do metacommunity theories explain spatial variation in fish … · 2019. 4. 17. · disturbance...

Freshwater Biology 201964367ndash379 wileyonlinelibrarycomjournalfwb emsp|emsp367copy 2018 John Wiley amp Sons Ltd

Received14June2018emsp |emsp Revised15October2018emsp |emsp Accepted31October2018DOI101111fwb13229

O R I G I N A L A R T I C L E

Do metacommunity theories explain spatial variation in fish assemblage structure in a pristine tropical river

Edwin O Loacutepez-Delgado12 emsp|emspKirk O Winemiller1emsp|emspFrancisco A Villa-Navarro2

1DepartmentofWildlifeandFisheriesSciencesTexasAampMUniversityCollegeStationTexas2GrupodeInvestigacioacutenenZoologiacuteaFacultaddeCienciasUniversidaddelTolimaIbagueacuteTolimaColombia

CorrespondenceEdwinOLoacutepez-DelgadoDepartmentofWildlifeandFisheriesSciencesTexasAampMUniversityCollegeStationTXEmaileolopezdtamuedu

Funding informationWorldWildlifeFoundation(WWF)ColombiaFundacioacutenOmachaInstitutodeInvestigacioacutendeRecursosBioloacutegicosAlexandervonHumboldtandUniversidaddelTolima

Abstract1 UnderstandingprocessesdrivingpatternsofspeciesdistributionanddiversityisoneofthemainobjectivesofcommunityecologyTherehasbeenagrowingrec-ognitionthat localenvironmentalconditionsarenottheonlyfactorstructuringecologicalcommunitiesand that large-scalespatialvariationanddispersalalsohavemajorinfluences

2 TheaimofourstudywastoevaluatespatialvariationinfishassemblagestructurealongthelongitudinalfluvialgradientoftheBitaRiveranearlypristinetributaryoftheOrinocoRiverintheLlanosregionofColombiaStandardisedsurveyscon-ductedat34sitesthroughoutthebasinduringthelowwaterperiodinJanuaryandMarch2016yielded25928fishspecimensrepresenting201speciesTwenty-sevenenvironmentalvariableswererecordedateachsiteandasymmetriceigen-vectormapswereusedtomodelspatialvariables

3 Tounderstandspatialvariation in local fishassemblagesandtheir relationshipswiththeenvironmentalandspatialvariablestwoapproacheswereusedFirstweappliedtheelementsofmetacommunitystructureframeworkfollowedbyavari-ation decomposition analysis that allowed themetacommunity to be classifiedaccordingtosixalternativepatternsofspeciesdistributionandfouralternativemetacommunityparadigms

4 Wehypothesisedthatatabasinscaleamajorfractionofvariationinstructureisexplainedbyapureenvironmentaleffectandmetacommunitypatterns shouldrevealaClementsiandistributionAtamoreregionalscale(localitieswithinariversection)assemblagesinupstreamanddownstreamregionsmayreflectdifferentmetacommunityprocessesBecauseheadwaterstreamsare isolatedwithin theriver network they should receive fewermigrants andmay have local assem-blagesstronglyinfluencedbylocalenvironmentalconditionsandspeciessortingwith one of three possible distributional patterns (ClementsianGleasonian orevenlyspaced)wouldbeobservedConverselydownstreamsitescloser to therivermouthshouldbeinfluencedbyhighdispersalresultinginagreaterimpor-tanceofspatialfactorsandthemasseffectwithmetacommunitypatternsnestedalongthelongitudinalgradient

5 Our results suggest that the fish metacommunity in the Bita River exhibits aClementsiandistributionimplyingthatspeciesrespondtotheenvironmentalandfluvial gradient as groups along the longitudinal gradient These replacements

368emsp |emsp emspensp LOacutePEZ- DELGADO Et AL

1emsp |emspINTRODUC TION

Thespatialdistributionsof speciesand resultantdiversityof localassemblages are affected by multiple factors operating at differ-ent temporal and spatial scales (Pease Gonzaacutelez-Diacuteaz Rodiles-Hernaacutendez ampWinemiller 2012 Presley Higgins ampWillig 2010)andunderstandinghowthese factors influencepatternshasbeenthe main objective in community ecology (Landeiro MagnussonMeloEspiacuterito-SantoampBini2011)Streamecologistshavebeenpar-ticularly active in research addressing relationships of assemblagepatterns with abiotic (Mouillot Dumay amp Tomasini 2007) biotic(MacarthurampLevins1967)spatial(Peres-NetoampLegendre2010)and stochastic influences (Chase Kraft Smith Vellend amp Inouye2011)Thesefactorscanactindependentlysequentiallyorsimulta-neouslyandofteninteract(CarvalhoampTejerina-Garro2015)

Until recentlymost field studieshave focusedonassociationsbetweenlocalspeciesassemblagesandlocalenvironmentalfactorsDuringthepastdecadetherehasbeenarapidincreaseinresearchshowinghow local communities are influencedbydispersal of or-ganismsacrossvarious spatial scalesand involvingecologicalpro-cesses suchashabitat selectionand responses todisturbancesorcompetition (Erős 2017 Erős Takaacutecs Specziaacuter Schmera amp Saacutely2017LeiboldampMikkelson2002Leiboldetal2004Presleyetal2010)

Metacommunitytheoryexploreshowlocalinteractionsandre-gionalprocesses(dispersalsub-populationextirpation)shapecom-munity structureThewordmetacommunity isused todescribeanetworkof localcommunitiesof interactingpopulations linkedviadispersal Themetacommunityparadigmhas stimulated investiga-tiononhowpopulations interactatregionalaswellas localscales(LogueMouquetPeterampHillebrand2011)At least fourmodelsofmetacommunitydynamicshavebeenproposedeachdefinedbytherelativeinfluencesofdispersalenvironmentalfilteringhabitatselection habitat disturbance biotic interactions and stochasticfactors(Leiboldetal2004)

Three statistical approaches have been developed to studymetacommunitiesTheseareelementsofmetacommunitystructure(EMS)variationpartitioningandzero-summultinomialdistribution(Logueetal2011MonteiroPaivaampPeres-Neto2017)Thecur-rentstudyadoptsthefirsttwoapproachestotestmetacommunitytheoriesbyanalysingspatialvariationinfishassemblagesinapris-tineneotropicalriver

TheEMSmethod(LeiboldampMikkelson2002)analysesspeciesdistributionpatternsalongenvironmentalgradientstorevealmul-tipleEMSEMSusesanorderedsites-by-speciesincidencematrixby reciprocal averaging (RA) to evaluate assemblage coherenceturnover and boundary clumping The method can distinguishamongsixpossiblepatternsofspeciesdistributioncheckerboard(nonrandom patterns of species co-occurrence) Clementsian(clumps of co-occurring species distributed along spatial gradi-ents)Gleasonian(independentspeciesdistributionsalongspatialgradients) evenly spaced (hyperdispersed species distributions)nestedsubsetsandrandom(LeiboldampMikkelson2002Presleyetal 2010)Because the focal unitsof analysis are speciesdis-tributions rather than assemblage structure among sites thisapproach allows testingof hypotheses about competitive exclu-sion(negativecoherencendashcheckerboarddistribution)randomas-semblies (non-significantcoherence)andenvironmental filtering(positive coherencendashClementsian and Gleasonian distributions)(Meynardetal2013)

Variation partitioning another commonly used method incommunity ecologywasdesigned to reveal potentialmechanismsresponsibleforspatialvariation inassemblagecomposition(Peres-Neto LegendreDrayampBorcard 2006) This approach integratesenvironmentalvariationandspatialprocessesandfacilitates infer-encesaboutthelikelihoodthatoneormoreofthefourmetacom-munityprocesses(speciessortingmasseffectpatchdynamicsnullmodel)accountforobservedassemblagepatterns(Cottenie2005)

Spatial configurations such as dendritic structures of rivernetworks also affect fish assemblage structure (Vitorino-Juacutenior

were associatedwith environmental heterogeneity especially regarding habitatfeaturesSimilarlythevariationpartitioninganalysisshowedthatthepureenvi-ronmentalcomponentwashigherthanthepurespatialcomponentwhichiscon-sistentwithspeciessorting

6 In this paperwe demonstrated that variation partitioning andmetacommunitystructureanalysesprovidedcomplementaryfindingstoinferprocessesstructur-ingfishassemblagesintheBitaRiverBothapproachesidentifiedspeciessortingastheprincipalstructuringprocessesinthissystemThereforestrategiestopre-servefishdiversityinthissystemmustemphasisemaintenanceofhabitathetero-geneityandconnectivity

K E Y W O R D S

Clementsiandistributionenvironmentalfilteringfluvialgradientspeciessorting

emspensp emsp | emsp369LOacutePEZ- DELGADO Et AL

Fernandes Agostinho amp Pelicice 2016) Brown and Swan (2010)foundthatprocessesstructuringaquaticcommunitiesdifferedde-pendingonthenetworklocation(headwatersstreamsversusmainstem)Headwaters streamsare relatively isolatedwithin rivernet-workspossesshighhabitatheterogeneityandtendtohavestrongdisturbance regimes Patch dynamics may dominate metacom-munity dynamics under these conditions (Winemiller Flecker ampHoeinghaus2010)Converselyhabitatsinlowerreachesaremuchless isolated with greater potential for dispersal conditions thatshould facilitate species sorting and themass effect Communitystructure along river longitudinal gradients also couldbe affectedbytheunidirectionalflowofwaterDatryetal(2016)inferredthatspeciesdiversityincreasesfromupstreamtodownstreamduetothecombinedeffectofheadwaterisolationandwaterflowlimitingup-streamdispersal

