite methodological differences, our results are
broadly congruent with previous studies on dendrobatid
frogs (Graham et al. 2004) and Aphelocoma jays (Rice et
FIG. 4. Relationship between patristic distance and ecological-envelope overlap in species pairs of the Anolis sagrei
group.
July 2006
NICHE EVOLUTION IN ANOLIS LIZARDS
S37
PLATE 1. Example of a Cuban trunk-ground anole lizard species (Anolis rubribarbus) from Guantanamo Province at Sendero
Natural et Recreo de Nibujon, Cuba. Photo credit: R. E. Glor.
al. 2003): in all three cases, many instances of closely
related taxa that diverge greatly in niche have been
discovered. Thus, the balance of evidence to date
provides little consistent evidence that environmental
niches are phylogenetically conservative. The main
counterexample to date is a study of Mexican bird
species that showed that allopatric populations on either
side of the Isthmus of Tehuantepec tend to exhibit niche
conservatism (Peterson et al. 1999, Peterson and Holt
2003). Although it is tempting to suggest that the focus
on allopatric populations explains these discrepant
findings, this conclusion is unwarranted, as closely
related allopatric sister taxa show divergence in dendrobatid frogs and anoles.
The broad-scale environmental data used in ENM
algorithms successfully predict anole occurrence on a
regional scale. Investigating the role that adaptation to
different climatic niches has played in anole evolution
will contribute importantly to understanding the genesis
of the incredible diversity of this species-rich clade.
Nonetheless, this approach has limitations. First, the
resolution of the GIS data limits analysis to regions,
rather than specific localities. Thus, this approach can
only investigate environmental determinants of regional
co-occurrence, rather than true sympatry. Second,
current species distributions may be a result of recent
allopatric speciation and not a consequence of species
distributions actually tracking climatic conditions. This
can present an interpretive dilemma for sister species
that appear to diverge in niche characteristics. This
scenario is displayed by only one species pair in our data
set (A. jubar–A. homolechis). Finally, an important
component of anole community ecology and evolution
is partitioning of habitats (e.g., cool/hot; high/low)
within a site (Schoener 1968, Williams 1983, Losos et al.
2003), a scale of habitat far too small to be detected by
these sorts of data. Clearly, a next step in niche modeling
will be the integration, of broad- and fine-scale niche
characteristics to elucidate the determinants of local and
regional distributions and habitat use.
ACKNOWLEDGMENTS
We greatly appreciate the time and efforts of the Curators
and Collection Managers at the American Museum of Natural
History, the California Academy of Sciences, Field Museum,
the Museum of Comparative Zoology, Harvard University, the
Museum of Vertebrate Zoology, University of California at
Berkeley, the Smithsonian National Museum of Natural
History, and the University of Kansas Natural History
Museum who assisted in providing locality data for species in
this study. We also greatly appreciate the efforts of D. Ullman
who georeferenced a large percentage of these data. J. H.
Knouft was supported by a grant from the National Science
Foundation (DBI-204144).
S38
JASON H. KNOUFT ET AL.
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Ecology, 87(7) Supplement, 2006, pp. S39–S49
Ó 2006 by the Ecological Society of America
PHYLOGENY AND THE HIERARCHICAL ORGANIZATION
OF PLANT DIVERSITY
JONATHAN SILVERTOWN,1,4 MIKE DODD,1 DAVID GOWING,1 CLARE LAWSON,2
2
AND
KEVIN MCCONWAY3
1
Department of Biological Sciences, The Open University, Walton Hall, Milton Keynes, MK7 6AA UK
Centre for Agri-Environmental Research, School of Agriculture, Policy and Development, The University of Reading,
Earley Gate, P.O. Box 237, Reading, RG6 6AR UK
3
Department of Statistics, The Open University, Walton Hall, Milton Keynes, MK7 6AA UK
Abstract. R. H. Whittaker’s idea that plant diversity can be divided into a hierarchy of
spatial components from a at the within-habitat scale through b for the turnover of species
between habitats to c along regional gradients implies the underlying existence of a, b, and c
niches. We explore the hypothesis that the evolution of a, b, and c niches is also hierarchical,
with traits that define the a niche being labile, while those defining b and c niches are
conservative. At the a level we find support for the hypothesis in the lack of close significant
phylogenetic relationship between meadow species that have similar a niches. In a second test, a
niche overlap based on a variety of traits is compared between congeners and noncongeners in
several communities; here, too, there is no evidence of a correlation between a niche and
phylogeny. To test whether b and c niches evolve conservatively, we reconstructed the
evolution of relevant traits on evolutionary trees for 14 different clades. Tests against null
models revealed a number of instances, including some in island radiations, in which habitat (b
niche) and elevational maximum (an aspect of the c niche) showed evolutionary conservatism.
Key words: coexistence; community assembly; diversity; evolutionary lability; geographical range;
habitat; hydrology; niche overlap; plant community; plant phylogeny.
INTRODUCTION
R. H. Whittaker (1975) proposed that diversity should
be analyzed at a hierarchy of spatial scales. At the local
scale, a diversity represents the number of species found
within a habitat. These species occur in sufficient
proximity to interact with one another. At intermediate
scales, b diversity quantifies the turnover in species that
takes place between habitats or along environmental
gradients. At a still wider scale, c diversity is the species
diversity of a region. Of a and b diversity, Whittaker
(1975:119) wrote that they ‘‘will be recognized as
consequences of niche differentiation and habitat diversification of species, respectively.’’ Not long afterwards,
Pickett and Bazzaz (1978) explicitly referred to these
concepts as the a niche and the b niche. (A glossary of
terms is given in Table 1.) Although these terms were not
widely adopted when they were first introduced, recent
research on the phylogenetic structure of ecological
communities suggests that the distinction between a and
b niches should receive greater attention, because the
hierarchical relationship between them might reflect the
hierarchical structure of evolutionary trees (Fig.1).
For a given phylogeny, heritable traits that vary freely
among the terminals (tips) of the tree are likely to be
Manuscript received 18 January 2005; revised 9 September
2005; accepted 13 September 2005. Corresponding Editor (ad
hoc): C. O. Webb. For reprints of this Special Issue, see
footnote 1, p. S1.
4
E-mail: [email protected]
evolutionarily labile. If these traits determine a species’
niche, community structure will appear free of phylogenetic conservatism. In contrast, traits that vary little
among terminals on the same tree indicate that their
evolution is likely to be more conservative. Niche-related
traits of this kind can potentially produce a phylogenetic
signal in the structure of ecological communities. Whether
conservatively evolving niche traits actually do produce
this signal depends upon the ecological processes of
community assembly that determine how many representatives of a conservatively evolving clade are present.
In areas of high endemism such the Cape Floristic Region
of South Africa or oceanic archipelagos such as Hawaii,
some communities might have been assembled, at least in
part, by adaptive radiation in situ. This is where we might
expect to find recent evolutionary events influencing
community structure most strongly. However, this form
of community assembly is a rare event, and most plant
communities, including some on islands such as those in
Macaronesia (Santos 2001), have been assembled from
plants with quite disparate phylogenetic histories (Pennington et al. 2004, Pennington and Dick 2004).
We ask two central questions. First, do ecological
traits evolve in a conservative manner? Second, is there a
difference in evolutionary lability between the traits that
underlie a and b niches? Recent studies of the
phylogenetic distribution of ecological traits have tended
to emphasize the conservative nature of plant trait
evolution and suggested that this influences community
assembly (Tofts and Silvertown 2000, Webb 2000,
S39
S40
JONATHAN SILVERTOWN ET AL.
Ecology Special Issue
TABLE 1. Definitions of terms used in the text.
Term
a niche
b niche
c niche
Community
Habitat
Lability
Niche
Niche trait
Realized niche
Definition
Source
The region of a species’ realized niche corresponding to species diversity at the local (a) scale
where interactions among species occur
The region of a species’ niche that corresponds to the habitat(s) where it is found; equivalent to
the ‘‘habitat niche’’ Grubb (1977)
The geographical range of a species
The collection of species that predictably co-occurs within a particular type of habitat
The kind of environment where a species occurs, defined largely by physical conditions; note
that conditions will usually be influenced by organisms as well as physical factors, but direct
interactions among organisms are not used to define habitats
The property of evolutionary changeability in a trait
An n-dimensional hypervolume defined by axes of resource use and/or environmental conditions
and within which populations of a species are able to maintain a long-term average net
reproductive rate 1
A measurable property of a species, by which its niche (a, b, or c ) can be defined
The region of its niche that a species is able to occupy in the presence of interspecific
competition and natural enemies
2, 5
2, 3
6
2
1, 4
1
Sources: 1, Hutchinson (1957); 2, Whittaker (1975); 3, Pickett and Bazzaz (1978); 4, Chase and Leibold (2003); 5, Silvertown
(2004); 6, Silvertown et al. (2006).
Prinzing et al. 2001, Webb et al. 2002, Ackerly 2003,
2004, Chazdon et al. 2003). One example of this pattern
is the long-standing observation that, in many communities, there is a higher ratio of species per genus than
would be expected if communities were assembled by
random draws from the species pool (e.g., Williams
1964). If congeneric species are overrepresented in
communities, then it follows that they must share
ecological traits that influence community assembly
and that these traits evolve more slowly than the rate
of appearance of new species.
Other studies, however, suggest that some traits that
influence community structure do not evolve conservatively. Cavender-Bares et al. (2004) detected labile evolution in the soil moisture tolerances of North American
oak species and found that these species segregated along
soil moisture gradients. Silvertown et al. (1999) found
that plant species in English meadow grasslands also
segregated on hydrological gradients and later reported
that there is no correlation between the ecological distance between species in hydrological niche space and
their phylogenetic distance as measured by the evolution
of the rbcL gene (Silvertown et al. 2006). How can these
data be reconciled with the many other examples of the
conservative evolution of ecological traits?
Silvertown et al. (2006) suggested that the apparent
contradiction between the lack of phylogenetic signal in
their data, which implies evolutionary lability in hydrological niches, and contrary findings by other authors
implying conservative evolution in some traits could be
explained if the traits have different evolutionary lability.
They proposed that habitat-determining traits that
influence b diversity, and which may be said to define
the b niche (Pickett and Bazzaz 1978), evolve conservatively. By contrast, traits involved in coexistence and that
influence a diversity, defining the a niche, are evolutionarily labile. Such a pattern could arise if, as most
theories of coexistence demand (Chesson 2000), species
must differ from each other in order to coexist. The
corollary of this is that a niches and coexistence will
necessarily be determined by labile traits. In short,
Silvertown et al. (2006) proposed that competing species
must share b niches in order to occur in the same habitat,
but they must have different a niches in order to coexist.
Silvertown et al. (2006) proposed that, by extension of
the relationship between a and b niches and Whittaker’s
(1975) a and b diversity, the geographical range of a
species can be regarded as its c niche. Thus, there is a
hierarchy of three niche levels with c at the top (Fig. 1).
The little evidence that is so far available suggests that b
niche traits are evolutionarily conservative; data pertaining to the evolution of the c niche are even more sparse.
Prinzing et al. (2001) analyzed the niches of European
plant species using Ellenberg indicator values (Ellenberg
1979, Ellenberg et al. 1991) and found strong evidence of
evolutionary conservatism. These values were devised to
quantify on an ordinal scale where different plant species
are found in central Europe along major environmental
axes, such as soil moisture, pH, light, and soil fertility.
Several studies have found that Ellenberg values are
stable traits that consistently predict the b niche of
species across Europe more generally (Thompson et al.
1993, Hill et al. 2000, Schaffers and Sykora 2000,
Prinzing et. al. 2002). Ellenberg values can be regarded
as b niche traits, because they refer to large-scale
environmental gradients. However, since a niches are
nested within b niches, some correlation between traits
like soil moisture tolerance is to be expected.
Ackerly (2004) examined phylogenetic conservatism
in the evolution of leaf traits that are associated with
adaptation to Mediterranean climates in California
chaparral habitat. These are a good example of traits
associated with the b niche. Specific leaf area was
significantly conserved in all four families analyzed, and
leaf size in three. These results suggest that sclerophylly
and other leaf traits associated with Mediterranean
habitats evolved before California chaparral was colo-
July 2006
HIERARCHY OF PLANT DIVERSITY
nized, supporting the view that the b niche traits evolve
in a conservative manner.
Very little evidence is available concerning the
evolution of c niches. Qian and Ricklefs (2004) found
that the latitudinal ranges, and hence c niches, of 57
plant genera with disjunct distributions in North
America and Asia were correlated between continents,
suggesting that the genera had highly conserved c niches
that dated to before the origin of the disjunctions,
perhaps 18 million years ago in the case of woody
species. How typical this result will prove to be of c
niches in general is not clear at present.
Other studies have examined the degree of range
overlap between members of the same clade (Barraclough and Vogler 2000, Graham et al. 2004). Barraclough and Vogler (2000) found that in a range of
vertebrate and insect phylogenies range overlap was low
between recently diverged taxa, but increased with time
since divergence. This indicates that speciation occurs
more often in allopatry (no range overlap) than
sympatry, but does not directly address the issue of c
niche evolution, since ranges can be split (i.e., become
allopatric) by the appearance of environmental barriers,
without any need for evolutionary change in the c niche.
In this paper we present four new lines of evidence
that have a bearing on the evolutionary conservatism of
a, b, and c niche traits. Each piece of evidence applies a
different kind of test, as appropriate to the data
available.
First, we take a closer look at the English wet meadow
communities in which Silvertown et al. (2006) found a
niche traits to be evolutionarily labile. We perform a
new analysis of the data in which we ask whether groups
of specialists that are confined to particular subcommunity types are more closely related to one another
than would be expected for randomly drawn samples
from the same community. A smaller phylogenetic
distance between specialists than between randomly
drawn nonspecialists would imply evolutionary conservatism in the specialist group.
Second, we reanalyze published data on various
ecological traits in a number of plant communities to
determine whether a niche overlap between congeners is
greater or less than between noncongeners in the same
community. Although we recognize that there is no
consistent phylogenetic definition of a genus, and that
some may be very old, congeners can usually be expected
to be more closely related than species from other genera
drawn from the same community. Trait variation ought
therefore to be smaller between congeners than noncongeners for conservatively evolving traits that predate
the origin of the genus, but similar for labile traits.
Third, we conduct a test of a prediction derived from
the hypothesis that b niche evolution is conservative by
examining the number of inferred transitions in habitat
(reflecting the b niche) within plant phylogenies for a
sample of 12 independent clades. Changes of habitat
should be fewer than expected by chance if b niche
S41
FIG. 1. A Venn diagram showing the nested hierarchy of a,
b, and c niches superimposed upon a hypothetical phylogenetic
tree. Note that the rectangles representing each kind of niche
intersect the phylogenetic tree at progressively deeper levels
from a to b to c niches, indicating earlier origin and greater
conservatism.
evolution is conservative. As a subsidiary hypothesis, the
expected patterns of conservatism should be stronger in
clades that have evolved in continental areas, where
available habitats are likely to have been already occupied by competitors, than in clades that have radiated
on islands where most habitats were unoccupied.
Finally, we apply the same kind of test to c niche
evolution by optimizing maximum elevation (reflecting
the c niche) onto phylogenies for two clades.
METHODS
Are habitat specialists phylogenetically clustered?
Silvertown et al. (1999, 2001) previously analyzed the
niche relationships of species in two mesotrophic grassland (meadow) plant communities classified as MG5 and
MG8 by the British National Vegetation Classification
(Rodwell 1992). Silvertown et al. (2001) suggested that
some of the niche separation observed in these
communities arose as deep as the split between monocots and eudicots, indicating that niche specialization
occurs within particular clades. For the present study,
we identified specialists from within each of the two
community types using data from an extensive survey
made by Gowing et al. (2002). This survey recorded an
estimate of percent cover of all species present in 3904 1
3 1 m quadrats across 18 sites representative of MG5,
MG8, and other floodplain hay meadow types in
England. Quadrats were classified into 12 communities
S42
JONATHAN SILVERTOWN ET AL.
Ecology Special Issue
TABLE 2. Comparison of a niche overlaps between congeneric pairs of herb species in nine genera and mean overlaps between the
congeners and species in other genera.
Genus
Drosera
Prosopis
Dentaria
Galium
Senna
Trillium
Aster
Helictotrichon
Festuca
Total no.
Overlap
Overlap
overlapping
with
with
species
congeners Comparison noncongeners
9
16
17
17
16
17
10
10
10
0.00
0.15
0.42
0.45
0.48
0.51
0.76
0.87
0.88
,
,
.
¼
.
,
.
.
.
0.60
0.38
0.37
0.45
0.31
0.52
0.44
0.83
0.83
a niche axes
Data
source
water table gradient in a bog community
soil moisture and nutrients in a desert community
forest understory microtopography and light
forest understory microtopography and light
soil moisture and nutrients in a desert community
forest understory microtopography and light
phenology and microtopography in forest understory
shoot phenology in grassland
shoot phenology in grassland
5
4
1
1
4
1
2
3
3
Notes: There is no significant difference overall between the degree of overlap found between congeners and the overlap between
noncongeners (Wilcoxon matched-pairs test, Z ¼ 0.42, P ¼ 0.67).
Sources: 1, Mann and Shugart (1983); 2, Beatty (1984); 3, Sydes (1984); 4, Shaukat (1994); 5, Nordbakken (1996).
and subcommunities using the program TWINSPAN
(Hill 1979).
A group of seven species characteristic of the MG5a
subtype of the MG5 community consisted of Trifolium
pratense (Fabaceae), Rhinanthus minor (Scrophulariaceae), Dactylis glomerata (Poaceae), Prunella vulgaris
(Lamiaceae), Heracleum sphodylium (Apiaceae), and
Leucanthemum vulgare and Leontodon saxatilis (both
Asteraceae). Within the MG8 community type, 10
species were identified as specialists associated with a
Carex disticha subcommunity. These were Carex disticha, C. distans, and Eleocharis uniglumis (Cyperaceae);
Senecio aquaticus and Bellis perennis (Asteraceae);
Juncus inflexus, J. articulatus, and J. subnodulosus
(Juncaceae); Festuca arundinaceae (Poaceae), and Trifolium fragiferum (Fabaceae). Specialists occurred more
frequently in the designated subcommunity types
(MG5a, MG8 C. disticha) than in any other of the 12
communities identified in the TWINSPAN analysis.
Phylogenetic distances between all pairwise combinations of 52 species belonging to MG5 and MG8
communities were calculated by Silvertown et al.
(2006). Distances were calculated as the sum of branch
lengths connecting species in a tree fitted to rbcL
sequences using maximum likelihood in PAUP* (Swofford 1996). For each of the two specialist groups, we
computed the mean and variance of pairwise phylogenetic distances among members of the group and
compared these with expected (null) distributions produced by randomization. Null distributions were derived
by sampling groups of n species at random from the 52
species in the meadow species pool for which rbcL
sequences are known, where n was the number of species
in the specialist group. To avoid bias in the species pool
caused by underrepresentation of sequences for Carex
and Juncus, we added extra copies of rbcL sequences for
species in these genera when conducting the test on the
Carex disticha subcommunity type. Using substitutes in
this way does not introduce bias, because rbcL sequence
differences among species of Carex and among Juncus
species are very small. A total of 104 randomizations were
run for each null model. If specialists are significantly
clustered phylogenetically, then the mean and variance of
pairwise rbcL distances should fall in the lower 5% of
values in the null distribution of each statistic.
a niche overlap among congeners vs. other species
Through an extensive review of the literature on plant
niches, we identified five studies of plant communities
from which it was possible to compute a niche overlaps
within and between genera. There were nine sets of
congeners in total. The validity of generic names was
checked against the online versions of Clayton and
Williamson (2003) for grasses and Brummitt (1992) for
other species. A name change affected one genus
(Dentaria to Cardamine), but did not alter the implied
evolutionary relationships between this genus and the
rest of the community with which it was compared.
Niche axes varied between studies (Table 2), but overlap
was measured using Pianka’s index in all cases (Pianka
1973). The pairwise overlap, Ojk, between the niche of
species j and the niche of species k is
P
pij pik
Ojk ¼ qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
ð1Þ
P 2 2
pij pik
for all resource states, i. In Eq. 1, pij is the proportion of
total resources used by j that consist of resource state i,
and pik is the proportion of total resources used by k that
consist of resource state i. Values of Ojk range from 0 to
1. The difference in mean overlap between congeners,
and between congeners and the rest of the community,
was tested by a Wilcoxon matched-pairs test (Sokal and
Rohlf 1995).
b and c niche transitions
We conducted a search of articles and citations in
American Journal of Botany, Systematic Botany, and
TreeBASE (University of Buffalo, New York, USA;
available online)5 to identify molecular phylogenetic
5
hwww.treebase.orgi
July 2006
HIERARCHY OF PLANT DIVERSITY
studies of plants in which 50% of the extant species in a
clade had been sampled (Tables 3 and 4). Phylogenies
with ,20 species were excluded because randomization
tests of the kind we used to detect phylogenetic
conservatism have low statistical power with sample
sizes below this limit (Blomberg et al. 2003). Habitat and
elevational data were obtained from the same source as
the phylogeny wherever they were given, or from
standard floras where they were not (Tables 3 and 4).
Habitat is by definition a b niche trait. We treated
elevational maximum as a c niche trait, because it
delimits the vertical dimension of a species’ range and is
clearly related to climate.
Evolution of habitat and elevational maximum (EM)
were optimized onto trees using MacClade 3.06 (Maddison and Maddison 1992). Habitat was treated as a
polymorphic character for species that were present in
more than one habitat type. Elevational maximum was
scored as a categorical variable with four classes: 0, EM
1000 m; 1, 1000 m , EM 2000 m; 2, 2000 m , EM
3000 m; 3, EM . 3000 m. Tests for phylogenetic
conservatism were performed by comparing the number
of transitions (steps) between habitat or EM states
required to account for the observed distribution of
habitats among terminal taxa with a null distribution.
We obtained a null distribution for the number of
habitat or EM transitions to be expected in any given
tree by randomly shuffling the observed states among
its terminals (Maddison and Slatkin 1991). Using
MacClade 3.06, we performed 103 randomizations for
each tree. The probability that an observed number of
steps occurred by chance was the frequency of
transitions of the same or smaller value found in the
null distribution. Frequencies ,0.05 were treated as
evidence of significant conservatism in the evolution of
habitat preference or EM. The randomization test we
used is normally employed on binary characters, but
some of our tests involved more than two niche
categories (e.g., four EM classes of the c niche). In
order to test the robustness of our results against the
unconventional use of multistate characters, where
variables could be combined on the basis of some
ecological variable (e.g., dry vs. mesic), we ran tests on
data recoded as a single binary character.
RESULTS
Are habitat specialists phylogenetically clustered?
The mean and variance of pairwise rbcL distances
among the seven specialists in the MG5a community
were 0.112 (P ¼ 0.144) and 0.0017 (P ¼ 0.226),
respectively. For the 10 specialists in the Carex disticha
community, the mean and variance were respectively
0.125 (P ¼ 0.428) and 0.0023 (P ¼ 0.362). In neither
community was the mean or the variance significantly
lower than expected by chance; thus the null hypothesis
of no phylogenetic clustering among specialists cannot
be rejected.
S43
a niche overlap among congeners vs. other species
Table 2 compares a niche overlaps between congeneric pairs of species in nine genera with mean overlaps
between the congeners and species in other genera.
There is no significant difference overall between the
degree of overlap found between congeners and the
overlap between noncongeners (Wilcoxon matchedpairs test, Z ¼ 0.42, P ¼ 0.67).
b and c niche transitions
Table 3 presents b niche transitions in seven island and
five continental clades. There was significant conservatism in the evolution of the b niche in five of the seven
island clades and in three of the six continental cases.
Habitat factors associated with conservatism included
the six major altitudinal zones in the Canary Islands (the
principal archipelago of Macaronesia) in the case of the
Aeonium clade, but not in the Echium or Argyranthemum
clades. When a binary coding of b niche into dry vs.
mesic was used, Argyranthemum and Sonchus did show
conservatism, but Aeonium and Sideritis did not (Table
3). Similar patterns were found in Hawaii, with species in
the Schiedea clade showing conservative evolution in
respect of eight habitat types; this clade and the
silversword alliance showed fewer transitions than
expected between wet and dry environments (Table 3).
In continental clades, conservatism occurred in both
habitat variables (serpentine soils and forest vs. open
habitats) analyzed in Calochortus, in one of three
variables (occurrence in vernal pools) in Mimulus, and
in preference among four habitat types in Narcissus
(Table 3). Neither Linanthus nor Primula clades showed
evidence of b niche conservatism.
Of the two clades analyzed for c niche conservatism,
EM evolved conservatively in Pinus, but not in Mimulus
(Table 4). Whether the data were coded as four EM
classes or two did not affect either outcome.
DISCUSSION
Collectively, the analyses performed here demonstrate
a lack of phylogenetic signal in the ecological structure
of communities, but, in contrast, indicate its presence in
at least some instances of how speciation populates
different habitats and how elevational range evolves.
The results support the suggestion that a niche traits are
evolutionarily labile, while b and c niche traits might
evolve in a more conservative manner. However, there
are caveats.
The species in the samples used to examine ecological
structure in communities on the one hand and adaptive
radiation among habitats and elevations on the other
were differently constituted. In the first instance, we
measured phylogenetic distances between a collection of
species that had passed through the various ecological
filters involved in community assembly. This resulted in
an extremely rarefied sampling of disparate branches of
the angiosperm phylogeny, including monocot and
eudicot clades. It would be necessary to analyze a less
S44
JONATHAN SILVERTOWN ET AL.
Ecology Special Issue
TABLE 3. Characteristics and numbers of observed b niche (habitat) transitions in island and continental clades estimated by
phylogenetic optimization, along with the number expected from a null model.
Clade
Sample/Clade size Types of b niche (no. habitats)
Macaronesia
51/63
Argyranthemum
Macaronesia
51 pops/23 spp.
Echium
Macaronesia
21/27
Sideritis
Sonchus
Macaronesia
Macaronesia
32/32
31/34
Schiedea and
Alsinidendron
Hawaii
30/28
Silversword alliance
Hawaii
36/36
cliffs and rocks, xerophytic scrub,
thermophile woodland, laurel forest,
pine forest, subalpine (6)
dry, mesic (2)
cliffs and rocks, xerophytic scrub,
thermophile woodland, laurel forest,
pine forest, subalpine (6)
dry, mesic (2)
cliffs and rocks, xerophytic scrub,
thermophile woodland, laurel forest,
pine forest, subalpine (6)
dry, mesic (2)
dry, mesic (2)
dry, coastal, mesic (3)
dry and coastal, mesic (2)
dry forest, dry shrubland, dry cliffs,
dry subalpine, shrubland, diverse
mesic forest, wesic forest, wet
forest (8)
dry, mesic (2)
dry, mesic (2)
Western North America
67/67
Narcissus
Europe
23/27
Linanthus, Leptosiphon clade
California
28/28
Mimulus
Northwestern North America
Primula sect. Auricula
Alps
Island clades
Aeonium, Greenovia, and
Monanthes
Continental clades
Calochortus
Region
88/;114
25/25
serpentine, other habitats (2)
forest, open habitats (2)
dry rocky open Mediterranean
hillsides, montane wet meadows,
oak woodland, lowland mesic
Mediterranean (4)
woodland and chaparral, grassy areas,
serpentine, desert, drying areas in
conifer forest (5)
dry, other habitats (2)
serpentine, other habitats (2)
vernal pool, other habitats (2)
serpentine, other habitats (2)
limestone, acid substratum (2)
cliffs and rocks, turf, woodland,
alpine/subalpine/tundra (4)
*P 0.05, **P 0.01, ***P 0.001.
Sample/Clade size is the ratio of the no. species (or, in the case of Argyranthemum, no. populations) in the phylogenetic
analysis to the estimated no. extant species that belong to the clade.
à The expected number of transitions shown is the mode of the distribution of 1000 runs of the null model.
§ P values are for the difference between the number of expected transitions and the number of observed transitions (number
observed not exceeding number expected).
Sources: 1, Hickman (1993); 2, Baldwin and Robichaux (1995); 3, Wagner et al. (1995); 4, Weller et al. (1995); 5, Bohle et al.
(1996); 6, Francisco-Ortega et al. (1996); 7, Kim et al. (1996); 8, Barber et al. (2000); 9, Bell and Patterson (2000); 10, Bramwell and
Bramwell (2001); 11, Mort et al. (2002); 12, Beardsley et al. (2004); 13, Graham and Barrett (2004); 14, Patterson and Givnish
(2004); 15, Zhang et al. (2004); 16, S. C. H. Barrett, personal communication.
rarefied sample if we were asking a solely evolutionary
question, but the following question is specific and
ecological: ‘‘Are specialist members of MG5a communities phylogenetically clustered?’’ In this case, the
method we have used is appropriate and it gives the
unequivocal answer, ‘‘no.’’
It is interesting that Kembel and Hubbell (2006) found
an absence of phylogenetic structure in the overall
tropical forest community in the 50-ha plot at Barro
Colorado Island, but that phylogenetic structure did
occur within specific habitats. Our null model and those
of Kembel and Hubbell (2006) were different, and this
cannot be ruled out as a source of the opposing results
(Gotelli and Graves 1996). It may also be that the much
larger species pool for tropical forest (n ¼ 312) than for
English meadows (n ¼ 52) makes phylogenetic structure
more likely to occur or easier to detect among specialists.
The approach used to compare niche overlap among
congeners with that among other species does not raise
phylogenetic sampling issues, but it does assume that a
niche dimensions relevant to coexistence have been
correctly identified. In each case the dimensions
measured do seem likely to fulfil this assumption (Table
2), but as yet very few field studies of putative plant
July 2006
HIERARCHY OF PLANT DIVERSITY
S45
Observed
Expectedà
P§
Data
sources
used specific leaf area (SLA) as a proxy measure of the a
niche in Ceanothus and found that this diverged earlier
than their climatically defined measure of the b niche.
This finding is at odds with our hypothesis that the a
niche is more labile than the b niche (Fig. 1).
20
25
0.003**
10, 11
b niche transitions
TABLE 3. Extended.
No. b niche transitions
10
21
11
24
0.303
0.071
6
11
10
12
0.001***
0.485
5
13
8
6
18
6
13
11
9
21
0.272
0.681
0.020*
0.036*
0.019*
3, 4
6
5
9
8
0.020*
0.011*
2
10
13
8
15
17
12
0.011*
0.049*
0.003**
17
18
0.284
6
3
3
5
5
12
8
3
6
6
7
12
0.092
1.000
0.002**
0.156
0.120
0.641
6
5, 10
8
7
14
13, 16
1, 9
1, 12
15
niches have proven their role in coexistence beyond all
doubt (Silvertown 2004). The result of the congeners
test performed here is consistent with large a niche
differences that have been found between sympatric
species in, among others, the following genera: Acer
(Sipe and Bazzaz 1994, 1995), Adenostoma (Redtfeldt
and Davis 1996), Dryobalanops (Itoh et al. 2003),
Macaranga (Davies 2001), Piper (Fleming 1985), Psychotria (Valladares et al. 2000), Quercus (CavenderBares et al. 2004), Ranunculus (Harper and Sagar 1953),
Salix (Dawson 1990), and Typha (Grace and Wetzel
1981). It is clear that coexisting congeners are often as
ecologically different from each other as they are from
unrelated members of the same communities. This
implies that a niche traits are evolutionarily labile,
although proof of this requires evolutionary changes to
be analyzed against an explicit, and preferably dated,
phylogeny.
Ackerly et al. (2006) tested the order in which a and b
niche traits evolved in the shrub genus Ceanothus. They
Oceanic islands and island-like habitats, such as
vernal pools and serpentine barrens in California,
contain multiple radiations that provide replicates for
the test of b niche conservatism. There are several
examples in the endemic flora of vernal pools in the
California Floristic Province (CFP). An extreme case is
the monophyletic genus Downingia that contains 13
species (Schultheis 2001), all but one of which occur in
vernal pools (Ayers 1993). In section Navarretia of the
genus Navarretia, four vernal pool species form a clade
that is sister to a species that is facultatively associated
with the same habitat (Spencer and Rieseberg 1998). In
the much larger genus Mimulus, there are roughly six
vernal pool species, and four of them are concentrated in
one small clade, indicating significant conservatism in
this genus, as well (Thompson 1993, Beardsley et al.
2004) (Table 3).
Also in the CFP, significantly conservative evolution
of serpentine tolerance is found in the large genus
Calochortus, where seven of a total of 18 species
occurring on serpentine soils belong to a single clade
(Patterson and Givnish 2004) (Table 3). Serpentine
species in Mimulus show slight, though non-significant
phylogenetic association (Table 3). Phylogenetic relationships among Mimulus species are well resolved, but
the weak evidence of phylogenetic conservatism might
easily be strengthened by more ecological data. Just
three of 28 species in the Leptosiphon clade of the genus
Linanthus occur on serpentine, but they represent three
independent evolutionary events (Patterson 1993, Bell
and Patterson 2000), so there is no evidence of
conservatism in this case.
Kelch and Baldwin (2003) compared the mean genetic
divergence measured at ITS and ETS rDNA loci among
terminal taxa in seven clades that have evolved within
the CFP, in addition to the cases already mentioned.
There was a positive relationship between genetic
divergence within a clade and the number of plant
communities in which its members are found. A clade of
Cirsium species endemic to the CFP was an outlier from
the relationship as a whole, inhabiting a greater variety
of plant communities than would be expected for the
degree of genetic divergence among its members. This
deviation could result either from an abnormally high
rate of evolutionary shifts between habitats in the CFP
Cirsium clade, or an abnormally low rate of molecular
evolution. High ecological diversity relative to rDNA
variation also occurs in the larger North American
Cirsium clade of which the CFP endemics form one part
(Kelch and Baldwin 2003). This could indicate that the
evolutionary lability of habitat depends on lineage.
S46
JONATHAN SILVERTOWN ET AL.
Ecology Special Issue
TABLE 4. Characteristics and no. c niches, along with no. b niche transitions, based upon upper elevational maximum in two large
clades estimated by phylogenetic optimization
Clade
Region
No. b niche transitions
Sample/Clade
size
No.
c niches
Observed
Expected
P
4
2
4
2
27
19
29
13
27
19
37
19
0.399
0.680
0.003
0.003
Mimulus
Northwestern North America
88/;114
Pinus
Europe, Asia, North and Central America
101/;120
Data
sources
2, 3
1, 4
Notes: Data were analyzed for c niches coded into four (0–999 m, 1000–1999 m, 2000–3000 m, .3000 m) and two (,2000 m vs.
.2001 m) elevational maximum classes (i.e., c niches). Also see Table 3 footnotes for further explanatory details.
Sources: (1) Mirov 1967, (2) Hickman 1993, (3) Beardsley et al. 2004, (4) Gernandt et al. 2005.
Radiations on islands also show a mixed picture,
although conservatism here is more evident than might
have been expected given the extreme evolutionary
lability of plant form that is present in Aeonium
(Jorgensen and Olesen 2001), Sonchus (Kim et al.
1996), the silversword alliance (Baldwin and Robichaux
1995), and other island endemics. The Hawaiian mints
are another endemic group in which considerable
morphological variation among species occurs within a
restricted range of climatic conditions (Lindqvist et al.
2003). It should be recognized that the crude distinction
between wet and dry habitats used for the silverswords
in Table 3 does not do justice to the enormous range of
soil moisture conditions present in different habitats in
Hawaii. The Hawaiian lobeliods are a group that have
radiated across the entire soil moisture gradient
(Givnish et al. 2004). Nonetheless, the unexpected
presence of conservatism of habitat evolution in several
island radiations is remarkable. It suggests that speciation often involves interisland colonization between
similar habitats (Francisco-Ortega et al. 1996) and that
conservative habitat evolution is not confined to
continental radiations.
c niche transitions
The distinction between b and c niches is not clearcut, but neither should we expect it to be. The three
niche types of a, b, and c are segments in Hutchinson’s
(1957) n-dimensional hypervolume and are bound to
overlap along some dimensions. On some dimensions
they may be nested, on others they may not. For
example, since elevation and habitat are closely
correlated in Macaronesia (Bramwell and Bramwell
2001), conservative evolution of habitat in Aeonium and
Sonchus (Table 3) also implies conservative evolution of
elevational distribution. We analyzed elevational maximum in Mimulus, where its evolution was not
conservative and in Pinus where it was (Table 4).
Differences in elevational distribution between the pines
of different regions of the world were noted by Mirov
(1967). Grotkopp et al. (2004) found that species in the
subgenus Pinus occupied significantly lower elevations
than those in subgenus Strobus. This implies that
elevational distribution has been conserved since the
two subgenera diverged, which dates it to the deepest
node in the phylogeny of Pinus (Gernandt et al. 2005).
Extant members of the genus comprise a mixture of
ancient and quite recently evolved species (Farjon
1996), so conservatism in their elevational distribution
cannot simply be attributed to the lack of recent
speciation.
Why should a, b, and c niches evolve
with different degrees of lability?
All theories of coexistence based upon nonneutral
processes require that species have different a niches in
order to coexist (Chesson 2000). Silvertown et al. (2006)
argued that, for this reason, community assembly will
create structure based upon labile traits. (It will not do
so if neutral processes dominate community assembly.)
The argument is not that competitive exclusion forces a
niches to evolve in a labile manner, but rather that it
prevents any traits that might, for whatever reason, not
be evolutionarily labile from facilitating coexistence.
Nonlabile traits are prevented from defining the a niche
by default. Webb et al.’s (2006) study of the effect of
interspecific relatedness on seedling mortality implies
that apparent competition mediated by disease, as well
as direct competition, could cause related species that
are insufficiently different to exclude one another at the
local scale.
A filtering process might also operate upon the traits
that define the b niche, but with opposite effect.
Coexisting species must by definition occupy the same
habitat and must therefore have b niches that overlap.
Thus, the b niche might come to be defined by nonlabile
traits.
The filtering processes that could determine the
lability of the a niche and the conservatism of the b
niche do not as easily explain the conservatism of c
niches, such as the latitudinal ranges of woody plants
with disjunct distributions (Qian and Ricklefs 2004). For
c niches, we must invoke either phylogenetic constraint,
such as a lack of appropriate genetic variation, or
phylogenetic niche conservatism (PNC) (Harvey and
Pagel 1991). Although the result of stabilizing selection,
it is not clear why PNC should operate with particular
effect on the c niche; we therefore offer a third
July 2006
HIERARCHY OF PLANT DIVERSITY
explanation. If one thinks of the c niche as being a
geographical area with climatically defined boundaries,
then the problem of why it evolves conservatively is
closely allied to another evolutionary question: what
prevents species at range boundaries from evolving the
ability to escape beyond those boundaries? Haldane
(1956) proposed the following answer to this question:
Adaptation at range boundaries, which is necessary for
spread to be possible, might be genetically constrained
by the swamping effect of gene flow from individuals in
the hinterland that are not adapted to condition at the
boundary. This process requires that populations at the
periphery of a distribution exist as demographic sinks
that require an input of migrants for persistence
(Kirkpatrick and Barton 1997, Barton 2001). This is a
condition that can be tested.
In this paper we have developed earlier ideas that the
hierarchical organization of plant diversity at the a, b,
and c scales proposed by Whittaker (1975) corresponds
to a hierarchical set of niches. The traits that define the a
niche appear to be evolutionarily labile, whereas the
phylogenetic evidence suggests that the b niche evolves
in a conservative manner. Perhaps most conservative of
all is the c niche, which is related to geographic
distribution. The more conservative a trait, the more
remote its origin in evolutionary time and the deeper this
lies in a phylogenetic tree. Further exploration of the
correspondence between the ecological and evolutionary
hierarchies should illuminate our knowledge of both.
ACKNOWLEDGMENTS
J. Silvertown acknowledges the support of a Royal Society
travel grant and is grateful to Spencer Barrett and the Botany
Department of the University of Toronto for their hospitality
during the inception of this paper. We thank Konrad Dolphin,
Mike Fay, and Jeffrey Joseph for rbcL sequences. D. Gowing
and C. Lawson acknowledge the support of Defra and M.
Dodd the support of The Open University.
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Ecology, 87(7) Supplement, 2006, pp. S50–S61
Ó 2006 by the Ecological Society of America
NICHE EVOLUTION AND ADAPTIVE RADIATION:
TESTING THE ORDER OF TRAIT DIVERGENCE
D. D. ACKERLY,1,4 D. W. SCHWILK,2
2
AND
C. O. WEBB3,5
1
Department of Integrative Biology, University of California, Berkeley, California 94720 USA
U.S. Geological Survey/WERC, Sequoia and Kings Canyon Field Station, Three Rivers, California 93271 USA
3
Department of Ecology and Evolution, Yale University, New Haven, Connecticut 06511 USA
Abstract. In the course of an adaptive radiation, the evolution of niche parameters is of
particular interest for understanding modes of speciation and the consequences for coexistence
of related species within communities. We pose a general question: In the course of an
evolutionary radiation, do traits related to within-community niche differences (a niche)
evolve before or after differentiation of macrohabitat affinity or climatic tolerances (b niche)?
Here we introduce a new test to address this question, based on a modification of the method
of independent contrasts. The divergence order test (DOT) is based on the average age of the
nodes on a tree, weighted by the absolute magnitude of the contrast at each node for a
particular trait. The comparison of these weighted averages reveals whether large divergences
for one trait have occurred earlier or later in the course of diversification, relative to a second
trait; significance is determined by bootstrapping from maximum-likelihood ancestral state
reconstructions. The method is applied to the evolution of Ceanothus, a woody plant group in
California, in which co-occurring species exhibit significant differences in a key leaf trait
(specific leaf area) associated with contrasting physiological and life history strategies. Cooccurring species differ more for this trait than expected under a null model of community
assembly. This a niche difference evolved early in the divergence of two major subclades
within Ceanothus, whereas climatic distributions (b niche traits) diversified later within each of
the subclades. However, rapid evolution of climate parameters makes inferences of early
divergence events highly uncertain, and differentiation of the b niche might have taken place
throughout the evolution of the group, without leaving a clear phylogenetic signal. Similar
patterns observed in several plant and animal groups suggest that early divergence of a niche
traits might be a common feature of niche evolution in many adaptive radiations.
Key words: adaptive radiation; Ceanothus; Cerastes; Coast Range; community assembly; Euceanothus;
habitat, niche conservatism; phylogenetic comparative methods; specific leaf area; Sierra Nevada; trait
divergence; Transverse Range.
INTRODUCTION
Ecologists have long considered niche differences
among species to be essential for species coexistence
(Chesson 2000, Chase and Leibold 2003; but see
Hubbell [2001]). The evolution of niche differences
among closely related species has received particular
attention. Because close relatives tend to be ecologically
similar in many respects (Darwin 1859, Felsenstein
1985, Harvey and Pagel 1991, Webb et al. 2002), those
features that do diverge during speciation will provide
important insights into ecological differentiation and
consequences for coexistence of closely related species.
It is useful in this context to distinguish two scales of
niche differentiation, corresponding to different scales
Manuscript received 21 January 2005; revised 9 August 2005;
accepted 11 August 2005. Corresponding Editor: A. A.
Agrawal. For reprints of this Special Issue, see footnote 1, p.
S1.
4
E-mail: [email protected]
5
Present affiliation: Arnold Arboretum of Harvard University.
of species distributions. At large spatial scales, species
can occupy different macrohabitats or climatic envelopes; the resulting distributions will be largely allopatric, or, if they do overlap geographically, individuals
of the two species would rarely encounter one another
due to habitat differentiation. At smaller scales, related
species that co-occur in local communities usually
exhibit spatial or temporal differentiation in microhabitat, resource use, diet, or other factors. It is at this
local scale, where the balance of intra- and interspecific
interactions influences coexistence and community
structure, that the niche concept has played the most
important role. Following Pickett and Bazzaz (1978)
and Silvertown et al. (2006), we employ the term a niche
to describe these small-scale components of the niche
that differ among co-occurring species, corresponding
to Whittaker’s (1975) use of a diversity for diversity of
local communities. In contrast, the b niche is defined as
macrohabitat and climate factors related to larger scale
distributions, corresponding to the b component of
diversity among habitats in a landscape. In this paper,
we do not distinguish the proposed b and c niche, which
S50
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NICHE EVOLUTION AND ADAPTIVE RADIATION
refers to distributions at the scale of habitat vs.
geographic range (Silvertown et al. 2006), as these have
equivalent implications in terms of species interactions
at the community level.
It has been argued that species within local communities tend to be phylogenetically overdispersed; i.e.,
closely related species co-occur less than expected,
relative to an appropriate null model (Elton 1946,
Williams 1964, Gotelli and Graves 1996, CavenderBares et al. 2004). This pattern would suggest that a
niche parameters are evolutionarily conserved and/or b
niche parameters are highly divergent, such that close
relatives tend to occupy different macrohabitats and
hence different communities (Elton 1946, Williams 1964,
Gotelli and Graves 1996, Cavender-Bares et al. 2004).
The alternative, if a niche traits were more labile, would
facilitate coexistence and divergent resource use in
sibling species within local communities. Divergence of
macrohabitat parameters among sibling taxa is compatible with the allopatric model of speciation, as disjunct
populations in a heterogeneous landscape are likely to
encounter distinct habitats (Graham et al. 2004; but see
Wiens [2004]). Differentiation of the a niche would then
represent a later stage of evolutionary divergence, either
resulting from or directly promoting species coexistence
as the ranges of the now distinct species expand into
each other’s territory.
In an important paper, Diamond (1986) argued for
this ‘‘habitat-first’’ model of speciation, based on his
observations that closely related bird species in New
Guinea tend to be allopatric and occupy distinct
macrohabitats along elevational or climatic gradients
(see review by Schluter [2000]). In addition, Diamond
and Schluter both argued that the habitat-first speciation model can be extended to the analysis of adaptive
radiations, based on a parsimonious assumption that
rates of evolution do not dramatically change. In other
words, if habitat divergence represents the first stage of
speciation among close relatives, then it would also be
characteristic of early speciation events at the base of an
adaptive radiation. As a corollary, if differentiation of a
niche occurs late in speciation, or is observed among
distant relatives, then it would be characteristic of the
later stage of adaptive radiation. Streelman and Danley
(2003) presented a related model, arguing that vertebrate
adaptive radiations follow a trajectory of divergence
along three axes: habitat, trophic morphology, and
communication, usually in that order. However, their
use of ‘‘habitat’’ refers more to microhabitats within a
community (e.g., benthic vs. limnetic sticklebacks),
rather than Diamond’s larger scale differentiation
among elevational bands and different forest types
occupied by New Guinea birds. This ambiguity over
the use of the word habitat is unfortunate, as there is a
substantive difference between these models in their
emphasis on large-scale habitat differences, implying
allopatric populations, vs. microhabitat differentiation
within local communities.
S51
Recent developments of phylogenetic methodology
offer outstanding opportunities to reevaluate these
classic questions. Here, we present a comparative
approach to the problem of niche evolution by
introducing a new comparative method designed to test
the relative timing of divergence in two ecological traits
(e.g., a vs. b niche axes); we then use the test to evaluate
the sequence of trait divergence in the radiation of the
woody plant group Ceanothus in California. Our results
indicate that a niche traits related to local scale
coexistence diverge first in the radiation of this group,
a pattern that is shared with several other plant and
animal radiations.
DIVERGENCE ORDER TEST
The divergence order test (DOT) was designed to
address these questions of niche evolution, as well as
other questions regarding the relative sequence of
diversification events in a clade. The test examines the
relative timing of evolutionary divergence for two
continuous characters, and it is based on a modification
of the method of phylogenetic independent contrasts
(Felsenstein 1985). Independent contrasts transform the
trait data for N species into a set of N – 1 contrasts, each
based on the difference between trait values across a
phylogenetic divergence. Under the assumption that the
trait evolved independently at each divergence, the
contrasts provide a robust basis on which to test
hypotheses of correlated evolution, addressing the
underlying historical pattern of trait evolution as well
as better meeting the assumptions of standard parametric statistics (Garland et al. 1992). For the DOT, we
modify this method and use the absolute differences
between related nodes derived from maximum-likelihood estimates of ancestral trait reconstructions,
obtained using ANCML (Schluter et al. 1997). This
approach allows us to incorporate the uncertainty of
reconstructions in deeper nodes.
The divergence order test is based on two sets of
numbers: (1) the absolute value of the unstandardized
contrasts for trait i across the nodes (k ¼ 1, 2, . . . , N) of a
phylogeny (Cik), which measures the magnitude of the
divergence that occurred at each node regardless of the
direction of change; and (2) the age of each node (Ak).
We then calculate a weighted mean age of divergence for
each trait as follows:
N
X
Ak Cik
Wi ¼ k ¼N1
X
:
ð1Þ
Cik
k¼1
The result is an average age, in units of time, that
indicates whether the large divergences in a trait tended
to occur early or late in the diversification of a group
(Fig. 1). Note that this age will not generally correspond
to the age of any single divergence; it is simply a
statistical measure of the tendency toward early or late
S52
D. D. ACKERLY ET AL.
FIG. 1. Example of the divergence order test (DOT). Two
patterns of trait divergence are illustrated on a simple
phylogeny. Numbers at the tips of the phylogeny indicate two
possible trait states, 0 or 1. Numbers at interior nodes (in italics)
show the contrast for that node. Trait A (open circles) exhibits a
pattern of late divergence, whereas Trait B (shaded circles)
exhibits a pattern of early divergence. The lower panel plots
contrast magnitude vs. age and shows the calculated weighted
divergence age for Trait A (WA ¼ 1 Ma [i.e., 1 million years
ago]) and Trait B (WB ¼ 2 Ma).
divergence for the trait in question. The DOT is then
based on the comparison of the weighted divergence age
for two traits (D ¼ Wi Wj) to determine whether one
trait diverged significantly earlier than the other, on
average.
The DOT statistic is derived from contrasts between
ancestral states and does depend on the accuracy of the
ancestral estimates themselves (Oakley and Cunningham
2000). We do not consider the traits of outgroups in
calculating ancestral states, as these are only necessary
to identify trends in trait evolution within a group, and
not the magnitude of divergences. The maximumlikelihood algorithm of ANCML assumes that the
pattern of trait evolution fits a model of Brownian
motion. Global squared-change parsimony, which is
also based on Brownian motion, provides the same
ancestral estimates, but no confidence limits. The fit to
Brownian motion can be tested in several ways. First,
using Felsenstein’s (1985) algorithm, the correlation of
Ecology Special Issue
the absolute value of standardized independent contrasts
and the standard deviation of those contrasts (the square
root of subtending branch lengths) should be nonsignificant (Garland et al. 1992). A negative correlation
would indicate larger contrasts than expected on rapid
bifurcations, and vice versa for a positive correlation.
The absolute values of standardized contrasts should
also fit a half-normal distribution, and this can be
checked visually using truncated normal probability
plots. In addition, if several distinct clades are present
(as in the Ceanothus case), homogeneity of evolutionary
rates can be tested using a nonparametric comparison of
standardized contrasts between groups (Garland 1992),
or a recently introduced maximum-likelihood approach
(O’Meara et al. 2005). In general, methods based on
independent contrasts are fairly robust to violations of
Brownian motion (Diaz-Uriarte and Garland 1996,
Ackerly 2000), but this has not been evaluated for the
calculation of standard errors by ANCML or the DOT
analysis.
It can be useful to examine correlations between the
two traits under consideration, though DOT does not
require that the traits exhibit any particular pattern of
correlated or independent evolutionary change. If
changes in the two traits are tightly linked, then DOT
will certainly not be significant, as the contrasts will be
similar in magnitude at each node. However, differences
in the magnitude of a few basal or distal nodes could
result in a significant DOT outcome, and trait evolution
could still be correlated overall on the tree.
We have explored several approaches to significance
testing of the D statistic (see Appendix A). We present
here our preferred method, based on a bootstrapping
approach, to obtain confidence intervals for the two
estimates of W and their difference, D. The rationale for
this approach is that comparative methods, particularly
independent contrasts, tend to underestimate the magnitude of older divergences for rapidly evolving traits.
As a simple example, consider a bifurcating tree with
four species at the tips. If each pair of sister taxa has
divergent trait values, reflecting rapid trait evolution,
then the averages for their respective common ancestors
could be virtually identical and the basal contrast will be
nearly or exactly zero (e.g., Fig. 1, Trait A). However,
given the rapid rate of evolution for this trait, it is also
possible that a large divergence occurred at the first
node, followed by reversals at the subsequent nodes
resulting in convergence among extant taxa. Maximumlikelihood estimates of ancestral states allow for this
possibility by placing confidence limits on the ancestors
(Schluter et al. 1997). If a trait evolves rapidly, then the
confidence limits at deeper nodes will be large (see
Appendix A, Fig. A1).
We use ANCML (Schluter et al. 1997) to generate
maximum-likelihood estimates and confidence limits of
ancestral states at each node; we then create bootstrap
distributions of the potential magnitude of each
divergence event (see Appendix A, Fig. A1). Hypo-
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NICHE EVOLUTION AND ADAPTIVE RADIATION
thetical ancestral values are sampled from the distribution for each trait at each node, and from each sample
we calculate the corresponding values of Cik, Cjk, Wi,
Wj, and D. We then examine the distribution of D values
to determine whether D ¼ 0 falls outside the 95%
confidence limits on the mean, indicating significance of
the observed values at P 0.05. The calculation and
significance of the DOT method were implemented on
Mac OS X, using awk scripts, R (R-project 2004), and
ANCML (see Supplement).
Branch lengths and node ages
As the objective of this test is to calculate relative
timing of divergence events, the analysis should ideally
be conducted with ultrametric, calibrated phylogenies
based on a molecular clock, or rate-smoothed branches
if the branch lengths violate a molecular clock (Sanderson 2002). Methods for obtaining relative ages are
improving, although there can still be considerable
uncertainty due to heterogeneous rates of molecular
evolution and difficulty in establishing calibration points
across different clades (Sanderson 2002, 2003). The
DOT method will be robust to much of this uncertainty,
because the same ages are used in the calculation of
weighted divergence times for both traits. For the nodes
along any contiguous path from the root to the tips, ages
will always be correctly ordered, even if the actual values
are uncertain. Problems would most likely arise if
incorrect calibrations were applied to two or more
independent segments of the tree (i.e., along different
root-to-tip paths) in which different traits exhibited
large evolutionary divergences. Attention to this problem is warranted in applications of the test.
S53
Despite these consistent differences in drought tolerance and fire response, both clades are widespread in the
California Floristic Province, inhabiting chaparral,
semiarid forests, and oak woodland. Often, species pairs
representing one species from each of the subgroups cooccur, and it has been suggested that differences in fire
response and/or tolerance of water stress facilitate this
coexistence (Keeley 1977, Keeley and Zedler 1978, Davis
et al. 1999). These patterns suggest that the basal
divergence between the two clades involved a niche traits
related to drought tolerance or postfire regeneration
strategies. In contrast, both clades are represented by
species throughout California, suggesting more recent
differentiation of the climatic niche envelope, representing the b niche (Knight and Ackerly 2001). Here we
undertake four new analyses to quantify and test these
observations. (1) We have assembled a co-occurrence
data set from the literature, and we use null models to
test for nonrandom patterns of co-occurrence between
species from the two subgroups. (2) We combine the cooccurrence data with a trait data set to test whether cooccurring species differ significantly for traits related to
plant growth strategies (a niche) and are more similar
than expected for traits related to climatic envelopes (b
niche). (3) We reanalyze the Ceanothus phylogeny, based
on internal transcribed spacer (ITS) sequence data, to
obtain an ultrametric tree with branch lengths fit to a
molecular clock. (4) We use this phylogeny and the trait
data set to apply the DOT analysis, testing the
prediction that a niche traits diverged earlier than b
niche traits in the evolution of Ceanothus.
