Environment and predation govern fish community assembly ...depts.washington.edu/.../01/GlobalEcolBiogeog_2016.pdfquestions: (1) are fish communities structured non-randomly, and (2)

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

http://www.horizon-systems.com/NHDPlus

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).



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



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).



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).



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.



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.

REFERENCES

Albouy, C., Guilhaumon, F., Vill�eger, S., Mouchet, M., Mercier,

L., Culioli, J.M., Tomasini, J.A., Le Loc’h, F. & Mouillot, D.

(2011) Predicting trophic guild and diet overlap from func-

tional traits: statistics, opportunities and limitations for

marine ecology. Marine Ecology Progress Series, 436, 17–28.

Almeido-Neto, M., Guimar~aes, P., Guimar~aes, P.R., Loyola,

R.D. & Ulrich, W. (2008) A consistent metric for nested-

ness analysis in ecological systems: reconciling concept and

measurement. Oikos, 117, 1227–1239.

Alofs, K.M. & Jackson, D.A. (2014) Meta-analysis suggests biotic

resistance in freshwater environments is driven by consump-

tion rather than competition. Ecology, 95, 3259–3270.

Blanchet, S., Helmus, M.R., Brosse, S. & Grenouillet, G.

(2014) Regional vs. local drivers of phylogenetic and spe-

cies diversity in stream fish communities. Freshwater Biol-

ogy, 59, 450–462.



Connor, E.F. & Simberloff, D. (1979) The assembly of species

communities: chance or competition. Ecology, 60, 1132–1140.

Diamond, J.M, (1975) Assembly of species communities.

Ecology and evolution of communities (ed. by M.L. Cody

and J.M. Diamond), pp. 342–444. Harvard University

Press, Cambridge, MA.

Dias, M.S., Oberdorff, T., Hugueny, B., Leprieur, F., J�ez�equel,

C., Cornu, J.-F., Brosse, S., Grenouillet, G. &Tedesco, P.A.

(2014) Global imprint of historical connectivity on fresh-

water fish biodiversity. Ecology Letters, 17, 1130–1140.

Dietz, E. (1983) Permutation tests for association between

two distance matrices. Systematic Zoology, 32, 21–26.

Elleouet, J., Albouy, C., Ben Rais Lasram, F., Mouillot, D. &

Leprieur, F. (2014) A trait-based approach for assessing

and mapping niche overlap between native and exotic spe-

cies: the Mediterranean coastal fish fauna as a case study.

Diversity and Distributions, 20, 1333–1334.

Englund, G., Johansson, F., Olofsson, P. & Salonsaari,€Ohman, J. (2009) Predation leads to assembly rules in frag-

mented fish communities. Ecology Letters, 12, 663–671.

Frimpong, E.A. & Angermeier, P.L. (2009) Fish Traits: a data-

base of ecological and life-history traits of freshwater fishes

of the United States. Fisheries, 34, 487–495.

Frimpong, E.A. & Angermeier, P.L. (2010) Trait based

approaches in the analysis of stream fish communities.

American Fisheries Society Symposium, 73, 109–136.

Gilliam, J.F., Fraser, D.F. & Alkins-Koo, M. (1993) Structure

of a stream fish community: a role for biotic interactions.

Ecology, 74, 1856–1870.

Godoy, O., Kraft, N.J.B. & Levine, J.M. (2014) Phylogenetic

relatedness and the determinants of competitive outcomes.

Ecology Letters, 17, 836–844.

Gotelli, N.J. (2000) Null model analysis of species co-

occurrence patterns. Ecology, 81, 2606–2621.

Gotelli, N.J. & Graves, G.R, (1996) Null models in ecology.

Smithsonian Institution Press, Washington, DC.

Gotelli, N.J. & McCabe, D.J. (2002) Species co-occurrence: a

meta-analysis of J.M. Diamond’s assembly rules model.

Ecology, 83, 2091–2096.

Gotelli, N.J. & Ulrich, W. (2010) The empirical Bayes

approach as a tool to identify non-random species associa-

tions. Oecologia, 162, 463–477.