Relatively few investigations have examined metacommunitypatternsalonglongitudinalfluvialgradients(AlmeidaampCetra2016Carvajal-Quintero etal 2015 Datry etal 2016 Vitorino-Juacutenioretal2016)andevenlessresearchhasbeenconductedinrelativelypristine riverswith catchments lacking significanthuman impactsKnowledge gained fromunperturbed systems enables predictionsabout future impacts to biodiversity and can help to guide con-servationandrestorationeffortsforriversthathavealreadybeendegraded

Weinvestigatedspatialvariationinfishassemblagestructurealong the longitudinal fluvialgradientof theBitaRiver anearlypristine tributary of the Orinoco River in the Llanos region ofColombiaWeanalysedfishspeciesdistributionsacrossthelongi-tudinalgradientinanefforttorevealrelativeinfluencesofabioticenvironmental and spatial factors and to infermetacommunitymodelsthatmayaccountforpatternsWepredictedthatatthebasin-scaledispersalratesaresufficiently lowtoallowlocalas-semblages to track spatial environmental variation (eg speciessorting)withamajorfractionofvariationinstructureexplainedbyapureenvironmentaleffect(HeinoMeloampBini2015HeinoMelo Siqueira etal 2015) Given that environmental condi-tions should have greater variation over broader spatial scalesmetacommunity patterns should reveal high turnover and fairlydiscrete local assemblageswithaClementsiandistributionAt amoreregionalscale(localitieswithinariversection)assemblagesinupstreamanddownstreamregionsmayreflectdifferentmeta-communityprocessesBecauseheadwaterstreamsarerelativelyisolatedwithintherivernetwork theyshouldreceivefewermi-grantsandmayhavelocalassemblagesstronglyinfluencedbylocalenvironmental conditions and species sorting Metacommunitypatterns of headwaters should reveal high turnover with threepossible distributional patterns Clementsian Gleasonian orevenlyspacedConversely sites locateddownstreamandclosertotherivermouthshouldbeinfluencedbyhighdispersalresult-inginagreaterimportanceofspatialfactorsandthemasseffectIn the downstream section in-channel environmental variationwillbelowerandmetacommunitypatternsmaybenestedalongthelongitudinalgradient

2emsp |emspMETHODS

21emsp|emspStudy area

TheBitaRiverBasinislocatedintheeasternplainsoftheColombianLlanos in theVichadadepartmentdraininganareaof812312haflowingeastroughly700kmtotheOrinocoRiverAsinmostnaturaldrainagesystemsstreamenvironmentalcharacteristics intheBitaBasinvaryalongthelongitudinalgradientMostheadwaterstreamsbeginatanelevationof300mabovesealevelThemiddleandlowersectionsare characterisedbyabroad sinuouschannel and flood-plainswithnumerouslagoonsandseasonallyfloodedforestsandsa-vannasTheaverageelevationalgradientislow(0357mkm)withtheriverenteringtheOrinocoRiveratanelevationof50mabovesea level near the municipality of Puerto Carrentildeo Riparian veg-etationiscomprisedofdensegalleryforestsdominatedbyCaraipa llanorum (Calophyllaceae) Astrocaryum jauari (Arecaceae) andParahancornia oblonga (Apocynaceae) and savannasdominatedbyAxonopus ancepsPanicum cayennense (Poaceae)Bulbostylis capilla-risCyperus haspanRhynchospora cephalotes(Cyperaceae)Caladium macrotites (Araceae) and C llanorum (Calophyllaceae) (Trujillo ampLasso2017)Themainriverandstreamscontainmultiplehabitatsthatvarymainly insubstratecompositionriparianvegetationanddepthDuringMayndashNovembertheriverfloodsadjacentareascre-atingcomplexanddiversehabitatsAverageairtemperatureintheregion is27degCandaverageannualprecipitation is2300mmwithmostrainfallfromMaytoAugust(TrujilloampLasso2017)

Twofieldexpeditionseachofwhich lasted30dayswereper-formedduringJanuaryandMarch2016whenlow-waterconditionsfacilitateefficientcaptureoffishesandrelationshipsbetweenfishassemblagestructureandhabitatshouldbestrongest(Peaseetal2012)Weselected34surveysitesdistributedalongtheentirebasin(E1uppermosttoE34lowest)followingtheriverrsquoslongitudinalgra-dient(SupportingInformationTableS1)Thebasinwasdividedintofoursections(highmidndashhighmidndashlowlow)usingthelineardistanceofthebasin(265km)divideditbythenumberofsections(four)Thisvalue(665km)wasusedtodefinethedistancebetweeneachsec-tionalongthelongitudinalgradient(Figure1)

22emsp|emspFish surveys

At each survey sitewe selected a 200-m reach encompassing allapparentmacrohabitatstocollectfishesanddataforlocalenviron-mental variables Fishes were collected using a seine (10times15m3-mmmesh)andtwogillnets(10times2m100-mmmesh)Withineachstudyreachsixseinehaulsof20mwereperformedandthegillnetwasdeployed for2hrAfter fisheswere removed fromnets theywere anaesthetised according to an approved TexasAampMuniver-sityanimaluseprotocol (IACUC2015-0360)by immersion in tric-aine methane sulfonate (MS-222) and euthanised in an overdoseofMS-222Specimenswere fixed in10formalin transported tothelaboratoryandtransferredto70ethanolforpreservationAllspecimenswereidentifiedcataloguedanddepositedinthevoucher


collectionsoftheUniversidaddelTolimaandInstitutovonHumboldtinColombia

23emsp|emspEnvironmental variables

Environmentalvariablesweredividedintosixcategorieswaterpa-rameters substrate instream cover channelmorphology local ri-parianbufferandlandscapevariablesfollowingPeaseetal(2012)(SupportingInformationTableS2)Priortofishsamplingwaterqual-ity parameters such as pH conductivity (μS) water temperature(degC)dissolvedoxygen(mgL)andtotalsolidsweremeasuredateachsurveysiteusingamultiparameterwaterqualitymeter (YSImodel85) To characterise substrate and instreamcover thepercentageof cobble (diameter6ndash25cm) sand (006ndash2mm)mud (lt006mm)filamentousalgaelargewoodydebris(gt50cm)smallwoodydebris(lt50cm)submergedrootsoverhangingterrestrialmaterialandleaflitterwerevisuallyestimatedalongthe200-mreachVariablessuchaswidthofriparianbuffer(m)areaoftheriparianforest(ha)andlandscape(presenceofroadscropsdistancetotheOrinocoRiverandaltitude)weremeasuredbygeoreferencedsatelliteimagesusing

ArcMap (Version1031) inacircularbufferof1kmStreamorderwascalculatedusingthefunctionstreamorderinArcMap(Version1031)methodStrahlerrsquosclassification

24emsp|emspData analysis

Analyses were performed at basin (number of sites=34) and re-gional scales Regional-scale datasets were grouped according tofoursectionsalongthelongitudinalfluvialgradient(highmidndashhighmidndashlow low)Thisapproachaimed toexplore thepossibility thatenvironmental or spatial variationwithin river sections influencesassemblage patterns according tometacommunity processes thatmay differ among sections Using the method of Dufrene andLegendre (1997) we calculated indicator values for species mostcommonwithinagivenriversectionThismethodisbasedonspe-ciesabundance(specificity)andfrequency(fidelity)atsurveysitesValuesfromthismethodrangefrom0to1with1beingaperfectindicator of a river section Significance of indicator values wasevaluatedusingthedifferencebetweentheobservedvalueandthemeanofvaluesobtainedfrom1000randompermutationsSpecies

F IGURE 1 emspMapoftheBitaRiverBasinshowingthefoursectionsandthe34surveysites

E1E2E3

E4

E9 E8E7E6

E34E33

E31

E30E29

E28E27

E26E20

E19

E15E11

68deg0prime0primeprimeW69deg0prime0primeprimeW

6deg0

0N

6deg0prime

0primeprimeN

Casanare River

Meta River

Orin

oco

Riv

er

Bita River

BrazilPeru

Bolivia

Argentina

Colombia

Venezuela

ChileParaguay

Ecuador

GuyanaSuriname

Uruguay

French Guiana (France)

E1

E2E3E4

E5E9

E8E7

E6E17

E15E14E13

E12

E10

E31

E30E29

E28

E27E26E25E23E22

E20E19

E34E33

E32

Meta River

Mid_High

Mid_Low Low

Venezuela

Colombia

South America

ColombiaBita River

Bita River

Bita R

iver

High

Bita River

Orin

oco

Riv

er

Bita river basin

0 40 8020 Kilometers


indicator analysiswas performed using the function Indval of thepackagelabdsv(Roberts2007)intheRstatisticallanguage(Version341)(RCoreTeam2017)

25emsp|emspSpatial variables

Becausefishdispersalinriversisdifficulttoestimateweusedspa-tial predictors as proxies for dispersal potential Spatial eigenvec-torswereusedaspredictors to control for spatial autocorrelationandestimate the influenceof spatialdistanceon speciesdistribu-tionsandpatternsofassemblagestructure(Peres-NetoampLegendre2010) Spatial patternsweremodelled through asymmetric eigen-vectormaps (AEM)anapproachproposedbyBlanchetLegendreandBorcard(2008b)Thismethodtakesintoaccountthedirectionalaspectofinter-sitedistancesofsystemssuchasrivernetworks(egwaterflowrelativeposition)a limitationofmethodssuchasprin-cipal coordinates of neighbour matrices and Moranrsquos eigenvectormaps(BlanchetLegendreMarangerMontiampPepin2011SharmaLegendreDeCaacuteceresampBoisclair2011)

