Environment and predation govern fish
community assembly in temperate
streams
Xingli Giam* and Julian D. Olden
School of Aquatic and Fishery Sciences,
University of Washington, Seattle,
WA 98105, USA
*Correspondence: Xingli Giam, School of
Aquatic and Fishery Sciences, University of
Washington, Seattle, WA 98105, USA.
E-mail: [email protected]
ABSTRACT
Aim The elucidation of patterns and drivers of community assembly remains a
fundamental issue in ecology. Past studies have focused on a limited number of
communities at local or regional scales, thus precluding a comprehensive
examination of assembly rules. We addressed this challenge by examining
stream fish community assembly within numerous independent watersheds
spanning a broad environmental gradient. We aimed to answer the following
questions: (1) are fish communities structured non-randomly, and (2) what is
the relative importance of environmental filtering, predator–prey interactions
and interspecific competition in driving species associations?
Location The conterminous USA.
Methods We used null models to analyse species associations in streams.
Non-random communities were defined as those where the summed number
of segregated and aggregated species pairs exceeded the number expected by
chance. We used species traits to characterize species dissimilarity in
environmental requirements (ENV), identify potential predator–prey
interactions (PRED) and estimate likely degree of competition based on species
similarity in body size, feeding strategies and phylogeny (COMP). To evaluate
the effect of environmental filtering, predation and competition on species
associations, we related ENV, PRED and COMP to the degree of species
segregation.
Results The majority (75–85%) of watersheds had non-random fish
communities. Species segregation increased with species dissimilarity in
environmental requirements (ENV). An increase in competition strength
(COMP) did not appear to increase segregation. Species pairs engaging in
predator–prey interactions (PRED) were more segregated than non-predator–
prey pairs. ENV was more predictive of the degree of species segregation than
PRED.
Main conclusions We provide compelling evidence for widespread non-
random structure in US stream fish communities. Community assembly is
governed largely by environmental filtering, followed by predator–prey
interactions, whereas the influence of interspecific competition appears
minimal. Applying a traits-based approach to continent-wide datasets provides
a powerful approach for examining the existence of assembly rules in nature.
KeywordsAssembly rules, co-occurrence, competition, ecological interactions, null
models, North America, rivers, species traits.
DOI: 10.1111/geb.12475VC 2016 John Wiley & Sons Ltd http://wileyonlinelibrary.com/journal/geb1194
Global Ecology and Biogeography, (Global Ecol. Biogeogr.) (2016) 25, 1194–1205
RESEARCHPAPER
INTRODUCTION
The quest to understand how species communities assemble
remains one of the most fundamental, and often controver-
sial, topics in ecology. Since the pivotal publication of Jared
Diamond’s ‘The assembly of species communities’ (Diamond,
1975), intense investigation has centred on the operation of
environmental filtering, the definition of assembly rules, the
importance of null models and the role of species neutrality
(Hubbell 2001; Leibold et al., 2004). Although their relative
roles are debated, key processes involved in community
assembly include biotic interactions in the form of interspe-
cific competition and predation (M’Closkey, 1978; Connor &
Simberloff, 1979), environmental filtering (Heino, 2013; Kraft
et al., 2015) and historical effects such as dispersal limitation
owing to physical barriers (Dias et al., 2014). These processes
can shape co-occurrence patterns among species pairs
(Gotelli & McCabe, 2002; Veech, 2014) and in whole meta-
communities (Leibold & Mikkelson, 2002; Almeido-Neto
et al., 2008; Presley et al., 2010) as well as produce patterns
in phylogenetic or trait dispersion within local communities
(Webb et al., 2011; Liu et al., 2013).
Ecological theory and empirical evidence suggest that com-
petition and predation can limit co-occurrences of interacting
species (i.e. negative species associations) (Diamond, 1975;
Englund et al., 2009). By contrast, environmental filtering and
historical processes can either: (1) increase species co-
occurrences when two or more species are adapted to similar
environments, have similar niche requirements or have similar
biogeographical histories, or (2) limit co-occurrences when
different species are adapted to different environments, have
different niche requirements or disperse from different histori-
cal pools (Heino, 2013; Dias et al., 2014).
Null models are commonly used to test whether an
observed pattern of species co-occurrence is likely to be real
or the result of random processes (Gotelli & Graves, 1996).
