Download Regional and local impact on species diversity – from pattern to

Oecologia (2002) 132:479–491
DOI 10.1007/s00442-002-0988-3
REVIEW
Helmut Hillebrand · Thorsten Blenckner
Regional and local impact on species diversity –
from pattern to processes
Received: 20 August 2001 / Accepted: 23 May 2002 / Published online: 16 July 2002
© Springer-Verlag 2002
Abstract The impact of regional factors (such as speciation or dispersal) on the species richness in local communities (SL) has received increasing attention. A prominent method to infer the impact of regional factors is the
comparison of species richness in local assemblages (SL)
with the total number of species in the region (SR). Linear relations between SR and SL have been interpreted as
an indication of strong regional influence and weak influence of interactions within local communities. We
propose that two aspects bias the outcome of such comparisons: (1) the spatial scale of local and regional sampling, and (2) the body size of the organisms. The impact
of the local area reflects the scales of ecological interactions, whereas the ratio between local and regional area
reflects the inherent moment of autocorrelation. A proposed impact of body size on the relation is based on the
high dispersal and high abundance of small organisms.
We predict strongest linearity between SR and SL for
large organisms, for large local areas (less important ecological interactions) and for sampling designs where the
local habitat area covers a high proportion of the regional area (more important autocorrelation). We conducted a
meta-analysis on 63 relations obtained from the literature. As predicted, the linearity of the relationship between SL and SR increased with the proportion of local to
regional sampling area. In contrast, neither the body size
of the organisms nor the local area itself was significantly related to the relation between SL and SR. This indicated that ecological interactions played a minor role in the
shape of local to regional richness plots, which instead
was mainly influenced by the sampling design. We found
that the studies published so far were highly biased towards larger organisms and towards high similarity between the local and regional area. The proposed prevalence of linear relationships may thus be an artefact and
H. Hillebrand (✉) · T. Blenckner
Erken Laboratory, Department of Limnology,
Evolutionary Biology Centre, University of Uppsala,
Norr Malma 4200, 76173 Norrtälje, Sweden
e-mail: [email protected]
Fax: +46-176-229315
plots of SL to SR are not a suitable tool with which to infer the strength of local interactions.
Keywords Diversity · Dispersal · Body size · Area ·
Species pool
Introduction
The diversity in local assemblages can be regulated by
local factors (such as competition, disturbance, abiotic
conditions) and by regional factors (such as history of
climate, evolution and migration). Much research has
been devoted to local mechanisms which increase, maintain or decrease diversity (Tilman and Pacala 1993;
Chesson 2000). In recent years, however, large-scale
processes have been increasingly regarded as important
determinants for the species richness of local communities (SL) (Ricklefs 1987; Cornell and Lawton 1992;
Lawton 1999). These reports also comprised the question
of how regional differences in diversity are generated
and maintained during time (Rosenzweig 1995; Hillebrand and Azovsky 2001) and how these regional differences transfer into the diversity of local communities
(Zobel 1997).
Conceptually, the assembly of a local community can
be visualised as species passing through a series of filters, which represent historical (e.g. dispersal, speciation) and ecological (e.g. competition, predation, disturbance, abiotic environmental factors) constraints on the
arrival and survival of organisms at a certain locality
(Zobel 1997; Lawton 1999). In this concept, the local diversity is related to the diversity of the regional pool if
processes connected to the dispersal of organisms are
mainly responsible for the assembly of local communities (Fig. 1). A dominant impact of the local environment
(abiotic and biotic) was supposed to lead to independence between SL and regional species richness (SR)
(Fig. 1). Several contributions tried to disentangle the regional and local constraints of SL (Cornell and Lawton
1992; Ricklefs and Schluter 1993; Cornell and Karlson
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pact of both factors and connect these to a conceptual
predictive model (see Background). We will then test
these predictions in a quantitative synthesis of the literature available. For this meta-analysis, we will also revise
the statistics used to distinguish between type I and
type II communities.
Background
The impact of area
Fig. 1 Conceptual model visualising the assembly of local communities from regional species pool (see text for more details)
1996, 1997; Cornell 1999; Srivastava 1999; Shurin
2001). Generally, these studies agree on an important influence of both regional and local factors, but the relative importance of these factors is still uncertain since
they act on different temporal and spatial scales.
Some of these scales are difficult to manipulate or
are not at all tractable, reducing the possibility to experimentally test the predictions on regional and local influence. Therefore, the importance of regional and local
processes has been derived from the analysis of patterns. A central method used for this discussion is the
regression of SL on SR (Lawton 1999; Srivastava 1999).
Significant linear regressions are interpreted as an indication of the high impact of regional factors on local diversity (type I communities), whereas saturating or nonlinear functions would indicate an upper limit of SL set
by ecological interactions (type II communities)
(Cornell and Lawton 1992). SL in type I communities
thus tends to be higher in species-rich regions, which
has also been called “regional enrichment” (Karlson and
Cornell 1998, 2002). Analyses of SL to SR have been
done in terrestrial, marine and freshwater systems,
mainly for vertebrates and host-specific organisms like
parasites. The studies published so far stressed the prevalence of type I communities (see reviews by Cornell
and Karlson 1997; Cornell 1999; Lawton 1999; Srivastava
1999). In many cases, this strong linear relationship between SR and SL has been interpreted as an indication of
“unsaturation” of local assemblages with species and of
generally weak effects of local interactions on species
richness (Oberdorff et al. 1998; Cornell 1999; Hugueny
and Cornell 2000).
