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Global Ecology & Biogeography (2000) 9, 39 – 58
I S L A N D B I OGE OG R AP HY S P E C I A L I S S U E
A species-based theory of insular zoogeography
Blackwell Science, Ltd
MARK V. LOMOLINO Oklahoma Biological Survey, Oklahoma Natural Heritage
Inventory and Department of Zoology, University of Oklahoma, Norman, OK 73019, U.S.A.
E-mail: [email protected]
ABSTRACT
1 I present an alternative to the equilibrium
theory of island biogeography, one which is based
on the premise that many of the more general patterns in insular community structure
result from, not despite, nonrandom variation
among species.
2 For the sake of simplicity, the model is limited
to patterns and processes operating over scales
of ecological space and time: evolution is not
included in the current version of the model.
3 The model assumes, as did MacArthur and
Wilson’s model, that insular community structure
is dynamic in ecological time, but the model
does not assume a balance, or equilibrium, of
immigration and extinction.
INTRODUCTION
The great diversity of patterns we study in biogeography all derive from three fundamental forces:
immigration, extinction and evolution. Therefore,
our ability to understand patterns in insular
community structure will rest upon our understanding how these processes vary with characteristics of the species and islands in question.
The tripartite model of island biogeography
(see Lomolino, 2000; see also Heaney, 1986,
2000) represents a very general conceptualization of these interrelationships, explaining how
the relative importance of immigration, extinction and evolution should vary with island area
and isolation, and with differences in resource
requirements and immigration abilities among
species groups. My purpose in this paper is to
present a more focused, species-based model
that can serve as a useful tool for understand-
4 The model presented here is hierarchical,
phenomenological (it requires little parameterization beyond that which is directly derived from
distributional data), graphical, and it includes
potential feedback processes (including interspecific interactions).
5 The model offers an alternative explanation
for a variety of patterns ranging from distributions of individual species, species–area and species–
isolation relationships, to patterns of assembly
of insular communities. The model also generates some new predictions and identifies some
potentially important areas for future studies.
Key words Area, assembly, equilibrium theory,
extinction, immigration, insular distribution
function, island biogeography, isolation.
ing a diversity of patterns within ecological
space and time. The model presented here thus
operates within a subset of the tripartite space,
reducing it to a two-dimensional model where
speciation is assumed to be too infrequent to
have a significant influence on insular community structure. Speciation will, of course, set the
pool of species that can colonize a given island,
but the species pool is assumed to be constant
across the archipelago. Even within ecological
time, however, this assumption may be challenged. For example, focal archipelagoes may
lie within transition zones between two or more
prospective species pools (e.g. montane forest mammals of the American South-west [Lomolino
et al., 1989; Lomolino & Davis, 1997;] and many
insular biotas of Wallacea).
My hope is that other researchers will take up
the challenge of expanding this two-dimensional
model or will offer alternative models that include
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M. V. Lomolino
the influence of speciation and other events occurring over evolutionary and geological time scales
(see Heaney, 2000; Ward & Thornton, 2000).
Despite this limitation, the model presented
here can explain a diversity of patterns in insular
community structure ranging from distributions
of particular species within an archipelago to
differences in patterns of species richness and
composition across archipelagoes and among
faunal groups. Let me first summarize some
of the salient features of this model (see also
Lomolino, 1986).
1 The model is species-based. Not only do
islands vary in fundamental characteristics such
as isolation and area, but species also differ
with respect to their abilities to immigrate to and
to survive on islands. To the degree to which
such interspecific variation is nonrandom, it
will result in regular patterns in insular community structure. Again, many of the common
patterns in biogeography may result from, not
despite, nonrandom variation among species (see
Diamond, 1975).
2 The model assumes that insular community
structure is dynamic in ecological time. Immigration and extinction of focal species is assumed
to be recurrent, but the model does not assume
a balance, or equilibrium, of these processes.
3 The model is primarily phenomenological. It
requires very little parameterization beyond that
which can be directly derived from distribution
patterns of the focal species.
4 The model is hierarchical. It addresses the
factors that may influence distribution patterns
at scales ranging from single species and particular islands to complexes of different faunal
groups and archipelagoes.
5 The model includes potential feedback among
system components. One factor that becomes
important at the scale of entire communities is
interspecific interaction. The community level version
of this model explicitly considers interspecific
interactions and the potential for covariation
of immigration abilities, resource requirements
and ecological dominance (i.e. the ability of one
species to outcompete or prey upon another).
6 The model is graphical and, at least in terms
of the examples presented here, restricted to
zoogeographic patterns. These features reflect my
limitations and not any ultimate constraints of
the model. I hope that others, more proficient
with mathematical modelling and with more
knowledge regarding biogeography of insular
plants will choose to fill in these gaps. Indeed,
metapopulation biologists such as Hanski and
colleagues (e.g. see Hanski & Gilpin, 1997) have
made great progress modelling the dynamics of
populations occupying patchy habitats. Unlike
the model presented here, metapopulation
models are largely stochastic and equilibrial. The
deterministic, graphical model presented here,
and metapopulation models may be viewed as
alternative, but complementary approaches, both
derived from the dynamic theory of island biogeography championed by MacArthur & Wilson
(1967).