In freshwater ecosystems, for instance, Matthews (1982)
found the number of negative associations among stream
fishes to be no more than that derived from random com-
munity assembly. By contrast, Winston (1995) found mor-
phologically similar fish species to co-occur less often than
random (inferring the importance of interspecific competi-
tion), whereas Peres-Neto (2004) demonstrated that environ-
mental filtering shaped fish communities in Brazilian
streams. Divergent mechanisms influencing fish communities
are also evident in lakes, where studies support both environ-
mentally mediated patterns (Jackson et al., 1992) and assem-
bly rules resulting from biological interactions (Englund
et al., 2009). Regardless of taxonomy, the mechanisms (or
lack thereof) governing how communities are assembled vary
in both time and space (Lockwood et al., 1997). However,
most existing studies have investigated species co-occurrence
and community assembly rules in a single region or interact-
ing metacommunity (e.g. Connor & Simberloff, 1979; Mat-
thews, 1982; Jackson et al., 1992; Winston, 1995; Peres-Neto,
2004; Englund et al., 2009). It remains unclear whether the
results of these studies are context specific or represent gen-
eral assembly patterns for each taxonomic group.
Emerging from the burgeoning literature on species assem-
bly was a meta-analysis indicating that most animal com-
munities had fewer species co-occurrence than expected by
chance (Gotelli & McCabe, 2002). Notably, that study
reported that negative species co-occurrences were more
common in warm-blooded than cold-blooded animals, and
that among cold-blooded taxa, fish communities were prob-
ably randomly structured. Despite representing a significant
advance in the field, the approach used by Gotelli & McCabe
(2002) was complicated by the fact that C-scores (which
quantify the degree of segregation or aggregation between a
pair of species) were averaged over all species pairs. Gotelli &
Ulrich (2012) suggested that this approach might miss poten-
tially important pairwise associations between particular pairs
of species. Thus, the particular processes contributing to
community structure require further examination.
Here, we examined the patterns and drivers of fish com-
munity assembly across diverse taxonomies (500 species) and
geographies (c. 8000 stream locations) in the conterminous
USA. Freshwater fishes are a good model for community
assembly analyses because watersheds represent naturally
bounded, independent regions within which species disperse
and interact (Leprieur et al., 2011). This facilitated a
robust test of the assembly rule concept using numerous
independent sets of interacting communities across a broad
environmental gradient. By combining pairwise species
co-occurrence analyses with trait-based inference of species
interactions (McGill et al., 2006; Frimpong & Angermeier,
2010), we aimed to answer the following questions: (1) are
freshwater fish communities structured non-randomly within
watersheds, and (2) what processes (i.e. environmental filter-
ing, predator–prey interactions, interspecific competition)
drive species associations? By doing this we hope to advance
the current understanding of the nature of assembly rules in
freshwater fish communities.
METHODS
Species community dataset
We compiled a database of species occurrence for 7846 sites
(i.e. fish communities) across 1502 watersheds (i.e. HUC8
hydrological units as defined by the United States Geological
Survey) in the conterminous USA (Fig. 1). The sites were
surveyed between 1990 and 2012 by US federal government
agencies [e.g. the EPA and Regional Environmental Monitor-
ing and Assessment Program (EMAP and REMAP), the EPA
National Rivers and Streams Assessment (NRSA), the USGS
National Water Quality Assessment Program (NAWQA)],
state natural resource and environmental agencies and uni-
versity researchers (see Appendix S1 in Supporting Informa-
tion for full list). All surveys were designed to characterize
the entire fish community, which includes both native and
Community assembly in freshwater fishes
Global Ecology and Biogeography, 25, 1194–1205, VC 2016 John Wiley & Sons Ltd 1195
non-native species, at each location in terms of species occur-
rence, and we assumed that communities are more or less in
equilibrium.
Survey sites were selected to ensure comparability across
fish communities. To minimize any bias introduced by differ-
ent sampling techniques, we included only those surveys in
which electrofishing was the primary method of fish collec-
tion. Backpack electrofishing was the common primary
method of sampling for small wadeable streams, whereas raft
electrofishing was used for deep and large rivers. Sampling
reach length depended on the width of the river – wider riv-
ers require longer sampling reaches – a standard protocol to
accurately characterize fish communities in streams of differ-
ent widths (Hauer & Lamberti, 2006). Though not exactly
the same, the sampling reach length:river width ratio and
actual sampling reach length were comparable across datasets.
In general, all sites had sampling reaches over 150 m and
were considered to accurately reflect the true composition of
the fish communities at the time of collection.
We assigned sampling sites in our dataset to stream
reaches in the National Hydrography Dataset (NHDPlus v.2;
http://www.horizon-systems.com/NHDPlus/) and retained
only those sites that were located on natural stream reaches.
Sites located in drainage canals, artificial connectors and
ditches were removed. We manually identified overlapping
and repeat sites by searching for duplicated reach names and
geographical coordinates; for those sites, we used data from
the most recent survey to standardize sampling effort. To
maximize the spatial independence of sites, we randomly
subsampled sites to ensure they were at least 1 km apart.