Although this type of analysis seems to be straightforward, several pitfalls have been discussed (Srivastava
1999; Shurin et al. 2000). In this paper, we analyse two
factors interfering with the analysis of local to regional
richness plots, the spatial scale of the analysis and the
body size of the organisms. We hypothesise that these
two factors will affect the linearity between SL and SR
more than the intensity of ecological interactions. In the
following, we will thoroughly describe the proposed im-
Previous studies showed that using different areas for
censuses of SL or SR, respectively, resulted in highly different outcomes of the regression of SL to SR (Westoby
1993; Angermeier and Winston 1998; Karlson and
Cornell 2002). Area plays an important role in these analyses: it affects the estimates of species richness (species
area relationship), it reflects the scales on which species
interact (ecological factors) and the size of the regional
pool (SR), and it describes the degree of similarity if the
local and regional area are close to each other (autocorrelation).
Species-area relationships
Increasing the area of sampling will generally result in
an increase in species richness, as described by the wellknown species-area relationship (SAR). The different
size of regions itself can be one explanation for different
sizes of SR pools, e.g. on islands of different size
(Ricklefs 1987). However, the use of naturally bound local habitats of different size for the determination of SL
will result in a biased estimate of SL (Shurin et al. 2000).
These differences can be avoided by removing the trend
with area from the data (Karlson and Cornell 1998;
Shurin et al. 2000) or by using standardised areas for localities and regions (Westoby 1993; Caley and Schluter
1997; Weiher 1999).
Ecological factors
The scale of local interactions should be used to determine local habitat size, since the local extinction of species due to competition or predation is implicitly assumed to cause the non-linearity between SL and SR.
This scale may be a complete lake for relatively wellmixed pelagic communities, but only a few square metres for plants competing for soil nutrients (Huston and
De Angelis 1994). In one of the most thorough analyses
so far, Caley and Schluter (1997) presented strong evidence for a linear relationship between SR and SL for a
variety of organisms. These results were independent of
the size of the local community, which comprised either
1% or 10% of the 250,000 km2 regional squares. However, even the lower marginal of these two local assemblages covered an area on which animals or plants do not
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interact (Huston 1999). Therefore, an upper limit of SL
due to ecological interactions cannot be expected on
these large scales. On the other hand, sampling areas for
SL which are too low may result in increasing undersampling of rare species and thus the artificial creation of a
non-linear pattern between SL and SR, which has been
called pseudosaturation (Caley and Schluter 1997; Karlson
and Cornell 2002).
Regional species richness
The delimitation of the regional species pool is an important factor in analyses of relationships between SL
and SR. First, the suitable extent of the regional area is
difficult to determine (Srivastava 1999; Shurin et al.
2000). Most studies used an arbitrarily chosen large area
(Caley and Schluter 1997) or an area delimited by natural geography (Oberdorff et al. 1998). The regional pool
should comprise all species able to invade the local community, which integrates a component of dispersal ability
(see below) and a component of time. At extreme time
scales, almost all species will at least possibly invade almost every community. To delimit a regional species
pool, a theoretical ecological time period has to be assumed – but this has not been done explicitly so far. Secondly, all species able to reach a certain community may
not be able to live there. Simply counting the number of
species within a taxonomic group will overestimate the
regional species pool and a correction for the suitability
of the habitat would have to be done (Pärtel et al. 1996).
Thirdly, different regional species pools are not independent, e.g. due to an overlap in species composition
(Srivastava 1999; Ricklefs 2000).
Autocorrelation
The pattern between SL and SR will highly depend on the
proportion of the region covered by the local communities. If the area of the local habitat compared to the region is extremely large, almost all species of a regional
pool should be present in any “local” community and the
linear relationship between SL and SR would be just a
statistical consequence. This moment of autocorrelation
is implicit in the SL to SR regressions and is more important if the proportion of local to regional area is large
(Creswell et al. 1995). Loreau (2000) discussed this
problem by dividing total regional diversity (γ) into a between-habitat (β–) and a within-habitat (α–) diversity
component (see also Whittaker 1972; Godfray and
Lawton 2001). Saturation of local α– with increasing γdiversity is only possible if the γ– and β– components
are highly correlated, i.e. higher regional pools encompass more habitats, increasing the β-component of diversity. This is only possible if the area of the “local” assemblage is small, i.e. if higher regional areas easily include more habitats (Cornell and Lawton 1992; Loreau
2000).
The impact of body size
The probability that organisms will invade a local habitat
is clearly related to dispersal ability and the abundance
of propagules. If the regional species pool is supposed to
reflect the species able to invade the target locality, both
factors have to be taken into account. However, the studies presented so far did not adjust the definition of regions to dispersal ability, e.g. Caley and Schluter (1997)
used the same regional area for each group. These differences become even more important by extending the approach to small organisms such as zooplankton, protozoans or unicellular algae (Shurin et al. 2000; Hillebrand et
al. 2001). These organisms exhibit a very high abundance and high transportability, which will increase the
statistical probability of successful dispersal (Finlay et
al. 1996; Fenchel et al. 1997). Additionally, any individual unicellular organism can be seen as a propagule.
Consequently, these organisms exhibit a comparably
small global species richness, leading to a unimodal relation of global diversity with highest richness in intermediate body size classes (Godfray and Lawton 2001). The
proposition of low global and high SL of unicellular organisms (Fenchel 1993) has repeatedly been criticised
based on difficulties in applying morphospecies concepts
to theses groups (Mann and Droop 1996; Foissner 1999;
Sabbe et al. 2001). However, Godfray and Lawton
(2001) summarise some evidence on reduced speciation
rates of unicellular organisms by high dispersal ability
and thus by high gene flow. Additionally, the high dispersal ability of small organisms is reflected by the similarity of species composition between distant sites
(Hillebrand et al. 2001), the low proportion of unicellular endemism in remote lakes (Cocquyt 2000) and the
rarity of large biogeographical patterns (Hillebrand and
Azovsky 2001). These reports are contrasted by increasing evidence of dispersal limitation in higher plants
(Tilman 1997; Cain et al. 1998; Hubbell et al. 1999;
Turnbull et al. 2000) and benthic invertebrates
(Roughgarden et al. 1988; Cowen et al. 2000). Body size
is thus a rough proximate variable to indicate dispersal
ability across different size classes (Fenchel 1993;
Hillebrand and Azovsky 2001).
