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Ecology and Evolution. 2017;1–11. | 1 www.ecolevol.org Received:30June2016 | Revised:28December2016 | Accepted:14January2017 DOI:10.1002/ece3.2793 ORIGINAL RESEARCH Prey diversity as a driver of resource partitioning between riverdwelling fish species Javier Sánchez-Hernández1,2 | Heidi-Marie Gabler1 | Per-Arne Amundsen1 ThisisanopenaccessarticleunderthetermsoftheCreativeCommonsAttributionLicense,whichpermitsuse,distributionandreproductioninanymedium, providedtheoriginalworkisproperlycited. ©2017TheAuthors.Ecology and EvolutionpublishedbyJohnWiley&SonsLtd. 1DepartmentofArcticandMarineBiology, FacultyofBiosciences,Fisheriesand Economics,UiTTheArcticUniversityof Norway,Tromsø,Norway 2DepartmentofZoology,Geneticsand PhysicalAnthropology,FacultyofBiology, UniversityofSantiagodeCompostela, SantiagodeCompostela,Spain Correspondence Per-ArneAmundsen,DepartmentofArctic andMarineBiology,FacultyofBiosciences, FisheriesandEconomics,UiTTheArctic UniversityofNorway,Tromsø,Norway. Email:per[email protected] Funding information NorwegianResearchCouncil;Xuntade Galicia,Grant/AwardNumber:PlanI2C Abstract Althoughfoodresourcepartitioningamongsympatricspecieshasoftenbeenexplored inriverinesystems,thepotentialinfluenceofpreydiversityonresourcepartitioningis littleknown.Usingempiricaldata,wemodeledfoodresourcepartitioning(assessedas dietaryoverlap)ofcoexistingjuvenileAtlanticsalmon(Salmo salar)andalpinebullhead (Cottus poecilopus).Explanatoryvariablesincorporatedintothemodelwerefishabundance,benthicpreydiversityandabundance,andseveraldietarymetricstogivea totalofseventeenpotentialexplanatoryvariables.First,aforwardstepwiseprocedure basedontheAkaikeinformationcriterionwasusedtoselectexplanatoryvariables withsignificanteffectsonfoodresourcepartitioning.Then,linearmixed-effectmodels wereconstructedusingtheselectedexplanatoryvariablesandwithsamplingsiteasa randomfactor.Foodresourcepartitioningbetweensalmonandbullheadincreased significantlywithincreasingpreydiversity,andthevariationinfoodresourcepartitioningwasbestdescribedbythemodelthatincludedpreydiversityastheonlyexplanatory variable. This study provides empirical support for the notion that prey diversityisakeydriverofresourcepartitioningamongcompetingspecies. KEYWORDS biodiversity,coexistence,dietaryoverlap,interindividualvariation,mixedmodels,nichetheory 1 | INTRODUCTION Resourcepartitioning,assumedtobeaprincipalmediatorofbiodiversity,hasbeencentralforunderstandinghowacommunityofspeciespersistsovertime.Consumerinteractionshavegenerallybeen viewedfromtheperspectiveofpredatordiversity(e.g.,Griffin,Haye, Hawkins, Thompson, & Jenkins, 2008; Ives, Cardinale, & Snyder, 2005;Northfield,Snyder,Ives,&Snyder,2010),withthediversity ofpreyspeciesrarelybeingtakenintoaccount(butseeDuffyetal., 2007). Biodiversityofthepreycommunitycouldbeimportantinfood resource partitioning, because increased prey diversity should enhance the possibility of interactive segregation in resource utilization(Hillebrand&Matthiessen,2009;Hillebrand&Shurin, 2005).Thus,itispertinenttoaskwhetherthereisadecreasein competitionforfoodamongspecieswhenpreydiversityishigh. Thatis,doespreydiversityinfluencecompetitiveinteractionsand food resource partitioning between