METHODS
Occurrence data
CASE STUDY: COMMUNITY ASSEMBLY AND NICHE
EVOLUTION IN CEANOTHUS
The woody plant group Ceanothus comprises ;55
minimum-rank taxa (species or subspecies) that have
primarily diversified within the California Floristic
Province (McMinn 1942, Hardig et al. 2000). The group
is divided into two well-supported clades (Jeong et al.
1997, Hardig et al. 2000) that differ consistently in
several morphological and physiological traits related to
drought tolerance. The two groups are considered
subgenera, and are currently designated Cerastes and
Ceanothus; for greater clarity (and consistency with
phylogenetic naming conventions), we prefer the older
name Euceanothus for the latter group. Species in
Cerastes have thick leaves with stomatal crypts and have
shallower roots and more embolism-resistant xylem than
do members of Euceanothus (McMinn 1942, Hellmers et
al. 1955, Davis et al. 1999). Additionally, species of
Cerastes establish only from seed following fire, while
Euceanothus species generally resprout as well (Wells
1969, Schwilk and Ackerly 2005). In a Mediterraneantype climate, seedlings of nonsprouting species must
survive an intense summer drought period after winter or
spring germination.
A matrix of co-occurrence data for Ceanothus in
California was obtained from a search of the literature
and consultation with colleagues. A total of 51 sites were
obtained that had two or more co-occurring Ceanothus,
with a total of 16 different taxa (plots in the same
location with the same species composition were
recorded as one site; Nicholson 1993). Of these sites,
35, with 13 taxa, were located in chaparral of the Coast
or Transverse ranges, while 16 sites with 7 taxa were
from the Sierra Nevada region (CT and SN, respectively). Of the 51 sites, 48 had just two Ceanothus
species, while the remainder had three. (See Appendix B
for details of the occurrence data matrix.)
Trait selection
Although coexistence of Ceanothus species, in a matrix
of other taxa in the community, can involve contrasting
physiological or regeneration strategies, no direct studies
of coexistence mechanisms in chaparral have been
conducted. To reflect differences in drought tolerance,
the ideal traits would be either a direct measure of xylem
tolerance to embolism under water deficit or wood
density, which is a close correlate of xylem tolerance
(Hacke et al. 2001). These traits are known to vary
S54
D. D. ACKERLY ET AL.
between the two subgroups (Davis et al. 1999), but they
are not available for large numbers of species. As a proxy
for contrasting physiological strategies, we used specific
leaf area (SLA), the ratio of fresh leaf area to dry mass.
This well-studied leaf functional trait plays an important
role in the ‘‘leaf economic spectrum’’ of variation in plant
metabolic rates (Wright et al. 2004). In general, species
with higher SLA have shorter leaf life span and higher
photosynthetic rates. In chaparral shrubs such as
Ceanothus, high SLA is associated with less droughttolerant leaves (Ackerly 2004b), and we have a large data
set available for roughly two-thirds of the Ceanothus taxa
(Ackerly 2004a). We do not claim that differences in SLA
per se promote coexistence, but rather that they could be
associated with suites of traits that are related to niche
partitioning and differentiation in ecological strategies of
co-occurring species. Data for SLA are species means
collected previously from field, herbarium, and botanic
garden specimens (Ackerly 2004a); three new taxa that
appeared in the occurrence data set were added to this
trait data set (C. greggii, C. parvifolius, and C. sanguineus)
based on measurements of specimens in the University
and Jepson Herbaria (University of California–Berkeley,
California, USA). Values were log-transformed for all
analyses, as relative differences are a better measure of
physiological differentiation (Reich et al. 1997). (See
Appendix C for details of the trait data set.)
At larger spatial scales (b niche), Ceanothus are
differentially distributed with respect to edaphic conditions (e.g., serpentine specialists), habitat (chaparral
vs. conifer forests), elevational range, and macroclimate
(precipitation and temperature) (Hickman 1993, Nicholson 1993, Davis et al. 1999). We quantified the realized
climatic niche, to characterize these large-scale distributions, by overlaying species distributions on climate
maps of California and calculating mean climatic
parameters for each species (Knight and Ackerly
2001). We have selected the mean precipitation and the
mean January temperatures within the geographic range
of each species, reflecting distributions along geographic
and elevational gradients in California. The climate
niche analysis serves as an indirect surrogate for
unmeasured physiological traits related to species
tolerances and distributions along climate gradients.
This interpretation assumes that distributions do not
simply reflect historical factors and limited dispersal
potential. The contraction and expansion of chaparral
during the interglacial periods (Graham 1999) argues
against a strong role for dispersal limitation as a longterm constraint on species distributions.
Community assembly
We used five null models for community assembly to
test several predictions, relative to patterns that would be
expected by chance: (1) Species from the two clades
(Cerastes and Euceanothus) co-occur more often than
expected. (2) Values of SLA show greater variation
among co-occurring species than expected. (3) Climate
Ecology Special Issue
niche parameters show greater similarity among cooccurring species than expected. The measure of trait
dissimilarity within communities was simply the difference between species values for two-species samples, and
the mean of the successive differences among ranked
values in three-species samples. We also calculated the
mean trait value for each community, which allowed us
to test our additional null models. (4) The standard
deviation of site means should be higher for climate niche
parameters than expected by chance, due to turnover of
species along climatic gradients. Finally, (5) among-site
standard deviation in mean SLA should be lower than
expected under the null, as the combinations of species
from the two clades result in high trait disparity within
sites and low disparity across sites. One-tailed tests were
conducted for all hypotheses, based on these predictions,
comparing the observed data to 999 randomizations.
As a null model, we use the ‘‘independent-swap’’
algorithm (Gotelli and Entsminger 2001), preserving
both site diversity and species frequency of occurrence
while randomizing assignments of species to sites. This is
critical to ensure that patterns of trait assembly do not
simply reflect differential abundance of particular
species. All calculations were carried out in R (R-project
2004); the swap algorithm was implemented by S.
Kembel in C as part of the PHYLOCOM package
(Webb et al. 2004).
Phylogeny
The Ceanothus phylogeny of Hardig et al. (2000) was
reanalyzed to obtain an ultrametric tree fit to a
molecular clock for the taxon sample in our trait data
set. Conflicting phylogenies for Ceanothus based on ITS
vs. matK sequence data could reflect lineage sorting
during rapid radiations or hybridization (Hardig et al.
2000), and ITS (which is a nuclear marker) was selected
as a more reliable estimator of the ‘‘true’’ species tree.
Limited sampling of ndhF (10 taxa; Jeong et al. 1997) is
insufficient to incorporate in the broader analysis
considered here. ITS sequence data for 76 accessions
(73 Ceanothus and 3 outgroups [Adolphia californica,
Zizyphus obtusifolia, and Spyridium parvifolium]) were
obtained from GenBank (accessions GBAN-AF048901
through GBAN-AF048975; Hardig et al. 2000). Sequences were aligned with ClustalX using default parameters,
and alignments were checked by eye (no manual
adjustments were made); total aligned sequence length
for ITS1, ITS2, and the intervening 15S region was 627
nucleotides. Taxa with identical sequences were kept in
the analysis, for use later in comparative analyses.
Multiple sequences for individual taxa were pruned to
one representative sequence, based on preliminary
analyses (Hardig et al. 2000). The resulting analysis
included 56 Ceanothus sequences and 96 informative
characters. Phylogenetic analysis was conducted with
PAUP*, using parsimony criteria and a heuristic search
(random addition sequence with 10 replicates, TBR,
MULTREES in effect, collapse zero-length branches in
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NICHE EVOLUTION AND ADAPTIVE RADIATION
effect, and steepest descent not in effect) (Swofford
2002). This analysis resulted in 174 equally parsimonious trees of length 322 on one island, which was hit in
all 10 replicate searches. The strict consensus of the
equally parsimonious trees was very similar to the results
reported by Hardig et al. (2000). The most significant
difference was that we obtained more resolution within
Euceanothus, with the Western group (Hardig et al.
2000: Fig. 1) monophyletic and nested within a paraphyletic Eastern group.
For comparative analysis, ANCML (Schluter et al.
1997) requires fully bifurcating phylogenies. It is
possible to generate these by randomly resolving the
trees obtained in the analysis just described (in which
zero-length branches were collapsed), but we are not
aware of software that will provide alternative resolutions while maintaining branch lengths. As an
alternative, we conducted a second parsimony search
in PAUP* with the same parameters, but with zerolength branches not collapsed and MAXTREES ¼
10 000. Ceanothus oliganthus ssp. oliganthus, which was
present in our ecological data but missing from the
molecular data, was added to the matrix with the same
sequence as C. oliganthus ssp. sorediatus. Given taxa
with identical sequences, this search quickly reached
the maximum number of trees, with length ¼ 322, as in
the prior case. We then pruned the trees to include only
the 39 taxa for which we had phenotypic data, resulting
in 3254 unique topologies (outgroups were not
considered in the analysis of ancestral states). For
further analysis, 100 trees were selected from this set by
sampling every 20th tree (because immediately adjacent
trees tend to be similar to each other). This set of 100
alternative, fully bifurcating trees was used for all
subsequent analyses.
Branch lengths
Maximum-likelihood methods were used to fit branch
lengths to the 100 topologies, based on the HKY85
model with transition/transversion rates fit empirically
from the data (Swofford 2002). A molecular clock was
not rejected using this model (P . 0.05 for all trees), so
the branch lengths fit with a molecular clock were used
for comparative analyses. Based on an independent
analysis of rates of rbcL evolution in Ceanothus (Jeong
et al. 1997), the split between Cerastes and Euceanothus
was calibrated at 18–39 Ma. Fossil evidence provides
independent confirmation of the minimum age, as taxa
assignable to both clades appear in the fossil record by
18 Ma (Chaney 1927, Axelrod 1956). Using this
calibration for the basal node, the clock-calibrated tree
suggests that radiation of each of the two (Cerastes and
Euceanothus) began no more recently than 4–5 Ma, at
about the same time as the onset of the Mediterraneantype climate in California.
The inclusion of taxa with identical sequences (a
common occurrence for rapidly speciating groups)
results in zero-length branches in the phylogeny (i.e., a
S55
polytomy). Zero-length branches create problems for
both ANCML and independent contrasts, as they imply
instantaneous evolutionary divergence (a hard polytomy), whereas they might reflect uncertainty of the
sequence of speciation events (soft polytomy) as much
as rapid radiation. Zero-length branches represent
truncated estimates of elapsed time since speciation,
since each branch will remain at zero length until the first
fixed base change occurs. We addressed this problem by
adjusting zero-length branches to a small nonzero value
(1.0 3 104) slightly lower than the shortest branches
resulting from the maximum-likelihood fit to the
molecular clock, which ranged from 1.03 3 104 to 4.94
3 104 across the 100 trees. Based on our calibration, this
adjustment represents an absolute time of ;5 3 104 yr.
Note that these small divergences (especially if they occur
on both sides of a bifurcation) will lead to an inflated
estimate of the Brownian motion rate parameter and,
hence, broader confidence intervals on ancestral states.
This will increase the standard errors of weighted
divergence age (W ) from our bootstrap procedure,
leading to a more conservative test of significance for
the DOT statistic. We recognize that this is an imperfect
solution and hope that the problem of zero-length
branches will be addressed in future research on branch
length calibration for comparative analysis.
Divergence order test analysis
We conducted the DOT analysis based on the average
age of internal nodes, weighted by the absolute value of
the unstandardized independent contrasts for each trait.
Significance of the difference in ages was assessed by
bootstrapping trait histories from the mean and standard deviations obtained from ANCML. Given the
strong preliminary data that we have introduced, we
conducted one-tailed tests of the hypothesis that SLA
divergence occurred earlier than divergences in climate
niche parameters.
RESULTS
Trait variation
Across the entire trait data set (39 taxa), specific leaf
area (SLA) was significantly higher in Euceanothus than
Cerastes. Precipitation and SLA were weakly correlated
overall (R ¼ 0.25), and they were essentially independent
based on independent contrasts (R ¼ 0.04) (Fig. 2A).
January temperature and SLA were negatively correlated across species (R ¼ 0.35) and based on
independent contrasts (R ¼ 0.31). This negative
relationship was also apparent within Euceanothus, but
not in Cerastes (Fig. 2B). The decline in Euceanothus
reflects the transition from deciduous taxa occupying the
coldest ranges (e.g., C. parvifolius) to evergreens at lower
latitudes or elevations. The most striking aspect of both
relationships is the marked difference in SLA between
the clades that is maintained across the climatic
gradients, consistent with the prediction of local cooccurrence between species that differ in SLA.
S56
D. D. ACKERLY ET AL.
Ecology Special Issue
FIG. 2. Specific leaf area (SLA; originally measured in mm2/mg) vs. (A) mean precipitation, and (B) January temperatures
within species ranges for species of Cerastes (solid circles) and Euceanothus (open circles).
Community assembly
Taxonomic co-occurrence.—Of the 48 sites with two
species, 38 had one from each of the two major clades
(Cerastes and Euceanothus); this pattern was particularly
striking in the Coast/Transverse (CT) chaparral sites (31
of 35 sites), but was not significant in the Sierra Nevada
(SN) (Table 1). For both the full data set and the coastal
partition, the elevated co-occurrence of species from
different clades was highly significant, relative to the null
model that maintained both site diversity and species
occurrence (P 0.001). The frequent co-occurrence of
taxa from the two clades has long been noted in the
literature, but not previously quantified and tested
relative to a null model.
Trait disparity.—Across all sites, the mean difference
in log10(SLA) between co-occurring species was 0.32
units, and as predicted this value was significantly
greater than the null expectation (P , 0.04; Table 2). In
contrast, differences between climate niche parameters
of co-occurring species were much smaller than expected
(P 0.001 in both cases; Table 2). When the data are
partitioned geographically, the within-site disparity in
SLA was still significantly greater in CT, but disparity in
SN was less than expected. Disparity in climate niche
parameters is significantly lower than the null, as
predicted, in both areas (Table 2). The among-site
standard deviation in trait means was significantly
greater than expected for climate niche traits, as
predicted, in the entire data set and both geographic
partitions. For SLA, it was significantly lower than
expected in CT, but the pattern was reversed in SN
(Table 2).
Collectively, these analyses indicate that in Coast and
Transverse range chaparral, co-occurring Ceanothus
species exhibit greater disparity in SLA than expected
under a null model of community assembly. In
contrast, in the Sierra Nevada it appears that SLA
varies among sites, while within-site disparity is low,
perhaps due to distribution of evergreen vs. deciduous
(low- vs. high-SLA) species along elevational gradients.
As expected, climate niche parameters are always
similar among co-occurring species and significantly
different among sites.
Divergence order test analysis
These results support the selection of SLA and
realized climate niche parameters as traits reflecting
local (a) vs. regional (b) differentiation, respectively. The
patterns are stronger in the Coast and Transverse
ranges, and if there were a monophyletic group in
Ceanothus restricted to these communities we would
limit our analyses to this group. However, the evolutionary radiation into the two major clades, and
subsequently within clades, encompasses both geographic areas (and beyond). We feel it is important to
TABLE 1. Relative frequency of local co-occurrence patterns
for Ceanothus, in terms of the number of species from each
major subgroup (CT ¼ Coast and Transverse ranges; SN ¼
Sierra Nevada).
No. communities
Community pattern
Cerastes
Euceanothus
Total
(P 0.001)
0
1
2
0
1
2
3
2
1
0
3
2
1
0
7
38
3
2
1
0
0
CT
(P 0.001)
SN
(NS)
2
31
2
0
0
0
0
5
7
1
2
1
0
0
Notes: Example of how to read Table 1: The first row
indicates that a total of seven communities were recorded (two
in CT and five in SN) with two co-occurring Euceanothus
species and no Cerastes. P values indicate the probability of
obtaining the observed number of sites occupied by species
from the two subgroups, relative to a null model of community
assembly (see text).
July 2006
NICHE EVOLUTION AND ADAPTIVE RADIATION
S57
TABLE 2. Mean trait disparity among co-occurring species, and standard deviation of trait means among sites.
Mean disparity
Trait
SD
of site means
Prediction All (N ¼ 51) CT (N ¼ 35) SN (N ¼ 16) Prediction All (N ¼ 51) CT (N ¼ 35) SN (N ¼ 16)
Specific leaf area
(mm2/mg, log)
Observed mean
Expected mean (SD)
P
.
Annual precipitation
(mm)
Observed mean
Expected mean (SD)
P
January minimum
temperature (8C)
Observed mean
Expected mean (SD)
P
0.32
0.29 (0.018)
,0.04
0.35
0.28 (0.021)
0.001
0.25
0.28 (0.016)
,
117
280 (25.2)
0.001
123
187 (17.3)
0.002
103
121 (8.7)
,0.03
,
1.25
2.53 (0.19)
0.001
0.92
1.24 (0.093)
0.002
1.95
2.49 (0.21)
, 0.02
0.170
0.14 (0.014)
NS
0.093
0.15 (0.013)
0.001
.
217
155 (12.1)
0.001
159
124 (10.5)
0.002
83
67 (8.22)
,0.02
.
2.001
1.50 (0.11)
0.001
0.985
0.81 (0.065)
, 0.01
1.571
1.23 (0.14)
,0.02
,
NS
0.158
0.17 (0.012)
NS
Notes: Observed values are compared to expectations based on a null model of community assembly. Analyses were conducted
for all sites, and for Coast and Transverse ranges (CT) and Sierra Nevada (SN) sites separately. Prior predictions regarding the
direction of the difference between observed and expected values are listed, and all significance tests are one-tailed.
conduct evolutionary analyses on all available data for
the entire group.
The three traits considered here generally fit the
Brownian motion model underlying the use of ANCML
for ancestral states. For all three traits, inspection of
normal probability plots for the absolute value of
standardized contrasts indicated a close fit to a
truncated normal distribution. For SLA and January
temperatures, correlations of standardized contrasts and
their standard deviation (i.e., the square root of the
subtending branch lengths) were not significant. For
precipitation, the correlation was significantly negative
across all trees, reflecting larger than expected divergences across rapid speciation events, and a small divergence across the long branches between Cerastes and
Euceanothus. This pattern will lead to a relatively high
Brownian motion rate parameter, to accommodate these
rapid divergences, and will thus inflate the confidence
intervals around the ancestral estimates for precipitation, making the DOT more conservative. Across most
of the 100 trees, rates of trait evolution were homogeneous between Cerastes and Euceanothus; in 3 and 24
trees, rates of SLA and January temperature evolution,
respectively, were significantly higher in Euceanothus
(0.04 , P , 0.05).
Fig. 3 illustrates trait divergences on a randomly
selected tree from the analysis. The largest contrast for
SLA was located at the basal node between Cerastes and
Euceanothus (Fig. 3). The weighted divergence age for
SLA, averaged across the 100 alternative phylogenies,
was 6.5 3 103 branch length units (Table 3). For the
climate parameters, the basal contrasts were much
smaller, and larger divergences were noted within each
of the two major clades (Fig. 3). The weighted
divergence ages were 4.30 3 103 and 4.24 3 103 for
January temperatures and precipitation, respectively.
For all 100 alternative phylogenies, weighted divergence
ages were older for SLA than for both climate
parameters, and the DOT was significant in 94 of 100
trees for the SLA vs. January temperature difference,
and for 92 out of 100 trees for SLA vs. precipitation.
DISCUSSION
This analysis of the radiation of Ceanothus demonstrates an initial shift in a niche traits that subsequently
promote (or at least facilitate) coexistence among related
species. These traits were then conserved as the radiation
progressed, and later speciation events were characterized primarily by divergence in climate envelopes,
corresponding to geographic differentiation along latitudinal and elevational gradients. The result is that cooccurring Ceanothus species within local communities
are more distantly related than expected by chance,
relative to the group as a whole.
Similar patterns are evident in other clades that have
been analyzed in a phylogenetic context. In the oaks
(Quercus) of Florida, local communities tend to be
phylogenetically overdispersed, often with one or two
members each drawn from three distinct clades (red,
white, and live oaks). Habitat preferences diverge
repeatedly within each of these clades; deeper divergences between the clades involve traits, such as seed
maturation time and wood density, which may promote
coexistence through differential regeneration or pathogen tolerance, respectively (Cavender-Bares et al. 2004).
In studies of Phylloscopus warblers in Kashmir, Richman and Price (1992, Richman 1996) argued that deep
divergences in the group involved differentiation in
feeding strategies, while more recent speciation events
were related to macrohabitat distributions (coniferous
vs. deciduous forests) (but see Forstmeier et al. [2001]).
In the radiation of Anolis lizards, distinctive ecomorphs,
which coexist by feeding in different parts of the canopy,
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D. D. ACKERLY ET AL.
FIG. 3. Divergence order test (DOT) for Ceanothus,
illustrated for one randomly chosen tree out of 100 equally
parsimonious trees used for analysis. For all panels, ages along
the x-axis are clock-calibrated molecular branch length units;
the breaks in the axis indicate the long branches connecting the
two recently diversifying clades. (A) Ceanothus phylogeny,
based on reanalysis of internal transcribed spacer (ITS)
sequence data from Hardig et al. (2000). Species names are
omitted for clarity. (B–D) Open circles indicate the magnitude
of unstandardized contrasts vs. node age for specific leaf area
(SLA), January temperatures, and mean precipitation, respectively (see Table 3 for weighted mean ages). Standard errors of
the contrasts (derived from the bootstrap procedure) are
illustrated for the basal contrast only. The solid diamonds
indicate weighted mean divergence age for each trait (6SE)
based on 200 bootstrap randomizations of the contrasts
(vertical position of this point is arbitrary).
have evolved independently on multiple islands (Losos
et al. 1998, Knouft et al. 2006). On the larger islands,
however, there has been continued speciation involving
diversification of macrohabitats within ecomorph
Ecology Special Issue
groups (Glor et al. 2003). In our terminology, the earlier
divergence events in each of these cases involve shifts in
the a niche, whereas b niche traits continue to diverge
later in the radiation. Streelman and Danley (2003)
include the anoles as one of the case studies in their
review, considering the diversification of ecomorphs as
an example of the first stage of radiation, involving
microhabitat divergence. This is consistent with our
interpretation of ecomorphs as diversification of the a
niche, although our terminology is different.
These case studies provide a striking contrast to
Diamond’s (1986) habitat-first model, which proposed
that the first stage of speciation and adaptive radiation
involved allopatric divergence along habitat gradients.
These contrasting interpretations reflect differences in
the interpretation of fast- vs. slow-evolving traits, and
they highlight underlying philosophical differences in
the interpretation of comparative data.
It is well known that phylogenetic inference works
best for slowly evolving traits (Felsenstein 1988). Rapid
evolution erodes the signal of early events on a
phylogeny, due to reversals on the same or adjacent
branches and convergence among the terminal states
(strictly speaking, this is only true for traits with a finite
number or range of states; Donoghue and Ree 2000,
Ackerly and Nyffeler 2004). For this reason, independent contrasts will generally provide an extremely poor
estimate of the timing of divergence for rapidly evolving
traits, as it will appear that there was little divergence in
deeper nodes (e.g., Fig. 1). The high level of uncertainty
in ML ancestral state reconstructions reflects this
problem for rapidly evolving traits (Schluter et al.
1997, Cunningham et al. 1998) and led us to adopt the
bootstrap method proposed here. In this situation, if the
most recent speciation events in an adaptive radiation
involve divergence in macrohabitat or habitat-related
traits, it is parsimonious to assume that deeper events
also involved such divergences (T. Price, personal
communication), though evidence of this will not be
available from phylogenetic analysis.
In contrast, for slowly evolving traits phylogenetic
methods are quite powerful, leading to a different
interpretation of the comparative data. For this case,
as in the contrasting SLA values in Ceanothus, or
divergent ecomorphs in Anolis, the greatest trait differences will generally be observed among distantly related
species. The conservatism of these traits in diverse clades
within each group strongly argues that the underlying
divergences occurred during the initial speciation events
early in the overall radiation. The view that a niche
divergence occurs late in the course of species differentiation, because these traits tend to differ between
distantly related species, is not compatible with comparative phylogenetic analysis.
Taken together, these views lead to a possible
synthesis of contrasting interpretations. In the cases
discussed here, adaptive radiation may have proceeded
by ‘‘a niche early’’ and ‘‘b niche throughout.’’ In other
words, habitat divergence can occur frequently at all
July 2006
NICHE EVOLUTION AND ADAPTIVE RADIATION
S59
TABLE 3. Results of the divergence order test (DOT) for relative divergence times of specific leaf area (SLA) and climate niche
parameters in Ceanothus.
Trait
Mean age (SE)
D (SE)
No. nonzero
SLA
January temperature
Precipitation
0.00654 (0.00083)
0.00430 (0.00071)
0.00424 (0.00083)
0.00225 (0.00108)
0.00230 (0.00117)
94
92
Notes: Mean age is the average, over 100 alternative trees, of the weighted divergence times (W) for each trait. For each tree, W is
derived by bootstrapping the ancestral states from maximum likelihood distributions for ancestral states, with 200 replicates; D is the
mean difference in age of each climate parameter vs. SLA, over the 100 alternative trees; no. nonzero is the number of trees (out of
100) in which D was significantly different from zero, based on a one-tailed test of the hypothesis that mean age for SLA was older.
depths in the tree, although it can only be reconstructed
with confidence in recent speciation events. Habitat
differences clearly can play an important role in
allopatric speciation, though it is important to note that
allopatric populations might also occupy similar environments in different geographic areas (Peterson et al.
1999, Wiens 2004). In some cases, speciation also
involves a shift in a niche traits related to resource
partitioning in local communities, but these events are
apparently less frequent. Divergence in a niche traits
might be due to incidental divergence under conditions
that favor different traits (e.g., island vs. mainland
populations), or divergence by character displacement in
sympatry due to direct competitive interactions between
the incipient species (Schluter 2000, Levin 2004).
If correct, the ‘‘a early, b throughout’’ model presents
two unresolved questions about the evolution of a niche
traits. First, why should these traits exhibit evolutionary
transitions early on in adaptive radiations? (At the time
of this initial divergence, the adaptive radiation is still an
unrealized future of the clade.) And second, why do a
niche traits often exhibit phylogenetic conservatism
during subsequent diversification. With regard to the
first question, it is tempting to identify divergence in a
niche traits as key innovations, or invasions of a novel
adaptive zone (sensu Simpson 1953), contributing to the
subsequent radiation. For example, in the case of
Ceanothus we have strong evidence that California
chaparral communities are capable of supporting at
least one sprouting and one nonsprouting Ceanothus
species. Thus, when this trait diverged early in the
evolution of Ceanothus (the direction of evolution
between ancestral and derived states is unknown), the
two descendent subclades were both able to diversify in
parallel across a broad gradient of climatic conditions.
While such scenarios are plausible, and may be correct,
it is difficult to conduct rigorous analyses of diversification hypotheses in terms of the timing of phenotypic
innovation and hypothesized shifts in speciation rates
(Sanderson and Donoghue 1994, Hodges 1995).
The second question addresses the important topic of
niche conservatism: What are the mechanisms promoting
evolutionary stasis in ecological traits through speciation
and diversification of a clade? Recent theoretical analyses
support the view that contrasting selection pressures in
heterogeneous environments, combined with gene flow,
interspecific competition, and/or habitat selection, can
generate stabilizing selection effects that lead to evolutionary stasis in niche parameters (Holt 1987, 2003,
Kirkpatrick and Barton 1997, Case and Taper 2000).
Empirical studies of these predictions are needed. The
roles of niche conservatism in speciation, the evolution of
regional biota and the assembly of communities has
recently received increased attention (Webb et al. 2002,
Ackerly 2003, Wiens 2004), and each of these offers a
counterpoint to the emphasis on ecological divergence as
a key component of evolutionary radiations.
The conclusion that a niche traits evolve relatively
slowly during an adaptive radiation contrasts with the
views of Silvertown et al. (2006a, b) on niche evolution.
They argue, based on several lines of evidence, that the a
niche evolves rapidly, and as a corollary local communities usually show little phylogenetic signal in their
species composition. This conclusion is supported by
their analysis of the phylogenetic structure of English
meadow communities (Silvertown et al. 2001, 2006a, b)
and by comparison of niche overlap between congeneric
and noncongeneric species within local communities.
The apparent conflict between their analyses and our
conclusions is most likely due to the different scales of
analysis and sampling of taxa. The species of the English
meadow communities are widely dispersed across the
angiosperm phylogeny. When the phylogeny is pruned
for analysis of niche distributions, the closest relatives
remaining on the tree are rarely if ever immediate
sibling species. As a result, even the most recent ‘‘events’’
represented on such a phylogeny are relatively old
compared to our analysis of adaptive radiations. Thus,
Silvertown et al.’s (2006a) conclusions and our analyses
may be entirely compatible, but focused on different
scales of analysis.
CONCLUSIONS
The divergence order test (DOT) introduced here
provides a quantitative approach to test hypotheses
about the relative sequence of divergence for continuous
traits. Considering the potential pitfalls in comparative
analysis of rapidly evolving traits, the bootstrap method
incorporating the uncertainty of ancestral state reconstructions provides a conservative approach for significance testing. Our application of the DOT to Ceanothus,
and interpretation of other cases in the literature, leads
to the conclusion that a niche traits often diverge early
in the course of adaptive radiations. The b niche traits,
S60
D. D. ACKERLY ET AL.
which are related to macrohabitat distributions, might
evolve rapidly throughout the radiation, although the
signal of early divergences is erased by the high rates of
evolution. Further application of the DOT, or improved
tests addressing these questions, will provide an important step towards synthesis of niche evolution and
adaptive radiation.
ACKNOWLEDGMENTS
D. D. Ackerly thanks C. O. Webb and J. B. Losos for the
invitation to contribute this paper to the ESA symposium and
this special issue on Phylogenetics and Community Ecology.
The authors thank D. Schluter and T. Price for valuable
discussions and comments that improved the manuscript. This
research was supported by National Science Foundation grants
0212873 to C. O. Webb, M. J. Donoghue, and D. D. Ackerly
and 0078301 to D. D. Ackerly.
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APPENDIX A
Discussion of alternative null models (Ecological Archives E087-110-A1).
APPENDIX B
Ceanothus occurrence matrix (Ecological Archives E087-110-A2).
APPENDIX C
Ceanothus trait matrix (Ecological Archives E087-110-A3).
SUPPLEMENT
Source code for DOT (Ecological Archives E087-110-S1).
S61
Ecology, 87(7) Supplement, 2006, pp. S62–S75
Ó 2006 by the Ecological Society of America
PHYLOGENETIC DISPERSION OF HOST USE IN A TROPICAL INSECT
HERBIVORE COMMUNITY
GEORGE D. WEIBLEN,1,6 CAMPBELL O. WEBB,2,7 VOJTECH NOVOTNY,3 YVES BASSET,4
AND
SCOTT E. MILLER5
1
Department of Plant Biology, University of Minnesota, Saint Paul, Minnesota 55108 USA
Section of Evolution and Ecology, University of California, Davis, California 95616 USA
Institute of Entomology, Czech Academy of Sciences and Biological Faculty, University of South Bohemia,
370 05 Ceske Budejovice, Czech Republic
4
Smithsonian Tropical Research Institute, Balboa, Ancon, Panama
5
National Museum of Natural History, Smithsonian Institution, Washington, D.C. 20013-7012 USA
2
3
Abstract. Theory has long predicted that insect community structure should be related to
host plant phylogeny. We examined the distribution of insect herbivore associations with
respect to host plant phylogeny for caterpillars (Lepidoptera), beetles (Coleoptera), and
grasshoppers and relatives (orthopteroids) in a New Guinea rain forest. We collected
herbivores from three lineages of closely related woody plants and from more distantly related
plant lineages in the same locality to examine the phylogenetic scale at which host specificity
can be detected in a community sample. By grafting molecular phylogenies inferred from three
different genes into a supertree, we developed a phylogenetic hypothesis for the host
community.
Feeding experiments were performed on more than 100 000 live insects collected from the 62
host species. We examined patterns of host use with respect to the host plant phylogeny. As
predicted, we found a negative relationship between faunal similarity, defined as the
proportion of all herbivores feeding on two hosts that are shared between the hosts, and the
phylogenetic distance between hosts based on DNA sequence divergence. Host phylogenetic
distance explained a significant fraction of the variance (25%) in herbivore community
similarity, in spite of the many ecological factors that probably influence feeding patterns.
Herbivore community similarity among congeneric hosts was high (50% on average)
compared to overlap among host families (20–30% on average). We confirmed this pattern
using the nearest taxon index (NTI) and net relatedness index (NRI) to quantify the extent of
phylogenetic clustering in particular herbivore associations and to test whether patterns are
significantly different from chance expectations. We found that 40% of caterpillar species
showed significant phylogenetic clustering with respect to host plant associations, somewhat
more so than for beetles or orthopteroids. We interpret this as evidence that a substantial
fraction of tropical forest insect herbivores are clade specialists.
Key words: community ecology; community phylogenetics; herbivory; host specialization; host
specificity; plant–insect interactions; phylogenetic dispersion; phylogeny; tropical rain forest.
INTRODUCTION
In the era before automated DNA sequencing and
molecular phylogenetics, Daniel H. Janzen stated that
‘‘the systematics and taxonomy of interactions is hopeless’’ (Janzen 1977). As robust phylogeny estimates for
plants and insects become available, investigating the
evolutionary history of their interactions is no longer a
fruitless endeavor. It is now possible to examine the
historical associations of plants and insects by comparing molecular phylogenies for the interacting lineages
(Becerra 1997, Weiblen and Bush 2002, Percy et al.
2004). Phylogenetic studies of host use by phytophagous
Manuscript received 24 January 2005; revised 20 June 2005;
accepted 23 June 2005. Corresponding Editor: A. A. Agrawal.
For reprints of this Special Issue, see footnote 1, p. S1.
6
E-mail: [email protected]
7
Present affiliation: Arnold Arboretum of Harvard University.
insects have tended to focus on the reconstruction of
ancestral associations for particular groups (Kelley and
Farrell 1998) or whether particular insect groups and
their host plants have diversified in parallel (Farrell and
Mitter 1990, 1998). Other macroevolutionary studies
have examined patterns of phylogenetic conservatism in
the host plant associations of phytophagous insects
(Farrell 1998, Janz and Nylin 1998, Ward et al. 2003).
Ecologists interested in patterns of herbivore community structure are faced with a different set of
questions. For example, to what extent do insects feed
on closely related host plants in a particular community?
How likely are host shifts to occur between divergent
host lineages? Few studies have attempted to integrate
the knowledge of phylogeny in the study of community
structure (Connor et al. 1980, Strong et al. 1984,
Marquis 1991, Losos 1996, Ødegaard 2003, Ødegaard
et al. 2005). Concern over the lack of statistical
independence among species led Kelly and Southwood
S62
July 2006
PHYLOGENY OF HOST ASSOCIATIONS
(1999) to control for phylogenetic effects in demonstrating that host plant abundance can predict herbivore
species richness in the temperate forest of Britain. But
still more can be learned from phylogeny. The members
of any biotic community are related in some fashion,
and insights can be gained by examining ecological
patterns with respect to patterns of descent from
common ancestors.
The incorporation of phylogenetic knowledge in
ecological studies can inform our understanding of
community structure (Webb et al. 2002) and of evolutionary constraints on the distribution of traits in
ecological communities (Chazdon et al. 2003). A useful
approach is to apply clustering indices to the phylogenetic distribution of species that belong to a particular
community sample drawn from a larger species pool
(Futuyma and Gould 1979, Webb 2000). Such indices
were first applied to the distribution of phytophagous
insects across a host plant phylogeny in order to quantify
diet breadth (Symons and Beccaloni 1999, Beccaloni and
Symons 2000). Early studies of diet breadth failed to
consider the phylogenetic nonequivalence of taxonomic
ranks (e.g. families and orders), and the phylogenetic
diversity index and the clade dispersion index, in
particular, were proposed to address this problem
(Symons and Beccaloni 1999). However, these indices
measured relatedness in terms of the branching order,
not branch lengths, of phylogenies. Branch lengths are
especially critical for studies of phylogenetic dispersion in
ecological communities with an uneven distribution of
closely related and distantly related species (CavenderBares et al. 2004). Consider lowland tropical rain forest
tree communities, for example, which are often dominated by a relatively small number of highly species-rich
genera and families (Novotny et al. 2002). In such cases,
narrow host specificity of herbivores has been invoked to
explain the maintenance of high insect species richness,
but this conclusion was reached with little regard for host
plant relatedness (Basset 1992).
The analysis presented here builds on an earlier study
(Novotny et al. 2002), expanding a New Guinea host
plant assemblage from 51 to 62 species and applying new
indices of phylogenetic dispersion to herbivore associations. The island of New Guinea is the third largest
remaining area of tropical forest wilderness in the world
and includes ;5% of global plant and insect diversity
while occupying only 0.5% of the land area (Miller
1993). Our study site near Madang, on the north coast
of Papua New Guinea, includes ;150 tree species/ha
that measure .5 cm dbh, and species richness is
dominated by approximately a dozen genera.
We quantified the relationship between the herbivore
community similarity of host trees and the phylogenetic
distance between hosts. We defined similarity as the
ratio of the number of herbivore species sharing two
hosts to the total number of herbivore species feeding on
the pair of hosts. Phylogenetic distance between host
species was based on DNA sequence divergence
S63
integrated across three genes and rate-smoothed across
the community phylogeny using penalized likelihood. If
herbivores tend to feed on closely related plants more
than on distantly related plants, as we expect, then
faunal similarity should decline with increasing phylogenetic distance between host species.
Indices of phylogenetic dispersion that incorporate
null models can be especially useful as quantitative tests
of host specificity in community samples. We used the
nearest taxon index (NTI) and net relatedness index
(NRI) to quantify the extent of phylogenetic clustering
in particular herbivore associations and to test whether
patterns are significantly different from chance expectations (Webb 2000, Webb et al. 2002). These indices
measure the mean phylogenetic distance between plants
that share a particular herbivore, relative to the mean
and standard deviation of herbivore associations randomly distributed on the phylogeny, as obtained by
multiple iteration. The NRI measures the average
distance between all plants that share an herbivore
species (i.e., the extent of overall clustering), while the
NTI measures the average minimal distance between
plants that share an herbivore species (i.e., the extent of
terminal clustering).
METHODS
Community ecology
Leaf-chewing insects were collected from 62 plant
species representing 41 genera and 18 families (Table 1).
Sampling effort was equalized across all host plants to
provide quantitative estimates of herbivore relative
abundance. Parataxonomists and village collectors
surveyed 1500 m2 of foliage over nearly 1600 field-days
and .6 3 104 tree inspections. Live insects were
subjected to feeding trials with fresh foliage of the plant
species from which they were collected in the field. These
procedures are detailed in Novotny et al. (2002). We
recorded 961 species and 62 193 individuals feeding on
the 62 host plant species. Additionally, 40 000 insects
that failed to feed on the plant from which they were
collected were discarded. Local parataxonomists assigned feeding specimens to morphospecies (Basset et al.
2000), and taxonomic specialists later identified known
taxa. Details on plant and insect identification are
reported in Miller et al. (2003). One-quarter of all species
were identified to named species, and 44% were
identified to genus, but taxonomic knowledge varied
from group to group. For example, 90% of the
Lepidoptera species were assigned to a genus, and 72%
were associated with a known species, while only 39% of
beetles were assigned to genus and 19% to species. The
locality, collection date, and host plant species for 37 972
mounted specimens are also available in our database.
Digital photographs of many species are archived and
available online.8 Sampling included 388 species and
8
hhttp://www.entu.cas.cz/png/index.htmli
S64
TABLE 1.
GEORGE D. WEIBLEN ET AL.
Ecology Special Issue
Plant species and gene sequences included in a phylogenetic study of host use in a tropical insect herbivore community.
Species
Code
Family
Order
Clade
GenBank
Amaracarpus nymanii Valeton
Artocarpus camansi Blanco
Breynia cernua (Poir.) Muell. Arg
Casearia erythrocarpa Sleum.
Celtis philippensis Blanco
Codiaeum ludovicianum Airy Shaw
Dolicholobium oxylobum K. Schum.
Dracaena angustifolia Roxb.
Endospermum labios Schodde
Eupomatia laurina R. Br.
Excoecaria agallocha L.
Ficus bernaysii King
Ficus botryocarpa Miq.
Ficus conocephalifolia Ridley
Ficus copiosa Steud.
Ficus dammaropsis Diels
Ficus hispidioides S. Moore
Ficus microcarpa L.
Ficus nodosa Teysm. & Binn.
Ficus phaeosyce Laut. & K. Schum.
Ficus pungens Reinw. ex Bl.
Ficus septica Burm. f.
Ficus tinctoria Forst.
Ficus trachypison K. Schum.
Ficus variegata Bl.
Ficus wassa Roxb.
Gardenia hansemannii K. Schum.
Gnetum gnemon L.
Homalanthus novoguineensis (Warb.) K. Schum.
Hydriastele microspadix (Becc.) Burret.
Kibara cf. coriacea (Bl.) Tul.
Leucosyke capitellata (Poir.) Wedd.
Macaranga aleuritoides F. Muell.
Macaranga bifoveata J. J. Smith
Macaranga brachytricha A. Shaw
Macaranga densiflora Warb.
Macaranga novoguineensis J. J. Smith
Macaranga quadriglandulosa Warb.
Mallotus mollissimus (Geisel.) Airy Shaw
Melanolepis multiglandulosa (Reinw. ex Bl.) Reichb. f.
Morinda bracteata Roxb.
Mussaenda scratchleyi Wernh.
Nauclea orientalis (L.) L.
Neonauclea clemensii Merr. & Perry
Neuburgia corynocarpa (A.Gray) Leenh.
Osmoxylon sessiliflorum (Lauterb.) W.R.Philipson
Pavetta platyclada Lauterb. & K. Schum.
Phyllanthus lamprophyllus Muell. Arg.
Pimelodendron amboinicum Hassk.
Pometia pinnata Forster
Premna obtusifolia R.Br.
Psychotria leptothyrsa Miquel
Psychotria micralabastra (Laut. & Schum.) Val.
Psychotria micrococca (Laut. & Schum.) Val.
Psychotria ramuensis Sohmer
Pterocarpus indicus Willd.
Randia schumanniana Merrill & Perry
Sterculia schumanniana (Lauterb.) Mildbr.
Tabernaemontana aurantica Gaud.
Tarenna buruensis (Miq.) Val.
Timonius timon (Spreng.) Merr.
Versteegia cauliflora (K. Schum. & Laut.)
AMA
ART
BRE
CAS
CEL
COD
DOL
DRA
END
EUP
EXC
BER
BOT
CON
COP
DAM
HIS
MIC
NOD
PHA
PUN
SEP
TIN
TRA
VAR
WAS
GAR
GNE
HON
ARE
STG
LEU
MAA
MAP
MAF
MAD
MAU
MAQ
MAL
MEL
MOR
MUS
SAR
NEO
NEU
OSM
PAV
PHY
PIM
POM
PRE
PSF
PSM
PSS
PSL
PTE
MEN
STR
TAB
TAR
TIT
VER
Rubiaceae
Moraceae
Phyllanthaceae
Flacourtiaceae
Ulmaceae
Euphorbiaceae
Rubiaceae
Agavaceae
Euphorbiaceae
Eupomatiaceae
Euphorbiaceae
Moraceae
Moraceae
Moraceae
Moraceae
Moraceae
Moraceae
Moraceae
Moraceae
Moraceae
Moraceae
Moraceae
Moraceae
Moraceae
Moraceae
Moraceae
Rubiaceae
Gnetaceae
Euphorbiaceae
Arecaceae
Monimiaceae
Urticaceae
Euphorbiaceae
Euphorbiaceae
Euphorbiaceae
Euphorbiaceae
Euphorbiaceae
Euphorbiaceae
Euphorbiaceae
Euphorbiaceae
Rubiaceae
Rubiaceae
Rubiaceae
Rubiaceae
Loganiaceae
Araliaceae
Rubiaceae
Phyllanthaceae
Euphorbiaceae
Sapindaceae
Verbenaceae
Rubiaceae
Rubiaceae
Rubiaceae
Rubiaceae
Fabaceae
Rubiaceae
Malvaceae
Apocynaceae
Rubiaceae
Rubiaceae
Rubiaceae
Gentianales
Rosales
Malphigiales
Malphigiales
Rosales
Malphigiales
Gentianales
Asparagales
Malphigiales
Magnoliales
Malphigiales
Rosales
Rosales
Rosales
Rosales
Rosales
Rosales
Rosales
Rosales
Rosales
Rosales
Rosales
Rosales
Rosales
Rosales
Rosales
Gentianales
Gnetales
Malphigiales
Arecales
Laurales
Rosales
Malphigiales
Malphigiales
Malphigiales
Malphigiales
Malphigiales
Malphigiales
Malphigiales
Malphigiales
Gentianales
Gentianales
Gentianales
Gentianales
Gentianales
Apiales
Gentianales
Malphigiales
Malphigiales
Sapindales
Lamiales
Gentianales
Gentianales
Gentianales
Gentianales
Fabales
Gentianales
Malvales
Gentianales
Gentianales
Gentianales
Gentianales
euasterids 1
eurosids 1
eurosids 1
eurosids 1
eurosids 1
eurosids 1
euasterids 1
monocots
eurosids 1
basals
eurosids 1
eurosids 1
eurosids 1
eurosids 1
eurosids 1
eurosids 1
eurosids 1
eurosids 1
eurosids 1
eurosids 1
eurosids 1
eurosids 1
eurosids 1
eurosids 1
eurosids 1
eurosids 1
euasterids 1
outgroup
eurosids 1
monocots
basals
eurosids 1
eurosids 1
eurosids 1
eurosids 1
eurosids 1
eurosids 1
eurosids 1
eurosids 1
eurosids 1
euasterids 1
euasterids 1
euasterids 1
euasterids 1
euasterids 1
euasterids 2
euasterids 1
eurosids 1
eurosids 1
eurosids 2
euasterids 1
euasterids 1
euasterids 1
euasterids 1
euasterids 1
eurosids 1
euasterids 1
eurosids 2
euasterids 1
euasterids 1
euasterids 1
euasterids 1
AJ002176 AY289288
AY374311
AF206746 D86309 AY374312
AJ318445
AF206729 AY374313
L12644 AY374314
AF165378
AF165379
AF165381
AF165382
AF165383
AF165388
AF165393
AF165395
AF165401
AF165404
AF165409
AF165413
AF165414
AF165415
AF165418
AJ318446
AY056577
AY374315 AY012504 AF050221 AY208707 AY374319
AY374321
AY374316
AY374317
AY374320
AY374318
AY374322
AY374323
AJ318448
AJ318447
AJ318449
AJ318450
AJ001755
U50257 AJ318451
AY374325
AY374324
AJ403008 U28883 AJ318452
AJ318453
AJ318454
AJ318455
AF308721 AJ318456
AJ233140 X91772 AJ318457
AJ318458
AJ318459
Notes: When sequences were not available for particular species, substitutions of near relatives from GenBank were made
(hhttp://www.ncbi.nlm.nih.govi). For example, rbcL sequences from Artocarpus altilis (AF500345) and Ficus heterophylla
(AF500351) were substituted for ART and VAR, respectively. Additional substitutions are footnoted.
Substituted rbcL sequences Amaracarpus sp. (AMA), Casearia sylvestris (CAS), Celtis sinensis (CEL), Agave ghiesbreghtii
(DRA), Eupomatia bennetti (EUP), Gnetum parvifolium (GNE), Kibara rigidifolia (STG), Hydriastele wendlandiana (ARE), Urtica
dioica (LEU), Teraplasandra hawaiensis (OSM), Talisia nervosa (POM), Premna microphylla (PRE), Willardia mexicana (PTE),
Sterculia apetala (STR), and Tabernaemontana divaricata (TAB).
July 2006
PHYLOGENY OF HOST ASSOCIATIONS
24 481 individuals for beetles (Coleoptera), 464 species
and 31 108 individuals for moths and butterflies (Lepidoptera; see Plate 1), and 109 species and 6605
individuals of orthopteroids (Orthoptera and Phasmatodea). Among the caterpillars, ;14 000 were matched
with adults, amounting to 298 species of Lepidoptera
with known larval and adult stages.
Molecular phylogenetics
Phylogenetic relationships for the 62 host plant
species were drawn from multiple molecular data sets
including a three-gene phylogeny for all angiosperms
(Soltis et al. 1998). We used additional molecular
markers for species of Moraceae, Rubiaceae, and
Euphorbiaceae, including the internal transcribed spacer
(ITS) region of nuclear ribosomal DNA for Ficus
(Weiblen 2000), rbcL, encoding the large subunit of
ribulose-1,5-bisphosphate carboxylase, and the 30S
ribosomal protein S16 gene (rps16) for Rubiaceae
(Novotny et al. 2002), and ndhF, encoding a subunit
of NADH-plastoquinone oxidoreductase, for the Euphorbiaceae. Phylogenetic analyses of Euphorbiaceae
based on ndhF are presented in the Appendix.
Community phylogenetics
A phylogeny estimate for the community sample was
obtained by grafting less inclusive single-gene phylogenies for Ficus, Euphorbiaceae, and Rubiaceae into a
more inclusive phylogeny of angiosperms based on three
genes (Soltis et al. 1998). The assembly of a community
phylogeny can follow supertree methods (Sanderson et
al. 1998) or other approaches (Lapointe and Cucumel
1997), but one crucial difference is that only members of
the community are retained in the supertree, while all
other lineages are pruned away.
It is important to consider the impact of branch length
considerations on indices of phylogenetic clustering
drawn from community samples. When branch lengths
are assumed equal, using the number of intervening
nodes as a proxy for phylogenetic distance (Novotny et
al. 2002), relationships between intensively sampled
congeneric species are given the same weight as relationships among representatives of major clades. Branch
length information can distinguish between these two
very different cases, short distances between congenerics
and long distances between members of major lineages.
Therefore, to incorporate information from all three
molecular data sets, we scaled branch lengths in the
supertree to the relative rate of change in two genes
compared between pairs of taxa. For example, the
relative rate of ITS to ndhF was calculated by counting
the absolute number of character differences in each
gene between Ficus microcarpa and F. variegata.
Including all characters, there were 15 ndhF differences
between these species and 58 ITS differences, yielding a
relative rate of 0.259 for ndhF to ITS (Weiblen 2000,
Datwyler and Weiblen 2004). Fifty-eight pairwise differences between Artocarpus camansi and Ficus variegata
S65
for rbcL and 111 for ndhF yielded a rate of 1.914 for
ndhF relative to rbcL. We rescaled the branch lengths by
these rates to approximate the phylogenetic distance
between taxa sampled for genes showing radically
different rates of molecular divergence. The assumption
of this method is that rates of divergence for each gene
are homogeneous among the lineages comprising the
community sample. In the case of plant families other
than Moraceae, Rubiaceae, and Euphorbiaceae, rbcL
sequences were not necessarily available from the
particular species, and in these instances sequences from
related species or genera were obtained from GenBank
as indicated in Table 1.
The next challenge is to obtain a phylogeny for
which all distances from the root of the tree to the tips
are equal, also known as an ultrametric tree. Ultrametricity is necessary to make direct comparisons of
phylogenetic distance (as measured by rescaled molecular branch lengths) among pairs of host species
distributed across the phylogeny. Each individual data
set rejected a molecular clock assumption, so we
applied nonparametric rate smoothing and penalized
likelihood as implemented in the program r8s (Sanderson 2002) to the rescaled branch lengths of the
supertree to obtain an ultrametric tree accommodating
rate heterogeneity across lineages. Penalized likelihood
is a semiparametric method that allows substitution
rates to vary among lineages according to a smoothing
parameter (Sanderson 2002). The optimal smoothing
parameter was chosen on the basis of the data by
cross-validation involving the sequential pruning of
taxa from the tree and parameter estimation to best
predict the branch length of the pruned taxon (Sanderson 2003). We compared 20 cross-validation parameters beginning with zero and increasing by increments
of log10(0.05) and chose the optimal smoothing
parameter to minimize v2 error. Cross-validation was
performed with the age of the root node fixed at one.
Penalized-likelihood search parameters included 2000
maximum iterations, 10 multiple starts, and 30
optimization runs.
Phylogenetic dispersion of herbivore associations
Herbivore associations with each of the 62 host
species were coded as either present or absent under
two different assumptions, including or excluding
solitary observations. Where r denotes the number of
feeding records for a particular herbivore species on a
particular host species, associations were coded as
present when r . 1 or when r . 0 to exclude or include
singletons, respectively. Varying this threshold allowed
us to examine the sensitivity of findings based on
presence/absence to extreme variation in herbivore
abundance. We examined the distribution of herbivore
associations across the host phylogeny, with indices of
phylogenetic clustering as implemented in the program
Phylocom (Webb et al. 2004).
S66
GEORGE D. WEIBLEN ET AL.
Ecology Special Issue
FIG. 1. Phylogenetic relationships of host plant species included in the study (see Table 1 for abbreviations). Brackets indicate
the three major angiosperm clades that were sampled intensively. A supertree was assembled from separate analyses of DNA
sequences for Rubiaceae (Novotny et al. 2002), Ficus (Weiblen 2000), Euphorbiaceae (see the Appendix), and angiosperms as a
whole (Soltis et al. 1998, Angiosperm Phylogeny Group 2003). Branch lengths based on ITS, rbcL, and ndhF sequences for partially
overlapping sets of taxa were rescaled in proportion to pairwise differences between selected species with published ITS and ndhF,
or ndhF and rbcL, sequences (see Methods). Branch lengths as shown are proportional to absolute numbers of nucleotide changes
under parsimony. The scale bar indicates 10 changes.
The net relatedness index measured the mean
phylogenetic distance between all plants sharing a
particular herbivore: NRI ¼ –(Xnet – X(n))/SD(n) where
Xnet is the mean phylogenetic distance between all pairs
of n host plants sharing an herbivore, and X(n) and SD(n)
are the mean and standard deviation of phylogenetic
distance for n host plants randomly distributed on the
phylogeny, obtained by multiple iteration. The nearest
taxon index measured the distance between the two
nearest hosts sharing a particular herbivore. This index
is calculated in the same manner as NRI, except that
Xnear is substituted for Xnet, where Xnear is the shortest
July 2006
PHYLOGENY OF HOST ASSOCIATIONS
S67
FIG. 2. Molecular divergence among 62 selected, woody host plant species in lowland tropical rain forest on the island of New
Guinea (see Table 1 for species abbreviations). The ultrametric tree was derived from penalized-likelihood analysis. (a) Shallowest
split between families, Loganiaceae and Apocynaceae, (b) deepest crown radiation of a genus, Psychotria, and (c) shallowest crown
radiation of a genus, Ficus. Brackets mark the angiosperm families and genera that were the focus of herbivore sampling. Branch
lengths as shown are proportional to the number of nucleotide changes per site under maximum likelihood. The scale bar indicates
0.05 substitutions per site.
distance between all pairs of n host plants sharing an
herbivore. High values of these indices suggest clustering, whereas low values point to evenness (i.e., overdispersion). We tested whether these measures of
phylogenetic dispersion of herbivore associations across
the community phylogeny were significantly different
from chance expectations. Under a null model of
random association, we performed 1000 permutations
of host associations to simulate a distribution of NRI
and NTI for each herbivore species. A two-tailed test of
significance evaluated the rank of observed values at P ¼
0.05. For example, a rank of ,25 or .975 of 1000
permutations constituted significant overdispersion or
clustering, respectively.
