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Home , Assembly rules, Community (ecology)

Global and , (Global Ecol. Biogeogr.) (2016) 25, 1194–1205

RESEARCH Environment and govern fish PAPER assembly in temperate streams Xingli Giam* and Julian D. Olden

School of Aquatic and Fishery Sciences, ABSTRACT University of Washington, Seattle, Aim The elucidation of patterns and drivers of community assembly remains a WA 98105, USA fundamental issue in ecology. Past studies have focused on a limited number of at local or regional scales, thus precluding a comprehensive examination of . 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 in driving 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. *Correspondence: Xingli Giam, School of Keywords Aquatic and Fishery Sciences, University of Washington, Seattle, WA 98105, USA. Assembly rules, co-occurrence, competition, ecological interactions, null E-mail: [email protected] models, North America, rivers, species traits.

DOI: 10.1111/geb.12475 1194 VC 2016 John Wiley & Sons Ltd http://wileyonlinelibrary.com/journal/geb Community assembly in freshwater fishes

INTRODUCTION

The quest to understand how species communities assemble results of these studies are context specific or represent gen- remains one of the most fundamental, and often controver- eral assembly patterns for each taxonomic group. sial, topics in ecology. Since the pivotal publication of Jared Emerging from the burgeoning literature on species assem- Diamond’s ‘The assembly of species communities’ (Diamond, bly was a meta-analysis indicating that most animal com- 1975), intense investigation has centred on the operation of munities had fewer species co-occurrence than expected by environmental filtering, the definition of assembly rules, the chance (Gotelli & McCabe, 2002). Notably, that study importance of null models and the role of species neutrality reported that negative species co-occurrences were more (Hubbell 2001; Leibold et al., 2004). Although their relative common in warm-blooded than cold-blooded animals, and roles are debated, key processes involved in community that among cold-blooded taxa, fish communities were prob- assembly include biotic interactions in the form of interspe- ably randomly structured. Despite representing a significant cific competition and predation (M’Closkey, 1978; Connor & advance in the field, the approach used by Gotelli & McCabe Simberloff, 1979), environmental filtering (Heino, 2013; Kraft (2002) was complicated by the fact that C-scores (which et al., 2015) and historical effects such as dispersal limitation quantify the degree of segregation or aggregation between a owing to physical barriers (Dias et al., 2014). These processes pair of species) were averaged over all species pairs. Gotelli & can shape co-occurrence patterns among species pairs Ulrich (2012) suggested that this approach might miss poten- (Gotelli & McCabe, 2002; Veech, 2014) and in whole meta- tially important pairwise associations between particular pairs communities (Leibold & Mikkelson, 2002; Almeido-Neto of species. Thus, the particular processes contributing to et al., 2008; Presley et al., 2010) as well as produce patterns community structure require further examination. in phylogenetic or trait dispersion within local communities Here, we examined the patterns and drivers of fish com- (Webb et al., 2011; Liu et al., 2013). munity assembly across diverse taxonomies (500 species) and Ecological theory and empirical evidence suggest that com- geographies (c. 8000 stream locations) in the conterminous petition and predation can limit co-occurrences of interacting USA. Freshwater fishes are a good model for community species (i.e. negative species associations) (Diamond, 1975; assembly analyses because watersheds represent naturally Englund et al., 2009). By contrast, environmental filtering and bounded, independent regions within which species disperse historical processes can either: (1) increase species co- and interact (Leprieur et al., 2011). This facilitated a occurrences when two or more species are adapted to similar robust test of the assembly rule concept using numerous environments, have similar niche requirements or have similar independent sets of interacting communities across a broad biogeographical histories, or (2) limit co-occurrences when environmental gradient. By combining pairwise species different species are adapted to different environments, have co-occurrence analyses with trait-based inference of species different niche requirements or disperse from different histori- interactions (McGill et al., 2006; Frimpong & Angermeier, cal pools (Heino, 2013; Dias et al., 2014). 2010), we aimed to answer the following questions: (1) are Null models are commonly used to test whether an freshwater fish communities structured non-randomly within observed pattern of species co-occurrence is likely to be real watersheds, and (2) what processes (i.e. environmental filter- or the result of random processes (Gotelli & Graves, 1996). ing, predator–prey interactions, interspecific competition) In freshwater , for instance, Matthews (1982) drive species associations? By doing this we hope to advance found the number of negative associations among stream the current understanding of the nature of assembly rules in fishes to be no more than that derived from random com- freshwater fish communities. munity assembly. By contrast, Winston (1995) found mor- phologically similar fish species to co-occur less often than METHODS random (inferring the importance of interspecific competi- Species community dataset tion), whereas Peres-Neto (2004) demonstrated that environ- mental filtering shaped fish communities in Brazilian We compiled a database of species occurrence for 7846 sites streams. Divergent mechanisms influencing fish communities (i.e. fish communities) across 1502 watersheds (i.e. HUC8 are also evident in lakes, where studies support both environ- hydrological units as defined by the United States Geological mentally mediated patterns (Jackson et al., 1992) and assem- Survey) in the conterminous USA (Fig. 1). The sites were bly rules resulting from biological interactions (Englund surveyed between 1990 and 2012 by US federal government et al., 2009). Regardless of , the mechanisms (or agencies [e.g. the EPA and Regional Environmental Monitor- lack thereof) governing how communities are assembled vary ing and Assessment Program (EMAP and REMAP), the EPA in both time and space (Lockwood et al., 1997). However, National Rivers and Streams Assessment (NRSA), the USGS most existing studies have investigated species co-occurrence National Water Quality Assessment Program (NAWQA)], and community assembly rules in a single region or interact- state natural and environmental agencies and uni- ing (e.g. Connor & Simberloff, 1979; Mat- versity researchers (see Appendix S1 in Supporting Informa- thews, 1982; Jackson et al., 1992; Winston, 1995; Peres-Neto, tion for full list). All surveys were designed to characterize 2004; Englund et al., 2009). It remains unclear whether the the entire fish community, which includes both native and

