Download Scale, Environment, and Trophic Status: The Context Dependency

vol. 167, no. 6
the american naturalist
june 2006
E-Article
Scale, Environment, and Trophic Status: The Context
Dependency of Community Saturation
in Rocky Intertidal Communities
Roly Russell,1,* Spencer A. Wood,2,† Gary Allison,3,‡ and Bruce A. Menge1,§
1. Department of Zoology, Oregon State University, Corvallis,
Oregon 97331;
2. Department of Zoology, University of British Columbia,
Vancouver, British Columbia V6T 1Z4, Canada;
3. Aquatic Ecology Laboratory, Department of Evolution, Ecology,
and Organismal Biology, Ohio State University, Columbus, Ohio
43210
Submitted September 13, 2005; Accepted February 15, 2006;
Electronically published May 8, 2006
abstract: Our understanding of the relative influence of different
ecological drivers on the number of species in a place remains limited.
Assessing the relative influence of local ecological interactions versus
regional species pools on local species richness should help bridge
this conceptual gap. Plots of local species richness versus regional
species pools have been used to address this question, yet after an
active quarter-century of research on the relative influence of local
interactions versus regional species pools, consensus remains elusive.
We propose a conceptual framework that incorporates spatial scale
and ecological interaction strength to reconcile current disparities.
We then test this framework using a survey of marine rocky intertidal
algal and invertebrate communities from the northeast Pacific. We
reach two main conclusions. First, these data show that the power
of regional species pools to predict local richness disintegrates at
small spatial scales coincident with the scale of biological interactions,
when studying ecologically interactive groups of species, and in generally more abiotically stressful habitats (e.g., the high intertidal).
Second, conclusions of past studies asserting that the regional species
pool is the primary driver of local species richness may be artifacts
of large spatial scales or ecologically noninteractive groups of species.
* Corresponding author. Present address: Earth Institute and Ecology, Evolution, and Environmental Biology Department, Columbia University, New
York, New York 10027; e-mail: [email protected]. Order of the first two
authors’ names determined by an appropriately stochastic process.
†
E-mail: [email protected].
‡
E-mail: [email protected].
§
E-mail: [email protected].
Am. Nat. 2006. Vol. 167, pp. E158–E170. 䉷 2006 by The University of
Chicago. 0003-0147/2006/16706-41302$15.00. All rights reserved.
Keywords: local richness, marine intertidal, species diversity, northeast Pacific, biodiversity, regional species pool.
Ecologists can say with uncommon certainty that numerous individual factors can influence the number of species
found in a place. These factors include key drivers such
as predation (Paine 1966), herbivory (Lubchenco and
Gaines 1981), competition (Gurevitch et al. 1992), recruitment (Gaines and Roughgarden 1992), facilitation
(Bruno et al. 2003), dispersal (Huffaker 1958), disturbance
(Mackey and Currie 2000), area (Rosenzweig 1995), and
biogeography (Caley and Schluter 1997). Nonetheless, we
still have a poor understanding of the relative influence
and interactions of multiple factors acting in concert on
the number of species in a place.
Plots of local species richness against regional species
pools have been used to assess the relative influence of
local constraints (e.g., biological interactions, such as competition) versus regional historico-evolutionary drivers
(e.g., speciation) on local species richness (Terborgh and
Faaborg 1980; Lawton 1999; Ricklefs 2004). If regional
species pools are driving local species richness, we expect
to see a positive linear relationship (observing such a linear
relationship, however, could also result from an independent factor driving both local and regional richness). Conversely, if ecological interactions and niche packing saturate communities with species and set an upper limit to
species richness, an asymptotic relationship should be observed. An asymptotic relationship, however, can be difficult to unequivocally interpret mechanistically (Rosenzweig and Ziv 1999) and ultimately implies only that
factors other than regional species pools are driving local
richness (Srivastava 1999). Nonetheless, as Rosenzweig and
Ziv (1999) reiterate, demonstrations of the form of this
relationship in the real world have much to teach us.
Following the publication of Terborgh and Faaborg’s
(1980) seminal article, numerous (1100) studies have addressed the relationship between local and regional species
pools in the real world, cumulatively spanning a large
Local Richness, Regional Pools E159
Figure 1: Conceptual model demonstrating the spatial scale and species grouping dependency of predictions from local-regional theory. When a
survey chooses a large, ecologically heterogeneous area as a local scale, or when group of species investigated are not interactive, we would not
expect to find saturation. Only when species groupings and local scale both incorporate interacting species should we expect saturation potential.
