Environmental and spatial processes: what controls the functional structure of fish assemblages in tropical rivers and headwater streams?

Environmental and spatial processes: whatcontrols the functional structure of fishassemblages in tropical rivers and headwaterstreams?Rodrigo A. Carvalho1, Francisco L. Tejerina-Garro21Departamento de Ecologia, Programa de P�os-graduac�~ao em Ecologia e Evoluc�~ao, Universidade Federal de Goi�as, Instituto de Ciencias Biol�ogicas, RodoviaGoiania-Ner�opolis km 5, Campus II Itatiaia, Goiania, 74001-970, GO Brasil2Centro de Biologia Aqu�atica, Pontif�ıcia Universidade Cat�olica de Goi�as – Campus II, Av. Engler s/n, Jardim Mariliza, Goiania, 74605-010, GO Brasil

Accepted for publication April 27, 2014

Abstract – In this study, we investigated functional structure patterns of tropical headwater and river fishassemblages. We hypothesised that environmental conditions are primarily structuring headwater streams leading tofunctionally clustered assemblages, whereas processes that favour functional overdispersion would guide riverassemblages. For 27 headwater streams and 22 rivers, we used eight functional traits for calculating two functionalindexes: mean pairwise distance (MPD) and net relatedness index (NRI). We performed linear regressions betweenindexes and species richness, a multiple regression between NRI and eight environmental variables and a variationpartitioning to disentangle the role of environment and space on NRI. Our findings indicate that fish assemblages ofheadwaters are structured by environmental conditions as most assemblages in this habitat displayed a tendency toclustering and MPD/NRI were not correlated with species diversity, whereas the opposite pattern was observed forriver habitat. Four environmental variables (channel depth, water velocity, dissolved oxygen and turbidity) explain56% of functional structure variation. These variables seem to function as selective filters in headwaters, whereaschannel depth may be determinant for functional overdispersion of river fish assemblages. Components associatedwith space are also influencing the functional structure. Limitations of species dispersal through space (betweenboth habitat types) appear as a possible cause to this. In this sense, both environmental conditions and processeslinked with space are capable of influencing the functional structure of tropical headwater streams and river fishassemblages.

Key words: Central Brazil; mean pairwise distance; net relatedness index; Tocantins-Araguaia basin; variation partitioning

Introduction

A central issue of ecological research relies on under-standing which processes underlie the diversity pat-terns of biological communities. A wide range ofexplanations has been proposed to explain these pat-terns in aquatic systems, including abiotic factors(Poff & Allan 1995; Peres-Neto 2004; Hoeinghauset al. 2007; S�uarez et al. 2007) and biotic interactionsamong species (Winston 1995; Taylor 1996, 1997).The co-occurrence of species and their persistence,

independent of their traits and/or interactions, wouldbe another possibility as proposed by the neutral the-ory of Hubbell (2001). It has been demonstrated thatall three processes may act at the same time (Helmuset al. 2007) or sequentially – in the case of environ-mental gradients – to determine diversity patterns(Mason et al. 2007). Therefore, the current challengeno longer seems to be finding a valid mechanism butcomprehending which one most strongly influenceson local community organisation (Mouchet et al.2010). For freshwater fish assemblages, some studies

Correspondence: Rodrigo A. Carvalho, Departamento de Ecologia, Universidade Federal de Goi�as – UFG, Caixa Postal 131, CEP, Goiania 74001-970, GO, Brasil.E-mail: [email protected]

doi: 10.1111/eff.12152 1

Ecology of Freshwater Fish 2014 � 2014 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd

ECOLOGY OFFRESHWATER FISH

investigated such mechanisms and different findingshave been presented. Poff & Allan (1995) found thatthe presence of fish species with generalist or specialistfeeding strategies is directly associated with hydrologi-cal variability of streams. Peres-Neto (2004) showedthat habitat features are of greater importance for deter-mining species co-occurrence than species interactions.On the other hand, Winston (1995) demonstrated thatcompetition is a powerful force in determining speciesco-existence and Gilliam & Fraser (2001) found outthat predation threat is capable of influencing themovement of fish species along streams.Traditionally, environmental conditions have been

used to explain the variation of diversity patterns inbiological communities (Sharma et al. 2011). Therole of environmental filters on freshwater fishassemblages was explored by Smith & Powell (1971)who proposed the filter model, which was later modi-fied to include species traits (Poff 1997). Accordingto this model, fish species from the regional poolmust pass through a series of environmental filters toestablish themselves locally (Tonn et al. 1990).Therefore, species from a local fish assemblage areexpected to respond similarly to environmental fac-tors and probably share many traits (Mouillot et al.2007). In this context, environmental filters can bedeterminant factors in driving the composition oflocal fish assemblages.Despite the role of environment in structuring fish