S68
GEORGE D. WEIBLEN ET AL.
Ecology Special Issue
TABLE 2. Numbers and percentages of insect herbivore species with significantly clustered (and overdispersed) patterns of host
association across a community sample of 62 woody plant species from New Guinea lowland rain forest.
Excluding singletons
NRI
Taxon
Lepidoptera
Lepidoptera (%)
Coleoptera
Coleoptera (%)
Orthopteroids
Orthopteroids (%)
Total herbivores
Herbivores (%)
NB
76
55
30
30
12
30
118
43
(0)
(0)
(1)
(1)
(0)
(0)
(1)
(1)
BL
65
47
43
43
14
35
122
44
(1)
(1)
(0)
(0)
(0)
(0)
(1)
(1)
NTI
PL
61
45
46
46
9
22
116
42
(5)
(4)
(1)
(1)
(0)
(0)
(6)
(2)
LF
61
45
46
46
9
22
116
42
(5)
(4)
(1)
(1)
(0)
(0)
(6)
(2)
NB
68
50
26
26
8
20
102
37
(0)
(0)
(0)
(0)
(0)
(0)
(0)
(0)
BL
58
42
27
27
4
10
89
32
(3)
(2)
(0)
(0)
(1)
(2)
(4)
(1)
PL
60
44
24
24
4
10
88
32
(9)
(6)
(3)
(3)
(2)
(5)
(14)
(5)
LF
60
44
24
24
4
10
88
32
(9)
(6)
(1)
(1)
(6)
(15)
(16)
(6)
Notes: Two-tailed tests of phylogenetic dispersion assessed significance at P ¼ 0.05 with ranks .975 (or ,25) out of 1000
randomizations. Abbreviations: NRI ¼ net relatedness index; NTI ¼ nearest taxon index; NB ¼ no. branch lengths (no. intervening
nodes); BL ¼ rescaled molecular branch lengths (nonultrametric); PL ¼ rescaled ultrametric branch lengths (penalized likelihood);
LF ¼ rescaled ultrametric branch lengths (Langely-Fitch nonparametric rate smoothing).
We further examined the relationship of herbivore
community similarity to the phylogenetic distance
between hosts. We calculated community similarity as
the percentage of the total number of herbivore species
feeding on any pair of host species that were shared
between the hosts (Novotny et al. 2002). We estimated
phylogenetic distance from branch lengths based on
DNA sequence divergence under penalized likelihood as
implemented in r8s (Sanderson 2002). We used linear
regression to analyze the direction and linear regression
and Mantel tests to assess the significance of this
relationship.
RESULTS
Community ecology
Among the 62 193 insects, including 464 caterpillar
species reared to adults, 388 beetle species, and 109
orthopteroids, there were 281 species collected as single
individuals (singletons). Singleton species were excluded
from subsequent analyses, because it is impossible to
assess host range when a species is known from only one
feeding record (Novotny and Basset 2000). Apart from
singletons, our sample also included 156 herbivore
species that fed on a single plant species. Our analysis
did not examine whether these species are truly monophagous or were simply sampled in insufficient numbers.
Rather, we focused on the host phylogenetic distribution
of associations for the remaining 524 herbivore species
(55% of the total) that were found to feed on more than
one plant species.
Community phylogeny
A phylogeny was obtained for the host plant
community sample by grafting hypotheses of relationship for selected Euphorbiaceae (see Appendix). Rubiaceae (Novotny et al. 2002), and Ficus (Weiblen 2000) to
an ordinal phylogeny based on multiple data sets
(Angiosperm Phylogeny Group 2003). The phylogeny
is shown in Fig. 1 with rbcL and ITS branch lengths
rescaled in terms of ndhF substitutions. Nonparametric
rate smoothing (Langely-Fitch) and penalized likelihood yielded highly similar ultrametric trees (Fig. 2).
As expected, phylogenetic distances between congeneric
species were lower than between confamilial genera and
extaordinal families.
Phylogenetic dispersion
Each of 226 Lepidoptera, 212 Coleoptera, and 87
orthopteroid species observed on multiple hosts was
tested for nonrandom patterns of association with
respect to host plant phylogeny. Under a more
stringent coding of host association that excluded all
solitary feeding records, the 137 Lepidoptera, 99
Coleoptera, and 40 orthopteroid species encountered
on multiple hosts (multiple times each) were also
analyzed with respect to host phylogenetic dispersion.
Results under four different branch length assumptions,
two different indices of phylogenetic dispersion, and
two feeding thresholds indicated that herbivores with
nonrandom dispersion of associations feed on closely
related hosts more often than on distantly related hosts
(Table 2). In particular, 25–43% of the herbivore
species we analyzed were significantly clustered on the
host plant phylogeny compared to 0–6% that were
overdispersed.
The incorporation of sequence divergence in branch
length estimation had a dramatic impact on the
detection of phylogenetic dispersion. In the case of
nearest taxon index, for example, results under the
assumption of equal branch lengths only agreed with
those under molecular branch length assumptions in
65% of cases, three variations on the latter agreed in 93%
of cases, and the two assumptions based on ultrametric
trees agreed in all cases. Exclusion of feeding records
represented by single observations also enhanced the
detection of nonrandom associations with respect to
host plant phylogeny. Without singletons, 32–37% of
herbivore species rejected the null model of association
July 2006
PHYLOGENY OF HOST ASSOCIATIONS
S69
TABLE 2. Extended.
Including singletons
NRI
NB
124
55
41
19
20
23
185
35
(0)
(0)
(6)
(3)
(1)
(1)
(7)
(1)
BL
93
41
70
33
27
31
190
36
(0)
(0)
(0)
(0)
(0)
(0)
(0)
(0)
NTI
PL
90
40
75
35
23
26
188
36
(4)
(2)
(3)
(1)
(0)
(0)
(7)
(1)
LF
91
40
76
36
24
27
191
36
(4)
(2)
(3)
(1)
(1)
(1)
(8)
(2)
compared to 25–31% including singletons in the
analysis, a trend that was upheld by each of three insect
groups.
Community similarity and phylogenetic distance
Herbivore community similarity, defined as the fraction of the total herbivore species on two host species that
are shared between the hosts (Novotny et al. 2002), was
negatively associated with phylogenetic distance as
estimated by rate-smoothed molecular divergence under
penalized likelihood (Fig. 3). The regression of community similarity against phylogenetic distance was highly
significant (ANOVA, F1, 3842 ¼ 1243.5, P , 0.0001), and
the correlation between these variables was also significant according to a Mantel test (Pearson’s productmoment correlation, r ¼ 0.423, P , 0.01). Declining
community similarity with increasing phylogenetic distance between hosts indicates that herbivores tend to feed
on closely related plants more often than on distantly
related plants.
NB
103
46
45
21
13
15
161
31
BL
(0)
(0)
(0)
(0)
(0)
(0)
(0)
(0)
78
34
44
21
8
9
130
25
(7)
(3)
(0)
(0)
(3)
(3)
(10)
(2)
PL
75
33
49
23
9
10
133
25
(13)
(6)
(1)
(0)
(2)
(2)
(16)
(3)
LF
76
34
50
23
10
11
136
26
(13)
(6)
(1)
(0)
(2)
(2)
(16)
(3)
extant taxa. This is why we applied indices of
phylogenetic dispersion to examine the relationship
between herbivore associations and host plant phylogeny.
Phylogenetic dispersion of host associations
Community phylogenies, null models, and measures
of phylogenetic dispersion taken together increase the
precision with which herbivore associations can be
studied. Previous attempts to quantify host specificity,
for example, have either relied on taxonomic ranks that
are not commensurate with plant lineages or ignored the
branch length information contained in molecular
phylogenies (Symons and Beccaloni 1999, Novotny et
al. 2002). Branch length assumptions influence our
DISCUSSION
While it is tempting to trace ecological character
evolution on community phylogenies, ancestral reconstructions of host associations in community samples
often yield implausible inferences. Equally weighted
parsimony for highly polyphagous species implies that
these herbivores colonized the common ancestors of
major angiosperm clades and were subsequently lost
from some host lineages. Consider for example the
ancestral association of Rhinoscapha tricolor under
equally weighted parsimony (Fig. 4). It is highly unlikely
that this particular polyphagous species was associated
with the common ancestor of the angiosperms and the
gymnosperm Gnetum. Ancestral state reconstructions
are sensitive to taxon sampling (Cunningham et al. 1998,
Cunningham 1999), and colonization or extinction
patterns cannot necessarily be inferred from local
assemblages because community phylogenies are incomplete by definition. This problem is not unique to the
evolution of host associations, but also occurs whenever
the included taxa might be a subset of an entire clade of
FIG. 3. Herbivore community similarity as a function of the
phylogenetic distance between host plants. Similarity is the
fraction of the total fauna on two hosts that is shared between
the hosts. Phylogenetic distance was derived from the penalized-likelihood ultrametric phylogram shown in Fig. 2. Means,
standard deviations, and ranges of community similarity are
shown for selected distance intervals. The outgroup is excluded
from the regression.
S70
GEORGE D. WEIBLEN ET AL.
FIG. 4. Erroneous inference of ancestral host use in
community samples under equally weighted parsimony. Rhinoscapha tricolor is a polyphagous generalist that parsimony
suggests had an implausible, ancient association with the
common ancestor of Gnetum and flowering plants. See Table
1 for species abbreviations.
power to detect patterns of phylogenetic dispersion in at
least one important way. Failure to consider the extent
of molecular divergence between hosts will underestimate the extent of herbivore clustering (or overdispersion) given that closely related hosts and extremely
divergent hosts with the same number of intervening
Ecology Special Issue
nodes in the community phylogeny are assumed to be
equidistant when they are not. Branch lengths scaled to
molecular divergence distinguish between these cases
and enhance the power to detect significant patterns in
host use (Table 2). Ultrametric molecular branch lengths
approximate relative ages of lineages, and thus the
length of time for ecological associations or adaptations
to arise. We found that a large proportion of herbivores
feed on closely related plants, including congeneric
species and confamilial genera, and that a small number
of herbivores feed on more divergent hosts than
expected by chance. The former pattern is expected in
cases of herbivore specialization (Jaenike 1990, Futuyma
et al. 1993) and the latter pattern when herbivores are
tracking convergent chemical, morphological, or ecological host traits (Becerra 1997).
The incorporation of molecular branch lengths in a
community phylogeny assembled from multiple genes
poses interesting methodological challenges that invite
further exploration. Communities are usually composed
of heterogeneous taxa, some very closely related and
others distantly so. Grafting of multiple phylogenies
based on different genes could be necessary when no
single gene resolves phylogenetic relationships at all
taxonomic levels in the community sample. This was the
case in our sample, where ITS sequences were employed
to resolve relationships among Ficus, but this region
could not be aligned across plant families. Rescaling of
branch lengths from different gene regions based on the
ratio of absolute character differences between taxon
pairs represents one possible solution among many. An
improvement on our method would be to correct for
multiple substitutions in a model-based maximum-likelihood framework when rescaling branch lengths across
grafted phylogenies.
We do not know the extent to which the phylogenetic
dispersion of herbivores in our samples is representative
of herbivore community structure on the complete local
plant community or tropical rain forests in general. The
scope of our sampling universe is incomplete for even
the local community. Fifteen figs, 13 Rubiaceae, 13
Euphorbiaceae, and 21 other angiosperms hardly
encompass the woody vegetation of a study area that
contains hundreds of flowering plant species. The
selection of study plants was made to replicate the
taxonomic ranks of family and genus, and is at best a
highly skewed sample in terms of local abundance and
distribution. At least one way to avoid artifacts due to
taxonomic unevenness is to restrict analyses to single
representatives of given taxonomic ranks, such as
families, but this is not satisfactory owing to the
phylogenetic nonequivalence of taxa at any single rank.
Age estimates of family clades in a recent study of
angiosperms range from ,25 Ma to .150 Ma (Davies et
al. 2004). The problem of taxonomic unevenness could
be addressed by including all members of a local
community, provided that the boundaries of the
community can be defined. We intend to explore these
July 2006
PHYLOGENY OF HOST ASSOCIATIONS
S71
FIG. 5. Phylogenetic dispersion of host range in 30 herbivore species arbitrarily selected from the community sample to
illustrate the extremes of variation. Herbivores are grouped into nonsignificantly clustered species including polyphagous
generalists, and significantly clustered species including oligophagous specialists feeding on Macaranga, Ficus, or Psychotria.
Branch lengths of the host community phylogeny are proportional to molecular divergence as in Fig. 4, except for the truncated
root indicated by a slash. Host species codes are defined in Table 1, and herbivore species codes are defined in Table 3. As in Fig. 4,
solid boxes indicate herbivore presence and open boxes indicate herbivore absence.
issues in the future through the complete enumeration of
vegetation in specific areas of forest (Novotny et al.
2004a). At the very least, it is encouraging that the
relationship between herbivore community similarity
and host phylogenetic distance was strengthened by the
expansion of our sample from 51 host species in
Novotny et al. (2002) to 62 in the present study, and
through the incorporation of branch length information.
It is remarkable that a full quarter of the variance of
herbivore community similarity can be explained by the
phylogenetic relationships among hosts (r2 ¼ 0.244)
when we consider the variability that environmental and
S72
GEORGE D. WEIBLEN ET AL.
Ecology Special Issue
TABLE 3. Herbivore species from Fig. 5 arranged alphabetically by morphospecies code.
Code
Order
Family
Species
N
H
NRI
NTI
ACRI001
ACRI014
ACRI044
ARCT002
BUPR002
CHOR008
CHRY004
CHRY076
CHRY124
CRAM003
CRAM005
CRAM006
CRAM044
CURC002
CURC005
GEOM021
LYCA006
NOCT002
NYMP001
NYMP002
PHAS002
PHAS004
PHAS016
PSYC001
PYRA002
SPHI004
TORT006
TORT008
TORT040
TORT075
XXXX021
XXXX048
XXXX076
Orthopteroid
Orthopteroid
Orthopteroid
Lepidoptera
Coleoptera
Lepidoptera
Coleoptera
Coleoptera
Coleoptera
Lepidoptera
Lepidoptera
Lepidoptera
Lepidoptera
Coleoptera
Coleoptera
Lepidoptera
Lepidoptera
Lepidoptera
Lepidoptera
Lepidoptera
Orthopteroid
Orthopteroid
Orthopteroid
Lepidoptera
Lepidoptera
Lepidoptera
Lepidoptera
Lepidoptera
Lepidoptera
Lepidoptera
Lepidoptera
Lepidoptera
Lepidoptera
Pyrgomorphidae
Acrididae
Eumastacidae
Arctiidae
Buprestidae
Choreutidae
Chrysomelidae
Chrysomelidae
Chrysomelidae
Crambidae
Crambidae
Crambidae
Crambidae
Curculionidae
Curculionidae
Geometridae
Lycaenidae
Noctuidae
Nymphalidae
Nymphalidae
Heteronemiidae
Phasmatidae
Phasmatidae
Psychidae
Pyralidae
Sphingidae
Choreutidae
Tortricidae
Tortricidae
Thyrididae
Pyralidae
Gelechiidae
Gelechiidae
Desmopterella biroi (Bolivar, 1905)
Valanga papuasica (Finot, 1907)
Paramnesicles buergersi Bolivar, 1930
Darantasia caerulescens Druce, 1899
Habroloma sp.
Brenthia salaconia Meyrick, 1910
genus indeterminate
Deretrichia sp.
Deretrichia sp.
Glyphodes margaritaria (Clerck) 1794
Talanga deliciosa (Butler) 1887
Talanga sexpunctalis (Moore) 1877
‘‘Coelorhycidia’’ nr. purpurea Hampson, 1907
Apirocalus ebrius Faust, 1892
Alcidodes elegans (Guerin) 1838
Cleora repetita Butler, 1882
Philiris helena Snellen, 1887
Asota heliconia Linnaeus, 1758
Euploea leucosticos Gmelin, 1788
Cyrestis acilia Godart, 1819
Neopromachus vepres (Brunner von Wattenwyl) 1907
Dimorphodes prostasis Redtenbacher, 1908
Eurycantha insularis Lucas, 1869
Eumeta variegata Snellen, 1879
Orthaga melanoperalis Hampson, 1906
Macroglossum melas Rothschild & Jordan, 1930
Choreutis lutescens (Felder and Rogenhofer) 1875
Adoxophyes templana complex
Homona mermerodes Meyrick, 1910
Mellea ramifera Warren, 1897
Unadophanes trissomita Turner, 1913
Dichomeris ochreoviridella (Pagenstecher) 1900
Dichomeris sp. nr. resignata Meyrick, 1929
2215
273
111
3
20
389
125
16
16
318
856
329
234
2349
141
12
121
257
108
156
211
98
38
33
246
167
332
482
815
56
301
394
324
58
49
29
3
3
7
6
5
6
11
14
13
5
54
22
9
8
9
11
12
41
35
25
19
7
5
13
29
25
7
5
6
10
0.36
1.09
1.28
0.54
3.34
5.60
6.60
5.01
0.55
9.04
10.02
9.50
2.65
1.31
1.94
0.11
5.54
7.70
8.12
9.52
0.15
0.06
1.65
0.72
5.99
4.32
9.50
1.54
0.1
5.99
4.97
5.79
6.01
0.74
0.55
1.94
0.89
2.33
2.38
2.60
2.45
0.26
3.22
3.37
3.21
1.51
1.38
2.51
0.21
2.00
2.94
3.04
3.32
1.43
0.74
0.68
0.12
2.55
2.11
3.21
1.65
1.53
2.55
2.35
2.48
1.88
Notes: The total number of individuals (N) and the total number of host species (H), including solitary feeding records, are
indicated. Net relatedness (NRI) and nearest taxon (NTI) indices are reported as calculated under the penalized-likelihood tree
(Fig. 2).
population demographical heterogeneity must inevitably
contribute to samples of herbivore associations from
any site over any period of time. A recent community
phylogenetic analysis of host use by beetles in Panamanian rain forest revealed the same pattern (Ødegaard et
al. 2005). These findings provide quantitative support
for long-standing theory (Ehrlich and Raven 1964,
Strong et al. 1984, Schoonhoven et al. 1998). There are
at least two explanations for the decline in herbivore
similarity with increasing phylogenetic distance between
hosts. The first has to do with the phylogenetic
conservatism of host choice as manifest in the tendency
for herbivore offspring to feed on the same host lineages
as their parents. Second, it is possible that host choice is
influenced by the conservatism of chemical, morphological, ecological, and physiological plant traits affecting herbivore performance. Power to detect phylogenetic
conservatism in community samples could be improved
by considering species abundance and increasing the
universe of sampled hosts. Nonetheless, species presence/absence and a limited sample of the local plant
community indicated a relatively strong influence of host
relatedness on herbivore community composition.
Tests of host specificity
Community phylogenies and null models provide a
quantitative test of significance for host specificity at the
clade level. Examples of specialists on Macaranga, Ficus,
and Psychotria that rejected null models of host
association are shown in Fig. 5 and Table 3, along with
nonspecialists that failed to reject null models. These
examples were chosen to illustrate extreme cases and to
reinforce the point that a quantitative definition of host
specificity based on phylogenetic dispersion is more
practical and powerful than definitions based on
arbitrary taxonomic ranks. When singleton records were
excluded from analyses, more host clade specialists were
detected in all herbivore assemblages (Table 2). This
result is not surprising given that herbivores tend to have
a highly skewed distribution of abundance across the
host range. The average herbivore species in New
Guinea secondary forest, for example, has .90% of
individuals aggregated on a single host species and feeds
on one to three host species (Novotny et al. 2004b).
Singletons representing rare or anomalous feeding
events are likely to increase error rates in analyses such
as ours that ignore abundance distributions and simply
treat the associations as present or absent.
July 2006
PHYLOGENY OF HOST ASSOCIATIONS
S73
PLATE 1. Darantasia caerulescens Druce (Lepidoptera: Arctiidae): (A) Adult, (B) larva, and (C) male genitalia, with aedoeagus
separated and vesica inflated. Genitalia, illustrated here for the first time, allow differentiation from similar-looking species. The
caterpillars of this moth fed on three distantly related plant species (Fig. 5). Photo and dissection credit: Karolyn Darrow.
Excluding singletons and considering molecular
branch lengths, the NRI and NTI differed as expected,
considering that NTI quantifies dispersion near the tips
of the phylogeny whereas NRI measures overall
dispersion. According to NRI, Lepidoptera was proportionally the most specialized fauna, with 42–44% of
species significantly clustered with respect to host plant
phylogeny, compared with 24–27% of coleopterans and
10% of orthopteroids. By contrast grasshoppers and
relatives showed the highest proportion of overdispersed
species (2–15%), compared with Lepidoptera (2–6%)
and Coleoptera (0–3%). We attribute these differences to
variation among feeding guilds. We expected Lepidoptera to show the greatest overall degree of host
specificity, due to the fact that caterpillars feeding on
foliage were reared from larvae to adults and host
specificity is manifest at the larval stage. Coleoptera, on
the other hand, were tested for feeding only as adults
and potentially feed on a more restricted set of hosts as
larvae. Root-, stem-, and wood-boring beetle larvae are
expected to exhibit greater host specialization than adult
stages, because the impact of plant chemistry on insect
development is strongest in the early life stages (Mattson
et al. 1988). The nonholometabolous assemblage of
grasshoppers and relatives, feeding as nymphs and
adults, are widely regarded as polyphagous (Chapman
and Sword 1997) and therefore expected to show less
phylogenetic clustering and greater overdispersion than
the other assemblages. Orthopteroid nymphs are more
mobile than caterpillars, enhancing opportunities to
graze on multiple hosts and presumably selection for
greater breadth of diet (Chapman and Sword 1997).
Clustering of similar plant traits in close relatives due
to phylogenetic conservatism (Cavender-Bares et al.
2004) provides a simple explanation for the extreme
patterns of clade specialization observed in many
herbivore species. We believe that herbivore tracking
of phylogenetically conserved plant traits is a more
plausible explanation than co-cladogensis for patterns of
association in many plant-herbivore interactions. Predation and parasitism might also indirectly promote
specialization in phytophagous insect communities
(Bernays and Graham 1988). Attack rates of caterpillar
parasitoids in temperate forests, for example, vary
among host plant species, and this variation has the
potential to influence the evolution of herbivore host
range (Lill et al. 2002). Apart from patterns of clade
specialization, we also detected a small number of cases
of phylogenetic overdispersion (3–6% of all herbivores)
that could have a biological explanation.
Significant overdispersion is expected for herbivores
feeding on distantly related hosts when hosts share a set
of convergent traits that are palatable to particular
herbivores (Cavender-Bares and Wilczek 2003). Convergence in plant traits can result from adaptive evolution
(Agrawal and Fishbein 2006) or habitat specialization
(Fine et al. 2006). For example, Ficus tinctoria (Moraceae) and Excoecaria agallocha (Euphorbiaceae) share
an extreme environment and a unique set of herbivores
along the seacoast. The identification of convergent
ecophysiological, morphological, and chemical traits in
distantly related hosts sharing similar herbivores might
point to factors that limit the evolution of host range.
Where trait convergence enables similar insects to feed on
highly diverged plant lineages, we expect significant
S74
GEORGE D. WEIBLEN ET AL.
herbivore clustering in more than one place on the plant
phylogeny, causing the nearest taxon index to be
significantly high when the net relatedness index is not.
Conclusions
This study of herbivore associations illustrates how
the integration of community ecology and phylogeny
can detect patterns of host specialization. A community
phylogeny with molecular branch lengths and null
models identified patterns of phylogenetic clustering in
the associations of insect herbivores feeding on a sample
of tropical rain forest vegetation in New Guinea.
Quantitative, community phylogenetic studies such as
ours show a general tendency for insects to feed on
closely related hosts (Ødegaard et al. 2005). As
predicted, we found greater phylogenetic structure in
caterpillar associations than in herbivorous beetles or
orthopteroids. Quantifying the phylogenetic dispersion
of host associations has advantages over approaches
that ignore phylogenetic distance or assume the equivalence of taxa of the same rank. Indices of phylogenetic
dispersion provide a quantitative definition of host
specificity that can be compared among studies, solving
a problem that has plagued herbivore community
ecology from the very beginning. The approach provides
not only a standard for the identification of specialists,
but also holds promise for the study of host shifts and
the identification of alternative hosts.
ACKNOWLEDGMENTS
We thank D. Althoff, K. Seagraves, and two anonymous
reviewers for helpful comments; S. I. Silvieus for Euphorbiaceae
sequencing; S. L. Datwlyer and S. Swenson for analytical
assistance; C. Bellamy, J. Brown, K. Darrow, D. R. Davis, J. D.
Holloway, M. Horak, S. James, E. G. Munroe, P. Nasrecki, D.
Perez, G. Robinson, G. A. Samuelson, K. Sattler, M. Shaffer,
M. A. Solis, G. Setliff, W. Takeuchi, K. Tuck, H. C. M. Van
Herwaarden, and P. van Welzen for taxonomic assistance; and
the staff of the New Guinea Binatang Research Center for field
assistance. This material is based upon work supported by the
National Science Foundation under Grants 9407297, 9628840,
9707928, 0212873, and 0211591, the Czech Ministry of
Education (ME646, 6007665801), the Czech Grant Agency
(206/04/0725), the Czech Academy of Sciences (Z50070508),
and a David and Lucille Packard Fellowship in Science and
Engineering.
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APPENDIX
A description of Euphorbiaceae phylogeny (Ecological Archives E087-111-A1).
Ecology, 87(7) Supplement, 2006, pp. S76–S85
Ó 2006 by the Ecological Society of America
ECOLOGICAL FITTING AS A DETERMINANT OF THE COMMUNITY
STRUCTURE OF PLATYHELMINTH PARASITES OF ANURANS
DANIEL R. BROOKS,1,5 VIRGINIA LEÓN-RÈGAGNON,2 DEBORAH A. MCLENNAN,3
2
AND
DEREK ZELMER4
1
Department of Zoology, University of Toronto, Ontario M5S 3G5 Canada
Laboratorio de Helmintologı´a, Instituto de Biologı´a, Universidad Nacional Autónoma de Me´xico,
C. P. 04510, D. F. México, Me´xico
3
Department of Zoology, University of Toronto, Ontario M5S 3G5 Canada
4
Department of Biological Sciences, Emporia State University, Emporia, Kansas 66801 USA
Abstract. Host–parasite associations are assumed to be ecologically specialized, tightly
coevolved systems driven by mutual modification in which host switching is a rare
phenomenon. Ecological fitting, however, increases the probability of host switching, creating
incongruences between host and parasite phylogenies, when (1) specialization on a particular
host resource is a shared characteristic of distantly related parasites, and (2) the resource being
tracked by the parasite is widespread among many host species. We investigated the effect of
ecological fitting on structuring the platyhelminth communities of anurans from a temperate
forest and grassland in the United States and tropical dry and wet forests in Mexico and Costa
Rica. The six communities all exhibit similar structure in terms of the genera and families
inhabiting the frogs. Parasite species richness is highly correlated with the amount of time a
host spends in association with aquatic habitats, a conservative aspect of both parasite and
host natural history, and determined in a proximal sense by host mobility and diet breadth.
The pattern of parasite genera and families within host genera across the regions examined is
consistent with the prediction that ecological fitting by phylogenetically conservative species,
coupled with historical accidents of speciation and dispersal, should be evidenced as a nestedsubset structure; the shared requirement for aquatic habitats of tadpoles provides a baseline
assemblage to which other parasite taxa are added as a function of adult host association with
aquatic habitats. We conclude that parasite communities are structured by both ecological
fitting and coevolution (mutual modification), the relative influences of which are expected to
vary among different communities and associations.
Key words: anurans; coevolution; community structure; Costa Rica; ecological fitting; frogs; Mexico;
nested subset; parasitic platyhelminths; phylogenetic conservatism; toads; United States of America.
INTRODUCTION
There are two approaches to studying the evolution of
host–parasite associations. The first and newer research
program, maximum co-speciation, assumes that hosts
and their parasites share such a specialized and exclusive
evolutionary association (Page 2003, Clayton et al. 2004,
Johnson and Clayton 2004) that speciation in one
lineage causes speciation in the other (synchronous cospeciation; Hafner and Nadler 1988, 1990). Host–
parasite phylogenies are thus expected to be completely
congruent, with departures from congruence explained
by invoking extinction in one lineage or the other. The
second and original research program (Brooks 1979) is
also based upon comparing host–parasite phylogenies
and identifying points of congruence as instances of cospeciation (the term coined by Brooks in [1979]). There
are, however, no assumptions about underlying proManuscript received 20 January 2005; revised 12 September
2005; accepted 21 September 2005. Corresponding Editor (ad
hoc): C. O. Webb. For reprints of this Special Issue, see
footnote 1, p. S1.
5
E-mail: [email protected]
cesses, nor is there an expectation of complete congruence. Brooks proposed that the incongruent portions
of host–parasite phylogenies falsified the hypothesis of
co-speciation at those nodes and thus required investigations into the influence of other factors (e.g.,
dispersal and host switching) on the evolution of the
association. For example, parasites might diverge more
rapidly than their hosts via sympatric speciation, producing sister species inhabiting the same host (Brooks
and McLennan 1993; or ‘‘lineage duplication’’ sensu
Page [2003]), or ecological or immunological evolution
in the host lineage could cause parasite extinction
(lineage sorting or ‘‘missing the boat’’ sensu Page
[2003]).
Although the maximum co-speciation program has
been moving closer to Brooks’ propositions about the
way incongruences should be treated, there is still one
area of dispute between the two perspectives, the
importance of host switching during the evolution of
host–parasite associations. This debate is a logical
extension of the assumption that hosts and parasites
share a specialized exclusive evolutionary association,
making it extremely unlikely that a parasite could
S76
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ECOLOGICAL FITTING OF ANURAN PARASITES
S77
PLATE 1. Major habitat types in Area de Conservacion Guanacaste, Costa Rica (clockwise from upper left); Pitilla approach,
tropical cloud forest habitat at entrance to Pitilla Field Station; Cuajiniquil approach, Santa Elena Peninsula, seen from road to
Cuajiniquil, tropical dry forest habitat; Rio Pizote, permanent swamp near Rio Pizote, between Dos Rios and Brasilia, tropical rain
forest habitat. Pitilla Forest, tropical cloud forest habitat near Pitilla Field Station. All photos were taken in June 2005. Photo
credits: D. R. Brooks
change host species. This assumption, however, arises
from believing that it is the host species, not a biological
characteristic or combination of characteristics of the
host, that is important to the parasite (Brooks and
McLennan 1993). Once researchers began thinking in
terms of traits rather than taxonomy, it became evident
that parasites might be able to switch hosts if the trait
they were tracking was shared among two or more hosts.
The fact that present-day associations might be shaped
in part by the distribution of phylogenetically conservative traits is called ecological fitting (Janzen 1985).
There are many macroevolutionary manifestations of
ecological fitting. For example, any given parasite
species might be a resource specialist, but also might
share that specialist trait with one or more close
relatives. That is, specialization on a particular resource
can be plesiomorphic within a group (for an extensive
discussion and examples see Brooks and McLennan
[2002]). On the other hand, the resource itself might be
at once very specific and taxonomically and geographically widespread if it is a persistent plesiomorphic trait
in the hosts. The evolutionary basis for ecological fitting
is thus deceptively simple, yet powerful. If specific cues/
resources are widespread, or if traits can have multiple
functions (or both), then the stage is set for the
appearance of ecological specialization and close (co)evolutionary tracking as well as host switching. Ecological fitting thus explains how a parasite can be
ecologically specialized and still switch hosts: if the
resource is widespread across many host species, then
the parasite can take advantage of an opportunity to
establish a ‘‘new’’ specialized association without the cost
of evolving novel abilities (Brooks and McLennan 2002).
Just because a resource is widespread does mean that
it is automatically available. The geographic distribution
of the parasite might not coincide with the geographic
distribution of all hosts having the resource (Pellmyr
1992a, b), or some other aspect of host biology might
make the resource inaccessible to the parasite. For
example, if host species A bearing resource x is highly
abundant in a community, then less-abundant host
species B and C, which also bear x might not be
‘‘apparent’’ to a parasite specializing on that resource
(Feeny 1976, Wiklund 1984, Courtney 1985). Such
density-dependent factors provide the appearance of
close ecological tracking between the parasite and
species A at time T0. If some environmental stressor
later decreases the abundance of species A, and C
S78
DANIEL R. BROOKS ET AL.
Ecology Special Issue
FIG. 1. Ecological fitting in frog lung flukes. A clade of lung flukes (Haematoloechus spp.) arose in conjunction with the
evolution of leopard frogs (rpg; Rana pipiens group). Haematoloechus floedae arose through speciation by host switching to
bullfrogs (bg; Rana catesbeiana). Haematoloechus floedae was introduced into Costa Rica with bullfrogs, where it expanded its host
range to include local species of leopard frogs, members of the ancestral host group. Bullfrogs subsequently died out in Costa Rica,
but H. floedae survives today due to ecological fitting (from Brooks et al., in press). Thick lines indicate episodes of cospeciation;
thin lines indicate episodes of speciation by host switching.
becomes relatively more apparent, then the parasite will
become associated with C at time T1. This manifestation
of ecological fitting could explain seemingly rapid and
virtually unconstrained evolution of novel specialized
host associations. Finally, a parasite might have a
hierarchy of host preferences, even though it is tracking
the same resource (host rank order; Singer et al. 1971;
Janz and Nylin 1998 and references therein). The
hierarchy arises because the costs of accessing the
resource might not be identical across all host species
or even across individuals in the same species (Singer et
al. 1992). Such costs will depend on many different
factors, including concentration of the resource, host
density, and difficulty in extracting the resource. Overall,
parasites accessing a plesiomorphically (or, less often,
homoplasiously) distributed resource are ‘‘faux generalists’’ (Brooks and McLennan 2002): specialists whose
host range appears large, but who are in reality using the
same resource.
If a parasite species evolves the ability to utilize a
novel resource, a second and more complicated type of
host preference hierarchy can arise if the parasite also
retains sufficient information to use the plesiomorphic
resource (Wiklund 1981, Courtney et al. 1989). For
example, Haematoloechus floedae is a fluke native to the
southeastern United States where it lives in the lungs of
the bullfrog, Rana catesbeiana. When bullfrogs were
introduced to the southwestern United States, the
Yucatán, and Costa Rica, the parasite went with them,
and is now found in bullfrogs in those areas, as well as in
leopard frogs in the Yucatán and Costa Rica. Leopard
frogs (Rana pipiens clade) are the plesiomorphic hosts
for Haematoloechus (Fig. 1). Although the ancestor of
H. floedae switched to bullfrogs, the presence of the
fluke in leopard frogs indicates that the parasite has
retained its clade’s plesiomorphic ability to infect
leopard frogs (Brooks et al., in press). Interestingly,
bullfrogs have disappeared from Costa Rica, but the
parasite persists, having survived the ‘‘extinction’’ of its
preferred host. This is the first demonstration that
parasites, like phytophagous insects (Janz et al. 2001 and
references therein) might display ancestral host preferences under certain circumstances.
Ecological fitting is generally investigated in insect–
plant systems, because researchers can reconstruct
phylogenetic patterns of association between the two
clades, then examine the processes underlying those
patterns by (1) identifying the resource being tracked by
the insect, (2) determining the distribution of that
resource among host plants, and (3) delineating the
host preference hierarchy of the insects (Brooks and
McLennan 2002). Currently, we do not have this degree
of detailed information for any host–parasite system. It
is possible, however, to take advantage of ‘‘natural
experiments’’ (e.g., the case of H. floedae), or even to
make inferences based on contemporary patterns of
July 2006
ECOLOGICAL FITTING OF ANURAN PARASITES
host–parasite association, if hosts vary in their use of a
habitat to which parasite species are constrained. The
associations between anurans and their platyhelminth
parasites provide a model system for such an investigation, because the majority of helminths require water
for the development and transmission of infective stages,
while most, but not all, major groups of anurans have a
sexual and developmental tie to aquatic habitats. Brandt
(1936) suggested that species richness in anuran parasite
communities was directly related to the amount of time
the host spent in or near water, an observation
confirmed by subsequent studies (Prokopic and Krivanec 1975, Brooks 1976). A shared plesiomorphic
requirement for an aquatic habitat, coupled with a
gradient of adult anuran preferences ranging from
aquatic to arboreal, suggests that ecological fitting as a
determinant of the parasites associated with a given
anuran taxon should be evidenced as a nested-subset
structure (Patterson and Atmar 1986) of host–parasite
associations across anuran taxa (Zelmer et al. 2004).
At one extreme, if all the host–parasite associations
are the result of ecological fitting, then all host taxa are
interchangeable from the point of suitability for the
parasites, and associations will be determined solely by
the habitats the host utilizes and its feeding preferences.
The shared requirement of tadpoles for aquatic habitats
should thus provide a baseline assemblage of parasites
that infect the tadpole stage, while the parasites of adult
anurans should accumulate in anuran host species as a
function of the time they spend in aquatic habitats as
adults. If specialized coevolutionary processes dominate,
sympatry between anurans and the infective parasitic
stages will result in parasitism of only appropriate hosts,
producing idiosyncratic (i.e., ‘‘unexpected’’) presences
and absences in the matrix of host–parasite associations.
MATERIALS
AND
METHODS
Compound parasite communities are defined as the
array of parasite species inhabiting an array of host
species in a given area (Holmes and Price 1986). We
have data for six compound communities of platyhelminths that parasitize frogs as definitive hosts in North
and Central America: the temperate hardwood forests of
North Carolina (Brandt 1936), the temperate grasslands
of Nebraska (Brooks 1976), and the tropical wet and dry
forests of Costa Rica (see Plate 1) and Mexico, derived
from biodiversity inventories currently being coordinated by D. R. Brooks (Costa Rica) and V. LeónRègagnon (Mexico) (see the Appendix).
We sampled 75 anuran species in the six areas; 59
were sampled in one area, 14 species were sampled in
two areas, and two species were sampled in three areas
(see the Appendix, Table A1). Of the 57 platyhelminth
species collected, 38 were found in one area, 13 species
were found in two areas, four species were found in
three areas, and two species (Langeronia macrocirra and
Haematoloechus complexus) were found in four areas
(see the Appendix, Table A2). The parasites inhabit 34
S79
of the 75 sampled anurans, only six of which (Rana
catesbeiana, Rana vaillanti, Smilisca baudenii, Smilisca
phaeota, Leptodactylus melanonotus, and Bufo marinus)
have been sampled in two areas, and one of which
(Bufo marinus) was sampled in three areas. From this
we conclude that comparisons of compound community structure among the six sites will not be confounded
by multiple samples of the same anuran community,
and therefore the same anuran parasite (i.e., pseudoreplication). Moreover, given the geographical and
taxonomic breadth of the surveys, it is assumed that
the resultant presence/absence matrices of host–parasite
associations, at the taxonomic levels examined (i.e.,
host genera and parasite genera and families) are
representative of the possible associations, and not
strongly biased by ecological factors, such as host and
parasite ranges and relative abundances.
Anuran species were ranked based on their association with aquatic habitats as follows: 7, riparian,
prolonged breeding (several months); 6, semiaquatic,
prolonged breeding; 5, terrestrial, prolonged breeding; 4,
terrestrial, explosive breeding (1–2 wk); 3, arboreal,
prolonged breeding; 2, arboreal, explosive breeding; 1,
fossorial. The relationship between the ranked association and trematode species richness was evaluated using
Spearman’s rank correlation analysis.
Without data from experimental infections, ecological
fitting and co-speciation cannot be distinguished as
explanations for extant, and apparently specific, host–
parasite associations. Thus, parasite species and host
species were grouped by genera for the purpose of
nested-subset analysis, increasing the likelihood that the
host and parasite clades had at one time been sympatric.
Given the degree of local adaptation for both the host
and parasite species, pooling hosts by genera and
parasites by genera and families should not increase
the likelihood of a nested-subset pattern occurring, given
a mechanism of co-speciation. Thus it is necessary to
view such a pattern as having been produced by
ecological fitting. Examination of the nested-subset
structure of parasite genera within the pooled anuran
genera across all six localities was conducted using the
nestedness temperature calculator (Atmar and Patterson
1995), which calculates the temperature of the matrix (a
measure of order, with lower temperatures indicating a
higher degree of order) and idiosyncratic host and
parasite temperatures, which indicate host species and
parasite species contributing disproportionately to the
lack of order in the matrix (Atmar and Patterson 1993).
Nested-subset patterns can arise as artifacts of
random draws of individual items from categories that
vary in their representation (Connor and McCoy 1979).
In a proximal sense, within a given locality, this would
involve host individuals acquiring parasites from a
species pool where the probabilities of infection varied
among the parasite species because of an uneven
distribution of infective stages within the environment.
Considering the patterns of association between host
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DANIEL R. BROOKS ET AL.
and parasite taxa, assuming that the various host taxa
are sympatric in a regional sense, nestedness could be
expected to arise by a similar passive mechanism if the
parasite taxa vary in the degree of sympatry between
their respective geographic ranges and those of the
hosts. For tests of passive sampling involving community data, the relative abundances of the species sampled
is not known for the source pool, requiring estimation of
these relative abundances from the available data.
Models that test for passive sampling typically base this
estimate on the occurrence of species in the sample
(RANDOM1; Patterson and Atmar 1986, Fischer and
Lindenmayer 2002), which will result in overestimation
of the colonization probabilities of rare species unless
none of the populations present in the sample were
further supplemented by dispersal from the source pool
following the initial colonization (Andrén 1994).
Constructing an appropriate null model for passive
sampling would require knowledge of the contribution
of immigration from the source pool to the observed
relative abundances. In the absence of such information,
a null model (RELABUND) defining the opposite
extreme, i.e., each individual present in a population is
assumed to be an immigrant from the source pool, can
be used in concert with RANDOM1, with the appropriate, but unavailable, null model falling between these
extremes (Zelmer et al. 2004). Given that the distributions of temperatures of matrices produced by these
models represent extremes in terms of the effect of
immigration on the observed population sizes within a
community, overlap with the tails of these distributions
cannot be evaluated with a simple decision rule and
must be interpreted in light of ecological evidence for the
expected effects of immigration.
By analogy, the evaluation of passive mechanisms
that produce nested-subset patterns of associations
between host taxa and parasite taxa would require an
understanding of the contribution of host capture to the
observed associations. Species-level host and parasite
phylogenies do not yet exist for the taxa in question (an
exception is Haematoloechus; León-Règagnon and
Brooks 2003), so the number of times a particular host
genus acquired any particular parasite genus or family
cannot be directly inferred, and must be estimated from
the available presence/absence data. Analogous models
to RANDOM1 and RELABUND were employed, using
the occurrence of parasite taxa within host taxa to
parameterize the Monte Carlo simulations for RANDOM1, and using the number of independent host–
parasite associations to parameterize RELABUND.
(For example, there are two species of Langeronia, one
infecting four host species, the other infecting one. Thus,
for the RELABUND model considering parasite genera,
five ‘‘individuals’’ of Langeronia are distributed randomly among the host taxa. Within the Lecithodendriid
family, in addition to the associations mentioned for
Langeronia, there are two other parasite species, one
infecting two host species, and one infecting a single
Ecology Special Issue
species. For the RELABUND model considering parasite families, eight ‘‘individual’’ lecithodendriids are
distributed randomly among the host taxa.) As with
the interpretation of these models for nestedness in
communities, some of the associations observed will be
the result of host capture, and some by inheritance,
placing the appropriate, but unknown, null model
between the extremes. Both the RANDOM1 and
RELABUND null models were applied to presence/
absence matrices of platyhelminth genera within anuran
genera, trematode genera within anuran genera, and
matrices where parasite genera not represented in all
three regions were pooled by family and evaluated
within host genera and host ecotype ranking.
To evaluate whether the patterns of presence and
absence revealed across regions by the nested-subset
analysis were reflected at smaller scales, we employed
Spearman’s rank correlation analysis to assess covariance between the total number of host genera
occupied by a parasite genus or family (pooled across
all six localities), and the number of host species, genera,
and families occupied by each species within that taxon
within each of the six localities. We also used Pearson’s
analysis to determine covariance between the total
number of host genera occupied by a parasite genus or
family (pooled across all six localities) and the number
of host species, genera, and families occupied by those
taxa within each region (United States, Mexico, and
Costa Rica).
RESULTS
The ranked association with aquatic habitats of the
anuran species with nine or more individuals necropsied
per locality positively covaried (r ¼ 0.785; P , 0.0001)
with the trematode richness of the frog host species
(Fig. 2), with no clear differences in the pattern of
increase among the six localities.
The temperature (the measure of matrix order derived
by Atmar and Patterson [1993]) of the presence/absence
matrix of platyhelminth genera within anuran genera
(Fig. 3) was significantly more ordered than the matrices
produced by the RANDOM1 (P ¼ 0.00063) or
RELABUND (P ¼ 0.00002) null models. Nested-subset
analysis designated four of the 21 parasite taxa as
idiosyncratic; two monogenean genera and two cestode
genera. Such idiosyncrasies are an indication of different
colonization histories for these genera (Atmar and
Patterson 1993) relative to the other parasites considered, suggesting the importance of phylogenetic congruence as a determinant of the anuran associations with
monogenean and cestode species. Consequently, the
remaining analyses focused on the trematodes.
The nested-subset structure of the trematode genera
within the pooled anuran genera (Fig. 4) also was
significantly colder than the matrices generated from
both null models (RANDOM1, P ¼ 0.000001; RELABUND, P ¼ 0.0000004), and also revealed idiosyncratic
parasite genera. These idiosyncrasies all occurred in
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ECOLOGICAL FITTING OF ANURAN PARASITES
S81
FIG. 2. Increasing trematode species richness in anuran host species from each of six localities, with increased anuran
association (ranked) with aquatic habitats. Multiple observations at a single coordinate are indicated parenthetically above the
coordinate. ACG denotes Area de Conservación Guanacaste.
genera that were missing from at least one of the three
regions sampled (North America, Mexico, or Costa
Rica). Thus, in order to evaluate the potential for the
interaction of the anuran host genera with specific
landscapes to produce nested subset patterns, we
arbitrarily pooled parasite genera not represented in all
three areas into their respective families, thereby
ensuring that all anuran genera included in the analysis
have the potential to draw from the same parasite pool.
The resulting matrix is depicted in Fig. 5.
The temperature of the presence/absence matrices of
trematode genera/families in pooled anuran genera
overlaps the cold tail of the distribution produced by
the RANDOM1 model (P ¼ 0.0025) and the cold tail of
the distribution produced by the RELABUND model
(P ¼ 0.0885). Thus, the observed nested-subset pattern
could only be attributed to passive mechanisms by
adhering to the RELABUND model’s assumption that
all host–parasite associations occur independently.
Given that extreme assumption, however, interpretations of the observed matrix as nonsignificant with
regards to passive sampling must be made with caution.
Examination of the presence/absence matrix of trematode genera and families within ranked anuran ecotypes
(Fig. 6) supports the contention that the semiaquatic
anuran habitat creates overlap with infective stages of a
greater number of trematodes than a purely aquatic
habit. The temperature of this matrix falls within the cold
tail of the distribution of the temperatures produced by
both models (RANDOM1, P ¼ 0.0018; RELABUND, P
¼ 0.165). As with the parasite associations with anuran
genera, one must conclude that passive mechanisms could
produce this pattern only under the independent-association assumption of the RELABUND model, again with
the caveat that the distribution of the appropriate null
model presumably has a warmer central tendency than
that produced by the RELABUND model.
The total number of host genera occupied by a
parasite genus or family (pooled across all six localities)
positively covaried with the number of host species (r ¼
0.321; P ¼ 0.0104), genera (r ¼ 0.425; P ¼ 0.0005), and
families (r ¼ 0.426; P ¼ 0.0005) occupied by each species
within that taxon within each of the six localities. The
total number of host genera occupied by a parasite
genus or family also positively covaried with the number
of host species (r ¼ 0.492; P ¼ 0.0147), genera (r ¼ 0.668;
P ¼ 0.0004), and families (r ¼ 0.645; P ¼ 0.0007) occupied
by each species within that taxon within each of the
three regions (United States, Mexico, and Costa Rica).
DISCUSSION
Parasite habitat preference and transmission patterns
Fifty-four of the 57 parasite species (see the Appendix,
Table A3) exhibit the plesiomorphic pattern of requiring
water for transmission, either by utilizing aquatic
intermediate hosts (digeneans and cestodes), or by
swimming from one host to another (monogeneans).
In other words, transmission patterns are phylogenetically conservative in this phylum (Brooks and
McLennan 1993, Adamson and Caira 1994). This
explains why 45 of the 57 platyhelminth species were
found only in aquatic and semiaquatic frogs. Of the
remaining 12 species, 10 occur in terrestrial, arboreal,
and fossorial frogs, but infect the tadpole stage of their
hosts. Digeneans in the genus Glypthelmins and the
family Paramphistomidae cluster with the brachycoelids
(Brachycoelium and Mesocoelium) in the maximally
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DANIEL R. BROOKS ET AL.
Ecology Special Issue
FIG. 3. Maximally packed presence/absence matrix for pooled parasite genera (columns) within pooled host genera (rows) from
all six localities. Stars indicate idiosyncratic hosts and parasite species. Letters within the matrix denote geographic region where
associations were observed: A, United States only; B, Mexico only; C, Costa Rica only; D, United States and Mexico; E, United
States and Costa Rica; F, Mexico and Costa Rica; G, United States, Mexico, and Costa Rica.
packed matrix (Fig. 5) as parasite taxa common to a
number of anuran genera within localities and regions,
as well as across regions. All that is required for infection is that the frog species comes to water to breed in
a population density high enough to ensure infection.
This behavior is plesiomorphic for, and phylogenetically
conservative among, frogs. The last two species,
members of the sister groups Brachycoelium and
Mesocoelium, have terrestrial life cycles, which explains
why they occur so frequently in terrestrial anurans and
in frogs that occasionally forage away from water.
Choledocystus intermedius (one of the idiosyncratic
taxa in the presence/absence matrix depicted in Fig. 4)
inhabits only Bufo marinus, and it is the only adult platyhelminth restricted to that host. Razo-Mendivil et al. (in
press) recently have shown C. intermedius to be closely
related to members of the families Ochetosomatidae and
Telorchiidae. Life cycles for members of those families
involve aquatic molluscs as first intermediate hosts, and
tadpoles as second intermediate hosts, which are ingested
by the final host. The absence of C. intermedius from other
anuran hosts that ingest tadpoles might indicate that the
FIG. 4. Maximally packed presence/absence matrix for pooled trematode genera (columns) within pooled host genera (rows)
from all six localities. Stars indicate idiosyncratic host and parasite species. Letters within the matrix are as in Fig. 3.
July 2006
ECOLOGICAL FITTING OF ANURAN PARASITES
FIG. 5. Maximally packed presence/absence matrix for
trematodes (columns) pooled by genera, or by family for those
genera that did not occur in all three regions (United States,
Mexico, and Costa Rica), within pooled host genera (rows)
from all six localities. Stars indicate idiosyncratic host and
parasite species. Letters within the matrix are as in Fig. 3.
association between C. intermedius and Bufo marinus
involves more specialization than ecological fitting.
The remaining parasite taxa, whose associations with
their hosts cannot easily be interpreted as manifestations
of ecological fitting (based on the idiosyncratic patterns
revealed in the nested subset analysis), also infect
tadpoles at some stage in their lives. The monogeneans
Polystoma naevius, and the probable sister species
Neodiplorchis scaphiopi and Pseudiplorchis americanus,
infect tadpoles and develop into adults when the tadpoles
metamorphose. Anecdotal reports exist of tadpoles
eating proglottids of nematotaeniid cestodes, suggesting
that four additional species (Cylindrotaenia americana
and C. sp., Distoichometra bufonis and D. kozloffi) gain
infection in a manner similar to the first three species.
Perhaps infection of a tadpole requires greater specificity
on the part of the parasite than infections of adult
anurans, making parasites with such life cycle patterns
less amenable to ecological fitting.
S83
in plethodontid salamanders and has a terrestrial life
cycle. Not surprisingly it is more common in a terrestrial
nonranid (Pseudacris brimleyi) than in a ranid host.
It is clear that a role for coevolutionary processes
exists for variations in associations of parasite genera
within families, and species within those genera in terms
of their specific host association. However, no explanatory power is gained from considering anurans by their
genera, as opposed to their ecotype, in terms of
associations with the genera and families of the
trematodes infecting them. This equivalence occurs, in
part, because ecotype preference for anuran hosts (e.g.,
all aquatic and semiaquatic host species in all six sites
are members of the same genus, Rana), and transmission
dynamics for the parasites (e.g., all species of Haematoleochus utilize odonate naiads as second intermediate
hosts) are phylogenetically conservative (Snyder and
Janovy 1994, Wetzel and Esch 1996).
The host landscape
As evidence for phylogenetic conservatism in host and
parasite biology, 80% of the parasite species discovered
in these six communities inhabit only 13% of the frog
species sampled. How do these species coexist? Part of
the answer lies in perhaps the most fundamental element
of ecological fitting: allopatry. Only 38% of the 48
parasite species inhabiting aquatic and semiaquatic
anurans occur in more than one community.
Another aspect of the process of co-ocurrence lies in
parasite microhabitat diversification, or, as commonly
Habitat use by hosts
Forty-two of the 58 species of adult platyhelminths
(72%) occur in ranids. Of those 42 species, 27 are found
only in ranids, indicating that some character or suite of
characters associated with being a ranid is the resource
being tracked by the parasites. Of the remaining 15
species, 11 always occur in higher prevalences in ranids
(one measure of the host preference hierarchy), two
occur at lower prevalences, and two are equivocal
(possibly an artifact of small sample size). The lowprevalence occurrences in nonranid hosts might be an
additional example of ecological fitting if the nonranids
are suitable hosts, but their lack of exposure to aquatic
habitats renders them ‘‘not apparent’’ to the parasites.
Brachycoelium hospitale, for example, is generally found
FIG. 6. Maximally packed presence/absence matrix for
trematodes (columns) pooled by genera, or by family for those
genera that did not occur in all three regions (United States,
Mexico, and Costa Rica), within host species from all six
localities, pooled by ecotype (ranked association with aquatic
habitats). Stars indicate idiosyncratic host and parasite species.
Variation in rank within genera was (no. species at rank) as
follows: ranids, two at rank 7, six at rank 6, and two at rank 5;
bufonids, two at rank 4, three at rank 5; leptodactylids, two at
rank 4, one at rank 5; hylids, two at rank 3; see Materials and
methods for rank designations. No trematodes were found to
infect the fossorial species.