The darker shading in this diagram signifies higher potential to detect saturation of communities. For example, a study assessing ecological saturation
at very large local scales and across various taxonomic groupings might fall into the large region bounding A (e.g., Caley and Schluter 1997), and
we would not predict to see saturation, even if the hypothesized mechanisms are acting to saturate communities at smaller local scales. An experiment
at an ecologically interactive local spatial scale, however, and with a highly interactive group of species (area bounding B; e.g., Munguia 2004),
would allow us to predict saturation if the mechanisms are in fact operating.
range of taxonomic groups, habitat types, and spatial scales
(for reviews, see Cornell 1999; Hillebrand and Blenckner
2002; Srivastava 1999). Despite such intensive study, conclusions continue to expound the primacy of both local
ecological interactions (e.g., Winkler and Kampichler
2000; Munguia 2004) and regional species pools (e.g.,
Caley and Schluter 1997; Witman et al. 2004) in driving
local richness, and a widely applicable theoretical interpretation of the discrepancies between these conclusions
is still rudimentary (see Rosenzweig and Ziv 1999). We
believe that much of the disagreement in these results can
be attributed to studies in which spatial scales of the mechanism do not match those of the observations (Huston
1999; Loreau 2000) or to other contextual discrepancies
that lead to noninteractive groups of species being studied.
Ecological theory provides a simple prediction for saturation of communities: if species are interacting, then a
saturation pattern is often predicted, and if species are not
interacting, then saturation is rarely predicted (Huston
1999; Loreau 2000; Shurin and Allen 2001). Ecological
models that do not rely on species interactions (Caswell
1976; Hubbell 1997, 2001; Bell 2000) do not predict that
local competitive interactions will limit local richness. Even
accepting interaction-based ecological theories (Elton
1927; Hutchinson 1959; Oksanen et al. 1981; Menge and
Sutherland 1987) and assuming that saturation does limit
local species richness, we do not predict saturation at spa-
tial scales beyond those at which individuals interact or
among groups of species that are weakly interactive or not
interactive (fig. 1). For example, sponges living under
boulders in wave-sheltered low intertidal areas are not
expected to influence the abundance of barnacles living
on nearby wave-exposed high intertidal open rock surfaces. Even if species interactions saturate community
membership at a particular scale, increasing the spatial
breadth of the defined local community should eventually
incorporate heterogeneous environments and lead to a
shift from an asymptotic relationship into a linear relationship (i.e., apparent dominance by the regional species
pool). Similarly, groups of species that interact strongly
may demonstrate saturation in some contexts, whereas
groups of species that do not interact should not demonstrate saturation patterns in any contexts (see fig. 1).
Finally, saturated communities have been shown theoretically (Rosenzweig and Ziv 1999; Mouquet et al. 2003) and
empirically (Rivadeneira et al. 2002; Munguia 2004) to
conceal saturation patterns at certain spatial scales or during intermediate stages of community assembly. Therefore,
empirical observations of the contexts in which we observe
asymptotic (saturation-type) relationships between local
and regional richness should tell us a great deal about the
mechanisms driving these patterns.
Here we use a spatially extensive set of surveys of marine
rocky intertidal communities along the West Coast of the
E160 The American Naturalist
United States to address how three elements—scale, species groupings, and habitat—influence the relative dominance of the evolved regional species pool and local species interactions on local species richness. We predict an
increased predictive power of regional species pools as we
assess increasingly large local spatial scales, groups of species that are only weakly interacting, and habitats with
weakly interacting species.
Methods
Ecosystem Description
Marine rocky intertidal ecosystems along the West Coast
of the United States, ranging from northern Washington
(48.4⬚N, 124.7⬚W) to southern California (32.7⬚N,
117.3⬚W), are the empirical focus of this study. Vertical
zones in the intertidal are characterized by gradients in
environmental factors and potential stress levels, ranging
from a nearly continuously submerged benthic marine
community, low in the intertidal, to nearly terrestrial communities high in the intertidal (Lewis 1964). We focus on
communities at three tidal heights: mean lower low water
(MLLW), mean sea level (MSL), and mean higher high
water (MHHW), referred to hereafter as the low, mid, and
high zones, respectively. As has been documented repeatedly at sites along the West Coast (Paine 1966, 1974; Dayton 1971; Menge et al. 1994; Connolly and Roughgarden
1998; Sanford and Menge 2001), zones at wave-exposed
sites are dominated by macrophytes (low zone), mussels
(mid zone), and barnacles and macrophytes (high zone).