Ecological trait dataset
Ecological traits represent a powerful currency for studying
interactions among species and between species and their
environment (McGill et al., 2006; Frimpong & Angermeier,
2010; Morales-Castilla et al., 2015). A traits-based approach
is often used to describe the similarity of fishes in terms of
their environmental and trophic niches (Poff & Allan, 1995;
Olden et al., 2006; Albouy et al., 2011; Elleouet et al., 2014).
Here, we collated data from the literature on nine ecological
traits to quantify the degree to which pairs of species: (1)
have similar environmental requirements; (2) potentially
compete for resources, and (3) are likely to engage in preda-
tor–prey interactions. We describe species traits and their
data sources in Table 1.
Environmental requirements of fishes were described by
six traits: affinity for different freshwater bodies (HAB), alti-
tudinal affinity (ALT), mean stream size (STR), temperature
preference (TEMP), substrate preference (SUB) and affinity
to different flow speeds (FLOW). To quantify the degree of
potential interspecific competition, we used three ecological
traits to characterize species similarity in food acquisition:
maximum body length (BL), adult trophic guild (TROPH)
and vertical feeding position in water (or activity position if
non-feeding) (VERT). Two additional traits, family (FAM)
and genus (GEN) membership, were also included to account
Figure 1 (a) Map of 7846 candidate sites/fish communities located within 1502 watersheds. We selected only those watersheds with at
least 10 sites and 10 species (224 watersheds containing 3670 communities) for our null model analysis because of statistical power
considerations. Abiotic and biotic interactions that could structure fish communities include: (b) environmental filtering – many species
such as central stoneroller (Campostoma anomalum) display strong habitat affinities; (c) predator–prey interactions – predators such as
the largemouth bass (Micropterus salmoides) may affect the abundance of prey species; and (d) interspecific competition – competitive
exclusion may result from species such as the brown trout (Salmo trutta) and mountain whitefish (Prosopium williamsoni) competing
for similar resources. Photographs courtesy of Freshwater Illustrated [(b) and (c) Jeremy Monroe, (d) Dave Herasimtschuk].
X. Giam and J. D. Olden
1196 Global Ecology and Biogeography, 25, 1194–1205, VC 2016 John Wiley & Sons Ltd
for likely competition owing to phylogenetic relatedness
(Violle et al., 2011). We identified potential predator–prey
interactions between species pairs based on TROPH, BL and
VERT while taking into account predator selectivity for prey
size (Juanes, 1994) (see ‘Drivers of species associations’).
Data analyses
Structure of fish communities within watersheds
We considered fish communities within a given watershed to
be an interacting metacommunity, following Blanchet et al.
(2014). Watersheds are small enough (mean 4513 km2, inter-
quartile range 3027–5563 km2; n 5 224 basins included in
our analyses) for us to realistically assume that fishes can
freely disperse among sites therein. Using a larger spatial
scale (USGS HUC6 drainage basins) to define metacommun-
ities would likely result in a higher proportion of segregated
species pairs owing to increased environmental heterogeneity
(Troia & Gido, 2015) and/or historical vicariance. To mini-
mize the confounding effect of the latter, the USGS HUC8
watershed scale is therefore more appropriate for our analy-
ses. The mean site density across watersheds is 0.0043 (inter-
quartile range 0.0025–0.0050) sites/km2. The positive
correlation between site number and watershed area,
although significant, is weak (Spearman’s q 5 0.20,
P 5 0.003) but high sampling completeness among sites
within each watershed (mean 80%, interquartile range 75–
86%; Methods S1) indicates that sampling effort is adequate
within and comparable across watersheds.
To determine whether fish communities within a water-
shed have a non-random structure, we compared the
observed sum of positive and negative species associations
with that expected from two null models of community
assembly. Null models quantify the degree of association
between species pairs with specific assumptions of commu-
nity assembly in the absence of species interactions (Gotelli
& Graves, 1996). We used two null models: (1) the fixed
rows–fixed columns (FF) model, which preserves total occur-
rences among species and total species richness among sites
within each watershed, and (2) the fixed rows–equiprobable
columns (FE) model, which preserves differences in total
occurrences among species but not differences in richness
Table 1 Traits used to characterize dissimilarity in environmental requirements (ENV), identify potential predator–prey interactions
(PRED) and estimate the level of competition (COMP) between species (marked by ‘X’)
No. Traits Variable type Data sources
Variables
ENV COMP PRED
1 HAB: affinity to different
freshwater bodies
Multichoice nominal
Levels: lake, spring, headwater, creek, small river, medium
river, large river
1, 3 X
2 ALT: altitudinal affinity Multichoice nominal
Levels: lowland, upland, montane
1 X
3 STR: mean stream size Continuous
Average of values assigned from 1 (smallest stream size:
spring) to 6 (largest: large river)
1, 3 X
4 TEMP: temperature
preference
Ordinal
Levels: cold, cold–cool, cool, cool–warm, warm
2, 5 X
5 SUB: substrate affinity Multichoice nominal
Levels: fine, coarse, rocky, vegetation
1 X
6 FLOW: flow velocity Multichoice nominal
Levels: slow, moderate, fast
1 X
7 BL: maximum body length
(in mm)
Continuous. 2, 5 X X
8 TROPH: adult trophic guild Nominal
Levels: herbivore–detritivore, invertivore, omnivore, inverti-
vore–piscivore, piscivore, non-feeding, parasitic
2, 5 X X
9 VERT: vertical feeding posi-
tion in water
Multichoice mominal
Levels: benthic, surface
1, 5 X X
10 GEN Nominal
Levels: genera
4 X
11 FAM Nominal
Levels: families
4 X
Multichoice nominal variables are those in which species can be assigned to more than one variable level (Pavoine et al., 2009).