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DANIEL R. BROOKS ET AL.
phrased, diversity in preferred site of infection within the
host (Brooks and McLennan 1993, 2002, Adamson and
Caira 1994, Radtke et al. 2002). Platyhelminth species
occur in the buccal cavity, lungs, gall bladder and hepatic
ducts, small intestine, rectum, and urinary bladder of
anuran hosts, such that multiple species from different
clades can infect the same host and form complicated
communities without interacting physically, i.e., they are
microallopatric. Many species having similar transmission modes occur in the same hosts, but live in
different parts of that host. On the other hand, because
the diversification of infection site is phylogenetically
conservative, multiple, distantly related parasite species
living within a given geographic area can exhibit the
same kind of site specificity, which should amplify
competition. In some cases, the parasite species occur
in different host species; for example, polystome monogeneans and gorgoderid digeneans live in the urinary
bladder, but do not infect the same species of frogs. In
other cases, the parasites have markedly different
biological requirements. Cestodes living in the host gut
absorb nutrients from the host intestinal contents,
whereas digeneans living in the host gut forage for host
epithelial cells, cell and tissue exudates, and blood.
CONCLUSIONS
These six communities of frog parasites are both
complex and similar to each other. Their complexity
rules out simple phylogenetic replication, namely, that
these communities are products of a simple history of
vicariance and/or co-speciation. The taxonomic similarity of the communities, coupled with their occurrence in
such markedly different environments, rules out the
possibility that they are the result of convergent
adaptation. Brooks (1980, 1985), Futuyma and Slatkin
(1983), and Janzen (1985) suggested that relatively weak
phenomena (in this case, phylogenetic conservatism in
host and parasite natural history) have the potential to
produce marked ecological structure. That a great deal
of the stable and predictable structure of contemporaneous anuran parasite communities appears to be a
result of phylogenetic conservatism in the evolution of
both parasite and host biology, coupled with the
historical biogeographic contingencies of speciation
and dispersal, is consistent with those views.
These observations, of course, do not rule out the
possibility of ongoing strong evolutionary interactions
between any of these parasites and their hosts or each
other, particularly at the small spatial and short
temporal scales associated with Thompson’s (1994)
coevolutionary mosaic. Nor do the observations imply
that ecological fitting explains everything; only that
assumptions about the low probability of host switching
must be viewed with far more caution in the future.
Tracking a plesiomorphic resource in parasites is the
equivalent of free-living organisms dispersing into new
habitat, but retaining their ecological niche; both are
aspects of ecological fitting. Given this, we expect that
Ecology Special Issue
parasite communities will be macroevolutionary mosaics
of ecological fitting and co-speciation, just as are
communities of free-living organisms (e.g., colubrid
snakes; Cadle and Greene 1993). Additionally, because
communities and associations are subject to evolutionary forces that will vary across space and time, we
also expect that the importance of ecological fitting and
co-speciation will vary among communities and among
associations.
Finally, our analysis implies that many parasites
currently restricted to particular hosts in particular
localities could survive in other hosts and other localities
if they could get there. Episodes of major climate change,
for example, result in range contractions and expansions
bringing together species that have been allopatric
during their previous evolutionary histories. In such
cases, we would expect an increase in host switching, not
as a result of evolution of novel host utilization
capabilities, but as a manifestation of ecological fitting.
As discussed above (see the Introduction), some parasites
might well survive extinction of their original hosts.
Discovering the importance of ecological fitting as a
determinant of the structure of anuran parasite communities thus underscores the need for more comprehensive ecological and evolutionary understanding of
host specificity in assessing the risk of parasite transmission into native hosts resulting from the introduction
of exotic host species along with their parasites.
ACKNOWLEDGMENTS
We express our appreciation to Cam Webb for inviting us to
contribute to this issue. D. R. Brooks thanks the scientific and
technical staff of the Area de Conservación Guanacaste for
support of this study, in particular: Elda Araya, Roger Blanco,
Carolina Cano, Maria Marta Chavarrı́a, Felipe Chavarrı́a,
Roberto Espinoza, Dunia Garcia, Guillermo Jimenez, Elba
Lopez, Sigifredo Marin, Alejandro Masis, Calixto Moraga,
Fredy Quesada, and Petrona Rios. Thanks to Dan Janzen and
Winnie Hallwachs, scientific advisers to the ACG, for their
support and insights. D. R. Brooks and D. A. McLennan
acknowledge support from the Natural Sciences and Engineering Research Council (NSERC) of Canada. V. León-Règagnon
thanks Ma. Antonieta Arizmendi, Luis Garcı́a, Rosario Mata,
Laura Paredes, Elizabeth Martı́nez, Agustı́n Jiménez, Rogelio
Rosas, Ulises Razo (Instituto de Biologı́a, Universidad
Nacional Autónoma de México [UNAM]), Edmundo Pérez,
Adrián Nieto, (Facultad de Ciencias, UNAM), David Lazcano,
and Javier Banda (Universidad Autónoma de Nuevo León) for
their help in the field and lab, and acknowledges support from
NSF grant DEB-0102383.
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APPENDIX
Geographic distributions of anuran hosts and their platyhelminth parasites in six areas in North and Central America
(Ecological Archives E087-112-A1).
Ecology, 87(7) Supplement, 2006, pp. S86–S99
Ó 2006 by the Ecological Society of America
THE PHYLOGENETIC STRUCTURE OF A NEOTROPICAL FOREST
TREE COMMUNITY
STEVEN W. KEMBEL1,3
1
AND
STEPHEN P. HUBBELL2
Department of Biological Sciences, University of Alberta, Edmonton, Alberta T6G2E9 Canada
2
Department of Plant Biology, University of Georgia, Athens, Georgia 30602 USA
Abstract. Numerous ecological and evolutionary processes are thought to play a role in
maintaining the high plant species diversity of tropical forests. An understanding of the
phylogenetic structure of an ecological community can provide insights into the relative
importance of different processes structuring that community. The objectives of this study
were to measure the phylogenetic structure of Neotropical forest tree communities in the
Forest Dynamics Plot (FDP) on Barro Colorado Island, Panama, to determine how the
phylogenetic structure of tree communities varied among spatial scales and habitats within the
FDP, and to study the effects of null-model choice on estimates of community phylogenetic
structure. We measured community phylogenetic structure for tree species occurring together
in quadrats ranging in size from 10 3 10 m to 100 3 100 m in the FDP. We estimated phylogenetic structure by comparing observed phylogenetic distances among species to the distribution of phylogenetic distances for null communities generated using two different null
models. A null model that did not maintain observed species occurrence frequencies tended to
find nonrandom community phylogenetic structure, even for random data. Using a null model
that maintained observed species frequencies in null communities, the average phylogenetic
structure of tree communities in the FDP was close to random at all spatial scales examined,
but more quadrats than expected contained species that were phylogenetically clustered or
overdispersed, and phylogenetic structure varied among habitats. In young forests and plateau
habitats, communities were phylogenetically clustered, meaning that trees were more closely
related to their neighbors than expected, while communities in swamp and slope habitats were
phylogenetically overdispersed, meaning that trees were more distantly related to their
neighbors than expected. Phylogenetic clustering suggests the importance of environmental
filtering of phylogenetically conserved traits in young forests and plateau habitats, but the
phylogenetic overdispersion observed in other habitats has several possible explanations,
including variation in the strength of ecological processes among habitats or the phylogenetic
history of niches, traits, and habitat associations. Future studies will need to include
information on species traits in order to explain the variation in phylogenetic structure among
habitats in tropical forests.
Key words: community phylogenetic structure; environmental filtering; Forest Dynamics Plot; null
models; Panama; phylogenetic clustering; phylogenetic overdispersion.
INTRODUCTION
Why are there so many species of trees in the tropics?
Tropical forests are incredibly biologically diverse, and
numerous ecological and evolutionary processes, such as
niche differentiation, herbivory, dispersal, competition,
parasitism, and disease, appear to interact to play a role
in maintaining the high species diversity of tropical tree
communities at a range of spatial scales (Wright 2002).
Although all of these processes have been demonstrated
to occur, their relative importance in structuring
ecological communities is not well understood. Numerous studies have demonstrated niche differentiation and
Manuscript received 21 January 2005; revised 6 September
2005; accepted 8 September 2005. Corresponding Editor (ad
hoc): J. B. Losos. For reprints of this Special Issue, see footnote
1, p. S1.
3
E-mail: [email protected]
habitat specialization in tropical tree species (Hubbell
and Foster 1983, Condit et al. 1996, Clark et al. 1998,
Webb and Peart 2000, Harms et al. 2001), but the
strength of habitat specialization is often not sufficient
to explain observed levels of species richness in tropical
forests (Webb and Peart 2000, Harms et al. 2001). Similarly, finding niche differentiation among species does
not mean that niche differences are more important than
species-neutral processes in structuring a community
(Hubbell 2001). The phylogenetic structure of ecological
communities should provide insights into the relative
importance of different ecological processes, as these
processes interact with the evolutionary history of plant
traits and leave their signature on the phylogenetic
structure of a community (Webb et al. 2002).
Previous studies have identified two general types of
processes that can interact with phylogenetic patterns of
niche and trait evolution to give rise to nonrandom
S86
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FOREST COMMUNITY PHYLOGENETIC STRUCTURE
phylogenetic community structure, namely, competitive
exclusion and environmental filtering processes (Webb
2000, Webb et al. 2002, Cavender-Bares et al. 2004).
Competitive exclusion and other negative densitydependent interactions among ecologically similar species can create nonrandom community phylogenetic
structures. Many processes, such as herbivory (Novotny
et al. 2002) and competition (Uriarte et al. 2004), can
have negative density-dependent effects, not only on
conspecific individuals, but on close phylogenetic relatives as well. Density-dependent processes that negatively
affect close phylogenetic relatives could give rise to
phylogenetic overdispersion of co-occurring species,
meaning that co-occurring species are more distantly
phylogenetically related than expected by chance. Direct
competitive exclusion, as well as indirect interactions
among relatives mediated by herbivores, parasites, or
pathogens, could all give rise to the same pattern of community phylogenetic overdispersion (Webb et al. 2002).
Environmental filters (Weiher and Keddy 1995, Webb
et al. 2002, Cavender-Bares et al. 2004) or assembly rules
(Weiher and Keddy 1999) restricting community membership to individuals possessing certain traits might
also affect community phylogenetic structure. If environmental conditions in a habitat act as a filter that
select for species in possession of certain traits, we would
expect to find either phylogenetic clustering or overdispersion, depending on the evolutionary history of
ecologically important traits and species niches (Cavender-Bares et al. 2004, Ackerly et al. 2006, CavenderBares et al. 2006, Silvertown et al. 2006).
The phylogenetic structure of a community might also
be random. If species-neutral interactions (Hubbell 2001)
structure a community, if the strength of density-dependent and environmental filtering processes are balanced or
weak, or if species niches or traits are phylogenetically
random, local communities could exhibit phylogenetic
structures indistinguishable from random.
Given the variation of measures of community
structure, such as taxonomic and functional diversity
with spatial scale and extent (Levin 1992), and changes in
the phylogenetic signal of niches and traits with spatial
scale (Cavender-Bares et al. 2006, Silvertown et al. 2006),
we would predict that the phylogenetic structure of a
community will be highly dependent on the spatial scale
and extent used to define the community.
The choice of an appropriate null model to use when
measuring the structure of ecological communities has
been very contentious (Gotelli and Graves 1996, Gotelli
2004), since analyses of the same data set with different
null models can lead to very different conclusions. Simulation studies have been used to directly assess the performance of different null models when measuring species
co-occurrence patterns (e.g., Gotelli 2000), and a number
of different null models have been used to measure community phylogenetic structure, but the effects of different
null models on estimates of community phylogenetic
structure have not been evaluated quantitatively.
S87
In this study, we used large data sets on the
phylogenetic relationships and co-occurrence patterns
of Neotropical forest tree species in communities at a
range of local spatial scales (10 3 10 m to 100 3 100 m)
to address several questions. First, we compared the
performance of different null models used to study the
phylogenetic structure of ecological communities. Second, to test the relative importance of different
ecological processes in structuring these communities
and to test whether community phylogenetic structure
changes with spatial scale, we asked whether trees
occurring together in communities at a range of local
spatial scales are phylogenetically clustered or overdispersed. Third, we asked whether the phylogenetic
structure of tree communities differs among habitats
characterized by different environmental conditions.
METHODS
Ecological data
We estimated the phylogenetic structure of a Neotropical forest tree community using data from the 50-ha
Forest Dynamics Plot (FDP) on Barro Colorado Island
in the Republic of Panama. The moist lowland forests in
the 1000 3 500 m FDP receive ;2600 mm of rain each
year, with a dry season during January–April, and mean
annual temperatures of 278C (Dietrich et al. 1982).
Within the FDP, a variety of habitats have been
identified (Harms et al. 2001), including (in approximate
order of decreasing water availability during the dry
season) swamp, stream, slope, and upland plateaus. The
majority of the FDP contains old-growth primary
forests, although some relatively young secondary forest
habitat is found within the plot (Harms et al. 2001).
Within the 1000 3 500 m FDP, all tree and shrub stems
1 cm dbh have been mapped and identified to species in
repeated censuses conducted since 1982 (Condit 1998).
We measured the phylogenetic structure of the forests on
Barro Colorado Island using data on occurrence of tree
and shrub species in the FDP. Mapped tree locations
from the 1982 census of the FDP were divided into square
nonoverlapping quadrats of four different spatial scales
(10 3 10 m, 20 3 20 m, 50 3 50 m, and 100 3 100 m). We
defined communities at a given spatial scale to include
species of all tree and shrub stems 1 cm dbh present
together in individual quadrats at that scale. Although
we refer to the communities in the FDP as tree
communities throughout this paper for the sake of
convenience, shrubs 1 cm dbh were also included in
all analyses of community structure.
Phylogenetic data
We constructed a hypothesized phylogenetic tree for
the 312 tree species occurring in the FDP using
Phylomatic version R20031202 software (Webb and
Donoghue 2005), a phylogenetic database and toolkit
for the assembly of phylogenetic trees. The tree created by
Phylomatic used information from numerous published
molecular phylogenies to create a tree containing all of
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STEVEN W. KEMBEL AND STEPHEN P. HUBBELL
Ecology Special Issue
FIG. 1. Hypothesized phylogenetic relationships among woody plant species occurring in the 50-ha Forest Dynamics Plot on
Barro Colorado Island, Panama. Circles indicate nodes dated based on divergence dates reported by Wikström et al (2001).
Undated nodes were spaced evenly between dated nodes to minimize variation in branch length.
the species in the FDP, based on Phylomatic reference
tree R20031202 with the APG II (Angiosperm Phylogeny
Group 2003) phylogenetic classification of flowering
plant families (P. F. Stevens, available online)4 forming
the backbone of the tree. In the absence of detailed
information on phylogenetic relationships within many
of the 55 families and 192 genera found in the FDP, we
assumed most families and genera were monophyletic
and polytomous when placing them on the tree. We
assigned branch lengths to the phylogenetic tree using the
BLADJ module of the Phylocom version 3.19 software
package (Webb et al. 2004), creating a pseudochronogram with branch lengths based on clade ages reported by
Wikström et al. (2001). Nodes in the phylogenetic tree for
which age estimates were available were fixed at their
estimated ages (Wikström et al. 2001), and all remaining
branch lengths were set by spacing undated nodes in the
tree evenly between dated nodes to minimize variance in
branch lengths. Age estimates were available for 34 of the
122 internal nodes in the phylogeny. Although 57 of the
122 internal nodes were polytomous, the majority of
these polytomies were within families and genera, with
the backbone of the tree relatively well resolved and
dated. The resulting phylogenetic tree (Fig. 1; also, see the
Supplement) was used for all subsequent analyses of
community phylogenetic structure.
Community phylogenetic structure
We calculated several measures of community phylogenetic structure for all quadrats at each spatial scale.
4
hhttp://www.mobot.org/MOBOT/research/APwebi
All analyses of community phylogenetic structure were
conducted using the Phylocom version 3.19 software
package (Webb et al. 2004). Three steps were taken to
measure community phylogenetic structure in each
quadrat. First, we calculated raw phylogenetic distances
among species occurring together in each quadrat.
Second, we created numerous randomly generated null
communities corresponding to each quadrat and estimated raw phylogenetic distances among species occurring together in the null communities. Finally, we
calculated measures of standardized effect size (Gotelli
and Rohde 2002) of community phylogenetic structure
for our raw measures of phylogenetic distance in each
quadrat, by comparing observed phylogenetic distances
to the distribution of phylogenetic distances for the
randomly generated null communities.
We calculated raw phylogenetic distances among
species in quadrats in two ways, each of which captures
a different aspect of the phylogenetic relatedness of cooccurring species (Webb 2000). The mean pairwise
distance (MPD) was calculated as the mean phylogenetic
distance among all pairwise combinations of species
occurring together in each quadrat, and the mean
nearest neighbor distance (MNND) was calculated as
the mean phylogenetic distance to the nearest relative
for all species occurring together in each quadrat (Webb
2000, Webb et al. 2002).
Null models
To determine whether the phylogenetic structure of
local tree communities differed from the phylogenetic
community structure expected by chance, we compared
observed phylogenetic distances among species in each
July 2006
FOREST COMMUNITY PHYLOGENETIC STRUCTURE
quadrat to the distribution of phylogenetic distances for
randomly generated null communities (Gotelli and
Graves 1996). To assess the effect of null-model choice
on ability to measure community phylogenetic structure,
we generated null communities in two ways.
The first method maintained the total species richness
of each quadrat, with species in each quadrat chosen
equiprobably at random, without replacement from the
pool of species present in the FDP. We refer to this null
model as the ‘‘unconstrained’’ model, since the species
richness of each quadrat remained the same in the null
communities, but species occurrence frequencies in the
null communities were not constrained to be equal to
their actual occurrence frequency among quadrats in the
FDP data set. This null model assumes that all species
present in the FDP are equally able to colonize any
quadrat.
The second method maintained both the total species
richness of each quadrat, as well as the occurrence
frequency of each species, by randomly swapping species
occurrences among all quadrats at a scale subject to the
constraint that the species richness of each quadrat
remain constant and that the relative frequency of all
species occurrences in quadrats remain constant. We
refer to this null model as the ‘‘constrained’’ model, since
the occurrence frequencies of species in the null
community were constrained to be equal to their actual
frequency in quadrats at that spatial scale. This null
model assumes that a species’ ability to colonize a
quadrat is proportional to its frequency in the FDP.
We generated the constrained null communities using
the independent swap algorithm (Gotelli 2000, Gotelli
and Entsminger 2003) by holding the row and column
sums of the quadrat/species occurrence matrix constant
while swapping species among quadrats using a checkerboard swap. The checkerboard swap searches the
quadrat/species matrix for submatrices of the form
(0,1)(1,0) or (1,0)(0,1) (where 1 and 0 represent species
presence and absence, respectively, in two quadrats) and
swaps species presences between quadrats when these
checkerboard submatrices are found. This maintains
species frequencies and quadrat species richnesses while
randomizing patterns of species co-occurrence. Each
null community was created by swapping subsequent
matrices many times relative to the number of species
presences in the quadrat/species matrix, creating serially
independent randomized matrices. The first null community for each spatial scale was created by checkerboard swapping the original quadrat/species matrix for
that scale 30 000 times, with each subsequent null
community generated by checkerboard swapping the
previous matrix 10 000 times.
We recorded the mean and standard deviation of
MPD and MNND among species in each quadrat for
1000 null communities generated using the constrained
and unconstrained null models. We then calculated
measures of standardized effect size (Gotelli and Rohde
2002) of the observed phylogenetic distances among
S89
species occurring in each quadrat, relative to the
distribution of distances calculated for null communities
in each quadrat. These effect size measures compared
the observed phylogenetic distance in each quadrat to
the distribution of phylogenetic distances in null
communities corresponding to that quadrat, and can
be used to test for phylogenetic clustering or overdispersion. We used two metrics of community phylogenetic structure similar to those first proposed by Webb
(2000), but based on comparisons with different null
models.
The net relatedness index (NRI) of each quadrat
(Webb 2000, Webb et al. 2002) was defined as [–(MPD –
MPDnull)/SD(MPDnull)], where MPD is the mean pairwise phylogenetic distance among species in the quadrat,
MPDnull is the mean MPD for that quadrat in 1000 null
communities, and SD(MPDnull) is the standard deviation
of MPD for that quadrat in 1000 null communities. The
net relatedness index has been proposed as a measure of
tree-wide phylogenetic clustering and overdispersion of
species (Webb 2000). Positive NRI scores indicate that
species occurring together in a quadrat are more closely
phylogenetically related than expected by chance,
generally due to tree-wide phylogenetic clustering of
co-occurring species. Negative NRI scores indicate that
co-occurring species are less phylogenetically related
than expected by chance, generally due to tree-wide
phylogenetic overdispersion of co-occurring species.
The nearest taxon index (NTI) of each quadrat (Webb
et al. 2002) was defined as [–(MNND – MNNDnull)/
SD(MNNDnull)], where MNND is the mean nearest
neighbor phylogenetic distance among species in the
quadrat, MNNDnull is the mean MNND for that
quadrat in 1000 null communities, and SD(MNNDnull)
is the standard deviation of MNND for that quadrat in
1000 null communities. The nearest taxon index has
been proposed as a measure of terminal (branch tip)
phylogenetic clustering of species on a phylogeny (Webb
2000). If species tend to occur together with other closely
related species (e.g., with congeners or confamilials),
NTI scores will generally be positive due to this terminal
phylogenetic clustering of species toward the tips of the
phylogenetic tree. If species tend not to occur together
with other closely related species, NTI scores will be
negative due to terminal phylogenetic overdispersion.
Estimating plot-wide phylogenetic structure
To test whether the average phylogenetic structure of
local tree communities at a given spatial scale differed
from random, we calculated the mean phylogenetic
structure of all quadrats at each scale as the mean NRI
and NTI of all quadrats at that scale. If the mean NRI
or NTI for all quadrats at a given spatial scale differed
from zero according to a one-sample t test, we could
conclude that the tree communities at that scale were
significantly phylogenetically clustered or overdispersed
on average, since both NRI and NTI are standardized
effect sizes whose expected values are zero for phylo-
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STEVEN W. KEMBEL AND STEPHEN P. HUBBELL
Ecology Special Issue
FIG. 2. Spatial patterns of (a) habitats (from Harms et al. [2001]), (b) net relatedness index (NRI), and (c) nearest taxon index
(NTI) in 20 3 20 m quadrats within the 50-ha Forest Dynamics Plot on Barro Colorado Island, Panama. The indices NRI and NTI
are measures of community phylogenetic structure based on a constrained null model that shuffled species co-occurrence patterns in
null communities while maintaining observed species occurrence frequencies and quadrat species richnesses (see Methods for
description). Positive NRI and NTI values indicate phylogenetic clustering, and negative values indicate phylogenetic
overdispersion of species occurring together in a quadrat.
genetically random communities, positive for phylogenetically clustered communities, and negative for phylogenetically overdispersed communities, with
approximately 95% of NRI and NTI values expected
to fall in the range of 2 to þ2 for random communities
(Gotelli and Rohde 2002). We estimated phylogenetic
structure for each quadrat using the constrained and
unconstrained null models.
We also assessed the phylogenetic structure of each
quadrat by comparing the observed MPD and MNND
values in each quadrat to the distribution of these values
in the null communities. Quadrats were considered to be
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FOREST COMMUNITY PHYLOGENETIC STRUCTURE
S91
TABLE 1. Results of a randomization study to assess the ability of different null models to measure community phylogenetic
structure for 50 randomly selected 20 3 20 m quadrats in the 50-ha Forest Dynamics Plot on Barro Colorado Island, Panama.
A) Mean pairwise phylogenetic distance (MPD) and net relatedness index (NRI)
MPD (Ma)
Randomization
null model
Constrained
Unconstrained
Analysis
null model
constrained
unconstrained
constrained
unconstrained
Randomization
Analysis
NRI
Mean
Mean
SD
Mean
Type I
error rate
214.3
214.3
217.9
217.9
214.4
217.9
217.9
217.9
5.9
7.3
7.2
7.2
0.00
0.49
0.00
0.00
0.06
0.04
0.06
0.06
B) Mean nearest neighbor phylogenetic distance (MNND) and nearest taxon index (NTI)
MNND (Ma)
Randomization
method
Constrained
Unconstrained
Analysis
method
constrained
unconstrained
constrained
unconstrained
Randomization
Analysis
NTI
Mean
Mean
SD
Mean
Type I
error rate
78.0
78.0
79.2
79.2
78.1
79.2
79.2
79.2
6.6
7.2
7.1
7.2
0.02
0.18
0.00
0.00
0.05
0.05
0.06
0.05
Notes: Species co-occurrences in these quadrats were first randomized using either the constrained or unconstrained null model
(see Methods for description). Phylogenetic distances among co-occurring species (mean pairwise phylogenetic distance [MPD] and
mean nearest neighbor phylogenetic distance [MNND]) were first calculated for the randomized data. Both MPD and MNND are
reported in terms of millions of years (Ma). The distributions of phylogenetic distances in null communities were then calculated for
999 subsequent randomizations of the data, using the analysis null model, and used to calculate measure of community
phylogenetic structure for each randomization (net relatedness index [NRI] and nearest taxon index [NTI]; see Methods for
description). This process was repeated 500 times for each combination of randomization and analysis null models. The average
community phylogenetic structure (mean NRI and NTI) and Type I error rate (proportion of randomizations indicating a
significant phylogenetic structure [absolute value of NRI or NTI . 2, P , 0.05]) were calculated for each combination of
randomization and analysis null models. Boldface type indicates mean NRI or NTI values that were significantly different from
zero according to a one-sample t test (N ¼ 50 quadrats 3 500 runs, P , 0.05).
significantly phylogenetically overdispersed or clustered
if they occurred in the lowest or highest 2.5% of the
distribution of distances from the null communities,
respectively (a ¼ 0.05). A one-tailed binomial test was
then used to assess whether the numbers of quadrats with
significantly overdispersed or clustered phylogenetic
distances were greater than expected at each spatial scale.
Differences in sample sizes among spatial scales were
accounted for using bootstrap estimation (Manly 1997)
of the mean and standard error of NRI and NTI at each
spatial scale. At the largest spatial scale examined (100 3
100 m), the sample size was 50 quadrats. At smaller
spatial scales (50 3 50 m, 20 3 20 m, 10 3 10 m), we
estimated the mean and standard error of phylogenetic
structure (NRI and NTI) for 4999 random draws
without replacement of 50 quadrats. The bootstrap
estimates of mean and standard errors of NRI and NTI
were then used to calculate a bootstrap t statistic and P
value for NRI and NTI at each spatial scale. This
method allowed direct comparisons of the mean and
standard error of NRI and NTI values among spatial
scales, taking into account the original differences in
sample size at each spatial scale.
Because phylogenetic similarities among co-occurring
species were positively spatially autocorrelated (Fig. 2),
we also tested whether NRI and NTI values at each
spatial scale differed from zero using generalized leastsquares models with simultaneous spatial autoregression
(SAR) covariance structures in SþSpatialStats (Kaluzny
et al. 1998). These models calculate an estimate of the
mean and standard error of the coefficients in the model
(NRI or NTI values), taking into account the nonindependence of spatially adjacent samples (Cressie
1993). In all cases, adding a first-order spatial-neighbor
autoregressive term to the model removed autocorrelation from the residuals and improved the fit of the
model, relative to a nonspatial model, and so we report
only the results of the spatial models.
Null model comparisons
To compare null models, we employed a method
similar to that used by Gotelli (2000), whereby random
communities are created by shuffling ‘‘real’’ data using
null models in order to randomize patterns of species cooccurrence, and then the randomized data are analyzed
using the same set of null models. We randomly chose 50
of the FDP’s 20 3 20 m quadrats to conduct our
randomization study. We first randomized the original
data using the unconstrained or constrained null models.
This created a new set of samples with the same phylogenetic relationships among species and sample species
richnesses as the original data, but for which species cooccurrences were completely randomized. We then
calculated phylogenetic distances (MPD and MNND)
and standardized effect sizes of community phylogenetic
structure (NRI and NTI) for the randomized samples
using 1000 runs of the unconstrained and constrained
null models. This process was repeated 500 times for
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STEVEN W. KEMBEL AND STEPHEN P. HUBBELL
Ecology Special Issue
FIG. 3. Tree community phylogenetic structure in 20 3 20 m quadrats within the 50-ha Forest Dynamics Plot on Barro
Colorado Island, Panama. Observed phylogenetic distances among co-occurring species are presented for 1250 quadrats, along
with phylogenetic distances (in millions of years, Ma) among species in corresponding null communities generated using a
constrained and an unconstrained null model (see Methods for description).
each combination of randomization and analysis null
models.
We estimated the proportion of randomized samples
for which each null model found a significantly nonrandom phylogenetic distance among species (number of
times observed distances were in the top or bottom 2.5%
of randomized distances, an estimate of the Type I error
rate), as well as calculating the average degree of phylogenetic clustering or overdispersion (mean NRI and
NTI) estimated by each null model.
Null models that maintain species frequencies have
been criticized for potentially including the effects of any
process that acts to determine the occurrence frequency
of a species (Colwell and Winkler 1984). If this were
occurring, we would expect some phylogenetic signal in
the distribution of species occurrence frequencies. To
determine whether the frequencies of species in the FDP
exhibited a phylogenetic signal, we calculated phylogenetic distances among all pairs of species occurring in
the entire FDP at each spatial scale examined. We then
calculated the dissimilarity of species frequencies as the
square root of the absolute difference in occurrence
frequency rank for all pairs of species, and we tested the
significance of the correlation among the resulting
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FIG. 4. Relationship between measures of phylogenetic community structure calculated using two null models in 1250 20 3 20
m quadrats within the 50-ha Forest Dynamics Plot on Barro Colorado Island, Panama. Net relatedness index (NRI) and nearest
taxon index (NTI) are measures of community phylogenetic structure based on constrained and unconstrained null models (see
Methods for description).
phylogenetic and frequency distance matrices using a
Mantel test (Legendre and Legendre 1998).
Habitat phylogenetic structure
Based on Harms et al.’s (2001) classification of 20 3 20
m quadrats within the FDP into seven habitat types (high
plateau, low plateau, mixed, slope, stream, swamp, and
young), we asked whether the phylogenetic structure of
tree communities at this spatial scale differed among
habitats within the FDP. We tested for overall differences in phylogenetic structure among habitats using
generalized least-squares models with simultaneous
spatial autoregression (SAR) covariance structures, as
described for the plot-wide tests. We estimated the
FIG. 5. Relationship between phylogenetic distances among species and similarity of species occurrence frequency ranks in 20 3
20 m quadrats within the 50-ha Forest Dynamics Plot (FDP) on Barro Colorado Island, Panama. Data points represent all pairwise
combinations of species present in the FDP. Frequency rank differences were calculated as the absolute difference in the frequency
occurrence ranks of each species pair. Phylogenetic distances were calculated as the branch length (in millions of years, Ma)
connecting each species pair. Mantel tests indicated that relationships between square-root transformed frequency rank difference
and phylogenetic distance were not statistically significant at any spatial scale examined.
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STEVEN W. KEMBEL AND STEPHEN P. HUBBELL
Ecology Special Issue
TABLE 2. Tree community phylogenetic structure in quadrats at four spatial scales within the 50-ha Forest Dynamics Plot on
Barro Colorado Island, Panama.
Net relatedness index
Nearest taxon index
Estimated mean
SE
P
Estimated mean
SE
P
Unconstrained null model
10 3 10 m
5000
20 3 20 m
1250
50 3 50 m
200
100 3 100 m
50
0.260
0.389
0.771
0.074
0.008
0.016
0.047
0.099
0.0001
0.0001
0.0001
0.4605
0.142
0.154
0.453
0.019
0.012
0.021
0.045
0.099
0.0001
0.0001
0.0001
0.8459
Constrained null model
10 3 10 m
5000
20 3 20 m
1250
50 3 50 m
200
100 3 100 m
50
0.061
0.056
0.051
0.043
0.016
0.033
0.093
0.180
0.0001
0.0952
0.5825
0.8110
0.070
0.020
0.046
0.003
0.014
0.028
0.076
0.161
0.0001
0.4854
0.5427
0.9858
Spatial scale
N
Notes: Net relatedness index (NRI) and nearest taxon index (NTI) are measures of community phylogenetic structure based on
constrained and unconstrained null models (see Methods for description). Positive NRI and NTI values indicate phylogenetic
clustering; negative values indicate phylogenetic overdispersion of species occurring together in a quadrat. Parameter estimates at
each scale are based on a spatial generalized least-squares model with a first-order spatial-neighbor simultaneous spatial
autoregression term (SAR). Significant P values indicate that the phylogenetic structure at a given spatial scale differed from zero
according to a two-tailed t test.
overall significance of differences in phylogenetic structure among habitats using NRI and NTI scores based on
the constrained null model, as well as estimating the
mean and standard errors of NRI and NTI scores within
each habitat type to test for significant phylogenetic
clustering or overdispersion. In all cases, adding a firstorder spatial-neighbor autoregressive term to the model
removed autocorrelation from the residuals and improved the fit of the model relative to a nonspatial model,
and so we report only the results of the spatial models.
RESULTS
Null model comparisons
When analyzing community data generated by
randomizing 50 randomly selected 20 3 20 m quadrats
from the Forest Dynamics Plot with the unconstrained
null model, both null models performed well (Table 1),
with appropriate Type I error rates and mean NRI and
NTI values of approximately zero. When analyzing data
generated by randomizing these quadrats with the
constrained null model, the unconstrained null model
concluded that the randomized data were significantly
phylogenetically clustered or overdispersed (mean NRI
and NTI different from zero), although the Type I error
rates of both null models remained correct (Table 1).
A comparison of the phylogenetic distances among
co-occurring species in all 1250 20 3 20 m quadrats in
the FDP with the corresponding mean pairwise phylogenetic distances in null communities (Fig. 3) showed
that phylogenetic distances in the null communities were
much less variable than the observed distances, especially the mean pairwise distances. Mean phylogenetic
distances among co-occurring species in the unconstrained null communities were higher than the mean
distances in the observed and constrained null communities. As a result of the higher mean phylogenetic
distances in the unconstrained null communities, NRI
and NTI values calculated for each quadrat using the
different null models were tightly correlated (Fig. 4), but
NRI and NTI values calculated using the unconstrained
null model tended to be higher than those calculated
using the unconstrained null model.
We found no statistically significant relationships
between phylogenetic distances among species and the
square root of differences in species frequency ranks at
any spatial scale (Mantel tests, P . 0.5 at all scales),
although there was a slight but nonsignificant trend of
the most closely related species pairs having similar
frequency ranks (Fig. 5).
Community phylogenetic structure
The phylogenetic structure of tree communities in the
FDP was highly variable among quadrats and dependent on the choice of null model. Using the unconstrained
null model, tree communities in the FDP were phylogenetically clustered on average (mean NRI and NTI .
0; Table 2, Fig. 6) at spatial scales from 10 3 10 m to 50
3 50 m. Although the mean NRI and NTI were greater
than zero at most scales examined, relatively few
quadrats were significantly phylogenetically clustered
or overdispersed according to the unconstrained null
model (Table 3, Fig. 6).
According to the constrained null model, mean
community phylogenetic structure (NRI and NTI)
across the entire plot did not differ from zero, except
at the smallest spatial scale examined (10 3 10 m), where
the mean NRI was greater than zero, indicating a slight
overall trend of phylogenetic clustering at this scale. The
magnitude of this effect was very small, and, after
accounting for differences in sample size among spatial
scales using bootstrap resampling (Table 4), the average
phylogenetic structure (NRI and NTI) of tree communities did not differ from zero at all spatial scales
examined according to the constrained null model.
However, there was substantial variation in phylogenetic
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FIG. 6. Tree community phylogenetic structure in quadrats at four spatial scales within the 50-ha Forest Dynamics Plot on
Barro Colorado Island, Panama. See Fig. 4 legend for definition and description of (a) NRI and (b) NTI. Dashed lines indicate
expected 95% CI.
structure around the mean at all spatial scales examined
(Fig. 6), and more quadrats than expected contained
communities that were significantly phylogenetically
overdispersed or clustered (Table 1).
In the 20 3 20 m quadrats (Fig. 2), the phylogenetic
structure of a quadrat was positively correlated with the
phylogenetic structures of that quadrat’s first-order
spatial neighbors (NRI, Moran’s I ¼ 0.34, P , 0.01;
NTI, Moran’s I ¼ 0.14, P , 0.01), whereas the residuals
of the spatial autoregression models for these values
were not significantly spatially correlated (NRI residuals, Moran’s I ¼ 0.05, P ¼ 0.93; NTI residuals,
Moran’s I ¼ 0.02, P ¼ 0.80). Similar patterns of signi-
ficant positive spatial autocorrelation of NRI and NTI
values and nonsignificant spatial autocorrelation of residuals from the spatial models were observed at all
spatial scales examined.
Habitat influences on community phylogenetic structure
Based on the constrained null model, the phylogenetic
structure of the tree communities in the FDP differed
among habitats at the spatial scale (20 3 20 m) for which
habitat data were available (Fig. 1, Table 5). Species
occurring together in the high plateau, low plateau, and
young habitats tended to be significantly phylogenetically clustered (NRI . 0 or NTI . 0, P , 0.05), while
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STEVEN W. KEMBEL AND STEPHEN P. HUBBELL
Ecology Special Issue
TABLE 3. Number of quadrats with statistically significant phylogenetic clustering or overdispersion at four spatial scales in the 50ha Forest Dynamics Plot on Barro Colorado Island, Panama.
MPD
Spatial scale
Total
quadrats
N
Unconstrained null model
10 3 10 m
5000
20 3 20 m
1250
50 3 50 m
200
100 3 100 m
50
Constrained null model
10 3 10 m
5000
20 3 20 m
1250
50 3 50 m
200
100 3 100 m
50
Overdispersed quadrats
MNND
Clustered quadrats
N
Binomial P
69
36
17
0
1.0000
0.2171
,0.001
1.0000
4
1
1
3
188
76
18
3
,0.001
,0.001
,0.001
0.1294
204
63
6
1
N
Binomial P
Overdispersed quadrats
Clustered quadrats
N
Binomial P
N
Binomial P
1.0000
1.0000
0.9937
0.1294
92
10
1
0
0.9992
1.0000
0.9937
1.0000
17
0
0
1
1.0000
1.0000
1.0000
0.7180
,0.001
,0.001
0.3840
0.7180
150
30
4
4
0.0151
0.6144
0.7385
0.0362
98
40
6
2
0.9950
0.0716
0.3840
0.3565
Notes: Phylogenetic distances among co-occurring species in each quadrat (mean pairwise phylogenetic distance [MPD] and mean
nearest neighbor phylogenetic distance [MNND]) were compared with phylogenetic distances in 1000 null communities generated
using the constrained and unconstrained null models (see Methods for description). Quadrats were considered to be significantly
phylogenetically overdispersed or clustered if they occurred in the lowest or highest 2.5% of the distribution of distances from the null
communities, respectively (a ¼ 0.05). A one-tailed binomial test was then used to assess whether the numbers of quadrats with
significantly overdispersed or clustered phylogenetic distances were greater than expected at each spatial scale.
communities in the swamp habitat tended to contain
species that were phylogenetically overdispersed (NRI ¼
1.096, P , 0.0001). Communities in the slope habitat
exhibited both tree-wide phylogenetic overdispersion
(NRI ¼ 0.286, P ¼ 0.0005) and marginally significant
terminal phylogenetic clustering (NTI ¼ 0.130, P ¼
0.0589).
DISCUSSION
Null model choice and community phylogenetic structure
Measures of phylogenetic distances among species in a
community must be compared with the phylogenetic
distances generated by a null model in order to determine whether a community is more phylogenetically
clustered or overdispersed than expected by chance. A
potential problem with analyses of the phylogenetic
structure of a community is that null models can simultaneously affect not only the co-occurrence patterns of
species, but also their occurrence frequencies across
samples and the distribution of frequencies on the
phylogeny.
Randomization tests showed that the unconstrained
null model can indicate nonrandom community phylogenetic structure (mean NRI and NTI different from
zero) when used with data containing nonuniform
species frequencies, even when patterns of species cooccurrence in samples are completely random (Table 1).
A similar pattern was found in the tree communities
within the FDP (Fig. 3). In the unconstrained null
communities, mean pairwise phylogenetic distance converged on the average pairwise phylogenetic distance
among all species occurring in the FDP, with every
species and its associated phylogenetic distance to other
species given equal weighting. In the constrained null
communities, species and associated phylogenetic
branches were effectively weighted by their frequency
TABLE 4. Tree community phylogenetic structure in quadrats at four spatial scales within the 50-ha Forest Dynamics Plot on
Barro Colorado Island, Panama.
Net relatedness index
Nearest taxon index
Estimated mean
SE
P
Estimated mean
SE
P
Unconstrained null model
10 3 10 m
50
20 3 20 m
50
50 3 50 m
50
100 3 100 m
50
0.261
0.388
0.771
0.074
0.077
0.079
0.094
0.099
0.0013
,0.0001
,0.0001
0.4583
0.143
0.156
0.452
0.019
0.118
0.103
0.090
0.099
0.2327
0.1347
,0.0001
0.8455
Constrained null model
10 3 10 m
50
20 3 20 m
50
50 3 50 m
50
100 3 100 m
50
0.058
0.053
0.047
0.043
0.156
0.157
0.186
0.180
0.7128
0.7389
0.8028
0.8122
0.070
0.021
0.047
0.003
0.140
0.139
0.151
0.161
0.6201
0.8786
0.7546
0.9852
Spatial scale
N
Notes: Parameter estimates at each scale are based on 4999 bootstrap resamples of 50 quadrats from that spatial scale, except at
the 100 3 100 m scale where parameters represent the actual parameter estimates for the 50 quadrats at that scale. Significant P
values indicate that the phylogenetic structure at a given spatial scale differed from zero according to a two-tailed t test. See Table 2
notes for additional definitions and explanations.
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TABLE 5. Tree community phylogenetic structure in 20 3 20 m quadrats in different habitats within the 50-h Forest Dynamics Plot
on Barro Colorado Island, Panama.
Net relatedness index
Nearest taxon index
Habitat
N
Mean
SE
P
Mean
SE
P
High Plateau
Low Plateau
Mixed
Slope
Stream
Swamp
Young
170
620
66
284
32
30
48
0.338
0.117
0.140
0.286
0.198
1.096
0.570
0.117
0.060
0.146
0.827
0.219
0.244
0.206
0.0039
0.0533
0.3393
0.0005
0.3666
0.0001
0.0057
0.021
0.054
0.141
0.130
0.315
0.116
0.440
0.094
0.049
0.129
0.069
0.191
0.206
0.169
0.8216
0.2619
0.2723
0.0589
0.0990
0.5720
0.0095
Notes: Net relatedness index (NRI) and nearest taxon index (NTI) are measures of community phylogenetic structure based on a
constrained null model that shuffled species co-occurrence patterns in null communities while maintaining observed species
occurrence frequencies and quadrat species richnesses (see Methods for description). Parameter estimates in each habitat are based
on a spatial generalized least-squares (GLS) model with a first-order spatial neighbor simultaneous spatial autoregression term
(SAR). Overall differences among habitats in NRI and NTI were statistically significant according to the spatial GLS tests (P ,
0.0001). Significant P values in the table indicate that the phylogenetic structure in a habitat differed from zero (significant
phylogenetic clustering or overdispersion) according to a two-tailed t test. See Table 2 notes for additional definitions and
explanations.
within the plot, leading to similar mean phylogenetic
distances in the observed and constrained null communities.
Our results highlight the sensitivity of measures of
phylogenetic community structure, not only to patterns
of species co-occurrence, but also to phylogenetic tree
topology, branch lengths, and species frequencies. By
giving species such as the tree fern Cnemidaria petiolaria
(which is both rare and distantly related to all other
species in the FDP) an equal chance of occurring in
every null community, phylogenetic distances among
species in the unconstrained null communities were
inflated relative to the observed communities, causing
mean NRI and NTI values to be greater than zero on
average. Conversely, by maintaining species frequencies
in null communities, the constrained null communities
tended to have the same mean phylogenetic structure as
the observed communities, leading to a distribution of
NRI and NTI values whose mean was close to zero.
When using the unconstrained null model with the
FDP data, it is difficult to attribute any differences in
phylogenetic structure between the observed and null
communities to the impact of ecological processes on the
phylogenetic structure of the community, since they
might simply be due to differences in species frequencies
between the observed and null communities. Additionally, given the widespread spatial aggregation of trees
(Condit et al. 2000) and strong dispersal limitation
(Hubbell et al. 1999) demonstrated to occur in the FDP
and other ecological communities, the assumption that
all species are equally able to colonize any sample in the
null communities is not realistic.
Much of the previous debate surrounding the use of
null models in ecology has focused on the relative merits
of null models that maintain or do not maintain species
frequencies (Gotelli and Graves 1996). Null models that
do not maintain species frequencies have been criticized
as being overly statistically liberal; this shortcoming has
been described previously as the ‘‘Jack Horner effect’’
(Wilson 1995). Conversely, null models that maintain
species frequencies have been criticized for potentially
‘‘smuggling in’’ the effects of processes such as competition or environmental filtering on species frequencies
and community phylogenetic structure (the ‘‘Narcissus
effect’’ [Colwell and Winkler 1984]), and for potentially
being too statistically conservative. If this were the case,
we might expect some relationship between species’
frequencies and their phylogenetic relatedness, but
relationships between frequency similarity and phylogenetic distance were not statistically significant at any
spatial scale examined according to Mantel tests (Fig. 2).
We might also expect the constrained null model to find
nonrandom community structure in fewer quadrats
within the FDP if it were too statistically conservative,
but in fact the constrained model found many more
quadrats to be phylogenetically nonrandom compared
to the unconstrained null model (Table 2), although
mean NRI and NTI values from the constrained null
model were always close to zero.
Recent attempts to resolve the null model debate have
focused on the use of simulation studies to quantify the
Type I and II error rates of different null models when
confronted with randomized or simulated community
co-occurrence data (Gotelli 2000). Although we quantified the Type I error rate and bias of null models in this
study, we did not assess the Type II error rate, which will
need to be measured using simulation studies with data
generated using different models of the interaction
among phylogenetic relationships, trait evolution, and
community structure.
Several additional issues related to the use of null
models to measure community phylogenetic structure
remain unresolved. The constrained null model used in
this study maintains observed species frequencies in null
communities, but swaps species presences among samples, and thus can only be used with species presence/
absence data matrices. Species abundances within
samples contain useful information that is discarded
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STEVEN W. KEMBEL AND STEPHEN P. HUBBELL
when using this approach, but it is probably not possible
to simultaneously constrain species frequencies, abundances, and sample species richnesses.
The constrained null model also ignores species in the
regional species pool when generating null communities,
since only species present in local communities are used
to generate the null communities. To determine how
community phylogenetic structure varies across a larger
range of spatial scales (Webb et al. 2002, CavenderBares et al. 2006), it will be necessary to compare the
phylogenetic relatedness of species present in communities at one scale to those present in some regional
species pool. It is not clear how to separate the effects of
variation in species frequency from the effects of
ecological and evolutionary processes on community
phylogenetic structure for these types of data, but clearly
more research on the effects of null-model and species
pool choice is needed.
Community phylogenetic structure in the FDP
Although the average phylogenetic structure of the
tree communities in the FDP was close to random on
average across the entire plot at spatial scales from 10 3
10 m to 100 3 100 m according to the constrained null
model (Tables 2 and 4), the phylogenetic relatedness of
tree species occurring together in individual quadrats
varied greatly from phylogenetic overdispersion to phylogenetic clustering (Table 2, Fig. 6), and more quadrats
than expected exhibited significant phylogenetic clustering and overdispersion at most spatial scales (Table 1,
Fig. 6). Habitats within the plot differed strongly in
their phylogenetic structure (Table 5, Fig. 2). At the
spatial scale for which habitat data were available,
phylogenetic clustering in some habitats combined with
phylogenetic overdispersion in other habitats appeared
to result in a pattern of phylogenetic structure
indistinguishable from random on average across the
entire FDP, obscuring the strong differences in phylogenetic structure among habitats within the FDP.
In the seasonally dry plateau habitats and in the
young secondary growth forests within the plot, cooccurring species were phylogenetically clustered (Table
5). The tree-wide phylogenetic clustering in the plateau
habitats was largely due to the co-occurrence of numerous species from large, highly speciose clades such as the
eurosids. Numerous species from these clades tended to
occur together in plateau quadrats, leading to a trend of
tree-wide phylogenetic clustering of co-occurring species
in the plateau. Similarly, in the young habitats, species
from families and genera concentrated in a few orders
commonly occurred together in most quadrats, leading
to a pattern of both tree-wide and terminal phylogenetic
clustering.
Given the broad phylogenetic niche and trait conservatism documented across plants in general (Prinzing
et al. 2001, Cavender-Bares et al. 2006, Silvertown et al.
2006) and tropical trees in particular (Chazdon et al.
2003), the phylogenetic clustering in the plateau and
Ecology Special Issue
young forests in the FDP suggests that some sort of
environmental filter is structuring tree communities in
these habitats. Habitats such as the dry plateaus within
the FDP would be predicted to contain phylogenetically
clustered species, since the relatively stressful dry-season
soil moisture conditions in these environments would be
more likely to act as an environmental filter on broadly
conserved ecological traits than in moister habitats
(Harms et al. 2001, Webb et al. 2002).
Communities in the relatively moist slope and swamp
habitats were phylogenetically overdispersed (Table 5).
Tree-wide phylogenetic overdispersion in the swamp
habitats was caused by the presence of species from
families such as the Moraceae and Arecaceae (Harms et
al. 2001) that are widely scattered across the angiosperm
phylogeny. In the slope habitats, a pattern of tree-wide
overdispersion, but terminal phylogenetic clustering,
was found.
Several processes could give rise to these patterns of
phylogenetic overdispersion, depending on the interaction between the phylogenetic history of trait evolution
and contemporary ecological interactions in these
habitats. Even within a single habitat type, the hierarchical nature of trait and niche evolution (Ackerly et
al. 2006, Silvertown et al. 2006), interactions among
multiple ecological processes (Webb et al. 2002), and the
phylogenetic history of species habitat associations
(Brooks and McLennan 1991) could lead to complicated patterns of community phylogenetic structure,
making it difficult to attribute these patterns to any one
process.
More data on the ecological traits of species in the
FDP and the evolutionary history of habitat associations in tropical trees will be necessary to determine the
relative importance of processes such as environmental
filtering or competitive exclusion in different habitats
within the FDP. However, it is clear that either the
relative importance of nonneutral ecological processes
or the evolutionary history of niches, traits, or habitat
associations must vary along environmental gradients
within the FDP to explain the observed variation in
phylogenetic structure among habitats.
ACKNOWLEDGMENTS
Thanks to Cam Webb for inviting this contribution and for
suggesting the phylogenetic analysis of the Forest Dynamics
Plot (FDP) data. Thanks to Kyle Harms for providing the
habitat data, and to Rick Condit for making the FDP data
available. Thanks to J. F. Cahill and M. R. T. Dale for
statistical advice. This manuscript benefited greatly from comments by C. Webb and three anonymous reviewers. S. W.
Kembel acknowledges support from the Natural Sciences and
Engineering Research Council of Canada. The Forest Dynamics Plot of Barro Colorado Island has been made possible
through the generous support of the U.S. National Science
Foundation, The John D. and Catherine T. MacArthur
Foundation, and the Smithsonian Tropical Research Institute
and through the hard work of over 100 people from 10
countries over the past two decades. The BCI Forest Dynamics
Plot is part of the Center for Tropical Forest Science, a global
network of large-scale demographic tree plots.
July 2006
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SUPPLEMENT
A phylogenetic tree and key to species codes for tree species occurring within the 50-ha Forest Dynamics Plot (FDP) on Barro
Colorado Island, Panama (Ecological Archives E087-113-S1).
Ecology, 87(7) Supplement, 2006, pp. S100–S108
Ó 2006 by the Ecological Society of America
PHYLOGENETIC CLUSTERING AND OVERDISPERSION
IN BACTERIAL COMMUNITIES
M. CLAIRE HORNER-DEVINE1,3
1
AND
BRENDAN J. M. BOHANNAN2
School of Aquatic and Fishery Science, University of Washington, Seattle, Washington 98195 USA
2
Department of Biological Science, Stanford University, Stanford, California 94305 USA
Abstract. Very little is known about the structure of microbial communities, despite their
abundance and importance to ecosystem processes. Recent work suggests that bacterial
biodiversity might exhibit patterns similar to those of plants and animals. However, relative to
our knowledge about the diversity of macro-organisms, we know little about patterns of
relatedness in free-living bacterial communities, and relatively few studies have quantitatively
examined community structure in a phylogenetic framework. Here we apply phylogenetic
tools to bacterial diversity data to determine whether bacterial communities are phylogenetically structured. We find that bacterial communities tend to contain lower taxonomic diversity
and are more likely to be phylogenetically clustered than expected by chance. Such
phylogenetic clustering may indicate the importance of habitat filtering (where a group of
closely related species shares a trait, or suite of traits, that allow them to persist in a given
habitat) in the assembly of bacterial communities. Microbial communities are especially
accessible for phylogenetic analysis and thus have the potential to figure prominently in the
integration of evolutionary biology and community ecology.
Key words: bacteria; community structure; microbial ecology; phylogenetic clustering and overdispersion; phylogenetic diversity; phylogenetic structure; relatedness.
INTRODUCTION
Although there may be millions of species of bacteria
on Earth, we are only beginning to investigate patterns
in their diversity (Horner-Devine et al. 2004a). Understanding patterns of bacterial diversity is of particular
importance, because bacteria likely comprise the majority of the planet’s biodiversity, they mediate many
environmental processes that sustain life, and their
diversity is of great importance in medicine, agriculture,
and industry. Recent evidence suggests that bacteria can
exhibit patterns in taxonomic diversity and community
composition similar to those of plants and animals (e.g.,
Horner-Devine et al. 2003, 2004b). However, most of
these studies have relied on measures of diversity that do
not consider phylogenetic relatedness (Bohannan and
Hughes 2003), and few studies have quantitatively
examined bacterial communities within a phylogenetic
context (Martin 2002, Davelos et al. 2004, Martin et al.
2004).
Phylogenetic measures can reveal differences in the
richness or composition of two communities that would
be identical using standard measures of species richness
and composition (Martin 2002). Phylogenetic analyses
of diversity have proven valuable in studies of plant and
animal diversity, because such an approach can lend
Manuscript received 9 February 2005; revised 13 July 2005;
accepted 12 August 2005; final version received 7 September
2005. Corresponding Editor (ad hoc): J. B. Losos. For reprints
of this Special Issue, see footnote 1, p. S1.
3
E-mail: [email protected]
insight into the relative importance of evolutionary and
ecological forces in shaping communities (Elton 1946,
Webb et al. 2002, Cavender-Bares and Wilczek 2003).