To perform theAEMwe used the distribution of sites acrossthebasintoconstructaconnectiondiagramthatlinkssitestooneanotheraccording topathwayswithin the rivernetwork (Figure1SupportingInformationTableS3)followingtheflowdirectionfromupstreamtodownstreamUsingtheconnectiondiagramasites-by-edgesmatrixwasconstructedandthismatrixwasusedtocalculateasymmetricalspatialeigenfunctionsthroughsingularvaluedecom-positionwhichwereused laterasspatialdescriptorsAEMeigen-functionswerecalculatedusingthefunctionaemfromthepackageadespatial inR (Version341) (Drayetal2017)Toobtainanad-ditional spatial descriptorwemeasured thewatercourse distanceas thedistance fromeachpoint to the rivermouthalong thewa-tercourseusinggeoreferencedsatellite images inArcMap(Version1031)Thisvariableismorerelevanttofishdispersalthanstraight-linedistancethatincludesoverlandsegments(albeitsuchdispersalroutesarepossibleduringtheannualfloodpulse)

26emsp|emspElements of the metacommunity structure

Usingthesites-by-species incidencematrix (excludingrarespeciesthatoccurredatonlyonesite)asinputforRAEMSwereanalysedbasedonspeciesscoresonthefirsttwoaxesToevaluatepatternswithrespecttosixidealiseddistributionalpatterns(1)checkerboard(Diamond1975) (2)nestedsubsets (PattersonampAtmar1986) (3)Clementsian (Clements 1916) (4) Gleasonian (Gleason 1926) (5)evenlyspaced(Tilman1982)and(6)random(Simberloff1983)wefollowedtheframeworkproposedbyLeiboldandMikkelon(2002)andPresleyetal(2010)

Coherencewasassessedbycalculatingthenumberofgapsinspeciesrange(observedabsences)fromtheorderedmatrixfromRAStatisticalsignificanceofcoherencewasevaluatedusingthez-score test comparing observed absenceswith those expectedbasedon1000randomisedsimulationsWheneverobservedab-sencesaregreaterthanexpected(negativecoherence)thereisa

checkerboardpatternConversely ifobservedabsencesare lessthanexpectedunderthenullmodel(positivecoherence)theanal-ysis tests forevidenceofspeciesturnoverandboundaryclump-ing No significant difference between observed and expectedabsences indicates randomdistributions (no coherence) (LeiboldampMikkelson2002)

TurnoverwasevaluatedbycalculatingthenumberofspeciesreplacementsbetweensitesWeusedaz-score test tocompareobserved species replacements with the mean for species re-placements obtained from 1000 null simulations Significantlyfewerobservedreplacementsare indicativeof lowturnoveranddistributionsthatarenestedsubsetswhereasmoreobservedre-placementsindicatehighturnoverFinallyboundaryclumpingwasevaluatedusingMorisitarsquosindexforwhichavalueofoneindicatesthatboundariesarenotclumpedvalueslt1indicateclumpingandlt1indicateshyperdispersionStatisticalsignificanceofMorisitarsquosindexwasdeterminedusingtheχ2 testMore informationaboutinference of idealised patterns can be found in fig1 of Presleyetal(2010)

Weappliedafixedproportionalnullmodel(ier1 null model in MetacommunityfunctioninR)totestsignificanceofcoherenceandturnoverThisnullmodelmaintainsspeciesrichnessofeachsamplingsite but fills species ranges based on theirmarginal probabilitiesRandomsimulatedmatriceswerecalculatedusing1000simulations(Dallas2014)EMSwereassessedusingtheMetacommunity func-tionoftheMetacompackage(Dallas2014)inR

27emsp|emspVariation partitioning

Weusedvariationpartitioninganalysis(Peres-Netoetal2006)asanadditionalmeanstoidentifymetacommunitytypesandtoassesstherelativecontributionofenvironmentalandspatialpredictorsonfishdistributionsatbasinandregionalscalesThismethodallowedus to determine the contribution of environmental conditions in-dependentofspaceandvice versacontrollingfortypeIerrorasaproductofspatialautocorrelationintheenvironmentalcomponent(Peres-NetoampLegendre2010)Type I errormay lead to spuriousconclusions generating significant speciesndashenvironment relation-ships thatareartefactsPartitioninganalysiswasconductedusingthe adjusted R-squared in redundancy analysis on the Hellinger-transformedspecies-by-sitesabundancematrixandtwosetsofpre-dictorsenvironment[E]andspace[S]

To create each set of predictors firstwe reduced the num-berofvariablesineachmatrixbecausetheyreducethestatisticalpowerofthetesttoidentifyuniqueandsignificantenvironmentalandspatialcontributions(Peres-NetoampLegendre2010)Weper-formedavariableselectionforeachsetofpredictorsto identifysignificantvariables(plt05)associatedwiththespecies-by-sitesabundancematrixForthe[E]matrixwetransformedtheenviron-mentalvariablesduetononhomogeneousunitsandverifiedthatthedistributionwasnormalThosevariablesexpressedaspropor-tionsweretransformedtothearcsineoftheirsquarerootThere-mainingvariableswerelog(x+1)transformedwiththeexception


ofordinal andcategoricaldata forwhichno transformationwasdone Data for all variables were standardised and centred bycalculating z-scores (mean=0 standard deviation=1) (BorcardGilletampLegendre2011FalkeampFausch2010)Thefunctionfor-wardselfromtheRlibraryadespatial(Drayetal2017)wasusedtoperformvariableselectionusing999permutationswithasig-nificance level of p=05Selectedvariablescomprisedtheenvi-ronmentalmatrix[E]usedforthepartitionanalysis

Spatial predictors from the AEM analysis and inter-site dis-tancedataweretestedforinclusioninthespatialmatrix[S]usingtheforwardselfunctionoftheRlibraryadespatialusingthesameapproachasdescribedforthe[E]matrixSelectedvariableswereincludedinthe[S]matrixforthepartitioninganalysisFinallywithourthreedatasets(Hellinger-transformedsites-by-speciesmatrix[E]and [S])weapplied thevarpart functionof thevegan libraryin R to perform variation partitioning analysis as proposed byBlanchetLegendreandBorcard(2008a)andBorcardetal(2011)

Thesignificancelevelsofthecomponentsproducedbythevari-ation partitioning were used to classify the fish metacommunityaccording to one of the four metacommunity paradigms speciessorting mass effect neutral model and patch dynamics (Leiboldetal2004)TochoosetheproperparadigmweappliedthedecisiontreeproposedbyCottenie(2005)

28emsp|emspDrivers of metacommunity structure

We used two approaches to identify the underlying environmen-talandspatialfactorsthatcorrelatewithobservedpatterns inthemetacommunityorganisationat thebasinandregional levelFirstwecalculatedSpearman-rankcorrelationsbetweenthetwodomi-nantaxesextractedfromtheRAused in theEMSframeworkandeachvariablefromthesetofenvironmentalandspatialpredictorsSecond a variable selection procedure was performed for eachgroup (environmentalandspatial)usingthe forwardsel functionoftheRlibraryadespatialCorrelationswerecalculatedusingthefunc-tioncortestofthestatslibraryinR

3emsp |emspRESULTS

High and midndashhigh sections were characterised by higher valuesofdissolvedoxygenelevationdistance fromthemainsourcesa-vannaareaflowandpercentagesofgrassesandfilamentousalgae(Supporting Information Table S2) Channel width water tem-perature and total solids tended tobe lower atupper-basin sitesSubstratumandinstreamcoverwereheterogeneouswithinallfoursectionsSiteslocatedinthelowersectionstendedtohavemoreex-tensiveriparianforestmorelagoonsinadjacentfloodplainswiderchannelsandlowerflowvelocity(SupportingInformationTableS2)

A total of 25928 fish specimens representing 201 species 39familiesand10orderswerecollectedduringthestudySixtyspecieswerecollectedinthehighsection148inthemidndashhigh142inthemidndashlowand67inthelowsectionAveragenumberofspecimenspersite

was762andspeciesrichnessvariedfrom4to55ThemostabundantspecieswereAmazonprattus scintilla(Engraulidae)andtwospeciesofthegenusHemigrammus(Characidae)H elegans and H geisleriThesethreespeciesrepresentednearly30ofthecollectedfishspecimens(Supporting Information Table S4) Fifty-one rare species were col-lectedatsinglesitesSeveralspecieswereidentifiedasindicatorsforthe low (12spp)andhigh (sixspp) sectionsandfewornonewereidentifiedforthemidndashhigh(onesp)andlow(0sp)sections(Table1)

31emsp|emspElements of metacommunity structure

Atthebasinscale(34sites)theEMSrevealedpositivecoherencewithaClementsiandistributionalongbothof thedominantaxesmodel-ling gradientsof fish assemblage structure (RA1andRA2) (Table2Figure2)Thusatabroadspatialscalespeciesseemtorespondasgroupstoenvironmentalfactorsleadingtorelativelydiscreteassem-blagesalongthelongitudinalgradientTheClementsianpatterniden-tified forRA1waspositively correlatedwithchannelwidth streamorderandspatialpredictors(AEM21AEM25)andnegativelycorre-latedwithvariablesassociatedtosubstrateinstreamcoverandlocalriparianvegetation(Table3)ForRA2onlythespatialpredictorAEM1(positive)andflow(negative)werecorrelatedwithsitescores(Table3)

Metacommunitystructurewasanalysedforsiteswithineachof the foursectionsalongthe longitudinal fluvialgradient (highmidndashhighmidndashlowandlow)Assemblagesinthehighandlowsec-tionshadnegativecoherenceandarandompatternforbothRAaxes in thehighsectionandRA1 in the lowsectionRA2 in the