Global Ecology and Biogeography, 25, 1194–1205, VC 2016 John Wiley & Sons Ltd 1195 X. Giam and J. D. Olden

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 affinities; (c) predator–prey interactions – predators such as the largemouth bass (Micropterus salmoides) may affect the 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].

non-native species, at each location in terms of species occur- maximize the spatial independence of sites, we randomly rence, and we assumed that communities are more or less in subsampled sites to ensure they were at least 1 km apart. equilibrium. Survey sites were selected to ensure comparability across Ecological trait dataset fish communities. To minimize any bias introduced by differ- Ecological traits represent a powerful currency for studying ent sampling techniques, we included only those surveys in interactions among species and between species and their which electrofishing was the primary method of fish collec- environment (McGill et al., 2006; Frimpong & Angermeier, tion. Backpack electrofishing was the common primary 2010; Morales-Castilla et al., 2015). A traits-based approach method of sampling for small wadeable streams, whereas raft is often used to describe the similarity of fishes in terms of electrofishing was used for deep and large rivers. Sampling their environmental and trophic niches (Poff & Allan, 1995; reach length depended on the width of the river – wider riv- Olden et al., 2006; Albouy et al., 2011; Elleouet et al., 2014). ers require longer sampling reaches – a standard protocol to Here, we collated data from the literature on nine ecological accurately characterize fish communities in streams of differ- traits to quantify the degree to which pairs of species: (1) ent widths (Hauer & Lamberti, 2006). Though not exactly have similar environmental requirements; (2) potentially the same, the sampling reach length:river width ratio and compete for resources, and (3) are likely to engage in preda- actual sampling reach length were comparable across datasets. tor–prey interactions. We describe species traits and their In general, all sites had sampling reaches over 150 m and data sources in Table 1. were considered to accurately reflect the true composition of Environmental requirements of fishes were described by the fish communities at the time of collection. six traits: affinity for different freshwater bodies (HAB), alti- We assigned sampling sites in our dataset to stream tudinal affinity (ALT), mean stream size (STR), temperature reaches in the National Hydrography Dataset (NHDPlus v.2; preference (TEMP), substrate preference (SUB) and affinity http://www.horizon-systems.com/NHDPlus/) and retained to different flow speeds (FLOW). To quantify the degree of only those sites that were located on natural stream reaches. potential interspecific competition, we used three ecological Sites located in drainage canals, artificial connectors and traits to characterize species similarity in food acquisition: ditches were removed. We manually identified overlapping maximum body length (BL), adult trophic (TROPH) and repeat sites by searching for duplicated reach names and and vertical feeding position in water (or activity position if geographical coordinates; for those sites, we used data from non-feeding) (VERT). Two additional traits, family (FAM) the most recent survey to standardize sampling effort. To and genus (GEN) membership, were also included to account