The organisms in this study were identified as species
whenever possible, although coarser groupings were necessary in cases where key identifying features were not
expressed at the time of sampling (for a complete list of
taxa, see table A1). Most taxa observed in this study are
sessile in their mature stages and may interact directly or
indirectly via competition for space or facilitation (Connell
1961, 1983; Paine 1966; Menge et al. 1997). Trophic status
was assigned on the basis of published work, observations,
and expert opinion; we note that ambiguity remains in
identifying trophic roles of some of these taxa. Many invertebrate species in these systems have mobile larval
stages and potentially relatively long distance dispersal
(Grantham et al. 2003; Kinlan and Gaines 2003).
Data Collection
The survey data were obtained using a nested sampling
scheme. At the largest scale, we sampled within 16 regions
spread across 16 degrees of latitude (from 32⬚ to 48⬚N).
Regions were, on average, 168 km apart. Within each of
these regions, three sites were nested a few kilometers
apart. Within each site, three sets of 50-m transects were
placed in each of three zones (high, mid, and low), for a
total of nine transects in each site and thus 27 transects
in each region (fig. 2). In each transect, community composition was quantified in 10 randomly placed 0.25-m2
quadrats. Within each quadrat, we identified and estimated
the percent cover and/or density of all macroscopic organisms (for a complete list of taxa, see table A1). These
surveys were repeated annually from 2000 to 2004. Survey
sites were selected a priori using aerial photos and specific
criteria, including the presence of suitable open coast intertidal habitats with wave-exposed and moderately sloping rocky platforms. The purpose of these selection criteria
was to limit sampling to intertidal habitats that were as
physically similar as possible. These criteria and sampling
methods are described in more detail by Schoch and Dethier (1996).
Local and Regional Pool Estimation
Definitions of “local” and “regional” in the literature vary
dramatically and are context dependent. For example, local
scales in the literature range from the flesh of a fig (Hawkins and Compton 1992) or the gut of a fish (Aho and
Bush 1993) to areas more than half the size of Switzerland
(e.g., Caley and Schluter 1997). Given that local scales of
interaction for different species are different, spatial scales
relevant to local communities will inevitably vary. To better
understand the potential relevance of defined local scales
on the observed patterns of ecological saturation, we analyze three local scales. We define samples taken at the
quadrat scale of 0.25 m2 as a small local community, samples taken at the transect level on a scale of tens of meters
as an intermediate-sized local community, and samples
taken at the site level across several transects on a scale of
a few kilometers as a large local community. Because most
species included in this study are rather small and sluggish
or sessile when mature and competition for space in the
intertidal occurs on small spatial scales, we believe that
these scales provide a reasonable analysis of these patterns
(e.g., Connell 1961; Wootton 1993; Burnaford 2004). We
use the observed number of species at each scale to estimate local species richness.
Ideally, we would calculate the regional species pool
directly as we define it: the number of species that can
disperse to a locality in ecological time and survive in the
physical environment. The latter half of this definition is
critical because our definition must exclude, from the low
intertidal, for example, taxa such as caecilians and cetaceans that may disperse to—but cannot reside in—the
intertidal. The regional species pool is dependent on the
defined local community.
Regional species pools have been estimated in the past
Local Richness, Regional Pools E161
Figure 2: Map of surveys conducted on the West Coast of the United States. At the largest scale, points represent regions within which three sites
were surveyed, within which three sets of transects were placed in each of the low, mid, and high intertidal zones, in which 10 quadrats were
sampled. Low, mid, and high represent low, mid, and high zones in the intertidal, respectively.
primarily by two methods: literature-based range maps
(e.g., Caley and Schluter 1997) or cumulative species lists
from field observations (e.g., Terborgh and Faaborg 1980).
Both methods have associated pros and cons. Range maps,
for example, offer the advantage of a temporally integrated
measure of all species present in an area over many years.
However, range maps rely upon interpolation, at least at
some spatial scales. Many obscure intertidal species have
been recorded at only a handful of locations along the
coast, with published ranges spanning the coastline between these points. These coarse estimates overestimate
the regional pool (Hurlbert and White 2005) and produce
biased measures of local richness that logically lack the
signature of local ecological interactions. Alternatively, cumulative counts of species observed in an area, the method
we use here, provide a more direct measure of the species
actually extant within the defined region and detectable
by the survey scheme. This method provides more studyspecific estimates of regional species pools than do range
maps, at the expense of potentially underestimating the
number of species in the regional pool.