Gotelli, N.J. & Ulrich, W. (2012) Statistical challenges in null

model analysis. Oikos, 121, 171–180.

Gower, J.C. (1971) A general coefficient of similarity and

some of its properties. Biometrics, 27, 857–874.

Grossman, G.D., Ratajczak, R.E., Jr, Crawford, M. &

Freeman, M.C. (1998) Assemblage organization in stream

fishes: effects of environmental variation and interspecific

interactions. Ecological Monographs, 68, 395–420.

Halpern, B.S., Silliman, B.R., Olden, J.D., Bruno, J.P. &

Bertness, M.D. (2007) Incorporating positive interactions

in aquatic restoration and conservation. Frontiers in Ecol-

ogy and the Environment, 5, 153–160.

Hauer, F.R. & Lamberti, G.A. (Eds.) (2006) Methods in

stream ecology, 2nd edn. Academic Press, New York.

Heino, J. (2013) Environmental heterogeneity, dispersal

mode, and co-occurrence in stream macroinvertebrates.

Ecology and Evolution, 3, 344–355.

Heino, J., Nokela, T., Soininen, J., Tolkkinen, M., Virtanen, L.

& Virtanen, R. (2015) Elements of metacommunity struc-

ture and community–environment relationships in stream

organisms. Freshwater Biology, 60, 973–988.

Holomuzki, J.R., Feminella, J.W. & Power, M.E. (2010) Biotic

interactions in freshwater benthic habitats. Journal of the

North American Benthological Society, 29, 220–244.

Hubbell, S.P. (2001) The unified neutral theory of biodiversity

and biogeography. Princeton University Press, Princeton,

NJ.

Jackson, D.A., Somers, K.H. & Harvey, H.H. (1992) Null

models and fish communities: evidence of nonrandom pat-

terns. The American Naturalist, 139, 930–951.

Jackson, D.A., Peres-Neto, P.R. & Olden, J.D. (2001) What

controls who is where in freshwater fish communities – the

roles of biotic, abiotic and spatial factors. Canadian Journal

of Fisheries and Aquatic Sciences, 58, 157–170.

Johnston, C.E. & Page, L.M, (1992) The evolution of com-

plex reproductive strategies in North American minnows

(Cyprinidae). Systematics, historical ecology, and North

American freshwater fishes (ed. by R.L. Mayden), pp. 600–

621. Stanford University Press, Stanford, CA.

Juanes, F. (1994) What determines prey size selectivity in pis-

civorous fishes? Theory and application in fish feeding ecol-

ogy (ed. by D.J. Stouder, K.L. Fresh and R.J. Feller), pp.

79–100. University of South Carolina Press, Columbia, SC.

Kraft, N.J.B., Adler, P.B., Godoy, O., James, E.C., Fuller, S. &

Levine, J.M. (2015) Community assembly, co-existence and

the environmental filtering metaphor. Functional Ecology,

29, 592–599.

Legendre, P., Lapoint, F.-J. & Casgrain, P. (1994) Modeling

brain evolution from behavior: a permutational regression

approach. Evolution, 48, 1487–1499.

Leibold, M.A. & Mikkelson, G.M. (2002) Coherence, species

turnover, and boundary clumping: elements of meta-

community structure. Oikos, 97, 237–250.

Leibold, M.A., Holyoak, M., Mouquet, N., Amarasekare, P.,

Chase, J.M., Hoopes, M.F., Holt, R.D., Shurin, J.B., Law,

R., Tilman, D., Loreau, M. & Gonzalez, A. (2004) The

metacommunity concept: a framework for multi-scale

community ecology. Ecology Letters, 7, 601–613.

Leprieur, F., Tedesco, P.A., Hugueny, B., Beauchard, O., Durr,

H.H., Brosse, S. & Oberdorff, T. (2011) Partitioning global

patterns of freshwater fish beta diversity reveals contrasting

signatures of past climate changes. Ecology Letters, 14,

325–334.