sympatric species? If prey diversity is high, sympatric species may be able to segregate in resource use and partitioning may occur, as predicted by niche theory (Schoener, 1974, 1989).Alternatively, if prey diversity is low,sympatricspeciesmayutilizethesameresources,andniche overlapwillbehighorcompetitiveexclusioncouldoccur(Keddy, 2001; Schoener, 1989). The potentially important relationship between prey diversity and dietary overlap between sympatric specieshasrarelybeenexplored,butthefewexamplesthatexist fromfish(Barili,Agostinho,Gomes,&Latin,2011;Targett,1981; Wuellneretal.,2011)andothervertebrates(Jiang,Feng,Sun,& Wang,2008;Martin&Garnett,2013;Zapata,Travaini,Delibes,& Martinez-Peck, 2005)indicatethatincreasedpreydiversitymay
2 | SÁNCHEZHERNÁNDEZ Et al. mitigatecompetitionthroughenhancedresourcepartitioning(but see Wuellner etal., 2011). It should, however, be kept in mind thatvariablesotherthanpreydiversity,suchaspreyabundance, foraging mode, diel patterns, and habitat segregation for feeding,mayalsobemajordeterminantsoffoodresourcepartitioning (e.g.,Crow,Closs,Waters,Booker,&Wallis,2010;Kronfeld-Schor & Dayan, 2003; Nakano, Fausch, & Kitano, 1999; Sánchez- Hernández,Vieira-Lanero, Servia,& Cobo,2011).Consequently, thestudyoffoodresourcepartitioningrequiresaframeworkthat includesthecomplexinterplayamongpreydiversity,preyabundance,fishabundance,anddietvariation. Weexaminedtherelationshipbetweenseveralpossibleexplanatoryvariables(preydiversity,preyabundance,fishabundance,and dietvariationofspecies)andfoodresourcepartitioning(measured asdietaryoverlap)ofcoexistingjuvenileAtlanticsalmon(Salmo salar Linnaeus, 1758; henceforth salmon) and alpine bullhead (Cottus poecilopusHeckel,1836;henceforthbullhead).Weusedthesetwo fishesasmodelspeciesbecausetheirfeedingecologyandcompetitiveinteractionsarewelldocumented(Amundsen&Gabler,2008; Gabler&Amundsen,1999,2010).Bothspeciesfeedonsimilarprey withapreferenceforbenthicinvertebrates(Gabler&Amundsen, 1999),andtheyarepresumedtoberesourcecompetitorsbecause theirdietsandhabitatusearesimilarevenwhenfoodresources arelimited(Amundsen&Gabler,2008;Gabler&Amundsen,1999, 2010).Further,thetwospeciesdonotshowsignificantdielsegregationinfeedinginsubarcticrivers(Gabler&Amundsen,1999). Thisprovidesanopportunitytoexaminepreychoiceandfoodresourcepartitioninginsympatricfishspeciesbycomparingmultiple sitesthatdifferinpreydiversity,preyabundance,andfishdensity. Themainobjectivesweretoexplore(i)whetherfoodresourcepartitioningoccurredbetweenthetwospeciesand(ii)whetherprey diversityoranyotherofthepotentialexplanatoryvariablescould beidentifiedassignificantpredictorsoffoodresourcepartitioning. We hypothesized that food resource partitioningwould increase with increasing prey diversity irrespective of other site-specific characters. 