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Complex relationships between species
niches and environmental heterogeneity
affect species co-occurrence patterns in
modelled and real communities
Research
Avi Bar-Massada
Cite this article: Bar-Massada A. 2015
Complex relationships between species niches
and environmental heterogeneity affect species
co-occurrence patterns in modelled and real
communities. Proc. R. Soc. B 282: 20150927.
http://dx.doi.org/10.1098/rspb.2015.0927
Received: 20 April 2015
Accepted: 8 July 2015
Subject Areas:
ecology
Keywords:
co-occurrence, niche overlap, niche breadth,
environmental heterogeneity, null models
Author for correspondence:
Avi Bar-Massada
e-mail: [email protected]
Electronic supplementary material is available
at http://dx.doi.org/10.1098/rspb.2015.0927 or
via http://rspb.royalsocietypublishing.org.
Department of Biology and Environment, University of Haifa, Kiryat Tivon 36006, Israel
Species co-occurrence analysis is commonly used to assess how interspecific
interactions dictate community assembly. Non-random co-occurrences,
however, may also emerge from niche differences as well as environmental
heterogeneity. The relationships between species co-occurrence patterns,
environmental heterogeneity and species niches are not fully understood, due
to complex interactions among them. To analyse the relationships among
these patterns and processes, I developed synthetic community models and analysed a large dataset of tree species across the conterminous United States. Niche
overlap and environmental heterogeneity had significant and contrasting effects
on species co-occurrence patterns, in both modelled and real communities.
Niche breadth, in turn, affected the effect sizes of both variables on species cooccurrence patterns. The effect of niche breadth on the relationship between
co-occurrence and niche overlap was markedly consistent between modelled
and real communities, while its effect on the relationship between co-occurrence
and environmental heterogeneity was mostly consistent between real and modelled data. The results of this analysis highlight the complex and interactive
effects of species niche overlap, niche breadth and environmental heterogeneity
on species co-occurrence patterns. Therefore, inferring ecological processes from
co-occurrence patterns without accounting for these fundamental characteristics
of species and environments may lead to biased conclusions.
1. Background
The question of which ecological processes drive species assembly in communities has been a major focus of ecological research in the past decades.
Specifically, it has been hypothesized that competitive, facilitative or neutral
interactions among species play varying roles in affecting community composition [1–3]. Analysis of patterns of species co-occurrence in real communities
compared with those generated by null models has been a focal tool in the
attempt to disentangle the opposite roles of interspecific interactions in determining species assembly [4– 9]. While the early debate between Diamond [1]
and Connor & Simberloff [10] on the utility of using such approaches has
mostly subsided [11], there remain considerable methodological, statistical
and theoretical challenges in the attempt to link species co-occurrence patterns
to the true ecological processes behind them [11 –14].
An inherent problem in co-occurrence analysis is that it ignores species
resource requirements, and consequently cannot account for co-occurrence patterns (or the lack of them) that arise from niche differentiation among species.
Species segregation can simply be an outcome of non-overlapping habitat
requirements between species, which leads to differences in geographical distributions; therefore, significant segregation patterns can emerge with little or no
interspecific interactions between species [13]. In the same manner, species can
exhibit aggregation patterns simply due to similarity in habitat requirements
within heterogeneous landscapes (with little or no interspecific interactions),
or due to facilitative or mutualistic interactions [15– 17]. Unfortunately, it is
impossible to disentangle the roles of these unique processes by identifying
& 2015 The Author(s) Published by the Royal Society. All rights reserved.
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2. Material and methods
(a) Tests with synthetic species pairs
To test the effects of species’ niche similarity on their cooccurrence patterns at different levels of niche breadth and
environmental heterogeneity, I developed a community model
in R [30] (code available in the electronic supplementary
material), based on a simplified version of a previously published model [31]. I generated, at random, communities of
10 – 100 species, consisting of 30 – 100 sites. In the initial set of
simulations, each site hosts 30 individuals and has an environmental condition E, which is derived at random from a normal
distribution with a mean of 50 and a standard deviation of 20.