Liu, X., Swenson, N.G., Zhang, J. & Ma, K. (2013) The

environment and space, not phylogeny, determine trait

dispersion in a subtropical forest. Functional Ecology, 27,

264–272.

Lockwood, J.L., Powell, R.D., Nott, P. & Pimm, S.L. (1997)

Assembling ecological communities in time and space.

Oikos, 80, 549–553.



McGill, B.J., Enquist, B.J., Weiher, E. & Westoby, M. (2006)

Rebuilding community ecology from functional traits.

Trends in Ecology and Evolution, 21, 178–185.

Matthews, W.J. (1982) Small fish community structure in Ozark

streams: structured assembly patterns or random abundance

of species. American Midland Naturalist, 107, 42–57.

M’Closkey, R.T. (1978) Niche separation and assembly in

four species of Sonoran desert rodents. The American Nat-

uralist, 112, 683–694.

Mims, M.C., Olden, J.D., Shattuck, Z.R. & Poff, N.L. (2010)

Life history trait diversity of native freshwater fishes in

North America. Ecology of Freshwater Fish, 19, 390–400.

Minns, C.K. (1995) Allometry of home range size in lake and

river fishes. Canadian Journal of Fisheries and Aquatic Sci-

ences, 52, 1499–1508.

Monta~na, C.G., Winemiller, K.O. & Sutton, A. (2014) Inter-

continental comparison of fish ecomorphology: null model

tests of community assembly at the patch scale in rivers.

Ecological Monographs, 84, 91–107.

Morales-Castilla, I., Matias, M.G., Gravel, G. & Araujo, M.B.

(2015) Inferring biotic interactions from proxies. Trends in

Ecology and Evolution, 30, 347–356.

Moyle, P. & Light, T. (1996) Biological invasions of fresh

water: empirical rules and assembly theory. Biological Con-

servation, 78, 149–161.

Oksanen, J., Blanchet, F.G., Kindt, R., Legendre, P., Minchin,

P.R., O’Hara, R.B., Simpson, G.L., Solymos, P., Stevens,

M.H.M. & Wagner, H. (2015) vegan: community ecology

package. R package v. 2.3-0. Available at: http://CRAN.R-

project.org/package5vegan (accessed June 2015).

Olden, J.D., Poff, N.L. & Bestgen, K.R. (2006) Life-history

strategies predict fish invasions and extirpations in the

Colorado River Basin. Ecological Monographs, 76, 25–40.

Pavoine, S., Vallet, J., Defour, A.-B., Gachet, S. & Daniel, H.

(2009) On the challenge of treating various type of varia-

bles: application for improving the measurement of func-

tional diversity. Oikos, 118, 391–402.

Page, L.M. & Burr, B.M. (2011) Peterson field guide to fresh-

water fishes of North America north of Mexico, 2nd edn.

Houghton Mifflin Harcourt, Boston, MA.

Page, L.M., Espinosa-P�erez, Findley, L.T., Gilbert, C.R., Lea,

R.N., Mandrak, N.E., Mayden, R.L. & Nelson, J.S. (2013)

Common and scientific names of fishes from the United

States, Canada, and Mexico, 7th edn. American Fisheries

Society, Bethesda, MD.

Pendleton, R.M., Pritt, J.J., Peoples, B.K. & Frimpong, E.A.

(2012) The strength of Nocomis nest association contrib-

utes to patterns of rarity and commonness among New

River, Virginia cyprinids. American Midland Naturalist,

168, 202–212.

Peoples, B.K. & Frimpong, E.A. (2016) Biotic interactions

and habitat drive positive co-occurrence between facilitat-

ing and beneficiary stream fishes. Journal of Biogeography,

43, 923–931.

Peoples, B.K., Blanc, L.A. & Frimpong, E.A. (2015) Lotic

cyprinid communities can be structured as nest webs and

predicted by the stress-gradient hypothesis. Journal of Ani-

mal Ecology, 84, 1666–1677.