2 | MATERIALS AND METHODS 2.1 | Study area ThestudywascarriedoutinRiverReisa(Figure1),asubarctic,oligotrophicriverinnorthernNorway(latitude69°N).Theriver,approximately140kmlongandaround40mwidealongthestudiedsections, drainsacatchmentareaof2,516km².Theriverdoesnothaveany significantflowregulationstructure,andthemeanannualdischargeis 34m³/swiththewaterflowtypicallypeakingat200–250m³/sinlate June(Gabler&Amundsen,1999).TheReisaNationalParkislocatedin theheadwateroftheReisabasin,andtheparkandsurroundingareas provide grazing for semidomesticated reindeer. The Reisa basin includesamixtureofgrasspaddocksandforest[birch(Betula pubescens Ehrh.)andscattered pine(Pinus sylvestrisL.)],with smallruralareas interspersedinthelowerpart.Thus,agriculture,stockbreeding,and domesticsewageeffluentsaretheprimarybutmodesthumanimpacts onthecatchment.Theclimateistypicallysubarcticwithlong,dark, andcoldwinters,andtheriverisusuallyice-coveredfromNovember untilApril.Geologically,thestudybasinischaracterizedbyanaccumulationofgraniteandgneiss,andboulders,cobble,andgravelconstitutethemainsubstratesoftheriverbottom.Theriparianvegetationis chieflycomposedbydeciduouswoodland(birch)andpineforests.No informationisavailableaboutdriftpatternsormagnitudeofterrestrial subsidiesintotheRiverReisa.Itshouldbenoted,however,thatthe contributionofterrestrialinsectstothedriftinNorwegiansubarctic riversmaybeverynoticeablefromJunetoOctober(Johansen,Elliott, &Klemetsen,2000).Infact,terrestrialinsectsarethelargestgroupin thedriftofanothernorthernNorwegianriver(RiverSaeterelva,latitude68°N)inAugust,butwithverylowdensitiesinMay(Johansen etal.,2000). RiverReisasupportsrecreationalsalmonanglingandtheannualreportedcatchesofsalmonoverthelast20yearshavevariedgreatlyfrom afewhundredkgtonearly12,000kg.Catcheswereparticularlyhigh from2008to2011(approximately 8,000–12,000kg/year)(Svenning, 2011).Thedistributionofthebullheadcoincideswiththatofsalmon, FIGURE1 LocationoftheRiverReisa (inred),northernNorway,showingthe samplingsites(SS,graycircle)labeledfrom theupperpart(SS1)tolowerpart(SS11)
| 3 SÁNCHEZHERNÁNDEZ Et al. TABLE1 Foodresourcepartitioning(measuredasdietaryoverlap,%)betweenAtlanticsalmonparrandalpinebullhead,preyavailability(preydiversity—measuredasShannon’sdiversity index,andabundance—estimatedasind./m2),fishabundance(fish/100m2),anddietarymetricsofthetwofishspecies(Levins’index,individualdietaryspecialization,andsurfaceprey contribution)fromthedifferentsamplingsites(SS)intheReisaRiver.1-IS=prevalenceofindividualdietaryspecialization,where1-ISisgivenasmean±SD.Alpinebullhead(bul),Atlanticsalmon (sal),contributionofsurfacepreyinthediet(surface).Overlaptotal=dietaryoverlapcalculatedusingallprey.Overlapaquatic=dietaryoverlapcalculatedwithoutsurfaceprey Sampling sites SS1 SS2 SS3 SS4 SS5 SS6 SS7 SS8 SS9 SS10 SS11 Preyresources Diversity 0.74 0.94 0.78 0.50 0.70 0.98 0.67 0.75 0.77 0.79 0.90 Abundance 393.2 304 82.2 95.3 63.6 83.3 120.1 123.1 63.6 144.6 77.9 Fishabundance Alpinebullhead 36.6 35.1 1.9 20 28.6 22.6 0.9 19.4 2.8 18.4 2 Atlanticsalmon 5.3 2.5 8.9 2.9 0.001 0.001 16.3 3.9 8.9 4.4 3.9 Browntrout 0 0 1.14 0.70 0 0 5.70 4.96 4.61 1.90 3.82 Arcticcharr 0 1.53 0 0 0 0 0.79 5.86 0.82 4.43 1.27 Total 41.90 39.13 11.94 23.60 28.60 22.60 23.73 34.10 17.14 29.13 10.99 Diet Levins(bul) 3.0 5.3 5.9 4.2 4.8 6.9 3.9 5.8 6.8 5.3 6.2 Levins(sal) 5.8 2.2 9.0 5.6 4.1 6.6 4.3 5.4 2.8 1.9 3.6 1IS(bul) 0.54±0.17 0.64±0.18 0.59±0.11 0.62±0.17 0.61±0.16 0.80±0.10 0.49±0.16 0.69±0.14 0.76±0.12 0.66±0.13 0.64±0.13 1IS(sal) 0.47±0.04 0.42±0.22 0.50±0.08 0.64±0.23 0.54±0.13 0.75±0.09 0.58±0.17 0.61±0.13 0.46±0.16 0.45±0.21 0.63±0.06 Surface(bul) 0 0 0 4.6 9.5 1.8 0 0 0.9 0.2 0 Surface(sal) 0 65 11.7 6 42.5 0 0 0 0 20 30 Overlaptotal 54.9 11.5 31.7 62.5 31.6 33.5 34.4 44.6 33.2 41.3 25.3 Overlapaquatic 54.9 44.0 37.5 65.4 57.4 34.4 35.5 44.6 33.6 51.4 40.3 Samplingsize Alpinebullhead(n)37 69 5 57 32 32 9 43 17 29 11 Atlanticsalmon(n) 9 8 16 12 13 961 20 10 7 14