In the following sections I develop the model
in a hierarchical fashion, starting with a singlespecies, one-archipelago version of the model.
I then expand the model to predict how the
distribution of a focal species should vary with
differences among archipelagoes: namely differences in immigration filters and regional productivity (i.e. area-specific carrying capacity of
insular environments). Finally, I model differences in insular distributions of suites of species
to explore patterns in assembly of communities
within as well as among archipelagoes.
A FOCAL SPECIES MODEL
The fundamental component of the hierarchical
model is the insular distribution function, which
describes how populations of a particular focal
species should be distributed across the two
geographical dimensions of island area and
isolation (Fig. 1; see Lomolino, 1986). The insular
distribution function can be represented by a
line, or curve, separating islands inhabited by a
focal species from those where the species is
absent. The salient features of the insular distribution function can be derived with a very
conservative set of assumptions.
1 The immigration rate of the focal species (number
of individuals of this species reaching the island
per unit time) decreases with increased isolation.
Therefore, the time between immigrations (Ti)
should increase with isolation. (Note that immigration rate as defined here is conceptually
different from that of the equilibrium model.
Because there is no reason to believe that the
number of individuals reaching an island per
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Fig. 1 The general form of the insular distribution
function for a focal species (dark symbols indicate
presence, open symbols indicate absence of the
focal species). The insular distribution function
delineates those combinations of area and isolation
for which expected persistence time equals time
between immigrations. The focal species is expected
to occur on islands that fall above the insular distribution function.
Fig. 2 Frequency distribution and cumulative frequency distribution of immigration abilities (distances,
units arbitrary) for individuals of a hypothetical focal
species. Because immigration abilities are expected
to exhibit a strong central tendency (likely a lognormal or log-skewed distribution), the cumulative
number of individuals whose immigration abilities
extend beyond a target island should decrease
(and Ti should increase) as a sigmoidal function
of island isolation.
unit time will vary with species richness, the
current model does not predict an equilibrium
of immigration and extinction).
2 The persistence time (T p), which equals the
expected time to extinction of a newly established population of the focal species, should
increase with island area.
3 Populations of the focal species should occur
on those islands where Tp > Ti, i.e. where additional immigrants reach an island before a resident population is expected to go extinct.
4 Immigration abilities of the focal species
should exhibit a strong central tendency. The
frequency distribution of this trait should be
unimodal and likely to take the form of a lognormal or log-skewed function, with most individuals clustering about the modal immigration
ability or distance (Fig. 2).
Given that the above assumptions hold true
for most species, we can derive the form of the
insular distribution function and explain some
common patterns as well as some apparent anomalies in island biogeography.
First, because Ti increases with isolation, Tp (and
therefore island area) also must increase with
isolation to maintain populations of the focal
species. Thus, the insular distribution function
should have a positive slope: i.e. critical minimum areas to maintain populations of the focal
species should increase with island isolation.
Second, given that Assumption 4 is valid, then
the cumulative number of individuals capable
of immigrating to an island should decrease as
a negative sigmoidal function of island isolation (dashed line in Fig. 2). Accordingly, critical
minimum areas (and insular distribution functions) should vary as positive sigmoidal functions
of isolation (Fig. 1). The predicted nonlinear
form of the insular distribution function has
important implications with respect to scale
dependence of biogeographic patterns, which I
discuss in a subsequent section.
The precise form of the insular distribution
function will depend on a variety of species’
and system characteristics that affect Ti and Tp.
Yet all insular distribution functions have two
readily interpretable components:
• an intercept, which is a direct measure of
resource levels required to maintain populations
of the focal species on the less-isolated islands,
and;
• a generalized slope, which is an inverse measure of immigration abilities of the species.
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Fig. 3 The predicted effects of interspecific differences in resource requirements on insular distribution
functions (size of circles depicts relative size of the
islands). Regional differences in primary productivity
(or carrying capacity per area of island) should
have equivalent effects on insular distribution functions; i.e. intercepts of insular distribution functions
should be higher for larger, more specialized and
more energy intensive species, or for the same
species occurring in archipelagoes with relatively low
primary productivity.
Therefore, intercepts of insular distribution
functions should increase as we consider larger,
more specialized, or otherwise more resourceintensive species (Fig. 3). The slope of the insular
distribution function, which describes how rapidly
critical minimum areas should increase with
isolation, should be higher for less vagile species
(Fig. 4). The actual intercepts and slopes of
insular distribution functions can be estimated
using empirical data on species distributions
and logistic regression, linear or nonlinear regression, or discriminant analysis (see Lomolino, 1986;
Lomolino et al., 1989).