1196 Global Ecology and Biogeography, 25, 1194–1205, VC 2016 John Wiley & Sons Ltd Community assembly in freshwater fishes

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’)

Variables

No. Traits Variable type Data sources ENV COMP PRED

1 HAB: affinity to different Multichoice nominal 1, 3 X freshwater bodies Levels: lake, spring, headwater, creek, small river, medium river, large river 2 ALT: altitudinal affinity Multichoice nominal 1X Levels: lowland, upland, montane 3 STR: mean stream size Continuous 1, 3 X Average of values assigned from 1 (smallest stream size: spring) to 6 (largest: large river) 4 TEMP: temperature Ordinal 2, 5 X preference Levels: cold, cold–cool, cool, cool–warm, warm 5 SUB: substrate affinity Multichoice nominal 1X Levels: fine, coarse, rocky, 6 FLOW: flow velocity Multichoice nominal 1X Levels: slow, moderate, fast 7 BL: maximum body length Continuous. 2, 5 X X (in mm) 8 TROPH: adult trophic guild Nominal 2, 5 X X Levels: –, invertivore, , inverti- vore–piscivore, piscivore, non-feeding, parasitic 9 VERT: vertical feeding posi- Multichoice mominal 1, 5 X X tion in water Levels: benthic, surface 10 GEN Nominal 4X Levels: genera 11 FAM Nominal 4X Levels: families

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). for likely competition owing to phylogenetic relatedness ses. The mean site density across watersheds is 0.0043 (inter- (Violle et al., 2011). We identified potential predator–prey quartile range 0.0025–0.0050) sites/km2. The positive interactions between species pairs based on TROPH, BL and correlation between site number and watershed area, VERT while taking into account predator selectivity for prey although significant, is weak (Spearman’s q 5 0.20, size (Juanes, 1994) (see ‘Drivers of species associations’). P 5 0.003) but high sampling completeness among sites within each watershed (mean 80%, interquartile range 75– Data analyses 86%; Methods S1) indicates that sampling effort is adequate Structure of fish communities within watersheds within and comparable across watersheds. To determine whether fish communities within a water- We considered fish communities within a given watershed to shed have a non-random structure, we compared the be an interacting metacommunity, following Blanchet et al. observed sum of positive and negative species associations 2 (2014). Watersheds are small enough (mean 4513 km , inter- with that expected from two null models of community 2 quartile range 3027–5563 km ; n 5 224 basins included in assembly. Null models quantify the degree of association our analyses) for us to realistically assume that fishes can between species pairs with specific assumptions of commu- freely disperse among sites therein. Using a larger spatial nity assembly in the absence of species interactions (Gotelli scale (USGS HUC6 drainage basins) to define metacommun- & Graves, 1996). We used two null models: (1) the fixed ities would likely result in a higher proportion of segregated rows–fixed columns (FF) model, which preserves total occur- species pairs owing to increased environmental heterogeneity rences among species and total among sites (Troia & Gido, 2015) and/or historical vicariance. To mini- within each watershed, and (2) the fixed rows–equiprobable mize the confounding effect of the latter, the USGS HUC8 columns (FE) model, which preserves differences in total watershed scale is therefore more appropriate for our analy- occurrences among species but not differences in richness

Global Ecology and Biogeography, 25, 1194–1205, VC 2016 John Wiley & Sons Ltd 1197 X. Giam and J. D. Olden among sites within each watershed. The FF and FE models community structure and species pair associations (random, represent ecologically plausible colonization processes and segregated, aggregated) between FF and FE null models. have low Type I error probabilities (Gotelli, 2000). Cohen’s j ranges from 0 (totally incongruent classifications) We performed the null model analyses for each of the 224 to 1 (totally congruent classifications). watersheds containing 10 or more sampling sites and 10 or Drivers of species associations more species to ensure adequate statistical power (Gotelli & Ulrich, 2010; Veech, 2013). Each watershed defines the The effect size for the degree of association between species i regional from which species are drawn under and j within a given watershed was quantified by ranking the the two null models. For each pair of species in a watershed, observed CS with respect to its null distribution and rescaling we compare the observed rescaled C-score [CS; which ranges the rank to [0,1] where 0 is maximum aggregation and 1 is from 0 (maximum species aggregation) to 1 (maximum spe- maximum segregation. For each species pair, we used the cies segregation)] with 4999 sets of randomized (FE and FF median effect size (across all watersheds; n 5 224) to quantify null) . The rescaled CS of species i and j the overall degree of association (hereafter, species segrega-