assemblages, ecologists have found evidence that thespatial structure of fish assemblages is also a result ofspatial patterns. Sharma et al. (2011) found out thatassemblage composition of native and non-native fishspecies in temperate lakes is driven by different eco-logical rules. While native fish assemblages are struc-tured by environmental conditions suggestingenvironmental filtering, non-native fish assemblagesare structured by human-mediated dispersal. Assess-ing the role of environmental and spatial componentsthrough the use of variation partitioning (Borcardet al. 1992; Peres-Neto & Legendre 2010) combinedwith spatial statistical techniques [Moran’s Eigenvec-tors Maps (MEM) Dray et al. 2006; AsymmetricEigenvectors Maps (AEM) Blanchet et al. 2008] isnow recognised as a key step for understanding theecological processes that rule community composi-tion patterns.In freshwater systems, headwater streams can be

considered as the most upstream areas in a river sys-tem, and they are generally classified from first orderup to third order (Vannote et al. 1980). Abiotic fac-tors in headwaters – such as water velocity, substratetype, dissolved oxygen, water temperature and trans-port of particulate organic matter (Poff 1997) – maypredominate over biotic interactions in structuringlocal fish assemblages (Ostrand & Wilde 2002;

Grenouillet et al. 2004). Due to high variability intheir environment, these low-order streams frequentlysupport low-diversity and less-structured assemblages(Jackson et al. 2001) and a few hardy species tend topersist (Rahel & Hubert 1991). On the other hand,there is an increase in habitat diversity and environ-mental stability further downstream along fluvial gra-dients (Meffe & Minkley 1987; Jackson et al. 2001).This fact frequently allows the persistence of fishassemblages with a higher number of species (Mat-thews 1986). Given the potential influence of abioticfactors as selective filters at headwater streams andthe higher stability of downstream areas (high-orderrivers), it is possible to derive predictions about thefunctional structure of fish assemblages in these habi-tat types. In headwater streams, where environmentalfiltering seems to be more preponderant, and fishassemblages are composed of few hardy species, weexpect the coexistence of functionally similar species(functional clustering). By contrast, we may expectthe coexistence of more functionally dissimilar spe-cies (functional overdispersion) in downstream areas,where the environment is more stable and diverse,and fish assemblages tend to be richer.In this study, we used a set of tropical headwater

streams and rivers to understand functional diversityand functional structure patterns of their fish assem-blages, trying to elucidate which ecological processesdrive their functional structure patterns. First, weinvestigated how the functional diversity and thefunctional structure of fish assemblages are related tospecies diversity in both habitat types (headwaterstreams and rivers). After that, we evaluated how thevariation of fish assemblages’ functional structure isrelated to different environmental variables. Finally,we explored the variation of fish assemblages’ func-tional structure between headwater streams and riv-ers. We hypothesise that headwater streams areprimarily structured by environmental variables, lead-ing to functionally clustered assemblages, whereasrivers would be less influenced by the environmentand would be structured by processes that favour theexistence of functionally overdispersed assemblages,such as competition or species dispersal ability. Here,we tested how much of the variation in the functionalstructure of fish assemblages is explained by multiplecomponents, namely environment, spatially structuredenvironment and space. This kind of approach allowsus to identify which processes are more likely tostructure fish assemblages, as the spatial componentand the spatially structured environment variation areassumed to express the importance of neutral dynam-ics (Gilbert & Lechowicz 2004). On the other side,the environmental component may express the occur-rence of important niche-based processes, such asenvironmental filtering.

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Carvalho & Tejerina-Garro

Material and methods

Study area

We conducted our study in the Tocantins-AraguaiaRiver Basin (hereafter called Tocantins-AraguaiaBasin) that drains Goi�as State territory, Central Bra-zil. The Tocantins-Araguaia Basin has a drainagearea of approximately 767,000 km2 with a meanannual discharge of 11,000 m³�s�1 (Costa et al.2003). It has a well-defined dry season that occursbetween May and October and also a wet season thatextends between November and April (Albrecht &Pellegrini-Caramaschi 2003; Quesada et al. 2004). Inthis region, the knowledge about aquatic communitiesand its biodiversity is still very incipient (Tejerina-Garro 2008).