Differences in insular distribution
functions among archipelagoes
Abilities of species to immigrate to and maintain
populations on islands are influenced by a combination of species and system characteristics.
While islands vary substantially in their physical
characteristics, archipelagoes are typically assumed
by biogeographers to be internally homogeneous
Fig. 4 The predicted effects of interspecific differences in vagility (active immigration abilities) on
insular distribution functions. Regional differences
in impedances of immigration barriers should have
equivalent effects on insular distribution functions;
i.e. slopes of insular distribution functions should
be higher for less vagile species, or for the same
species occurring in archipelagoes with more formidable barriers.
with respect to other factors affecting immigration
and extinctions. That is, immigration filters, climate, primary productivity and other regional
scale factors are assumed to be invariant across
islands. When making comparisons among archipelagoes, however, this assumption becomes more
tenuous and may mask some interesting, large
scale patterns. In fact, archipelagoes are likely
to differ with respect to two principal features
that bear directly on insular distribution functions: the nature of immigration filters (i.e. the
characteristics of intervening seascapes or landscapes that impede immigration), and the level
of primary productivity, habitat characteristics
and other factors that influence carrying capacities of insular environments.
The effects of both of these regional scale
factors on differences in insular distribution functions for a given species among archipelagoes
are equivalent to those associated with differences among species. That is, their effects can be
modelled as changes in the slope and intercept of
the insular distribution function (Figs 3 and 4).
For a given focal species, the intercept of its
insular distribution function should be higher
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Fig. 5 Effects of different sampling regimes on apparent patterns of distribution of a hypothetical species (darkened symbols depict presence, open symbols depict absence). Depending on the region sampled,
the same species may appear to exhibit a variety of distribution patterns with respect to island area and
isolation (see text).
in archipelagoes with relatively low primary
productivity and the slope should be steeper in
archipelagoes with more formidable barriers to
immigration. Again, both immigration abilities
and abilities to maintain insular populations are
relative measures that may only have meaning
in the context of specific archipelagoes and
environmental conditions.
Scale-dependence of insular distribution
functions
If insular distribution functions were linear,
then patterns of insular distributions would be
scale independent. For each incremental increase
in isolation, critical minimum areas would increase
a constant amount regardless of the actual range
in isolation considered. However, insular distribution functions should not be linear, but
sigmoidal. Depending on the portion of the geographical template surveyed, different researchers
may make some apparently contradictory inferences about the factors limiting distributions
of the same species. For example, researchers
surveying islands in regions a or c of Fig. 5
would conclude that the insular distribution of
this species is primarily limited by island area,
but would estimate very different critical areas
(i.e. much smaller in a than in c). On the
other hand, researchers surveying islands in
region b of Fig. 5 would conclude that area
is relatively unimportant and that the insular
distribution of this species is most strongly
influenced by island isolation. These apparent
contradictions are clarified, however, when we
sample a much broader range of islands ( d
in Fig. 5) to reveal that the three patterns are
components of a more general pattern, the
insular distribution function.
Some examples of insular distribution
functions
The empirical insular distribution functions
illustrated in Fig. 6 exhibit the predicted form
of this function, i.e. critical minimum areas
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M. V. Lomolino
Fig. 6 Empirical distribution patterns of four species of terrestrial vertebrates inhabiting islands. a) masked
shrew in the Great Lakes archipelagoes, U.S.A., Lomolino (1993); b – d) birds of the guild Parus in the
Danish archipelago, data from Wiggins & Moller (1997); e) the endangered Ouachita Mountain river shiner
Lythrurus snelsoni Robison, among isolated pools of eastern Oklahoma, U.S.A., data from Taylor (1997);
darkened symbols depict presence, open symbols depict absence.
to maintain insular populations of these species increase with isolation. I have also analysed
insular distribution functions for a large number of other species (mostly mammals and birds)
from a variety of archipelagoes of true or habitat islands (Lomolino, 1986, 1988; Lomolino
et al., 1989; Lomolino & Davis, 1997, 1988). Not
surprisingly, insular distribution functions vary
markedly among species and archipelagoes, but
much of the variation appears consistent with
the model’s predictions. For example, insular
distribution functions should have a slope
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A species-based theory of insular zoogeography
near zero for relaxation faunas (Fig. 7a–c),
corresponding to the biogeographic space
circumscribed by c in Fig. 5 (relaxation faunas
are those experiencing gradual species loss due
to increased isolation following habitat change;
see Brown, 1971, 1978; Diamond, 1972; Heaney,
1986). In contrast, insular distribution functions of nonvolant mammals of the less-isolated
islands of the Great Lakes Region (Fig. 7d),
where immigration events occur on an annual
basis, are quite different. In this case, the slopes
of insular distribution functions vary in a
manner consistent with the relative immigration abilities of the species. Studies of cross-ice
movements of these species (Lomolino, 1988,
1989, 1993) indicate that, consistent with
bioenergetic considerations, winter immigration
rates increase with body size. Accordingly,
the slopes of insular distribution functions
are steepest for shrews and meadow voles
(corresponding to region b of Fig. 5), intermediate for tree squirrels and rabbits (regions
a and b), and not significantly different from
zero for deer, red fox and racoons (region a).