4 | SÁNCHEZHERNÁNDEZ Et al. andthesefisharethedominantspeciesinthefishcommunityofthe river.Otherfishspecies,suchasArcticcharrSalvelinus alpinus(Linnaeus, 1758),browntroutSalmo truttaLinnaeus,1758,andthree-spinesticklebackGasterosteus aculeatusLinnaeus1758,arealsopresentintheriver basin (Gabler &Amundsen, 2010). A natural waterfall located about 90kmfromthesearepresentstheupstreamlimittomigratingfish. 2.2 | Sampling Samplingprotocolsusedinthisstudyconformtotheethicallawsof the country. Based on previous knowledge of the study area (e.g., Amundsen & Gabler, 2008; Gabler & Amundsen, 1999, 2010), the samplingdesignwasperformedtomatchwiththedistributionofthe modelspeciesaswellastoensurevariationsinbioticconditions(fish andbenthicinvertebrates)amongsamplingsitesalongtheRiverReisa. Samplingoffishandbenthicinvertebrateswasconductedat11sites alongthemaincourseoftheriverinAugust2004(Figure1).Augustis thetimewhentheaquaticfoodresourcesupplyislowestrelativeto theenergeticrequirementsofsalmonandbullheadsandthustheperiodwhencompetitiveinteractionsshouldbestrongest(Amundsen, Bergersen,Huru, & Heggberget,1999; Amundsen & Gabler, 2008; Amundsen, Gabler, Herfindal, & Riise, 2000; Gabler & Amundsen, 2010).Someofthestudysectionswererelativelyclosetoeachother; theminimumdistanceapartwasbetweenSS11andSS10andwas FIGURE2 Proportionofdifferent preygroupsinthestomachcontentsof Atlanticsalmonparr(whitebars)andalpine bullhead(blackbars)(thecategory“others” includeschydorids,watermites,and unidentifiedpreytaxa).Dataarepresented foreachsamplingsiterankedfromthe highesttothelowestfoodresource partitioning(dietaryoverlapvalue).The presenteddietaryoverlapvaluesare calculatedwithallpreytypesincluded(i.e., withthehighesttaxonomicalresolutionas inTableS2)
| 5 SÁNCHEZHERNÁNDEZ Et al. approximately3km,whereasthemaximumdistanceapartwasabout 10kmbetweenSS5andSS4(Figure1).Weassumedfishmovement betweensamplingsiteswouldbenegligible,andthestudysections weredeemedindependent.Datafromapublishedstudy(Gabler& Amundsen,1999)withmonthlysamplingduringtheice-freeseason wereincludedintheanalysestoexamineforpossibleseasonalvariations in the prey diversity food resource partitioning relationship. Thesedatawerecollectedfollowingthesamesamplingprotocolasin thepresentstudy,allowingdirectcomparisonbetweenstudies. Fishandbenthicinvertebratesampleswerecollectedfromriffles withcobbleandgravelasthemainsubstrate.Priortosampling,siteselectionwasvisuallyperformedtoensurehabitatsimilarityamongsamplingsitestodiminishanypossibleerrorintheresultsrelatedtofield samplingtechniquessuchasbiasinfishremovalrateamongsampling sites. Thus, habitat conditions among sampling sites were deemed similar,butnospecifichabitatmeasurementsweretaken. Benthic invertebrates were collected at each site to study the preyavailability.Sampleswerecollectedimmediatelyafterfishsampling near to where electrofishing was conducted. Three parallel samplesweretakenusingthekickingmethod(Williams&Feltmate, 1992),standardizedbykickingfor3mininsideametalframedefining1.5×1.5mofthebottom.Benthicinvertebratesweresortedand