Data and sampling protocol

In this paper, we used a data set relative to 27 head-water streams and 22 rivers located in the Tocantins-Araguaia Basin (Fig. 1) at an average altitude of 450and 390 m, respectively. We defined a headwaterstream by its location in the river system, order(1st–3rd order; Vannote et al. 1980) and drainagearea (10 < drainage area < 1000 km²; Meybecket al. 1996). We defined a watercourse as a riverby its location in the river system, order (4th–6thorder) and drainage area (1000 < drainage area <100,000 km2; Meybeck et al. 1996). We determinedheadwater stream and river orders using Strahler’s

modification of Horton’s scale (Petts 1994), and wechecked it using a geographical information systemmap (1:250,000) available on the website of theSistema Estadual de Estat�ıstica e de Informac�~aoGeogr�afica de Goi�as (SIEG 2013).All headwater streams and rivers were sampled

during the dry season when the flows are low andfishes are captured more efficiently (Pease et al.2012). Furthermore, the relationship between fishassemblages’ structure and habitat structure in dryseason is stronger (Willis et al. 2005). All samplingswere conducted between May and September 2008,and the location of the sites was defined according tothe possibility of access. Sampling locations weremarked with an equipment of Global Position System(GPS; Garmin 12).Fish species from headwater streams were sampled

with a seine net (4 m 9 1.5 m 9 1 cm of meshbetween opposing knots), while species from riverswere collected with gillnets of different sizes ofopposing knots (10 m 9 1.5 m 9 12–70 mm).Seine nets are an efficient sampling technique for col-lecting fish fauna when the focus is on ecologicalpatterns of species richness and composition (Medei-ros et al. 2010), but the presence of rocks and woodydebris in the bottom substrate, as found in rivers,often limits the application of this method for captur-ing fish species (Tejerina-Garro & M�erona 2000).These authors indicate also that the use of differentmesh size of gillnets in repeated batteries is a usefulmethod that gives a reliable picture from the fractionof the fish community sampled in rivers.

Fig. 1. Spatial distribution of headwater streams (circles) and rivers (triangles) sampled in the upper section of the Tocantins-AraguaiaRiver Basin, Central Brazil. The Tocantins-Araguaia basin is represented in the small map.

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Functional structure of rivers and streams

For each headwater stream, a reach of 50 m wasdefined following Imhof et al. (1996), and the sam-pling effort was two persons per 50 m per 10 foot-steps. For rivers, the reach size was delimited as1000 m. All specimens collected were stored in iden-tified plastic bags and were put in plastic drums withformaldehyde 10%. The specimens were separatedand identified using taxonomic keys and expert opin-ion. Finally, specimens were stored in alcohol 70% atthe Aquatic Biology Center of the Pontif�ıciaUniversidade Cat�olica de Goi�as and the Laboratoryof Ichthyology of the Museum of Pontif�ıcia Univer-sidade Cat�olica do Rio Grande do Sul.

Data matrices

To perform the analyses in this paper, we used fourdata matrices. First, we constructed a presence–absence matrix (species vs. local sites) and a func-tional traits matrix (species vs. traits). Although wehad species abundance, this kind of data can be rela-tively sensitive to different sampling techniques andeffort (Poff & Allan 1995). Therefore, to avoid anybias generated by different sampling techniques usedin headwater and river habitats, we did not includeabundance data in our analyses. We used eight traitsto construct our functional traits matrix: average bodymass, trophic guild, water column position, parentalcare, foraging location, substrate preference, foragingmethod and migration (Tables S1 and S2). Body masswas obtained directly from collected fish specimens,and it was calculated as the average body mass foundfor all specimens of the species collected. Trophicguild was determined according to information pres-ent in Melo (2011). This author identified the maincomponent of diet for each species and then classifiedit in one of the following categories: algivorous,omnivorous, carnivorous, herbivorous, detritivorous,invertivorous and iliophagous. For species withoutinformation in Melo (2011), we searched for the maincomponent of species’ diet using an online database(FishBase; Froese & Pauly 2014) and specialised liter-ature. Data for parental care, water column position,foraging method and migration were taken from spec-ialised literature and an online database for fishes(FishBase; Froese & Pauly 2014; see Table S1 formore details of traits categories). To determine sub-strate preference, we looked for all locations wherethe species were collected and identified local sub-strate(s). Then, in the species traits matrix, we markedall substrates where the species has occurred. Forag-ing location was determined according to water velo-city. Watercourses with water velocity > 20 cm�s�1

were considered as running waters (riffles and runs),while those under this limit were considered as stand-ing waters or slower pools (see Rezende 2007). When

we were not able to find information for a species, weextrapolated the data for genus or family level when-ever it was possible. If extrapolation was not possible(a single species in the genus), species trait was con-sidered as not available (NA).Afterwards, we constructed an environmental vari-