The insular distribution function for eastern
chipmunks also has a slope of zero, but for
just the opposite reason. Chipmunks are not
just small, but they are the only hibernators
in this species group, and thus are very limited
in their abilities to travel across ice to colonize
islands in this region (much like relaxation
faunas; see above references).
To date, I have made only a limited number
of comparisons of insular distribution functions among archipelagoes, but the results
again appear consistent with the predicted shifts
in insular distribution functions illustrated in
Fig. 4. For example, in comparison with montane forest islands of the Great Basin, those
found further south in the American Southwest (in Arizona, New Mexico, and southern
Utah and Colorado) are separated by more
mesic, and therefore, less formidable barriers to
immigration by boreal forest mammals. Comparisons between these archipelagoes reveal that,
consistent with the model, populations of nonvolant mammals can maintain populations on
much smaller and geographically more isolated
islands in the American South-west (Lomolino
et al., 1989; Lomolino & Davis, 1997). Hazel
grouse (Bonasa bonasia) inhabiting fragmented
45
coniferous forests in Sweden exhibit the same
interarchipelago pattern (Aberg et al., 1995).
In contrast to landscapes where the intervening
habitat is second growth forests, in landscapes
dominated by more isolating, agricultural fields,
populations of this coniferous forest bird are
restricted to much larger and less isolated islands
(Fig. 7e).
ASSEMBLY OF INSULAR
COMMUNITIES
Many of the most general patterns in assembly
of insular communities may result from nonrandom variation in insular distribution functions among species. In order to expand the
focal species model to account for patterns in
insular community structure, I make the following assumptions.
1 Just as individuals of a focal species are
expected to exhibit strong central tendencies in
traits closely related to fitness (here, immigration abilities and resource requirements), groups
of closely related species (e.g. assemblages of
passerine birds, of bats, or of nonvolant mammals) should also exhibit strong central tendencies in these traits.
2 Immigration abilities and resource requirements (and therefore the slopes and intercepts
of insular distribution functions) should covary
among species.
3 Insular distributions of a group of species
may be strongly influenced by interspecific interactions, many of which may be highly asymmetrical (e.g. predation, amensalism, parasitism,
and asymmetrical competition).
Given the above assumptions, we can explain
the general form of species–area and species–
isolation relationships, species checkerboards
(sensu Diamond, 1975), and other patterns of
codistributions of species including community
nestedness (i.e. the tendency for species lists
on relatively depauperate islands to form proper
subsets of those on richer, larger or less-isolated
islands; see Patterson & Atmar, 1986; Lomolino,
1996; Wright et al., 1998).
Patterns in species richness
Let us first consider the general forms of the
species–area and species–isolation relationships,
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Fig. 7
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Fig. 7 a–d) Insular distribution patterns of nonvolant mammals on isolated archipelagoes. a) relaxation
fauna of montane forest islands of the Great Basin, North America, data from Brown (1978); b) relaxation
fauna of the islands of Bass Straits, Australia, data from Hope (1973); c) a hypothetical relaxation fauna;
and d) a nearshore archipelago of the Great Lakes region (after Lomolino, 1986). Species occur on islands
that fall above or to the left of the lines. Although distributions of the species illustrated in a and b
appear to be uninfluenced by isolation, this may be an artefact of the sampling regime. That is, isolation
effects may have been detected if less-isolated islands were included in these studies (depicted by dashed
line in c; darkened symbols depict presence, open symbols depict absence);
e) Effects of differences in immigration filters on insular distribution functions of Hazel grouse (Bonasa
bonasia) in fragmented forests in Sweden (Aberg et al., 1995). As predicted by the model shown in Fig. 4,
the slope of the insular distribution function for this species increased where forests were surrounded by
less hospitable habitats (agricultural fields [triangles] vs. second growth, managed forest [circles]; presence
and absence is indicated by filled and unfilled symbols, respectively).
which follow directly from Assumption 1, above.
Because most ecologically significant traits are
correlated with body size (see Peters, 1983;
Calder, 1984; Brown, 1995), I expect that the
frequency distribution of resource requirements
and immigration abilities among species will
take a similar form. That is, these frequency
distributions should take the form of log-normal
or log-skewed functions, with most species
clustering about relatively low, modal levels of
resource requirements and relatively low immigration abilities (Fig. 8a). Cumulative frequency distributions of these functions should thus take
the form of positive sigmoidal curves (dashed
line in Fig. 8a).