The regional species pool is estimated as the total number of species that were observed in the surveys within
each of the 16 regions (see fig. 2). For example, assessing
the regional species pool for filter feeders in the mid intertidal involves calculating the total number of filter
feeder species that were observed in the 90 quadrats surveyed in the mid zone within that specific region. These
regional estimates were calculated independently for each
species grouping.
For each region, we compared the observed richness
values with rarefied asymptotic richness estimates, calculated using jackknifed richness (Zahl 1977; Heltshe and
Forrester 1983; Rumohr et al. 2001) and the S⬁ metric
(Karakassis 1995). These estimates were only slightly
higher than the observed regional richness values (i.e, sampling intensity was approaching the asymptotic numbers
of species), and analysis using these extrapolated estimates
of total species richness gave similar results to using observed richness. We therefore use observed richness in the
analyses to allow for more direct interpretation. All of the
above computations were performed using the SAS System
for Windows, version 8.0 (SAS Institute 2001).
Statistical Analyses
We use nonlinear asymptotic regressions to test localregional relationships because the hypotheses predict an
asymptotic relationship and not a quadratic relationship.
In this article, we present a Michaelis-Menten-type function (eq. [1]), chosen because of the ecological interpretability of the parameters. Rather than traditional tests for
E162 The American Naturalist
a dichotomous presence or absence of saturation, we analyze the quantitative degree of saturation, which we argue
is a more ecologically realistic paradigm of actual communities:
local richness p
Vmax # regional pool
,
K m ⫹ regional pool
(1)
where Km is an estimated parameter that describes the
curvature of the relationship, also called the half-saturation
constant, and Vmax provides an estimate of the asymptote,
which approaches infinity as the relationship becomes
more linear (regionally dominated). This asymptote represents a quantitative measure of saturation: if the asymptote is estimated within the range of local richness
observed, we interpret this as a saturated system. If the
estimated asymptote is infinite, or occurs at an unattainably high local richness, the community is not saturated.
Because we are interested specifically in factors constraining the upper limits to local richness (rather than the mean
local richness), we use nonlinear quantile regression
(Scharf et al. 1998; Cade and Noon 2003) to regress the
ninetieth percentile of local richness on the regional species
pool and use all observations rather than mean observed
richness values. Quantile regression minimizes least absolute differences rather than least squared differences.
Data are necessarily spatially autocorrelated, and measures
of curve fit are neither presented nor analyzed: we are
interested in the relationship between the asymptote and
local richness data, not in measures of fit. All regression
analyses were performed using the statistical program R
(R Development Core Team 2004) and the quantreg package for quantile regression (Koenker 2004).
Testing the significance of the quadratic term in a quadratic model has been used in the past to test the asymptotic nature of this relationship. This quadratic approach
implies that the regional species pool driving local richness
is a more parsimonious model than alternative drivers
(local interactions or otherwise), which seems inappropriate, given the interpretation of the observed pattern.
Testing these patterns with a quadratic model forced
through the origin produces qualitatively similar results.
We use a calculated metric of percent saturation for
comparing saturation patterns across species groups and
scales that vary in total number of species. This metric
presents the most species-rich local community as a proportion of the estimated asymptote of local richness (and
analogous metric calculated from the half-saturation constant, Km, relative to the regional species pool produced
qualitatively similar results in all contexts). An index of
100% saturation indicates that some local communities
contain the same number of species as the estimated asymptote, while 0% saturation indicates that the observed
maximal local richness is negligible relative to the estimated asymptote of local richness (i.e., the regression is
a straight line). Thus, a saturated community should be
100% saturated, while a regionally dominated community
would be 0% saturated. This saturation index is bounded
between 0% and 100%.
Percent saturation was calculated, as above, for each of
the possible permutations of local scale (three: quadrats,
transects, and sites), taxonomic resolution (eight: all species combined; kingdoms: algae or invertebrates; and
trophic levels: filter feeders, primary producers, herbivores,
omnivores, and carnivores), habitat types (three: low, mid,
and high intertidal), and years (five: 2000–2004). This
equates to the assessment of saturation for 360 permutations, from which estimated parameters were used to
calculate the saturation index. The relationship between
the saturation index and spatial scale, zone, and species
groupings was estimated using a linear model (mixed effects, estimated by residual maximum likelihood, using R),
incorporating all main effect and interaction terms nested
within the repeated measures each year. Each main effect
(scale, habitat, and species grouping) was modeled as an
ordinal factor to correspond to our null hypotheses of
saturation levels changing as we increased spatial scales
(quadrats ≤ transects ≤ sites), tidal height (low (
mid ( high),
or
ecological
resolution
(all ≥
kingdoms ≥ trophic levels) of species groupings. Qualitative conclusions are not different between this ordinal
approach and a categorical approach (both methods are
presented in table D1); the ordinal approach is presented
here for ease of interpretation.