ables matrix (local sites vs. environmental variables)with altitude (m), channel depth (m), water velocity(cm�s�1), channel width (m), turbidity (NTU),dissolved oxygen (mg�l�1), pH, conductivity(lS�cm�1) and water temperature (°C). In each reach,three transversal transects were delimited where eachenvironmental variable could be measured. Altitudewas measured only in the first transect with a GPS(Garmin 12). In all transects, the following variableswere measured: channel width with a proper deviceto measure the distance (Bushnell Yardage Pro 500,Bushnell Company, Lenexa, KS, USA, for large riv-ers), dissolved oxygen and temperature with an oxi-meter (Lutron YK-22DO, Lutron Eletronic EntrepriseCo., Ltd., Taipei, Taiwan), pH with a pH meter(Lutron PH-208, Lutron Eletronic Enterprise Co.,Ltd., Taipei, Taiwan), conductivity with a conductiv-ity meter (WTW 315i, Xylem Group, White Plains,NY, USA), water velocity with a flowmeter (Flow-meter Model 2030, General Oceanics Inc., Miami,FL, USA) and channel depth with a measuring rope.Turbidity was measured only in the middle transectof headwater streams and all transects of rivers, bothwith a digital turbidimeter (LaMotte 2020, LaMotteCompany, Cherstertown, MD, USA). Each variablewas measured in one location inside the transect.Finally, we constructed a site-by-edge matrix (local

vs. link edges) that indicates the links among sitesaccording to a directional processes (for more detailssee Blanchet et al. 2008). We built our sites-by-edgesmatrix according to the direction of the flow(upstream–downstream) and considering that thisflow direction is used by fish species for larval dis-persion (Agostinho et al. 2007).

Analyses

For each fish assemblage, we calculated an index offunctional diversity using the mean pairwise distance(henceforth MPD, Webb et al. 2002). Despite itsusual use in the calculation of phylogenetic diversity,a phylogeny and a functional dendrogram have simi-lar structures and properties (Pavoine & Bonsall2010); therefore, this measure can be used to calcu-late functional diversity substituting the phylogeneticdistance matrix by a functional distance matrix (Kem-bel et al. 2010; Pavoine & Bonsall 2010). Then, forcalculating functional diversity using MPD, wesubstituted the phylogenetic distance matrix by afunctional distance matrix in the analysis (Kembel

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et al. 2010; Pavoine & Bonsall 2010) and used thefollowing protocol: (i) conversion of species traitsmatrix to a distance matrix, (ii) clustering distancematrix into a functional dendrogram, (iii) transform-ing the functional dendrogram into a phylogeny, thatis, converting it into an object with phylogeneticproperties, (iv) extracting distance matrix (‘cophenet-ic matrix’) and (v) calculating functional diversity asa measure of mean functional distances among alltaxa that occur in a sample. We obtained the distancematrix using a modification of Gower’s distance aswe had both qualitative and quantitative traitstogether (Pavoine et al. 2009) and the functional den-drogram using the unweighted pair group methodwith arithmetic averages (UPGMA) as the clusteringmethod.For each fish assemblage, we also calculated an

index of community functional structure using the netrelatedness index (henceforth NRI; Webb 2000;Webb et al. 2002). The NRI quantifies the distribu-tion of taxa in a sample relative to a species pool(Webb 2000) and gives the standardised effect of theobserved distances among species (Kembel &Hubbell 2006). The NRI for each locality can bemeasured as �1[(MPD � MPDnull)/SDMPDnull ], wherethe MPD is the observed mean pairwise distanceamong species in the locality, MPDnull is the meanMPD for that locality in 999 null communities andSDMPDnull is the standard deviation of MPD for thatlocality in 999 null communities (Webb et al. 2002).This index is calculated by measuring the mean dis-tance of each species to all other species in the samecommunity. Positive NRI values (P < 0.05) indicatethat co-occurring species in a community are morefunctionally similar than expected by chance (func-tional clustering), while negative values (P > 0.95)point to species less functionally similar thanexpected (Kembel & Hubbell 2006).To obtain the NRI, we used the functional traits

matrix to calculate a distance matrix with a modifica-tion of Gower’s distance (Pavoine et al. 2009) andUPGMA to develop the functional dendrogram. Here,we used the same approach for MPD to convert thefunctional dendrogram into a phylogeny and extractthe ‘cophenetic’ matrix. Based on the ‘cophenetic’matrix, we utilised the function ses.mpd (Kembelet al. 2010) available in R software (R Core Team2013) to create a series of null communities for eachfish assemblage. Then, the observed degree of clus-tering was compared with those expected by chance.We generated a set of 999 null communities for