Data sources are: 1, Frimpong & Angermeier (2009); 2, Mims et al. (2010); 3, Page & Burr (2011); 4, Page et al. (2013); 5, Olden & Giam
(unpublished).
Community assembly in freshwater fishes
Global Ecology and Biogeography, 25, 1194–1205, VC 2016 John Wiley & Sons Ltd 1197
among sites within each watershed. The FF and FE models
represent ecologically plausible colonization processes and
have low Type I error probabilities (Gotelli, 2000).
We performed the null model analyses for each of the 224
watersheds containing 10 or more sampling sites and 10 or
more species to ensure adequate statistical power (Gotelli &
Ulrich, 2010; Veech, 2013). Each watershed defines the
regional species pool from which species are drawn under
the two null models. For each pair of species in a watershed,
we compare the observed rescaled C-score [CS; which ranges
from 0 (maximum species aggregation) to 1 (maximum spe-
cies segregation)] with 4999 sets of randomized (FE and FF
null) metacommunities. The rescaled CS of species i and j
(CSij) is calculated as:
CSij5A2Jð Þ B2Jð Þ
AB; (1)
where A and B are the number of sites occupied by species i
and j, respectively, and J is the number of sites occupied by
both A and B (Gotelli & Ulrich, 2010). Species pairs with a
two-tailed Monte Carlo P-value� 0.05 were considered as
aggregated (if observed CS< expected CS under a null
model) or segregated (if observed CS> expected CS). For
each watershed, we summed the numbers of segregated and
aggregated species pairs.
Species pairs can appear non-random even when com-
munities are actually assembled randomly (Type I errors). To
determine whether fish communities within a watershed are
likely to indeed be non-random, we created 1999 randomized
metacommunities and repeated the procedures outlined
above to calculate the null distribution of the sum of segre-
gated and aggregated species pairs in each watershed. We
defined watersheds for which the sum of segregated and
aggregated species pairs exceed the 95th percentile of the cor-
responding null distribution as non-randomly structured.
We maximized the statistical power of the null model test
for fish community structure by including only species that
are present in (1) 2 to N – 2 sites (207 watersheds with 10
or more species) and (2) 4 to N – 4 sites (158 watersheds),
where N is the total number of sites present in a watershed.
We excluded very rare and very common species because the
absolute difference between observed and expected CS must
be greater than 1.6–3.3 for the test to have adequate power
(when N 5 10–50 under the FE null model; Veech, 2013).
When a species is present or absent in only one site, the
maximum difference between observed and expected co-
occurrence is only 1. In this case, even if a species pair was
truly associated, the test would not have power to detect the
association. Increasing the minimum numbers of sites and
species from 10 to 20 did not alter our findings, suggesting
that our results are robust to different exclusion criteria. We
therefore only reported results of analyses using the initial
criteria.
We used Cohen’s j to assess the congruence in the water-
shed classifications (i.e. random or non-random) of fish
community structure and species pair associations (random,
segregated, aggregated) between FF and FE null models.
Cohen’s j ranges from 0 (totally incongruent classifications)
to 1 (totally congruent classifications).
Drivers of species associations
The effect size for the degree of association between species i
and j within a given watershed was quantified by ranking the
observed CS with respect to its null distribution and rescaling
the rank to [0,1] where 0 is maximum aggregation and 1 is
maximum segregation. For each species pair, we used the
median effect size (across all watersheds; n 5 224) to quantify
the overall degree of association (hereafter, species segrega-
tion score). We included only species pairs present in 2 to N
– 2 sites, and 4 to N – 4 sites within 10 or more watersheds
(number of pairs 5 3020 and 1030, respectively) to increase
the reliability of the species segregation score.