The assumption that dispersal ability increases with
decreasing body size is clearly valid only for a very large
range of organism sizes. There are deviations from this
assumption, both for some general groups (birds will be
better dispersers than many smaller groups) and within
groups (different dispersal ability of snails). Still, dispersal is often a chance-related event, combining transportability and abundance of propagules, both being
higher for very small organisms. Decreasing body size
will roughly increase the range from which organisms
are able to reach the target locality and will thus influence the relation between SL and SR.
482
Fig. 2 Predicted effects of local area (a), the proportion of local to
regional area (b) and body size (c) on the relationship between local species richness (SL) and regional species richness (SR). Lines
represent extremes in a continuum of possible shapes of the regression of SL to SR
Predictions
In our analyses, we concentrate on two aspects of area:
(1) the spatial scale of ecological interactions, and (2)
the degree of autocorrelation. From the thoughts presented above, the following predictions can be derived.
1. The linearity of the relationship between SL and SR
will increase with increasing area of the local habitat
(Fig. 2a). The size of the local habitat determines the
extent to which ecological interactions are covered.
Increasing the area regarded as local habitat will thus
decrease the strength of ecological interactions and
increase the linearity.
2. The linearity of the relationship between SL and SR
will increase with an increasing ratio between the area
of the local habitat to the area of the region. The larger a locality is scaled in proportion to a region, the
more it is influenced by factors regulating the regional pool (cf. Karlson and Cornell 2002) and the more
Fig. 3a, b Conceptual model predicting the linearity between SL
and SR. Linearity is measured as the slope of the log-transformed
regression and depends on body size and the proportion of local to
regional sampling (pL) (a) or on body size and the area of the local
habitat (b). For abbreviations, see Fig. 2
SR is covered by SL. We introduce pL as the mean proportion of locality area to the area of the region and
predict an increasing importance of the regional pool
with increasing pL (Fig. 2b), reflecting the statistical
impact of area.
3. The linearity of the relationship between SL and SR
will increase with increasing body size of the organisms. The high dispersal ability and abundance of
small organisms will result in high SL, which in turn
should result in constant SL despite increasing SR. We
thus expect that the regional species pool SR constrains SL mainly within groups of large organisms
with a low dispersal ability and a low number of individuals (Fig. 2c).
The impacts of area and body size are clearly interwoven, since diatoms or mammals differentially perceive
the size of a certain local area. This is evident for both
the ecological and the statistical aspect of area. We therefore created two conceptual models pairing the impact of
body size with each of the area impacts (Fig. 3). For the
statistical impact of area, the most linear relationships
between SR and SL should be found at large body sizes
and at high pL, i.e. if local areas cover a high proportion
483
of the regional area. If organisms are small, numerous
and highly dispersed and if pL is small, the SL should be
independent of the regional species pool. A three-dimensional graphical model emerges, giving the importance
of SR for SL (i.e. the linearity of the relationship), depending on body size and pL (Fig. 3a). For the ecological
impact of area, we predict lowest linearity for small organisms in small areas. Increasing the area will increase
the linearity. For every given local area, the dependence
on regional pools will be highest for intermediate body
sizes, since the largest organisms may still be able to interact and the smallest may be highly dispersed. A second three-dimensional graphical model thus predicts a
unimodal function of the slope b dependent on body size
and local area (Fig. 3b)
Materials and methods
We use published data in a meta-analysis to test the predictions
derived above. A central aspect of meta-analysis is the use of a
suitable effect size (Osenberg et al. 1997), in this case a metric to
measure the linearity between SL and SR. Therefore, we first revise
the models used in the literature, before presenting the data basis
of our analysis.
Models to distinguish between type I and type II relationships
There is no consistent statistical test used to distinguish between
type I and type II relationships throughout the literature, but in
most studies linear and non-linear regression results were compared (Cornell 1985; Hawkins and Compton 1992; Cornell and
Karlson 1996). As non-linear regression models, second-order
polynomial (quadratic) or power functions have been used
(Creswell et al. 1995). Besides differences in the number of estimated parameters (Creswell et al. 1995), all curvilinear functions
initially allow a nearly linear increase (Fig. 4a). Within the range
of data used to compare SL and SR, the explained variance may
thus be very similar and highly correlated between linear and saturation curves, giving little contrast to distinguish between models.
However, a severe problem with these regression approaches is
the reliance on model I regression and the corresponding estimates
of explained variance (Cresswell et al. 1995; Pärtel et al. 1996).
Model I regression assumes that the dependency of one variable on
the other is known and that the independent variable is without error
(Sokal and Rohlf 1995). Both assumptions are not met by the data
sets presented in the literature. SR cannot be estimated without error
and may also be influenced by local pools as vice versa. The lack of
an error-free independent variable mostly affects the explained variance, and the error can be extremely large in non-linear regression
models. The implicit autocorrelation due to the interdependence of
SL and SR also biases the explained variance. Tests based on the
variance explained by different regression models are thus statistically flawed. Also correlation analyses are not suitable to indicate
the strength of a linear relationship due to autocorrelation (Cresswell
et al. 1995; Pärtel et al. 1996). In addition to these statistical concerns, there is also a general lack of rigorous hypothesis testing in
this approach: a slightly better fit in one type of relationship does
not falsify the validity of the alternative regression approach.