identifiedtothelowesttaxonpossible,andpreyabundancewascalculatedasnumberofindividualsperm2.PlecopteraandEphemeroptera nymphsandTrichopteralarvaewereidentifiedtospecieslevel,and othertaxatothegenusorfamilylevel.Preydiversity(Hʹ)wascalculatedasShannon’sdiversityindex(Shannon&Weaver,1949): where pi is the proportion of species i in the benthic invertebrate samples. Fishwere collected using portable backpack electrofishing gear withpulseddirectcurrent(GeOmegabackpackmodel;700–1,400V, 5Amaximumintensity,40–80Hz)andasingleanodeof30cmdiameter.Three-passremovalelectrofishingwasconductedateachsampling sitewith30minbetweenpassesfollowingthestandardizedprocedures described for the EU Water Framework Directive (European Commission,2000)bytheCENdirectiveonfishingwithelectricityin wadeablerivers(CEN,2003).However,duetolargeriverwidthsand depths,nonetswereusedtoblocktheupstreamanddownstream boundaries. Fish sampling was conducted in an upstream direction fromtheriverbanktoawaterdepthofabout70cmoverastream sectionof100m.Eachfishwasidentified,measured(forklength,mm), andpreservedin96%ethanolforlaterdissectionanddietaryanalysis.Althoughthedepletionmethodwithonlythreepassesmaybe inadequatetoestimatefishabundance,particularlyinsamplingevents withlowcaptureprobabilities(e.g.,Dorazio,Jelks,&Jordan,2005),the abundanceofeachfishspecies(hereoverallfishdensityregardlessof fishlength)ateachsitewasestimatedasnumberoffishper100m2 usingZippinmultiple-passdepletionmethod(Zippin,1956).Although thisfishdensityestimationmightberough,itisassumedtoprovide representativeestimatesoftherelativefishabundanceamongsamplingsites.FishabundancesaregiveninTable1. Toavoidbiasresultingfrompossibledifferencesinfeedingbehaviorofdifferentsizeclassesoffish(e.g.,Dineen,Harrison,&Giller,2007; Hesthagen,Saksgård,Hegge,Dervo,&Skurdal,2004),onlyindividuals <100mmwereusedfordietanalysis.Intotal,179salmonparrand 341bullheadswerecaught,ofwhich142salmonand341bullheads wereusedforstomachcontentanalyses(SCA).Weattemptedtocollectatleasttenindividualsofeachfishspeciesfromeachsamplingsite. Althoughthisgoalwasnotalwaysachievedforsalmon(successfulin sevenofelevensamplingsites)andbullhead(nineofelevensampling sites;seesamplingsizesofeachlocalityinTable1),weassumethat thecapturedindividualsarerepresentativeoftheentirepopulation. Additionally,todismissanypossibleimpactoftheunequalsampling sizes,wegenerated1,000bootstrapsamples(seeSection2.3below). Thestomachswereopened,andthepercentageoftotalfullness wasvisuallydetermined,rangingfromempty(0%)tofull(100%)(see subjectivemethodsinHyslop,1980).Eachpreyitemwasthenidentifiedtothesametaxonomiclevelasforthebenthicinvertebratesamples.Therelativecontributionofeachpreytothetotalstomachfullness was estimated according toAmundsen, Gabler, and Staldvik (1996). Thatis,thesumofallpreycategoriesofastomachmeetsthevisually determinedtotalfullness.Inmathematicalterms,thecontributionof eachpreytothedietisdescribedaspercentpreyabundance(Ai): where Siisstomachfullnessofpreytypei,Stisthetotalfullnessofall preycategories,andnisthenumberoffishwithpreyiinthestomach.For thegraphicalrepresentation,preytypicallycaughtatthewatersurfaceincludingbitingmidges(Culicoidesspp.),aerialstagesofaquaticinsects,spiders,andunidentifiedterrestrialinsectswerecombinedanddesignated