The idea that closely related taxa are more likely to
interact intensely with each other than with more
distantly related taxa is an old one (Darwin 1859); more
recently, this idea has been expanded to suggest that
interspecific interactions are influenced by the net
ecological similarity of taxa, and closely related taxa
tend to be more similar ecologically than distantly
related taxa (Harvey and Pagel 1991). For example, cooccurring rainforest tree species have been observed to
be more closely related than expected by chance (Webb
2000); such a pattern of phylogenetic attraction or
clustering can indicate that these closely related taxa
share traits important for their persistence in a
particular environment (Webb et al. 2002). Such habitat
filtering is important and might be more important than
competition, in maintaining rain forest tree species
diversity (see also Tofts and Silvertown 2000, Webb
2000, Kembel and Hubbell 2006).
In contrast, a community could be composed of
distantly related taxa as a result of current or past
competitive exclusion between similar (and thus closely
related) taxa and/or as a result of convergent evolution
in traits important for persistence in a given environment (Cavender-Bares et al. 2004, Kembel and Hubbell
2006). However, even for macro-organisms, relatively
few studies have quantitatively examined community
structure in a phylogenetic framework (but see other
articles this issue), and even fewer have done so for
S100
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RELATEDNESS IN BACTERIAL COMMUNITIES
microbes (but see Martin 2002, Francis et al. 2003,
Martin et al. 2004). We know of no other microbial
studies that employ the approach used here to quantify
community structure.
Bacteria offer a unique opportunity to examine the
phylogenetic structure of multiple communities, because
most recently published bacterial diversity data are
molecular in nature and thus can be more easily
interpreted within a phylogenetic context than data
from many community studies of macro-organisms
(Bohannan and Hughes 2003). A large proportion of
microbes cannot be cultured with current laboratory
techniques (Brock 1987), and thus bacterial taxa are
often identified from the sequences of indicator genes
extracted from environmental samples (Stackebrandt
and Goebel 1994).
Here we take a quantitative approach to examining
the phylogenetic structure of bacterial communities from
a number of different environments. We ask whether
bacterial communities exhibit phylogenetic structure
(e.g., significant degrees of clustering or overdispersion
of taxa across a phylogenic tree) and whether such
patterns vary along environmental gradients.
METHODS
Data
We used existing bacterial sequence data from four
different environments and for three different genes. We
selected data sets that were of high resolution (e.g.,
cloned and sequenced DNA sequences, rather than
gradient gel or restriction fragment length data), that
were from extensively sampled communities (relative to
many studies of bacteria diversity), and that were
replicated or spanned ecologically interesting environmental gradients in aquatic, soil, and sediment habitats.
The first data set consists of partial 16S rDNA
sequences (the most common indicator gene used for
bacterial diversity studies) sampled from freshwater
mesocosms that span a productivity gradient (HornerDevine et al. 2003). Each mesocosm consisted of a 2 m
diameter polyethylene cattle tank with a screen cover,
filled with well water. Each mesocosm was inoculated
from the same-pooled sample collected from six ponds
in southern Michigan that spanned a natural gradient in
primary productivity. A gradient of primary productivity was established across the mesocosms by maintaining
otherwise identical mesocosms with different input
concentrations of nitrogen and phosphorus. At the end
of a 4-mo growing season, one composite water column
sample was used to generate a clone library from each of
the five mesocosms. We selected ;100 clones from each
of these libraries and sequenced 500 nucleotides from the
5 0 terminal of each clone (GenBank accession numbers
DQ064816–DQ065575).
The second data set consists of 16S rDNA sequences
sampled from soil communities at different depths that
differed in water saturation and total organic carbon
(Zhou et al. 2002). Soil cores were collected from
S101
previously described sites in northern Virginia (Abbot’s
Pitt) and central Delaware (Zhou et al. 2002) at both the
soil surface (depth ¼ 0.05 m; GenBank accession
numbers AY280351–AY289492) and subsurface (approximately at the depth of the water table, depth . 4.0
m; GenBank accession numbers AY456755–AY456883
and AY456885–AY456903) for a total of five samples
(note that two samples were collected from the subsurface of Dover Air Force Base; hereafter, subsurface-D1
and subsurface-D2).
The third data set consists of 16S rDNA sequences
sampled specifically from ammonia-oxidizing bacteria in
Costa Rican soils (Carney et al. 2004). Samples were
collected from three land use types on sandy loam soils:
forest, pasture, and tree plantations. The tree plantation
sites included three different site types that differed in
plant community composition and richness. The onespecies sites contained only Cordia alliodora, the threespecies sites contained C. alliodora, an herb, and a palm,
and the five-species sites contained C. alliodora, two
palm species, and two other hardwoods. Each of the five
site types (forest, pasture, and the three plantation
treatments) was replicated three times, with the exception of the five-species site, which had two replicates.
For each site type, a composite soil sample was collected
from each of the replicate plots for a total of 14 samples.
Partial 16S rDNA sequences from these samples were
deposited in GenBank under accession numbers
AY631475–AY631851.
The fourth data set consists of sequences of functional
genes amplified from five sediments samples collected
along a salinity and nitrogen gradient in the Chesapeake
Bay (Francis et al. 2003; C. A. Francis, J. C. Cornwell,
and B. B. Ward, unpublished manuscript). One of these
functional genes (amoA) codes for a subunit of ammonia
monooxygenase, an enzyme found only in ammoniaoxidizing bacteria (bacteria that mediate the transformation of ammonia into nitrite). A 450 base pair
(bp) region was chosen for phylogenetic analysis,
representing 150 amino acids (GenBank accession
numbers AY352899–AY353054; Francis et al. 2003).
The second functional gene (nirS) codes for a subunit of
nitrite reductase, an enzyme found in denitrifying
bacteria (bacteria that mediate the transformation of
nitrite into nitrogen gas). A 233-bp region was used for
analyses (C. A. Francis, J. C. Cornwell, and B. B. Ward,
unpublished manuscript). Both gene fragments span the
active site of their respective proteins (Berks et al. 1995,
Rotthauwe et al. 1997, Braker et al. 2000).
For each data set, we screened sequences for chimeras
and aligned them using the 2002 version of the ARB
software package (for 16S genes; available online)4 or
Sequencher software (for functional genes; Gene Codes
Corporation, Ann Arbor, Michigan, USA). We used
only unambiguously aligned positions to construct the
4
hhttp://www.arb-home.de/i
S102
M. C. HORNER-DEVINE AND B. J. M. BOHANNAN
phylogenetic hypotheses, and duplicate sequences were
not used when generating phylogenetic trees. Thus
sample sizes represent the number of unique sequences
observed, rather than the total number of sequences
analyzed.
Analysis
We used indices of community phylogenetic structure
to compare these communities (Webb 2000). The net
relatedness index (NRI) and nearest taxa index (NTI)
measure the degree of phylogenetic clustering of taxa
across a phylogenetic tree in a given sample relative to
the regional pool of taxa. Positive values indicate that a
community is clustered, whereas negative values indicate
that community members are evenly spread or overdispersed across a phylogenetic tree. In other words, a
positive NRI value indicates a community where
members are on average more closely related to one
another than they are to members of the regional taxon
pool. Such a community thus appears to be clustered on
a phylogenetic tree of the regional taxa. The NRI
measures overall clustering across the phylogeny as the
average distance between all pairs of taxa in a
community. Specifically, NRI ¼ (Xnet – X(n))/SD(n),
where Xnet is the mean phylogenetic distance, measured
as the mean pairwise branch lengths and thus a measure
of pairwise sequence divergence, between all pairs of n
taxa in a particular community; X(n) and SD(n) are the
mean and standard deviation of phylogenetic distance
for n taxa randomly distributed on the phylogeny. We
obtain these latter values by 1000 random draws from
the entire pool of taxa in the phylogeny. Alternatively,
NTI measures the extent of terminal clustering on the
phylogeny by determining the minimal distance or
branch length between taxa in a particular community.
The two indices are calculated similarly, except NTI
substitutes Xnear for Xnet, where Xnear is the shortest
mean distance between all pairs of n taxa in a
community sample. We calculated NRI and NTI using
Phylocom 3.22 (Webb et al. 2005).
High and positive values of these indices indicate
clustering of taxa across the overall phylogeny, whereas
low or negative values indicate overdispersion of taxa
across the phylogeny. We tested whether these values
(and thus whether the extent of clustering) significantly
differed from that of a randomly assembled community
with a null model (1000 permutations of randomly
drawn communities). We used a two-tailed significance
test to evaluate the rank of observed values at P ¼ 0.05,
such that an observed rank of ,25 or .975 was
assumed to be significant overdispersion or clustering,
respectively.
Calculation of NRI and NTI relies on a community
phylogeny. We used ModelTest 3.06 to determine the
best models of sequence evolution for the unique amoA
and nirS sequences from the Chesapeake Bay sediment
samples (Posada and Crandall 1998). Using the Akaike
Information Criterion (Akaike 1973), we selected
Ecology Special Issue
K81ufþIþG as the best model of sequence evolution
for the amoA sequences and TVMþIþG for the nirS
sequences. The 16S rDNA trees were constructed using
neighbor-joining distance clustering with a HKY þ
gamma substitution model (Hasegawa et al. 1985),
where gamma was estimated from the data. We used
PAUP* to construct trees for all data sets (Swofford
2002). Maximum likelihood methods were used to
estimate branch lengths based on the above HKY and
gamma DNA substitution models. Trees were bootstrapped to examine phylogenetic robustness.
We also examined how phylogenetic clustering varies
along environmental gradients. NRI and NTI values
were standardized by the mean expected value for the
number of taxa found in each community (Webb et al.
2002). We then used regression and ANOVA implemented in JMP, version 4.0, to examine the relationship
between clustering values and environmental parameters
(Sokal and Rohlf 1995). Where data were not normally
distributed (as determined by the Shapiro-Wilk W test),
we used the Kruskal-Wallis one-way analysis of variance
by ranks (Sokal and Rohlf 1995). The following
environmental parameters were considered: chlorophyll
a (for the mesocosm data from Horner-Devine et al.
[2003]), carbon content (for the soil community data
from Zhou et al. [2002]), plant diversity and ammonia
(for the ammonia oxidizer data from Carney et al.
[2004]), ammonia and salinity (for the amoA data), and
nitrate and salinity (for the nirS data). These environmental parameters were identified in the respective prior
studies as important to taxonomic richness and/or
community composition.
RESULTS
Phylogenetic structure
We observed that most of the bacterial communities
we examined exhibited significant phylogenetic structure
(i.e., bacteria tended to co-occur with other bacteria
that were more closely related than expected by chance).
For example, bacterial communities from freshwater
mesocosms exhibited significant and positive net relatedness index (NRI) and nearest taxa index (NTI) values
(Table 1). This was true when all bacteria were considered, as well when the three most common groups of
bacteria sampled from each of the mesocosms (Alphaproteobacteria, Betaproteobacteria, and CytophagaBacteroides-Flavobacteria, or CFB) were considered
separately. While there was some variation in relatedness among the different communities and groups of
taxa, bacteria in the three most common taxonomic
groups in these communities tended to be clustered.
Soil communities sampled at different depths showed
different patterns of phylogenetic structure (Table 2).
Subsurface soil communities showed significant clustering
for both NRI and NTI. In contrast, one surface sample
was randomly structured phylogenetically, and the other
exhibited significant overdispersion for both indices.
July 2006
RELATEDNESS IN BACTERIAL COMMUNITIES
TABLE 1. Net relatedness index (NRI) and nearest taxa index
(NTI) results for the 16S rDNA sequences from the five
freshwater mesocosm communities.
TABLE 3. The NRI and NTI results for the Costa Rican soil
nitrifiers.
Group
Community
N
NRI
NRI_gt
NTI
NTI_gt
All bacteria
1
2
3
4
5
108
114
87
117
104
1.47*
7.28*
2.47*
2.18*
3.8*
934
999
988
13
998
5.54*
4.95*
2.81*
3.18*
2.51*
999
999
998
998
998
Betaproteobacteria
1
18
2
35
3
2
4
19
5
7
2.34*
4.62*
1.02
0.47
0.31
991
999
76
690
627
2.71*
0.87
1.36
1.89 0.98
999
814
77
972
825
Alphaproteobacteria
1
18
1.48
2
28
2.96*
3
14
1.53
4
33
2 5
42
4.03*
74
999
935
25
999
2.44*
1.97*
1.35
1.94*
2.35*
998
994
913
994
998
Cytophaga-Bacteroides-Flavobacteria (CFB)
1
26
3.44*
999
4.57*
2
20
2.52*
996
3.29*
3
13
1.21
889
0.48
4
24
2.38*
996
2.68*
5
7
0.08
504
0.78
999
999
673
996
234
Notes: N ¼ no. taxa in a community. NRI_gt and NTI_gt
represent the number of times the observed NRI and NTI
values for a community, respectively, were greater than the
value for randomly permuted communities.
* Communities that are significantly structured at the P ¼
0.05 level.
Communities that are significantly structured at the P ¼
0.10 level.
Ammonia-oxidizer communities from Costa Rican
soils exhibited the most variation in phylogenetic
structure of all the data sets considered (Table 3). While
communities from forest soils showed no significant
phylogenetic structure, pasture communities tended to
be overdispersed. Communities from the experimental
plant treatments with one, three, or five plant species
TABLE 2. The NRI and NTI results for the 16S rDNA
sequences from soil communities at different depths.
Community
N
NRI
NRI_gt
NTI
NTI_gt
Subsurface A
Subsurface D1
Subsurface D2
Surface D
Surface A
43
27
20
66
65
3.15*
2.59*
3.81*
3.4*
0.98
999
994
999
0
159
3.72*
2.97*
3.81*
1.88 0.29
999
999
999
27
382
Notes: Labels D and A refer to samples collected at Dover
Air Force Base (Delaware, USA) and Abbot’s Pitt (Virginia,
USA), respectively. As two subsurface samples were collected at
Dover Air Force Base, they are denoted D1 and D2. Other
abbreviations and symbols are as in Table 1.
* Communities that are significantly structured at the P ¼
0.05 level.
Communities that are significantly structured at the P ¼
0.10 level.
S103
Forest
Forest
Forest
Pasture
Pasture
Pasture
One
One
One
Three
Three
Three
Five
Five
Community N
F1
F2
F3
PF
PR
PS
One1
One2
One3
Three1
Three2
Three3
Five1
Five2
18
18
18
20
20
19
29
33
34
36
35
31
30
27
NRI
NRI_gt
NTI
NTI_gt
0.58
0.05
0.46
2.06*
2.42*
4.71*
1.99*
2.73*
2.32*
2.97*
1.19
2.43*
0.91
2.5*
264
508
314
26
9
0
23
997
996
998
887
995
802
998
0.77
0.77
1.4
0.16
2.41*
3.76*
1.53
1.84*
1.26
1.93*
0.45
1.8*
0.3
2.25*
751
755
937
394
10
0
959
986
882
987
332
983
588
999
Notes: ‘‘One,’’ ‘‘Three,’’ and ‘‘Five’’ indicate the three
plantation treatments containing the respective number of
different species. Community labels describe the group and
sample number. Other abbreviations and symbols are as in
Table 1.
* Communities that are significantly structured at the P ¼
0.05 level.
tended to be phylogenetically clustered overall with no
clear pattern in the NTI.
Finally, sediment bacterial communities sampled at
five sites in the Chesapeake Bay were phylogenetically
structured (i.e., clustered) and contained less genetic
diversity than a randomly assembled community
(Table 4). This was true for both the ammonia-oxidizing
bacteria and denitrifying bacteria sampled. All but one
sample showed significant overall phylogenetic structure
(as estimated by NRI). Interestingly, one community of
denitrifying bacteria exhibited significant overdispersion
as measured by NRI. Phylogenetic clustering measured
by NTI was more common for denitrifying bacteria than
for ammonia-oxidizing bacteria. Only one of the five
TABLE 4. The NRI and NTI results for the five Chesapeake
sediment communities.
Community
N
NRI
amoA
CB1
CB2
CB3
CT1
CT2
NRI_gt
NTI
NTI_gt
24
18
22
26
14
3.58*
5.66*
6.17*
5.04*
1.95*
999
999
999
999
965
0.64
2.79*
0.47
0.88
1.56
275
999
688
789
940
nirS
CB1
CB2
CB3
CT1
CT2
79
46
53
75
87
4.66*
5.13*
4.27*
0.39
3.09*
0
999
999
631
999
0.32
3.56*
3.45*
2.45*
5.54*
363
998
999
990
999
Note: Sampling stations were located in the Choptank River
(CT) as well as in the main channel of the Chesapeake Bay
(CB). CT1 was located in the upper Choptank, while CT2 was
located in the lower Choptank. Main channel stations were
located in the north bay (CB1), mid-bay (CB2), and south bay
(CB3). Other abbreviations and symbols are as in Table 1.
* Communities that are significantly structured at the P ¼
0.05 level.
S104
M. C. HORNER-DEVINE AND B. J. M. BOHANNAN
Ecology Special Issue
was a negative trend between NRI estimated for
Betaproteobacteria and primary productivity in aquatic
mesocosms (Fig. 1A). Relatedness did not vary with
productivity for any of the other bacterial groups
examined in these mesocosms (results not shown).
Phylogenetic structure varied significantly with depth
for soil communities (NRI, t ¼ 5.319, df ¼ 3, P ¼ 0.013;
NTI, t ¼ 6.693, df ¼ 3, P ¼ 0.0068). In addition,
communities sampled from soils with high total organic
carbon had lower relatedness values than those sampled
from low total organic carbon soils (Fig. 1B).
In the Chesapeake Bay, relatedness of denitrifying
bacteria exhibited a nonsignificant trend with nitrate
and salinity (Fig. 1C for nitrate results; salinity: NRI, NS,
results not shown; NTI, R2 ¼ 0.5121, P ¼ 0.107). In
contrast, relatedness measures of ammonia-oxidizing
bacteria did not vary with any environmental parameters measured (ammonium and salinity, results not
shown).
Phylogenetic structure of communities of ammoniaoxidizing bacteria varied with plant community composition in Costa Rican soils for NRI (ANOVA, F4,9 ¼
5.507, MSE ¼ 2.33, P ¼ 0.0160), but not for NTI
(Kruskal-Wallis: v2 ¼ 6.5095, df ¼ 4, P ¼ 0.164). Pairwise
post-hoc comparisons of the different treatments revealed that the bacterial communities from sites with
one, three, or five focal plant species were more clustered
than pasture communities as measured by NRI. There
was a weak positive relationship between ammonia and
NTI, but not NRI, for these bacteria (R2 ¼ 0.218, P ¼
0.052).
DISCUSSION
FIG. 1. Variation of relatedness along environmental
gradients. (A) Relatedness decreased with increasing productivity in freshwater mesocosms for Betaproteobacteria (NRI, R2
¼ 0.829, P ¼ 0.0588; mesocosm three was excluded due to small
community size). (B) Relatedness decreased with total organic
carbon in bacterial soil communities (NRI, R2 ¼ 0.925, P ¼
0.0058; NTI, R2 ¼ 0.966, P ¼ 0.0017). (C) There was a
significant negative relationship between the relatedness of nirS
genes and nitrate in Chesapeake Bay sediment communities
(NRI, R2 ¼ 0.909, P ¼ 0.0077; NTI, R2 ¼ 0.601, P ¼ 0.0768).
ammonia-oxidizing communities sampled showed significant phylogenetic clustering as estimated by NTI.
Phylogenetic structure and the environment
We also observed that measures of phylogenetic
structure can vary along environmental gradients. There
Our results suggest that bacteria tend to co-occur with
other closely related bacteria more often than expected
by chance, as has been observed for some plant species
(Webb 2000; also see Cavender-Bares et al. 2006,
Kembel and Hubbell 2006, Lovette and Hochachka
2006, Weiblen et al. 2006). In addition, we observed that
phylogenetic structure can vary along environmental
gradients.
We observed significant net relatedness index (NRI)
and nearest taxa index (NTI) values for freshwater
bacterial communities from experimental mesocosms.
Relatedness information provides a different window
into bacterial communities than does information
concerning richness or taxonomic composition. Accordingly, our observations that the relatedness of cooccurring Betaproteobacteria decreases with productivity, is in contrast to previous observations that their
taxonomic richness does not (Horner-Devine et al.
2003). We observed that each of the five communities
contained approximately the same number of taxa
regardless of productivity, but these taxa tended to be
more distantly related at higher productivities. Decreasing relatedness with increasing productivity might
indicate that low productivity environments are more
‘‘stressful’’ (e.g., impose a stronger ‘‘filter’’ on a
July 2006
RELATEDNESS IN BACTERIAL COMMUNITIES
community) for Betaproteobacteria than do more
productive environments. In contrast, we did not
observe a relationship between relatedness of Alphaproteobacteria or Cytophaga-Bacteroides-Flavobacteria
(CFB) and productivity in the mesocosm study, despite
our previous observations of changes in taxonomic
richness of these groups with productivity (HornerDevine et al. 2003).
Clustered distributions have been interpreted as
evidence of habitat filtering, where a group of closely
related species shares a trait, or suite of traits, that allow
them to persist in a given habitat (Webb et al. 2002).
Alternatively, significant phylogenetic clustering could
be the result of differential dispersal and/or colonization
abilities, or an adaptive radiation event. While it is
beyond the scope of this work to determine the process
responsible for the clustering we observed, the results
from the freshwater mesocosms suggest that habitat
filtering, rather than an adaptive radiation event or
colonization effects, was important in the assembly of
these bacterial communities. In this mesocosm study,
bacterial communities were established in each mesocosm from the same inoculum of bacteria from natural
pond communities (Horner-Devine et al. 2003). Thus,
the history of colonization could not play a role in the
patterns we observed. Although dispersal among mesocosms was not prevented (and likely occurred), the
productivity gradient was randomized in space (i.e.,
mesocosms with similar productivities were not clustered
in space), and thus clinal dispersal is unlikely to underlie
the patterns we observed. Finally, since the mesocosm
communities were sampled four months after they were
initiated, it is unlikely that the ribosomal gene evolved
during the course of the experiment, and thus it is
unlikely that the clustering is due to adaptive radiation
during the experiment. Without such information for the
other data sets we analyzed, it is difficult to distinguish
among habitat filtering, adaptive radiation, or colonization processes in the systems that were not manipulated. However, the results from the mesocosm study
suggest that habitat filtering could be an important force
in the assembly of at least some bacterial communities.
Overdispersion of taxa across a phylogeny has been
observed in natural communities (Ackerly et al. 2006,
Cavender-Bares et al. 2006, Silvertown et al. 2006) and
could indicate that negative interactions (e.g., competition) are important in community assembly (Graves and
Gotelli 1993, Webb et al. 2002). Although we did
observe significant phylogenetic overdispersion in one of
the freshwater bacterial communities, our observations
do not suggest that competition played an overwhelming
role in structuring the communities we studied at the
scales examined here. However, it is important to note
that habitat filtering and competition likely act in
concert to produce the communities we observe. Thus
even where the phylogenetic signature suggests the
importance of habitat filtering, local competition can
also be occurring.
S105
Phylogenetic scale (the taxonomic group or rank
under consideration) has been shown to influence the
observation of phylogenetic patterns (Silvertown et al.
2001, 2006, Cavender-Bares et al. 2004, 2006). However,
the effect of phylogenetic scale was not evident for
bacteria from the freshwater mesocosms. We tended to
observe phylogenetic clustering both when we examined
all bacteria together and when we examined taxonomic
subsets of the bacteria (Alphaproteobacteria, Betaproteobacteria, and CBF). It is possible that Alphaproteobacteria, Betaproteobacteria, and CBF encompass such
a broad range of bacterial ecotypes that, even with the
NTI (which focuses on terminal clustering and thus is
particularly sensitive), it is less likely that one will
observe overdispersion.
Recent work by Zhou et al. (2002) and Treves et al.
(2003) suggests that spatial isolation plays an important
role in structuring soil bacterial communities. They
observed that unsaturated, surface communities had
more uniform rank abundance patterns than did
communities from saturated, subsurface communities,
which exhibited high-dominance distributions (Zhou et
al. 2002). They interpreted the uniform distribution
from the surface samples as evidence that local
competition does not play a significant role in structuring the soil communities they studied. Their observations (and subsequent mathematical modeling and
laboratory experimentation) suggested that spatial isolation might limit competition in the surface soils
(Treves et al. 2003). Our results do not support this
hypothesis. We observed phylogenetic clustering in the
subsurface samples, where spatial isolation was predicted to be minimized due to high water content and
thus where there was an expectation for strong
competition and phylogenetic overdispersion. In contrast we observed phylogenetic overdispersion in one of
the surface samples, where isolation was predicted to be
high and competition weak.
We did observe that phylogenetic clustering decreased
with increasing total organic carbon (which covaried
with depth) in the Zhou et al. (2002) soil data set. Thus
as carbon availability increased, the strength of clustering and perhaps habitat filtering decreased. The
potential decrease in the strength of filtering is unlikely
to be related to an increase in the role of competition for
carbon, since competition would likely decrease with
increasing carbon. More information on the types of
carbon present, as well as C:N ratios, might lend more
insight into the underlying processes.
We observed both phylogenetic clustering and overdispersion for ammonia-oxidizing bacteria from Costa
Rican soils. It is possible that, for ammonia-oxidizing
bacteria, a more restricted (and potentially more
ecologically similar) group of taxa, it might be easier
to detect interactions among taxa, because ammoniaoxidizing bacteria likely compete for similar resources.
Carney et al. (2004) found that neither bacterial richness
nor composition changed across plant diversity treat-
S106
M. C. HORNER-DEVINE AND B. J. M. BOHANNAN
ments (one, three, or five focal plant species). Similarly,
we did not observe pairwise differences in phylogenetic
structure among the plant treatments using post-hoc
comparisons. Carney et al. (2004) also observed differences in the ammonia-oxidizer community among landuse types in some measures of diversity and in
composition. We observed that, while pasture and forest
did not differ in phylogenetic clustering, pasture
communities were less phylogenetically diverse (i.e., less
clustered) than each of the plant treatments. In fact,
pasture communities tended to be overdispersed. We
also found a weak positive relationship between terminal
clustering and ammonia, such that clustering (and
perhaps the importance of habitat filtering rather than
competition) increased with ammonia. This is consistent
with an increase in the importance of habitat filtering
(and conversely, perhaps a decrease in the relative
strength of competition) for ammonia as ammonia
concentrations increased.
Analysis of amoA and nirS genes sampled from the
Chesapeake Bay offered us an opportunity to examine
community patterns deduced from potentially ‘‘ecologically relevant’’ genes. We assumed that phylogenetic
overdispersion would be more prevalent in such data,
where the taxa sampled are essentially from a single
guild (i.e., a group performing the same function and
requiring the same resources). However, we did not
observe overdispersion for amoA, and we observed
overdispersion in only one sample of nirS. In hindsight
this is not overly surprising for nirS, given that
denitrifying bacteria can be ecologically very different
despite sharing the nirS gene (Shapleigh 2000). However,
ammonia-oxidizing bacteria are believed to be a
physiologically constrained group, existing solely on
the oxidation of ammonia. The lack of overdispersion
suggests that the amoA gene may be too conserved to
reveal ecological differences or that sequence variation
at this scale does not reflect ecological differences.
Previous analysis of the amoA samples demonstrated
a nonsignificant trend toward decreasing richness with
increasing salinity (Francis et al. 2003). We did not
observe a relationship between phylogenetic structure as
inferred from this gene and salinity or ammonia. In
contrast, we observed a significant decrease in phylogenetic clustering as inferred from the nirS gene with
increasing nitrate and a weak relationship between
clustering and salinity. It is difficult to interpret the
relationship with nitrate; one might expect that increasing nitrate availability would lead to decreased competition for this substrate by denitrifiers or stronger
habitat filtering for denitrifying bacteria with genes that
confer an advantage at high nitrate concentrations; we
observed the opposite trend.
The interpretations of our results are based on the
assumption that closely related taxa are more ecologically similar than distantly related taxa. This assumption
has been shown to be true for some plants, animals, and
microbes (Kuittinen et al. 1997, Nubel et al. 1999,
Ecology Special Issue
Morgan et al. 2001, Prinzing et al. 2001), but not all
(Losos et al. 2003, Rice et al. 2003, Knouft et al. 2006).
How universally the assumption about similarity of
closely related organisms applies to microorganisms is
currently unknown. If this assumption does not hold for
most bacteria, other explanations might be necessary for
the patterns we observe. For example, some bacteria are
capable of lateral gene transfer (LGT; Ochman et al.
2000, Lerat et al. 2003). Lateral gene transfer among cooccurring bacteria could weaken or uncouple the
relationship between ecological similarity and evolutionary relatedness, if ecologically relevant genes are
exchanged more often than phylogenetically informative
housekeeping genes (e.g., ribosomal genes) as has been
suggested (Lerat et al. 2003). Rampant LGT would
reduce the prevalence of phylogenetic clustering or
overdispersion due to ecological processes. However,
we observed a significant level of phylogenetic clustering
in the communities that we examined, suggesting that
LGT does not substantially overwhelm phylogenetic
patterns in these communities. In addition, recent work
in environmental genomics suggests that on recent
evolutionary time scales horizontal gene transfer is not
rampant in natural microbial communities (Lerat et al.
2003).
Martin et al. (2004) used a different approach
(lineage-per-time analysis) to look for phylogenetic
patterns in microbial diversity data. They failed to show
significant phylogenetic structure (i.e., an overabundance of closely related or distantly related sequences)
across several different data sets. However their study
differed from ours in that they assumed a ‘‘universal’’
null model (an exponential increase in lineages) for all
data sets, rather than creating a null expectation for each
data set by resampling of a regional phylogenetic tree. In
the approach used here, we are interested in whether
observed communities differ in phylogenetic diversity
from communities created by a random draw from the
available taxa in the regional pool. The Martin et al.
(2004) approach suffers from a lack of power if the
fraction of diversity sampled is small, making the task of
detecting phylogenetic structure very difficult. While the
communities examined here are also undersampled, the
use of a relative measure of community relatedness
decreases the influence of undersampling, provided
communities in a given analysis are sampled with equal
effort. Furthermore, if the questions of interest concern
community assembly (as they do in our study), assuming
a null model based on regional tree resampling is
appropriate because it should model the assembly
process.
We have observed that bacterial communities exhibit
phylogenetic structure, in some cases similar to that
observed for plants, and that this structure can vary
along environmental gradients. Our results suggest that
habitat filtering might be relatively more important to
the assembly of bacterial communities than competition.
Why might this be the case? Recent work suggests that
July 2006
RELATEDNESS IN BACTERIAL COMMUNITIES
the greater the degree of environmental heterogeneity
over which one samples a community, the more likely
that phylogenetic clustering, rather than overdispersion,
will be present (Cavender-Bares et al. 2006, Silvertown
et al. 2006). The data sets used in our study consisted of
samples that were extremely large relative to the size of
the target organisms and the scales over which
individuals interact (as is the case in most studies of
microbial diversity) and thus likely included substantial
environmental heterogeneity. As such, the spatial scale
of sampling might bias the results towards clustering
rather than overdispersion.
Recent studies have also suggested that phylogenetic
scale should affect the prevalence of clustering; increasing phylogenetic scale (i.e., an increase in taxonomic
lumping) might result in an increased prevalence of
clustering (Silvertown et al. 2006). Substantial ecological
diversity has been shown to be present within microbial
taxa defined using molecular markers, especially ribosomal markers (e.g., Ward et al. 1998, Rocap et al. 2002).
The molecular approach used to characterize microbial
diversity might bias microbial diversity data sets toward
detection of phylogenetic clustering when relatively large
groups of organisms are targeted. Finer scale markers
(e.g., internal transcribed spacer (ITS) or multi-gene
approaches) might reveal the presence of increased
phylogenetic overdispersion. The growing possibility of
using environmental genomics to examine full genomes
of different lineages from environmental samples will
provide even more power to this approach (R. Whitaker
and J. F. Banfield, unpublished manuscript).
The search for patterns in microbial biodiversity is in
its infancy, and it is premature to make strong
conclusions regarding the exact mechanisms responsible
for the patterns we have described. To make such
conclusions with confidence, a better understanding of
the relationship between community assembly mechanisms and phylogenetic patterns is necessary. Such an
understanding could be developed through studies of
controlled experimental systems, where, for example,
one can manipulate environmental parameters such as
resource availability and tease apart the effects of
mechanisms that occur on large scales of time and space
(such as evolution and differential colonization) from
those that occur on smaller scales (such as habitat
filtering and competition). Microbial model systems
could serve as excellent experimental systems in which to
explore these ideas (Jessup et al. 2004). We also suggest
that future work in natural systems examine gradients
that span a greater range of environmental characteristics to test the hypothesis that phylogenetic clustering
and thus habitat filtering increases with environmental
extremes. Such studies coupled with a better understanding of the extent of LGT, how traits map onto
phylogenies, and the evolutionary history of these traits
(Ackerly et al. 2006, Silvertown et al. 2006) will help to
explain the patterns of phylogenetic structure we
observed. Where we do observe similar patterns of
S107
phylogenetic structure for microbial and macrobial
communities, it is possible that similar mechanisms
could be responsible for the structure of communities
from these very different forms of life.
ACKNOWLEDGMENTS
We are grateful to David Ackerly, Will Cornwell, Clara
Davis, and Cam Webb for many informative discussions about
phylogenetic structure and community assembly. We would
also like to thank Karen Carney, Chris Francis, and Jizhong
Zhou for allowing us to use their data for these analyses.
Finally, we thank Jonathan Losos and two anonymous
reviewers for their comments and suggestions. M. C. HornerDevine was supported by funding from the American Association of University Women and the Center for Evolutionary
Studies at Stanford. This work was also supported by a grant
from the NSF (DEB0108556) to B. Bohannan.
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Ó 2006 by the Ecological Society of America
PHYLOGENETIC STRUCTURE OF FLORIDIAN PLANT COMMUNITIES
DEPENDS ON TAXONOMIC AND SPATIAL SCALE
JEANNINE CAVENDER-BARES,1 ADRIENNE KEEN,
AND
BRIANNA MILES
Department of Ecology, Evolution, and Behavior, University of Minnesota, St. Paul, Minnesota 55108 USA
Abstract. Consideration of the scale at which communities are defined both taxonomically and spatially can reconcile apparently contradictory results on the extent to which plants
show phylogenetic niche conservatism. In plant communities in north central Florida, we
collected species abundances in 55 0.1-ha plots in several state parks. When communities were
defined narrowly to include a single phylogenetic lineage, such as Quercus, Pinus, or Ilex,
neighbors tended to be less related than expected (phylogenetic overdispersion) or there was
no pattern. If the same communities were defined more broadly, such as when all seed plants
were included, neighbors tended to be more related than expected (phylogenetic clustering).
These results provide evidence that species interactions among close relatives influence
community structure, but they also show that niche conservatism is increasingly evident as
communities are defined to include greater phylogenetic diversity. We also found that, as the
spatial scale is increased to encompass greater environmental heterogeneity, niche
conservatism emerges as the dominant pattern. We then examined patterns of trait evolution
in relation to trait similarity within communities for 11 functional traits for a single phylogenetic lineage (Quercus) and for all woody plants. Among the oaks, convergent evolution of
traits important for environmental filtering contributes to the observed pattern of phylogenetic
overdispersion. At the broader taxonomic scale, traits tend to be conserved, giving rise to
phylogenetic clustering. The shift from overdispersion to clustering can be explained by the
increasing conservatism of traits at broader phylogenetic scales.
Key words: environmental heterogeneity; Florida; Ilex; niche conservatism; overdispersion; phylogenetic
structure of communities; Pinus; Quercus; taxonomic scale; trait convergence.
INTRODUCTION
There is growing recognition that species evolve
within communities and that community interactions
influence the evolutionary process (Antonovics 1992,
Neuhauser et al. 2003, Whitham et al. 2003). At the
same time, we are becoming increasingly aware that
evolutionary processes, particularly the way that traits
evolve within lineages, influence species distributions
and assembly in communities (McPeek 1996, Webb et al.
2002, Ackerly 2003, Chazdon et al. 2003, CavenderBares et al. 2004a). This study seeks to understand the
role that trait evolution plays in determining the
phylogenetic structure of communities and the extent
to which phylogenetic structure depends on how
communities are defined.
Two processes are often considered as central to the
assembly of communities: (1) filtering of species that can
persist within a community on the basis of their
tolerance of the abiotic environment (e.g., Weiher and
Keddy 1995), and (2) competitive interactions among
species that limit their long-term coexistence (Elton
Manuscript received 26 January 2005; revised 17 August
2005; accepted 18 August 2005. Corresponding Editor: A. A.
Agrawal. For reprints of this Special Issue, see footnote 1, p.
S1.
1
E-mail: [email protected]
1946, MacArthur and Levins 1967, Chesson 1991,
Leibold 1998). The two processes lead to opposite
predictions about the phenotypic similarity and phylogenetic relatedness of co-occurring species (Tofts and
Silvertown 2000, Webb et al. 2002). If closely related
species share similar physiological limitations and
exhibit evolutionary niche conservatism, environmental
filtering will tend to cause closely related species to cooccur (phylogenetic clustering). In contrast, competitive
exclusion should limit the coexistence of closely related
species if species compete for the same limiting
resources, leading to the opposite pattern of phylogenetic overdispersion. Both processes can operate simultaneously in real communities, but have greater
influence at different scales. Keddy and Weiher (1999)
hypothesized that limiting similarity should have greater
importance at smaller spatial scales, whereas environmental filtering should predominate at larger spatial
scales. Evidence supporting this view has been found in
meadow communities in Great Britain (Silvertown et al.
2005).
At the same time, the ecological process that appears
to predominate might also depend on how broadly or
narrowly communities are defined. For example, phylogenetic clustering was found among tree species in
rainforest communities in Borneo (Webb 2000), as well
as in herbaceous communities in Great Britain (Tofts
S109
S110
JEANNINE CAVENDER-BARES ET AL.
Ecology Special Issue
FIG. 1. (A) Phylogenetic structure of communities. An observed correlation (solid line) that is more negative than expected
(dashed line) indicates phylogenetic clustering (left panel), because closely related species occur together more often than expected
by chance; an observed correlation that is more positive than expected indicates phylogenetic overdispersion (right panel), because
closely related species do not occur together. (B) Trait similarity in communities. An observed correlation that is more negative
than expected indicates that co-occurring species show similar trait values (phenotypic clustering, left panel); an observed
correlation that is more positive than expected indicates that trait values can be highly variable within communities (phenotypic
overdispersion, right panel).
and Silvertown 2000). Both studies considered a large
number of angiosperm lineages, providing support for
the generalization that evolutionary stasis, or phylogenetic niche conservatism, is widespread among plant
communities around the globe (Harvey and Pagel 1991,
Eldridge 1995, Wen 1999, Webb et al. 2002, Ackerly
2003, Qian and Ricklefs 2004). In contrast, phylogenetic
overdispersion has been found in narrowly defined
communities that include a single phylogenetic lineage,
including within Caribbean lizard communities (Losos et
al. 2003) and among co-occurring oaks in north central
Florida (Cavender-Bares et al. 2004a). In the latter
study, evidence of convergence, rather than conservatism, in the evolution of species niches highlighted the
prevalence of species interactions over niche conservatism in community assembly. These studies suggest that
patterns of phylogenetic dispersion might vary systematically with taxonomic scale. The present study seeks to
evaluate this possibility by examining the vegetation of
Florida at various taxonomic and spatial scales. In
addition, the study addresses the role that trait evolution
plays in the phylogenetic structure of communities.
Specifically, we make the following predictions:
1) The pattern of phylogenetic structure among
communities depends on how a community is defined
in terms of the taxa included. The more broadly a
community is defined, the more likely it will be to show
phylogenetic clustering as a result of trait conservatism
and environmental filtering. Narrowly defined communities, in contrast, are more likely to show phylogenetic
overdispersion, either as a result of trait convergence,
trait overdispersion, or both.
2) The phylogenetic structure of communities depends
on the spatial scale of the analysis. As the spatial scale is
increased to encompass greater environmental heterogeneity, species interactions should become less important, and phylogenetic clustering should emerge as the
dominant pattern.
3) The relationship between trait evolution (conservative vs. convergent) and trait similarity within communities (clustered vs. overdispersed) should predict the
phylogenetic structure of communities at different scales
of analysis.
MATERIALS
AND
METHODS
Tests of phylogenetic structure of communities
We examined the phylogenetic structure of communities by comparing the degree of co-occurrence of species
pairs in relation to the phylogenetic distance between
them (Fig. 1A). Due to nonindependence and nonnormality of the data points, these correlations were
compared to null models. Tests for phylogenetic
clustering and overdispersion were conducted for a suite
of different data sets in north central Florida and in the
entire state of Florida that vary in spatial extent and
July 2006
SCALE AND FLORIDIAN PLANT COMMUNITIES
resolution of data. We compared the correlation
coefficient of the relationship between co-occurrence
and phylogenetic distance of species pairs to a null
model in which either species distributions were
permuted or phylogenetic relationships among species
were randomized (Fig. 1A; Cavender-Bares and Wilczek
2003). The pairwise values of co-occurrence (C) were
calculated based on proportional similarity (Schoener
1970) as follows: Cih ¼ 1 – 0.5 Rjpij – phjj, where Cih is the
co-occurrence of species i and h, and pij is the proportion
of total basal area or the proportion of occurrences of
the ith species in the jth plot. We calculated phylogenetic
distances from the estimated intervening branch length
distances (measured in millions of years) between species
pairs based on community phylogenies created for each
test (see Phylogenetic analyses).
Three null models were used. The first two generated
a null distribution of expected species occurrence
patterns against which to compare the observed data.
These allow us to ask the following question: Given the
evolutionary history of the taxa in the regional species
pool, does phylogenetic relatedness influence the way
that species have assembled in communities? In null
model 1, basal area of species within plots was
randomized by reshuffling raw data values 999 times
across plots, but constraining the total basal area per
species. Null model 2 used presence/absence data,
instead of basal area, and constrained both total occurrences per species and total number of species per
plot, using the sequential swap algorithm (Gotelli and
Entsminger 2001b). The null model for the presence/
absence data may be the most biologically realistic,
because it constrains both species abundances and plot
diversity levels (Gotelli and Graves 1996). However,
presence/absence data provide a lower degree of
resolution for the distributions of species than basal
area, because differences in relative abundance within
plots are not considered. A null model that constrains
total basal area per species and plot would probably be
impossible to construct and was not attempted.
Randomizations were carried out using Ecosim (Gotelli
and Entsminger 2001a), and the co-occurrence vs.
phylogenetic distance correlations were carried out in
a self-written Visual Basic program, modified to
accommodate large data sets from Cavender-Bares et
al. (2004a).
A third null model kept constant the distribution of
species within communities, but randomized the phylogenetic tree topology. Null model 3 allows us to ask,
given the distribution of species in communities, does
the phylogenetic relatedness of species within those
communities differ from random expectation? To
randomize, we used the ‘‘random branch moves’’
algorithm in Mesquite (Maddison and Maddison 2000)
and assigned the number of branch moves to equal the
number of taxa in the tree. The total branch length
distance from the basal node to the tips was kept
constant. A distance matrix was then automatically
S111
computed from each of the 999 randomized trees, as well
as from the original phylogeny, using a program in
Visual Basic (J. Cavender-Bares, unpublished software).
We used the result of this computation to calculate a
null distribution of correlation coefficients between
pairwise species co-occurrence and phylogenetic distance. In all analyses, a P value was calculated based on
a two-tailed test. Since the null models use 999
randomizations plus the observed value, the minimum
P value is 0.002 (Manly 1991).
Trait similarity within communities
We used a similar test to examine trait similarity
within communities (Fig. 1B) by comparing the absolute
value of pairwise differences in trait values to the degree
of co-occurrence between species pairs. The observed
correlation coefficients were tested against null model 2,
in which the presence/absence of species within plots was
randomized keeping row and column totals constant.
The ranking of the observed correlation coefficient
relative to that of the null model provides a way to
order the measured traits in terms of their similarity
within communities. The term ‘‘phenotypic clustering’’
refers to high trait similarity within communities, while
the term ‘‘phenotypic overdispersion’’ refers to low trait
similarity within communities (Fig. 1B)
The spatial extent of communities
In order to test the importance of (1) the spatial
resolution of data, and (2) how communities are defined,
we analyzed a series of community data sets that differ
in the resolution of species abundances and in the
community definition. First, we examined the influence
of using basal area or presence/absence to evaluate
species abundance in random 0.1-ha plots of north
central Florida. Second, we examined the influence of
the way community boundaries are defined by using
these random 0.1-ha plots, where community boundaries are fixed by a standard area (hereafter referred to as
the plot survey), or by using previously established
community classifications and vegetation maps within
state parks (hereafter referred to as community classifications). In the latter case, community boundaries in
north central Florida were defined by environmental
variation and the vegetation itself according to the
Florida Natural Areas Inventory and the Florida
Department of Natural Resources (FNAI and FDNR
1990). Previously determined community classifications
for the entire state of Florida were also used, so that
environmental variation among communities encompasses topographic and edaphic variation, as well as
climatic variation.
To examine the influence of the inclusion or exclusion
of communities on phylogenetic structure, we performed
tests of co-occurrence vs. phylogenetic distance for three
individual genera (Quercus, Pinus, and Ilex), using
community classification data sets for six state parks in
north central Florida relative to two null models (both
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JEANNINE CAVENDER-BARES ET AL.
corresponding to null model 2). We created the first null
model by permuting the presence of species across only
those communities that contained a member of the
genus (no community had zero as a matrix element). We
created the second null model by permuting species’
presences across all communities that existed in the six
parks examined, regardless of whether any members of
the genus were found in them or not (a number of
communities were represented by zeros in the matrix).
This allowed us to test whether increasing the number of
communities (effectively increasing the spatial scale and
the degree of environmental heterogeneity) would alter
the results.
Inclusiveness of taxa within communities
To examine the influence of phylogenetic scale, or the
inclusiveness of taxa, on phylogenetic structure of
communities, we carried out the same tests for the plot
survey data to examine the phylogenetic structure of
plant species within communities for (1) species within a
monophyletic lineage (Quercus), (2) all angiosperms in
the same guild (tree and shrub strata), (3) all plants in
the tree and shrub strata, and (4) all recorded plants in
all strata, including trees, shrubs, vines, epiphytes, and
herbaceous species. These were tested against null
models 1, 2, and 3, and against null models 2 and 3
only in the latter case, as only presence/absence data
were available. We ran the same tests for the community
classification data. The three most diverse woody plant
genera within north central Florida, including the oaks
(18 species), the hollies (seven species), and the pines (six
species), were included to examine lineage-specific
patterns in the context of their biogeographic and
evolutionary history.
Trait similarity within communities
in relation to trait evolution
For suites of life history and functional traits, we
used the relationship between trait conservatism and
trait similarity within communities to understand the
phylogenetic structures of communities for the oaks
only, as well as for all trees and shrubs in the plot
survey. Trait similarity within communities was determined from the correlation of trait differences between
species pairs and their degree of co-occurrence, relative
to null model 1 (Fig. 1B). Trait conservatism (or phylogenetic signal) was calculated using methods developed by Ackerly in the ‘‘Analysis of Traits’’ module in
Phylocom (Webb et al. 2004), based on trait differences
between nodes, normalized by the standard deviation of
the trait within a given lineage relative to a null model
in which species are randomized across the phylogeny
(Moles et al. 2005). We used the ranking of the
observed phylogenetic signal relative to the simulations
of the null model to order the traits by their degree of
conservatism. We recognize that measures of trait
conservatism are influenced by taxon sampling and
might be biased in highly pruned phylogenies, such as
Ecology Special Issue
those used in the present study (Ackerly 2000). Nevertheless, examination of patterns of trait evolution with
increasing taxonomic diversity is useful for understanding how trait conservatism shifts with phylogenetic scale in a single study system.
Trait data collection
We measured a suite of leaf traits on mature trees
across the range of their local environmental distributions, collected from five state and city parks in north
central Florida, including San Felasco Hammock State
Preserve, Ichetucknee Springs State Park, Morning Side
Nature Center, Payne’s Prairie State Preserve, and
O’Leno State Park. For five individuals of each species,
three sun and three shade leaves from each individual
were measured for leaf area, scanned, and dried for leaf
mass. We then used scanned leaf images to calculate
perimeter (P), the perimeter-to-area ratio (P/A), which
has been shown to be correlated with leaf hydraulic
conductance (Sack et al. 2003), and lobedness (PL/A),
determined as the perimeter-to-area ratio, scaled by leaf
length (L). In addition, we took plant height, seed mass,
and cotyledon type (photosynthetic or storage cotyledons), which has been shown to influence growth
dynamics (Kitajima and Fenner 2000), from published
(Kurz and Godfrey 1962, Mirov 1967, Schopmeyer
1974, Godfrey and Wooten 1981, Wenger 1983, Godfrey
1988) or online sources. We were able to collect leaf trait
data for 90 of the 122 species examined in the plot
survey; maximum height and leaf habit were obtained
for 115 species, seed mass for 63 species, and cotyledon
type for 87 species.
It is useful to include as many different kinds of
traits as possible that might contribute to ecological
filtering and interactions among species. The present
study, however, examined a limited number of easily
measured traits. A suite of additional traits, including
maximum hydraulic conductivity, vessel diameter,
wood density, leaf longevity, and others were available
from previous studies for the Quercus species (Cavender-Bares and Holbrook 2001, Cavender-Bares et al.
2004b), and these are presented in the oak analysis for
comparison.
Community data collection
Plot survey data in north central Florida.—Quantitative vegetation data were collected from randomly
located 20 3 50 m (0.1 ha) plots established in a
previous study (Cavender-Bares et al. 2004a, b) in
several state parks in north central Florida. Seventy
four plots were originally established in 1998 for a
study on oaks, and 55 of these original plots were
resampled to determine the basal area of all woody
species as well as the presence/absence of all plant
species common enough within a plot to be conspicuous. Insufficient time and inability to relocate some of
the plots prevented a complete resampling of all of the
original plots. Within each plot, the diameter at breast
July 2006
SCALE AND FLORIDIAN PLANT COMMUNITIES
height of each tree over 1 m height was measured for
calculation of basal area for a total of 122 tree and
shrub species (see Appendix A). The presence/absence
of all seed plants was also recorded for each of the
plots (141 species; see Appendix A). The majority of
the plots are located in three state parks, including San
Felasco Hammock State Preserve (28 plots), a 2803-ha
park in Alachua County; Ichetucknee Spring State
Park (13 plots), a 921-ha park bridging Columbia and
Suwanee counties and bisected by the Ichetucknee
River; and Manatee Springs State Park (12 plots), a
960-ha park abutting the Suwanee River in Levy
County. Two more plots were resampled at other sites
in the region, including one at Morning Side Nature
Center and one at Paynes Prairie Preserve State Park.
An effort was made to sample across a range of the
major woody plant community types in north central
Florida.
Community classification data in north central Florida.—We obtained species lists for each of six state parks
in north central Florida and corresponding maps of
natural community types from the District 2 Park
Service in Gainesville, Florida, USA. Parks included
San Felasco Hammock, Ichetucknee Springs, O’Leno
State Park, Big Shoals State Park, Stephen Foster State
Park, and Goldhead Branch State Park. In Florida, 63
terrestrial natural communities were identified and
defined by the Florida Natural Areas Inventory and
Florida Department of Natural Resources (FNAI and
FDNR 1990), 29 of which occur in terrestrial areas of
these state parks in north central Florida. Aquatic
communities, which comprised nearly half of the
defined communities, were excluded from the analyses
because plant species were either poorly represented or
poorly documented. A natural community is defined by
the FNAI and FDNR (1990) as a ‘‘distinct and
reoccurring assemblage of populations of plants,
animals, fungi, and microbes naturally associated with
each other and their physical environment.’’ In each
park, we assigned species to designated communities,
either by data directly from the Park Service or from a
master list from the FNAI and FDNR (1990). If
community assignments were not available from these
sources, we determined them from two other sources
(Kurz and Godfrey 1962, Godfrey 1988). Data were
sufficient to include only the tree and shrub species (216
species). There were 29 possible community types,
replicated according to whether they occurred in multiple state parks, giving a total of 75 sample communities
used for the community classification analysis.
Community classification data for the state of Florida.—A master species list with the community affiliations of these species was developed for the entire state
of Florida from the FNAI and FDNR report. We ran
two analyses: one for all listed plant species with
identified community types (383 species), and a second
for plants in the tree and shrub guild only (255 species)
(see Appendix A). We included 50 terrestrial commun-
S113
ities in the analyses, including wetlands and coastal
communities. Unlike the analyses for the parks in north
central Florida, each community was represented only
once, regardless of how commonly it occurs.
Phylogenetic analyses
Phylogenetic reconstruction.—Community phylogenies were created for each test using Phylomatic (Webb
and Donoghue 2005). Phylomatic is an online application for creating a backbone phylogeny based on family
and genera.2 The maximally resolved seed plant tree
used for our trees relies on the online resource
continually updated by P. F. Stevens, i.e., Angiosperm
Phylogeny.3 Sources for tree construction down to the
family level are extensively documented on this web site,
although phylogenies created through the Phylomatic
maximally resolved seed plant tree are not entirely
resolved to the family level. We used a supertree of the
angiosperms (Davies et al. 2004) to manually resolve all
trees to the family level. Higher taxa were resolved
according to published phylogenies (see Appendix B). If
no published information was available to resolve
polytomies, they remained unresolved. (See Appendix
C for all reconstructed phylogenies.) It is important to
recognize that the community phylogenies generated
using this approach represent only approximations of
true species relationships and should be refined as more
data and other methods for constructing supertrees
become available. The Quercus phylogeny was based
on Cavender-Bares et al. (2004a) and was consistent
with Manos et al. (1999). The four most parsimonious
trees from that analysis were tested, as well as several
less resolved phylogenies that collapsed nodes with low
bootstrap support. We converted branch lengths to
millions of years. A less resolved topology was used in
the analyses presented here (see Appendix C), but the
results were indistinguishable from those using the
more resolved phylogenies (data not shown). We based
the Ilex phylogeny on Cenoud (2000), and the Pinus
phylogeny on Millar (1993), Schwilk and Ackerly
(2001), Grotkopp et al. (2004), and Gernandt et al.
(2005).
Branch length estimation.—Branch lengths were based
on minimum ages of nodes determined for genera,
families, and higher orders from fossil data, and we
extrapolated to higher order branches by spacing
undated nodes in the tree evenly between dated nodes.
This was done using an averaging algorithm in
Phylocom (Webb et al. 2004) called ‘‘BLADJ’’ (Branch
Length ADJustment, available online).4 Most of the
node ages at the family level were taken from Wikström
et al. (2001). At the genus level, we used additional
sources, including Daghlian and Crepet (1983) for
Quercus, based on the first appearance of the genus in
2
3
4
hhttp://www.phylodiversity.net/phylomatici
hhttp://www.mobot.org/MOBOT/research/APwebi
hhttp://www.phylodiversity.net/bladji
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JEANNINE CAVENDER-BARES ET AL.
Ecology Special Issue
TABLE 1. Tests for patterns of phylogenetic structure for community data sets that vary in how communities are defined. For each
data set, the number of taxa, the number of communities, the data type (basal area or presence/absence), and the null model used
in the analysis are given.