Several studies have tried to overcome these limitations. Pärtel
et al. (1996) used correlation coefficients in connection with minimum strength null hypothesis and Monte-Carlo simulations.
Griffiths (1997) used log-log regressions of SL on SR, arguing that
slopes similar to 1 indicate proportional sampling (type I communities), while slopes <1 indicate levelling off in local richness
(type II communities). Linear relationships going through the origin tend to have a slope b=1 in log-log space, whereas non-linear
Fig. 4a, b Models used to distinguish between linear and non-linear regressions of SL on SR (see text for details). For abbreviations,
see Fig. 2
saturating functions have a slope b<<1 (Fig. 4b). Using log-transformed variables, the deviation of b from 1 can thus be seen as a
measure of linearity (see also Ricklefs 2000). This latter test is advantageous since estimates of the slope are not biased by the errors present in the independent variable (Sokal and Rohlf 1995)
and the slope can be used as an effect metric in meta-analyses
(Hillebrand et al. 2001).
Therefore, we conducted a meta-analysis on the linearity of the
relationship between SR and SL, calculating the slope b for every
study (see Data basis) using the equation:
(1)
where SL is the mean SL and a and b the parameters to be estimated.
We used b to test first each of the three single predictions concerning local area, pL, and body weight. Since a weighted metaanalysis was not possible due to biased estimates of SEs for the
slope, we used Spearman rank-correlations between b and each of
the three predicting variables. We then tested the predictions of the
three-dimensional models by conducting a multiple regression
analysis on b. We used a fixed non-linear model (second-order
polynomials) to allow unimodal responses of the dependent variable. To avoid errors inclined with the errors in the independent
variables, all variables were rank-transformed.
Data
Surveying the literature in addition to recent reviews by Cornell
(1999) and Srivastava (1999), we found 54 studies on the relation-
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ship between regional and SL (Appendix 1). It became obvious
that to date most investigations were on vertebrate animals and
host-specific organisms like galling insects, parasitoids or helminth parasites of fish. Studies on small organisms were rare, but
we derived estimates of SR and SL for ciliates and diatoms, thus
adding four more data sets (Appendix 1).
The following criteria were used to select the data to be included in the analysis:
1. The minimum number of observations had to be n≥3.
2. The underlying model for SL to SR relationships analysed in the
study should follow the argumentation in Srivastava (1999).
Therefore, we included only studies investigating the richness
of one community type in different regions, since Srivastava
(1999) argued that using different habitat types in one region
as regional pools (see Pärtel et al. 1996) confounds habitat differences and differences in SR.
3. Host-specific taxa like pathogens or phytophagous insects had
to be excluded from our analysis, since their dispersal is not related to their body size but to the dispersal of their hosts.
4. When the original approach could not be used [e.g. due to
pseudoreplication, cf. Srivastava (1999)], we obtained the original data from the study and recalculated the regression. If the
presentation of the original study did not allow the recalculation, we deleted the study from the analysis.
5. If different local areas were used to estimate SL, we calculated
the species area relationship and used the residuals as measures
of SL.
Our final data set comprised 63 analyses from 32 studies, which
could be used to test our hypotheses. For each organism group, we
estimated body mass from literature values and obtained estimates
of local area from the original contribution. Furthermore, we calculated pL, for which alternative methods had to be used, since
original approaches for defining regional and local pools differed.
In most cases, the proportion could be calculated on the basis of
the areas of local and regional sampling (Caley and Schluter
1997), whereas in some studies it was calculated on the basis of
sample numbers (Van Valkenburgh and Janis 1993). In several
studies, pL could not be obtained directly from the original analysis and had to be estimated (Appendix 1), but the impact of this error was minimised by log-transformation of the variables.
Results
We found positive correlations between the linearity of
the relationship between SL and SR (i.e. slope b) and the
proportion pL (rS=0.285, n=62, P=0.025). No significant
relationship was found for the body weight of the organisms (rS=0.182, n=63, P=0.153) and the local area
(rS=0.201, n=45, P=0.186), respectively.
The combined impact of pL and body size on the linearity of the relationship between SL and SR was evident
across the studies included in our analysis (Fig. 5, upper
graph). We found highest linearity if organisms were
large and if pL was high. This was especially evident if
pL was ≥0.01 (1%). On the other hand, studies comprising small organisms and/or small pL revealed slopes
<<1, indicating non-linear relationships (Fig. 5, upper
graph). This pattern was reflected by a significant multiple regression model on slope b with pL and body size as
independent variables (Table 1). Although the overall
model was significant, the single factors were only significant for pL, but not for body weight (Table 1). Both
factors comprised strong positive linear interactions and
Fig. 5 Slope b of regressions of SL to SR dependent on pL and
body size (upper graph) or on local area and body size (lower
graph). For abbreviations, see Figs. 2 and 3
Table 1 Results of fixed non-linear (second-order polynomial)
multiple regression on the slopes b of log-transformed regressions
of local to regional species richness. The table gives the overall
model statistics and the estimates for the independent variables for
two different models. The first model comprised the proportion of
local to regional sampling (pL) and body weight as linear and quadratic predictor variables, whereas the second model comprised
local area and body weight. All variables were rank-transformed
Model
Overall
Model 1 r2=0.2181
F4,57=3.98
P=0.006
Model 2 r2=0.056
F4,43=0.64
P=0.639
Variables
Estimate
of b
P-level
Intercept
pL Linear
pL Quadratic
Weight linear
Weight quadratic
Intercept
Local area linear
Local area quadratic
Weight linear
Weight quadratic
0.206
0.042
–0.0006
0.005
–0.00007
0.899
0.002
–0.00004
–0.003
0.00009
0.261
<0.001
0.001
0.659
0.693
0.003
0.916
0.823
0.869
0.881
weaker negative quadratic terms, indicating a tendency
towards non-linearity.