TABLE 1emspSummaryoftheindicatorspeciesanalysisforeachsectionintheBitaBasin

Species Section Indicator value

Hemigrammus schmardae High 059

Parotocinclus eppleyi High 040

Ammocryptocharax elegans High 040

Farlowella vittata High 038

Mastiglanis asopos High 037

Phenacorhamdia anisura High 035

Steindachnerina argentea Midndashhigh 048

Nannostomus unifasciatus Low 089

Microphilypnus ternetzi Low 084

Hemigrammus elegans Low 083

Apistogramma minima Low 066

Brittanichthyssp Low 061

Curimatopsis evelynae Low 060

Hemigrammus analis Low 060

Acaronia vultuosa Low 059

Hemigrammus rhodostomus Low 058

Ochmacanthus alternus Low 058

Acestrorhynchus minimus Low 057

Hemigrammus barrigonae Low 033

Onlyspecieswithstatisticallysignificantvalues(plt005)arelisted


TABLE 2 emspResultsofEMSanalysisoffishmetacommunitystructureattheleveloftheentireBitaBasinandattheregionallevelforhighmidndashhighmidndashlowandlowsectionsoftheriver

Axis of variation Basin High Midndashhigh Midndashlow Low

Species 145 37 84 85 55

Sites 34 5 12 14 3

Primaryaxis

Coherence

Observedabsences 2506 55 318 453 14

Expectedabsences 3032 41 445 554 14

p-value lt0001 0127 lt0001 0001 09

Turnover

Observedreplacements 347752 543 20498 27534 630

Expectedreplacements 186586 624 14397 20631 621

p-value lt0001 015 lt0001 lt0001 089

Clumping

Morisitarsquosindex 158 102 137 114 1

p-value lt0001 017 lt0001 lt0001 gt0999

Best-fitpatterns Clementsian Random Clementsian Clementsian Random

Secondaryaxis

Coherence



p-value 0001 096 003 064 0007

Turnover



p-value lt0001 065 lt0001 lt0001 0004

Clumping


p-value lt0001 0004 lt0001 lt0001 gt0999

Best-fitpatterns Clementsian Random Clementsian Random Nesteddistribution

Statisticallysignificantvalues(plt005)areshowninboldtype

F IGURE 2 emspSpeciesdistributionsamong34sitesintheBitaBasinorderedaccordingtoscoresonthefirstaxisofthereciprocalaveragingrevealingaClementsiangradientSitesareinrowsspeciesareincolumns

E16E8E7

E17E24E30E20E10E18E33E27E31E9

E26E2E1

E12E4E5

E25E15E34E14E21E32E22E11E23E3

E13E6

E29E28E19

Species

AbsencePresence


lowsectionrevealedanesteddistributionNosignificantcorrela-tionsbetweenassemblagestructureandenvironmentalorspatialfactorswerefoundforthehighandlowsections(Tables2and3Figure3)

Midndashhigh and midndashlow sections displayed Clementsian pat-terns of assemblage structure for both the first and second RAaxes For the midndashhigh section no significant correlations wereobtained between RA1 and environmental or spatial variableshoweverRA2waspositivelycorrelatedwiththepresenceofroadsandthespatialpredictorAEM1(Tables2and3Figure3)ForthemidndashlowsectionRA1waspositivelycorrelatedwithsubstrateandinstreamcoverandRA2withthespatialpredictorAEM1(Tables2and3Figure3)

32emsp|emspVariation partitioning analysis

Atthebasinscaleallthecomponentsfromvariationpartitioning(ESE+SE|SS|E)explainedsignificant(plt05)variationinfishassem-blagestructuresupportingtheideathatmetacommunitydynamicsinfluencepatternsat that scale (Table4)Around19of the totalexplainedvariation (E+S)wasmodelledbythecombined influenceofenvironmentandspatialpredictors69byapureenvironmentalcomponent(E|S)and46byapurespatialcomponent(S|E)Eighty-onepercentofthevariationwasunexplainedEnvironmentalvaria-tionatthebasinscalewassignificantlyinfluencedbypercentageof

mudsubstratesmallwoodydebrisandwaterconductivity(Table4)Onlyfourof33spatialpredictorsweresignificant(AEM1AEM16AEM18AEM21Table4)

Attheregionalscalethenumberofsiteswithinthehighandlow sections was too small for statistical analysis and only themidndashhigh and midndashlow sections were analysed Only the envi-ronmental component (E) explained significant variation in fishassemblagestructurewithineachofthesesections(Table4)indi-catingthatitwasnotpossibletoinferanymetacommunitymodelIn themidndashhigh section a significant environmental componentinfluenced most strongly by percentage of mud substrate andsmallwoodydebrisexplained16ofassemblagevariationInthemidndashlow section 14of assemblage variationwas explainedbyan environmental componentwith conductivity andpercentageofsmallwoodydebrismostinfluential

33emsp|emspDrivers of fish community structure

The set of variables selected by variation partitioning includedmostof theones thathadhighestSpearman rankcorrelationsAtthe basin level environmental variables related to substrate andinstream coverwere identified by bothmethods (Tables3 and 4)EnvironmentalpredictorssuchaspercentagesofmudsubstrateandsmallwoodydebrisandthespatialpredictorAEM1weresignificantatbothbasinandregionalscales(Tables3and4)

TABLE 3emspSpearman-rankcorrelationsbetweenpredictorsandscoresobtainedfromthefirsttwoaxesfromthereciprocalaveragingordinationofthesites-by-speciesincidencematrixintheelementsofmetacommunitystructureframework

Variables

Basin Midndashhigh Midndashlow

r p r p r p

Primaryaxis

()Mud minus055 lt001 061 002

()Largewoodydebris minus048 lt001

()Smallwoodydebris minus048 lt001 055 004

()Submergedroots minus048 lt001 061 002

()Streamshadedbytreecanopy minus047 lt001 061 002

Width 037 003

Streamorder 052 lt001

AEM21 039 002

AEM25 039 002

Secondaryaxis

()Mud minus071 001

()Largewoodydebris minus066 002

()Smallwoodydebris minus075 lt001

()Streamshadedbytreecanopy minus075 lt001

Flow minus047 lt01

Neartoroads 066 002

AEM1 051 lt01 06 004 069 001

Onlystatisticallysignificantvalues(plt005)areshownNosignificantcorrelationswerefoundforhighandlowsectionsAEMasymmetriceigenvectormaps


F IGURE 3 emspSpeciesdistributionswithinfourriversectionsorderedaccordingtoscoresonthefirstreciprocalaxis

High

E5

E2

E4

E3

E1

Species

Midminushigh

E16

E8

E7

E9

E14

E17

E10

E15

E12

E11

E13

E6

Species

Midminuslow

E30E20E24E18E26E27E31E25E21E23E22E29E28E19

Species

Low

E33

E34

E32

Species

Absence

Presence

Component

Bita basin Midndashhigh Midndashlow

AdjR2 p AdjR2 p AdjR2 p

E 014 0001 016 0007 014 0002

S 012 0001 000 05 000 056

E+S 019 0001 018 02 014 013

E|S 0069 0001 020 03 016 016

S|E 0046 0025 002 06 000 051

b 0075

R 081 082 086

Variablesselected Mud[E] Smallwoodydebris[E] Conductivity[E] AEM1[S] AEM16[S] AEM18[S] AEM21[S]

Mud[E] Smallwoodydebris[E]

Conductivity[E] Smallwoodydebris[E]

AEMasymmetriceigenvectormapsAdjR2adjustedR2forthepercentageofvariationEenviron-mentalvariationSspatialvariationE+STotalexplainedvariationE|SPureenvironmentalvaria-tion S|E Pure spatial variation b Variation shared by environmental and spatial factorsRUnexplainedvariation(residual)Significantvalues(plt005)areshowninbold

TABLE 4emspVariationpartitioninganalysisforfishassemblagesandselectedgroupsofenvironmentalandspatialvariablesatbasinandregionalscales


4emsp |emspDISCUSSION

In this study we applied complementary statistical analyses toinvestigate fish species distributions and variation in assemblagestructureacrossalongitudinalfluvialgradienttorevealtherelativeinfluenceoffactorsassociatedwithenvironmentalconditions(eghabitat features environmental filtering) and spatial relationshipsinfluencingdispersalResultsfromtheseanalysesprovidedabasistoinfertherelativeinfluenceofbiotic interactionsandstochasticprocessesincommunityassemblyattwospatialscalesOurresultssuggest that the fishmetacommunity in theBitaRiver exhibits aClementsiandistributionimplyingthatspeciesrespondtotheen-vironmental and fluvial gradient as groups with fairly consistentcompositions (Clements1916)Thispatternalso impliesapoten-tialeffectofenvironmentalfilteringatthescaleoftheentirebasinSimilarlythevariationpartitioninganalysisshowedthatthevaria-tion explainedby the pure environmental componentwas higherthanthepurespatialcomponentwhichalsoisconsistentwithspe-ciessortingSpeciessortingoccurswhenorganismsselecthabitatsthat convey relativelyhigh fitness andor avoidor areotherwiseeliminatedfromhabitatsthatreducetheirfitness(Soininen2014)Thesefindingssupportourpredictionthatatthebasinscaledis-persalratesarelowandassemblagestructureisstronglyinfluencedbyspeciessorting

Ourregional-scaleresultsalsorevealedaClementsiandistribu-tionoflocalassemblagecompositionThispatterncouldhavebeencausedbyspeciesresponsestospatialvariationofabioticandbioticenvironmentalfactorsSpearmancorrelationbetweenenvironmen-talvariablesandsitescoresongradientsofassemblagevariationin-dicatedthatstreamorderandchannelsizehadstrongassociationswithlocalassemblagestructureVariablesassociatedwithsubstratecomposition and instream cover also had significant associationsFishes in theBitaRiver apparently respond to theseenvironmen-talvariablesasrelativelyclumpedgroupsClementsianassemblagestructure has been found in plants (Meynard etal 2013 Willigetal2011)mammals(Loacutepez-GonzaacutelezPresleyLozanoStevensampHiggins 2012PresleyampWillig 2010) andvarious freshwateror-ganismsincludingplanktonmacroinvertebratesandfish(DallasampDrake2014Erősetal2017FernandesHenriques-SilvaPenhaZuanonampPeres-Neto2014TonkinStollJaumlhnigampHaase2016TorresampHiggins2016)