The variance of saturation index for explanatory groups
(e.g., spatial scale) was calculated by randomly permuting
(10,000 times) the observed association between saturation
indexes and each contextual factor (i.e., scale, habitat, and
species groupings) pooled across years, calculating the variance in observed data relative to variance in random permutations as our test statistic and its distribution. These
probabilities provide a test of whether observed differences
in variation of saturation indexes between groups are different from expected, given a null hypothesis of no relationship between saturation and spatial scale, habitat, and
species groupings.
Results
Local-Regional Plots
Modeling saturation as a function of spatial scale, habitat,
and species groupings demonstrates increased saturation
potential at smaller local scales, higher in the intertidal,
and among trophic species groupings rather than kingdom-level groupings (regression coefficients presented in
Local Richness, Regional Pools E163
table 1). In other words, the regional species pool predicts
local species richness best at larger local scales, lower in
the intertidal, and in coarser groupings of species. Appendix C graphically presents the regressions of each of
the 360 permutations of scales, habitats, taxonomic
groups, and years. Summaries of parameters estimated
from the regressions are presented in table B1. Figure 3
presents an example comparison of local and regional richness for each of the eight species groupings in one intertidal habitat (mid intertidal), at one local scale (0.25 m2),
in one year (2003), across the Pacific coastline of the contiguous United States.
Effects of Spatial Scale
As local scale increases, communities become dominated
by regional species pool effects (negative coefficient in table 1). At the smallest local scale, richness reached, on
average, 73% of the estimated asymptote of local richness,
dropping to 39% in the intermediate transect scales and
to only 18% at the site level, the coarsest spatial resolution
(fig. 4).
Small local scales also demonstrate a broader range of
potential saturation levels. Permutation of the variance of
saturation indexes associated with each local scale indicates
that variance tends to decrease as local spatial scales increase: quadrat-level variance is greater than transect level
(P(1 tail) ! .001), and transect-level variance is greater than
site level (P(1 tail) ! .001).
Effects of Species Groupings
Incorporation of all taxa in one analysis results in a predominately regionally dominated relationship (mean saturation index p 18%), while finer and potentially more
interactive species groupings show progressively higher
levels of saturation: 30% with kingdoms as groups and
39% with trophic levels as groups (fig. 5; table B1). The
positive coefficient (P ! .001) of species groupings indicates that the predictive power of the regional pool declines
as we shift from grouping all taxa to grouping taxa into
trophic levels.
Different groups do not exhibit different variability of
saturation indexes. The variance in the saturation index
of all-inclusive groupings is not less than in kingdom-level
groups (P(1 tail) p .152) or trophic-level groupings
(P(1 tail) p .073).
Effects of Intertidal Zone
Saturation is less common in habitats lower on the shore
than in habitats higher on the shore (fig. 6; table B1).
Mean saturation in the low, mid, and high zones is 19%,
Table 1: Regression of community saturation index as
a function of resolution, scale, and habitat
Regression
term
Value
Standard
error
P
value
(Intercept)
Resolution
Resolution2
Scale
Scale2
Habitat
Habitat2
Resolution # scale
Resolution2 # scale
Resolution # scale2
Resolution2 # scale2
Resolution # habitat
Resolution2 # habitat
Resolution # habitat2
Resolution2 # habitat2
Scale # habitat
Scale2 # habitat
Scale # habitat2
Scale2 # habitat2
.346
.121
.005
⫺.321
.069
.251
⫺.073
⫺.102
.024
⫺.031
.015
⫺.069
⫺.010
⫺.010
.025
⫺.253
.004
.078
⫺.023
.022
.025
.023
.024
.024
.024
.024
.043
.040
.043
.040
.043
.040
.043
.040
.034
.034
.034
.034
!.001
!.001
.831
!.001
.004
!.001
.002
.018
.544
.471
.707
.107
.808
.806
.533
!.001
.915
.022
.497
Note: Terms are ordered factors, each with three levels, and
thus the linear components test for a linear change across the
gradient (e.g., low to high zone habitat), while the quadratic terms
indicate whether the relationship levels off or is unimodal across
each gradient. Resolution refers to species groupings (all, kingdom, or trophic level), scale refers to the size of the local scale
(quadrat, transect, or site), and habitat refers to intertidal zone
(low, mid, or high). Terms with P ! .05 are in bold.