Furthermore, it became obvious from Fig. 5, that
there is strong bias in the investigations published so far
485
towards large organisms and high pL. Clearly, only few
studies incorporated invertebrates and small relative local areas. Several studies revealed even slopes >>1,
which indicated that low SR regions had too low estimates of SL which may be due to undersampling.
The predicted impact of local area in connection to
body weight was not shown in the data (Fig. 5, lower
graph). While linearity still somewhat decreased for
small organisms, there was no trend with the size of the
local habitat. Also the multiple regression was clearly
non-significant (Table 1). It became thus evident that the
local impact of area was absent, whereas the statistical
impact was the most important factor influencing the
shape of the relation between SL and SR.
Discussion
Our results indicated that the linear relation between SL
and SR is mainly driven by a strong impact of autocorrelation, that is increasing linearity was observed when the
sizes of local and regional areas became more similar.
Although there was some indication that SL becomes independent of SR for small organisms, this pattern was not
significant. An ecological impact of area on the results
could not be found. The proposed prevalence of linear
type I relations (Cornell 1999; Lawton 1999; Srivastava
1999) was thus clearly based on a biased selection of test
organisms and locality sizes. In the following, we first
will discuss the use of regional to local richness plots to
detect mechanisms of SL assembly, then proceed by focusing on processes instead of patterns and finally give
some future implications of our analysis.
The use of regressions between SL and SR
Linear relationships between SR and SL have been interpreted as an indication that local assemblages are not saturated with species and SL is largely influenced by processes on regional scales (Ricklefs 1987; Cornell 1999).
Additional evidence in favour of these regional imprints
on SL has been taken from the equivocal results of experiments on the dependence of invasibility on species richness (Cornell 1999; Levine and D'Antonio 1999), and
from the existence of large-scale gradients in regional as
well as in local data sets (Gaston 2000; Hillebrand and
Azovsky 2001). In this context, type I relationships have
even been discussed as indicators of weak or absent ecological interactions (Lawton 1999; Hugueny and Cornell
2000) and a higher importance of dispersal limitation
than ecological interactions (Cornell and Karlson 1997).
Also our study revealed that most investigations
found linear relationships between SL and SR (slopes ~1),
thus corroborating previous statements that SL does not
converge in similar habitats in regions of differing diversity (Lawton 1999; Srivastava 1999). However, the predominance of linear (type I) relationships indicated
merely the scale of analysis, but not the unsaturation of
local assemblages with species or the strength of ecological interactions (cf. Huston 1999; Loreau 2000). The
clear increase in the linearity with pL and the generally
high values of pL across the studies make it obvious that
the linear relationship found in many investigations follows from the autocorrelation between SL and SR as defined in these studies. At high pL, the regression between
SR and SL is linear without any underlying ecological
mechanism, simply indicating that estimates of SL and SR
are not independent (Loreau 2000). Only few studies
tried to circumvent this problem by using very large regions and small local samples (Cornell and Karlson
1996; Karlson and Cornell 2002). In addition to the statistical artefact, our analysis strongly suggests that the
prevalence of linear relationships between SL to SR is
also based on a biased selection of test organisms
(favouring organisms with comparably low dispersal
ability).
Thus, there seems to be a limited validity in the use of
regressions of SL on SR to infer biological processes
(Loreau 2000; Shurin et al. 2000). Shurin et al. (2000)
found strong linear regressions of SL to SR for crustacean
zooplankton, but simultaneously found evidence that dispersal was not an important factor influencing zooplankton diversity. Moreover, linear relations between SL and
SR were not incompatible with strong local interactions
(Shurin 2000; Shurin et al. 2000). But even if dispersal is
high and local interactions are strong, SL may not be
consistently reduced irrespective of SR. First, if regions
contain only some localities where competitive exclusion
is prevented, the mean SL will increase with the species
pool SR (Huston 1999). Secondly, the reduction of SL requires time and disturbances may decrease the effects of
biological interactions (Huston 1999; Karlson and Cornell
2002). In patch-occupancy models, even low rates of disturbance were shown to obscure the effect of strong
competitive interactions on SL, leading to a linear relations between SL and SR (Caswell and Cohen 1993).
Thirdly, in the context of saturation of local assemblages
with species, the discussion of ecological interactions
has been mainly restricted to one trophic level, i.e. competition (Cornell and Lawton 1992; Huston 1999). Other
local interactions, such as grazing, can have a strong impact on species diversity (Proulx et al. 1996; Hillebrand
et al. 2000) and should be included (Shurin and Allen
2001). Finally, even if local assemblages may saturate
with species in time scales corresponding to ecological
interactions, the saturation pattern may be invisible. In
evolutionary time, new species may invade or evolve
within the regional pool, which may lead to shifts in the
species composition in local communities – and prevent
SL from reaching hard limits (Cornell and Lawton 1992).
In a thorough critical review on the use of local to regional richness regressions, Srivastava (1999) discussed
additional pitfalls of the regional to local plots, such as:
(1) in defining “regional” and “local” (cf. Huston 1999;
Loreau 2000; Shurin et al. 2000), (2) in choosing the
right type of data for this kind of comparison, and (3) in
avoiding spatial and temporal pseudoreplication. The
486
sizes of the local and regional areas have been defined
rather arbitrarily and differ widely. Not all studies have
standardised the SL measurements to a certain area
(Westoby 1993; Caley and Schluter 1997; Weiher 1999).