(CSij) is calculated as: 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 ðÞA2J ðÞB2J (number of pairs 5 3020 and 1030, respectively) to increase CSij 5 ; (1) AB the reliability of the species segregation score. We quantified differences between species in terms of their where A and B are the number of sites occupied by species i environmental requirements (ENV) by calculating Gower’s and j, respectively, and J is the number of sites occupied by dissimilarity coefficient for each species pair based on traits both A and B (Gotelli & Ulrich, 2010). Species pairs with a associated with environmental preferences (HAB, ALT, STR, two-tailed Monte Carlo P-value 0.05 were considered as TEMP, SUB, FLOW) (Gower, 1971). To estimate the degree aggregated (if observed CS < expected CS under a null of competition between species pairs (COMP), we calculated model) or segregated (if observed CS > expected CS). For Gower’s similarity coefficient based on traits related to food each watershed, we summed the numbers of segregated and acquisition (BL, TROPH, VERT), and phylogeny (FAM, aggregated species pairs. GEN), where the more similar two species are in terms of Species pairs can appear non-random even when com- these traits, the higher the degree of potential competition munities are actually assembled randomly (Type I errors). To between the species. determine whether fish communities within a watershed are Potential predator–prey interactions between species pairs likely to indeed be non-random, we created 1999 randomized (PRED) were identified based on TROPH, BL and VERT. metacommunities and repeated the procedures outlined Using data from a meta-analysis of the prey size selectivities above to calculate the null distribution of the sum of segre- of piscivorous fishes (Juanes, 1994), we found that piscivores gated and aggregated species pairs in each watershed. We select prey fishes that are on average 3.73 times smaller than defined watersheds for which the sum of segregated and their own body length. We therefore defined species pairs aggregated species pairs exceed the 95th percentile of the cor- likely to have predator–prey interactions (PRED 5 1) as those responding null distribution as non-randomly structured. (1) comprising at least one species that is classified as an We maximized the statistical power of the null model test invertivore–piscivore or piscivore and that the invertivore– for fish community structure by including only species that piscivore or piscivore is at least 3.73 times the maximum are present in (1) 2 to N – 2 sites (207 watersheds with 10 body length of the other species or at least one parasitic spe- or more species) and (2) 4 to N – 4 sites (158 watersheds), cies, and (2) having overlapping vertical feeding positions. where N is the total number of sites present in a watershed. Because phylogenetic similarity is related to competition in We excluded very rare and very common species because the some communities but not others (Violle et al., 2011; Godoy absolute difference between observed and expected CS must et al., 2014 and references therein), we calculated a second be greater than 1.6–3.3 for the test to have adequate power Gower’s similarity coefficient (COMP2) based on the above (when N 5 10–50 under the FE null model; Veech, 2013). variables but excluding FAM and GEN as an alternative esti- When a species is present or absent in only one site, the mate of competition strength. Using COMP2 had little effect maximum difference between observed and expected co- on the findings (results not shown); we therefore present the occurrence is only 1. In this case, even if a species pair was analysis based on COMP. truly associated, the test would not have power to detect the To examine the effect of environmental filtering, interspe- association. Increasing the minimum numbers of sites and cific competition and predator–prey interactions on species species from 10 to 20 did not alter our findings, suggesting associations, we fitted linear models that predict the species that our results are robust to different exclusion criteria. We segregation score as a of ENV, COMP, PRED and therefore only reported results of analyses using the initial the two-way interactions ENV 3 COMP and ENV 3 PRED. criteria. The continuous variables ENV and COMP were mean- We used Cohen’s j to assess the congruence in the water- centred. Some traits were used to quantify two variables (e.g. shed classifications (i.e. random or non-random) of fish BL, TROPH and VERT used to quantify both COMP and