42%, and 56%, respectively. The regression coefficients for
habitat (P ! .001) and habitat2 (P p .002) indicate that
mean saturation in each of the three habitats is different
(table 1). Variation in saturation indexes is not different
between habitats (P(2 tail) 1 .05).
Discussion
Ecological theory predicts that as the potential strength of
local ecological interactions increases, there should be concomitant decreases in the potential for the regional species
pool to determine local species richness. We analyzed data
on three axes along which we might expect such a shift
in the relative strength of local ecological interactions: spatial scale, taxonomic grouping, and habitat type.
Scale Influence
Our prediction that smaller spatial scales should be more
saturated was supported by the data: mean saturation
dropped from 73% at the smallest local scales to only 18%
at the largest spatial scales. Thus, increases in the spatial
scale of local communities were associated with an increase
E164
Figure 3: Local species richness versus regional species pool regressions from intertidal communities on the West Coast of the United States. The black dashed line is a nonlinear asymptotic model
fit through the ninetieth quantile. These results demonstrate the number of species in local communities at the scale of a single quadrat (1/4 m2) in the mid intertidal (mean sea level). The regional
pool is calculated as the number of species that were observed in a surveyed region surrounding each local community, which is on the order of tens of kilometers. There are multiple solutions
for the half-saturation constant (Km) for omnivores, and so only the asymptote is plotted. Points have been jittered to aid in visualization of data density.
Local Richness, Regional Pools E165
Figure 4: Saturation indexes across various local scales of observation. The small (sml) scale refers to the average number of species within 1/4-m2
quadrats, medium (med) relates to 50-m horizontal transects, and large (lrg) represents groups of three nested transects across approximately 1 km
of shoreline. Data are calculated from groups of different trophic status and different habitats (high, mid, and low intertidal communities), and
thus each point represents the saturation of a particular trophic level in a particular zone at each spatial scale (e.g., all filter feeders in the low
intertidal would be one point at each scale).
in dominance by the regional species pool, as predicted
theoretically (Westoby 1998; Huston 1999; Loreau 2000)
and empirically (Hillebrand and Blenckner 2002; Rivadeneira et al. 2002). There has been disparity in previous
research between the scale at which the proposed mechanism for saturation (e.g., ecological interaction) operates
and the scale of observation (Huston 1999). Observations
at spatial scales greater than that within which species
interact will lead us to conclude that the regional pool has
a dominant effect, regardless of whether local interactions
determine richness at smaller scales. For example, at large
local scales (e.g., 2,500 km2 and 25,000 km2 in Caley and
Schluter 1997), we expect to—and do—see an apparent
prevalence of the regional species pool in determining local
community composition. Studies at local scales that more
closely match expected scales of ecological interactions often do demonstrate saturation (Winkler and Kampichler
2000; Rivadeneira et al. 2002; Borges and Brown 2004;
Munguia 2004). If the goal is to understand the potential
influence of local ecological interactions in driving local
species richness, as the tool was originally presented and
used (Terborgh and Faaborg 1980), then local scales must
be small enough for the encompassed populations to be
able to interact.
Sampling minute areas may lead to a pseudosaturated
relationship in which there are too few individuals to properly sample the relative abundance distribution and rare
species are not assessed adequately (Karlson and Cornell
2002; Caley and Schluter 1997). The large numbers of
individuals sampled at the smallest scales surveyed in this
study (a mean of 318 mobile individuals and 133% cover
of sessile individuals per quadrat) make it unlikely that
small sample sizes generated the relationships observed.
Group Membership Influence
Our prediction that coarser species groupings should be
more regionally dominated than finer groupings was supported, with percent saturation more than doubling (from
18% when all taxa were combined to 39% when trophic
E166 The American Naturalist
Figure 5: Demonstration of the range of saturation indexes across a range of taxonomic resolutions. all p grouping of all species; king p
kingdom-level division, parsing species in two groups (i.e., Protista and Animalia); and troph p groups of species based on trophic status, including
primary producers, suspension/filter feeders, herbivores, omnivores, and carnivores. Data are calculated from communities at different habitats (high,
mid, and low intertidal). Each point represents the saturation of the grouping resolution indicated on the X-axis in a particular tidal height, using
transects as the local spatial scale (e.g., one of the points in the all-inclusive category represents the saturation level of an analysis counting all
species found in quadrats in the low intertidal).
levels were analyzed separately) as groupings became finer.