Because of the increase in species richness with area, using different sizes of “local” and “regional” in one study
will affect the results (Srivastava 1999). It would be
preferable to standardise local size and to relate the size
of the locality to the size of the organisms, their habitat
use and especially to the scale of interactions. The size
of the regional species pool is even more difficult to define. Counting the number of species in a certain taxa
may combine species from different trophic guilds,
which do not compete, (Huston 1999) or may comprise
species which cannot survive under the present local
conditions (Pärtel et al. 1996; Zobel 1997; Dupré 2000;
Shurin 2000). The area used to calculate the regional
pool should furthermore be adjusted to the dispersal ability of the organisms (since it should contain all species
able to reach the local habitat), but most studies have
used one arbitrarily defined large area for different
groups (Caley and Schluter 1997).
From these arguments it becomes clear that SL is connected to the regional species pool, irrespective of the
importance of local interactions. Thus, the only conclusions to be drawn from the SR to SL plots is that local interactions are not strong enough to limit SL to a consistent level in all localities in different regions (Cornell
and Lawton 1992; Loreau 2000).
From pattern to process
Our analysis revealed the limited applicability of SR to
SL regressions to infer processes of diversity assembly.
However, the statistical issues do not contradict the importance of regional processes on local communities. It
is without question that total species richness differs between regions for many organism groups. Different regional diversity can be based on different processes, e.g.
variation in net diversification rates due to higher physiogeographical heterogeneity and subsequent allopatric
speciation (Ricklefs 1987; Qian and Ricklefs 2000).
There is considerable discussion about the mechanisms
creating and maintaining highly diverse regions (Gaston
and Williams 1996; Rohde 1999; Hillebrand and Azovsky
2001). There is also good evidence beyond the SL to SR
plots that high SR is connected to higher SL (Ricklefs
1987; Westoby 1993). Instead of inferring the saturation
of local communities with species from patterns, it is
more important to analyse how different levels of SR
transfer into SL and which mechanisms affect this transfer.
Recruitment limitation in fact seems to be an important process determining SL, influenced by organism
traits (size of the dispersal stage, number of propagules
produced, durability of the dispersal stages) and habitat
characteristics (such as the degree of flow in aquatic
communities). Propagule density does not only affect the
colonisation of local communities (the supply side), but
also the internal interactions within these communities
(Menge and Sutherland 1987; Palmer et al. 1996). The
continuous supply of propagules is proposed to intensify
competitive and trophic interactions in local communities (Menge and Sutherland 1987). Palmer et al. (1996)
made predictions about regional and local impacts under
different scenarios of dispersal and disturbance. If both
are high, they proposed a regional control of SL since
random colonisation events will determine community
composition. If disturbance and dispersal are low, a
mainly local influence was proposed based on local interactions. At low disturbance but high dispersal, Palmer
et al. (1996) assumed combined local and regional control. Menge and Sutherland (1987) made similar predictions for different trophic levels, finding important and
distinct effects of recruitment on the relative importance
of environmental stress, competition and predation for
basic, intermediate and top trophic levels.
The relative importance of these interacting forces
and the mechanisms linking them can be best analysed
by models and careful experimentation. Amarasekare
and Nisbet (2001) explicitly considered how dispersal
modified local competitive interactions and found that
increased immigration rates can reduce coexistence.
Rather than the patch occupancy approach with an emphasis on exclusion and colonisation, source-sink dynamics and spatial variation in competitive success were
important features with regard to understanding coexistence [see Moore et al. (2001) and Mouquet and Loreau
(2002) for similar approaches]. In a model linking predation, competition and dispersal, Shurin and Allen (2001)
investigated the effects of predator-mediated coexistence
on local and regional richness, proposing that processes
other than dispersal limitation can promote linear relations between local and regional richness. If only competitive exclusion was included as a local interaction, the
model predicted an asymptote of SL with increasing SR.
However, if a keystone predator was introduced, regional
coexistence was promoted due to an expanded array of
conditions allowing competitors to coexist. Mean local
diversity was affected positively or negatively, depending on the dispersal rates of the predator and its prey
(Shurin and Allen 2001).
Few experiments included the presence of a regional
species pool (Palmer et al. 1996), but especially aquatic
communities seem to be suitable for experimental manipulations. These experiments included recruitment and
species pools on small laboratory scales (Long and Karel
2002) and larger field studies (Menge et al. 1997, 1999).
The colonisation history and competitive dominance determined interactively the community structure in aquatic microcosms (Long and Karel 2002). In coastal benthic
communities, differences in community structure can be
tracked to differences in near-shore oceanography and
thus food supply and density in recruits (Menge et al.
1997, 1999; Connolly and Roughgraden 1998). Regional
and local factors also interacted when two different predators (fish, insect) were introduced into zooplankton as-
487
semblages that were or were not connected to a regional
pool of zooplankton species (Shurin 2001). Both predators reduced zooplankton species richness if dispersal
was not possible. With dispersal, however, the fish had
positive effects on richness by facilitating the invasion
by species from the regional species pool.
Conclusions and future perspectives
The linearity of relationships between SL and SR increased with the proportion of the region (pL) sampled to
obtain SL. The strong relation between pL and b showed
that the sampling design plays a major role in the outcome of these analyses. The body size as a proximate
variable for abundance and dispersal ability of the organisms was not significantly related to the linearity and
neither was the local area itself. Regressions of local to
regional richness do not allow one to infer processes of
community assembly and should be used cautiously.
There is ample evidence for regional differences in diversity, but no evidence for the absence of strong ecological interactions that can be derived from linear SL to SR
relations.