1198 Global Ecology and Biogeography, 25, 1194–1205, VC 2016 John Wiley & Sons Ltd Community assembly in freshwater fishes

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 Figure 2 Distribution of watersheds with random and non- 0.01, to reduce Type I error rates. We used a backward elimi- randomly structured fish communities when (a) FF and (b) FE nation procedure suggested by Legendre et al. (1994), but null models are applied to species pairs present in 2 to N –2 with regression coefficient P-values calculated from Student’s sites in each watershed. We obtained similar results when we t-distribution rather than matrix permutation, to obtain analysed species pairs present in 4 to N – 4 sites (see Fig. S1). the final model (Bonferroni-corrected, P-to-remove 5 0.001, 0.005 or 0.01). Using the final model, we quantified the rela- There was moderate congruence between FF and FE null tive contributions of ENV, COMP, and PRED in driving spe- models with respect to whether a given fish community was cies associations by calculating the mean reduction in R2 non-randomly structured (Cohen’s j 5 0.46–0.65; Table 2). (DR2) between predicted and observed values of the species Similarly, the null models were moderately congruent with segregation score when each predictor variable (ENV, COMP, respect to associations between individual species pairs PRED) is permuted (Strobl et al., 2007). The higher the DR2, (Cohen’s j 5 0.56–0.60). The FF null model classified 1.3 the more important the variable. Because our main findings times more species pairs as segregated than aggregated. By were consistent across the different alpha values, we pre- contrast, the FE null model identified 3.6–4.6 times more sented results based on an alpha value of 0.005. species pairs as aggregated than segregated (Table S1). All analyses were performed in R 3.2.2 (R Core Team, 2015). We used the vegan package (Oksanen et al., 2015) to Drivers of species associations perform null model simulations. The species segregation score (a high score indicating high RESULTS degree of segregation) was always positively correlated with ENV, indicating that species with greater differences in their Patterns of fish community structure environmental requirements were more segregated (Figs 3 & S2, Fish communities across the majority of US watersheds were Tables 3 & S2). Predator–prey pairs (PRED) were more segre- non-randomly structured. The sum of aggregated and segre- gated than species that do not form such pairs in all analyses. gated species pairs was higher than expected in 75–76% Theonlytimecompetitionstrength(COMP)wasincludedin (under the FF null model) and 84–85% (FE) of watersheds the final model was when we considered species pairs present (Figs 2 and S1). There was little evidence for geographical in 2 to N – 2 sites under the FE null model (Table 3). However, clustering of non-random fish assemblages. species segregation decreased, instead of increased, with COMP.

Global Ecology and Biogeography, 25, 1194–1205, VC 2016 John Wiley & Sons Ltd 1199 X. Giam and J. D. Olden

Table 2 Number of watersheds classified as having random or DISCUSSION non-randomly structured fish communities under the FF or FE The aim of our study was to uncover patterns and drivers of null model. fish community assembly over a large spatial scale. By inter- rogating patterns in fish community composition across 5 (a) Species occupying 2 to N – 2 sites (Cohen’s j 0.65) diverse phylogenies and geographical contexts, we have dem- onstrated that the majority of temperate stream fish commun- FE null model ities in the conterminous USA are structured non-randomly. Random Non-random FF total Our results provide important resolution on past studies (e.g. Matthews, 1982; Winston, 1995; Gotelli & McCabe, 2002; FF null model Random 30 21 51 Peres-Neto, 2004) by contributing new evidence that supports Non-random 3 153 156 the preponderance of non-random fish community assembly FE Total 33 174 207 across multiple independent watersheds. Environmental filtering appeared to be by far the most (b) Species occupying 4 to N – 4 sites (Cohen’s j 5 0.46) important process structuring the composition of fish com- munities. Species demonstrating greater similarity in environ- FE null model mental requirements (i.e. size and type of stream, stream temperature and elevation) tend to be more positively associ- Random Non-random FF total ated with respect to patterns in co-incidence. The importance FF null model Random 17 21 38 of environmental filtering in driving the community struc- Non-random 6 114 120 ture of temperate fishes (as shown here) is supported by FE Total 23 135 158 research in tropical streams (Peres-Neto, 2004). It is well known that most are not able to establish and per- Only watersheds with at least 10 species are included. sist in all environments (Kraft et al., 2015) and the commu- nity structure of stream fishes is governed by a multitude of In all analyses, the dissimilarity in environmental require- abiotic conditions that include stream order, stream mor- ments of species (ENV) was much more predictive of the phology, flow regime and riparian and instream habitat degree of species segregation than the contribution of inter- (Jackson et al., 2001). specific competition (COMP) and predator–prey interactions The effect of predator–prey interactions was also evident, 2 (PRED). Permuting ENV reduced the R between predicted albeit to a smaller degree than the role of environmental fil- and observed species segregation scores by 0.11–0.13, whereas tering. Species pairs potentially engaging in predator–prey 2 permuting PRED and COMP reduced the R by 0.02–0.05 interactions are more likely to be segregated in space. Rela- and 0–0.007 respectively (Fig. 4). tively few field-based studies have investigated the role of