Environmental heterogeneity is defined by two variables: the
range of E values and the coefficient of variation of E values
across sites. Both measures of heterogeneity are based on the
same environmental axis (E) that is used to quantify niche overlap and niche breadth, and thus they are not independent.
I argue, however, that analysing the effect of heterogeneity on
ecological communities is only informative when conducted
this way [32], as the predominant ecological dogma on the
importance of heterogeneity relies upon its interaction with
species niches (i.e. more heterogeneous habitats comprise more
niches); therefore, environmental heterogeneity should ideally
be quantified in the same niche axes that determine species
occurrence. Both measures of heterogeneity are calculated for
each species pair separately, based only on sites that host at
least one species from the corresponding pair (i.e. species A,
species B, or both). Each species has a Gaussian niche with a
mean (m), chosen at random from a uniform distribution
bounded by the range of environmental conditions, and a
breadth (s) between 10 and 35. The range of niche breadths
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Proc. R. Soc. B 282: 20150927
spatial extents. Focusing on niche characteristics is fundamental, as they are the main driver behind species’ habitat
requirements and geographical ranges [25]. Moreover, following the results of Barberán et al. [24], I suggest that analysing
species co-occurrence patterns in the context of species niche
characteristics requires us to account for another independent variable, habitat heterogeneity, which can affect the
co-occurrence/niche relationship. Habitat heterogeneity affects
species co-occurrence patterns by facilitating spatial segregation
of species within heterogeneous habitats. For example, a
meta-community of stream macroinvertebrates exhibited significantly more segregated co-occurrence patterns across streams
than within streams, and this pattern corresponded to differences between heterogeneity levels across these scales [26].
Another study on stream insects found that in streams with
similar abiotic conditions (i.e. homogeneous patterns) species
exhibited strong aggregated patterns, while strong segregated
patterns were found in communities occurring across streams
with different abiotic conditions [27]. Positive relationships
between segregated co-occurrence patterns and resource heterogeneity were also found for weed species in agricultural
fields [28] and natural grassland communities [29].
How would environmental heterogeneity, species niche
overlap and species niche breadth interact to affect species cooccurrence patterns? The complex interaction among all these
separate patterns and processes is difficult to envision. To quantify this relationship, I used two approaches. First, I developed a
simple community model to analyse this relationship under
controlled settings. Secondly, to evaluate the relationship in
real-world communities, I analysed a large empirical dataset
of tree species across the entire conterminous United States.
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segregation or aggregation patterns in a void, without
accounting for differences in species habitat requirements
[18], or more broadly differences in their geographical ranges.
Several recent studies attempted to devise statistical frameworks to disentangle the roles of habitat requirements from
interspecific interactions in driving species co-occurrence
patterns. Ovaskainen et al. [13] developed a multivariate
logistic regression approach to incorporate species-specific
habitat requirements into co-occurrence analysis, and tested
their approach on wood-decaying fungal species in forest
sites. They found that species aggregation patterns were
mostly due to similarities in habitat requirements, without
any evidence of interspecific interactions among species. Similarly, Pollock et al. [19] developed a joint species distribution
model (SDM) based on a hierarchical multivariate probit
regression model [20]. They analysed the relative roles of
shared environmental responses and other ecological processes in determining species co-occurrences, according to
the residual correlations in co-occurrence patterns and environmental correlations in the model, respectively. For frogs and
eucalypts in Australia, they found that shared environmental
responses were the main driver of species distributions,
though there was some evidence of interspecific interactions.
Finally, Wittman et al. [21] studied the role of thermal tolerance
on ant community structure in the Siskiyou Mountains, USA.
They found that species pairs with increased thermal niche
overlap tended to be more spatially segregated compared
with species pairs with little niche overlap.