The majority of species in these ecosystems have weak or
undetectable interactions (Menge 1995; Berlow 1999); how
we group species should influence whether we expect species to interact. For example, separating an ecological community into phylogenetic groups, rather than mutual resource users, should lessen the potential strength of the
mechanism (in this case, competitive interactions) proposed to drive the pattern. The relative mobility of different groups of species can also influence expected patterns resulting from ecological interactions by changing
historical and contemporary dispersal patterns (Williams
et al. 2003; Munguia 2004). Again, when designing studies
to assess the relative importance of local species interactions and regional species pools, we must ensure that the
observed taxa have the necessary potential to functionally
interact.
Habitat Influence: Biotic Interactions
Because environmental stress and potential productivity
vary across habitats, the average strength of interactions
among habitats is expected to vary and thus influence
where saturation is predicted. We assessed the prevalence
of saturation patterns across different intertidal habitats,
ranging from high intertidal zones, where abiotic stress
should be higher (Bertness and Leonard 1997; Hacker and
Gaines 1997), to the low zone, where such stress should
be lower (Harley 2003). Interestingly, we see differences,
yet the low intertidal is where we see the strongest signature of regional dominance (19% saturation in the low
intertidal, in contrast to 42% in the mid and 56% in the
high). This result appears inconsistent with our prediction
that higher stress would decrease the importance of biotic
interactions as a determinant of community composition
(Connell 1961; Menge and Sutherland 1987) and lead to
Local Richness, Regional Pools E167
Figure 6: Saturation levels of intertidal communities at different intertidal heights. low p mean lower low water; high p mean higher high water;
and mid p mean sea level. Data are calculated from groups of different trophic status. Each point represents the saturation of a particular trophic
level in transects, given the intertidal zone indicated on the X-axis (e.g., the saturation of filter feeders found in transects is one point in each
intertidal zone).
more regionally dominated communities higher in the intertidal. One alternative hypothesis is that increased predation lower in the intertidal could decrease the relative
role of competition (Connell 1961; Menge and Sutherland
1987), allowing for an increase in the influence of the
regional species pool in the low zone. Similarly, facilitation
has been documented to become increasingly important
at higher levels of abiotic stress (Bertness and Leonard
1997; Hacker and Gaines 1997; Bruno et al. 2003). Facilitation could drive these observed patterns (Shurin and
Allen 2001) if we hypothesize that once a threshold number of species are present in the regional pool, the community is released from abiotic determination and stabilizes at some local richness level dictated by the presence
of stress-ameliorating species.
pect to see a decrease in the relative importance of the
regional species pool on local richness in this habitat, as
we observed. Note that this prediction is founded on a
hypothesized decrease in the influence of the regional species pool as a result of increased influence of locally acting
abiotic factors rather than the traditionally assumed increased influence of local ecological interactions. Our data
indicate that higher in the intertidal variables other than
the regional species pool have increased potential to structure local communities (140% saturated); this relationship
could be an expression of an increase in the importance
of local abiotic stress in determining local richness rather
than local ecological interactions.
Incorporation of Abiotic Stress and Facilitation into
Traditional Local-Regional Models
Habitat Influence: Abiotic Stress
If abiotic factors, however, drive local species richness in
the high intertidal (Menge and Sutherland 1976), we ex-
Environmental stress models generally predict that as the
influence of abiotic stress increases (cf. increased intertidal
elevation; Bertness and Callaway 1994; Bertness and Leon-
E168 The American Naturalist
ard 1997), the relative influence of predatory interactions
declines, and competitive interactions follow with an increase and then decrease in importance, followed by compensatory increase in the relative influence of facilitation
and then direct abiotic stress (Bertness and Callaway 1994;
Bertness and Leonard 1997). The power of the regional
pool to predict local richness should be strongest in contexts away from those engendering strong competitive interactions or those where facilitation ameliorates stress levels and thus becomes increasingly important. Our results,
which indicate that regional dominance is decreased higher
in the intertidal, could thus be interpreted as an increase
in importance of facilitation, a decrease in predation, or
an increase in direct abiotic stress rather than exclusively
as a function of competitive ecological interactions.