Weproposed that somemetacommunity processes and resul-tantpatternswouldnotbethesameattwodifferentspatialscalesofanalysisWefoundpositivecoherenceandturnoverinupstreamregionsandpositivecoherenceandnegativeturnoverwithnesteddistributionsindownstreamregionsThesepatternswerenotpre-dicteda prioriAttheregionalscaleaClementsiandistributionwasfoundonlyinthemidndashhighandmidndashlowsectionsofthebasinsug-gesting that species responded as groups to environmental filtersmore strongly in these middle sections of longitudinal gradientAssemblage coherence was non-significant (ie not statisticallydifferent from a random distribution) for the high and low sec-tionsoftheriverAccordingtoPresleyetal(2010)non-significant

coherence is not always an indicatorof randomstructuresDallasandDrake(2014)foundthatinmoststudiesinwhichrandompat-ternswerefoundsamplescontainedfewspeciesfewsitesorbothand therefore therewas not enough statistical power to test co-herenceusing randomisationprocedures Inourstudy therewerefewersurveysitesforthehighestandlowestsectionsoftheriverwhichmayaccountfornon-significantcoherenceinthosesectionsNonethelessFigure3revealsapatternofspeciesdistributionwithhigh turnover and boundary clumping in the high section such apatternislessdiscernibleforthelowsection

Inthelowsectionoftheriverdistributionpatternswerediffer-ent for the twoassemblageordinationaxesRandomdistributionswere inferredbasedon the firstordinationaxisandnesteddistri-butionswerefoundforthesecondaxisResultsofthesecondaxissupport our hypothesis that environmental gradients over smallerspatial scales in the low sectionmay promote nested distributionpatternsSitesinthissectionwerecharacterisedbysubstratescom-prised almost entirely of sandwith patches of leaf litter and lowvariation inwater physicochemistryDistribution patternsmay bemorelikelytobenestedwhensamplesizeissmallandenvironmen-talgradientsareshort(Tonkinetal2016)ConverselylargesamplesizesandlongenvironmentalgradientsmayincreasethelikelihoodoffindingClementsianandGleasonianpatterns

At the basin scale the total variation in assemblage structurecapturedbythemodel(19)wasdecomposedinto69purelyen-vironmental 75 shared by environmental and spatial and 46purelyspatialcomponents(eachfractionwithplt0001)Thepureenvironmental component was strongly associated with the per-centage of mud substrate small woody debris and conductivitySharedenvironmentalandspatialfactorsalsowereassociatedwiththepercentageofmudandconductivityThepure spatial compo-nentwas represented by eigenvectors fromAEM predictors thatare not easily interpretable because they integrate multiple spa-tial scales (Peres-Netoamp Legendre 2010)with small-scale spatialvariables tending tobeassociatedwithmasseffectdynamics andlarge-scalespatialvariablestendingtobeassociatedwithdispersallimitation (HeinoMeloampBini2015HeinoMeloSiqueiraetal2015) At the scale of the entire basin both processes probablyoccursimultaneously

Thepercentageofvariationexplainedbyourpartitioninganal-ysiswas low(19)acommonfinding incommunityecologystud-ies (Castillo-Escrivagrave etal 2016 Devercelli Scarabotti MayoraSchneider amp Giri 2016 Erős etal 2017 Legendre amp Legendre2012TerBraakampŠmilauer2012)Thelargeamountofunexplainedvariationcouldbeduetoseveralfactorssuchasfailuretoincludeother relevant environmental variables and spatial predictors orvariablesrelatedtootherprocessessuchasbiotic interactionsOfcoursetherealsocouldbealargeinfluenceofstochasticdynamicsSoininen(2016)reviewed322datasetsandfoundthatspatialpredic-torsobtainedfromspatialeigenvectoranalysisgenerallyexplainedasmall fractionof totalassemblagevariationwhichsuggests thatspatialpredictors fromAEMmayperformpoorly incapturingpat-ternsreflectingdispersaldynamicsMonteiroetal(2017)proposed


theuseofpatchconnectivitymetricsaspredictorsofdispersaldy-namicsratherthanspatialeigenvectorsTheyfoundthatthepatchconnectivityframeworkincreasedtheamountofexplainedvariationbyasmuchas50

Becausebothpureenvironmentalandpurespatialcomponentswere statistically significant both species sortingandmasseffectdynamics could have influenced fish assemblage structure at thebasin scale (Cottenie 2005) However because the proportionexplained by the pure environmental fractionwas larger than thespatialfraction(69versus46)speciessortingwasprobablythemoreinfluentialcomponentstructuringfishassemblagesinthissys-temTheseresultsareinaccordancewithHeinoMeloampBini(2015)whofoundthatanintermediateamountofdispersalwasneededforthespeciessortingprocesssincespeciesneedtodispersefromoneplace toanother searching forsiteswith theappropriateenviron-mentalconditionstothrive

Soininen(2014)reviewed326investigationsthatusedvariationpartitioning analysis finding that environmental variables usuallyexplainedthemostvariationinassemblagecompositionandspeciessortingwasmostofteninferredSpeciessortingmaybethepreva-lentmetacommunitymodel for river and stream fish assemblages(Cottenie2005FalkeampFausch2010HeinoMeloampBini2015HeinoMeloSiqueiraetal2015)

At a smaller spatial scale we predicted that species sortingstrongly influencesassemblages inheadwaterswherehabitatsarerelativelyisolatedwithintherivernetworkwhereasassemblagesindownstreamreacheswouldhavegreaterdispersalandbemorein-fluencedbymasseffectdynamicsThesepredictionswerenotcon-firmedBasedonvariationpartitioninganalysisandthedecisiontreeofCottenie(2005)noevidencewasobtainedtosupportanyofthemetacommunity paradigmsproposedby Leibold etal (2004) Thevariation partitioning procedure could only be performed for themidndashhighandmidndashlowsectionsthathadsufficientlylargesamplesOnlytheenvironmentalfractionwassignificantandthisresultcanbepronetotypeIerror

Insummaryvariationpartitioningandmetacommunitystructureanalysesprovidedcomplementaryfindingstoinferprocessesstruc-turingfishassemblagesintheBitaRiverBothapproachesidentifiedenvironmentalfilteringandspeciessortingastheprincipalstructur-ingprocessesinthissystemVariationpartitioninganalysisimpliedthatdispersalplaysasignificantroleatthebasinscalehowevertheenvironmentalfractionexplainedmorevariationBothanalysesre-vealedassemblagepatternscorrelatedwithcomponentsofhabitatstructureSimilarconclusionshavebeenreportedfromstudiescon-ducted at smaller spatial scales inother tropical rivers (ArringtonWinemillerampLayman2005WillisWinemillerampLopez-Fernandez2005)with some also inferring that biotic interactionsplay a sig-nificant role in structuring fish assemblages within local habitats(MontantildeaWinemillerampSutton2014) Inastudyof theCinarucoRiveranOrinocotributaryintheVenezuelanLlanoswithcharacter-isticssimilartotheBitaRiverWillisetal(2005)foundthathabitatstructural complexity was strongly correlated with the functionaldiversity of fish assemblages These findings combinedwith ours

suggest that fish community structure of Neotropical rivers andstreams is influencedby localenvironmentalconditionsespeciallyaspectsassociatedwithsubstrateandhabitatstructuralcomplexitythat in turnaffect species sorting that results inClementsianpat-ternsofspeciesdistribution Improvedunderstandingof the influ-enceofdispersalenvironmentalfilteringandbioticinteractionsonspeciesassemblagesinpristinesystemswillbeessentialforeffortstoconserveandrestorebiodiversityForexampleifthespatialdis-tributionoffishesintheBitaRiverderiveslargelyfromdispersalandenvironmental filtering then strategies to conserve fish diversitymustemphasisemaintenanceofhabitatheterogeneityandconnec-tivityatappropriatespatialandtemporalscales

ACKNOWLEDG MENTS

WethankWorldWildlifeFoundation(WWF)ColombiaFundacioacutenOmachaInstitutodeInvestigacioacutendeRecursosBioloacutegicosAlexandervonHumboldtandUniversidaddelTolimaforlogisticsandfinancingpartof this studyWealsowould like to thankDTaphornCDoNascimentoJGAlbornozandDMontoyafortheirassistanceinthefieldandlaboratorywork

ORCID

Edwin O Loacutepez-Delgado httpsorcidorg0000-0002-4010-1880

R E FE R E N C E S

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BlanchetFGLegendrePampBorcardD (2008a)Forwardselection of explanatory variables Ecology 89 2623ndash2632 httpsdoiorg 10189007-09861

BlanchetFGLegendrePampBorcardD(2008b)Modellingdirectionalspatialprocesses inecologicaldataEcological Modelling215325ndash336httpsdoiorg101016jecolmodel200804001

BlanchetFGLegendrePMarangerRMontiDampPepinP(2011)Modelling the effect of directional spatial ecological processes atdifferent scalesOecologia166 357ndash368 httpsdoiorg101007s00442-010-1867-y

BorcardDGillet Famp Legendre P (2011)Numerical ecology with R NewYorkSpringerhttpsdoiorg101007978-1-4419-7976-6

Brown B L amp Swan C M (2010) Dendritic network structure constrains metacommunity properties in riverine ecosystems Journal of Animal Ecology 79 571ndash580 httpsdoiorg101111 j1365-2656201001668x

Carvajal-Quintero J D Escobar F Alvarado F Villa-Navarro F AJaramillo-VillaUacuteampMaldonado-OcampoJA (2015)Variation infreshwater fish assemblages along a regional elevation gradient inthenorthernAndesColombiaEcology and Evolution52608ndash2620httpsdoiorg101002ece31539