Beyond the effect of niche overlap per se, niche breadth may
also be related to species co-occurrence patterns, as it is known
to affect community assembly of various taxa. From a pure
statistical standpoint, species with broader niches are likely
to exhibit increased niche overlap if niche space is finite,
and this might increase the role of stochastic processes in determining community composition compared with interspecific
interactions (because increased niche overlap corresponds
with more neutral community dynamics). Dividing communities into assemblages of generalists and specialists
and analysing their patterns separately may provide insights
into how niche characteristics affect species assembly.
A multi-year study of the meta-community in rock pools in
Jamaica [22] found that the abundance of species with narrow
niches (specialists) is affected by environmental variables
more strongly than it is affected by spatial variables, while generalist species exhibited the opposite pattern. Thus, specialist
species exhibited assembly patterns that are mostly in line
with the species sorting meta-community model [23], while
generalist species exhibited patterns more similar to those
suggested by the neutral theory [2] or the patch dynamics
meta-community model [23]. Another study [24] used network
analysis approaches to identify co-occurring soil microbial
species in soil samples collected in different regions worldwide.
They found that co-occurrence networks of specialists were
separate from those of generalists (that is, specialists tended to
co-occur with other specialists, but not with generalists, and
vice versa), and the generalist network was more compartmentalized. This is probably due to the increased environmental
variability of generalist habitats, which suggests that environmental heterogeneity interacts with species niche breadth to
influence species co-occurrence patterns.
Here, I build on the findings of the studies described
above and ask to what extent niche breadth interacts with
niche overlap to affect co-occurrence patterns across broad
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The degree of niche overlap between two species, D, is calculated
using the Bray– Curtis similarity metric:
P
jP1 ðEÞ P2 ðEÞj
,
ð2:2Þ
D ¼ 1 PE
ðP
E 1 ðEÞ þ P2 ðEÞÞ
where Pi is the suitability of site conditions for species i (equation
(2.1)). Thus, the likelihood of occurrence for a species in a given
site depends only on the suitability of site conditions to the
species’ niche requirement relative to their suitability to other
species. However, as the number of individuals per site is finite
(initially 30), species engage in interspecific competition for
space. To assess the effect of competition for space on the results,
I repeated the entire analysis four more times with different site
sizes (10, 20, 40 and 50). I analysed co-occurrence patterns for a
single pair of species (hereafter the ‘focal pair’), consisting of
the third and fourth species in the community table, to avoid
biasing the results by over-accounting for the environmental conditions in communities with large numbers of species pairs. As
species do not interact in an explicit manner and have arbitrary
niche requirements, it does not matter which pair of species is
being analysed for co-occurrence patterns.
To analyse the effect of niche breadth on the relationships
between niche overlap, environmental heterogeneity and species
co-occurrence patterns, I conducted separate analyses for communities comprising species with different niche breadths (s),
including each value from 10 to 35, inclusive.
Overall, I generated 1000 communities at each level of niche
breadth, which I subsequently analysed using null models to
determine whether they exhibited significant co-occurrence patterns. For the focal species pair, I quantified co-occurrence
using the C-score metric of Stone & Roberts [6].
Cscore ¼ ðN1 N12 ÞðN2 N12 Þ,
ð2:4Þ
where N1 and N2 are the number of exclusive occurrences
of species 1 and 2, respectively, and N12 is the number of their
co-occurrences.
I generated 1000 null models for each community, using the
trial-swap algorithm [35], with a thinning parameter of 10 000.