as“surfaceprey.”Similarly,theaquatictaxaweregroupedintosevenprey categories (Ephemeroptera, Plecoptera,Trichoptera, Diptera, Mollusca, Coleoptera,andothers)fortheplottingofthedietgraphs(Figure2). Dietaryoverlap(Pjk)wascalculatedaspercentageoverlap(Krebs, 1989)usingthelowesttaxonomicresolutionsofprey: where Pjkisthepercentageoverlapbetweenspeciesjandk,andAij andAikarethepercentpreyabundanceofresourceiusedbyspecies jandk,nisthetotalnumberofresourcecategories.Preydiversity analyseswererestrictedtobenthicinvertebrates,sodietaryoverlaps werealsocalculatedandanalyzedwithoutinclusionofsurfaceprey. Weaddressedthetrophicnicheatthepopulationlevelbyestimatingnichebreadth(B)usingLevins’index(Levins,1968): where Piistheproportionofeachpreytypeiinthedietexpressedas fractionratherthanpercentage(Amundsen,Knudsen,&Bryhni,2010). To study individual dietary specialization, the proportional similarity (PSi) index was calculated (Bolnick, Yang, Fordyce, Davis, & Svanbäck,2002): (1) H �=− ∑ pilog10p i (2) A i=100 n ∑ i=1 Si ( n ∑ i=1 St )−1 (3) P jk = [n ∑ 1 (minimum Aij,Aik) ] (4) B =1∕ ∑ P 2 i
6 | SÁNCHEZHERNÁNDEZ Et al. where Pijistheproportionofresourcecategoryjinthedietofindividuali,andQjtheproportionofresourcecategoryjinthedietofthe population.Thisindexcompareseachindividual’sdiettothatofthe population,withvaluesrangingbetween0and1.Forindividualsthat specializeonasingleorfewpreytypes,PSivaluesarelow,whereas forindividualsthatconsumeresourcesinasimilarproportiontothe populationasawhole,PSivaluesapproach1(Bolnicketal.,2002). Theoverallprevalenceofindividualspecializationwascalculatedas theinverseoftheaverageindividualPSivalues(Quevedo,Svanbäck, &Eklöv,2009). 2.3 | Statistical analyses Therelationshipsbetweendietaryoverlap(henceforthfoodresource partitioning)andthebioticvariables(explanatoryvariables)wereinvestigatedwithlinearmixed-effectmodelsusingsamplingsiteasa randomfactor.Thedatawerehierarchicallystructuredwithexplanatoryvariables being nestedwithin samplingsites,and mixed-effect models were used to account for potential random effects among sampling sites. Thus, the random part contains components that allowforheterogeneityofvariablesamongthestudiedsamplingsites. Seventeenpotentialexplanatoryvariablesoffoodresourcepartitioningwereconsidered(Table2).First,weselectedfixedterms(i.e.,explanatoryvariablesthataredeterministic)thatdescribetheresponse variableY(herefoodresourcepartitioning)asafunctionoftheexplanatory variables. The optimal fixed component was established basedonastepwiseforwardselectionmethod(stepfunction).This procedureenabledustoselectwhichexplanatoryvariablesaresignificant,andwhicharenot.ThisselectionwasmadeaccordingtoAkaike informationcriteria(AIC)(Akaike,1974).Nineexplanatoryvariables wereselectedformodelsimulations(seesignificantexplanatoryvariablesinTable2).Next,webuiltmodelsbasedonthenineselected explanatoryvariablesusingtherestrictedmaximumlikelihood(REML) estimation for linear regression models. REML aims to correct the estimatorforthevariance,andassuggestedbyZuur,Ieno,Walker, Saveliev, and Smith (2009), this procedure should be used to fit modelswithmanyfixedterms(heren = 9).Modelselectionwasalso