Thus, the importance of regional impacts on SL has to
be seen in conjunction with local processes. The models
and experiments described above showed that the relative importance of regional and local influences and the
mechanisms underlying this relation are analytically
tractable. For such an analysis, the intensity of local interactions should be measured independently (e.g. as
presence of competitive exclusion, resource limitation,
density compensation, disturbance frequency or interaction strength) and compared to the regional species pool
and the SR to SL relation (Srivastava 1999).
Like other topics in macroecology, the question of regional impacts on SL leads to an inspiring discussion of
community assembly with a strong impact on basic and
applied issues. The saturation of local communities is
linked to conservation issues in at least two aspects, first
the success of invasive species in relation to SL (Levine
and D'Antonio 1999), and second the usefulness of conserved areas if target species have a limited dispersal
ability. Regional impacts may be important for the richness of local assemblages, but it is premature to infer a
prevalence of regional factors from the studies presented
so far. Studies beyond the plots of SL to SR have to be
conducted to disentangle the scales on which the factors
controlling community composition (inter-)act.
Acknowledgements We are gratefully indebted to Robert E.
Ricklefs and Ron Karlson for encouragement and critical discussion. Ron Karlson also provided us with original data. We thank
all authors of the primary contributions on this topic for the possibility to conduct this meta-analysis. The manuscript profited from
the comments of Stephanie Blenckner and Monika Feiling. We are
moreover grateful to Russell Monson and two anonymous reviewers. This work has been funded by a grant to H. H. (Deutscher
Akademischer Austauschdienst D/99/08944) and by the Erken
Laboratory.
Appendix 1
Studies on regional and local species richness analysed during
this investigation
thermore it is stated if the study was included (Incl.) in our analysis and how the proportion of local to regional sampling (pL) was
calculated
The table gives authors and references for each study, the regions
and organisms studied, and the number of observations (n). FurAuthors
Reference
Abele (1984)
In: Strong DR, et al. (eds)
Worldwide
Ecological communities.
Princeton University Press,
Princeton, N.J., pp 123–137
In: Esch GW, et al. (eds)
Parasite communities: patterns
and processes. Chapman and Hall,
London, pp 157–195
In: Ricklefs RE, Schluter D (eds) North America
Species diversity in ecological
communities.
University of Chicago Press,
Chicago, Ill.,pp 185–193
Ecology 79:911–927
Virginia, USA
Aho (1990)
Aho and Bush
(1993)
Angermeier and
Winston (1998)
Caley and
Schluter (1997)
Clarke and
Lidgaard (2000)
Cornell and
Karlson (1996)
Cornell 1985)
Region
Organisms
Decapods
Parasites of
amphibians
Parasites of
bass fish and
sunfish
Fish
n
Incl.
Comments
PL
4
Yes
–
Estimated
area
13
No1
–
–
No1
–
–
13/13 Yes10 –
–
3/9
Ecology 78:70–80
Worldwide
J Anim Ecol 69:799–814
North Atlantic
Birds, mammals,
3–5
reptiles, fish, trees
Bryozoans
14
J Anim Ecol 65:233–241
Worldwide
Corals
Ecology 66:1247–1260
California
Galling insects
Yes
Yes
39/26 Yes2
9
No1
Two local
sizes
–
Two
estimates SL
–
Precise area
Estimated
area
Estimated
area
–
488
Appendix 1 (Table continued)
Authors
Reference
Region
Organisms
Cornell (1985)
Dawah et al.
(1994)
Findley and
Findley (2001)
Foissner
(1992–1996)
Foissner
(1996–1999)
Frenzel and
Brandl (2000)
Gaston and
Gauld (1993)
Griffiths (1997)
Hawkins and
Compton (1992)
Am Nat 126:565–569
J Anim Ecol 64:708–720
California
Great Britain
Ecol Monogr 71:69–91
Incl.
Comments
PL
Galling insects
7
Parasitoids insects 15
No1
No1
–
–
–
–
Worldwide
Fish
18
Yes
Diversec
Germany
Aquatic ciliates
7
Yes
Two region
sizes
–
Estimated
area
Sample
Diversed
Worldwide
Soil ciliates
16
Yes
–
Sample
Global Ecol Biogeogr 9:293–309
Central Europe
–
No1
–
–
J Trop Ecol 9:491–499
Worldwide
Phytophagous
insects
Insects
4
No1
–
–
J Anim Ecol 66:49–56
J Anim Ecol 61:361–372
North America
South Africa
15
Yes
15/15 No1
–
–
–
–
–e
Worldwide
Fish, lacustrine
Galling insects
and their
parasitoids
Diatoms
10
Yes
–
–f
Worldwide
Diatoms
29
Yes
–
Estimated
area
Sample
Am Nat 146:162–169
Cote d’Ivoire
Fish
10
Yes2
–
–
Ecol Monogr 68:259–274
Worldwide
Corals
No7
–
–
J Biogeogr 26:825–841
Asia, N America Mammals
Yes
–
Kennedy and
Guegan (1994)
Parasitology 109:175–185
Great Britain
No1
–
Estimated
area
–
Lawes and
Eeley (2000)
J Biogeogr 27:1421–1435
Worldwide
Lawes et al.
(2000)
Biodiv Conserv 9:683–705
South Africa
Birds, mammals,
Lepidoptera
5–15 Yes
Lawton et al.