each fish assemblage using the independent swapalgorithm (Gotelli & Entsminger 2001). This algo-rithm randomises the community data matrix main-taining constant species occurrence frequency andspecies richness of each community. In the end, each

null community has a NRI value, and all 999 valueswill be used to create a null distribution of NRI val-ues expected at random. Then, the observed NRIvalue can be compared with the null distribution. Weconsidered as our regional pool the set of fish speciesfound in all rivers and streams sampled.We performed two simple linear regressions for

each habitat type to examine the relationship of spe-cies diversity with functional diversity and commu-nity functional structure: (i) species richness versusMPD and (ii) species richness versus NRI. MPD andNRI were considered as response variables, while Swas the predictor variable. All analyses were per-formed in the R software (R Core Team 2013).We performed a multiple linear regression between

NRI and environmental variables to explore whetherenvironmental features are predicting the variation inthe functional structure among sites. We used a for-ward stepwise selection of the environmental vari-ables to reduce the cases of co-linearity (Oberdorffet al. 1995). We ran the multiple linear regression inR software (R Core Team 2013).To test the contribution of the environment and

space for NRI variation, we performed a variationpartitioning analysis (Borcard et al. 1992; Peres-Neto& Legendre 2010) using spatial descriptors and envi-ronmental variables matrices as predictors and theNRI values as our response variable. To obtain thespatial descriptors, we used the AEM method pro-posed by Blanchet et al. (2008). It takes into accountthe distance among watercourses and the direction ofthe flow to represent dispersal patterns among sam-pled sites. Based on our sites-by-edges matrix andthe spatial coordinates of sampled sites, we created aconnexion diagram linking the sites to one anotheraccording to the asymmetric processes influencingour response variable. Then, the connexion diagramwas used to calculate the spatial descriptors. Giventhe different nature and scales of our environmentalvariables, we performed a log-transformation toincrease linearity and avoid extreme values (Sharmaet al. 2011), excepting for pH values.When a single variable is used as a predictor, the

partition of the variance is based on a linear regres-sion, and it uses the adjusted R2 to estimate theunique and combined effects of environmental andspatial predictors (Borcard et al. 1992; Peres-Neto &Legendre 2010). The adjusted R2 values are capableto provide unbiased estimates on the real contributionof explanatory variables to the response variable (Oh-tani 2000; Peres-Neto et al. 2006). All these analyseswere performed in R environment (R Core Team2013) using AEM package for Asymmetric Eigen-vectors Maps (Blanchet & Legendre 2013) and var-part function present in vegan package for partitionanalysis (Oksanen et al. 2013).

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Results

Fish assemblages from headwater streams and riv-ers contained 150 fish species. Of these, 121 spe-cies were found in rivers and 72 in headwaterstreams. Thirty-one species occurred only in rivers,while 12 species only in headwater streams. Themean average of species per local site was 11.75species considering both rivers and headwaterstreams. Taking into account only rivers, the meanaverage of species per local site was 14, while themean average for headwater streams was 9.9 spe-cies per local site.The spatial patterns of functional grouping found

in this study showed that most rivers evaluated hadnegative NRI values (18 out of 22), whereas most ofheadwater streams (25 out of 27) had positive NRIvalues (Fig. 2 and Table S3). According to our nullmodel, eight of the 22 rivers (36.4%) presented a sig-nificant functional grouping (P < 0.05), and six ofthem (27.3%) had fish assemblages with functionaloverdispersion and two (9.1%) with functional clus-tering (Table S3). On the other hand, 10 of the 27headwater streams (34.5%) presented a significantfunctional grouping (P < 0.05), with functional clus-tering of fish assemblages (Table S3).For rivers, linear regressions showed that S is not

correlated with MPD (Fig. 3a; adjusted R2 = 0.30,P = 0.16), but it is negatively correlated with NRI(Fig. 3b; adjusted R2 = �0.48, P = 0.02). This nega-tive association shows that patterns of functional

overdispersion (lower NRI values) of river fishassemblages are partially explained (48%) by anincrease in species richness suggesting that functionalpatterns in this habitat type may also be associatedwith other factors. For headwater streams, linearregressions indicated that S is not significantly corre-lated with MPD (Fig. 3c; adjusted R2 = 0.23,P = 0.22) or NRI (Fig. 3d; adjusted R2 = 0.20,P = 0.30).The multiple regression demonstrated that environ-

mental variables explain 56% of the total variation inNRI values (Table 1; adjusted R2 = 0.56,P = 0.0001). Based on the forward stepwise selec-tion, four environmental variables were indicated asinfluencing NRI variation: channel depth, watervelocity, dissolved oxygen and turbidity (Table 1).Variation partitioning revealed that neither the

local environment alone (fraction a, Table 2) nor thespatially structured environment (fraction b, Table 2)is the best predictor to explain NRI variation. Thespace alone presented the higher fraction of explana-tion for NRI variation (fraction c, Table 2), but theunexplained fraction of variation was also high(fraction d, Table 2).