Peres-Neto, P.R. (2004) Patterns in the co-occurrence of fish spe-

cies in streams: the role of site suitability, morphology and

phylogeny versus species interactions. Oecologia, 140, 352–360.

Peters, R.H. (1986) The ecological implications of body size.

Cambridge University Press, Cambridge.

Poff, N.L. & Allan, J.D. (1995) Functional organization of

stream fish assemblages in relation to hydrologic variabili-

ty. Ecology, 76, 606–627.

Power, M.E., Stout, R.J., Cushing, C.E., Harper, P.P., Hauer,

F.R., Matthews, W.J., Moyle, P.B., Statzner, B. & Wais de

Badgen, I.R. (1988) Biotic and abiotic controls in river and

stream communities. Journal of the North American Bentho-

logical Society, 7, 456–479.

Presley, S.J. & Willig, M.R. (2010) Bat metacommunity struc-

ture on Caribbean islands and the role of endemics. Global

Ecology and Biogeography, 19, 185–199.

Presley, S.J., Higgins, C.L. & Willig, M.R. (2010) A compre-

hensive framework for the evaluation of metacommunity

structure. Oikos, 119, 908–917.

R Core Team (2015) R: A language and environment for statistical

computing. R Foundation for Statistical Computing, Vienna.

Resetarits, W.J. Jr (1997) Interspecific competition and quali-

tative competitive asymmetry between two benthic stream

fishes. Oikos, 78, 428–439.

Sfenthourakis, S., Tzanatos, E. & Giokas, S. (2006) Species

co-occurrence: the case of congeneric species and a causal

approach to patterns of species association. Global Ecology

and Biogeography, 15, 39–49.

Shurin, J.B., Gruner, D.S. & Hillebrand, H. (2006) All wet or dried

up? Real differences between aquatic and terrestrial food webs.

Proceedings of the Royal Society B: Biological Sciences, 273, 1–9.

Strobl, C., Boulesteix, A-L., Zeileis, A. & Hothorn, T. (2007)

Bias in random forest variable importance measures: illus-

trations, sources, and a solution. BMC Bioinformatics, 8,

25. doi:10.1186/1471-2105-8-25.

Taylor, C. (1996) Abundance and distribution within a guild

of benthic stream fishes: local processes and regional pat-

terns. Freshwater Biology, 36, 385–396.

Tonkin, J.D., Stoll, S., J€ahnig, S.C. & Haase, P. (2016) Ele-

ments of metacommunity structure of river and riparian

assemblages: communities, taxonomic groups and decon-

structed trait groups. Ecological Complexity, 25, 35–43.

Troia, M.J. & Gido, K.B. (2015) Functional strategies drive

community assembly of stream fishes along environmental

gradients and across spatial scales. Oecologia, 177, 545–559.

Veech, J.A. (2013) A probabilistic model for analysing species

co-occurrence. Global Ecology and Biogeography, 22, 252–260.

Veech, J.A. (2014) A pairwise approach to analysing species

co-occurrence. Journal of Biogeography, 41, 1029–1035.

Violle, C., Nemergut, D.R., Pu, Z. & Jiang, L. (2011) Phylo-

genetic limiting similarity and competitive exclusion. Ecol-

ogy Letters, 14, 782–787.

Warton, D.I. & Hui, F.K.C. (2011) The arcsine is asinine: the

analysis of proportions in ecology. Ecology, 92, 3–10.



http://CRAN.R-project.org/package=vegan



info:doi/10.1186/1471-2105-8-25

Webb, C.O., Ackerly, D.D., McPeek, M.A. & Donoghue, M.J.

(2011) Advances, challenges and a developing synthesis of

ecological community assembly theory. Philosophical Trans-

actions of the Royal Society B: Biological Sciences, 366,

2403–2413.

Winston, M.R. (1995) Co-occurrence of morphologically

similar species of stream fishes. The American Naturalist,

145, 527–545.

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



Documents

Environment and predation govern fish community assembly ...depts.washington.edu/.../01/GlobalEcolBiogeog_2016.pdfquestions: (1) are fish communities structured non-randomly, and (2)