(1993)
In: Ricklefs RE, Schluter D (eds)
Species diversity in ecological
communities. University of
Chicago Press, Chicago, Ill.,
pp 175–184
Divers Distrib 5:91–103
Great Britain
Insects on fern
6
No1
Spain,
South Africa
Worldwide
Coleoptera
4
Yes
Ectoparasites
of fish
Lizards
35
No1
Two different Precise area
regions
–
–
2
No8
–
–
Fish in rivers
9
Yes2
–
14
No9
27
No9
16, 203 Yes2
–
–
–
Estimated
area
–
–
Estimated
area
H. Hillebrand,
unpublished data
H. Hillebrand,
unpublished data
Hugueny and
Paugy (1995)
Karlson and
Cornell (1998)
Kelt et al. (1999)
Lobo and Davis
(1999)
Morand et al.
(1999)
Morton (1993)
Oberdorff et al.
(1998)
Pärtel et al. (1996)
Pärtel et al. (2000)
Pearson and
Juliano (1993)
Pearson (1977)
Int J Parasitol 29:663–672
In: Ricklefs RE, Schluter D (eds)
Species diversity in ecological
communities. University of
Chicago Press, Chicago, Ill.,
pp 159–169
J Anim Ecol 67:472–484
Oikos 75:111–117
Oikos 90:191–193
In: Ricklefs RE, Schluter D (eds)
Species diversity in ecological
communities. University of
Chicago Press, Chicago, Ill.,
pp 199–202
Condor 79:232–244
Australia and
North America
NW France
Parasites of
endemic and
introduced fish
Primates
Estonia
Plants
Estonia
Plants
North America, Tiger beetles
India, Australia
Tropical forests Birds
n
3
11–23 Yes
6
Yes2
Three
different
regions
Three
different
regions
–
Estimated
area
Estimated
area
–
Reanalysed Estimated
by Srivastava area
(1999)
489
Appendix 1 (Table continued)
Authors
Reference
Region
Organisms
Pejler (1997)
Worldwide
Rotifers
Richardson et al.
(1985)
Ricklefs (1987)
Arch Hydrobiol [Suppl]
53:255–306
J Veg Sci 6:329–342
Australia
Banksia (plants)
Science 235:167–171
Caribbean
Birds
Ricklefs (2000)
J Anim Ecol 69:1111–1116
Caribbean
Birds
Rørslett (1991)
Aquat Bot 39:173–193
Scandinavia
Roslin (2001)
Shurin et al.
(2000)
Smith (2001)
Ecography 24:511–524
Ecology 81:3062–73
Finland
Worldwide
Ecology 82:792–801
New Zealand
Aquatic
macrophytes
Dung beetles
Crustacean
zooplankton
Zoobenthos
Soares et al.
(2001)
Stevens (1986)
Stevens and
Willig (2002)
Terborgh and
Faaborgh (1980)
Tonn et al. (1990)
Aust Ecol 26:187–192
Brazil
Am Nat 128:35–46
Ecology 83:545–560
Incl.
Comments
PL
6
Yes
–
40
Yes2
–
Estimated
area
Precise area
5
No
7
Yes
Reanalysed
by Ricklefs
(2000)
–
18
No10
–
Estimated
area
–
131
24
Yes
Yes
–
–
Precise area
Precise area
24
Yes
–
Ants
10
Yes
–
Estimated
area
Precise area
Eastern USA
America
Coleoptera
Bats
20
32
No1
Yes
–
–
Am Nat 116:178–195
Caribbean
Birds
10
Yes
–
Am Nat 136:345–376
Finland and
Wisconsin
USA
Fish in lakes
2
No8
–
–
Estimated
area
Estimated
area
–
Mammals, fossils
24
Yes
–
Sample
USA
Bivalvia
14
Yes
–
Van Valkenburgh In: Ricklefs RE, Schluter D (eds)
and Janis (1993) Species diversity in ecological
communities. University of
Chicago Press, Chicago, Ill.,
pp 330–340
Vaughn (1997)
Ecography 20:107–115
Weiher (1999)
Westoby (1993)
Winkler and
Kampichler
(2000)
Wisheu and
Keddy (1996)
Zobel and Liira
(1997)
n
–
J Ecol 87:1005–1011
In: Ricklefs RE, Schluter D (eds)
Species diversity in ecological
communities. University of
Chicago Press, Chicago, Ill.,
pp 170–177
Ecography 23:385–392
Canada
Australia and
North America
Plants, herbaceous
Lizards, birds,
plants, fish
10
2
Yes
No9
–
–
Estimated
area
Precise area
–
Austria
Collembola
10
Yes
–
Precise area
Oikos 76:253–258
North America
Plants
28
Yes
–
Precise area
Oikos 80:325–332
Estonia
Plants
27
No9
1. Organisms depend or presumably depend on their hosts for
dispersal.
2. Original regression pseudreplicated, reanalysed.
3. Comprises data from Foissner (1997) (Limnologica 27:179–
238) and Foissner et al. (1992) (Limnologica 22:97–104).
4. Comprises data from Foissner (1996) (Biol Fertil Soils
23:282–291), Foissner (1997) (Biodiv Conserv 6:1627–1638),
Foissner (1997) (Biol Fertil Soils 25:317–339), Foissner
(1999) (Biodiv Conserv 8:319:389) and Foissner (1996) (Acta
Protozool 35:95–123).
5. This data set contains the regional species richness of large
scale floras (Central Europe, British Isles, Baltic Sea) and the
mean number of species found in local assemblages (lakes,
coastal sites) within these regions.
–
6. This data set contains the species richness at one site as regional richness and the richness within one sample as local
richness.
7. Data set identical to Cornell and Karlson (1996) (J Anim Ecol
65:233–241).
8. Too few data.
9. Data set used not suitable for the analysis (see Srivastava
1999) (J Anim Ecol 68:1–16).
10. Original regression pseudoreplicated, original data not obtainable.
490
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