Carvalho R A amp Tejerina-Garro F L (2015) The influence of en-vironmental variables on the functional structure of headwa-ter stream fish assemblages A study of two tropical basins in


Central Brazil Neotropical Ichthyology 13 349ndash360 httpsdoiorg1015901982-0224-20130148

Castillo-Escrivagrave A Rueda J Zamora L Hernaacutendez R Del MoralM ampMesquita-Joanes F (2016) The role of watercourse versusoverland dispersal and niche effects on ostracod distribution inMediterraneanstreams(easternIberianPeninsula)Acta Oecologica731ndash9httpsdoiorg101016jactao201602001

ChaseJMKraftNJSmithKGVellendMampInouyeBD(2011)Usingnullmodelstodisentanglevariationincommunitydissimilarityfromvariationinα-diversityEcosphere21ndash11

Clements F E (1916)Plant succession An analysis of the development of vegetationWashingtonDCCarnegieInstitutionofWashingtonhttpsdoiorg105962bhltitle56234

Cottenie K (2005) Integrating environmental and spatial processesin ecological community dynamics Ecology Letters 8 1175ndash1182httpsdoiorg101111j1461-0248200500820x

Dallas T (2014) metacom An R package for the analysis of meta-community structure Ecography 37 402ndash405 httpsdoiorg101111j1600-0587201300695x

DallasTampDrakeJM(2014)RelativeimportanceofenvironmentalgeographicandspatialvariablesonzooplanktonmetacommunitiesEcosphere51ndash13

DatryTMeloASMoyaNZubietaJDeLaBarraEampOberdorffT(2016)MetacommunitypatternsacrossthreeNeotropicalcatch-mentswithvaryingenvironmentalharshnessFreshwater Biology61277ndash292httpsdoiorg101111fwb12702

DevercelliMScarabottiPMayoraGSchneiderBampGiriF(2016)Unravelling the role of determinism and stochasticity in struc-turing the phytoplanktonic metacommunity of the Paranaacute Riverfloodplain Hydrobiologia 764 139ndash156 httpsdoiorg101007s10750-015-2363-5

Diamond JM (1975)Assemblyof species communities InMartinLCodyandJaredMDiamondeditorsEcology and evolution of com-munities(pp342ndash444)CambridgeMassBelknapPressofHarvardUniversityPress

DraySBlanchetGBorcardDGuenardGJombartTLarocqueGhellipWagnerHH(2017)adespatialMultivariatemultiscalespatialanalysisRpackageversion009

DufreneMampLegendreP (1997)Speciesassemblagesand indicatorspecies The need for a flexible asymmetrical approachEcological Monographs67345ndash366

Erős T (2017) Scaling fish metacommunities in stream networksSynthesisandfutureresearchavenuesCommunity Ecology1872ndash86httpsdoiorg10155616820171819

Erős T Takaacutecs P Specziaacuter A SchmeraD amp Saacutely P (2017) Effectof landscapecontextonfishmetacommunitystructuringinstreamnetworksFreshwater Biology62215ndash228httpsdoiorg101111fwb12857

FalkeJAampFauschKD(2010)Frommetapopulationstometacom-munities linking theorywith empirical observations of the spatialpopulationdynamicsofstreamfishesInAmericanFisheriesSocietySymposium(Vol73pp207ndash233)

FernandesIMHenriques-SilvaRPenhaJZuanonJampPeres-NetoPR(2014)Spatiotemporaldynamicsinaseasonalmetacommunitystructure is predictable The case of floodplain-fish communitiesEcography37464ndash475

Gleason H A (1926) The individualistic concept of the plant asso-ciation Bulletin of the Torrey Botanical Club 53 7ndash26 httpsdoiorg1023072479933

Heino JMeloASampBiniLM (2015)Reconceptualising thebetadiversity-environmental heterogeneity relationship in runningwater systems Freshwater Biology 60(2) 223ndash235 httpsdoiorg101111fwb12502

HeinoJMeloASSiqueiraTSoininenJValankoSampBiniLM(2015) Metacommunity organisation spatial extent and dispersal

in aquatic systems Patterns processes and prospects Freshwater Biology60845ndash869httpsdoiorg101111fwb12533

Landeiro V L Magnusson W E Melo A S Espiacuterito-Santo H MV amp Bini L M (2011) Spatial eigenfunction analyses in streamnetworks Do watercourse and overland distances produce dif-ferent results Freshwater Biology 56 1184ndash1192 httpsdoiorg101111j1365-2427201002563x

LegendrePLegendreLampLegendreP(2012)Numerical ecology 3rd EnglishedPierreLegendreandLouisLegendreAmsterdamBostonElsevier

LeiboldMAHolyoakMMouquetNAmarasekarePChaseJMHoopesMFhellipGonzalezA(2004)ThemetacommunityconceptAframeworkformulti-scalecommunityecologyEcology Letters7601ndash613httpsdoiorg101111j1461-0248200400608x

LeiboldMAampMikkelsonGM(2002)Coherencespeciesturnoverand boundary clumping Elements of meta-community structureOikos 97 237ndash250 httpsdoiorg101034j1600-07062002 970210x

Logue J B Mouquet N Peter H Hillebrand H amp TheMetacommunityWorkingGroup (2011)Empirical approaches tometacommunities A review and comparisonwith theoryTrends in Ecology amp Evolution 26 482ndash491 httpsdoiorg101016jtree201104009

Loacutepez-GonzaacutelezCPresleySJLozanoAStevensRDampHigginsCL(2012)MetacommunityanalysisofMexicanbatsEnvironmentallymediatedstructure inanareaofhighgeographicandenvironmen-tal complexity Journal of Biogeography 39 177ndash192 httpsdoiorg101111j1365-2699201102590x

MacarthurRampLevinsR (1967)The limitingsimilarityconvergenceanddivergenceofcoexistingspeciesThe American Naturalist101377ndash385httpsdoiorg101086282505

Meynard C N Lavergne S Boulangeat I Garraud L Van Es JMouquetNampThuillerW(2013)Disentanglingthedriversofmeta-community structure across spatial scales Journal of Biogeography401560ndash1571httpsdoiorg101111jbi12116

MontantildeaCGWinemillerKOampSuttonA(2014)IntercontinentalcomparisonoffishecomorphologyNullmodeltestsofcommunityassembly at thepatch scale in riversEcological Monographs84(1)91ndash107httpsdoiorg10189013-07081

MonteiroVFPaivaPCampPeres-NetoPR (2017)Aquantitativeframework to estimate the relative importance of environmentspatial variation and patch connectivity in driving communitycomposition Journal of Animal Ecology 86 316ndash326 httpsdoiorg1011111365-265612619

Mouillot D Dumay O amp Tomasini J A (2007) Limiting similaritynichefilteringandfunctionaldiversityincoastallagoonfishcommu-nitiesEstuarine Coastal and Shelf Science71443ndash456httpsdoiorg101016jecss200608022

Patterson B D amp Atmar W (1986) Nested subsets and thestructure of insular mammalian faunas and archipelagosBiological Journal of the Linnean Society 28 65ndash82 httpsdoiorg101111j1095-83121986tb01749x

Pease A A Gonzaacutelez-Diacuteaz A A Rodiles-Hernaacutendez R ampWinemiller K O (2012) Functional diversity and traitndashenviron-ment relationships of stream fish assemblages in a large tropi-cal catchment Freshwater Biology 57 1060ndash1075 httpsdoiorg101111j1365-2427201202768x

Peres-Neto P R amp Legendre P (2010) Estimating and con-trolling for spatial structure in the study of ecological commu-nities Global Ecology and Biogeography 19 174ndash184 httpsdoiorg101111j1466-8238200900506x

Peres-NetoPRLegendrePDraySampBorcardD(2006)VariationpartitioningofspeciesdatamatricesEstimationandcomparisonoffractionsEcology872614ndash2625httpsdoiorg1018900012-9658(2006)87[2614vposdm]20co2


Presley S J Higgins C L ampWillig M R (2010) A comprehensiveframework for the evaluation ofmetacommunity structureOikos119908ndash917httpsdoiorg101111j1600-0706201018544x

Presley S J amp Willig M R (2010) Bat metacommunity structure on Caribbean islands and the role of endemics Global Ecology and Biogeography19185ndash199httpsdoiorg101111j1466-82382009 00505x

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RobertsDW (2007) labdsvOrdinationandmultivariateanalysis forecologyR package version1(1)

SharmaSLegendrePDeCaacuteceresMampBoisclairD(2011)Theroleofenvironmentalandspatialprocessesinstructuringnativeandnon-native fish communities across thousands of lakes Ecography 34762ndash771httpsdoiorg101111j1600-0587201006811x

SimberloffD(1983)Competitiontheoryhypothesis-testingandothercommunityecologicalbuzzwordsThe American Naturalist122626ndash635httpsdoiorg101086284163

Soininen J (2014) A quantitative analysis of species sorting acrossorganisms and ecosystems Ecology 95 3284ndash3292 httpsdoiorg10189013-22281

SoininenJ(2016)SpatialstructureinecologicalcommunitiesmdashAquan-titative analysis Oikos 125 160ndash166 httpsdoiorg101111oik02241

TerBraakCampŠmilauerP (2012)Canoco reference manual and userrsquos guide Software for ordination (version 50)IthacaNYMicrocomputerPower

Tilman D (1982) Resource competition and community structure PrincetonNJPrincetonUniversityPress

Tonkin J D Stoll S Jaumlhnig S C amp Haase P (2016) Elements ofmetacommunity structure of river and riparian assemblagesCommunities taxonomic groups and deconstructed trait groupsEcological Complexity 25 35ndash43 httpsdoiorg101016jecocom201512002