Data type and taxa included
North central Florida
Plot survey data (three parks, 55 plots)
Quercus species
Quercus species
Quercus species
Quercus species
Angiosperm trees and shrubs
Angiosperm trees and shrubs/no oaks
All tree and shrub species
All tree and shrub secies
All tree and shrub species
All tree and shrub species
All angiosperms
All angiosperms/no oaks
All plant species
All plant species
r
Communities No. taxa No. communities Data type Null model (observed)
all
all
all
all
all
all
all
all
all
all
all
all
all
all
17
17
17
17
113
96
122
122
122
122
130
113
141
141
55
55
55
55
55
55
55
55
55
55
55
55
55
55
basal area
pres/abs
basal area
pres/abs
basal area
basal area
basal area
pres/abs
basal area
pres/abs
pres/abs
pres/abs
pres/abs
pres/abs
1
2
3
3
1
1
1
2
3
3
2
2
2
3
0.184
0.157
0.184
0.157
0.030
0.019
0.036
0.043
0.036
0.043
0.008
0.014
0.027
0.027
North central Florida
Community classification data (six parks, 29 terrestrial community types)
Quercus species
all
18
Quercus species
oak only
18
Quercus species
all
18
Pinus species
all
6
Pinus species
pine only
6
Pinus species
all
6
Ilex species
all
7
Ilex species
holly only
7
Ilex species
all
7
Angiosperm trees and shrubs
all
204
Angiosperm trees and shrubs
all
204
All tree and shrub species
all
216
All tree and shrub species
all
216
75
38
75
75
29
75
75
35
75
75
75
75
75
pres/abs
pres/abs
pres/abs
pres/abs
pres/abs
pres/abs
pres/abs
pres/abs
pres/abs
pres/abs
pres/abs
pres/abs
pres/abs
2
2
3
2
2
3
2
2
3
2
3
2
3
0.153
0.153
0.153
0.284
0.284
0.284
0.243
0.243
0.243
0.046
0.046
0.015
0.015
State of Florida
Florida community classifications (50 terrestrial community types)
All tree and shrub species
all
255
All species
all
383
50
50
pres/abs
pres/abs
2
2
0.023
0.057
Notes: Observed and expected correlation coefficients (r) for the relationship between co-occurrence and phylogenetic distance of
species pairs are shown. P values (two-tailed test) are determined from a null distribution of 999 randomizations plus the observed
value.
the Americas, and Cenoud et al. (2000) for Ilex, based
on the Eocene radiation of the genus, not the earliest
appearance of extinct basal lineages. Higher level branch
lengths, if available, were converted to millions of years,
based on the fossil age for the deepest node. This
method of branch length calculation provides only a first
approximation of relative evolutionary distances between species. However, given that molecular data could
not be used for branch length estimation, this method is
an improvement over using only the tree topology itself.
RESULTS
Plot survey data
Oak species: basal area vs. presence/absence.—Quercus
species showed significant phylogenetic overdispersion
when we compared pairwise co-occurrence values to
phylogenetic distances and used null models 1 or 3 with
basal area (Table 1, Fig. 2A). We obtained similar
results when using presence/absence data, rather than
basal area, and applying null models 2 or 3, although the
results were somewhat less statistically significant (Table
1). These results are very similar to previous analyses
(Cavender-Bares et al. 2004a), despite reduced sampling
and a less resolved phylogeny.
Increasing taxonomic inclusiveness.—The effect of
increasing the number of species in the community
analysis was examined with data sets that included
angiosperm trees and shrubs, all trees and shrubs, all
recorded angiosperm species, and all recorded plants
(Table 1). When angiosperm tree and shrub species were
included, we found no pattern to the data. This result
did not change significantly if we excluded oaks from the
analysis. However, the observed correlation coefficient
became more negative than 818 of the simulated r
values, up from only 650 (Table 1), indicating a shift
toward greater clustering. When examining all tree and
shrub species (122 species), again using basal area and
July 2006
SCALE AND FLORIDIAN PLANT COMMUNITIES
TABLE 1. Extended.
r
(expected)
Obs.Sim
0.014
0.004
0.024
0.019
0.025
0.006
0.009
0.010
0.005
0.009
0.009
0.008
0.014
0.005
P
Phylogenetic pattern
8
31
1
33
650
818
979
977
992
997
578
990
999
960
0.016
0.064
0.002
0.066
0.700
0.364
0.042
0.046
0.016
0.006
0.844
0.020
0.002
0.080
overdispersion
weak overdispersion
overdispersion
weak overdispersion
no pattern
no pattern
clustering
clustering
clustering
clustering
no pattern
clustering
clustering
weak clustering
0.013
0.028
0.014
0.424
0.135
0.005
0.029
0.062
0.005
0.005
0.038
0.005
0.026
45
1
9
254
465
943
109
34
175
999
999
998
979
0.090
0.002
0.018
0.508
0.930
0.114
0.218
0.068
0.350
0.002
0.002
0.004
0.042
weak overdispersion
overdispersion
overdispersion
no pattern
no pattern
no pattern
no pattern
weak overdispersion
no pattern
clustering
clustering
clustering
clustering
0.021
0.005
999
999
0.002
0.002
clustering
clustering
either null model 1 or 3, a clear pattern of phylogenetic
clustering emerged. A nearly identical result was obtained when presence/absence and null models 2 or 3 were
used. When all recorded plant species were examined
(141 species), the pattern of phylogenetic clustering
became even more significant using null model 1. All
recorded angiosperm species (130 species) showed no
pattern. However, upon removal of the 17 oak species
from the analysis, the angiosperms were also significantly clustered. We obtained very comparable results
for the two types of null models (community randomizations vs. phylogeny randomizations) (Table 1),
although we did not attempt an exhaustive comparison.
Community classification data
In general, similar results were obtained when
community classifications were used, rather than actual
plot survey data. Based on the community classification,
angiosperm trees and shrubs, as well as all trees and
shrubs, showed significant phylogenetic clustering. Oaks
still showed a pattern of phylogenetic overdispersion
S115
(Table 1, Fig. 2B). This pattern was marginally
significant if all communities were used in the analysis
and became highly significant if communities where oaks
did not occur were excluded from the analysis. One
difference that is apparent between the plot survey and
the community classification is that closely related oak
species show higher degrees of co-occurrence when the
community classification is used (Fig. 2A, B). This is not
surprising, given that defined community types necessarily include all species that regularly occur in them, and
they do not take into account spatial distances among
individuals or smaller scale environmental variation that
might occur within community types. Despite this
difference, the overall pattern of phylogenetic overdispersion in the oaks was clear in both analyses.
The hollies, although much less diverse, showed a
similar but only marginally significant pattern of overdispersion, based on null model 2 only (Fig. 2C), due in
large part to the non-co-occurrence of pairs of close
relatives, i.e., Ilex cassine and Ilex opaca, as well as Ilex
ambigua and Ilex decidua. Note, however, that the
overdispersion pattern was only apparent if community
types that did not include any Ilex species were excluded
from the analysis. The pattern was not significant using
null model 3. The pines, a much older lineage, showed a
somewhat different pattern (Fig. 2D). The two closest
relatives, Pinus palustris and Pinus taeda (subsection
Australes), do not co-occur, similar to the pattern in the
other two genera. However, at the other extreme,
distantly related pines also do not occur. Pinus clausa,
which is in a different subsection of the genus
(Contortae) from all of the other pines in the region
(section Australes), does not co-occur with any other
Pinus species.
Extending the analysis to communities for the entire
state of Florida, a clear pattern of phylogenetic
clustering emerged (Table 1). At this scale, which
includes coastal and subtropical communities, there is
an increase in the number and variety of community
types and the degree of environmental heterogeneity
that is encompassed. Correspondingly, the number of
taxa also increases. Acknowledging the incompleteness
of the species list, the number of plant species for the
entire state more than doubles that of the north central
Florida communities. The large spatial scale and high
environmental heterogeneity, along with the greater
inclusiveness of taxa, leads to a highly significant pattern
of phylogenetic clustering.
Trait evolution and trait similarity within communities
Analysis of trait evolution for the leaf data and life
history attributes for the 120-species data set from the
plot survey showed that all traits examined were
significantly conserved (Fig. 3A). A subset of these
traits also showed high similarity within communities
(clustering), including specific leaf area for both sun and
shade leaves, leaf habit, maximum height, leaf perimeter-to-area ratio, and cotyledon type.
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JEANNINE CAVENDER-BARES ET AL.
Ecology Special Issue
FIG. 2. Results of tests for phylogenetic overdispersion or clustering within three lineages in north central Florida. (A) There is
a higher than expected correlation between pairwise co-occurrence (based on measurements of basal area of species within 55
0.10-ha plots) and phylogenetic distance (measured in millions of years [Ma], based on fossil records) for oak species. Closely
related species tend not to co-occur, whereas species from different lineages do tend to co-occur. (B) A similar pattern emerges when
the analysis is based on presence/absence of oaks in defined community types (based on community classification data) within six
state parks in the same region. The significance of the depicted relationship is dependent on whether the null model only includes
communities in which oaks occur, or whether all defined terrestrial communities found in the parks are included. (C) A similar, but
less significant pattern is found for the less speciose Ilex genus. (D) The Pinus genus shows a different pattern. Note that in all
graphs multiple data points may be superimposed.
In contrast, when the same traits were examined only
for the oaks, many fewer traits showed conservatism
(Fig. 3B). Conserved traits included leaf mass, leaf area,
leaf habit, seed mass, and cotyledon type. None of these
traits showed high similarity within communities. Other
traits showed a lack of conservatism, which can also be
interpreted as various degrees of convergence. Maximum height was the most convergent of the traits
examined in this study. The high convergence results
from the fact that in each of the major oak lineages,
including the red oaks, the white oaks, and the live oaks,
there exist both short and tall species. This trait also
showed very high similarity within communities. A
number of other traits were examined in a previous
study (Cavender-Bares et al. 2004a) and are indicated
with small, open circles in Fig. 3B. Relatively convergent
traits from this analysis included maximum hydraulic
conductivity, whole-shoot transpiration rate, vulnerability to cavitation during drought, absolute and relative
growth rates, ability to resprout from rhizomes, and
bark thickness. At the same time, traits that were fairly
conserved tended to show overdispersion (or low
similarity within communities), such as seed mass and
specific leaf area (particularly for shade leaves), or wood
density, acorn maturation time, and leaf lifespan from
the previous analysis. In both the present study and the
previous analysis, there was a remarkable absence of
conserved traits that showed clustering (or high similarity) within communities. This contrasts sharply with
the pattern seen for all woody plants (Fig. 3A). For the
oaks, trait similarity was highest for convergent traits
and lowest for conserved traits. These results indicate
that the pattern of phylogenetic overdispersion in oak
communities is generated by the combination of
environmental filtering of convergent traits and overdispersion of conserved traits.
DISCUSSION
It has long been recognized that plants can show a
high degree of evolutionary stasis (e.g., Li 1952, Wen
1999, Qian and Ricklefs 2004) and niche conservatism
(Webb et al. 2002, Ackerly 2003, Reich et al. 2003). At
the same time, evolutionary processes that cause differentiation of sister taxa, such as character displacement
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SCALE AND FLORIDIAN PLANT COMMUNITIES
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FIG. 3. Conservatism of traits in relation to trait similarity within communities for (A) all woody species and (B) oak species
only. Traits are ordered on the x- and y-axes by the ranking of the observed similarity or phylogenetic signal of each trait relative to
999 simulations in the null model (the ranking of the r values of observed data for trait similarity relative to simulated data
increases with overdispersion [vs. clustering], and the ranking of the observed phylogenetic signal relative to simulated data
decreases with conservatism [vs. convergence]). Traits include the following (labeled solid circles): leaf area, specific leaf area (SLA)sun, SLA-shade (SLA-sh), perimeter-to-area ratio (P/A), leaf habit, leaf mass, lobing, petiole length, cotyledon type, seed mass, and
height. Open circles represent a suite of other morphological, physiological, and life history traits, measured from trees in the field
as well as from seedlings in a common garden from a previous study (Cavender-Bares et al. 2004). The contrasting patterns of trait
evolution and community similarity for the whole woody plant community and the oak lineage help explain the contrasting
phylogenetic structures of communities observed at these different taxonomic scales.
(e.g., Schluter 2000) and adaptive radiation (e.g.,
Givnish et al. 2000) are well documented. To the extent
that evolutionary stasis predominates, close relatives are
likely to occur in similar habitats, given that plants will
track environments for which they are adapted (Ackerly
2003). The principle of competitive exclusion presents a
paradox, however, because close relatives that are too
similar cannot coexist (e.g., Elton 1946, MacArthur and
Levins 1967, Chesson 2000). In this study, we suggest a
solution for this paradox by showing that plant
communities can simultaneously exhibit niche conservatism and overdispersion of close relatives. The signal
that dominates depends on the scale at which communities and the taxa in them are examined.
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JEANNINE CAVENDER-BARES ET AL.
FIG. 4. The phylogenetic structure of communities is
dependent on the scale at which they are defined. Apparent
overdispersion within forest communities (A), for example, can
appear as clustering when grassland and wetland communities
are included (B). At the same time, phylogenetic structure also
depends on the phylogenetic scale or taxonomic inclusiveness.
Apparent overdispersion within a lineage is more likely to
appear as a dominant pattern of clustering when the analysis
includes many lineages.
Phylogenetic structure of communities at different
spatial and taxonomic scales
Both the spatial scale and resolution of species
abundance data influence the observed phylogenetic
structure in community assemblages. Greater overdispersion is detected at smaller spatial scales and with
higher resolution data. This is demonstrated by more
significant overdispersion when using basal area within
plots rather than presence/absence (Table 1). Basal area
provides higher resolution for species distributions, and
the extent to which they overlap within communities,
and hence could provide more precision for detecting
overdispersion. The difference in null model is apparently not a factor in this outcome, as the same results
were found using null model 3 (randomization of the
phylogeny instead of the communities; Table 1).
Phylogenetic overdispersion is more easily detected
among close relatives when the communities included
in the analysis are limited to only those in which the
focal lineage occurs (Table 1). This effectively reduces
the spatial area and environmental heterogeneity examined. These results make clear that phylogenetic overdispersion can occur at small spatial scales, even while
Ecology Special Issue
clustering occurs at larger spatial scales when a greater
range of community types is included (Fig. 4).
The phylogenetic structure of communities depends
even more strikingly on the phylogenetic scale, or inclusiveness of taxa. Communities within a region may appear overdispersed at one phylogenetic scale (e.g., within
one lineage), but clustered at a broader phylogenetic scale
(e.g., when including all seed plants in the community).
This allows a reconciliation of apparently contradictory
results, as both niche conservatism and species interactions are important forces in community assembly, but
are dominant at different scales. Species interactions that
give rise to phylogenetic overdispersion show a stronger
signal when communities are delimited to include a single
monophyletic lineage. Niche conservatism shows a
stronger signal the more broadly a community is defined
taxonomically. Speciose lineages, such as the oaks, may
be more likely to show overdispersion than less speciose
lineages if, for example, increased diversity leads to
increased competition among closely related species.
Phylogenetic patterns are also likely to be lineagespecific, and it is not known the extent to which the oaks
represent an exceptional scenario. Hence, the specific
results found here might not be generalizable to other
systems (see, for example, Kembel and Hubbell 2006).
Nevertheless, the study does demonstrate the potential of
phylogenetic patterns to vary predictably with scale and
demonstrates that both phylogenetic overdispersion and
clustering can occur at different spatial and taxonomic
scales in the same study system.
Phylogenetic structure within single lineage communities
Phylogenetic dispersion patterns of species in communities that are narrowly defined taxonomically appear
to be lineage specific, and may depend on intrinsic
properties of lineages, biogeographic history, and
lineage diversity. One possibility is that phylogenetic
overdispersion might be more likely in speciose lineages
due to intensification of species interactions. In addition,
overdispersion might be more likely in lineages that have
undergone adaptive radiations more or less in situ, as
the evolutionary process should result in close relatives
occupying different habitats as well as high local
diversity. The American oaks are a likely example of
such a radiation. Oaks are believed to have reached the
Americas from Eurasia ;40 million years ago and are
subsequently thought to have undergone a rapid
radiation with all of the major subgenera appearing in
the fossil record by 35 million years ago (Crepet and
Nixon 1989). Many of the southeastern oaks are
endemic to the region, and biogeographic evidence
suggests that diversification of the oaks in the Americas
occurred in their current localities (Manos et al. 1999,
Manos and Stanford 2001). The Florida peninsula was
not connected with North America and was submerged
under the ocean until 20 million years ago (Webb 1990).
The colonization of Florida, therefore, occurred subsequent to this time period. Fossil records indicate that
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SCALE AND FLORIDIAN PLANT COMMUNITIES
the flora was originally tropical, but increasingly
invaded by deciduous hammock communities, similar
to those of today, starting about 17 million years ago as
the climate cooled. The availability of new habitats for
colonization might have facilitated rapid adaptation,
possibly aided by promiscuous exchange of genetic
material among species. The present-day overdispersion
of oaks (Table 1; Mohler 1990, Cavender-Bares et al.
2004a) could thus be the result of their evolutionary
history of adaptive radiation into novel habitats.
The hollies show some degree of overdispersion
(Fig. 3C) under null model 2, although this pattern is
not significant under null model 3, and hence the signal is
somewhat ambiguous. Ilex diversified in the Eocene, ;50
million years ago and was already dispersed over all of
the major continents by that time (Cuenoud et al. 2000).
These small-seeded, bird-dispersed species arrived in
Florida over the course of the past 20 million years, but
for the most part evolved before then on other continents
in other contexts. With the exception of two pairs, Ilex
cassine and Ilex myrtifolia, and Ilex decidua and Ilex
ambigua, speciation did not occur in the context of the
other hollies that currently inhabit Florida, and currently
sympatric congeners could have large intervening branch
length distances (Cuenoud et al. 2000). The lack of cooccurrence between the closely related members in each
pair gives rise to the apparent trend of overdispersion
under null model 2 (Fig. 3C). The relatively weak signal,
however, might result from a very different biogeographic history of the hollies or, alternatively, from the
fact that the hollies are much less speciose than the oaks,
particularly in this study region.
The pines show a hump-shaped phylogenetic pattern,
in which only pines of intermediate relatedness co-occur
(Fig. 3D). It has previously been noted for the pines in
Florida that sister species do not co-occur and that cooccurring species are not closely related (Adams and
Jackson 1997). Hence, some degree of phylogenetic
overdispersion has been observed within the genus, even
though we did not detect it in north central Florida. The
lack of clear phylogenetic signal in pine communities
might result from the lower diversity of the pines relative
to the oaks, causing congeneric competition to be less
important among the pines.
The strong signal of overdispersion in the oaks might
result from their history of adaptive radiation and their
high local diversity. Additional studies are needed to
determine (1) the extent to which overdispersion among
close relatives depends on historical and lineage-specific
factors and (2) the extent to which local diversity within
lineages might influence the intensity of species interactions that give rise to phylogenetic overdispersion in
communities.
Trait evolution and community assembly
Nonneutral ecological processes that influence community assembly act on the phenotypes of species.
Therefore, the phylogenetic structure of communities
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ultimately depends on (1) the evolutionary history of
species’ traits and (2) the extent to which traits influence
species distributions across environmental gradients or
prevent coexistence due to species interactions. Closely
related species are likely to co-occur if traits important
for environmental filtering are conserved. On the other
hand, if traits important for environmental filtering are
convergent, or many different strategies for existing in a
given habitat are possible, then closely related species
might not co-occur. Likewise, if similarity in particular
traits prevent coexistence, and such traits are conserved,
then closely related species are again unlikely to co-occur.
Of course, if species distributions are not related to
their phenotypes, as predicted by neutral models (Hubbell 2001), then phylogenetic patterns in community
structure are unlikely to emerge (Kembel and Hubbell
2006). However, lack of clear phylogenetic patterns
might also result from species interactions and environmental filtering operating in opposing directions. In this
study, for example, phylogenetic overdispersion of oaks
apparently masks the pattern of phylogenetic clustering
that predominates among angiosperm lineages. When
oaks are removed from the analysis, the clustering
pattern becomes apparent (Table 1).
Among species in the broadly defined Floridian
communities, all traits examined showed a fairly high
degree of conservatism (Fig. 3A). A subset of these traits
showed high similarity within communities (clustering),
including specific leaf area for both sun and shade
leaves, leaf habit, maximum height, leaf perimeter-toarea ratio, and cotyledon type. These traits have
generally been shown to be important in the ability of
species to respond to abiotic stress factors and to
influence species distributions across environmental
gradients, critical evidence for their potential role in
environmental filtering. For example, specific leaf area
and leaf habit, a proxy for leaf lifespan, are both
important in the carbon economy of plants (e.g.,
Kikuzawa 1991, Damesin et al. 1998, Kikuzawa and
Ackerly 1999) and have been well documented to vary
with soil nutrient availability at various spatial scales
(Monk 1966, Reich et al. 1999, Wright et al. 2002, Reich
et al. 2003). Specific leaf area is also likely to be
associated with canopy openness and soil moisture
availability (Wright et al. 2002). Maximum height has
been linked to growth rate (Thomas 1996), and taller
height is a competitive strategy for accessing light in
productive environments (Tilman 1988). In lower
productivity environments or in communities prone to
burning, less investment in aboveground biomass is
expected (Schwilk and Ackerly 2001, Cavender-Bares et
al. 2004b). Leaf perimeter-to-area ratio has been
correlated to hydraulic conductance (Sack et al. 2003)
and is likely to be important in distribution patterns
across soil moisture gradients (Brodribb and Holbrook
2003). The primary role of cotyledons as either storage
organs or photosynthetic organs represents a trade-off
in regeneration strategies that depend on resource
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JEANNINE CAVENDER-BARES ET AL.
availability (Kitajima and Fenner 2000). The previously
established linkages to resource capture and use for all
of the traits that show high similarity (clustering) within
communities support the interpretation that these traits
are likely to be important for habitat tracking and
environmental filtering. Clustering of conserved traits is
critical in explaining the emergent pattern of phylogenetic clustering in Floridian plant communities.
Other traits, including leaf mass, leaf area, lobedness,
petiole length, and seed mass showed low similarity
within communities. These traits might be less adaptive
to environmental factors, or at least to those that vary at
the same scale as the plot survey, and might be
associated with architectural constraints. Alternatively,
there could be multiple trait strategies that are successful
in a given community. For example, leaf size and leaf
lobing have been linked to boundary layer conductance
and heat load (Givnish 1976), which could be important
in environmental filtering. However, various combinations of leaf size, shape, leaf angle, leaf display, and
pubescence can achieve a similar energy balance
(Lambers et al. 1998, Ackerly 1999). Leaf size, seed
size, and petiole length might also be more related to
plant architecture than to external environmental
gradients (e.g., Mazer 1989, Lord et al. 1995, Moles et
al. 2005), and these traits have not generally shown
strong trait–environment correlations.
While all the traits measured in this study were fairly
conserved in the broad analysis, many of the same traits
tended to be convergent, or not different from random
expectation, within the oak genus (Fig. 3B). For
example, maximum height, the perimeter-to-area relationship, and specific leaf area were more convergent
within the oaks than among the larger species pool. This
result is not surprising if trait variance among lineages is
higher than the variance within a lineage (Felsenstein
1985, Harvey and Pagel 1991). In other words, even if
sister taxa show divergent morphology, recent common
ancestry of closely related species is likely to limit the
amount of divergence within a lineage, relative to that
found among distantly related lineages. This could
explain the shift to conservatism at broader taxonomic
scales. Specific leaf area, a fairly labile trait within the
oaks, for example, varied roughly threefold within the
genus, but exceeded fivefold among all species sampled.
However, in a study of almost 13 000 seed plants, Moles
et al. (2005) showed that higher divergences sometimes
occurred within a family than between families. A
number of genera, particularly speciose genera in which
many members co-occur, have also been shown to have
divergences within them equal to or greater than those
between co-occurring species that are distantly related
(Silvertown et al. 2006). Therefore, the shift toward trait
conservatism at the broader phylogenetic scale can also
be explained as a result of swamping out the signal of
high trait lability within the oaks by the addition of
many more taxa that have conserved traits. Maximum
height, for example, shows as much variation within the
Ecology Special Issue
oaks (;60-fold difference) as it does across all species
examined at the broader scale, but this level of variation
within a clade is unique to the oaks in our study. The
shift in patterns of trait evolution toward increasing
conservatism at broader taxonomic scales is likely to
explain the concomitant shift in the phylogenetic
structure of communities toward clustering. It also lends
support to the view that niche conservatism is widespread among plants (Wen 1999, Webb et al. 2002,
Ackerly 2003, Qian and Ricklefs 2004).
The oaks, which dominate many woody communities
in north central Florida, appear to represent a special
case in this study system. They show an unusual amount
of lability in certain functional traits, such as maximum
height, water transport capacity, growth rate, and the
ability to resprout from rhizomes (Cavender-Bares et al.
2004a), traits important for habitat specialization
(Cavender-Bares et al. 2004b). The observed phylogenetic overdispersion among the Floridian oaks could be
a result of their history of adaptive radiation and might
be maintained by reduced competition among cooccurring species of different subgenera or lower
density-dependent mortality (Cavender-Bares et al.
2004a). The latter possibility has been hypothesized as
a result of pathogen specificity at taxonomic levels above
the species (Janzen 1970, Webb and Gilbert 2006). At
larger taxonomic and spatial scales, however, Floridian
plant communities show phylogenetic clustering. This
can be accounted for by conservatism of functional
traits that influence species distributions. These contrasting patterns that emerge within the same study
system illustrate the importance of scale in detecting
opposing ecological and evolutionary forces.
ACKNOWLEDGMENTS
J. Cavender-Bares would like to thank Cam Webb and
Jonothan Losos for the invitation to participate in this special
issue. We thank Kelly McPhearson and Anne Barkdoll at the
District 2 Park Service for providing species lists and
community data maps for the six state parks in north central
Florida. Ginger Morgan is acknowledged for facilitating
permits. George Otto, Tony Davanzo, and Michael Stevens
are gratefully acknowledged for resampling 32 of the permanent
plots. Elizabeth Salisbury collected and sent to us all of the
leaves for the leaf trait analysis, and Marjorie May Haight
helped scan the leaves. We thank Doug Hornbeck, Sue Mauk,
Kent Cavender-Bares, David Ackerly, Jason Teisinger, Hannah
Nendick-Mason, Erik Smith, and Park Manager Randy Brown
for logistical and other support or assistance with various
aspects of the work in Florida. Anurag Agrawal and two
anonymous reviewers are gratefully acknowledged for valuable
comments on the manuscript.
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APPENDIX A
Species used in each analysis (Ecological Archives E087-114-A1).
APPENDIX B
References for phylogenetic data (Ecological Archives E087-114-A2).
APPENDIX C
Phylogenetic trees used in each analysis (Ecological Archives E087-114-A3).
Ecology, 87(7) Supplement, 2006, pp. S123–S131
Ó 2006 by the Ecological Society of America
PHYLODIVERSITY-DEPENDENT SEEDLING MORTALITY, SIZE
STRUCTURE, AND DISEASE IN A BORNEAN RAIN FOREST
CAMPBELL O. WEBB,1,3 GREGORY S. GILBERT,2
AND
MICHAEL J. DONOGHUE1
1
Department of Ecology and Evolutionary Biology, Yale University, New Haven, Connecticut 06520 USA
2
Environmental Studies Department, University of California-Santa Cruz, California 95064 USA
Abstract. Density-dependent models that partition neighbors into conspecifics and
heterospecifics ignore the great variation in effect of heterospecifics on focal plants. Both
evolutionary theory and empirical results suggest that the negative effect of other plants on a
focal plant should be higher for closely related neighbors than for less related neighbors. Using
community-wide seedling mortality data from a forest where density dependence has
previously been found, we searched for significant phylogenetic neighborhood effects (the
‘‘phylodiversity’’ neighborhood) on seedling (,50 cm tall) survival at various spatial scales.
Logistic regression models were used, with 19-mo survival of individual seedlings as the
response.
We found a significant positive effect of nearest taxon phylodiversity on seedling survival at
the 36-m2 scale and the 4-m2 scale, indicating that seedling survival is enhanced by being in a
neighborhood where heterospecifics are not closely related. At all scales there was a strong
negative effect of conspecific seedling density on focal survival, and at small scales there was
also an effect of heterospecific density, indicating generalized competition. We place these
results (for seedling dynamics over a relatively short period of time) in the context of changes
in phylodiversity between different size classes of plants in the same forest, which integrate the
effects of dynamics of all size classes over long time periods. At the 36-m2 scale, there was an
increase in nearest taxon phylodiversity (i.e., a decrease in phylogenetic clustering) from the
seedlings (,50 cm tall) to the poles (1–5 cm diameter), consistent with the positive effect of
local phylodiversity on seedling survival. In contrast, there was a marked decrease in average
phylodiversity from seedlings to saplings at the same scale. The trees in the 1600 m2
surrounding the seedling plots had much lower phylodiversity than either the seedlings or
saplings.
Taken together, these results suggest that (1) over short time and spatial scales, local
seedling phylodiversity has a positive effect on seedling survival, possibly via interaction with
pathogens (which we discuss in detail), but (2) over longer time periods and larger spatial
scales the effect of abiotic-related mortality results in habitat filtering for phylogenetically
conserved traits.
Key words: Borneo; community phylogenetic structure; density dependence; phylodiversity; plant
pathogen infection; seedlings.
INTRODUCTION
In both theoretical and empirical studies, intraspecific
negative density dependence has been shown to be
important for slowing competitive exclusion and maintaining diversity in rain forest trees (Wright 2002). Most
analyses focus on the statistical effects of conspecific
density on either focal-plant performance or species
demographic parameters (Martinez-Ramos et al. 1988,
Condit et al. 1994, Harms et al. 2000). In some cases, the
effect of distance from focal plant to conspecifics has been
used as a correlate of plant density (Hubbell 1980, Connell
et al. 1984, Hubbell et al. 1990, Gilbert et al. 1994).
Manuscript received 26 January 2005; revised 18 July 2005;
accepted 21 July 2005; final version received 9 September 2005.
Corresponding Editor: A. A. Agrawal. For reprints of this
Special Issue, see footnote 1, p. S1.
3
Present affiliation: Arnold Arboretum of Harvard University. E-mail: [email protected]
The strength of conspecific density (or interplant
distance), relative to the effect of overall competitive
pressure, can be measured by including the density (or
distance) of heterospecifics as a separate factor in
models (Connell et al. 1984, Uriarte et al. 2004).
However, simply dividing species into conspecifics and
heterospecifics obscures the great variation in effects of
different species on any focal species (Pacala et al. 1996).
One solution is to give separate ‘‘competition’’ parameters to each pairwise interaction (Canham et al. 2004,
2006). Unfortunately, a great deal of data is required to
fit a model with so many free parameters. If the effect of
one species on another was random with respect to the
plants’ phenotypes, there would be no solution other
than this multiparameter modeling. However, interspecific interactions are influenced by the anatomical and
physiological similarity of the species involved, and
gross similarity is not distributed at random among
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CAMPBELL O. WEBB ET AL.
Ecology Special Issue
PLATE 1. The Air Putih River as it passes through granite hill forest at the Cabang Panti study area, Gunung Palung National
Park, Indonesia. Photo Credit: C. Webb.
organisms; it is generally highest among taxa that share
a recent ancestor (Felsenstein 1985, Harvey and Pagel
1991). In general, we expect greater negative interactions
among individuals that are more phenotypically similar,
be they competing for a similar vector of resources or
negatively affecting each other via shared seed predators. Thus, we expect that closely related taxa should
have a greater negative influence on each other than taxa
that are more distantly related. Indeed, Uriarte and
colleagues (2004) recently analyzed neighbor-dependent
sapling growth at Barro Colorado Island and found that
the negative effect of a neighbor on a target was greater
when both plants were in the same taxonomic family.
Adding a relatedness parameter to models of density
dependence might therefore increase the predictive
power of these models significantly, at the expense of
just a few degrees of freedom.
The development of phylogenetic methods over the
past few decades has exposed the nonequivalence of taxa
assigned to the same traditional rank. For example, in a
recent supertree study of the relationships among
angiosperms (Wikström et al. 2001), the estimated
minimum age of family clades varied over an order of
magnitude (e.g., 108 Ma for the Aristolochiaceae, 9 Ma
for the Rhizophoraceae). Additionally, higher taxonomic groups defined using morphological (e.g., floral)
characters might have little ecological coherence; either
more- or less-inclusive clades than named-rank clades
might be more ecologically meaningful classes. With the
great progress made recently in resolving the phylogenetic relationships among organisms, we can now move
beyond ranks (such as family and genus) to use
phylogenetic distances (e.g., age) among taxa. In this
paper, we include such a phylogenetic distance factor in
the analysis of community seedling mortality in lowland
rain forest at Gunung Palung in Indonesian Borneo (see
Plate 1; Webb and Peart 2000). At this site, densitydependent seedling mortality has previously been found
both in single species (Webb and Peart 1999, Blundell
and Peart 2004) and as a community-wide compensatory
trend (Webb and Peart 1999).
We take two approaches toward testing the contribution of the phylogenetic relatedness structure of
neighbors (hereafter the neighborhood ‘‘phylodiversity’’;
cf. Faith 1992) to seedling dynamics. First, we examine
its effect on the survival of seedlings in small quadrats in
multifactorial models that also include the effect of
conspecific density and heterospecific density. The
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PHILODIVERSITY AND SEEDLING DYNAMICS
period chosen for the analysis is the same as that for
which community-wide density dependence was found
(Webb and Peart 1999). Second, we analyze changes in
local phylogenetic structure with plant size class. Any
influence of phylodiversity on seedling survival (a
dynamic measure) should eventually leave an imprint
on the phylogenetic structure of surviving plants (a static
pattern). If, for example, mortality is higher in species
that are sharing a neighborhood with closely related
species than in species that are phylogenetically isolated
from their neighbors, then this should be observed as an
increase in phylodiversity (and a decrease in net
relatedness) of plants of increasing size. Alternatively,
if habitat filtering leads to strong phenotypic and
phylogenetic attraction (Webb et al. 2002, CavenderBares et al. 2004), the plants in increasingly taller size
classes should show decreased phylodiversity.
We thus ask the following questions: (1) Does adding
a phylogenetic neighborhood (phylodiversity) term
increase the fit of density-driven models of seedling
mortality? (2) Does the influence of neighborhood
phylodiversity vary with neighborhood scale? (3) Are
changes in the (static) phylogenetic structure with
increasing size classes consistent with phylodiversitydependent seedling mortality?
Finally, we develop a mechanistic hypothesis for the
mediation of these phylodiversity effects by pathogens,
the most likely causal agents of density dependence in
these forests.
METHODS
In 1993, Webb set up 28 36-m2 seedling plots in
lowland rain forest in the Gunung Palung National
Park, West Kalimantan (Indonesian Borneo; site location: 1.21258 S, 110.10788 E; see Plate 1). The height of
all woody plants was measured for plants taller than 5
cm and ,1.0 cm dbh (diameter at breast height [1.3 m
high]), and dbh was measured for plants 1.0–5.0 cm dbh.
Each seedling plot was nested inside a 40 3 40 m tree
plot in which all trees .10.0 cm dbh were measured and
identified (see Webb and Peart [1999, 2000] for further
details).
We constructed a single phylogeny that included all
plant species occurring in any of the plots. We first ran
the species list through the online tool Phylomatic
(Chazdon et al. 2003, Webb and Donoghue 2005) to
produce a tree topology based on the Angiosperm
Phylogeny Group (APG) II backbone (APG 2003; P. F.
Stevens, Angiosperm Phylogency Website, version 6,
(available online),4 using the Phylomatic reference
megatree R20040402. We then used the BLADJ
algorithm of Phylocom (Webb et al. 2004, Moles et al.
2005) to constrain the internal nodes of the tree to the
age estimates of Wikström et al. (2001). The algorithm
then interpolates the other nodes of the tree for which
4
hhttp://www.mobot.org/MOBOT/research/APwebi
S125
direct age estimates are not available. Because many of
the genera in the plot have yet to be sampled in a
phylogenetic study, many of the families were modeled
in the final supertree as a polytomy, and all the genera
were polytomies. This means that the significant
phylogenetic results reported here are not dependent
on, for example, phylogenetic conservatism in pathogen
host use only among sibling species. Instead, they must
reflect patterns of conservatism that extend at least to
the root of genera nodes, and possibly beyond. It is
likely that some ecologically relevant characters are even
conserved at family nodes and above.
For the analysis of seedling dynamics, we used
measures of survival of all individuals of all seedling
species (5–50 cm tall) over a 27-month period from
March 1994, in a subset of 12 of the plots (n ¼ 2296
seedlings total). This subset included only plots with
completed seedling identification as of November 1994,
and was a sample of all 28 plots stratified by elevation
and subhabitat. Individual plants were mapped to a
0.25-m2 quadrat within the 36-m2 seedling plot, allowing
analysis in nested square neighborhoods of 0.25, 1, 4,
and 36 m2 (the design of the seedling plots included two
1-m walkways, which precluded using neighborhoods
between 4 m2 and 36 m2; see plot diagram in Webb and
Peart [1999]). We first counted the total seedling number
per quadrat (for each sized quadrat: 0.25, 1, 4, and 36
m 2) at the beginning of the census period, and
determined whether each individual was alive or dead
at the end of 27 months. We also calculated the number
of conspecific individuals and the relative phylodiversity
of each species in the quadrat. Phylogenetic diversity
(Faith 1992), or phylodiversity, is negatively related to
the extent of phylogenetic clustering in a sample (Webb
2000, Webb et al. 2002, Cavender-Bares et al. 2004), and
positively related to measures of phylogenetic distance
among taxa in a sample. We measured relative
phylodiversity in two ways, (1) using the mean
phylogenetic distance (in units of millions of years)
from the species of the focal individual to all other (n –
1) species in the quadrat, and (2) using the minimum
phylogenetic distance to any heterospecific species in the
quadrat (i.e., to the nearest taxon, or taxa, where several
were equidistant). Both distances were then standardized
by the mean expected phylogenetic distance, given the
number of species in the quadrat, in order to correct for
the effect of sample species richness; details of the same
standardization for the indices of phylogenetic clustering, net relatedness index (NRI) and nearest taxon index
(NTI), are given in Webb et al. (2002). We refer to the
resulting metrics as relative average phylodiversity,
APd 0 , and relative nearest taxon phylodiversity, NTPd 0
(the prime indicates that the metric is relative to the
identity of focal species; absolute APd and NTPd are
used in analysis of size class; and ‘‘Pd’’ is used to
differentiate from Faith’s [1992] ‘‘PD,’’ which is
measured differently). Phylodiversity is oriented here
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CAMPBELL O. WEBB ET AL.
so that larger positive values of phylodiversity indicate
communities whose species are less closely related.
We modeled survival of each seedling (categorical
data: lived vs. died) using four separate logistic
regressions: (1) survival as a function of the total
number of seedlings in the same quadrat, (2) survival
as a function of the number of conspecifics in the same
quadrat and the number of heterospecifics in the same
quadrat, (3) survival as a function of the number of
conspecifics in the same quadrat, and (4) survival as a
function of the number of conspecifics in the same
quadrat, number of heterospecifics in the same quadrat,
and relative phylodiversity of the community in the
same quadrat. The total number of species in the
quadrat was included in preliminary analyses, but was
never a significant effect, and was dropped from final
models.
For the static size class analysis, we used identity and
height for all species alive in June 1996 in the full set of
28 seedling and tree plots. Based on plant size at that
time, we assigned plants to four size classes: seedlings (0–
50 cm tall), saplings (50 cm tall to 1 cm dbh), poles (1–5
cm dbh), and trees (.10 cm dbh). A total of 389 species
were recorded as seedlings, 277 as saplings, 161 as poles,
and 325 species as trees. We drew up a species list for
each size class in each of the 28 plot locations, and this
was passed through Phylocom (Webb et al. 2004) to
calculate the net relatedness index and the nearest taxon
index (Webb et al. 2002) of these species lists on the
phylogeny of the whole pool of 548 species (see the
Supplement). We recognize that not all taxa in the pool
can occur in every size class (e.g., shrub and liana taxa
will not be found in the tree plot samples), but we use a
common phylogeny because measures of phylogenetic
structure (NRI, NTI) are directly comparable only
within a common pool phylogeny. We do not attempt
to test whether the indices are different from zero
(indicating either phylogenetic clustering or evenness;
Webb 2000), because the null model of random
sampling from the pool would be inappropriate. Because
we are using estimates of absolute age between taxa, the
effect of taxon sampling on phylogenetic distance is
largely removed.
The indices NRI and NTI were multiplied by negative
one to obtain measures of phylodiversity (average, APd,
and nearest taxon, NTPd, respectively), directly comparable to those used in the analysis we have described
of seedling mortality. Median values of phylodiversity
for the 28 plots were compared across size classes. We
conducted all analyses for both subprojects using the
statistical language R (R Project 2004) and Phylocom
(Webb et al. 2004).
RESULTS
Phylodiversity and seedling survival
A summary of the variation in numbers of individuals
and species follows: for quadrats of 36, 4, 1, and 0.25 m2,
respectively, the number of individuals per quadrat
Ecology Special Issue
(mean 6 SE) was 209 6 22, 48.4 6 4.4, 12.3 6 0.63, and
3.66 6 0.11; the number of species per quadrat was 48.0
6 3.5, 18.8 6 1.1, 7.46 6 0.29, and 2.88 6 0.072; the
mean number of APG family clades per quadrat was
23.1 6 1.1, 12.5 6 0.52, 5.97 6 0.19, and 2.70 6 0.061;
the mean number of individuals per species per quadrat
was 4.36 6 0.41, 2.56 6 0.13, 1.65 6 0.045, and 1.27 6
0.018.
A total of 20 models were fit (Table 1), and, in all
models in which it was present as a factor, the
(logarithm of the) number of conspecific individuals
was the most predictive factor for seedling survival. At
quadrat sizes of 4 m2, seedling survival was strongly
negatively related to (the logarithm of) total seedling
density. Heterospecific (log of) seedling density was
negatively related to survival only at the smallest
quadrat size (0.25 m2), at which scale the effect of total
density was also strongest. Relative nearest taxon
phylodiversity (NTPd 0 ) was positively associated with
survival at the 36-m2 and 4-m2 scales. At both scales, the
addition of the NTPd 0 phylodiversity term provided the
best overall model fit, as measured by Akaike’s
Information Criterion (AIC). Relative average phylodiversity (APd 0 ) was negatively correlated with seedling
survival at the largest (36-m2) scale.
Change in phylodiversity with increasing size class
From seedlings to poles, average phylodiversity (APd)
decreased and nearest taxon phylodiversity (NTPd)
increased, while phylodiversity declined from seedlings
to trees for both measures (Fig. 1). The influence of the
four size classes on phylodiversity was significant overall
(Kruskall-Wallis rank-sum test; APd, v2 ¼ 24.1, df ¼ 3, P
¼ 2.3 3 105; NTPd, v2 ¼ 34.7, df ¼ 3, P ¼ 1.4 3 107),
and the following pairwise comparisons were significant
using Wilcoxon rank-sum tests: APd, seedling vs.
sapling (W ¼ 209, P ¼ 0.0023), seedling vs. pole (W ¼
257, P ¼ 0.026), seedling vs. tree (W ¼ 130, P ¼ 6.5 3
106), pole vs. tree (W ¼ 195, P ¼ 0.0010); NTPd,
seedling vs. tree (W ¼ 207, P ¼ 0.002), sapling vs. tree (W
¼ 131, P ¼ 7.2 3 106), pole vs. tree (W ¼ 31, P ¼ 9.2
31012). In a separate analysis, lists of seedling and
sapling species were made for the 252 quadrats of 4 m2:
both APd and NTPd decreased from seedlings to
saplings (APd, v2 ¼ 7.17, df ¼ 1, P ¼ 0.0073; NTPd, v2
¼ 10.3, df ¼ 1, P ¼ 0.0012).
DISCUSSION
Our analysis of community-dependent seedling survival suggests that at larger scales (4 m2 and 36 m2) there
is a significant beneficial effect of local phylodiversity
(NTPd): that is, the chance that a seedling survives
increases if surrounding plants are not closely related to
it, even after the effects of conspecific and heterospecific
density have been removed. There was no such effect at
smaller scales. At the same scale of 36 m2, we also
observed an increase in phylodiversity from the seedling
to the sapling to the pole size classes, when measured as
July 2006
PHILODIVERSITY AND SEEDLING DYNAMICS
S127
TABLE 1. Effect of local seedling density and phylodiversity on seedling survival, for four quadrat sizes. Five logistic models were
fit for each of the four quadrat sizes: 0.25, 1, 4, and 36 m2.
Significance of factor
Log
(total N)
Quadrat size and model
AIC
df
36 m2
Total density
Partitioned density
Conspecifics only
Complete (APd 0 )
Complete (NTPd 0 )
3138
3114
3118
3103
3101
2295
2294
2295
2293
2293
NS
4 m2
Total density
Partitioned density
Conspecifics only
Complete (APd 0 )
Complete (NTPd 0 )
3129
3114
3112
3113
3111
2295
2294
2295
2293
2293
0.21**
1 m2
Total density
Partitioned density
Conspecifics only
Complete (APd 0 )
Complete (NTPd 0 )
3123
3109
3107
3110
3110
2291
2290
2291
2289
2291
0.20**
0.25 m2
Total density
Partitioned density
Conspecifics only
Complete (APd 0 )
Complete (NTPd 0 )
2838
2834
2838
2835
2837
2093
2092
2093
2827
2091
0.34**
Log
(conspecific N)
Log
(heterospecific N)
0.11***
0.13***
0.15***
0.085**
þ0.28*
þ0.25*
þ0.25*
0.17***
0.18***
0.19***
0.14**
NS
0.23***
0.24***
0.25***
0.21**
NS
0.31***
0.33***
0.33***
0.33**
0.18*
NS
Phylodiversity
(APd 0 )
0.25*
þ0.22**
NS
þ0.13**
NS
NS
NS
NS
0.18*
0.17*
Phylodiversity
(NTPd 0 )
NS
NS
NS
Notes: Survival is measured as a positive response, so a negative parameter estimate indicates a negative relationship of the
factor with seedling survival. Phylodiversity increases with decreasing relatedness of the focal taxon to the other taxa in the sample.
Akaike’s Information Criterion (AIC) measures the complexity of the fitted model; a lower value indicates a better fit of the model
to the data. Key to abbreviations: APd 0 , relative average phylodiversity; NTPd 0 , relative nearest taxon phylodiversity.
*P ¼ 0.05; **P ¼ 0.01; ***P ¼ 0.001.
nearest taxon phylodiversity, NTPd. These two results
are consistent with each other: if survival is higher for
seedlings that are more distantly related, then as the
cohort ages the overall net relatedness should decrease,
as closely related taxa are ‘‘weeded out.’’ However, a
contrasting pattern is observed in the measure of average
phylodiversity: at the 36-m2 scale, there is an apparent
detrimental effect of APd on seedling survival, and a
consistent decrease in APd phylodiversity is observed in
the static data, from seedlings to saplings, and from
seedlings to poles. We also observed that for both NTPd
and APd measures, there was a large decrease in
phylodiversity from seedlings to trees, i.e., tree species
were far more clustered phylogenetically than were
seedling species. Previous work showed trees in a plot
to be more phylogenetically clumped than expected by
chance (Webb 2000).
How can we reconcile these different results? When
comparing different size classes for the same area, we
must be aware that the different number of individuals in
the different size classes, and thus the different expected
number of species, could be leading to an artifactual
change in metrics. While this remains a possibility, we
feel it is not the cause of the patterns we observe, because
the standardization of the indices uses the expected value
of relatedness for a given n species (Webb et al. 2002),
which removes the main effect of sample species richness
on phylogenetic relatedness. Additionally, there was no
significant relationship of plot species richness with APd
or NTPd within any size class (8 tests, n ¼ 28 plots).
However, thorough simulation studies to explore the
behavior of these metrics are desirable and are underway
(S. Kembel, personal communication; N. Kraft, personal
communication).
Instead, we believe that the observed patterns result
from the different aspects of phylogenetic structure
captured by the two measures of phylodiversity. Imagine
that the seeds arriving at a particular site are a random
sample of all the plants in a forest, plants that occupy a
number of habitats. Imagine also that some large clades
of many taxa possess characters that will increase
survival in this particular habitat, while other clades
do not have these characters. Over time, there should be
a net increase in average relatedness of the survivors at a
site (i.e., a decrease in APd). However, if negative
interactions are highest among taxa that are very closely
related (sister species, or consectionals), there may
simultaneously be a reduction over time in the phylogenetic distance to the most closely related surviving
species (i.e., an increase in NTPd). All that is required
for these two apparently opposite changes to occur
simultaneously is that the characters for habitat filtering
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CAMPBELL O. WEBB ET AL.
Ecology Special Issue
become trees, nearest taxon phylodiversity (NTPd) must
also eventually decrease, as the only survivors will
belong to numerous ‘‘clumps’’ of related taxa. While this
explanation surely oversimplifies the phylogenetic distribution of characters necessary for survival, we believe
it helps explain the observed patterns. The large decrease
of average and nearest taxon phylodiversity in trees is
expected because of both the larger spatial scale of the
tree plots and the length of time over which processes
have operated as seedlings grow to trees. During this
time, the effects of habitat filtering have dominated
change in both measures of phylodiversity. We note that
it is the combination of short-term dynamic data and
static data reflecting long-term outcomes that provides
these insights into community dynamics.
A framework for the influence of pathogens
on phylodiversity
FIG. 1. Change in phylodiversity with size class (seedlings,
0–50 cm tall; saplings, 50 cm tall to 1 cm dbh; poles, 1–5 cm
dbh; trees, .10 cm dbh) for 28 plots (36 m2 for seedlings to
poles; 0.16 ha for trees, hence separation by dashed vertical
line); box indicates median (heavy line) and quartiles, whiskers
reach to points 1.5 times the interquartile range. Units of
both APd and NTPd are one standard deviation of a random distribution of phylogenetic distance (Pd), zeroed to
the distribution’s mean. (a) Average phylodiversity (APd ¼
NRI), which becomes more positive with decreasing mean
relatedness among all pairs of taxa in the sample. (b) Nearest
taxon phylodiversity (NTPd ¼ –NTI, which is the mean of the
relatedness to the most closely related taxon to each taxon).
Abbreviations are as follows: NRI, net relatedness index; NTI,
nearest taxon index.
(Webb et al. 2002, Cavender-Bares et al. 2004) must be
plesiomorphic to a clade, whereas negative interactions
must occur among the most closely-related taxa within
the clade. For instance, if disease cross-species susceptibility is highest in sister taxa, while drought tolerance
important for long-term survival on ridges is shared
among many taxa in a phylogenetic family, we should
see a simultaneous increase in nearest taxon phylodiversity (NTPd) and a decrease in ‘‘deeper,’’ average
phylodiversity (APd). If only taxa with a particular
synapomorphy can survive in a particular place to
As at other tropical rain forest sites (e.g., Augspurger
1984), available evidence points to pathogens being the
primary cause of density-dependent mortality at Gunung Palung (Webb and Peart 1999). Pathogens have
been shown to strongly influence plant community
structure and diversity in a number of natural and
agricultural plant systems (see review by Gilbert [2002]).
To the extent that pathogens are more likely to crossinfect closely related hosts (Parker and Gilbert 2004),
negative interactions between plant species should be
strongest among very close relatives (Mack 1996). While
the data presented in this paper are phenomenological,
and do not point directly to pathogen influences, we use
pathogens as an example in developing a framework
that integrates the expected phylogenetic component of
density dependence that we have outlined with a
mechanistic, causal hypothesis. Invertebrate herbivores,
with a species richness comparable to pathogens, might
interact with plant phylodiversity in a similar way
(Weiblen et al. 2006).
Within a plant community, a plant pathogen species
can have a single species of host plant (‘‘monophagy’’) or
several (‘‘polyphagy’’). Monophagous pathogen–host
dynamics have been well explored and tend to result in
classic negative density-dependent effects in the plant
(Burdon and Chilvers 1982). This will be the case either
in a monoculture of the plant species or in a mixture
with nonhost plant species. However, the population
dynamics that result when pathogens can attack several
plant species in a community are more complicated, and
have been addressed mainly in the context of apparent
competition (Price et al. 1988, Alexander and Holt
1998). Here, an increase in numbers of one plant species
leads to an increase in pathogen inoculum, which could
then affect other susceptible plant species in the
community. In a tropical rain forest, the hundreds of
plant species present are exposed to thousands of
potential fungal pathogens, mostly unidentified (Arnold
et al. 2000, Gilbert et al. 2002). Some of the pathogens
are monophagous and some have many hosts (Lindblad
July 2006
PHILODIVERSITY AND SEEDLING DYNAMICS
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FIG. 2. Theoretical framework for the phylogenetic effects of pathogens. (a) Model of spread of single pathogen to other host
plants (i þ 1 to n) in the community. The output vector of probabilities of cross-infection is a function of the phylogenetic
relatedness of other taxa to the host plant (i), the degree of polyphagy in the pathogen, and the ‘‘ease’’ of host switching to unrelated
host plants. (b) Model of community-wide cross-infection by all pathogens. The output is a matrix of cross-infection probabilities,
declining from a high rate (H) in closely related taxa to a low rate (L) in unrelated taxa. The mean baseline rate (l) depends on the
mean ‘‘ease’’ of pathogen host switching to unrelated taxa, both historically, and with contemporary rapid evolution in ecotypes.
Note that the lower half of the matrix can differ from the upper half if the probability of infection from plant i to j is not equal to
the probability from j to i.
2000, Woolhouse et al. 2001, Gilbert et al. 2002).
Individual plant species might host dozens of species of
fungal symbionts (Frohlich and Hyde 1999, Arnold et
al. 2001), many with negative effects on the host (reviews
in Gilbert 2002, 2005). Host susceptibility to pathogens
will also be influenced in plastic ways by plant
abundance, phenological state, season, nutrient availability, and other factors which stress a plant.
Analyzing and understanding this complicated network of interactions and effects might be greatly
simplified by modeling pathogen host breadth as a
phylogenetic function (Webb et al. 2002; cf. Weiblen et
al. 2006). The barriers for a pathogen to infect a host
plant are morphological and biochemical. Moreover,
because both morphology and plant secondary chemistry are often conserved phylogenetically (Farrell and
Mitter 1993, Farrell 2001), a pathogen that can infect
one species can most easily become adapted to
phylogenetically closely related species. At the same
time, while phylogenetic conservatism of morphology
and biochemistry is pervasive in nature, so is parallel
evolution, or homoplasy, of similar characters in
unrelated lineages. Hence, pathogen adaptations to
infection barriers might also be effective in phylogenetically unrelated plants, which partially explains why
some pathogens can infect many unrelated plants in a
community (Weste and Marks 1987, Eckenwalder and
Heath 2001).
In general, the probability that a single pathogen can
infect a particular set of hosts in a plant community
should decline with increasing phylogenetic separation
among the hosts, but with some host-sharing across some
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CAMPBELL O. WEBB ET AL.
apparently random plant species as well (Fig. 2a). Ecological association, through consistent community cooccurrence of unrelated but common species, might
facilitate more distant host jumps. Considering the many
pathogen species likely to be present, we expect variation
in (1) the number of hosts that pathogens can attack, (2)
the slope of their phylogenetically determined drop-off
from closely related cohosts to less related nonhosts, and
(3) the extent of nonphylogenetic host ‘‘jumps.’’ Combining the variation in all these pathogens, we expect that the
net interspecific cross-infection probability will decline
with increasing phylogenetic distance, down to a low
baseline that represents the frequency of phylogenetically
unrelated host ‘‘jumping’’ (Fig. 2b, matrix element L).