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).

1200 Global Ecology and Biogeography, 25, 1194–1205, VC 2016 John Wiley & Sons Ltd Community assembly in freshwater fishes

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. 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- Figure 4 Relative importance of ENV, COMP, and PRED in habitat use by potential prey species in a North Carolina predicting the degree of association between species found in (a) stream; however, the low abundance of predators might 2toN – 2 sites and (b) 4 to N – 4 sites in each watershed. explain the lack of an observed effect of predator–prey inter- action (Grossman et al., 1998). otherms of the same body weight (Peters, 1986); therefore, Our results suggest that competition plays a minor role in competition for food resources may be less intense in poiki- structuring fish communities across the continental USA. In lotherms than in homeotherms. the single final model that contained COMP, the relationship The spatial scale of investigation may also explain the between competition strength and species segregation was weak evidence for the role of competition in species assem- opposite to what would be expected if competition was bly. Our analyses focused on co-occurrence patterns at the indeed important in structuring communities. Alofs & Jack- scale of a stream reach, yet interspecific competition may be son (2014) showed that consumers (i.e. predators and herbi- manifest at even smaller spatial scales such as individual vores) not competitors provided biotic to pools and riffles or patches of resources/microhabitats within freshwater invaders, and they support previous studies that these (Taylor, 1996; Holomuzki et al., 2010). The rel- inferred weak or absent competitive effects in aquatic com- atively small size of the home ranges of many stream fishes munities from the lack of community saturation (Moyle & (Minns, 1995) supports the notion that competition, if pres- Light, 1996; Troia & Gido, 2015). By contrast, Winston ent, could operate at sub-reach scales. However, experimental (1995) argued for the importance of competition based on and observational studies provide mixed support for the role spatial segregation among morphologically similar minnows. of competition even at small spatial scales (see Grossman However, segregation occurred largely along long environ- et al., 1998; and Peres-Neto, 2004 versus Taylor, 1996 and mental gradients; this finding strengthens support for envi- Resetarits, 1997). It is possible that anthropogenic stressors ronmental filtering and weakens support for competition in such as dams and land-use change have attenuated competi- mediating species co-occurrence patterns. Weak or absent tive interactions in US streams, as predicted by the stress- competitive interactions may result from generalist feeding gradient hypothesis (Power et al., 1988). Given that a lack of habits of fish (Shurin et al., 2006) and/or their modest ener- competitive interactions has also been demonstrated in getic requirements as pokilotherms (Gotelli & McCabe, undisturbed streams (Grossman et al., 1998; Peres-Neto, 2002). Poikilotherms typically require less that home- 2004), future studies should investigate how competitive