We quantified differences between species in terms of their
environmental requirements (ENV) by calculating Gower’s
dissimilarity coefficient for each species pair based on traits
associated with environmental preferences (HAB, ALT, STR,
TEMP, SUB, FLOW) (Gower, 1971). To estimate the degree
of competition between species pairs (COMP), we calculated
Gower’s similarity coefficient based on traits related to food
acquisition (BL, TROPH, VERT), and phylogeny (FAM,
GEN), where the more similar two species are in terms of
these traits, the higher the degree of potential competition
between the species.
Potential predator–prey interactions between species pairs
(PRED) were identified based on TROPH, BL and VERT.
Using data from a meta-analysis of the prey size selectivities
of piscivorous fishes (Juanes, 1994), we found that piscivores
select prey fishes that are on average 3.73 times smaller than
their own body length. We therefore defined species pairs
likely to have predator–prey interactions (PRED 5 1) as those
(1) comprising at least one species that is classified as an
invertivore–piscivore or piscivore and that the invertivore–
piscivore or piscivore is at least 3.73 times the maximum
body length of the other species or at least one parasitic spe-
cies, and (2) having overlapping vertical feeding positions.
Because phylogenetic similarity is related to competition in
some communities but not others (Violle et al., 2011; Godoy
et al., 2014 and references therein), we calculated a second
Gower’s similarity coefficient (COMP2) based on the above
variables but excluding FAM and GEN as an alternative esti-
mate of competition strength. Using COMP2 had little effect
on the findings (results not shown); we therefore present the
analysis based on COMP.
To examine the effect of environmental filtering, interspe-
cific competition and predator–prey interactions on species
associations, we fitted linear models that predict the species
segregation score as a function of ENV, COMP, PRED and
the two-way interactions ENV 3 COMP and ENV 3 PRED.
The continuous variables ENV and COMP were mean-
centred. Some traits were used to quantify two variables (e.g.
BL, TROPH and VERT used to quantify both COMP and
X. Giam and J. D. Olden
1198 Global Ecology and Biogeography, 25, 1194–1205, VC 2016 John Wiley & Sons Ltd
PRED), giving rise to the possibility that the derived varia-
bles would be non-independent. Despite the trait overlap, the
maximum pairwise correlation between univariate terms was
less than 0.3 in all analyses, indicating that these variables
reflect environmental filtering, competition and predation
unambiguously. The independence is likely due to the fact
that COMP and PRED were not calculated in the same way;
COMP is a similarity metric whereas PRED was calculated
based on species pairs meeting multiple criteria. The species
segregation score was logit-transformed for normality (War-
ton & Hui, 2011): larger positive and negative values indicate
greater segregation and aggregation, respectively, whereas a
value of 0 indicates no association. Logit-transformed scores
derived from FF and FE null models were highly correlated
(Spearman’s q 5 0.98–0.99, P< 0.0001).
Because each observation is a pair of species, the observa-
tions are not independent (i.e. a species may be found in
multiple observations). Consequently, taking them as inde-
pendent in a correlation/regression analysis would yield
overly optimistic P-values and inflate Type I error rates
(Dietz, 1983). The framework of multiple regression of
distance matrices (Legendre et al., 1994) produces the correct
P-values by matrix permutation but it works only on associa-
tion matrices that are complete. Our association matrices are
not complete, because only a subset of all possible species-
by-species associations (i.e. of species pairs that occur within
the same watershed) are defined. The nature of our data thus
precludes the latter approach. We therefore used the former
approach but with stricter alpha values of 0.001, 0.005 and
0.01, to reduce Type I error rates. We used a backward elimi-
nation procedure suggested by Legendre et al. (1994), but
with regression coefficient P-values calculated from Student’s
t-distribution rather than matrix permutation, to obtain
the final model (Bonferroni-corrected, P-to-remove 5 0.001,
0.005 or 0.01). Using the final model, we quantified the rela-
tive contributions of ENV, COMP, and PRED in driving spe-
cies associations by calculating the mean reduction in R2
(DR2) between predicted and observed values of the species
segregation score when each predictor variable (ENV, COMP,
PRED) is permuted (Strobl et al., 2007). The higher the DR2,
the more important the variable. Because our main findings
were consistent across the different alpha values, we pre-
sented results based on an alpha value of 0.005.
All analyses were performed in R 3.2.2 (R Core Team,
2015). We used the vegan package (Oksanen et al., 2015) to
perform null model simulations.
RESULTS
Patterns of fish community structure
Fish communities across the majority of US watersheds were
non-randomly structured. The sum of aggregated and segre-
gated species pairs was higher than expected in 75–76%
(under the FF null model) and 84–85% (FE) of watersheds
(Figs 2 and S1). There was little evidence for geographical
clustering of non-random fish assemblages.