In this way, pathogens, which are already known to
exert strong influences on plant survival in natural
systems, might operate in a phylodiversity-dependent
manner and might underlay the phylodiversity-dependent seedling survival observed at Gunung Palung. The
most powerful way to confirm the phylogenetic-distance-dependent signal in pathogen interactions will be
to experimentally cross-inoculate pathogens in replicated samples of both closely and distantly related taxa
and to record the success in transmitting disease
symptoms. The overall influence of pathogens might
be confirmed with pathogen exclusion (i.e., fungicide)
experiments.
Irrespective of their underlying mechanisms, phylodiversity-dependent dynamics have the potential to contribute to high overall species diversity, in a different
way than other density-dependent phenomena. Singlespecies density dependence has the potential to cap the
population size of species (Hubbell 1980) and, if present
as a community compensatory trend across all species
(Connell et al. 1984, Webb and Peart 1999, Wright
2002), will stabilize community composition and promote diversity by allowing rare immigrants to increase in
number. Diversity-dependent dynamics, if found (Wills
et al. 1997; but see critique in Wright [2002]), should
increase overall survival in diverse sites and thus provide
source or refuge populations that influence the metapopulation dynamics of species that would otherwise be
poor competitors. None of these density and diversity
dynamics reacts to or influences the specific identity of
taxa: in theory, all taxa are considered to have
equivalent influence or response. Phylodiversity-dependent dynamics, however, should operate differently,
influencing the taxonomic (or rather phylogenetic)
structure of the species composition of communities.
The survival of species will be higher if they occur with
unrelated taxa rather than with close relatives, and thus
phylodiverse communities should be promoted, independently of their absolute species number, as found in
this study. There has been much concern in the literature
for the need to actively conserve sites and communities
that have high phylogenetic diversity (e.g., Vane-Wright
et al. 1991, Faith 1992, Moritz and Faith 1998). If
phylodiversity-dependent dynamics operate in natural
Ecology Special Issue
systems, then the persistence and recovery of phylogenetic diversity might be assisted by nature itself.
ACKNOWLEDGMENTS
C. O. Webb thanks David Ackerly for informative discussions about phylogenetic structure and David Peart for
many discussions on the nature of density dependence. Karen
Garrett provided feedback in the development of the conceptual model. C. O. Webb was funded during analysis and
writing by a grant from the National Science Foundation
(DEB-0212873) and during the field work by a graduate
fellowship (GER-9253849) and a Small Grants for Exploratory
Research (SGER) grant to David Peart (DEB-9520889). We
thank the various branches of the Indonesian government that
permitted the fieldwork, and Darmawan and Suryadi, C. O.
Webb’s field assistants at Gunung Palung. The review comments of Mark Westoby and an anonymous reviewer are much
appreciated.
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SUPPLEMENT
Supertree phylogeny files and taxonomic list (Ecological Archives E087-115-S1).
Ecology, 87(7) Supplement, 2006, pp. S132–S149
Ó 2006 by the Ecological Society of America
PLANT DEFENSE SYNDROMES
ANURAG A. AGRAWAL1,3
1
AND
MARK FISHBEIN2,4
Department of Ecology and Evolutionary Biology and Department of Entomology, Cornell University, Ithaca, New York 14853 USA
2
Department of Biological Sciences, Mississippi State University, Mississippi State, Mississippi 39762 USA
Abstract. Given that a plant’s defensive strategy against herbivory is never likely to be a
single trait, we develop the concept of plant defense syndromes, where association with specific
ecological interactions can result in convergence on suites of covarying defensive traits.
Defense syndromes can be studied within communities of diverse plant species as well as
within clades of closely related species. In either case, theory predicts that plant defense traits
can consistently covary across species, due to shared evolutionary ancestry or due to adaptive
convergence.
We examined potential defense syndromes in 24 species of milkweeds (Asclepias spp.) in a
field experiment. Employing phylogenetically independent contrasts, we found few correlations between seven defensive traits, no bivariate trade-offs, and notable positive correlations
between trichome density and latex production, and between C:N ratio and leaf toughness. We
then used a hierarchical cluster analysis to produce a phenogram of defense trait similarity
among the 24 species. This analysis revealed three distinct clusters of species. The defense
syndromes of these species clusters are associated with either low nutritional quality or a
balance of higher nutritional quality coupled with physical or chemical defenses. The
phenogram based on defense traits was not congruent, however, with a molecular phylogeny
of the group, suggesting convergence on defense syndromes. Finally, we examined the
performance of monarch butterfly caterpillars on the 24 milkweed species in the field; monarch
growth and survival did not differ on plants in the three syndromes, although multiple
regression revealed that leaf trichomes and toughness significantly reduced caterpillar growth.
The discovery of convergent plant defense syndromes can be used as a framework to ask
questions about how abiotic environments, communities of herbivores, and biogeography are
associated with particular defense strategies of plants.
Key words: Asclepias; cardenolides; chemical ecology; cluster analysis; coevolution; Danaus plexippus;
herbivory; latex; milkweed; monarch butterfly; phylogenetically independent contrasts; phytochemistry;
plant–insect interactions.
INTRODUCTION
Understanding the macroevolution of adaptive traits
has inspired biologists for decades, yet has been
challenging to study (Schluter 2000). The difficulty lies
in (1) identifying the trait, or more commonly suites of
traits, responsible for the adaptation of interest, (2)
having adequate phylogenies to examine macroevolutionary patterns, (3) distinguishing apparent adaptation
from ‘‘random’’ evolution along a diversifying phylogeny, and (4) matching the origins of adaptations to
contemporaneous biotic and abiotic environmental
factors that have likely driven adaptive changes. Despite
these difficulties, the study of plant–herbivore interactions has contributed substantially to understanding the
macroevolution of adaptive traits. Research on the
macroevolution of plant defense has played a prominent
Manuscript received 21 January 2005; revised 9 May 2005;
accepted 21 June 2005; final version received 1 August 2005.
Corresponding Editor (ad hoc): C. O. Webb. For reprints of
this Special Issue, see footnote 1, p. S1.
3
E-mail: [email protected]
4
Present address: Department of Biology, Portland State
University, Portland, Oregon 97207 USA.
role in the development of ideas on both the ecology and
evolution of plants and insect herbivores, two of the
most diverse lineages of eukaryotes (Ehrlich and Raven
1964, Coley 1983, Farrell et al. 1991, Farrell and Mitter
1993, 1998, Becerra 1997, Berenbaum 2001). In this
paper, we present a new synthesis of ideas on the
macroevolution of plant defense traits, with an attempt
to identify the relative roles of phylogenetic history and
ecological variables in shaping the expression of suites of
defense traits within species. We then test some of our
proposed ideas and assumptions utilizing new data on
the phylogeny and defense of milkweeds (Asclepias
spp.).
Although it is convenient to consider plant defense as
a single trait, plants typically utilize a broad arsenal of
defensive traits against herbivores (Duffey and Stout
1996, Romeo et al. 1996). Even when a plant species is
apparently defended by a single type or class of defense
chemical, there are typically many specific forms of
those compounds (Berenbaum et al. 1986, Malcolm
1991, Bennett and Wallsgrove 1994, Becerra 1997).
Thus, it is more useful to think about plant defense as a
suite of traits, which might include aspects of a plant’s
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PLANT DEFENSE SYNDROMES
nutritional quality (e.g., proteins and antiproteins),
physical characteristics (e.g., spines, trichomes, and leaf
toughness), toxicity (e.g., cyanides and alkaloids),
phenology, regrowth capacity (i.e., tolerance), and
indirect defenses (e.g., volatiles and branching architecture). Synergistic interactions between multiple traits is
particularly important in potentially providing a greater
level of defense than would be possible if the traits were
present independently (Broadway and Duffey 1988,
Gunasena et al. 1988, Berenbaum et al. 1991, Stapley
1998).
Nonetheless, most attention historically has considered defenses as singleton strategies, with the typical
prediction that there should be trade-offs among different antiherbivore strategies (because they could be costly
and/or redundant) (Steward and Keeler 1988, Herms
and Mattson 1992). We argue that this reasoning, in its
simplest form, is inaccurate, because plants do simultaneously employ multiple defense traits. Of course,
particular plant defenses might trade off against each
other, but this should not be the a priori expectation for
any two defense-related traits. If plant defenses, like
most adaptations, are composed of multiple traits, they
might be organized into coadapted complexes (Dobzhansky 1970).
The categorization of an organism’s phenotype into
suites of potentially covarying traits has a long tradition
in biology, including the concepts of guilds and
syndromes (Root 1967, Grime 1977, Fægri and van
der Pijl 1979, Simberloff and Dayan 1991, Chapin et al.
1993, Cunningham et al. 1999, Fenster et al. 2004,
Wilson et al. 2004). The syndrome concept has been
rightly criticized as overly simplistic when applied
uncritically (e.g., Waser et al. 1996); however, suites of
covarying traits can be usefully employed in some cases
to infer adaptation to specific selective contexts. For
example, suites of traits associated with hummingbirdpollinated flowers (i.e., red color, tubular morphology,
extended anther position, dilute nectar concentration,
etc.) have repeatedly evolved from those of beepollinated ancestors in diverse lineages (McDade 1992,
Beardsley et al. 2003, Castellanos et al. 2003, 2004,
Fenster et al. 2004). Patterns of natural selection
imposed by birds and bees implicate these pollinators
as the agents favoring floral syndromes (e.g., Schemske
and Bradshaw 1999). Although any two traits in a
syndrome can be positively or negatively (or not at all)
correlated across taxa, the plant defense syndrome
hypothesis rejects the prediction that any two plant
defenses are redundant, and thus should be negatively
associated across species (Steward and Keeler 1988,
Twigg and Socha 1996, Rudgers et al. 2004). Defense
syndromes as whole, however, can trade off if they truly
represent alternative adaptive strategies.
If a syndrome has independently evolved multiple
times, it suggests that the selective forces driving the
evolution of these convergent adaptations are common
and widespread. If, however, closely related species
S133
share a syndrome due to common ancestry, then
common ancestry is sufficient to explain the association,
and adaptive advantage need not necessarily be invoked.
Like trade-offs among supposedly redundant traits, the
correlated evolution of traits composing syndromes
needs to be tested explicitly rather than assumed.
Although plant defense syndromes have received little
attention in the past (but see Feeny 1976, Kursar and
Coley 2003), we suggest that where distantly related
plant species share a common assemblage of herbivores,
they are likely to defend themselves with similar
strategies. For example, plant species attacked primarily
by vertebrate grazers should employ quite different
strategies (e.g., spines, leaf toughness, and out-of-reach
morphologies) than plants primarily attacked by caterpillars (e.g., trichomes, toxins, and parasitoid-attracting
volatiles). Perhaps the original ‘‘plant defense syndrome’’ was the set of traits proposed to be associated
with highly apparent plants (Feeny 1976). Feeny argued
that apparent plants like oak trees were bound to be
found, and thus defended with suites of traits that make
them nutritionally poor: high concentrations of tannins,
low water and nitrogen content, and tough leaves. We
agree that such patterns are likely to have evolved
repeatedly to converge on distinct syndromes of
defensive traits, even in distantly related plant taxa. So
why have most ecologists not explicitly considered plant
defense syndromes? We believe that the traits of
importance in defense against herbivores are too often
unobservable by the naked eye of naturalists, and
therefore have escaped attention.
Plant defense syndromes in communities
versus taxonomic groups
The concept that plant species within a particular
community may coexist due to trade-offs in fitnessenhancing traits is an old and central paradigm in plant
ecology (reviewed by Tilman and Pacala [1993]). This
logic was later applied to plant defense traits for
coexisting species. Van der Meijden et al. (1988)
proposed that suites of traits associated with resistance
vs. regrowth (tolerance) might represent alternative
strategies for coexisting temperate herbaceous plants.
Spurred by Janzen’s (1974) study of tropical blackwater
rivers and emerging physiological plant defense theories
(Bryant et al. 1983), Coley and colleagues hypothesized
that plant species colonizing light gaps would have
divergent (trading-off) suites of defense traits, compared
to species colonizing the more resource-poor understory
in tropical forests (Coley 1983, 1987, Coley et al. 1985).
Such trade-offs explained divergent patterns of herbivory in different microenvironments and also likely
contributed to the maintenance of diversity at larger
spatial scales. Recent experimental work on phylogenetically paired species from white sand and clay soil
habitats of Peru support the hypothesis (Fine et al. 2004,
2006).
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ANURAG A. AGRAWAL AND MARK FISHBEIN
Kursar and Coley (2003) have also expanded these
ideas to explicitly consider the evolution of convergent
defense syndromes in tropical trees. They argue that
trees fall along an escape–defense continuum: extreme
‘‘escape’’ species are predicted to have few chemical
defenses, but rapid synchronous leaf expansion, and low
leaf nutritional quality during expansion; extreme
‘‘defense’’ species have high chemical defense, low
nutritional quality, and asynchronous leaf expansion
(Kursar and Coley 2003). Using phylogenetically
independent contrasts, Silvertown and Dodd (1996)
showed that herbaceous vs. woody plants had distinct
types of chemical defenses (tannins vs. alkaloids),
consistent with classic apparency theory (Feeny 1976).
In each of these examples, the general finding has been
that unrelated plant species within a particular community have converged on a suite of similar strategies
that maximize fitness, given a particular set of ecological
interactions. Thus within a regional community, plant
species can converge on a few defense syndromes, yet the
divergent strategies (across the syndromes) also can
promote the coexistence of species.
The approach of examining syndromes within communities is an ecological question, first and foremost,
and begins with identifying the traits of species that
occur within a plant community irrespective of their
evolutionary relationships. If patterns of defensive traits
consistently occur among coexisting taxa, then the
patterns of defense among species could be explained
by either shared phylogenetic history or convergence due
to similar selective agents causing repeated evolution of
a defensive syndrome. We propose that phylogenetic
history should provide the null hypothesis to explain the
distribution of defense traits among species (Silvertown
and Dodd 1996). The alternative hypothesis is that
species’ defensive traits are evolutionarily labile, and
species have repeatedly evolved homoplasious (i.e.,
independent, but similar) solutions to biotic and abiotic
environmental challenges. For instance, in a temperate
grassland community, patterns of plant defense could
consistently fall into a few categories (e.g., toxic and antdefended vs. tolerant and poor nutritional quality), but
each of these categories might be phylogenetically
homologous (e.g., evolving once each in legumes and
grasses). Alternatively, unrelated taxa could converge on
homoplasious suites of traits in response to the same or
similar selective forces (Fine et al. 2004).
In community studies of syndromes, phylogenetic
information need not be extremely detailed, and
taxonomic information might be sufficient. If two
species within a genus have different suites of defense
traits and are ecologically divergent (i.e., have different
types of species interactions, live in different microhabitats, etc), then selection is a reasonable hypothesis
for the differences between the close relatives; if multiple, phylogenetically independent congeneric pairs show
similar suites of defensive traits and divergent ecologies,
convergence can be invoked (Conway-Morris 2003, Fine
Ecology Special Issue
et al. 2004). Of course, such an association does not
prove adaptation, but, at minimum, implies an evolutionary association between traits and ecology.
The macroevolution of syndromes has also been
studied within clades. Within the plant genus Penstemon,
species have repeatedly evolved suites of traits associated
with hummingbird pollination from Hymenopterapollinated ancestors (Thomson et al. 2000, Wilson et
al. 2004). The repeated evolution of particular plant
defense trait combinations within a taxonomic group
(e.g., genus) is suggestive of ‘‘syndromes in clades.’’
Traits associated with induced resistance to herbivores
evolved multiple times in the cotton genus, Gosspium
(Thaler and Karban 1997). Work with ant-associated
and nonant-associated Acacia spp. may reveal patterns
of divergent defensive strategies in plants with altered
species interactions (Rehr et al. 1973, Seigler and
Ebinger 1987, Heil et al. 2000, 2002, 2004). Recent
work by Becerra and colleagues (Becerra 1997, Becerra
et al. 2001) demonstrates that there has been convergent
evolution of terpene chemical defense and ‘‘squirt-gun’’
defense combinations in tropical Bursera spp. Species of
specialist Blepharida beetles typically consume plants
with similar defense traits, which are not necessarily the
most closely related species or those with geographically
similar ranges (Becerra 1997, Becerra and Venable
1999). Thus, traits can covary within a clade and are
predicted to be associated with particular interactions
(although convergent taxa do not necessarily coexist).
The approach of examining syndromes within a clade
requires an accurate plant phylogeny and the existence
of two or more syndromes (suites of traits). Thus, if
suites of traits converge in different parts of the
phylogeny, the question becomes this: Are there
consistent ecological correlates that match these different syndromes? A key difference from the community
approach is that syndromes within a closely related set
of species in a clade can surface in geographically
separated species that do not naturally coexist. The
community approach specifically starts with species that
coexist under relatively similar ecological conditions.
Although similar in trying to disentangle the effects of
phylogenetic history and ecology, the ‘‘community’’ and
‘‘clade’’ approaches start at the opposing ends of the
phylogeny–ecology continuum.
Defense syndromes in milkweeds (Asclepias)
There are ;130 species of Asclepias native to North
America, including Mesoamerica and the Caribbean
(Woodson 1954; M. Fishbein et al., unpublished manuscript). Approximately six additional species are native
to temperate South America (Bollwinkel 1969). The
situation is more complicated in Africa, where up to 200
species have been or could potentially be included in the
genus Asclepias, depending on the breadth of circumscription and phylogenetic relationships among African
and American species (Fishbein 1996). However, preliminary analyses suggest that American and African
July 2006
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TABLE 1. Species sampled for defense-related traits, effects on herbivore performance, and phylogenetic relationships.
Species
Native Range
Asclepias amplexicaulis Sm.
Asclepias asperula (Decne.) Woodson ssp. asperula
Asclepias californica Greene
Asclepias cordifolia (Benth.) Jeps.
Asclepias curassavica L.
Asclepias eriocarpa Benth.
Asclepias exaltata L.
Asclepias fascicularis Decne.
Asclepias hallii A. Gray
Asclepias hirtella (Pennell) Woodson
Asclepias incarnata L. ssp. incarnata
Asclepias incarnata L. ssp. pulchra (Ehrh. ex Willd.) Woodson
Asclepias oenotheroides Schltdl. & Cham.
Asclepias perennis Walter
Asclepias purpurascens L.
Asclepias speciosa Torr.
Asclepias sullivantii Engelm. ex A. Gray
Asclepias syriaca L.
Asclepias tuberosa L.
Asclepias variegata L.
Asclepias verticillata L.
Asclepias viridis Walter
Gomphocarpus cancellatus (Burm. f.) Bruyns
Gomphocarpus fruticosus (L.) W.T. Aiton
Eastern USA
Southwestern USA and Mexico
Western USA
Western USA
Neotropics
Western USA
Eastern USA
Western USA and Mexico
Western USA
Central USA
Eastern USA
Eastern USA
Southwestern USA, Mexico, and Central America
Southeastern USA
Eastern USA
Western USA
Central USA
Eastern USA and Canada
Eastern USA, southwestern USA, and Mexico
Eastern USA
Eastern USA and Canada
Eastern USA
Southern Africa
Africa
species belong to distinct sister clades (Rapini et al. 2003;
M. Fishbein et al., unpublished manuscript), and thus
Asclepias may be considered an exclusively American
genus of ;150 species. In this study, we employ the
results of the most intensively sampled study of
Asclepias to date (M. Fishbein et al., unpublished
manuscript), focusing on the American species as a
framework for investigating macroevolution of defense
traits. Of the species we studied, all are perennial, some
are clonal, and genets are probably very long-lived
(Table 1). In addition, most of the species are relatively
rare in the landscape; those that are common now, such
as A. syriaca in eastern North America, were probably
much less abundant and widespread prior to the clearing
of the eastern deciduous forest by colonists.
Plants in the genus Asclepias have been of major
importance in the development of theories about plant–
herbivore and tri-trophic interactions, and historically
the focus has been primarily on cardenolides as a plant
defense and the Monarch butterfly as the herbivore
(Fig. 1) (Brower et al. 1967, 1972, Malcolm et al. 1989).
However, the herbivore community of Asclepias spp.
consists of tens of species that are principally hostspecific insects (Weiss and Dickerson 1921, Rothschild
et al. 1970, Wilbur 1976, Blakley and Dingle 1978, Price
and Willson 1979, Morse 1985, Malcolm 1991, Fordyce
and Malcolm 2000, Agrawal and Malcolm 2002). These
herbivores fill almost every conceivable feeding guild:
aphids feed on the phloem, beetles and caterpillars chew
the leaves, flies mine the leaves, hemipterans eat the
seeds, weevils bore through the stem and eat the pith,
and beetle larvae bore through the roots.
For example, the common milkweed, A. syriaca, has
12 reported insect herbivores, including members of all
of the guilds. Some of the herbivores, such as Monarchs
(Danaus plexippus) and Milkweed aphids (Aphis nerii),
are broadly distributed and feed on many milkweeds.
Other groups, such as the cerambycid beetles in the
genus Tetraopes have radiated with the milkweed genus,
and each of the 24 Tetraopes species is primarily
associated with a single milkweed species (Farrell and
Mitter 1998). Thus, each milkweed species is likely
attacked by several herbivores, although the species
composition and guilds attacking particular species vary
widely. An important future goal in the study of plant
defense macroevolution is to detail the particular insect
communities that correspondingly attack plants with
different defense syndromes.
The community of insects on milkweed thrives by
either actively avoiding defenses in the plant or by
consuming, sequestering, and advertising these same
defenses. Probably the two most potent aspects of
herbivore resistance in milkweeds are the production of
cardenolides and latex. Cardenolides are bitter tasting
steroids that occur in all milkweed tissues, including the
latex, that act by disrupting the sodium and potassium
flux in cells, and have toxic effects on most animals
(Malcolm 1991). The sticky white latex is delivered via
specialized canals (laticifers) to most plant parts, and
can be copiously exuded upon tissue damage (Fig. 1).
Latex of milkweed has been strongly implicated as a
physical impediment to herbivory (Dussourd and Eisner
1987, Zalucki et al. 2001). Other potentially defensive
and nutritional constituents that could influence herbivores include leaf toughness, trichome density, water
content, nitrogen content, and specific leaf area (Mattson 1980, Coley 1983, Haddad and Hicks 2000, Lavoie
and Oberhauser 2004). Most of these traits show plastic
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ANURAG A. AGRAWAL AND MARK FISHBEIN
Ecology Special Issue
FIG. 1. (A) Common milkweed (Asclepias syriaca) viewed from above early in the growing season. (B) A newly hatched
monarch caterpillar (Danaus plexippus). Before it can get its first meal, the caterpillar must graze the trichomes and avoid the
droplets of latex. Exposure to latex is reduced by clipping laticifers in a circle and feeding on the tissue in the middle.
variation within species, are genetically variable in
natural populations of single species, and are highly
variable across species (Table 2).
Using 24 species of Asclepias, we begin to address the
role of convergent evolution in giving rise to antiherbivore defense syndromes. We specifically addressed the
following questions: (1) What are the pairwise associations of defense traits across taxa? Are defense traits
typically negatively correlated as predicted by redundancy or trade-off theory, are they positively correlated
as predicted by syndrome theory, or are they not
correlated at all? (2) Do species cluster into syndromes
of common patterns of defense trait expression? (3) Are
phylogenetic relationships among Asclepias species
inferred from DNA sequences congruent with patterns
of defense trait similarity? If so, we can conclude that
phylogenetic history is sufficient to explain plant defense
trait associations; if not, there is a suggestion for
convergence in defense syndromes. And (4) Are particular defense traits or syndromes associated with the
performance of a common oligophagous herbivore of
milkweeds (monarch caterpillars, Danaus plexippus)?
MATERIALS
AND
METHODS
Measuring plant traits
Seeds for 24 species of milkweeds (Table 1) were
obtained from field collections, nurseries, and native plant
seed suppliers. We germinated seeds by nicking the tips
and placing the seeds on moist filter paper. Seedlings were
grown in potting soil (;10-cm pots) in growth chambers
for one month and out-planted in a completely randomized common garden in a plowed field at the Koffler
Scientific Reserve at Jokers Hill, Southern Ontario (44803 0
N, 79829 0 W; information available online).5 After
5
hhttp://www.zoo.utoronto.ca/jokershilli
accounting for plant mortality, our common garden had
approximately 12 replicate plants per species (mean 6 SE,
11.8 6 0.7). All measurements were taken from newly
expanded, undamaged leaves of plants in the common
garden.
We measured cardenolide concentrations as digitoxin
equivalents (grams per gram dry tissue) extracted from
50 mg dry leaf tissue (n ¼ 5 replicates/species); we
employed a spectrophotometric assay modified from
Nelson (1993). We adapted the assay for sampling using
a microplate reader (PowerWave X, Bio-Tek Instruments, Winooski, Vermont, USA). Field-collected leaf
tissue was kept on ice, then frozen, freeze-dried, ground
with a mortar and pestle, and weighed in 2-mL boilproof tubes. To each tube, we added 1.9 mL of 95%
ethanol, tubes were then vortexed, and floated in a
sonicating water bath (658C) for 10 min. We then
centrifuged the tubes at 5000 rpm for five minutes at
room temperature. Two 45-lL aliquots of the supernatant from each tube were then pipetted into the wells
of a 96-well plate, one above the other (active sample
and blank, respectively). Each plate also contained six
samples of digitoxin for the standard curve used to
determine concentrations of cardenolides (0.125–3 mg/
mL; Sigma Chemical, St. Louis, Missouri, USA). We
then added 90 lL of ethanol to the blanks and 90 lL of
0.15% 2,2 0 4,4 0 -tetranitrodiphenyl (TNDP) in ethanol to
the active samples. Finally, 70 lL of 0.1 mol/L aqueous
NaOH was added to all wells to make the solutions basic
and to catalyze the colorimetric reaction. After 15
minutes, all wells in the plate were read at 620 nm using
the microplate reader.
We measured latex from 5–10 replicates from each
species by cutting the tip off (0.5 cm) an intact leaf in the
field and collecting the exuding latex onto a 1 cm disc of
filter paper. Latex stopped flowing after ;10 s, all latex
was absorbed on the filter paper, and this disc was placed
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PLANT DEFENSE SYNDROMES
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TABLE 2. Seven putative defensive traits of milkweeds. Ranges for the actual trait values for treatment and for family and species
means are presented, followed by percentage variation (in parentheses).
Putative defense trait
Plastic variation
Full-sib variation
Species level variation
Latex (mg)
Cardenolides (% dry mass)
Trichomes (no. hairs/cm2)
Toughness (g)
C:N
Water content (%)
Specific leaf area (mm2/mg)
1.05–1.35 (29%)
2.7–4.0 (48%)
7.8–9.0 (15%)
0.9–4.5 (400%)
1.7–3.9 (129%)
580–1090 (88%)
96–128 (33%)
11–14 (27%)
NA
NA
NA
NA
0–4.8 (infinite)
2–16 (800%)
0–2019 (infinite)
58–177 (205%)
10–15 (50%)
78–90 (15%)
8–23 (188%)
NA
NA
Notes: In experiments, we have characterized the level of variation in these traits among control Asclepias syriaca plants and
those experimentally damaged by herbivores (plastic variation) (Van Zandt and Agrawal 2004a; A. A. Agrawal, unpublished data),
23 full-sibling families of A. syriaca (Agrawal 2004b, 2005), and 24 different species of Asclepias (Agrawal 2004a, this study). All
data were collected from at least five individuals (per treatment, family, or species) typically growing in growth chambers (plastic
variation) or independent field common gardens (full-sibling and species variation). There were significant differences between
treatments, families, and species for all traits measured (ANOVA for all traits, P 0.05); families and species vary continuously
between the extremes presented.
on top of another dry filter paper disc in a 24-well plate.
The discs were dried at 608C and then weighed to the
microgram. This method has proven to be highly repeatable (Van Zandt and Agrawal 2004a, Agrawal 2005).
We assessed the trichome density of 5–10 replicate
plants from each species by counting the tops and
bottoms of leaf discs (28 mm2) under a dissection
microscope. Leaf discs were taken from the tips of
leaves. Water content was assessed by first weighing leaf
discs wet and again after drying in an oven (608C).
Specific leaf area (SLA) was calculated as the area of the
leaf disc divided by the dry mass. This measure can be
thought of as an indicator of thickness or density; leaves
with a higher SLA are typically thin and have greater
levels of herbivory (Schädler et al. 2003).
We measured leaf toughness on 10 replicate plants
from each species with a force gauge penetrometer (Type
516; Chatillon, Largo, Florida, USA) that measures the
mass (in grams) needed to penetrate a surface. We
sandwiched the leaf between two pieces of Plexiglas,
each with a 0.5-cm hole, pushed the probe of the
penetrometer through the leaf, and recorded the
maximum force required for penetration. For each leaf,
we measured toughness on each side of the mid-rib;
these two measures were averaged and used as a single
data point per plant.
Total leaf carbon (C) and nitrogen (N) concentration
was measured from five replicates from each species by
microcombustion, using 5 mg of dried ground leaf
material in an Elemental Combustion System 4010,
CHNS-O analyzer (Costech Analytical Technologies,
Valencia, California, USA). We used the C:N ratio as
indicator of plant nutritional quality.
Means and standard errors of the seven defenserelated traits for each of the 24 milkweed species are
reported in Appendix A.
Statistical analyses of phylogenetic and
nonphylogenetic trait associations
Pairwise correlations among traits were calculated
using raw trait values and phylogenetically independent
contrasts (PICs) (Felsenstein 1985). We calculated PICs
using the maximum-likelihood (ML) estimate of the
phylogeny of the 24 species of Asclepias (Fig. 2) for
which defense traits were measured (Table 2), based on
noncoding plastid DNA (cpDNA) sequences (rpl16
intron and trnC–rpoB intergenic spacer; M. Fishbein et
al., unpublished manuscript). We obtained sequences
directly, using standard protocols, and manually aligned
them (M. Fishbein et al., unpublished manuscript). The
best fitting nucleotide substitution model under the ML
criterion (general time reversible plus invariant sites plus
gamma [GTRþIþC]) was selected using hierarchical
likelihood ratio tests and ML trees were sought using
a standard he uristic s ea rch strateg y ( us ing
PAUP*4.0b10; Swofford 2001) (see Fishbein et al.
2001). Clade support was assessed with Bayesian
probabilities estimated under the GTRþIþC model,
assuming uninformative priors for model parameters
and tree topologies (using MrBayes 3.0b4, Ronquist and
Huelsenbeck 2003) (see Fishbein and Soltis 2004).
Markov chains were iterated for 5 3 105 generations
with trees sampled during the first 105 generations
discarded as transitory samples. The ML tree for these
24 species did not differ significantly from relationships
found in a comprehensive analysis of 105 species of
Asclepias (M. Fishbein et al., unpublished manuscript;
results not shown).
We calculated PICs using Felsenstein’s (1985) method, as implemented in COMPARE (available online).6
We have no a priori evidence concerning rates of
evolution of defense traits. Thus, we conducted PIC
analyses under the assumptions of (1) equal branch
lengths, corresponding to a speciational model of
evolution (Mooers et al. 1999), and (2) branch lengths
estimated from cpDNA sequences. However, the branch
lengths estimated from molecular data caused pathological behavior in the PIC analysis: several exceedingly
short internodes (Fig. 2) resulted in widely inflated
6
hhttp://compare.bio.indiana.edui
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ANURAG A. AGRAWAL AND MARK FISHBEIN
Ecology Special Issue
FIG. 2. Maximum-likelihood phylogram of the 24 species of Asclepias employed in this study. Numbers indicate Bayesian
posterior probabilities. Letters to the right of taxon names represent defense syndrome clusters (see Fig. 4 and Results: Assessing
defense syndromes).
estimates of contrasts involving these branches. Thus,
the few data points involving these nodes drove the
pattern of correlation among contrasts (results not
shown), which often differed dramatically from those
found in equal branch length analyses. Despite the
drawbacks of assuming equal probabilities of change
across all branches of the phylogeny, this seems
preferable to alternative assumptions about unknown
rates of evolution. Thus, we only present the results
based on assuming equal branch lengths.
The nodes of the phylogeny subtended by extremely
short branches correspond to areas of uncertainty in the
phylogeny of Asclepias (see posterior clade probabilities
in Fig. 2). A consequence of this uncertainty is possible
error in the PIC analysis, due to using an incorrect
phylogenetic estimate. To examine this source of error,
we conducted PIC analyses on alternative topologies
and assessed the robustness of PIC-based correlations
(cf. Housworth and Martins 2001). Starting with a tree
in which all clades with posterior probabilities ,0.70 in
Fig. 2 were collapsed, we generated 100 trees that
randomly resolved all polytomies using MacClade 4.06
(Maddison and Maddison 2003). We present 95%
confidence limits for our correlation coefficients by
excluding the lowest and highest 2.5% of the r values.
We conducted PIC analyses, as described, with COMPARE on each of these 100 trees.
We used a hierarchical cluster analysis to produce a
defense phenogram (cf. Becerra’s ‘‘chemograms’’) (Becerra 1997, Legendre and Legendre 1998) to group
species by expression of defense traits. We used the mean
values for each of the seven plant traits measured for
each species to generate a phenogram. Mean trait values
were transformed to Z scores (mean ¼ 0, SD ¼ 1) so that
they were measured on a comparable scale. Following
Becerra’s (1997) model, we employed Ward’s method for
July 2006
PLANT DEFENSE SYNDROMES
S139
TABLE 3. Pairwise correlations of defense-related traits among Asclepias species.
Latex
(mg)
Latex (mg)
Trichomes
(no. hairs/cm2)
Toughness (g)
C:N
Water content
(%)
Cardenolides
(% dry mass)
SLA (cm2/mg)
Trichomes
(no. hairs/cm2)
Toughness
(g)
C:N
Water
content
(%)
Cardenolides SLA
(% dry mass) (cm2/mg)
0.60**
0.10
0.04
0.21
0.13
0.62**
0.10
0.17
0.04
0.05
(0.47–0.70)
0.06
0.05
0.32
0.19
0.06
(0.25–0.10)
(0.27–0.18)
0.03
0.18
0.43*
0.25
0.25
(0.25–0.10)
(0.43 to 0.115)
(0.18–0.455)
0.29
0.01
0.34 0.17
0.27
(0.32 to 0.045) (0.08–0.165) (0.35 to 0.13) (0.30 to 0.01)
0.08
0.1
0.01
0.24
0.16
(0.16–0.02)
(0.015–0.195)
(0.095–0.105) (0.32 to 0.14) (0.065–0.265)
0.57**
0.45*
0.15
0.07
0.57**
0.21
(0.58 to 0.42) (0.495 to 0.295) (0.24–0.055)
(0.215–0.01) (0.46–0.61) (0.12–0.27)
0.59**
0.43*
0.26
0.26
0.39 0.15
Notes: Correlation coefficients (r) above the diagonal are uncorrected for bias due to phylogenetic history; those below the
diagonal are phylogenetically independent contrasts (Felsenstein 1985) assuming a speciational model of evolution (with the 95%
confidence limits, based on random resolution of poorly supported nodes, shown in parentheses). SLA (last column) is specific leaf
area. None of the significant correlations represents a trade-off in defenses (see Results).
* P , 0.05, **P , 0.01; these values are also highlighted in boldface type; df ¼ 22.
P 0.1; df ¼ 22.
linkage and Euclidean distances, which combines subgroups (initially building from one species) at each
iteration so as to minimize the within-cluster ANOVA
sum of squares (Wilkinson 1997). Alternate joining
algorithms provided qualitatively similar relationships.
Clusters were designated by distances from nodes; nodes
separated by a distance of ,0.5 were included in the
same cluster, whereas nodes separated by .0.5 were
placed in separate clusters (Wilkinson 1997). We further
examined the strength to which particular traits
contributed to differences among clusters using discriminant function analysis (Wilkinson 1997).
To investigate whether factors related to geographical
distribution were associated with the repeated emergence
of trait combinations, we conducted a preliminary test
for an association between geography and membership
in the hierarchical clusters (Thaler and Karban 1997,
Becerra and Venable 1999). Biogeographical associations
of defense clusters were investigated by contingency table
analysis. The bulk of species we studied have ranges in
eastern North American (United States and Canada, east
of the Tall Grass Prairie–Great Plains ecotone) or
western North American (west of the ecotone) (Table 1).
Congruence between the ML phylogeny of Asclepias
and hierarchical clustering of species based on defense
traits was assessed using the test of Shimodaira and
Hasegawa (SH test) (Shimodaira and Hasegawa 1999,
Goldman et al. 2000), as implemented in PAUP* 4.0b10
(Swofford 2001), using RELL resampling to estimate
site likelihoods. Statistical incongruence between the
phylogeny of Asclepias and clustering of species by
defense traits would indicate that the evolution of
defensive traits does not passively track phylogeny, but
instead is evolutionarily labile, and suggests phylogenetically independent derivations of associations among
traits. This approach evaluates whether associations
among traits generate hierarchical similarity among taxa
that is statistically independent of phylogenetic relationships. Stochastically evolving traits will generate clusters
of taxa statistically indistinguishable from the estimated
phylogeny. Significant deviations in the phenotypic
clustering could be caused by convergent adaptation of
traits or other processes that generate trait correlations
that are independent of association due to phylogenetic
history. This test is similar in aim to tests of trait
conservatism (e.g., Ackerly and Donoghue 1998, Cavender-Bares et al. 2004). An advantage to our approach
is the ability to consider the associations among all traits
simultaneously, with the concomitant disadvantage that
the conservatism of a single trait cannot be assessed.
Effects of defense traits on caterpillars
To assess how individual plant traits and defense
syndromes affect the performance of one natural feeder
of milkweeds, we conducted a bioassay with monarch
caterpillars (Danaus plexippus) (Fig. 1). We placed single
eggs of freshly hatched caterpillars on all plants (n ’ 12
for each of the 24 plant species) in the field. Caterpillars
were from a laboratory colony established from local
individuals collected the previous summer and maintained on frozen milkweed foliage. Each caterpillar was
caged in a spun polyester bag (Rockingham Opportunities, Reidsville, North Carolina, USA) on the apical
meristem with four fully expanded leaves. After five
days, we recorded mortality and collected and weighed
each living caterpillar.
To assess whether plant species that were in the three
different defense clusters (see Results) showed differential resistance to monarch caterpillars, we conducted a
one-way ANOVA. The species cluster was used as the
factor (each species was assigned to one cluster) and
mean monarch mass and percentage survival on each
species were the response variables (total n ¼ 24). In
addition, we employed multiple regression to assess the
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ANURAG A. AGRAWAL AND MARK FISHBEIN
Ecology Special Issue
FIG. 3. Significant pairwise correlation between raw values of latex production and trichome density among 24 Asclepias
species (see Table 2). Raw correlations and those of phylogenetically independent contrasts (PIC) (Felsenstein 1985) are significant
and of similar magnitude.
effects of plant traits on monarch performance across
the 24 species. For this analysis, we used species means
for each of the seven traits (cardenolides, latex,
trichomes, water content, specific leaf area, toughness,
and C:N ratio) and mean caterpillar mass and percentage mortality as the response variables. We employed a
stepwise multiple regression with backwards removal (P
¼ 0.15 to enter or remove) (Wilkinson 1997).
RESULTS
Pairwise correlations between traits
Three of the 21 raw pairwise correlations (and five
based on phylogenetically independent contrasts, PICs)
among seven defense-related traits were statistically
significant (Table 3). This observed frequency of
phylogenetically independent correlations is unlikely to
have occurred by chance (binomial expansion test, P ¼
0.0028) (Zar 1996). Each of the significant raw
correlations involved just three traits showing complementary patterns of variation. Species with lower
specific leaf areas (SLA) exhibited higher leaf trichome
densities and latex production (Fig. 3). Species with
lower SLA also exhibited lower water content (P ¼ 0.06),
as expected (SLA is derived from leaf dry mass). Recall
that SLA is a physiological plant trait, often indicative
of rapid growth and high palatability to herbivores, and
that low SLA might defend against herbivory. Leaves of
species with high C:N ratios were also tougher, although
this relationship was not significant (P ¼ 0.13). Thus, we
found no indications of trade-offs among defense traits.
Indeed, all correlations were ‘‘positive’’; the significant
negative correlations in Table 2 represent negative
associations between nutritional quality and defense.
The phylogenetically independent correlations did not
differ substantially from raw correlations (Table 3,
Fig. 3). The phylogenetically independent correlation
between SLA and water content and between leaf C:N
ratio and toughness were stronger and statistically
significant compared to the raw correlations. In
addition, a weak and nonsignificant negative correlation
between leaf water content and toughness had a nearly
significant phylogenetically independent correlation
(P ¼ 0.10; Table 3).
Correlations based on PICs were generally robust to
phylogenetic uncertainty (Table 3). For the latex–
trichome, latex–SLA and SLA–percentage water correlations, every one of the randomly resolved topologies
generated significant correlations. The trichome–SLA
correlation was significant in just 41% of the random
resolutions, although the upper bound of the 95%
confidence limit (r ¼ 0.295) was still strongly negative.
The C:N–toughness correlation was less robust, with
only 12% of the random resolutions remaining significant. The only instance of a nonsignificant PIC
correlation that was found to be significant in randomly
resolved topologies was the C:N–trichome correlation,
but this occurred in only 3% of the resolutions. Overall,
little bias can be attributed to using the ML phylogeny
in the face of weak support for a number of clades.
Assessing defense syndromes
Our hierarchical cluster analysis of the seven defenserelated traits revealed three distinct clusters (Fig. 4). For
illustrative purposes only, in post hoc analyses, we
determined that clusters differed strongly from one
another (MANOVA results from discriminant function
analysis: Wilks’ lambda ¼ 0.033, F14,30 ¼ 9.688, P ,
0.001; see Appendix B). The analysis also indicates how
each trait correlated with each discriminant function
(Table 4); although most traits contributed substantially
to at least one function, cardenolides and leaf toughness
contributed the least to both. We also identified which
traits were significantly heterogeneous among the three
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PLANT DEFENSE SYNDROMES
S141
FIG. 4. A defense phenogram that depicts similarity among 24 species of Asclepias generated by hierarchical cluster analysis of
seven defense-related traits. Tightly clustered species are defensively similar and can be considered to form defense syndromes, A–C
(see Results: Assessing defense syndromes).
clusters (Table 4). These analyses revealed that most
traits were expressed differently in the three defense
clusters, and again cardenolides and leaf toughness were
the least distinct.
The three clusters (Fig. 4, Table 4) represent species
with combinations of: (A) high nitrogen (i.e., low C:N)
coupled with high physical defense traits (trichomes,
latex), (B) very high C:N ratio, coupled with tough
leaves and low water content (hard to eat, little reward),
and (C) low C:N and SLA coupled with high
cardenolides. Although differences in cardenolides were
not significantly different between clusters (Table 3), we
found 17% and 50% higher levels in cluster C than in A
or B, respectively. Clusters A and C are strategies that
both represent the coupling of a trait increasing the
reward to herbivores (nitrogen or SLA) with high
defense allocation. The three defense clusters were not
significantly associated with distribution in eastern vs.
western North America (v2 ¼ 8.77, df ¼ 5, P ¼ 0.12).
Phylogenetic congruence test
The phylogenetic relationships suggested by the
defense trait cluster analysis (Fig. 4) are significantly
incongruent with relationships estimated from noncoding cpDNA sequences (Fig. 2). The difference in
log likelihood [ln(L)] between the maximum likelihood
TABLE 4. The relationship between seven defensive traits and defensive clustering of milkweed species. Coefficients of standardized
canonical discriminant functions (CDFs) indicate how each trait contributes to the two factors generated by discriminant
analysis (Wilkinson 1997). Trait values (mean 6 SE) appear for the three defense syndromes (clusters) identified in this study.
Significance of differences among traits tested by ANOVA.
Latex (mg)
Trichomes (no. hairs/cm2)
Toughness (g)
C:N
Water content (%)
Cardenolides (% dry mass)
Specific leaf area (mm2/mg)
CDF1
CDF2
Cluster A (n ¼ 4)
Cluster B (n ¼ 6)
–0.495
–0.907
–0.089
0.637
0.102
–0.011
–0.456
0.404
0.316
0.285
0.703
–0.388
–0.008
–0.284
3.13
1630.08
111.08
11.48
0.83
4.97
9.98
1.28
355.40
131.00
14.09
0.81
3.89
10.41
6
6
6
6
6
6
6
0.72
146.57
17.93
0.08
0.01
0.67
0.69
6
6
6
6
6
6
6
0.56
185.22
12.01
0.32
0.01
0.76
0.99
Cluster C (n ¼ 14)
0.31
337.47
104.86
11.66
0.84
5.85
15.80
6
6
6
6
6
6
6
0.09
67.11
5.53
0.21
0.01
1.11
1.02
P
,0.001
,0.001
0.136
,0.001
0.088
0.508
0.002
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ANURAG A. AGRAWAL AND MARK FISHBEIN
Ecology Special Issue
FIG. 5. Schematic depiction of the lack of congruence between the molecular phylogeny of Asclepias and the defense trait
phenogram (see Fig. 4).
tree for the sequence data (–ln(L) ¼ 4225.61) and the
highest likelihood tree congruent with the phenogram
(–ln(L) ¼ 4532.10) was 306.49, which was highly
significant (SH test: P , 0.05). Thus, multivariate
defense clusters did not come about only as a consequence of tracking the speciational history of Asclepias
(Fig. 5).
Effects of defense traits on caterpillars
When plant species were classified by defense cluster
(Fig. 4), we found no difference in the performance of
monarch caterpillars among groups (mass, F2,20 ¼ 2.460,
P ¼ 0.111; survival, F2,21 ¼ 2.080, P ¼ 0.150). Nonetheless, in multiple regression analyses, plant traits significantly explained monarch performance (Fig. 6) (overall
model for mass, R2 ¼ 0.33, F2,20 ¼ 4.906, P ¼ 0.018,
trichome coefficient ¼ 0.130, P ¼ 0.022, toughness
coefficient ¼ 0.698, P ¼ 0.041; overall model for
survival, R2 ¼ 0.26, F1,22 ¼ 7.566, P ¼ 0.012, latex
coefficient ¼ 0.060, P ¼ 0.012). The latter positive
correlation between latex and percentage of caterpillar
survival is difficult to explain and is discussed below (see
the Discussion).
DISCUSSION
The concept of convergent expression of suites of
traits acting as syndromes has been a useful framework
for conceptualizing adaptations in ecological communities. For example, plant ecologists have proposed that
specific suites of traits might be associated with
particular types of environmental stress (Grime 1977,
Chapin et al. 1993). In animal ecology, the guild concept
is used to characterize species that exploit the same type
of resources in a similar manner (Root 1967). Species
forming a guild can share a common set of traits, but are
explicitly not necessarily unified by phylogenetic relatedness. Nonetheless, in the past there was limited ability to
disentangle the role of phylogenetic history and convergence in generating such species groups. For example, in the plant pollination literature, phylogenetic
analyses of convergence on plant traits associated with
bee and hummingbird pollination have only recently
been employed (McDade 1992, Fenster et al. 2004,
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PLANT DEFENSE SYNDROMES
S143
Wilson et al. 2004). Other assessments of syndromes
have also recently adopted a phylogenetically explicit
approach (Cunningham et al. 1999, Fine et al. 2004).
Thus, we see our concept of plant defense syndromes as
an extension and phylogenetic modernization of classic
plant defense theory (e.g., Feeny 1976).
Correlations between traits
In our study of Asclepias, none of the measured
defense-related traits showed the bivariate trade-off
predicted by traditional redundancy theory. Indeed, all
of the pairwise correlations between defensive traits were
positive; the significant correlations with a negative sign
were between nutritional quality and defense, thus, not
indicating a trade-off among traits providing resistance.
Using phylogenetically independent contrasts among
cotton (Gossypium) species, Rudgers et al. (2004) also
found no trade-off between the production of extrafloral
nectaries (EFNs) and trichomes, or between EFNs and
chemical defense glands; however, they did find a
negative association between trichomes and glands. This
negative correlation could reflect allocation costs of the
traits or redundancy in their ecological functions. Given
that the costs and ecological functions of most defenserelated traits are unknown, however, in this paper we
have argued that any two traits should not be a priori
predicted to negatively covary. This same conclusion has
been reached in a recent meta-analysis of intraspecific
genetic correlations of defense traits (Koricheva et al.
2004).
We found a positive (phylogenetically independent)
correlation between plant production of latex and
trichomes (Fig. 3). In an analysis of the correlation
between the same two traits across 23 genetic families of
A. syriaca, we found no correlation (Agrawal 2005).
Although other intraspecific correlations are needed,
Armbruster et al. (2004) and others have argued that
such correlations across species, but not within species,
are suggestive of adaptation over constraint. We
speculate that this association could be due to the fact
that latex is a water-intensive defense, and the protection
from water loss by trichomes facilitates use of latex.
Additionally, latex has widely been reported to function
as a defense against monarch caterpillars and other
milkweed herbivores (Dussourd and Eisner 1987,
Zalucki et al. 2001, Agrawal and Van Zandt 2003,
Agrawal 2004b, 2005), suggesting that the two strategies
might be employed in concert to produce a synergistic
defense. Although we did not detect a negative effect of
latex on monarchs in the current study, it is perhaps in
this situation where plants derive benefits from employing dual strategies. We speculate that the positive
correlation we observed between latex production and
percentage caterpillar survival is an artifact of a
correlation between latex and some unmeasured trait.
It is also possible that plant species with higher levels of
latex prevented caterpillars from feeding, which protected them from death due to other plant defenses; in
FIG. 6. Effects of milkweed defensive traits on monarch
caterpillar growth. Seven plant traits were assayed, and only
the significant predictors are shown: (A) trichome density and
(B) leaf toughness. Residual growth refers to the residuals from
the statistical model with the other factor in the analysis.
our short-term assays this protective effect is, thus,
perhaps an artifact.
A notable outlier in the positive correlation between
trichomes and latex (Fig. 3) was A. sullivantii, which has
high latex production but no trichomes. Two other
possible independent origins of species with high latex
production coupled with the absence of trichomes are A.
humistrata (southeastern USA) and a small clade of
Mexican species (A. glaucescens, A. elata, A. mirifica,
and A. lynchiana), neither of which were sampled in the
current study. These species (including A. sullivantii) all
have pronounced depositions of epicuticular wax (Fishbein 1996), which we speculate may be a substitute for
trichomes in a physical defense syndrome.
Thus, in summary, the admittedly adaptationist
hypothesis under the defense syndrome concept is not
of specific a priori trade-offs, but a prediction that the
multivariate trait complex is grouped such that costs are
minimized and defense is maximized, given the biotic and
abiotic environment of the species. Defense syndromes
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ANURAG A. AGRAWAL AND MARK FISHBEIN
Ecology Special Issue
FIG. 7. The plant defense syndrome triangle. Low nutritional defense syndrome is consistent with that outlined for apparent
plants by Feeny (1976); a similar group was found in this study. Tolerance follows the fast-growth and high-edibility pattern
outlined by Coley (Coley et al. 1985, Kursar and Coley 2003). Nutrition and defense is a strategy that couples a toxic defense or
barrier to feeding with relatively high edibility and digestibility. SLA denotes specific leaf area.
themselves might trade-off if they represent multivariate
strategies targeted at alternative ecological interactions.
Defense syndromes in milkweeds and beyond
We distinguished three syndromes (clusters) in our
multivariate analysis of seven defense-related traits,
which grouped as plant species employing (A) high
physical defenses, (B) low nutritional quality, and (C)
putatively high chemical defense (Figs. 1 and 3). We
found that, overall, our clustering of plant species by
defense traits was not congruent with the molecular
phylogeny of the group. At a minimum, this means that
the defense characteristics of Ascelpias are evolutionarily
labile, and do not simply track phylogenetic history. For
example, in the well-resolved group of three species that
co-occur in eastern North America (A. syriaca, A.
tuberosa, and A. exaltata) all three clusters are
represented. In addition, it is apparent that some taxa,
although distantly related, have converged on similar
defense phenotypes.
Although we have not reconstructed the relative
timing of changes in plant nutritional quality and
expression of defense traits, we assume that when such
traits are associated, changes in defense traits follow
after divergences in nutrition traits rather than the
reverse. For two of the three defense clusters (A and C in
Fig. 4) there is an association between high resource
quality (from the herbivore’s perspective) and high
expression of defense traits. In cluster A, low C:N ratio
(i.e., relatively high nitrogen levels) is coupled with high
latex production and trichome density. In cluster C,
both a low C:N ratio and high specific leaf area (SLA;
i.e., relatively concentrated plant tissue) is coupled with
higher levels of cardenolides than the other clusters. In
the case of the cluster-A species, repeated evolution of a
latex–trichome associated syndrome is statistically supported (Table 3, Fig. 3)
Although this analysis of Asclepias focused primarily
on resistance, plant growth traits and tolerance are likely
to be an important component of particular defense
syndromes (van der Meijden et al. 1988, Fornoni et al.
2004). For example, Kursar and Coley (2003) made the
argument that a trade-off between defense and growth
promoted divergent strategies among tropical tree
species. We offer the ‘‘defense syndrome triangle’’
hypothesis to include all defense categories (Fig. 7).
Presumably other traits, such as plant traits that affect
herbivore preference and attraction or facilitation of
enemies of herbivores will also be important to integrate
into the concept of plant defense syndromes.
Nearest to the origin of the two axes of edibility/
digestibility and toxicity/barriers is the low nutritional
quality syndrome (Fig. 7). This syndrome, empirically
demonstrated in Asclepias (Syndrome B in Fig. 4, Table
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PLANT DEFENSE SYNDROMES
4), very closely mirrors Feeny’s (1976) grouping of
defensive traits for apparent plants, and Coley et al.’s
(1985, Kursar and Coley 2003) ‘‘defense syndrome’’ for
plants that have evolved in low-resource environments.
Note that the defense syndrome concept does not
implicate a particular selective agent in generating the
suite of adaptations. Feeny hypothesized that the
selective agents were herbivores under the constraint of
the probability of being found; Coley also hypothesized
that herbivores were the selective agent; however, this
was presumed to be constrained by the abiotic (resource)
environment. The tolerance or escape syndrome (proposed by Kursar and Coley [2003], and not investigated
in the current study) predicts a lack of resistance traits in
very fast-growing and nutritive plants. Finally, our
study revealed two syndromes with intermediate levels
of edibility coupled with barriers to consumption
(Syndromes A and C, Fig. 4, Table 4). In some ways,
this mixed strategy is similar to Feeny’s prediction for
unapparent plants (i.e., qualitative defenses that cannot
be overcome by generalist herbivores, but specialists
feed on these plants with impunity). The important
distinction is the implicit recognition that these toxins or
barriers to consumption (latex, trichomes, cardenolides,
etc), typically have a dose-dependent (or quantitative)
effect on specialist herbivores (Berenbaum et al. 1989,
Adler et al. 1995, Agrawal and Kurashige 2003, Agrawal
and Van Zandt 2003, Agrawal 2004a, b, 2005). Plants
with high levels of toxicity or barriers to feeding,
however, are not predicted to have particularly high or
low levels of nutritional quality (Fig. 7). We reach this
conclusion based on the reasoning that plants with
extreme investments in toxins should not have the ability
to produce very nutritive tissues; likewise plants with
very low nutritive quality should not need to invest in
toxins.