Global Ecology and Biogeography, 25, 1194–1205, VC 2016 John Wiley & Sons Ltd 1201 X. Giam and J. D. Olden interactions change across a gradient of watershed using null models (Peres-Neto, 2004; Liu et al., 2013; Troia & . Gido, 2015). Community assembly mechanisms are likely to We recognize that our conclusions depend on the assump- be context specific (Montana~ et al., 2014). Future studies could tion that our traits correctly represent environmental require- use EMS and phylogenetic/trait dispersion approaches to ments, the strength of competitive interactions and likely examine watershed- and reach-scale correlates of community predator–prey interactions. It is possible that our study did assembly, respectively, in freshwater taxa. These methods, not find a competition effect simply because we did not exam- along with our traits-based, pairwise species co-occurrence ine those traits responsible for mediating competitive interac- approach, could also be applied to terrestrial taxa (e.g., Sfen- tions. However, we believe that our suite of traits – body size, thourakis et al., 2006; Presley & Willig, 2010) if the regional feeding strategies and phylogeny – are ecologically relevant in species pool is well defined. the sense that they is likely to capture both biological and spa- We sought to test whether temperate stream fish commun- tial factors that define competitive interactions. Excluding phy- ities are non-randomly structured and to elucidate the relative logeny, which is sometimes not related to competition, did not importance of environmental filtering, predator–prey interac- change our results, suggesting that our findings are robust. tions and interspecific competition in the species assembly The role of positive interactions, such as facilitation, in spe- process by examining thousands of fish communities spanning cies assembly remains somewhat understudied (Halpern et al., the conterminous USA. The gold-standard test for assessing 2007), yet may be important in structuring freshwater fish the environmental and biological determinants of species communities. For example, we expect nest associate species to assembly necessitates experimental manipulations that exam- co-occur with their host species (Pendleton et al., 2012; Peo- ine the ability of organisms to disperse to, and survive in, a ples et al., 2015) even after accounting for similarities in abiotic location in the absence versus presence of other potentially interactions (Peoples & Frimpong, 2016). A comprehensive interacting organisms (Kraft et al., 2015). Such data are often understanding of all nest associations is lacking, and most of difficult and expensive to obtain and impossible to collate at the known associations are limited to Nocomis hosts and more large spatial scales. Our study provides a complementary than 30 cyprinid nest associates (Peoples et al., 2015). This pre- traits-based approach that could be readily applied to large cludes a continental-scale assessment of the role of facilitation datasets of different taxonomies, making it particularly useful in community assembly. However, we hypothesize that the for comparative investigations over biogeographical scales. probable small number of nest associations, the lack of host specificity (Pendleton et al., 2012) and the ability to switch ACKNOWLEDGEMENTS between nest and broadcast spawning (Johnston & Page, 1992) We extend our sincere gratitude to all the individuals and in many known nest associates suggests a more limited role for agencies that shared their fish datasets, and applaud their col- facilitation in assembling entire fish communities. This topic lective hard work, collaborative generosity and foresight. We deserves further investigation. thank Lise Comte for comments on the manuscript. We also We showed some discrepancies between the FF and FE thank Simon Blanchet, two anonymous referees and the han- null models in their assessment of whether communities are dling editor for helping to improve the paper. Financial sup- randomly or non-randomly structured. The FE null model port was provided by a H. Mason Keeler Endowed tended to categorize species pairs as being more aggregated Professorship (School of Aquatic and Fishery Sciences, Uni- than the FF null model. Further simulation studies are versity of Washington) to J.D.O. required to examine why relieving the constraint on site rich- ness resulted in a lower null or ‘baseline’ level of species REFERENCES aggregation. 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Webb, C.O., Ackerly, D.D., McPeek, M.A. & Donoghue, M.J. Table S1 Classification of species pairs across all watersheds. (2011) Advances, challenges and a developing synthesis of Table S2 Final predictive model of species segregation in the ecological community assembly theory. Philosophical Trans- analysis including species pairs present in 4 to N – 4 sites. actions of the Royal Society B: Biological Sciences, 366, Appendix S1 Data sources for the fish community dataset. 2403–2413. Winston, M.R. (1995) Co-occurrence of morphologically BIOSKETCHES similar species of stream fishes. The American Naturalist, Xingli Giam is a post-doctoral research associate in 145, 527–545. the Freshwater Ecology and Conservation Laboratory at the University of Washington. His research focuses SUPPORTING INFORMATION on characterizing and mitigating anthropogenic Additional supporting information may be found in the impacts on the environment as well as elucidating online version of this article at the publisher’s web-site. large-scale biodiversity patterns, particularly in fresh- water ecosystems. Methods S1 Analysis of sampling completeness. Figure S1 Watersheds with random and non-randomly structured fish communities when null models are applied to Julian Olden is an Associate Professor who enjoys species pairs present in 4 to N – 4 sites. studying and squeezing fish, not necessarily in that Figure S2 Relationship between ENV, COMP, PRED and the order. degree of species segregation when null models are applied to species pairs present in 4 to N – 4 sites. Editor: Fabien Leprieur

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