We can now derive the form of the species–
area relationship. As lifetime energy budgets,
home range, territory size, and other measurements of area requirements are directly related
to resource requirements, we can substitute ‘area’
for resource requirements. As area increases along
the abscissa of Fig. 8a, an increasing number of
species should be able to find adequate resources
to maintain populations on the target islands
(i.e. Tp will exceed Ti for more and more species).
Thus, species richness of the focal group should
increase as a positive sigmoidal function of island
area (Fig. 8-b). The predicted form of the species–
area relationship is similar to those of models
commonly used to investigate this pattern (e.g.
the power and semi-log models), with one key
distinction. Only the sigmoidal model predicts
what MacArthur & Wilson (1967; pp. 30–33)
termed the ‘small island effect’ — the tendency
for species richness of some insular biotas to
remain relatively low and apparently independent of area for the smallest islands (small island
effects are reported by Wiens, 1962; Niering,
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Fig. 8 a) Frequency distribution (shaded) and cumulative frequency distributions (dashed lines) of resource
requirements (in units of area) and immigration abilities (in units of distance) of a group of species.
Because these traits are expected to show strong central tendencies within a species group, cumulative frequency distributions of these traits should take the form of positive sigmoidal functions; b) and
c) Given the patterns illustrated in a, the species–area and species–isolation relationships should also be
sigmoidal functions.
1963; Whitehead & Jones, 1969; Woodroffe, 1986;
Dunn & Loehle, 1988).
For reasons similar to those discussed above,
the species–isolation relationship should also
take the form of a sigmoidal function (Fig. 8c).
This relationship, however, is one of attenuation, not accumulation: as isolation increases,
fewer species are likely to immigrate to an
island frequently enough to maintain insular populations (i.e. Ti will exceed Tp for more and more
of these species). The rate of decline in species
richness (dashed line in Fig. 8c) should, in fact,
be inversely related to the cumulative frequency
distribution of immigration abilities. Therefore, as
isolation increases, richness should decline, very
gradually at first until isolation exceeds immigration abilities of the least vagile species, more
rapidly as isolation approaches the modal immigration abilities of the species group, and then
asymptotically approach zero beyond this point.
Again, sigmoidal curves imply scale dependence, in this case scale-dependent variation in
community parameters (species richness and species composition of insular communities). For
example, the effects of area or isolation on species richness will be difficult to detect (slopes of
species–area and species–isolation curves near
zero) if biogeographic surveys are limited to the
very small or very large islands, or to those too
close or too distant to the mainland to detect
the effects of isolation.
Also, just as insular distribution functions of
focal species should shift with differences among
species groups and archipelagoes, species–area and
species–isolation curves should shift in a similar
manner. For example, species–area curves should
shift toward the right (i.e. toward larger island size;
Fig. 9a) for groups of species with relatively high
resource and area requirements (e.g. for groups
of endotherms vs. ectotherms, or for relatively
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large vs. small animals). I expect equivalent
shifts in species–area curves among archipelagoes differing in primary productivity and
area-specific carrying capacity (i.e. right-ward
shifts for archipelagoes with relatively low carrying capacity). In a similar fashion, species–
isolation curves should shift to the right (i.e.
toward more isolated islands) for groups of species with relatively high vagilities (e.g. birds or
bats vs. nonvolant mammals) or, equivalently, for
archipelagoes surrounded by less formidable immigration filters (Fig. 9b,c).
Patterns in species composition
Fig. 9 Predicted and observed effects of differences
among species and among archipelagoes on species–
area and species–isolation relationships. a) Species–
area curves should shift toward the right (larger
islands required to support the same number of
species) for faunal groups with relatively high resource
requirements (e.g. groups comprised of relatively large
vs. small animals), or for the same species group
occurring in archipelagoes with relatively low primary
productivity (low area-specific carrying capacities).
b) Species–isolation curves should shift toward the
right (toward more isolated islands) for groups of
species with relatively high immigration abilities (birds
and bats vs. nonvolant mammals), or for the same
group of species in archipelagoes surrounded by less
formidable barriers. c) Species–isolation relationships
of non-volant mammals on archipelagoes in the
Great Lakes Region, U.S.A., appear consistent with
the predicted pattern illustrated in b. Species richness (measured as residuals about species–area curves
for each archipelago) declines more slowly in lacustrine archipelagoes (Lakes Huron and Michigan)
where currents are relatively weak and the ice is
more stable (after Lomolino, 1994).