Caveats
The performance of monarch butterfly caterpillars on
the 24 milkweed species did not differ on plants from the
three syndromes. This suggests that plant species might
have alternate mechanisms to achieve similar levels of
defense, and these might be driven by environmental
differences in the habitats in which these species live
naturally. Alternatively, our analysis should be viewed
as preliminary and is limited by (1) our somewhat
arbitrary distinction of clusters (see Materials and
methods: Effects of defense traits on caterpillars), (2)
unmeasured defense traits (epicuticular waxes, proteases, etc.), (3) a limited sample of species, and (4)
limited information on the ecology of each species that
would be informative for why particular defense
syndromes were expressed across these species. Thus,
further analyses are needed. In addition, our approach
was to sample broadly across Asclepias, but more
detailed analyses of well-resolved clades would also be
valuable. Such fine-scale sampling would better control
for the confounding effects of unmeasured traits by
S145
limiting the time of divergence among species and
permitting denser taxon sampling.
It is important to bear in mind that our working
hypothesis for the phylogeny of Asclepias (M. Fishbein
et al., unpublished manuscript) is not highly resolved with
strong statistical support (see Fig. 2). The lack of
resolution reflects the reality of the apparently rapid
initial radiation of Asclepias. It is also important to
consider the effect of our assumption of speciational
evolution of morphology (i.e., equal morphological
branch lengths across the tree) in calculating phylogenetically independent contrasts (PICs). Both of these
factors could bias our estimates of evolutionary
correlations among traits. We found that estimated
correlations among PICs were very robust to uncertainty regarding the correct relationships in poorly
supported regions of the tree. Conversely, the assumption concerning the mode of evolution of defense traits
had a dramatic effect on the sign and magnitude of
correlations. Ideally, an individual model of evolution
should be developed for each trait in order to accurately
estimate correlations (e.g., Pagel 1999, Lewis 2001).
Because of the relatively small number of taxa (i.e., low
sample size) and uncertainty regarding the correct
phylogenetic relationships, we did not pursue individualized fitting of evolutionary models for the defense
traits. We did attempt to analyze correlations assuming
that rates of morphological evolution were accurately
estimated by branch lengths inferred from the rates of
nucleotide substitution in the cpDNA sequences used to
estimate the tree. Correlations estimated under this
assumption differed substantially from those reported
here.
Although we are not comfortable assuming equal
branch lengths across the tree (which is almost certainly
incorrect), we prefer this assumption to the use of
molecular branch lengths. Generally, there is little
evidence for correspondence between rates of molecular
and morphological evolution (Bromham et al. 2002).
Specifically, the extreme branch length heterogeneity for
the molecular data is likely unreasonable for the
morphological traits under study here. Species of
Asclepias that exhibit very low levels of divergence in
cpDNA sequences can be dramatically different in
morphology, whereas species that differ only subtly (A.
fascicularis, A. verticillata, A. perennis, and A. incarnata)
are separated by branches that are orders of magnitude
longer than those separating morphologically diverse
species (see Fig. 2). Given the apparently poor
correspondence between rates of molecular and morphological evolution, we prefer the assumption of
speciational changes in morphology over any assumption about specific rates across lineages.
Directions
We see the identification of syndromes as a starting
point to test alternative hypotheses for why plant
defenses have converged. In this regard, the biotic and
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ANURAG A. AGRAWAL AND MARK FISHBEIN
abiotic environment could conspire to drive particular
syndromes. For example, plant trichomes are known to
have many functions, including defense against herbivory as well as a barrier against evapotranspirative
water loss (e.g., Woodman and Fernandes 1991,
Agrawal and Spiller 2004). Thus, trichomes might be
particular to plant species in environments with low
water availability (i.e., deserts) and subject to particular
types of herbivores. We have shown here that trichomes
are a significant barrier to growth of monarch caterpillars, but benefit some aphids (Agrawal 2004a). Thus,
trichomes might only be favored in particular combinations of biotic and abiotic conditions.
We suggest working toward formalizing hypotheses
about the particular types of herbivores that likely drive
the evolution of particular syndromes. It might be that
plants that share guilds of herbivores (i.e., those that
attack the same plants in a similar way) defend in a
similar manner. The example described initially (see
Introduction), that of plants defending against vertebrate
megafaunal grazers vs. insect herbivores, serves as a
starting point. For North American Asclepias, the
herbivore fauna of some species is .12 insect herbivore
species that eat every plant part, from the flowers and
seed pods, to the stems and leaves, to the phloem and
roots.
But all milkweeds are not subject to herbivory by all
insect species, and levels of attack presumably vary
considerably. For example, the very damaging stem
weevil (Rhyssomatus lineaticollis) is primarily known
from A. syriaca, and there apparently are not analogous
herbivores on other milkweeds (Agrawal and Van Zandt
2003). Twenty-four species of root-boring Tetraopes
beetles attack a single species of milkweed each;
apparently the ;100 other North American milkweeds
are not attacked in this way. Monarch butterflies only
attack a subset of the milkweed flora, probably because
their flight paths do not overlap with all species, and
some species inhabit forest understories or occur as
widely scattered individuals. Given that herbivore guilds
vary on plant species and there is a high level of
specificity in the effects of particular plant defenses on
particular herbivores (e.g., Da Costa and Jones 1971,
Giamoustaris and Mithen 1995, Van Dam and Hare
1998, Agrawal and Karban 2000, Van Zandt and
Agrawal 2004a, b), we predict that future studies will
find that certain herbivore species or guilds have been
critical in generating patterns of defense. In identifying
such convergent plant defense syndromes, we may
finally understand the evolutionary association between
herbivore communities and adaptive variation in plant
species.
ACKNOWLEDGMENTS
We thank Rowan Barrett, Erin Douthit, Dana Farmer,
Karen Hooper, Deborah Hopp, Shelley McMahon, Michael
Moody, Lisa Plane, Ana Lilia Reina G., Karin Rotem, Victor
Steinmann, Deborah Tam, Jennifer Thaler, Tom Van Devender, Pete Van Zandt, and Sergio Zamudio for help in the lab
Ecology Special Issue
and field; Robie Mason-Gamer and Steve Lynch for contributions to the phylogenetic study of Asclepias; David Goyder,
Mark Chase, and Marlin Bowles for providing DNA samples;
Bobby Gendron for seeds; Steve Malcolm for discussions on
cardenolide analysis; and Paul Fine, Marc Johnson, Rick
Karban, Marc Lajeunesse, Peter Price, Jennifer and Jon Thaler,
and Cam Webb for comments and discussion. This research
and our laboratories are supported by Natural Science and
Engineering Research Council of Canada, the U.S. National
Science Foundation, and the U.S. Department of Agriculture.
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APPENDIX A
Means and standard errors for seven defense-related traits of the 24 species of milkweed employed in this study (Ecological
Archives E087-116-A1).
APPENDIX B
The three defense clusters separated in multivariate space (Ecological Archives E087-116-A2).
Ecology, 87(7) Supplement, 2006, pp. S150–S162
Ó 2006 by the Ecological Society of America
THE GROWTH–DEFENSE TRADE-OFF AND HABITAT SPECIALIZATION
BY PLANTS IN AMAZONIAN FORESTS
PAUL V. A. FINE,1,2,3,9 ZACHARIAH J. MILLER,3 ITALO MESONES,4 SEBASTIAN IRAZUZTA,5 HEIDI M. APPEL,6
M. HENRY H. STEVENS,7 ILARI SÄÄKSJÄRVI,8 JACK C. SCHULTZ,6 AND PHYLLIS D. COLEY1
1
2
Department of Biology, University of Utah, Salt Lake City, Utah 84112 USA
Environmental and Conservation Programs and Department of Botany, Field Museum of Natural History, Chicago, Illinois 60605 USA
3
Department of Ecology and Evolutionary Biology, University of Michigan, Ann Arbor, Michigan 48109-1048 USA
4
Department of Forestry, Universidad Nacional de la Amazonı´a Peruana, Plaza Serafı´n Filomeno 246, Iquitos, Peru
5
Department of Biology, McMaster University, Hamilton, Ontario L8S4K1 Canada
6
Pesticide Research Laboratory, Pennsylvania State University, University Park, Pennsylvania 16802 USA
7
Department of Botany, Miami University, Oxford, Ohio 45056 USA
8
Zoological Museum, Centre for Biodiversity, FIN-20014, University of Turku, Finland
Abstract. Tropical forests include a diversity of habitats, which has led to specialization in
plants. Near Iquitos, in the Peruvian Amazon, nutrient-rich clay forests surround nutrient-poor
white-sand forests, each harboring a unique composition of habitat specialist trees. We tested
the hypothesis that the combination of impoverished soils and herbivory creates strong natural
selection for plant defenses in white-sand forest, while rapid growth is favored in clay forests.
Recently, we reported evidence from a reciprocal-transplant experiment that manipulated the
presence of herbivores and involved 20 species from six genera, including phylogenetically
independent pairs of closely related white-sand and clay specialists. When protected from
herbivores, clay specialists exhibited faster growth rates than white-sand specialists in both
habitats. But, when unprotected, white-sand specialists outperformed clay specialists in whitesand habitat, and clay specialists outperformed white-sand specialists in clay habitat.
Here we test further the hypothesis that the growth–defense trade-off contributes to habitat
specialization by comparing patterns of growth, herbivory, and defensive traits in these same six
genera of white-sand and clay specialists. While the probability of herbivore attack did not
differ between the two habitats, an artificial defoliation experiment showed that the impact of
herbivory on plant mortality was significantly greater in white-sand forests. We quantified the
amount of terpenes, phenolics, leaf toughness, and available foliar protein for the plants in the
experiment. Different genera invested in different defensive strategies, and we found strong
evidence for phylogenetic constraint in defense type. Overall, however, we found significantly
higher total defense investment for white-sand specialists, relative to their clay specialist
congeners. Furthermore, herbivore resistance consistently exhibited a significant trade-off
against growth rate in each of the six phylogenetically independent species-pairs.
These results confirm theoretical predictions that a trade-off exists between growth rate and
defense investment, causing white-sand and clay specialists to evolve divergent strategies. We
propose that the growth–defense trade-off is universal and provides an important mechanism
by which herbivores govern plant distribution patterns across resource gradients.
Key words: Amazon; ecological gradient; growth–defense trade-off; habitat specialization; herbivory;
phenolics; phylogenetic control; rainforest; reciprocal-transplant experiment; terpenes; tropical trees.
INTRODUCTION
The regional diversity of plant species arises, in part,
because a given species is restricted to a subset of
environmental conditions. But how and why does this
habitat specialization occur? The most common explanation is that habitat specialists are physiologically
adapted to growing in their particular abiotic environManuscript received 8 February 2005; revised 29 April 2005;
accepted 3 May 2005; final version received 11 July 2005.
Corresponding Editor: A. A. Agrawal. For reprints of this
Special Issue, see footnote 1, p. S1.
9
Present address: Department of Ecology and Evolutionary Biology, University of Michigan, Ann Arbor,
Michigan 48109-1048 USA. E-mail: paulfi[email protected]
ment and out-compete other plants that are not so
closely suited to the local conditions (Ashton 1969,
Cody 1978, Bunce et al. 1979). However, herbivore–
plant interactions can also contribute to the evolution of
habitat specialization. Theoretical work has demonstrated that herbivores can alter competitive relationships
among plants, especially when there is spatial heterogeneity of resources (Louda et al. 1990, Grover and Holt
1998). Empirical studies at the population and community levels have documented that herbivores can
reduce plants’ potential distributions, restricting them to
a subset of the habitats that they might physiologically
tolerate (Parker and Root 1981, Louda 1982, 1983,
Louda and Rodman 1996, Olff and Ritchie 1998,
S150
July 2006
PLANT TRADE-OFFS AND SPECIALIZATION
Carson and Root 2000, Harley 2003). Thus, herbivores
can play a major role in determining which species of
plants dominate in a community, as well as in which
habitats a species will be successful.
The lowland Amazonian rainforest near Iquitos, Peru
provides an ideal system to study habitat specialization
and the role of herbivores. Forests in the Iquitos area
grow on a mosaic of soil types; including red clay soils
and extremely infertile white-sand soils (Kauffmann et
al. 1998). The two soil types lie immediately adjacent to
each other, the boundaries are well defined, and each soil
type is associated with a distinctive flora (Gentry 1986,
Vásquez 1997, Fine 2004). White-sand forests are much
more resource limited than clay soil forests (Medina and
Cuevas 1989, Coomes and Grubb 1998, Moran et al.
2000). Resource availability theory proposes that
resource-limited species will have slower growth rates
and higher optimal levels of defense, reflecting the
decreased ability of a resource-limited plant to compensate for tissues lost due to herbivory (Janzen 1974, Coley
et al. 1985, Coley 1987b). Thus we predict that species
growing in white-sand forests should evolve to allocate
relatively more resources to defense than species
growing in clay forests (Fine et al. 2004).
Recently, we reported the results of a reciprocaltransplant experiment of 20 species of seedlings from six
genera of phylogenetically independent pairs of whitesand and clay specialist plants (Fine et al. 2004). We
manipulated the presence of herbivores and found that
clay specialists grew significantly faster than did whitesand specialists in both habitats when protected from
herbivores. But when herbivores were not excluded,
white-sand specialists out-performed clay specialists in
white-sand forests, and clay specialists grew faster than
white-sand specialists in clay forests. These results
strongly supported the existence of a growth–defense
trade-off, with habitat specialization being enforced by
herbivores (Fine et al. 2004).
Here, we test further the predictions of the growth–
defense trade-off by comparing species-level patterns of
growth, herbivory, and defense in this same phylogenetically diverse group of tree species. We predicted that
closely related species specialized to contrasting soil
types should diverge in traits that confer defense vs.
those that confer growth. We investigated the evidence
for such differential investment while controlling for
phylogeny. Therefore, any differences in defense allocation found between closely related white-sand and clay
specialists can be inferred to be traits derived for habitat
specialization. This phylogenetically controlled approach enabled us to investigate the degree of constraint
involved in the type and amount of defense, and to
separate this from the repeated and independent
evolution of defensive traits due to selection from
similar ecological conditions. Second, examining defense
investment with a reciprocal-transplant experiment
allowed us to identify which traits (if any) are phenotypi-
S151
cally plastic as opposed to genetically controlled
adaptations to a particular habitat.
Thus, to test whether the growth–defense trade-off
contributes to habitat specialization in white-sand and
clay forests, we combined field observations and a
reciprocal-transplant experiment to ask the following
questions: (1) Are there differences in herbivore abundance in the two habitats? (2) Is there a difference in the
impact of herbivory in the two habitats, suggesting
selection for greater defense investment in white-sand
habitats? (3) Do white-sand and clay specialists differ in
their type of defensive strategy or in their amount of
defense investment? Are these differences phylogenetically constrained or repeatedly and independently
evolved? (4) Are defensive traits in white-sand and clay
specialists affected by resource-driven phenotypic plasticity? (5) Do white-sand and clay specialists follow the
predictions of the growth–defense trade-off?
MATERIALS
AND
METHODS
Study site and study species
We conducted this research in the AllpahuayoMishana National Reserve near Iquitos, Peru (3857 0 S,
73824 0 W). This 57 600-ha reserve is at ;130 m elevation
and receives more than 3000 mm of precipitation during
the year, with no distinct dry season (Marengo 1998).
Many white-sand specialist trees belong to the same
genera as neighboring clay forest specialists, allowing for
a phylogenetically controlled experiment using edaphic
specialist species. For a reciprocal-transplant experiment, we chose 20 common white-sand and clay
specialists from six genera from five families (see Fine
et al. [2004] for a phylogeny). The genera were Mabea
(Euphorbiaceae), Oxandra (Annonaceae), Pachira (Malvaceae sensu lato), Parkia (Fabaceae), Protium (Burseraceae), and Swartzia (Fabaceae). Each genus was
represented by one white-sand specialist and one clay
specialist, except for Protium, which was represented by
six clay specialists and four white-sand specialists.
Designation of habitat for each species was accomplished by extensive inventories (Fine 2004, Fine et al.
2005) as well as consultation of local floras and other
published species lists from the western Amazon
(Vásquez 1997, Ruokolainen and Tuomisto 1998,
Jørgensen and Léon-Yánez 1999, Garcı́a et al. 2003).
Nitrogen availability
To test for differences in nitrogen availability between
white-sand and clay habitats, we filled 27 nylon stocking
bags filled with 8 g of Rexyn 300 (H-OH) analytical
grade resin beads. In May 2002, we placed the ionexchange resin bags beneath the litter layer and root mat
at the organic material–mineral soil interface at our
white-sand and clay sites (Binkley and Matson 1983).
The bags were collected after five weeks, extracted with
KCl, and measured by standard techniques with an
autoanalyzer (University of Wisconsin Soils Laboratory). Nitrate, ammonium, and root mat depth differ-
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PAUL V. A. FINE ET AL.
ences were tested for significance between soil types with
a Wilcoxon signed-ranks test.
The reciprocal-transplant experiment
We used a reciprocal-transplant experiment to test
whether white-sand and clay specialists had different
growth rates and defense investments as predicted by the
growth–defense trade-off hypothesis. In addition, the
reciprocal-transplant experiment allowed us to test for
phenotypic plasticity of defense investments under
different edaphic and herbivore treatments.
In May 2001, we built 22 control and 22 herbivore
exclosures (3 3 3 3 2 m); half were located in clay forest
and half in white-sand forest. We transplanted 880
seedlings from the six genera into the controls and
exclosures (see Fine et al. 2004). Using the results of the
reciprocal-transplant experiment reported in Fine et al.
(2004), we compared the amount of leaf and height
growth of the plants grown in herbivore exclosures to
the unprotected controls, and estimated the effect
herbivory had on growth rates for each white-sand
and clay specialist. This measure is referred to throughout as ‘‘protection effect.’’ The experiment lasted until
February 2003 (21 mo after transplanting, 18 mo after
first data collection), at which point leaves were collected
to measure defensive traits.
Insect abundance and species richness
To evaluate differences in insect abundance and
composition across habitats, we used a portable black
light attached to a battery to attract insects in five whitesand and five clay sites. During 8–20 December 2002, on
rain-free evenings between 1900–2000, the black-light
was illuminated and suspended above white sheets. We
collected all insects from the families/orders Blattoideae,
Coleoptera, Hemiptera, Homoptera, and Orthoptera.
We excluded all obvious predators and collected all
herbivorous insects from these five groups and counted
and identified them to order and family and then
separated them into morphospecies. Parasitoid wasps
were collected with malaise traps over a two-year period
in 15 white-sand and nonwhite-sand forest sites in the
Allpahuayo-Mishana National Reserve (from 15 of the
same sites described in Fine [2004]) as a part of a much
larger study on ichneumonid wasps (for detailed
methods see Sääksjärvi [2003]). Since these parasitoid
wasps attack either herbivorous insects (or predators of
herbivorous insects), we would expect parasitoid diversity and abundance to track herbivorous insect diversity
and abundance in white-sand and clay forests. To test
for differences between white-sand and clay habitats
(both the black light trap data and the wasp data),
Wilcoxon signed-ranks tests were conducted on the
ranked abundances and numbers of species.
Field herbivory
For herbivory comparisons in addition to those from
the transplant experiment, we chose six genera that were
Ecology Special Issue
common in both white sand and clay forests: Protium
(Burseraceae), Hevea and Mabea (Euphorbiaceae),
Pachira (Malvaceae s.l.), and Macrolobium (Fabaceae).
In September 2000, in the same white-sand and clay sites
where the wasps were collected, we sampled 355
individuals in the field from .20 species of Protium,
two species of Hevea, two species of Mabea, two species
of Pachira, and three species of Macrolobium. Most of
these species were found in only one of the two habitats.
Plants were 1–3 m tall (juvenile trees). We marked newly
expanding leaves (or leaves that had already expanded
but were not toughened) with small colored wires, from
1–10 leaves or leaflets per plant. After five to seven
weeks we estimated the amount of leaf area missing from
the marked leaflet (0–100%). Average amount of leaf
area missing was divided by number of days between
marking and the census (damage per day). These data
were arcsine square-root transformed to improve
normality, and a mixed-model ANOVA (including the
random factor genus and the fixed factor habitat) was
performed on the data to test for differences in
herbivory rate between white-sand and clay habitats.
Impact of herbivory (defoliation experiment)
In February 2003, after collecting leaf material for
chemical analyses from all of the seedlings in the
reciprocal-transplant experiment, we removed 100% of
the remaining leaves to test the effect of defoliation on
white-sand and clay specialists in the two habitats. After
three months, we counted the number of seedlings that
survived defoliation. To compare mortality rates, we
averaged mortality for white-sand specialists and clay
specialists in each of the 44 controls and exclosures
(Protium species in each control and exclosure were
weighted to give each genus equal importance in the
analyses). A fixed-factor ANOVA including the terms
habitat (white-sand or clay), origin (white-sand or clay
specialist), and the origin 3 habitat interaction was used
to assess the effects of origin and habitat on mortality
due to defoliation. Post hoc tests on the individual group
means were performed using the studentized t distribution (appropriate for equal sample sizes; Sokal and
Rohlf 1995).
Defensive characteristics of white-sand
and clay specialists
Comparing differences in herbivory and growth is the
best method of comparing defense investment in whitesand and clay specialists, since this approach takes into
account the entire arsenal of plant defenses as experienced by the actual herbivores (cf. Simms and Rausher
1987). However, to investigate which particular defensive traits are deterring herbivores, we measured two
classes of chemical defenses, a physical defense, and the
nutritional quality of white-sand and clay specialists.
After the transplant experiment was completed, we
collected leaves from all surviving plants to compare
defense investment in white-sand and clay specialists,
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PLANT TRADE-OFFS AND SPECIALIZATION
and to assess the effect of habitat and treatment on the
plasticity of defense investment for each species. We
collected marked mature leaves that were produced after
plants were transplanted. We measured terpenes, total
phenolics, toughness, and available protein for all
seedlings in the reciprocal-transplant experiment. Terpenes and phenolics are carbon-based secondary compounds common to many families of plants, including
those in our research (Mabry and Gill 1979, Bernays et
al. 1989, Schultes and Raffauf 1990). Although phenolics
and terpenes have alternative functions, they commonly
function in herbivore deterrence (Mabry and Gill 1979,
Bernays et al. 1989, Herms and Mattson 1992, Langenheim 1994; but see Harborne 1991, Close and McArthur
2002). Increased toughness of leaves (sclerophylly) is a
mechanical antiherbivore defense that is commonly
found worldwide in plants that live in resource-limited
environments (Coley 1983, 1987a, Grubb 1986, Turner
1994). Finally, available foliar protein is a good measure
of a plant’s nutritional quality. Moran and Hamilton
(1980) hypothesized that plant nutrition can be considered a defensive trait if it can be selected for by herbivore
attack. This can result if herbivores detect nutritional
differences and prefer plants with higher nutrition (cf.
Scheirs et al. 2003). A second mechanism is if slow
growth by herbivores due to low nutrition results in
higher predation rates (cf. Denno et al. 2002).
Chemical defenses
To compare terpene investment among the species,
;500 mg (fresh mass) leaves from the experimental
seedlings were collected at the experimental sites in 2-mL
glass vials and filled with dichloromethane (DCM). This
leaf–DCM mixture was used for qualitative and
quantitative analyses with gas chromatography (GC)
and gas chromatography–mass spectrometry (GCMS).
(See Appendix A for detailed methods of terpene
extraction and analysis.)
For comparisons of total phenolics, ;2 g fresh mass
of mature leaves of 16 individuals (8 protected and 8
unprotected) from each species in the reciprocal-transplant experiment were collected and immediately placed
in plastic tubes containing silica gel desiccant. Leaves
were later analyzed for phenolic compounds in the
Appel/Schultz laboratory at Penn State University.
Whenever possible, bulk tannins were prepared to
provide standards for the phenolic assays of individual
samples. This is a crude purification, and although
nonphenolic materials are unlikely to be present (Hagerman and Klucher 1986; H. M. Appel and J. C. Schultz,
unpublished data), the product is merely a more
representative sample of extractable polyphenols found
in the actual plant than is a commercial standard from
some other source. (See Appendix A for detailed
information on all methodology of phenolic extraction,
purification, and analysis.) Because total phenolics likely
function as an antiherbivore defense by precipitating
available protein (Herms and Mattson 1992), we divided
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our total phenolics obtained as described with available
foliar protein data to create a phenolic : protein ratio
(Nichols-Orians 1991).
Leaf toughness
A ‘‘penetrometer’’ (Chatillon Universal Tension and
Compression Tester, Largo, Florida, USA) was used to
puncture holes through the leaf (or leaflet) lamina to
give a measure of toughness. It was impossible to test the
pair of species from the genus Parkia, since both have
bipinnately compound leaves, with leaflets not much
larger than the 3 mm diameter of the testing machine’s
rod. We standardized the punch position to midway
between leaf tip and base, between the midrib and the
leaf margin, avoiding the main veins where possible. The
punch test measures a combination of shear and
compressive strength and resistance to crack propagation. For these reasons, it has been criticized as not
specifically measuring leaf toughness (Choong et al.
1992). Nevertheless, it is easy to perform in the field and
highly correlated with more specific tests to measure the
physical properties of leaf toughness (shearing and
tearing parameters) (Edwards et al. 2000).
Soluble protein assays
The amount of available foliar protein was measured
at the Appel/Schultz laboratory using the same driedleaf samples collected for the phenolics analyses. (See
Appendix A for detailed methods.)
Statistical analyses of growth and defensive traits
Clay and white-sand specialists in each of the six
genera were the experimental unit. Because there were
four white-sand specialists and six clay specialists from
the genus Protium, the responses for all Protium
individuals were weighted to give each genus equal
importance in the analysis. The four white-sand specialist Protium species were weighted at 0.25, the six clay
specialist Protium species were weighted at 0.167, and
species from all other genera were weighted at 1. We
used fixed factor ANOVAs to test for genus, origin (the
difference between white-sand specialists and clay
specialists), habitat (whether species responded differently depending on where they were planted), and
treatment (whether defense investment differed depending on whether the plants were exposed to herbivores).
Since we had a priori knowledge that different genera
would have different defensive strategies (i.e., some
species have terpene investment, others do not), we used
fixed-factor ANOVAs for defensive traits (genus was
treated as a fixed factor), since our ability to generalize
our results in these analyses to unsampled genera is
limited. Subsequent to the overall test, individual group
means were compared with Tukey hsd post hoc tests.
Defense index
Because different species of plants are likely to employ
different defensive strategies, we therefore devised a
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PAUL V. A. FINE ET AL.
simple method to combine all measures of chemical
defense, leaf toughness, and available protein to investigate whether, for each genus, white-sand specialists
were more defended than clay specialists. Values for
phenolics, terpenes, and leaf toughness were averaged
across both habitats and Z-transformed to give the
defense traits among the six pairs of white-sand and clay
specialists a mean of zero and a standard deviation of
one. Missing data was scored as zero. For available
protein, we standardized the inverse of the species
averages, because a larger amount of available protein
corresponds to lower defense. All four standardized
defense variables were then summed to create a defense
index (DI). For each genus, the DI for the clay specialist
was subtracted from the DI of the white-sand specialist.
This method has the assumption that each of these four
measures has equal weight, which is undoubtedly
incorrect, but preferable than subjectively assigning
different weights to defense types. These difference
scores (DIWS – DICL) were used to test the prediction
that white-sand specialists are more defended than clay
specialists with a one-tailed Wilcoxon paired signedranks test (Zar 1999).
Phylogenetic independence of growth, herbivory,
and defense traits
In order to evaluate whether growth, herbivory, and
defense traits were more similar in closely related genera,
we mapped each of the indices listed above, as well as
each individual defensive trait onto a phylogeny
representing the relationships among the six genera
and 20 species (see Fine et al. 2004). Using the program
Phylogenetic Independence 2.0, we tested whether traits
exhibited significant phylogenetic independence by
comparing the average contrast values (C-stat) among
the actual trait values for the plant species to the
distribution of contrast values created by randomly
placing the trait values at the tips within the topology
2000 times and testing for serial independence (TFSI)
(Abouheif 1999). If a trait is significantly phylogenetically constrained, then the average C-stat for the actual
value will be greater than 95% of the average contrast
values generated by the randomization.
Correlations of growth, defense, and herbivory data
for the six genera
Species averages for growth (leaf area and height,
averaged across habitats), the effect of herbivore
protection on growth (arithmetic difference between
the average leaf area and height with and without
protection, for each white-sand and clay genus averaged
across habitats) and defenses, as described, were Ztransformed and analyzed by a method analogous to
phylogenetically independent contrasts (Harvey and
Pagel 1991). To test for trade-offs, we plotted the values
for the six species pairs and analyzed the six slopes, to
see if the relationship between traits was consistent when
controlling for phylogenetic relationship. We used these
Ecology Special Issue
plots to test our predictions that (1) growth and
herbivory would be positively correlated, (2) growth
and defense would be negatively correlated, and (3)
herbivory and defense investment would be negatively
correlated. Hypotheses about the correlations of traits
were tested by the difference scores of the slopes and
were evaluated for significance with one-tailed Wilcoxon
paired sign-rank tests (Zar 1999).
RESULTS
Differences in nutrient availabilities
Clay forest sites contained significantly more available
nitrogen (Z ¼ 3.53, P , 0.0004) than white-sand forests,
more than twice as much available ammonium (Z ¼
2.71, P , 0.0061), more than an order of magnitude
more available nitrate (Z ¼ 3.59, P , 0.0003), and a
much thinner root mat (Z ¼ 4.89, P , 0.0001; Table 1).
Habitat differences in herbivore abundance
We found no significant differences in herbivore
abundance or species richness between habitats for all
herbivores or any of the six orders of herbivorous insects
that we collected (P . 0.05, Wilcoxon signed-ranks
tests; Table 1). Of the 311 morphospecies collected, 208
were collected only once (67%). Of the morphospecies
collected more than once, 41 were collected only in
white-sand forest, 28 were collected only in clay forest,
and 34 were collected in both forests (33% of the
morphospecies collected more than once). For parasitoid wasps, no statistical differences in abundance or
morphospecies diversity were found between white-sand
and nonwhite-sand forest sites (Table 1). Moreover, in
the reciprocal-transplant experiment, mean effect of
protection for white-sand and clay specialists did not
change between habitats (Fig. 1a, b).
Differences in the magnitude of herbivore attack
Clay specialists showed an average increase in growth
of 0.25 cm2/d in leaf area (paired t test, df ¼ 5, t ¼2.91, P
, 0.05) and 0.0018 cm/d in height (paired t test, df ¼ 5, t¼
2.59, P , 0.05) when protected from herbivores, while
white-sand specialists grew just as well or better in the
unprotected vs. protected treatments. When the effect of
herbivore protection on leaf area and height growth are
Z-transformed and summed, all genera show the same
pattern that clay specialists received a greater benefit from
herbivore protection than did white-sand specialists.
During our study of field herbivory rates in the two
habitats, plants in clay forest sites suffered more than
twice the herbivory rates on their new leaves than did
plants in white-sand sites (mixed model ANOVA, F1, 349
¼ 6.69, P , 0.01). Clay plants lost almost 23% of their
new leaves per month, while white-sand plants lost
slightly .10% (Table 1).
Habitat differences in the impact of herbivory
As predicted, seedlings overall suffered higher mortality due to total defoliation in white-sand habitat than
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PLANT TRADE-OFFS AND SPECIALIZATION
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TABLE 1. Comparisons of white-sand and clay forests for leaf litter depth, nitrogen availability, young-leaf herbivory, and insect
abundance and morphospecies richness (means 6 SE reported).
Variable
Clay forest sites
a
Root mat (cm) (N ¼ 44 plots)
0.91 6 1.0
Nitrogen availability (ppm) from ion-exchange resin bags (N ¼ 27 resin bags)
NO3–
NH4þ
Total nitrogen
349.2 6 25.7
135.2 6 32.7
484.4 6 43.0
8.48 6 0.6
b
b
b
b
Herbivory (% leaf eaten/mo) (N ¼ 355 individuals)
Insect herbivore abundance ((no. individuals)(light trap)1h1)
Total insect herbivore abundance
Blattoid abundance
Coleopteran abundance
Hemipteran abundance
Homopteran abundance
Orthopteran abundance
87.2
3.0
20.0
7.6
20.0
36.6
6
6
6
6
6
6
12.6 a
0.7 a
9.0 a
4.9 a
4.5 a
8.2 a
Insect herbivore species richness ((no. morphospecies)(light trap)1h1)
Total insect herbivore morphospecies
Blattoid morphospecies
Coleopteran morphospecies
Hemipteran morphospecies
Homopteran morphospecies
Orthopteran morphospecies
45.0
2.6
7.6
3.2
15.8
15.8
6
6
6
6
6
6
4.3
0.5
2.1
1.0
2.8
1.7
Parasitoid wasp ((no. individuals)site1(malaise trap)1 for 2 yr)
Total parasitoid wasp abundance
Total parasitoid species and morphospecies
67.7 6 28.5 a
25.5 6 6.4 a
22.8 6 4.3
White-sand forest sites
a
a
a
a
a
a
b
25.6 6 13.8
62.1 6 17.5
87.7 6 23.0
10.3 6 3.3
a
a
a
a
74.8
2.6
22.4
13.4
17.0
19.2
6
6
6
6
6
6
18.1 a
0.9 a
7.8 a
11.7 a
1.9 a
3.6 a
34.8
2.4
8.0
2.0
11.4
10.8
6
6
6
6
6
6
3.9
0.8
2.0
0.4
2.0
2.1
a
a
a
a
a
a
59.9 6 10.8a
22.0 6 3.3 a
Note: Significant differences between forests are indicated by different superscript letters within a row (mixed-model ANOVA,
effect of habitat for herbivory, Wilcoxon signed-ranks tests between habitats for litter depth, nitrogen availability, insect
abundance, and species richness).
they did in clay habitat (effect of habitat, F1,84 ¼4.96, P ,
0.05). In addition, white-sand specialists suffered significantly more mortality than did clay specialists in both
habitats (effect of origin, F1,84 ¼ 22.8, P , 0.0001; Fig. 2).
Differences in defense investment
Type of defense.—We found strong evidence for
phylogenetic constraint for type of defense. The main
effect of genus was always significant for differences in
terpenes, phenolics, leaf toughness, and available foliar
protein. Moreover, it is clear that different genera are
relying on different defense strategies, as each of the six
genera had a distinct defense investment pattern (see
Appendix C). For example, only two genera, Oxandra
and Protium, contained measurable terpenes identified
by GCMS (Appendix C). Similarly, only two genera,
Pachira and Parkia had white-sand specialists with
obviously tougher leaves than clay specialists. The
pattern of high phenolic investment and low available
foliar protein in white-sand specialists was more
consistent across the six genera, but still there were
exceptions (Oxandra and Protium for phenolics, Mabea
for available protein; Fig. 3).
Whereas different genera invest in different defensive
strategies, we found no consistent relationship between
any particular defensive traits that would suggest either
a negative trade-off or a synergistic relationship between
defense types (Fig. 3).
Amount of defense investment.—We found that fivesixths of the genera have a higher defense index (DI) in
the white-sand congener than in the clay congener, and
that our prediction of higher defense in the white-sand is
supported (one-tailed Wilcoxon paired signed-ranks
test, T0.05(1), 6 ¼ 1, P , 0.05, Fig. 3).
For phenolic compounds, white-sand specialists overall had significantly higher values for both total
phenolics (effect of origin, F1, 292 ¼ 50.3, P , 0.0001)
and phenolic : protein ratios (F1, 292 ¼ 128.2, P ,
0.0001) with, respectively, three-sixths and four-sixths
of the genera exhibiting significant relationships in the
predicted direction (Fig. 3, see Appendix D). The two
genera that invested in terpenes, Protium and Oxandra,
exhibited very different patterns of terpene investment in
their white-sand and clay specialists (see Appendix D).
Oxandra xylopiodies, the clay specialist, had significantly
higher sesquiterpene and total terpene concentrations
than O. euneura, the white-sand specialist (P , 0.05,
Tukey tests; see Appendix D). In contrast, Protium
white-sand specialists had higher monoterpene and total
terpene concentrations than did Protium clay specialists
(P , 0.05, Tukey tests; see Appendix D). Both Oxandra
and Protium white-sand species had significantly higher
concentrations of diterpenes and other resins than did
their respective clay specialists (see Appendix D).
There was no overall effect of origin on leaf toughness
(see Appendix D). In contrast, white-sand species had
lower available protein in their leaves than clay specialists (significant effect of origin, F1, 292 ¼ 393.5, P ,
0.0001; see Appendix D).
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PAUL V. A. FINE ET AL.
Ecology Special Issue
FIG. 1. The effect of origin and habitat in the reciprocal-transplant experiment for (a) the effect of herbivore protection on leaf
growth (cm2/d), (b) the effect of herbivore protection on height growth (cm/d), (c) total terpenes and resins (mL terpenes/mg of dry
leaf material), (d) total phenolics (g phenolics/g dry leaf material), (e) phenolic : protein ratio (phenolics divided by available
protein, a unitless ratio), (f) leaf toughness (grams of mass to punch a 3-mm rod through a leaf; 1.0 g ¼ 1.38 kPa), and (g) available
protein (g soluble protein/g dry leaf material). Histograms show means 6 SE. Different letters above bars indicate significant
differences among the different groups (Tukey tests).
Defensive traits and phenotypic plasticity
There was no significant overall effect of habitat for
terpenes (Fig. 1c). Aside from the outlier behavior by one
species, there was no evidence of phenotypic plasticity in
phenolic investment (Fig. 1d). Swartzia cardiosperma is
the only species of 20 in the experiment that showed a
significant effect of habitat for phenolic : protein ratios
(see Appendix C).
The effect of habitat on leaf toughness was highly
significant (F1, 388 ¼ 51.6, P , 0.0001; Fig. 1f). Sixteen of
17 species measured had greater leaf toughness in whitesand than clay habitat; three of those were significant (see
Appendix C). In contrast, even though nitrogen availabilities differed by more than five times in the two habitats,
there was no significant effect of habitat on available
protein for either white-sand or clay specialists (Fig. 1g).
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PLANT TRADE-OFFS AND SPECIALIZATION
S157
Phylogenetic independence of growth and defensive traits
FIG. 2. Mortality results of the 100% defoliation experiment. Bars show average mortality (6SE) for each origin and
habitat combination. Different letters above bars indicate
significant differences (post hoc tests, studentized t distribution).
Evaluating the trade-off: growth vs. defense vs. herbivory
The growth index (GI) and the herbivory index (HI)
showed a significant positive relationship (all six of the
genera with positive slope, T0.05(1), 6 ¼ 0, P , 0.025;
Fig. 4a). There was a significant negative trade-off
between GI and total DI, with five-sixths of the genera
showing a negative slope (T0.05(1),6 ¼ 1, P , 0.05,
Wilcoxon paired signed-ranks test; Fig. 4b). Finally, DI
showed a significant negative relationship with HI
(T0.05(1), 6 ¼ 1, P , 0.05; Fig. 4c).
There was evidence for significant phylogenetic
dependence for total phenolics (C-stat ¼ 0.34, P ,
0.002), terpenes (C-stat ¼ 0.34, P , 0.002), and leaf
toughness (C-stat ¼ 0.32, P , 0.012). The defense index
(C-stat ¼ 0.23, P , 0.11) and available protein (C-stat ¼
0.11, P , 0.148) exhibited a trend toward phylogenetic
constraint. We found no evidence for phylogenetic
constraint in GI (C-stat ¼ 0.07, P , 0.35) and the
protection effect index (C-stat ¼ 0.01, P , 0.399), results
that in part might reflect an artifact of our design
because our sampling within each genus was limited to
paired white-sand and clay specialists, which maximized
the variation between closely related species.
DISCUSSION
Habitat differences in herbivore populations
Two separate measures of herbivorous insect communities found statistically similar diversity and abundance in the two forest types. In addition, a full third of
the morphospecies that were collected more than once
occurred in both habitats. These results are likely
explained by the large home range and dispersal
capabilities of many herbivorous insects (Stork 1988),
coupled with the fact that most white-sand forest
FIG. 3. The defense index (DI) scores for each genus are plotted, showing the difference between clay (CL) and white-sand (WS)
specialists. The three-letter labels of the lines correspond to the genus table below the plot. Black boxes in the table indicate a
significantly higher defensive trait for that genus in the white-sand specialist, and shaded boxes indicate a significantly higher
defensive trait for the clay specialist (contrary to predictions). The final column shows the DI scores for each genus, with a negative
number signifying a score in the direction contrary to predictions.
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PAUL V. A. FINE ET AL.
Ecology Special Issue
insect herbivores indeed range into white-sand forests.
Moreover, these patterns are consistent with our
herbivory data from the reciprocal-transplant experiment showing that clay specialist seedlings were attacked
at similar frequencies whether they were transplanted
into clay or white-sand forests (Fig. 1a, b).
Habitat differences in the impact of herbivory
We predicted that the impact of herbivory would be
greater in a white-sand forest, because it is more difficult
for plants to replace the nitrogen lost to herbivores
(Coley 1987b, Craine et al. 2003). This prediction was
supported by the fact that all plants transplanted into
white-sand forest had significantly higher mortality
when defoliated than those transplanted into clay forest
(Fig. 2).
In the defoliation experiment, white-sand specialists
suffered a significantly higher mortality rate than did
clay specialists (Fig. 2), confirming a key prediction of
the growth–defense trade-off that white-sand specialists
ought to have more difficulty replacing tissue lost to
herbivores (Coley et al. 1985). This differential response
to defoliation by species adapted to low-fertility soils vs.
species adapted to high-fertility soils was also found in a
study in Singapore (Lim and Turner 1996). Thus, when
heavily defended white-sand species are defoliated, they
lose costly leaves that represent a high percentage of
their energy budget. Due to their slow growth rate, they
are then unable to compensate, and this in turn increases
their mortality rate (Coley et al. 1987b). For this reason
the impact of herbivory appears to be substantially
greater for plants adapted to low-resource conditions.
Differences in defense investment
FIG. 4. Plots of the six species pairs for (a) growth rate
index vs. protection effect index, (b) growth rate index vs.
defense index, and (c) herbivory vs. defense index. The
consistency and magnitude of these slopes were used to test
the predictions of the growth–defense trade-off hypotheses. The
three-letter labels correspond to the six genera listed in Fig. 3.
habitats in the Iquitos area are only a few square
kilometers. It is important to recognize that our
herbivore sampling was extremely limited and precludes
us from drawing definitive conclusions concerning the
relative abundance of herbivore populations in whitesand and clay habitats. Nevertheless, our herbivore
estimates represent two independent corroborations that
Type of defense.—Different genera adopted dramatically different defensive strategies. There was a consistent signal of phylogenetic constraint in our analyses
of plant defenses, as genus was a significant factor in
each defense variable (see Appendices B and D), and
tests for phylogenetic independence confirmed this. In
terms of terpenes, phenolics, toughness, and low
nutrition, there was no consistent ‘‘syndrome’’ of
defensive investment in the six genera; instead, each
genus allocated to different combinations of these (and
presumably other unmeasured) traits. Indeed, there is
little theoretical or empirical support for the idea of a
general negative trade-off between types of defensive
strategies (Koricheva et al. 2004, Agrawal and Fishbein
2006).
Amount of defensive investment.—For Protium, we
found higher concentrations of terpenes in white-sand
specialists as predicted, but for Oxandra the reverse
pattern was found (Fig. 3). The terpene profile of
Oxandra is driven by sesquiterpenes, which could
possibly be serving a function other than defense, or
do not function in a dosage-dependent fashion (Gershenzon and Croteau 1991, Langenheim 1994). In
contrast to sesquiterpenes, both Protium and Oxandra
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PLANT TRADE-OFFS AND SPECIALIZATION
white-sand specialists were found to have higher
diterpenes and other resins compared to clay specialists
(see Appendix B). Diterpenes are not volatile and are
thought to be either toxic (Lerdau and Penuelas 1993) or
a type of physical defense against herbivores or
pathogens (Langenheim 1994).
Total phenolics and phenolic : protein ratios were
significantly higher overall for white-sand specialists
than for clay specialists (see Appendix B). In our study,
percentage dry mass of total phenolics ranged 3–37%, a
large range that is certainly an overestimate and
highlights the difficulty of precise phenolic quantification in the laboratory (Appel et al. 2001). Finally, we
found significantly less available protein in white-sand
specialists. This was the most consistent trait, with fivesixths of the species pairs showing the same pattern (see
Appendix D).
Defensive traits and phenotypic plasticity
We did not find many cases of phenotypic plasticity in
the seedlings’ allocation to chemical defenses. Very few
species had significant increases or decreases in terpenes
or phenolics due to habitat (Fig. 1c, d). Similarly,
available foliar protein did not change depending on
where the seedlings were planted (Fig. 1g), even though
nutrient levels were significantly different between the
two habitats. We conclude that, for the genera in our
study, herbivore resistance due to chemical defenses and
available protein content is due to genetically based,
fixed traits (but see Boege and Dirzo 2004). Thus,
defense differences result from natural selection by
herbivores and are not just passive responses to differences of available nutrients in the soils.
In contrast to our results with chemical and nutritional defenses, we found a strong overall effect of
habitat on leaf toughness, which was significant for three
species (Fig. 1f; see Appendix C). Overall, we found that
leaf toughness was significantly higher for white-sand
species in only two genera, Parkia (which we were not
able to measure with our penetrometer, but for which
the pattern was obvious) and Pachira. In contrast, two
previous studies found that white-sand plants had
significantly tougher leaves than clay plants (Coley
1987a, Choong et al. 1992). These studies did not take
phylogeny into account, but their results for white-sand
and clay species averages were much more divergent
than ours. One possibility for the discrepancy is that
toughness in these two studies was only measured in the
plants’ home habitats. While our results in no way
negate the potentially strong selective effect of herbivores on sclerophylly, they do suggest that future
comparisons of white-sand and clay species should not
only be controlled for phylogenetic relationships, but
also for resource availability.
Evaluating the growth–defense trade-off
The evolutionary trade-off between growth and
defense is illustrated by the data graphed in Fig. 4.
S159
When the protection effect of each species is plotted
against the overall growth rate (Fig. 4a), all six genera
exhibited a positive relationship. In each genus, herbivores selectively attacked the faster growing species
more than the slower growing species. This is evidence
that faster growing plants have lower resistance to
herbivores, consistent with the predictions of the
growth–defense trade-off. Coley (1983, 1987b) found
the same relationship in Panama where the growth rates
of 40 species of trees were positively correlated with their
rates of herbivory.
In the graphs of Fig. 4a, the lengths of the lines
correspond to the amount of variation in growth rate
and antiherbivore traits within the species (white-sand
and clay) of each genus. For example, some genera like
Parkia are represented by longer lines in the horizontal
direction (Fig. 4a), because this genus includes both
shade-tolerant species and those that thrive in high-light
conditions. Therefore, the clay specialist in Parkia is a
very fast grower relative to the Protium and Swartzia
species, all of which are shade-tolerant species and never
found in tree-fall gaps (P. V. A. Fine, personal
observation). Yet the fact that the slopes of the six lines
in Fig. 4a are so similar suggests the existence of a
universal trade-off, even among species with such
disparate growth rates and defensive strategies.
When the defense index (DI) scores for the six genera
are plotted against their growth rate index (GI) (Fig.
4b), we found a significant negative relationship, with
five of the six genera having higher DI scores in the
slower vs. faster growing species. The slopes in this
graph exhibit much more variation than the growth vs.
herbivory graph (Fig. 4a), likely due to the coarse
method by which we attempted to quantify defense
investment in these species. The one outlier genus,
Mabea, shows the opposite relationship than the other
five genera, with a higher DI score in its faster growing,
clay specialist. Because the slower growing (white-sand
specialist) Mabea received the least amount of attack
from herbivores in the experiment (see Appendix B), it
seems likely that it actually is very well defended and we
failed to accurately quantify its defensive investment.
One reason for this may be that Mabea is the only genus
of the six that produces copious white latex, and we did
not quantify this trait in our comparisons. The
herbivory vs. defense graph (Fig. 4c) echoes this point,
with Mabea the only genus whose DI score does not
match its herbivory index score.
Phylogenetic approach to studying
the growth–defense trade-off
Our approach using multiple pairs of congeners from
ecologically divergent habitats differs from some other
more quantitative approaches that have used data on
branch lengths from a phylogenetic tree to test for
correlations between particular traits and habitat
association (Cavender-Bares et al. 2004). In our
approach, we ignore branch lengths by design, since
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PAUL V. A. FINE ET AL.
each of our genus pairs includes just one representative
from each habitat type. But in terms of comparisons of
growth rate, herbivory, and overall defense as it relates
to white-sand and clay specialization, our results
indicate that variation in branch lengths among our
pairs matters very little: All six pairs exhibit similar
trade-offs (Fig. 4a). Moreover, if this trade-off has a
bearing on a plant’s distribution onto white-sand and
clay soils, then evidence for it must be present both in
the most recently derived specialist pairs as well as in
pairs of species that have persisted for millennia in their
particular habitats. By contrast, if we were interested in
the evolution of particular traits (like phenolics per se),
then inclusion of some estimate of divergence time (and
denser sampling within genera) would certainly be
warranted.
One limitation of the congeneric pair approach is that
one’s sample is limited to genera that include species that
occur in both of the habitats of interest. It would be
interesting to compare genera that were restricted to
only white-sand or only clay habitats to see if the
growth–defense trade-off was evident in comparisons
with their sister taxa (that were specialists to the
contrasting habitat). Our way of calculating a defense
index (DI) works well precisely because defensive traits
are phylogenetically conserved between close relatives,
allowing for quantitative comparisons of the same
qualitative trait. If we used pairs of taxa that were not
closely related, it would become much more difficult to
capture the defense allocation of each contrasting
species within a DI, although protection effect would
still be an appropriate measure for comparison.
Including a phylogenetic context is vital for studies of
the growth–defense trade-off for at least two reasons.
First, controlling for phylogeny is critical because it
reduces the noise of interspecific variation that can easily
obscure the true patterns in the data (Agrawal and
Kotanen 2003). For example, there is substantial
variation in both growth and herbivory rates among
these six species pairs (Fig. 4a). Indeed, if phylogenetic
relationships are ignored and one plots all 12 species
averages for growth and herbivory together, the
correlation between growth and herbivory disappears.
Such an analysis treats each species’ average for growth
rate and defense as an independent data point, an
assumption that is clearly not valid (Harvey and Pagel
1991).
Second, it allows one to make direct inferences about
the phylogenetic patterns of plant defensive traits and
how they relate to habitat specialization. For example,
terpenes, phenolics, and leaf toughness in our genera
exhibit strong signals of phylogenetic constraint. But,
since species within those genera have a diverse group of
defensive options, this apparent lack of evolutionary
lability to completely turn on or off investment into a
particular class of defense does not result in lineages
becoming ecologically constrained to one particular soil
type. For this reason, we observed no signal of
Ecology Special Issue
phylogenetic constraint in protection effect (i.e., amount
of herbivory) or growth in the genera. This is almost
certainly due to the fact that the relevant traits that
confer resistance to herbivores in low-resource habitats
and faster growth in high-resource habitats are evolutionarily labile and involve quantitative increases and
decreases of already-present qualitative traits related to
growth and defense.
CONCLUSIONS
By manipulating the presence of herbivores, we
discovered that defense differences interact with edaphic
factors to restrict species to their specialized habitats.
Although the potential for herbivore attack was similar
in the two habitats, the impact of herbivory on growth
and survivorship was much stronger in white-sand
forest, giving solid evidence of strong selection for
effective defense in white-sand forests. Measurements of
defenses confirmed that white-sand specialists have a
higher overall defense investment, although each genus
expressed a different combination of defensive traits.
These results confirmed theoretical predictions that
species in low resource habitats evolve a higher optimal
defense investment. By testing for defense and growth
differences in white-sand and clay specialists within an
explicit phylogenetic framework, our results strengthen
the case that the trade-off between growth and defense is
universal and governs patterns of plant distribution.
This fundamental trade-off, mediated by herbivores,
represents an important mechanism of plant coexistence
that has been largely overlooked in studies of plant
habitat specialization and niche assembly. Furthermore,
this interaction between herbivory and resource heterogeneity should promote divergent selection in plant
growth and defense strategies that increase the potential
for ecological speciation.
ACKNOWLEDGMENTS
We thank the Peruvian Ministry of Natural Resources
(INRENA) for permission to conduct this study; D. Del
Castillo, L. Campos, E. Rengifo, and S. Tello of the Instituto de
Investigaciones de la Amazonı́a Peruana (IIAP) for logistical
support and permission to work in and around the Estación
Allpahuayo; E. Aquituari, M. Ahuite, J. Guevara, M. Jackson,
M. Olórtegui, J. Reed, and F. Vacalla for field assistance; P.
Evans, J. Becerra, and M. Lott, for advice on terpene analyses;
J. Heath, K. Pickering, and C. Cohen for assistance in the
Appel/Schultz lab; J. Álvarez, L. Bohs, D. Dearing, D. Feener,
R. Foster, T. Kursar, and S. Schnitzer, for advice during the
entire project; and A. Agrawal, M. Ayres, S. DeWalt, G. Paoli,
and an anonymous reviewer for improving the manuscript.
Support was provided by an NSF Predoctoral Fellowship to P.
V. A. Fine, an NSF Doctoral Dissertation Improvement Grant
to P. V. A. Fine and P. D. Coley, the Michigan Society of
Fellows to P. V. A. Fine, an NSF Long-term Research in
Environmental Biology Grant to J. C. Schultz, and NSF grant
DEB 0234936 to P. D. Coley.
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APPENDIX A
Detailed methods for the chemical analysis of terpenes, phenolics, and soluble protein (Ecological Archives E087-117-A1).
APPENDIX B
A table presenting all fixed-factor ANOVAs conducted on the growth and defense variables (Ecological Archives E087-117-A2).
APPENDIX C
A table presenting growth, herbivory, and defensive traits measured in the experiment for each species in the two soil types
(Ecological Archives E087-117-A3).
APPENDIX D
Figures showing the effect of origin (white-sand vs. clay specialists) and the genus 3 origin interaction for (a) leaf growth, (b)
height growth, (c) the effect of herbivory on leaf growth (protection effect), (d) protection effect on height growth, (e–j) chemical
defenses, (k) leaf toughness, and (l) available foliar protein (Ecological Archives E087-117-A4).
ENDPLATES. (Top) A British mesotrophic grassland (meadow) plant community. Plant diversity can be divided into a hierarchy
of special components, which correspond to a hierarchical set of niches. For an exploration of the correspondence between
ecological and evolutionary hierarchies, see the article by Silvertown et al. (pp. S39–S49). (Bottom) Larval monarch butterfly
(Danaus plexippus) on Asclepias californica. This plant species has high levels of latex and trichomes, which reduce feeding by
monarchs and form the basis for one of the milkweed defense syndromes (see the article by Agrawal and Fishbein, pp. S132–S149).