There was moderate congruence between FF and FE null
models with respect to whether a given fish community was
non-randomly structured (Cohen’s j 5 0.46–0.65; Table 2).
Similarly, the null models were moderately congruent with
respect to associations between individual species pairs
(Cohen’s j 5 0.56–0.60). The FF null model classified 1.3
times more species pairs as segregated than aggregated. By
contrast, the FE null model identified 3.6–4.6 times more
species pairs as aggregated than segregated (Table S1).
Drivers of species associations
The species segregation score (a high score indicating high
degree of segregation) was always positively correlated with
ENV, indicating that species with greater differences in their
environmental requirements were more segregated (Figs 3 & S2,
Tables 3 & S2). Predator–prey pairs (PRED) were more segre-
gated than species that do not form such pairs in all analyses.
The only time competition strength (COMP) was included in
the final model was when we considered species pairs present
in 2 to N – 2 sites under the FE null model (Table 3). However,
species segregation decreased, instead of increased, with COMP.
Figure 2 Distribution of watersheds with random and non-
randomly structured fish communities when (a) FF and (b) FE
null models are applied to species pairs present in 2 to N – 2
sites in each watershed. We obtained similar results when we
analysed species pairs present in 4 to N – 4 sites (see Fig. S1).
Community assembly in freshwater fishes
Global Ecology and Biogeography, 25, 1194–1205, VC 2016 John Wiley & Sons Ltd 1199
In all analyses, the dissimilarity in environmental require-
ments of species (ENV) was much more predictive of the
degree of species segregation than the contribution of inter-
specific competition (COMP) and predator–prey interactions
(PRED). Permuting ENV reduced the R2 between predicted
and observed species segregation scores by 0.11–0.13, whereas
permuting PRED and COMP reduced the R2 by 0.02–0.05
and 0–0.007 respectively (Fig. 4).
DISCUSSION
The aim of our study was to uncover patterns and drivers of
fish community assembly over a large spatial scale. By inter-
rogating patterns in fish community composition across
diverse phylogenies and geographical contexts, we have dem-
onstrated that the majority of temperate stream fish commun-
ities in the conterminous USA are structured non-randomly.
Our results provide important resolution on past studies (e.g.
Matthews, 1982; Winston, 1995; Gotelli & McCabe, 2002;
Peres-Neto, 2004) by contributing new evidence that supports
the preponderance of non-random fish community assembly
across multiple independent watersheds.
Environmental filtering appeared to be by far the most
important process structuring the composition of fish com-
munities. Species demonstrating greater similarity in environ-
mental requirements (i.e. size and type of stream, stream
temperature and elevation) tend to be more positively associ-
ated with respect to patterns in co-incidence. The importance
of environmental filtering in driving the community struc-
ture of temperate fishes (as shown here) is supported by
research in tropical streams (Peres-Neto, 2004). It is well
known that most organisms are not able to establish and per-
sist in all environments (Kraft et al., 2015) and the commu-
nity structure of stream fishes is governed by a multitude of
abiotic conditions that include stream order, stream mor-
phology, flow regime and riparian and instream habitat
(Jackson et al., 2001).
The effect of predator–prey interactions was also evident,
albeit to a smaller degree than the role of environmental fil-
tering. Species pairs potentially engaging in predator–prey
interactions are more likely to be segregated in space. Rela-
tively few field-based studies have investigated the role of
Table 2 Number of watersheds classified as having random or
non-randomly structured fish communities under the FF or FE
null model.
(a) Species occupying 2 to N – 2 sites (Cohen’s j 5 0.65)
FE null model
Random Non-random FF total
FF null model Random 30 21 51
Non-random 3 153 156
FE Total 33 174 207
(b) Species occupying 4 to N – 4 sites (Cohen’s j 5 0.46)
FE null model
Random Non-random FF total
FF null model Random 17 21 38
Non-random 6 114 120
FE Total 23 135 158
Only watersheds with at least 10 species are included.
Figure 3 Relationship between ENV, COMP, PRED and the degree of segregation between species (logit-transformed species segregation
score) as indicated by the final model when (a) FF and (b) FE null models are applied to species pairs present in 2 to N – 2 sites. We
obtained similar results when we analysed species pairs present in 4 to N – 4 sites (see Fig. S2).
X. Giam and J. D. Olden
1200 Global Ecology and Biogeography, 25, 1194–1205, VC 2016 John Wiley & Sons Ltd
predator–prey interactions in structuring freshwater com-
munities. Englund et al. (2009) demonstrated that northern
pike (Esox lucius) and Eurasian perch (Perca fluviatilis) were
negatively associated with two prey species in northern lakes
of Sweden. In a tropical watershed (Trinidad and Tobago),
the presence of predatory trahira (Hoplias malabaricus)
depressed the abundance of the prey Rivulus hartii (Gilliam
et al., 1993). By contrast, fish predators did not affect micro-
habitat use by potential prey species in a North Carolina
stream; however, the low abundance of predators might
explain the lack of an observed effect of predator–prey inter-
action (Grossman et al., 1998).