While the species–area and species–isolation relationships are island biogeography’s most common
and first recognized patterns, many of the field’s
most intriguing questions concern patterns in
species composition. To the degree to which
these patterns are predictable, they reflect nonrandom variation in insular distribution functions among species or among archipelagoes. I
have already predicted that, rather than being
uniformly or randomly distributed among species,
resource requirements and immigration abilities
should exhibit a strong central tendency (Assumption 1 above, Fig. 8). In addition, because most
biological traits are correlated with body size, it
is likely that resource requirements and immigration abilities (i.e. the intercepts and slopes of
the insular distribution function) will covary
with body size and therefore with each other
(Assumption 2). For example, immigration abilities and resource requirements of non-volant
mammals appear to show positive covariation:
larger species tend to be better immigrators, but
also require larger islands to maintain their
populations (Lomolino, 1988, 1989). Yet for other
groups of species, immigration abilities and
resource requirements may be inversely correlated. For example, small size in microsnails
and other small invertebrates may confer both
superior immigration abilities (primarily by aerial
dispersal) and lower resource requirements (see
Vagvolgyi, 1975).
These alternative forms of covariation have
important implications with respect to assembly
patterns of insular communities (Fig. 10). Where
immigration abilities are inversely correlated
with resource requirements (better immigrators
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Fig. 10 Effects of two alternative patterns of covariation of resource requirements and immigration
abilities on assembly and nestedness of insular communities. Solid lines depict insular distribution functions for three hypothetical species (A, B, C) in two archipelagoes. a) Where better immigrators require
smaller islands to maintain their populations, assembly sequences should be constant (i.e. first species A,
then B, and then C) and nestedness should be relatively high, regardless of the biogeographic space surveyed. b) Where better immigrators require more resources (larger islands) to maintain their populations,
assembly of insular communities should be complex and nestedness should be relatively low, especially
when biogeographic surveys include a broad range in island area and isolation. c) Expected patterns of
nestedness for the two hypothetical archipelagoes illustrated in a and b.
can maintain populations on smaller islands),
patterns of community assembly should be very
regular indeed. Regardless of the ranges of
isolation and area surveyed, nestedness of
insular communities should be very high, with
species accumulating in the same sequence across
all portions of the archipelago (in the hypothetical case of Fig. 10a, always species A first,
then B, then C). On the other hand, where
better immigrators require larger islands to
maintain insular populations, even this relatively
simple model predicts a complex pattern of
assembly. In the hypothetical case illustrated in
Fig. 10b, almost all possible combinations of
species are predicted to inhabit islands depending
on their isolation and area. Species accumulation sequences should be C first, then B, then
A on the less-isolated islands, B, then A, then
C on the islands of intermediate isolation, but
A, then B, then C on the most isolated islands
(note that community nestedness is inversely
related to how often insular distribution functions intersect). Therefore, under this scenario,
nestedness of insular communities should be
relatively low (Fig. 10c), unless biogeographic
surveys are conducted within just a limited range
of isolation or area. Unfortunately, logistics
often dictate that our surveys be limited in just
this manner (typically including islands that vary
less than an order of magnitude in isolation
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A species-based theory of insular zoogeography
from the mainland). This may contribute to the
fact that reported levels of community nestedness
are typically quite high. Yet, given appropriately
designed biogeographic surveys, species accumulation sequences should vary substantially, albeit
in a predictable manner, among islands and
archipelagoes.
The influence of interspecific interactions
When Jared Diamond first wrote about assembly
rules, it was in his seminal chapter, dedicated
to Robert MacArthur. Diamond accepted one
of MacArthur’s great challenges, to understand
‘the existence of alternative stable communities’
(Diamond, 1975; p. 345). While the hierarchical
model developed above contributes toward this
goal, it still needs an additional layer of complexity; namely feedback among the model’s components. The community level model of Fig. 10
describes the potential, or fundamental ranges of
insular populations. Their realized ranges, however,
may be strongly influenced by interspecific interactions (Fox & Fox, 2000).
The nature of these feedback effects can be
quite complex, including simple and diffuse,
direct and indirect interactions among many
species. We can, however, simplify the task of
modelling the effects of interspecific interactions
by reducing the problem to a ‘community’ of
just two species that engage in some type of
negative, asymmetrical interaction. That is, where
their fundamental ranges overlap, one species
will always exclude the other and therefore
reduce its realized range. It is again important
to consider potential covariation among species
traits. Just as immigration abilities and resource
requirements may covary among species, ecological dominance (the ability of populations
of one species to exclude those of another) may
covary with one of these traits. For example,
among terrestrial vertebrates, predators tend to be
larger than their prey and, thus, may also require
larger islands to maintain insular populations.
Whatever the pattern of covariation in a particular species group, it has strong implications
with respect to realized ranges of the focal species. In the two hypothetical cases illustrated in
Fig. 11, ecological dominants are predicted to
exclude the other species where their fundamental insular distribution functions overlap,
51
except on the very large islands where the second
species is likely to find ecological refugia. This
species should also find refugia on islands
lacking the ecological dominant. This condition
would be met on small islands if ecological
dominants have high resource requirements, or
on the more isolated islands if ecological dominants are relatively poor immigrators. Distribution
patterns of some freshwater fish in Oklahoma
(Fig. 12a), and of small mammals inhabiting
archipelagoes of the Great Lakes Region, U.S.A.