The trial-swap algorithm generates fixed – fixed null models,
which retain both overall species abundances and species richness levels in sites. I calculated C-score for each null model
separately, and the mean and the standard deviation of the distribution of null models for the focal species pair. Based on these
and on the observed C-score of the focal species pair, I calculated
the standardized effect size (SES) to quantify the number of
(b) Analysis of empirical co-occurrence patterns in
forest communities
To evaluate empirical relationships among co-occurrence patterns
of tree species, niche overlap, niche breadth and environmental
heterogeneity, I used a large database of tree species occurrence
in sample plots from the US Forest Inventory and Analysis
Program (FIA). The database comprised data from 10 000 permanent sample plots (a randomly selected subset from the entire
FIA database), each plot consisting of data aggregated from four
circular sub-plots of 7.32 m radius, in which the identity of each
tree species with a diameter at breast height larger than 500 was
noted. Sample plots were located across the entire conterminous
United States. Unfortunately, US Federal Government regulations
prevent the disclosure of actual plot locations. As an alternative,
information on species identity in plots, as well as data on the
values of multiple environmental variables in these plots were
supplied by the GIS Spatial Data Services unit of the US Forest
Service, which pre-processed the data according to the specifications requested for this study. In these data, species names and
plot locations were replaced by unique codes. For each plot, data
also included the values of 12 environmental variables that
are known to affect tree distributions, which were derived from
ancillary datasets. These variables are categorized as follows:
(1) climate variables: mean annual temperatures and precipitation,
and seasonality of temperature and precipitation (bio1, bio4, bio12
and bio15 variables, obtained from the BioClim database; [38]);
(2) topography variables: elevation and elevation range within a
1-km cell size (obtained and subsequently processed from the US
National Atlas); (3) coarse-scale soil data: minimum\maximum
values of water holding capacity, organic matter content, and
soil permeability (obtained from the STATSGO database, generated by the Soil Conservation Service, US Department of
Agriculture; available at http://water.usgs.gov/GIS/metadata/
usgswrd/XML/ussoils.xml; [39]).
Besides data on species occurrences and environmental conditions per plot, the dataset included a distance matrix, which
denoted the geographical distances between plots. I generated
multiple subsets of the dataset (representing ad hoc tree communities), by running a cluster analysis on the distance matrix using
the Ward algorithm, which divided it into 200 unique clusters,
each one comprising FIA sample points adjacent in geographical
space (electronic supplementary material, table S1). I chose to
generate 200 clusters to prevent community matrices from
becoming too large, as this is known to lead to statistical problems in null model analyses [11]. The average number of sites
in each cluster is therefore 46.3, with a standard deviation of
18.58. Then, for each cluster, I generated 1000 fixed– fixed null
models using the trial-swap algorithm [35]. For species that
occurred at least five times in a given community, I calculated
the C-score for each species pair within each cluster, and then calculated the pairwise SES of the C-score. Finally, for each cluster, I
calculated the number of significant segregated (and aggregated)
species pairs after correcting for the false-detection error rate due
to multiple tests using the Bayes mean-based criterion [40].
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Proc. R. Soc. B 282: 20150927
where P1(E) and P2(E) denote the suitability of environmental
condition E to species 1 and species 2, respectively. D ranges
from zero (no overlap) to one (complete overlap).
In each site, I used a multinomial model of species filtering to
determine the number of individuals of each species present in
the site [31]. The model is static and represents a process of filtering from a regional species pool where all species have the same
abundance. The number of individuals of species i that establish
in a given site is drawn from a multinomial distribution with a
probability Ri and a sample size that equals the size of the site
(i.e. the number of individuals it can host, regardless of species).
The probability of colonization Ri is denoted by
"
#
Pi
Ri ¼ P
,
ð2:3Þ
j Pj
standard deviations by which the observed C-score differs from
the mean of the null distribution of C-scores. Positive SES
values denote segregated co-occurrence patterns, whereas negative SES values denote aggregated co-occurrence patterns, and
SES values close to zero denote random co-occurrence patterns.
Given that the SES of a truly random community has a standard
normal distribution, SES values above 1.96 (and below 21.96)
denote significantly segregated (and aggregated) species pairs,
respectively, which correspond with the top (and bottom) 2.5%
of the distribution around the null expectation. I conducted all
co-occurrence analyses in R [30] using packages ‘vegan’ [36]
and ‘bipartite’ [37].