Discussion

Despite some recent efforts (Vill�eger et al. 2010,2012; Pease et al. 2012), functional diversity andfunctional structure of tropical fish assemblagesremain poorly explored. For headwater streams, our

Fig. 2. Spatial pattern of the NRI values of headwater streams (circles) and rivers (triangles) sampled in the Tocantins-Araguaia basin. Col-ours of the circles and triangles correspond to significant (grey) and nonsignificant (white) cases of functional grouping at a 0.05 levelunder the null model.

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findings revealed that neither functional diversity norfunctional structure of their fish assemblages issignificantly correlated with species diversity. Fur-

thermore, functional structure patterns showed that34.5% of the headwater streams sampled had a sig-nificant functional clustering, but almost all of thestreams exhibited positive raw values for NRI indi-cating a tendency of fish assemblages to clustering.Taking into account that headwater streams habitatsare highly affected by local environmental conditions(Ostrand & Wilde 2002; Grenouillet et al. 2004),such as channel depth and water velocity (S�uarezet al. 2007), it seems quite reasonable to think thatenvironmental conditions drive the functional compo-sition patterns of local fish assemblages in this study.If environmental conditions are leading to function-ally clustered assemblages, then they are probablyacting as filters to fish species traits. In this case,even assemblages with more species would be com-posed of functionally similar species, and an increasein species richness would not be accompanied by anincrease in functional diversity or functional structurevalues. For rivers, functional trends were opposite tothose observed in headwaters streams. Although only27.3% of the rivers sampled displayed fish assem-blages with significant functional overdispersion,almost all of them presented negative raw values forNRI suggesting a tendency to overdispersion. This

(a) (b)

(c) (d)

0.70

0.60

0.55

0.50

0.45

0.40

0.35

10 15 20 25 30 3550

10 15 20 25

R2 adj = –0.48; p = 0.02

R2 adj = 0.30; p = 0.16R2 adj = 0.23; p = 0.22

R2 adj = 0.20; p = 0.30

30 355

NR

IM

PD

10 15SS

S S

20 25 30 35 40 4550

10 15 20 25 30 35 40 4550

6

0

5

4

3

0

–1

1

2

0.8Rivers

0.7

0.6

0.5

0.2

0.3

0.4

6

7

5

4

3

0

–1

–2

–3

1

2

0.30

0.65

Headwater streams

Fig. 3. Linear regression results between species richness and two functional indexes: MPD (a–b) and NRI (c–d). Linear regressions wereperformed for headwater (a–c) and river (b–d) fish assemblages. R2 adj refers to adjusted R2 of the linear regression.

Table 1. Statistics of the multiple linear regression between the functionalstructure index (NRI) and environmental variables (adjusted R² = 0.56,P < 0000).

Functionalstructure index

Variable entered in the model(sign of the effect) P N

NRI Channel depth (�) 0.0002 49Water velocity (+) 0.0000 49Dissolved oxygen (�) 0.0005 49Turbidity (�) 0.0010 49

The sign of the effect indicates a positive (+) or negative (�) effect of thevariable on the functional structure index.

Table 2. Results of variation partitioning on NRI values.

a b c d

Fraction (%) 12.9 17.5 34.9 34.7

Fractions correspond to the variation explained by environment alone (a);environment spatially structured (b); space alone (c); and residuals (d).Fractions are based on adjusted R squares (R2 adj) of each component.