(Fig. 12b), appear consistent with the interactive
version of this model.
Returning to Diamond’s (1975) paper, one of
the most striking patterns he examined was species checkerboards, where members of the same
guild often exhibit exclusive distributions. The
interactive version of the hierarchical model can
provide a useful spatial context for this pattern
and may also help identify potential differences in immigration abilities, resource requirements and ecological dominance among species.
Without plotting species distributions across the
bivariate plot of area and isolation it would
be difficult to distinguish between two fundamentally different types of checkerboards. In the
first case, the component species may be distributed as true spatial checkerboards, i.e. uniformly
distributed across the biogeographic space illustrated in Fig. 13a. This is what we would expect
if the species were equivalent with respect to
immigration abilities and resource requirements
(i.e. if their fundamental insular distribution
functions were identical). Alternatively and
perhaps in most cases, however, immigration
abilities and resource requirements will differ
among species and the ‘checkerboard’ will
thus have a strong spatial bias (Fig. 12a,b). Codistributions of species within Diamond’s fruit
pigeon, cuckoo-dove and gleaning flycatcher
guilds appear consistent with this alternative,
biogeographically biased form of checker-boarding
(Fig. 13a,b,c). In fact, the pattern of exclusive
distributions of some guild members (specifically
Ptilinopus solomenensis, Macropygia mackinlayi,
and Pachycephala melanura) on smaller islands
is difficult to explain without invoking the potential
importance of negative interspecific interactions;
in this case, competition (see Diamond, 1975). In
comparison to other guild members, these species
may be competitively inferior but, because of
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M. V. Lomolino
Fig. 11 Effects of interspecific interactions on realized insular distribution functions of two hypothetical
species. Solid lines indicate the insular distribution functions of two species: these delimit the fundamental
range of each species (i.e. the ranges in isolation and area where each species is expected to occur in the
absence of ecological exclusion). a) Here the better immigrator, species B, suffers ecological exclusion
where the fundamental ranges of the two overlap. Species B, however, should persist in ecological refugia
afforded by relatively large islands, and on isolated islands lacking the less vagile, species A. b) Here, the
better immigrator, species B, excludes the second species where their fundamental ranges overlap. Species A,
however, should persist in ecological refugia afforded by relatively large islands, and on smaller islands
that lack species B.
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A species-based theory of insular zoogeography
53
Fig. 12 Empirical patterns of insular distributions reflecting the likely influence of interspecific interactions.
a) Distributions of two species of freshwater fish among pools of the Red River in Oklahoma (data from
Taylor, 1997). Small mouth bass (Micropterus dolomieu Lacapede) appear to dominate in interspecific
interactions with the creek chub (Semotilus atromaculatus Mitchell), but the bass appears more limited in
dispersal abilities. As a result, the distributions of these species is nearly exclusive, with populations
of small mouth bass being restricted to the less-isolated pools, while all but one population of the creek
chub are restricted to the most-isolated pools. The one exception is the occurrence of both species on a
relatively large, less-isolated pool (i.e. one large and near enough to maintain populations of creek chubs
despite the effects of competition and possible predation from small mouth bass). b) Insular distribution
patterns of two species of nonvolant mammals on islands of Lake Huron and Lake Michigan (S = shrews
[Blarina brevicauda Say and Sorex cinereus Kerr], D = deermice [Peromyscus maniculatus Wagner]). Because
shrews tend to be relatively poor immigrators, their populations are generally restricted to the less-isolated
islands. By preying on insular deermice (especially juveniles), shrews may be able to exclude populations of
these rodents from relatively small, less-isolated islands (see Lomolino, 1984 for a similar pattern with
insular voles and shrews). On the other hand, the ecologically subordinant but more vagile deermice may
find refugia on the more isolated or larger islands.
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M. V. Lomolino
Fig. 13 a) A biogeographic checker-board distribution of two hypothetical species. In this case, both
species are uniformly distributed across the bivariate, biogeographic space (i.e. their distributions are
independent of isolation and area), but they never occupy the same island. This pattern would be expected
where competition is intense, but species are essentially equivalent with respect to immigration abilities and
resource requirements. b–d) In contrast to the above, hypothetical pattern, analysis of insular distribution
patterns for three of Diamond’s (1975) avian guilds reveals that exclusive distributions are often achieved
by species segregating their realized distributions over different ranges of isolation and area (data from
D.R. Perault and M.V. Lomolino, unpublished report 1996; see also Fig. 12). Regression analysis was used
to estimate linear insular distribution functions of these species (see Lomolino, 1986). Insular distributions
were significantly (P < 0.05) associated with island area and isolation for Ptilinopus rivoli, P. solomenensis,
Macropygia nigrirostris, and Pachcephala pectoralis. Insular distributions were significantly associated with area
(but not with isolation) for P. melanura, and marginally (P < 0.09) associated with area for M. mackinlayi.