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denotes a gradient from specialists to generalist species. In a
given simulation, all species have the same niche breadth, but
may have different niche optima. The suitability P of a site
with environment E to a given species is denoted by [33,34]:
"
#
ðm EÞ2
PðEÞ ¼ exp :
ð2:1Þ
2s2
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(a)
4
(b)
4
6
2
–4
0
0.2
0.4 0.6 0.8
niche overlap
ge
ra
n
m
en
ta
l
–2
0
–4
0
0.2
0.4 0.6 0.8
niche overlap
1.0
vi
ro
n
–2
0.025
0.020
0.015
0.010
0.005
0
Figure 1. Relationships between species co-occurrence patterns (denoted by the SES of the checkerboard score) and two predictor variables: niche overlap between
pairs of species, and environmental heterogeneity (denoted by the range of the environmental axis). The gridded surface depicts the linear model fit to these data.
(a) Results from modelled communities comprising species with narrow niche breadths (s ¼ 15, or the 20% percentile of simulated breadths). (b) Results from a
large dataset of tree species in the conterminous United States, for species pairs in the 0 – 20% percentile bin of niche breadths. (Online version in colour.)
(c) Analysis of niche overlap among species in forest
communities
To quantify niche overlap among all species pairs in the FIA dataset, I employed an approach that is conceptually similar to my
analysis of niche overlap in the modelled communities. First, I generated a SDM for each species in the dataset based on its occurrence
sites and the 12 environmental variables detailed in §2b. For the
SDM, I used Maximum Entropy (MaxEnt, [41,42]), which is a
commonly used presence-only machine learning algorithm that
iteratively contrasts values of environmental predictor values at
occurrence locations with the values for a large background
sample taken from across the entire dataset. To improve model
performance and predictive capabilities, I restricted the analysis
to those species that appeared in 30 sites or more in the entire dataset, which led to the generation of models for 155 tree species
overall (out of 305 species that are present in the dataset). For
each one of those species, I used MaxEnt to generate a measure
of habitat suitability in any given site (a value between 0 and 1),
which is analogous to the variable P in equation (2.1). I then
used equation (2.2) to calculate the amount of similarity in habitat
requirements for each species pair (that is, the level of agreement
between their vectors of site suitability across all sites), under the
assumption that it can serve as a proximate measure of realized
niche overlap in terms of broad-scale climatic, topographic and
edaphic variables. To quantify niche breadth for each species, I tallied the number of sites in which its predicted habitat suitability
was larger than a threshold of 0.01. To evaluate the sensitivity of
the analysis to the value of this threshold, I repeated the process
with two additional thresholds, 0.1 and 0.001. I caution that this
measure of niche breadth is only meaningful in the context of
this analysis, as it depends on the distribution of environmental
conditions across the available sampling sites.
To quantify environmental heterogeneity at the cluster level, I
ran principal components analysis on the same 12 environmental
variables I used for modelling species distributions, after standardizing the variables to zero mean and unit variance. The
first four principal components explained 79.61% of the variation
in the environmental data (29.69%, 20.62%, 16.04% and 13.25%
by the first four axes, respectively). For each species pair, at
any given geographical cluster, I calculated the averages of the
range and the coefficient of variation of the first four principal
components across all sites in which either or both species
occurred. These serve as two species-pair-specific measures of
environmental heterogeneity at the cluster scale.
(d) Statistical analysis
To quantify the effects of environmental heterogeneity and niche
overlap on species co-occurrence patterns at different levels of
niche breadth, I used two approaches. For the modelled communities, I developed multivariate linear models with SES as
the dependent variable and niche overlap and environmental
heterogeneity as the independent variables. For the empirical communities, I developed linear mixed models with the same variable
designations as above, but added cluster identity as a random
effect. In both model types, I compared the effects of the two heterogeneity metrics (range and coefficient of variation of environmental
conditions). To account for the effect of niche breadth, I grouped
species according to different levels of niche breadths and developed the models separately for each group. To group species
according to niche breadth, I divided them according to their s
value in the analysis of modelled communities; in the analysis of
the empirical communities, I grouped them into bins according
to their percentile in the distribution of niche breadth values
(0–20%, 21–40%, 41–60%, 61–80% and 81–100%). I did not analyse the effects of niche breadth for species pairs in which each
species belonged to a different niche breadth percentile.