Our results suggest that competition plays a minor role in
structuring fish communities across the continental USA. In
the single final model that contained COMP, the relationship
between competition strength and species segregation was
opposite to what would be expected if competition was
indeed important in structuring communities. Alofs & Jack-
son (2014) showed that consumers (i.e. predators and herbi-
vores) not competitors provided biotic resistance to
freshwater invaders, and they support previous studies that
inferred weak or absent competitive effects in aquatic com-
munities from the lack of community saturation (Moyle &
Light, 1996; Troia & Gido, 2015). By contrast, Winston
(1995) argued for the importance of competition based on
spatial segregation among morphologically similar minnows.
However, segregation occurred largely along long environ-
mental gradients; this finding strengthens support for envi-
ronmental filtering and weakens support for competition in
mediating species co-occurrence patterns. Weak or absent
competitive interactions may result from generalist feeding
habits of fish (Shurin et al., 2006) and/or their modest ener-
getic requirements as pokilotherms (Gotelli & McCabe,
2002). Poikilotherms typically require less energy that home-
otherms of the same body weight (Peters, 1986); therefore,
competition for food resources may be less intense in poiki-
lotherms than in homeotherms.
The spatial scale of investigation may also explain the
weak evidence for the role of competition in species assem-
bly. Our analyses focused on co-occurrence patterns at the
scale of a stream reach, yet interspecific competition may be
manifest at even smaller spatial scales such as individual
pools and riffles or patches of resources/microhabitats within
these habitats (Taylor, 1996; Holomuzki et al., 2010). The rel-
atively small size of the home ranges of many stream fishes
(Minns, 1995) supports the notion that competition, if pres-
ent, could operate at sub-reach scales. However, experimental
and observational studies provide mixed support for the role
of competition even at small spatial scales (see Grossman
et al., 1998; and Peres-Neto, 2004 versus Taylor, 1996 and
Resetarits, 1997). It is possible that anthropogenic stressors
such as dams and land-use change have attenuated competi-
tive interactions in US streams, as predicted by the stress-
gradient hypothesis (Power et al., 1988). Given that a lack of
competitive interactions has also been demonstrated in
undisturbed streams (Grossman et al., 1998; Peres-Neto,
2004), future studies should investigate how competitive
Figure 4 Relative importance of ENV, COMP, and PRED in
predicting the degree of association between species found in (a)
2 to N – 2 sites and (b) 4 to N – 4 sites in each watershed.
Table 3 Final model that relates the degree of segregation
(logit-transformed species segregation score) between species to
their dissimilarity in environmental requirements (ENV), the
strength of competitive interactions (COMP) and potential pred-
ator–prey interactions (PRED).
Variables b P
FF null model
ENV 3.23 <0.000001
PRED 0.36 <0.000001
Model R2 5 0.12
FE null model
ENV 3.08 <0.000001
COMP 20.52 0.001
PRED 0.33 <0.000001
Model R2 5 0.11
b, model coefficient of variable.
P 5 two-tailed P-value of the variable.
This analysis includes species pairs present in 2 to N – 2 sites across
10 or more watersheds.
Community assembly in freshwater fishes
Global Ecology and Biogeography, 25, 1194–1205, VC 2016 John Wiley & Sons Ltd 1201
interactions change across a gradient of watershed
disturbance.
We recognize that our conclusions depend on the assump-
tion that our traits correctly represent environmental require-
ments, the strength of competitive interactions and likely
predator–prey interactions. It is possible that our study did
not find a competition effect simply because we did not exam-
ine those traits responsible for mediating competitive interac-
tions. However, we believe that our suite of traits – body size,
feeding strategies and phylogeny – are ecologically relevant in
the sense that they is likely to capture both biological and spa-
tial factors that define competitive interactions. Excluding phy-
logeny, which is sometimes not related to competition, did not
change our results, suggesting that our findings are robust.