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A species-based theory of insular zoogeography
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Fig. 13 continued.
their relatively low resource requirements and/
or superior immigration abilities, they are able
to maintain populations on relatively small or
isolated islands.
CONCLUSION
The hierarchical, species-based model remains
fundamentally simple in that its thesis is that
most patterns of insular community structure
result from non-random variation in immigration and extinction. It assumes that insular
populations of a focal species should be more
likely to occur on islands where they immigrate
more frequently than they go extinct, and that
immigrations and extinctions are strongly influenced by island isolation and island area, respectively. To explain some very general patterns in
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M. V. Lomolino
the assembly of insular communities, the model
makes the additional assumptions that immigration abilities and resource requirements vary in
a non-random manner among species, and that
these traits may covary. The community level
version of this model also assumes that insular
distributions can be strongly influenced by
interspecific interactions.
Thus, the model is based on a very conservative set of assumptions. Yet this simple model can
account for a variety of island biogeography’s
most general patterns, makes additional predictions, and identifies some key topics for future
studies. Perhaps the most interesting and potentially insightful topic for research concerns
patterns of covariation of immigration abilities
and resource requirements among species. Potential
covariation in system characteristics, such as
in the nature of immigration filters and regional
variation in productivity, also seems to be a
fertile subject for future research. Do islands
found in areas with relatively high productivity
(e.g. lower latitudes or lower elevations) also
tend to be surrounded by my more isolating
immigration filters? Or, to paraphrase Janzen
(1967), we may ask whether mountain passes
(immigration filters) really are ‘higher’, or more
isolating in the tropics?
A fundamental limitation of the speciesbased model is that it is limited to ecological
time scales. The assumption that speciation is
relatively unimportant should be challenged more
frequently, especially at large spatial scales (e.g.
when comparing patterns among archipelagoes).
Speciation events will of course be rare in
ecological time, but the legacies of such events
should be considered alternative, or complementary explanations for extant patterns. For
example, the current model assumes that speciation determines the species pool capable of
colonizing the focal islands. While this seems
unquestionable, the implicit assumption here
is that each archipelago is served by just one
species pool. Yet we know this is unlikely for
many archipelagoes, such as those situated along
a transition zone, or those subject to relatively
frequent, intra-archipelago speciation. In the latter
case, restriction of a species to what appear to
be the more isolated islands, rather than reflecting
ecological exclusion by less vagile predators or
competitors, may simply indicate that the focal
species is derived from a different species pool.
Again, the degree to which such historical
legacies need to be considered will vary with
the spatial scale of the study, as well as with
the propensity of the biotic group in question
to speciate on islands (see McCall et al., 1996).
The graphical and largely deterministic model
presented here and the mathematical models of
metapopulation biologists are both products of
the scientific revolution canonized by MacArthur
& Wilson’s (1967) theory (Harrison, 1991). Their
theory replaced the static view of isolated communities with one based on the premise that
insular community structure resulted from recurrent, rather than unique, immigrations and extinctions (see Dexter, 1978; Brown & Lomolino, 1998).
These two approaches to modelling dynamic,
isolated communities differ in some important
respects: the model presented here is deterministic, while metapopulation approaches are
primarily stochastic and equilibrial. The heuristic
value of such models, however, is difficult to
deny. Various metapopulation models now include
spatially explicit approaches, multispecies models,
and alternative (stochastic) explanations for patterns in species richness and species incidence
(e.g. see Ebenhard, 1991; Hanski, 1992, 1994,
1997; Hanski et al., 1996; Wahlberg et al., 1996;
Hanski & Gilpin, 1997; Hanski & Gyllenberg,
1997; Moilanen et al., 1998; Morrison, 1998).
Therefore, despite the differences in these alternative approaches, they may both be viewed as
valuable and complementary approaches derived
from the same paradigm of dynamic island
biogeography (i.e. MacArthur and Wilson’s model).
In conclusion, the hierarchical, species-based
model presented here appears to have high
heuristic potential for understanding and, by
extension, for conserving biological diversity of
many isolated communities. The degree to which
this potential is realized will strongly depend on
our ability to conduct biogeographic surveys and
other research focusing on the factors that most
strongly influence the fundamental biogeographic
processes — immigration, extinction and evolution.
ACKNOWLEDGMENTS
James H. Brown, Rob Channell, I. Hanski,
Lawrence Heaney, Michael Kaspari, Michael
Moulton, David Perault, Gregory A. Smith and
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A species-based theory of insular zoogeography
Chris Taylor provided valuable comments on the
manuscript. The conceptual development of the
species-based model was supported in part by
two grants (DEB-9322699 and DEB-9622137)
from the National Science Foundation.
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