TorresKMMampHigginsCL (2016)Taxonomicandfunctionalor-ganizationinmetacommunitystructureofstream-fishassemblagesamongandwithinriverbasinsinTexasAquatic Ecology50247ndash259httpsdoiorg101007s10452-016-9572-5

TrujilloFampLassoCA(2017)IVBiodiversidaddelriacuteoBitaVichadaColombia Serie Editorial Fauna Silvestre Neotropical (p 349)BogotaColombiaInstitutodeInvestigacioacutendeRecursosBioloacutegicosAlexandervonHumboldtFundacioacutenOmacha

Vitorino-JuacuteniorOBFernandesRAgostinhoCSampPeliciceFM(2016) Riverine networks constrain β-diversity patterns amongfishassemblagesinalargeNeotropicalriverFreshwater Biology611733ndash1745httpsdoiorg101111fwb12813

Willig M R Presley S J Bloch C P Castro-Arellano I CisnerosL M Higgins C L amp Klingbeil B T (2011) Tropical metacom-munities along elevational gradients Effects of forest type andother environmental factors Oikos 120 1497ndash1508 httpsdoiorg101111j1600-0706201119218x

WillisSWinemillerKampLopez-FernandezH (2005)Habitatstruc-turalcomplexityandmorphologicaldiversityoffishassemblagesinaNeotropicalfloodplainriverOecologia142284ndash295httpsdoiorg101007s00442-004-1723-z

WinemillerKOFleckerASampHoeinghausD J (2010)Patchdy-namicsandenvironmentalheterogeneityinloticecosystemsJournal of the North American Benthological Society29 84ndash99 httpsdoiorg10189908-0481

SUPPORTING INFORMATION

Additional supporting information may be found online in theSupportingInformationsectionattheendofthearticle

How to cite this articleLoacutepez-DelgadoEOWinemillerKOVilla-NavarroFADometacommunitytheoriesexplainspatialvariationinfishassemblagestructureinapristinetropicalriverFreshw Biol 201964367ndash379 httpsdoiorg101111fwb13229


1emsp |emspINTRODUC TION

Thespatialdistributionsof speciesand resultantdiversityof localassemblages are affected by multiple factors operating at differ-ent temporal and spatial scales (Pease Gonzaacutelez-Diacuteaz Rodiles-Hernaacutendez ampWinemiller 2012 Presley Higgins ampWillig 2010)andunderstandinghowthese factors influencepatternshasbeenthe main objective in community ecology (Landeiro MagnussonMeloEspiacuterito-SantoampBini2011)Streamecologistshavebeenpar-ticularly active in research addressing relationships of assemblagepatterns with abiotic (Mouillot Dumay amp Tomasini 2007) biotic(MacarthurampLevins1967)spatial(Peres-NetoampLegendre2010)and stochastic influences (Chase Kraft Smith Vellend amp Inouye2011)Thesefactorscanactindependentlysequentiallyorsimulta-neouslyandofteninteract(CarvalhoampTejerina-Garro2015)

Until recentlymost field studieshave focusedonassociationsbetweenlocalspeciesassemblagesandlocalenvironmentalfactorsDuringthepastdecadetherehasbeenarapidincreaseinresearchshowinghow local communities are influencedbydispersal of or-ganismsacrossvarious spatial scalesand involvingecologicalpro-cesses suchashabitat selectionand responses todisturbancesorcompetition (Erős 2017 Erős Takaacutecs Specziaacuter Schmera amp Saacutely2017LeiboldampMikkelson2002Leiboldetal2004Presleyetal2010)

Metacommunitytheoryexploreshowlocalinteractionsandre-gionalprocesses(dispersalsub-populationextirpation)shapecom-munity structureThewordmetacommunity isused todescribeanetworkof localcommunitiesof interactingpopulations linkedviadispersal Themetacommunityparadigmhas stimulated investiga-tiononhowpopulations interactatregionalaswellas localscales(LogueMouquetPeterampHillebrand2011)At least fourmodelsofmetacommunitydynamicshavebeenproposedeachdefinedbytherelativeinfluencesofdispersalenvironmentalfilteringhabitatselection habitat disturbance biotic interactions and stochasticfactors(Leiboldetal2004)

Three statistical approaches have been developed to studymetacommunitiesTheseareelementsofmetacommunitystructure(EMS)variationpartitioningandzero-summultinomialdistribution(Logueetal2011MonteiroPaivaampPeres-Neto2017)Thecur-rentstudyadoptsthefirsttwoapproachestotestmetacommunitytheoriesbyanalysingspatialvariationinfishassemblagesinapris-tineneotropicalriver

TheEMSmethod(LeiboldampMikkelson2002)analysesspeciesdistributionpatternsalongenvironmentalgradientstorevealmul-tipleEMSEMSusesanorderedsites-by-speciesincidencematrixby reciprocal averaging (RA) to evaluate assemblage coherenceturnover and boundary clumping The method can distinguishamongsixpossiblepatternsofspeciesdistributioncheckerboard(nonrandom patterns of species co-occurrence) Clementsian(clumps of co-occurring species distributed along spatial gradi-ents)Gleasonian(independentspeciesdistributionsalongspatialgradients) evenly spaced (hyperdispersed species distributions)nestedsubsetsandrandom(LeiboldampMikkelson2002Presleyetal 2010)Because the focal unitsof analysis are speciesdis-tributions rather than assemblage structure among sites thisapproach allows testingof hypotheses about competitive exclu-sion(negativecoherencendashcheckerboarddistribution)randomas-semblies (non-significantcoherence)andenvironmental filtering(positive coherencendashClementsian and Gleasonian distributions)(Meynardetal2013)

Variation partitioning another commonly used method incommunity ecologywasdesigned to reveal potentialmechanismsresponsibleforspatialvariation inassemblagecomposition(Peres-Neto LegendreDrayampBorcard 2006) This approach integratesenvironmentalvariationandspatialprocessesandfacilitates infer-encesaboutthelikelihoodthatoneormoreofthefourmetacom-munityprocesses(speciessortingmasseffectpatchdynamicsnullmodel)accountforobservedassemblagepatterns(Cottenie2005)

Spatial configurations such as dendritic structures of rivernetworks also affect fish assemblage structure (Vitorino-Juacutenior

were associatedwith environmental heterogeneity especially regarding habitatfeaturesSimilarlythevariationpartitioninganalysisshowedthatthepureenvi-ronmentalcomponentwashigherthanthepurespatialcomponentwhichiscon-sistentwithspeciessorting

6 In this paperwe demonstrated that variation partitioning andmetacommunitystructureanalysesprovidedcomplementaryfindingstoinferprocessesstructur-ingfishassemblagesintheBitaRiverBothapproachesidentifiedspeciessortingastheprincipalstructuringprocessesinthissystemThereforestrategiestopre-servefishdiversityinthissystemmustemphasisemaintenanceofhabitathetero-geneityandconnectivity