The role of positive interactions, such as facilitation, in spe-
cies assembly remains somewhat understudied (Halpern et al.,
2007), yet may be important in structuring freshwater fish
communities. For example, we expect nest associate species to
co-occur with their host species (Pendleton et al., 2012; Peo-
ples et al., 2015) even after accounting for similarities in abiotic
interactions (Peoples & Frimpong, 2016). A comprehensive
understanding of all nest associations is lacking, and most of
the known associations are limited to Nocomis hosts and more
than 30 cyprinid nest associates (Peoples et al., 2015). This pre-
cludes a continental-scale assessment of the role of facilitation
in community assembly. However, we hypothesize that the
probable small number of nest associations, the lack of host
specificity (Pendleton et al., 2012) and the ability to switch
between nest and broadcast spawning (Johnston & Page, 1992)
in many known nest associates suggests a more limited role for
facilitation in assembling entire fish communities. This topic
deserves further investigation.
We showed some discrepancies between the FF and FE
null models in their assessment of whether communities are
randomly or non-randomly structured. The FE null model
tended to categorize species pairs as being more aggregated
than the FF null model. Further simulation studies are
required to examine why relieving the constraint on site rich-
ness resulted in a lower null or ‘baseline’ level of species
aggregation. Nevertheless, both null models were consistent
in finding that most watersheds had non-randomly struc-
tured fish communities and produced consistent segregation
scores across species pairs.
We employed a traits-based, pairwise species co-occurrence
approach because our goal was to elucidate community assem-
bly mechanisms by relating different types of species interac-
tions to the degree of species co-occurrences among species
pairs. Alternative frameworks are available for complementary
studies at metacommunity or site resolutions. For example, the
elements of metacommunity structure (EMS) framework can
be used to evaluate the structure of a given metacommunity
(e.g. random, checkerboard, Gleasonian, Clementsian, etc.)
simultaneously (Leibold & Mikkelson, 2002; Presley et al.,
2010; Heino et al., 2015; Tonkin et al., 2016). Alternatively, one
could explore community assembly at the site scale by testing
for phylogenetic or trait dispersion within local communities
using null models (Peres-Neto, 2004; Liu et al., 2013; Troia &
Gido, 2015). Community assembly mechanisms are likely to
be context specific (Monta~na et al., 2014). Future studies could
use EMS and phylogenetic/trait dispersion approaches to
examine watershed- and reach-scale correlates of community
assembly, respectively, in freshwater taxa. These methods,
along with our traits-based, pairwise species co-occurrence
approach, could also be applied to terrestrial taxa (e.g., Sfen-
thourakis et al., 2006; Presley & Willig, 2010) if the regional
species pool is well defined.
We sought to test whether temperate stream fish commun-
ities are non-randomly structured and to elucidate the relative
importance of environmental filtering, predator–prey interac-
tions and interspecific competition in the species assembly
process by examining thousands of fish communities spanning
the conterminous USA. The gold-standard test for assessing
the environmental and biological determinants of species
assembly necessitates experimental manipulations that exam-
ine the ability of organisms to disperse to, and survive in, a
location in the absence versus presence of other potentially
interacting organisms (Kraft et al., 2015). Such data are often
difficult and expensive to obtain and impossible to collate at
large spatial scales. Our study provides a complementary
traits-based approach that could be readily applied to large
datasets of different taxonomies, making it particularly useful
for comparative investigations over biogeographical scales.
ACKNOWLEDGEMENTS
We extend our sincere gratitude to all the individuals and
agencies that shared their fish datasets, and applaud their col-
lective hard work, collaborative generosity and foresight. We
thank Lise Comte for comments on the manuscript. We also
thank Simon Blanchet, two anonymous referees and the han-
dling editor for helping to improve the paper. Financial sup-
port was provided by a H. Mason Keeler Endowed
Professorship (School of Aquatic and Fishery Sciences, Uni-
versity of Washington) to J.D.O.
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SUPPORTING INFORMATION
Additional supporting information may be found in the
online version of this article at the publisher’s web-site.
Methods S1 Analysis of sampling completeness.
Figure S1 Watersheds with random and non-randomly
structured fish communities when null models are applied to
species pairs present in 4 to N – 4 sites.
Figure S2 Relationship between ENV, COMP, PRED and the
degree of species segregation when null models are applied to
species pairs present in 4 to N – 4 sites.
Table S1 Classification of species pairs across all watersheds.
Table S2 Final predictive model of species segregation in the
analysis including species pairs present in 4 to N – 4 sites.
Appendix S1 Data sources for the fish community dataset.
BIOSKETCHES
Xingli Giam is a post-doctoral research associate in
the Freshwater Ecology and Conservation Laboratory
at the University of Washington. His research focuses
on characterizing and mitigating anthropogenic
impacts on the environment as well as elucidating
large-scale biodiversity patterns, particularly in fresh-
water ecosystems.
Julian Olden is an Associate Professor who enjoys
studying and squeezing fish, not necessarily in that
order.
Editor: Fabien Leprieur
Community assembly in freshwater fishes
Global Ecology and Biogeography, 25, 1194–1205, VC 2016 John Wiley & Sons Ltd 1205