3. Results
(a) Co-occurrence patterns of simulated species pairs
The co-occurrence patterns exhibited by species pairs in simulated communities were significantly related to both niche
overlap and one measure of environmental heterogeneity,
environmental range (figure 1a). Species pairs exhibited
mostly segregated patterns (positive SES values) at low levels
of niche overlap, and aggregated patterns (negative SES
values) at high levels of niche overlap. Environmental range
had a positive effect on species co-occurrence patterns, as
short ranges of environmental conditions (i.e. low
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0
140
120
100 e
g
80
ran
l
a
60 ent
40 onm
20 nvir
e
1.0
en
–2
SES
SES
4
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(b)
(a)
−2
−3
−4
−5
−6
−7
10
15
20
25
niche breadth
30
35
0.05
0.04
0.03
0.02
0.01
0
10
15
20
25
niche breadth
30
35
Figure 2. Relationships between niche breadth and the effect size (slope coefficient) of predictors ((a) niche overlap and (b) environmental heterogeneity) in the
multivariate linear model of SES of co-occurrence. Results are based on the analysis of modelled communities. Circle colours are based on a grey-level gradient that
depicts different parameter values of the local abundance in sites, from 10 individuals (black) to 50 individuals (white), in steps of 10 individuals. Negative slope
coefficients ( y-axis values in (a)) reflect cases where species tend to aggregate at higher values of niche overlap, whereas positive slope coefficients (y-axis values in
(b)) denote cases where species tend to segregate at higher values of environmental heterogeneity.
heterogeneity) led to aggregated co-occurrence patterns,
whereas longer ranges of environmental conditions (i.e. high
heterogeneity) led to more segregated co-occurrence patterns,
manifested by increasingly positive SES values. These results
were consistent among simulations with different parameter
settings for local abundance per site (i.e. from 10 to 50). The
second measure of heterogeneity, the coefficient of variation of
environmental conditions, was a significant predictor of SES
in only a small number of parameter combinations, regardless
of model setting.
Niche breadth had a clear and profound effect on the
relationship between both niche overlap and environmental
heterogeneity, and species co-occurrence. The effect size of
niche overlap on SES was larger (more negative) at lower
levels of niche breadth, and decreased linearly (became less
negative) as niche breadth grew (figure 2a). Co-occurrence
patterns of specialist species, therefore, are more strongly
affected by their niche overlap, compared with more
generalist species. Niche breadth affected the relationship
between environmental range and SES in the opposite direction, as the effect size of range decreased with increasing
levels of niche breadth (figure 2b). In other words, specialist
species exhibited the strongest effect of environmental
heterogeneity on co-occurrence patterns, whereas generalist
species were less affected by heterogeneity in terms of their
co-occurrence patterns.
The effects of niche breadth on the subsequent effects of
niche overlap and environmental heterogeneity on species cooccurrence depended on the parameter of local abundance in
sites. Communities comprising smaller sites (or increased interspecific competition for space) were characterized by smaller
(less negative) effect sizes of niche overlap on species cooccurrences patterns (figure 2a), and smaller (less positive)
effect sizes of environmental heterogeneity on species cooccurrence patterns (figure 2b). Hence, the competitive pressure
to establish in a site also has a clear impact on the relationships
among species niche breadth, niche overlap, and environmental
heterogeneity versus species co-occurrence patterns.
(b) Empirical co-occurrence patterns of forest trees in
the United States
According to the fixed—fixed null models, only 8.2% of species
pairs in geographical clusters generated from the FIA dataset
had co-occurrence patterns that differ significantly from
random (a ¼ 0.025). Among species pairs that exhibited
significant co-occurrence patterns, about half (52.1%) were
segregated, and the others (47.9%) were aggregated. The average percentage of aggregated species pairs per community
was 9.6% (s.d. 3.1%), whereas the average percentage of
significantly segregated species pairs was 10.6% (s.d. 3.12%).