K E Y W O R D S

Clementsiandistributionenvironmentalfilteringfluvialgradientspeciessorting





2emsp |emspMETHODS














E1E2E3

E4

E9 E8E7E6

E34E33

E31

E30E29

E28E27

E26E20

E19

E15E11


6deg0

0N

6deg0prime

0primeprimeN

Casanare River

Meta River

Orin

oco

Riv

er

Bita River

BrazilPeru

Bolivia

Argentina

Colombia

Venezuela

ChileParaguay

Ecuador

GuyanaSuriname

Uruguay


E1

E2E3E4

E5E9

E8E7

E6E17

E15E14E13

E12

E10

E31

E30E29

E28

E27E26E25E23E22

E20E19

E34E33

E32

Meta River

Mid_High

Mid_Low Low

Venezuela

Colombia

South America

ColombiaBita River

Bita River

Bita R

iver

High

Bita River

Orin

oco

Riv

er

Bita river basin






















3emsp |emspRESULTS
































Species 145 37 84 85 55

Sites 34 5 12 14 3

Primaryaxis

Coherence



p-value lt0001 0127 lt0001 0001 09

Turnover



p-value lt0001 015 lt0001 lt0001 089

Clumping


p-value lt0001 017 lt0001 lt0001 gt0999


Secondaryaxis

Coherence



p-value 0001 096 003 064 0007

Turnover



p-value lt0001 065 lt0001 lt0001 0004

Clumping


p-value lt0001 0004 lt0001 lt0001 gt0999




E16E8E7

E17E24E30E20E10E18E33E27E31E9

E26E2E1

E12E4E5

E25E15E34E14E21E32E22E11E23E3

E13E6

E29E28E19

Species

AbsencePresence











Variables


r p r p r p

Primaryaxis

()Mud minus055 lt001 061 002





Width 037 003


AEM21 039 002

AEM25 039 002

Secondaryaxis

()Mud minus071 001




Flow minus047 lt01

Neartoroads 066 002

AEM1 051 lt01 06 004 069 001




High

E5

E2

E4

E3

E1

Species

Midminushigh

E16

E8

E7

E9

E14

E17

E10

E15

E12

E11

E13

E6

Species

Midminuslow

E30E20E24E18E26E27E31E25E21E23E22E29E28E19

Species

Low

E33

E34

E32

Species

Absence

Presence

Component



E 014 0001 016 0007 014 0002

S 012 0001 000 05 000 056

E+S 019 0001 018 02 014 013

E|S 0069 0001 020 03 016 016

S|E 0046 0025 002 06 000 051

b 0075

R 081 082 086






















ACKNOWLEDG MENTS


ORCID


R E FE R E N C E S







































































2emsp |emspMETHODS














E1E2E3

E4

E9 E8E7E6

E34E33

E31

E30E29

E28E27

E26E20

E19

E15E11


6deg0

0N

6deg0prime

0primeprimeN

Casanare River

Meta River

Orin

oco

Riv

er

Bita River

BrazilPeru

Bolivia

Argentina

Colombia

Venezuela

ChileParaguay

Ecuador

GuyanaSuriname

Uruguay


E1

E2E3E4

E5E9

E8E7

E6E17

E15E14E13

E12

E10

E31

E30E29

E28

E27E26E25E23E22

E20E19

E34E33

E32

Meta River

Mid_High

Mid_Low Low

Venezuela

Colombia

South America

ColombiaBita River

Bita River

Bita R

iver

High

Bita River

Orin

oco

Riv

er

Bita river basin






















3emsp |emspRESULTS
































Species 145 37 84 85 55

Sites 34 5 12 14 3

Primaryaxis

Coherence



p-value lt0001 0127 lt0001 0001 09

Turnover



p-value lt0001 015 lt0001 lt0001 089

Clumping


p-value lt0001 017 lt0001 lt0001 gt0999


Secondaryaxis

Coherence



p-value 0001 096 003 064 0007

Turnover



p-value lt0001 065 lt0001 lt0001 0004

Clumping


p-value lt0001 0004 lt0001 lt0001 gt0999




E16E8E7

E17E24E30E20E10E18E33E27E31E9

E26E2E1

E12E4E5

E25E15E34E14E21E32E22E11E23E3

E13E6

E29E28E19

Species

AbsencePresence











Variables


r p r p r p

Primaryaxis

()Mud minus055 lt001 061 002





Width 037 003


AEM21 039 002

AEM25 039 002

Secondaryaxis

()Mud minus071 001




Flow minus047 lt01

Neartoroads 066 002

AEM1 051 lt01 06 004 069 001




High

E5

E2

E4

E3

E1

Species

Midminushigh

E16

E8

E7

E9

E14

E17

E10

E15

E12

E11

E13

E6

Species

Midminuslow

E30E20E24E18E26E27E31E25E21E23E22E29E28E19

Species

Low

E33

E34

E32

Species

Absence

Presence

Component



E 014 0001 016 0007 014 0002

S 012 0001 000 05 000 056

E+S 019 0001 018 02 014 013

E|S 0069 0001 020 03 016 016

S|E 0046 0025 002 06 000 051

b 0075

R 081 082 086






















ACKNOWLEDG MENTS


ORCID


R E FE R E N C E S











































































E1E2E3

E4

E9 E8E7E6

E34E33

E31

E30E29

E28E27

E26E20

E19

E15E11


6deg0

0N

6deg0prime

0primeprimeN

Casanare River

Meta River

Orin

oco

Riv

er

Bita River

BrazilPeru

Bolivia

Argentina

Colombia

Venezuela

ChileParaguay

Ecuador

GuyanaSuriname

Uruguay


E1

E2E3E4

E5E9

E8E7

E6E17

E15E14E13

E12

E10

E31

E30E29

E28

E27E26E25E23E22

E20E19

E34E33

E32

Meta River

Mid_High

Mid_Low Low

Venezuela

Colombia

South America

ColombiaBita River

Bita River

Bita R

iver

High

Bita River

Orin

oco

Riv

er

Bita river basin






















3emsp |emspRESULTS
































Species 145 37 84 85 55

Sites 34 5 12 14 3

Primaryaxis

Coherence



p-value lt0001 0127 lt0001 0001 09

Turnover



p-value lt0001 015 lt0001 lt0001 089

Clumping


p-value lt0001 017 lt0001 lt0001 gt0999


Secondaryaxis

Coherence



p-value 0001 096 003 064 0007

Turnover



p-value lt0001 065 lt0001 lt0001 0004

Clumping


p-value lt0001 0004 lt0001 lt0001 gt0999




E16E8E7

E17E24E30E20E10E18E33E27E31E9

E26E2E1

E12E4E5

E25E15E34E14E21E32E22E11E23E3

E13E6

E29E28E19

Species

AbsencePresence











Variables


r p r p r p

Primaryaxis

()Mud minus055 lt001 061 002





Width 037 003


AEM21 039 002

AEM25 039 002

Secondaryaxis

()Mud minus071 001




Flow minus047 lt01

Neartoroads 066 002

AEM1 051 lt01 06 004 069 001




High

E5

E2

E4

E3

E1

Species

Midminushigh

E16

E8

E7

E9

E14

E17

E10

E15

E12

E11

E13

E6

Species

Midminuslow

E30E20E24E18E26E27E31E25E21E23E22E29E28E19

Species

Low

E33

E34

E32

Species

Absence

Presence

Component



E 014 0001 016 0007 014 0002

S 012 0001 000 05 000 056

E+S 019 0001 018 02 014 013

E|S 0069 0001 020 03 016 016

S|E 0046 0025 002 06 000 051

b 0075

R 081 082 086






















ACKNOWLEDG MENTS


ORCID


R E FE R E N C E S























































































3emsp |emspRESULTS
































Species 145 37 84 85 55

Sites 34 5 12 14 3

Primaryaxis

Coherence



p-value lt0001 0127 lt0001 0001 09

Turnover



p-value lt0001 015 lt0001 lt0001 089

Clumping


p-value lt0001 017 lt0001 lt0001 gt0999


Secondaryaxis

Coherence



p-value 0001 096 003 064 0007

Turnover



p-value lt0001 065 lt0001 lt0001 0004

Clumping


p-value lt0001 0004 lt0001 lt0001 gt0999




E16E8E7

E17E24E30E20E10E18E33E27E31E9

E26E2E1

E12E4E5

E25E15E34E14E21E32E22E11E23E3

E13E6

E29E28E19

Species

AbsencePresence











Variables


r p r p r p

Primaryaxis

()Mud minus055 lt001 061 002





Width 037 003


AEM21 039 002

AEM25 039 002

Secondaryaxis

()Mud minus071 001




Flow minus047 lt01

Neartoroads 066 002

AEM1 051 lt01 06 004 069 001




High

E5

E2

E4

E3

E1

Species

Midminushigh

E16

E8

E7

E9

E14

E17

E10

E15

E12

E11

E13

E6

Species

Midminuslow

E30E20E24E18E26E27E31E25E21E23E22E29E28E19

Species

Low

E33

E34

E32

Species

Absence

Presence

Component



E 014 0001 016 0007 014 0002

S 012 0001 000 05 000 056

E+S 019 0001 018 02 014 013

E|S 0069 0001 020 03 016 016

S|E 0046 0025 002 06 000 051

b 0075

R 081 082 086






















ACKNOWLEDG MENTS


ORCID


R E FE R E N C E S






































































Species 145 37 84 85 55

Sites 34 5 12 14 3

Primaryaxis

Coherence



p-value lt0001 0127 lt0001 0001 09

Turnover



p-value lt0001 015 lt0001 lt0001 089

Clumping


p-value lt0001 017 lt0001 lt0001 gt0999


Secondaryaxis

Coherence



p-value 0001 096 003 064 0007

Turnover



p-value lt0001 065 lt0001 lt0001 0004

Clumping


p-value lt0001 0004 lt0001 lt0001 gt0999




E16E8E7

E17E24E30E20E10E18E33E27E31E9

E26E2E1

E12E4E5

E25E15E34E14E21E32E22E11E23E3

E13E6

E29E28E19

Species

AbsencePresence











Variables


r p r p r p

Primaryaxis

()Mud minus055 lt001 061 002





Width 037 003


AEM21 039 002

AEM25 039 002

Secondaryaxis

()Mud minus071 001




Flow minus047 lt01

Neartoroads 066 002

AEM1 051 lt01 06 004 069 001




High

E5

E2

E4

E3

E1

Species

Midminushigh

E16

E8

E7

E9

E14

E17

E10

E15

E12

E11

E13

E6

Species

Midminuslow

E30E20E24E18E26E27E31E25E21E23E22E29E28E19

Species

Low

E33

E34

E32

Species

Absence

Presence

Component



E 014 0001 016 0007 014 0002

S 012 0001 000 05 000 056

E+S 019 0001 018 02 014 013

E|S 0069 0001 020 03 016 016

S|E 0046 0025 002 06 000 051

b 0075

R 081 082 086






















ACKNOWLEDG MENTS


ORCID


R E FE R E N C E S













































































Variables


r p r p r p

Primaryaxis

()Mud minus055 lt001 061 002





Width 037 003


AEM21 039 002

AEM25 039 002

Secondaryaxis

()Mud minus071 001




Flow minus047 lt01

Neartoroads 066 002

AEM1 051 lt01 06 004 069 001




High

E5

E2

E4

E3

E1

Species

Midminushigh

E16

E8

E7

E9

E14

E17

E10

E15

E12

E11

E13

E6

Species

Midminuslow

E30E20E24E18E26E27E31E25E21E23E22E29E28E19

Species

Low

E33

E34

E32

Species

Absence

Presence

Component



E 014 0001 016 0007 014 0002

S 012 0001 000 05 000 056

E+S 019 0001 018 02 014 013

E|S 0069 0001 020 03 016 016

S|E 0046 0025 002 06 000 051

b 0075

R 081 082 086






















ACKNOWLEDG MENTS


ORCID


R E FE R E N C E S





































































High

E5

E2

E4

E3

E1

Species

Midminushigh

E16

E8

E7

E9

E14

E17

E10

E15

E12

E11

E13

E6

Species

Midminuslow

E30E20E24E18E26E27E31E25E21E23E22E29E28E19

Species

Low

E33

E34

E32

Species

Absence

Presence

Component



E 014 0001 016 0007 014 0002

S 012 0001 000 05 000 056

E+S 019 0001 018 02 014 013

E|S 0069 0001 020 03 016 016

S|E 0046 0025 002 06 000 051

b 0075

R 081 082 086






















ACKNOWLEDG MENTS


ORCID


R E FE R E N C E S



















































































ACKNOWLEDG MENTS


ORCID


R E FE R E N C E S



































































do metacommunity theories explain spatial variation in fish … · 2019. 4. 17. · disturbance...

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