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trend was partially explained by species diversity,thus rivers with a higher number of species tend topresent a higher degree of functional overdispersion.In general, habitat diversity and stability tend toincrease further downstream reaches (Meffe & Mink-ley 1987; Jackson et al. 2001), and greater habitatstructural complexity can be associated with greatermorphological diversity (Willis et al. 2005). There-fore, the functional overdispersion observed in riversmay be a result of the increase in the number of spe-cies with different functional traits due to habitatstructural complexity. Nevertheless, we suggest thatthis proposal should be evaluated more carefully infuture investigations as the average species richnessfound in this study for rivers was not much higherthan in headwaters. Moreover, species richnessexplained only 48% of NRI trends suggesting thatfunctional structure in this type of habitat is probablyaffected by other factors. If both habitat types(streams and rivers) are likely to be affected by dis-tinct ecological factors, the main question here is:Which processes are most strongly affecting theirfunctional structure?Several studies have pointed out a major role of

abiotic factors in shaping fish assemblages’ structure.Peres-Neto (2004) showed that habitat features aremore relevant than species interactions to determinespecies co-occurrence. Poff & Allan (1995) demon-strated that trophic specialist fishes are more associatedwith fish assemblages of stable watercourses, whiletrophic generalists are more numerous at sites withhigher hydrological variability, whereas Hoeinghauset al. (2007) revealed that both habitat type and stabil-ity have a powerful effect on fish assemblages andtheir functional organisation. Our results pointed toan important role of environmental variables on func-tional structure variation in headwater streams andrivers. More specifically, four of the eight variablesconsidered were strongly correlated with patterns infunctional structure: channel depth, dissolved oxygen,turbidity and water velocity. Water velocity and oxy-gen have been presented as possible selective filtersfor species traits (Poff 1997). For headwater streams,channel depth and water velocity are expected toaffect species co-occurrence because they restrictevents of colonisation and influence the persistenceof species in this kind of habitat (S�uarez et al. 2007).Based on this background, we suggest that a potentialecological process that might lead headwater streamfish assemblages to functional clustering is environ-mental filtering, and at least three environmental vari-ables are good candidates to act as selective filtersfor species functional traits: channel depth, watervelocity and dissolved oxygen. According to Poff(1997), there are other environmental conditions thatmay act in the selection of species traits such as: sub-

strate type and mobility, organic matter input, floodintensity, extreme temperatures, etc. Future investiga-tions should explore how the functional structure ofheadwater streams fish assemblages is related to theseenvironmental parameters. For rivers, the associationbetween functional structure and environmental vari-ables seems not to be clear as it is for headwaters.The tendency for functional overdispersion observedin rivers may be a consequence of the increase inspecies traits diversity due to a higher habitat struc-tural complexity (Willis et al. 2005). Given thatchannel depth is usually considered as a good indica-tor of habitat structural complexity (S�uarez et al.2007), we suggest that this variable could also beimportant for determining species co-occurrence inrivers.However, differences between headwater streams

and rivers related to environmental parameters areoften associated to an expressive altitudinal differen-tial. In this study, sampled sites in both habitats havesimilar levels of altitude, suggesting that the relation-ship between the functional structure of fish assem-blages and the environment can be even strongerwhen the altitudinal gradient is more evident. In thiscase, we could expect a higher level of functionalclustering in headwater streams.Despite the significant role of the environment on

the functional structure of headwater streams and riv-ers, it is worth noting that variation partitioningresults indicated a lower contribution of the environ-mental component. Furthermore, both spatial andspatially structured environment components showedsome contribution to functional structure variation.Together, the spatial component and the spatiallystructured environment are supposed to express theinfluence of neutral dynamics on community struc-ture (Gilbert & Lechowicz 2004; Diniz-Filho et al.2012). A neutral process guided by species dispersallimitations through the space is likely to occurbetween headwater and river habitats. For example,several species from rivers are not capable of reach-ing headwaters, whereas headwaters are known tosupport fish species that do not occur anywhere elsein the river system (Paller 1994; Meyer et al. 2007).Moreover, fish assemblages sampled in this study arelocated inside a region that is characterised by theoccurrence of many fish species with restricted spa-tial range (Nogueira et al. 2010), in which dispersionprobably occurs at short distances. Finally, althoughthe focus of this study is on a local scale, the entireset of our sampled sites is distributed between twowatersheds (Tocantins and Araguaia). Even if theyare capable of actively moving throughout differenthabitats (Agostinho & Zalewski 1995), fish speciesoften have limited rates of dispersal between distinctwatersheds, which is imposed by the drainage area