Conclusions
Our results yield three main points. First, the relative importance of multiple alternative hypotheses must be assessed empirically in order to understand ecosystem dynamics, a difficulty, given the requisite spatially and
taxonomically broad surveys necessary for such macroecological analyses (e.g., Wootton 2005). Furthermore, descriptions of complex systems such as ecosystems are necessarily not scale invariant (Bar-Yam 1999), and thus the
role of ecosystem drivers must be assessed at multiple
scales to produce generally applicable systemic knowledge
(Rahbek and Graves 2000). Our study provides such an
empirical and cross-scale assessment. By quantifying the
observed importance of regional species pools in predicting local richness across spatial scales, habitat types, and
species groupings, we have gained new insight into the
general dynamics of drivers of local richness. Local species
richness is driven by multiple scale-variant factors; even
if our goal is to interpret the ecological significance of a
single driver, we gain more germane ecological insight if
we couch our influence of interest (e.g., the regional species pool) within a diverse assemblage of potential drivers
and across spatial scales (e.g., Rosenzweig and Ziv 1999).
Second, as a future direction, we emphasize that plots
of local species richness versus regional species pools alone
are not an adequate tool to assess the relative influence of
local ecological interactions and regional species pools (see
Huston 1999; Srivastava 1999). This traditional approach
tells only part of the ecological story (i.e., the role of regional species pools) while veiling others. Our results begin
to illustrate how other factors such as facilitation and abiotic stress may have dramatic influences on the relationship between local richness and regional species pools. In
future research, an insightful approach would be to simultaneously test the influence of local ecological interaction strengths, the richness of the regional species pool,
and abiotic variables. We concede that such broad-scale
quantification of the strength of local ecological interactions concurrently with abiotic information would be a
major challenge. We hope that further tools may be developed that allow us to advance in this direction.
Last, this study emphasizes the volatility of predictions
of ecological theory as a result of the ecological context.
We will see saturation only where species can be expected
to interact, which is dependent on, among other factors,
temporal and spatial scales, habitat types, and operational
species groupings (see fig. 1). Even then, a community
driven by ecological interactions may hide any asymptotic
pattern (Rosenzweig and Ziv 1999). We argue that plots
of local richness against the regional species pool have not
produced a theoretical consensus because of the overwhelming influence of ecological context (Berlow 1997;
Menge 2003). If we assess patterns at landscape scales, we
should not expect to see—and do not see—strong signatures of local ecological interactions regardless of their
potential relevance at smaller scales. We also would not
expect to see saturation within less interactive groups of
species; for example, we demonstrated the lower likelihood
of seeing saturation in kingdom-level classifications than
within trophic-level groupings. Finally, we demonstrated
that even at small local scales and within ecologically defined groups of species, the characteristics of the habitat
we are studying can dictate whether we observe saturation
dynamics. With more explicit incorporation of experimental context into predictive theory, we have the potential to constructively reconcile the disjunctive conclusions
of past studies and gain more complete insights into the
general behavior of ecological systems.
Acknowledgments
The data used in our analyses exist because of the foresight,
dedication, and stamina of D. Digby, S. Etchemendy, M.
Kavanaugh, C. Schoch, S. A. Thompson, and the stalwart
Partnership for Interdisciplinary Studies of Coastal Oceans
(PISCO) “Team Biodiversity”: L. Ahlgren, C. Carlson, R.
Catullo, S. Clausen, M. Dutton, M. Foley, L. Keeton, K.
Lashenske, K. A. Miller, M. Packard, R. Pecore, A. Ryerson,
J. Uselman, F. Weeks, B. Williams, A. Wilson, and W.
Wood. We gratefully thank C. Carlson, F. Chan, A. Guerry,
J. Lawler, and J. Lubchenco for their intellectual insights.
We are also appreciative of the insightful comments provided by T. Miller and an anonymous reviewer. We thank
B. Abbott for her generous long-standing permission to
work at the Fogarty Creek site in Oregon. This work was
supported by a National Sciences and Engineering Research Council of Canada award, a Fulbright Fellowship,
and a Mamie Markham award to R.R., UBC Graduate
Fellowship to S.A.W., as well as grants from the David and
Local Richness, Regional Pools E169
Lucile Packard Foundation and the Andrew W. Mellon
Foundation. This is contribution 204 from PISCO, funded
primarily by the Gordon and Betty Moore Foundation and
David and Lucile Packard Foundation.
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