(c) Effects of niche characteristics and environmental
heterogeneity on empirical co-occurrence patterns
Similar to the modelled communities, empirical co-occurrence
patterns for tree communities had significant relationships with
both species niche overlap and environmental heterogeneity
(figure 1b). A linear mixed model of SES with both predictors
as fixed effects and cluster ID as a random effect had a significantly negative effect size for niche overlap (b ¼ 21.73, s.e.
0.05, t ¼ 234.29), and a significantly positive effect size for
environmental heterogeneity, denoted by the mean range of
the first four principal components of the 12 environmental
variables (b ¼ 0.05, s.e. 0.008, t ¼ 6.22). The random effect had
a variance of 0.015 (s.d. 0.12) and its residual had a variance
of 1.07 (s.d. 1.03). When environmental heterogeneity was
denoted by the mean coefficient of variation of the first four
principal components of the environmental variables, it did not
have a significant effect on SES.
When the analysis was conducted separately for each
niche breadth bin, I found that niche breadth affected the
Proc. R. Soc. B 282: 20150927
slope coefficient of niche overlap
−1
5
0.06
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slope coefficient of environmental heterogeneity
0
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Here, I analysed the roles of species niche characteristics and
environmental heterogeneity in dictating the patterns of
species co-occurrences in ecological communities. I found
that species niche overlap and environmental heterogeneity
interact in complex ways with niche breadth to drive species
co-occurrence patterns. These results were mostly consistent
between modelled communities and empirical data on tree
species across broad spatial scales.
Environmental heterogeneity, which manifests in difference in environmental conditions among sites, can lead to
niche differentiation [43], and consequently to segregated
co-occurrence patterns [44]. A positive relationship between
environmental heterogeneity and the prevalence of segregated co-occurrence patterns was reported before [27].
Another study, however, suggests that the relationship is
complex, as it depends on the covariance of species responses
to the environment, which depends on the similarity in
niche requirements among species, or in other words, the
degree of niche overlap [26]. Combining this suggestion
with my modelled and empirical findings reveals that niche
overlap is perhaps the most fundamental driver of species
co-occurrence, but its effects on co-occurrence patterns are
affected by environmental heterogeneity, as well as the
breadth of the niches of individual species. Yet, the magnitude of the relationships between niche overlap and
co-occurrence patterns (and to a lesser extent, the relationships between environmental range and co-occurrence
patterns) is affected by species niche breadth. While the
effect of niche breadth on the relationship between niche
overlap and co-occurrence was consistent between both
modelled and empirical analyses, its effect on the relationship
between environmental heterogeneity and co-occurrence was
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4. Discussion
more pronounced in modelled communities compared with
real communities due to the much larger number of breadth
values in the former. This difference is also present because
MaxEnt models were only developed for species that had
more than 30 occurrences, which makes it possible that species
with truly narrow niches were not included in the empirical
analyses; thus the range of niche breadth values in the real communities analysed here might be shorter compared with the
modelled communities. If this is the case, the results of real
communities correspond (and agree) only with the right-hand
side of figure 2b, whereas the breadth levels in the left-hand
size of that figure (which exhibit a slightly positive trend)
were simply not reflected in the empirical data.
As suggested by previous studies [13,14], the results of
the simple model I used here reveal that non-random cooccurrence patterns can emerge even when species have no
pre-determined interspecific interactions. In such cases, significant segregated patterns are expected to occur simply
due to spatial turnover when species have sufficiently different niche requirements (or are affected by dispersal
limitations) [18]. Similarly, significant aggregated patterns
are expected to occur when species with similar niche
requirements inhabit heterogeneous habitats, simply because
they tend to prefer similar sites that may be spatially aggregated within a matrix of unfavourable ones. These results,
coupled with those of [14] reiterate the risk of inferring
ecological processes from co-occurrence patterns without
explicitly accounting for range differences or differences in
niche requirements between species. Interestingly, the only
other study that I am aware of that analysed the relationship
between pairwise co-occurrence and similarity in niche
requirements [21], in this case for ant species, found contrasting results to those I report here for tree species. Ant species
with similar thermal tolerances tended to be more segregated
than ant species pairs with increasingly different thermal tolerances. The discrepancies between study results may be an
outcome of the inherent difference in the levels of biotic interactions among motile species (ants) compared with sessile
species (trees), where motile species can segregate in space
more readily when they encounter competitors.