8


itself or shifts in environmental conditions (Matthews& Robison 1988; Jackson et al. 2001). Under thesecircumstances, space also appears to play an impor-tant role for the functional structure of headwaterstreams and river fish assemblages. Limitations ofspatial species dispersion between headwaters andrivers seem to be a potential cause for this. It is worthto highlighting that the lower contribution of theenvironmental component on variation partitioningcan be a consequence of the fact that variables arespatially structured. Local variables as channel depth,water velocity, turbidity and/or dissolved oxygenmay present different levels for headwater streamsand rivers (for example, rivers tend to have a deeperchannel than headwater streams).It is necessary to consider the influence of two pos-

sible biases on the results. First, differences observedbetween rivers and headwater streams may be influ-enced by the different sampling methods used becauseof the distinct remarkable features of the two habitatssampled. This may facilitate plausible fish speciesselectiveness and differences on total number of indi-viduals sampled with potential consequences on spe-cies and/or functional richness. The second bias isassociated with the use of field data for fulfillinginformation about the functional data set, particularlysubstrate preference and foraging location traits, thatmay increase the association between functional indi-ces and the environmental component. However, wecall attention that these are the best data available forthe fish assemblages sampled as inventories of the fishfauna for the sampled region are incomplete (Tejeri-na-Garro 2008) and far from being well documented(Vari 1988; Revenga & Kura 2003), as observed forfreshwater ichthyofauna in other regions of the world(L�eveque et al. 2005; Dudgeon et al. 2006).Our findings suggest that there is a co-existence of

functionally similar species in fish assemblages ofheadwater streams, whereas a co-existence of func-tionally dissimilar species is more common in fishassemblages of rivers. The environment seems toplay an important role in driving these functional pat-terns and environmental filtering emerges as a poten-tial ecological process in determining functionalclustering in headwater habitats. This selective forcewould be guided by, at least, three main environmen-tal variables: channel depth, water velocity and dis-solved oxygen. For rivers, we argued that habitatstructural complexity might be the environmentalcondition driving the co-existence of dissimilar spe-cies as heterogeneous habitats may support specieswith different traits. Nevertheless, our results revealedthat spatial components also play a key role in deter-mining the functional trends in both habitat types.We suggest that a neutral dynamic process linked tospecies dispersal limitations between headwater

streams and rivers may be the explanation, althoughthe spatial structure of environmental variables mayaffect these patterns too. In this sense, the functionalstructure patterns observed for headwater streams andrivers are probably structured by both environmentand space. Finally, we found, excepting for a partialcontribution in the functional structure of riverassemblages, that species diversity is not related tofunctional diversity and functional structure trends inboth headwater stream and river fish assemblages.These findings are relevant for conservation of thesesystems. If sites with high species diversity do notharbour high functional diversity, the use of a conser-vation strategy based on a single aspect of diversityas a cure-all should be avoided.This study opens the possibility for further and

challenging investigations, such as: (i) Are thereother environmental variables that may act as selec-tive filters in headwater streams? (ii) Are environ-mental variables selecting the same functional traitsin different fish assemblages, that is, are fish assem-blages with functional clustering occupying the sameregion of the functional traits space? (iii) Which func-tional traits are more related to these selective envi-ronmental variables? (iv) Which traits are morerelated to the habitat structural complexity of rivers?(v) How the altitudinal gradient may influence func-tional patterns between headwater and river habitats?Answering these questions will help us to understandmore completely how different processes are actingon fish assemblages and how species traits are relatedto them in both freshwater habitats.

Acknowledgements

We are grateful to all students, technicians and researchersfrom Centro de Biologia Aqu�atica (CBA) that helped duringthe fieldwork, especially Waldeir Francisco de Menezes. Wealso thank Dr. Frabricio Barreto Teresa for helping us withrelevant suggestions in earlier versions of the manuscript; Dr.F. Guillaume Blanchet who helped with the understanding ofthe AEM framework and the construction of some R func-tions; and Dr. Thiago Santos for several debates and ideasabout spatial analysis and R software. We thank to researchersfrom Pontif�ıcia Universidade Cat�olica do Rio Grande do Sulwho assisted in species identification. We thank anonymousreviewers for their contributions to improve the initial draft ofthis study. To the National Council of Technological and Sci-entific Development – CNPq for the financial support givenfor the project (CNPq No. 471283/2006-1). RAC research issupported with a scholarship by Coordenac�~ao de Aperfeic�oa-mento de Pessoal de N�ıvel Superior – CAPES.

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Supporting Information

Additional Supporting Information may be found inthe online version of this article:Table S1. Fish traits used to calculate mean pair-

wise distance (MPD) index of fish assemblageslocated in headwater streams and rivers, Tocantins-A-raguaia River Basin, Brazil.Table S2. Description of the eight functional traits

and their respective categories used to calculate func-tional diversity (MPD) and functional structure (NRI)of headwater stream and river fish assemblages.Table S3. Results found during NRI calculation

via null model approach for fish assemblages ofheadwaters streams and rivers of the Tocantins-Ara-guaia basin.

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Documents

Environmental and spatial processes: what controls the functional structure of fish assemblages in tropical rivers and headwater streams?