The modelled results reveal that the strength of competition
for space, represented by the size of sites in which species compete for establishment, affects the subsequent relationships
between niche overlap, environmental heterogeneity and
species co-occurrence patterns (figure 2). Moreover, the effect
of competition strength varies with species niche breadth.
Both niche overlap and environmental heterogeneity have
stronger effects on co-occurrence when sites are large and competition for space is weak (white circles in figure 2). This
highlights the role of these two variables in generating nonrandom co-occurrence patterns even in the absence of strong
interspecific interactions. Furthermore, when niches are wide
(i.e. species are generalists), both niche overlap and environmental heterogeneity have weaker effects on co-occurrence
patterns. The weakening effect of niche overlap might be
due to an interaction between niche breadth and niche overlap (when niches are wider on a finite niche axis, niche overlap
increases). The reduced effect of environmental heterogeneity
is probably due to species’ increased capability of establishment
in a broader range of environmental conditions. Specialist
species, in contrast, exhibit the strongest effects of both niche
overlap and environmental heterogeneity on co-occurrence,
especially when there is weak competition for space.
rspb.royalsocietypublishing.org
slopes of the relationships between niche overlap and SES in
a manner mostly consistent with my findings from modelled
communities. Niche overlaps between species with lower
niche breadth percentiles had stronger (more negative)
effect sizes on SES than niche overlaps between species
with higher niche breadth percentiles; effect sizes (b) were
24.46, 23.27, 22.06, 21.12 and 20.92 for species pairs in
the 0– 20%, 21–40%, 41–60%, 61 –80% and 81 –100% percentiles of niche breadth, respectively. The trend in the effect of
niche breadth was qualitatively the same when niche breadth
was calculated based on different thresholds (above 0.1 and
above 0.001 of MaxEnt’s predictions of habitat suitability).
Environmental heterogeneity, denoted by the mean range
of the first four principal component axes, exhibited consistent effects on SES only in the three out of five niche
breadth percentiles. Its effect sizes decreased with increasing
niche breadth percentiles (0.21, 0.12 and 0.05, for the 0– 20%,
21–40% and 61–80% percentiles, respectively), in agreement
with the modelled results. When different thresholds for determining niche breadth were used, the trend in the effect size of
heterogeneity became less consistent (two out of five percentiles for a threshold of 0.1, and one out of five for a threshold
of 0.01), but its value was always positive. When the coefficient
of variation was used as the measure of environmental heterogeneity, it had a weaker overall effect on SES, which was
significant only in a small number of niche breadth percentiles.
Hence, it will not be discussed any further.
Downloaded from http://rspb.royalsocietypublishing.org/ on June 18, 2017
Data accessibility. FIA data used in this study can be obtained directly from
the US Forest Service, via their Spatial Data Services unit (http://www.
fia.fs.fed.us/tools-data/spatial/default.asp). R code for running the
community model is available in the electronic supplementary
material.
Competing interests. I declare I have no competing interests.
Funding. I received no funding for this study.
Acknowledgements. I thank Richard A. McCullough of the US Forest
Service for pre-processing the FIA data.
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I suggest, however, that this bias might be small because of a
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rspb.royalsocietypublishing.org
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Recent examples are studies that incorporated species resource
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of co-occurrence analysis to account for range differences
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study, in which no interspecific interactions were assumed,
raise yet another red flag about the potential of inferring interspecific interactions from co-occurrence patterns when species’
niche characteristics and relationships are unaccounted for.
I caution that the empirical approach used here to quantify niche characteristics has two main caveats for the
purpose of this study. First, niches generated from occurrence
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their corresponding habitat conditions. This, in turn, can also
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should be aware of another caveat. As both niche overlap and
species co-occurrence are determined from occurrence data, it
is possible that the results are biased to some extent as both
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