establishedusingAIC.Whensamplesizeissmallorthenumberofparametersislarge,AICc(AICcorrectedforsmall-samplebias)orQAICc (AICcforoverdisperseddata)shouldbeusedinsteadofAIC(Anderson & Burnham, 2002). In the present study AICc was used for model selection,withthebestmodelbeingtheonewith the lowestAICc (5) PS i=1−0.5 ||| Pij −Qj ||| = ∑ (Pij,Qj ) Explanatory variables Definition Correlation Preydiversity* Macrozoobenthosdiversitycalculatedas Shannon’sdiversityindex R=−.73,p = .011 Preyabundance* Macrozoobenthosabundanceestimatedasind./m2R=.07,p = .831 Atlanticsalmon abundance* Density(fish/100m2)ofAtlanticsalmonparr R=−.01,p = .992 Alpinebullhead abundance* Density(fish/100m2)ofalpinebullhead R=.14,p = .684 Browntroutabundance Density(fish/100m2)ofbrowntrout R=−.04,p = .895 Arcticcharrabundance* Density(fish/100m2)ofArcticcharr R=.03,p = .934 Totalfishabundance Totalfishcommunitydensity(fish/100m2) R=.18,p = .601 Surfaceprey(Atlantic salmon)* Contributionofsurfacepreyinthedietof Atlanticsalmonparr R=−.67,p = .024 Surfaceprey(alpine bullhead) Contributionofsurfacepreyinthedietof alpinebullhead R=.15,p = .666 Nichebreadth(Atlantic salmon) Levins’indexofAtlanticsalmonparr R=.32,p = .330 Nichebreadth(alpine bullhead)* Levins’indexofalpinebullhead R=−.50,p = .120 Individualspecialization (Atlanticsalmon) IndividualdietaryspecializationofAtlantic salmonparr R=.22,p = .508 Individualspecialization (alpinebullhead) Individualdietaryspecializationofalpine bullhead R=−.16,p = .628 Stomachfullness (Atlanticsalmon) Stomachfullness(%)ofAtlanticsalmonparr R=.54,p = .085 Stomachfullness(alpine bullhead) Stomachfullness(%)ofalpinebullhead R=.08,p = .818 Size(Atlanticsalmon)* Forklength(mm)ofAtlanticsalmonparr R=−.50,p = .114 Size(alpinebullhead)* Forklength(mm)ofalpinebullhead R=−.24,p = .484 TABLE2 Fulllistofexplanatory variablesusedtoexploretheirpossible influenceofonfoodresourcepartitioning (measuredasdietaryoverlap)between juvenileAtlanticsalmon(Salmo salar)and alpinebullhead(Cottus poecilopus). Significantexplanatoryvariablesafter stepwisevariableselection(*).Pearson’s rankcorrelationbetweeneachexplanatory variableandfoodresourcepartitioningis shown(significantonesmarkedinbold)
| 7 SÁNCHEZHERNÁNDEZ Et al. values.Thestrengthofassociationbetweenfoodresourcepartitioningandexplanatoryvariablesfromthebestmodelswastestedusing Pearson’srankcorrelation.Finally,weransensitivityanalysestotest whetherlinear mixed-effectmodelswere the sameafter excluding surfacepreyfromthedietaryanalyses.Asignificancelevelofp = .05 wasusedinallanalyses.ModelswereperformedusingR3.2.2(RCore Team2015)using“nlme”(Pinheiro,Bates,DebRoy,&Sarkar,2016) and“MuMIn”(Bartoń,2016)packages.Thebootstrappingtechnique wasperformedusingthe“boot”package(Canty&Ripley,2016)employingtechniquesoutlinedinZuuretal.(2009)foranadditionaltest ofthemodel.Weappliedaparametricbootstrap(n = 1,000)onthe bestlinearmixed-effectsmodelexplainingvariationoffoodresource partitioningbetweenAtlanticsalmonparrandalpinebullhead.The modelwasappliedonthebootstrappeddatafollowingthesamemodelingproceduresasdescribedabove.Residualsofthefinalselected model(originaldata,bootstrappeddata,andsensitivityanalyses)were visuallyinspectedfordeviationsfromnormalityandheteroscedasticity,withoutfindinganyevidenceforviolationofmodelassumptions (seeFig.S1). 3 | RESULTS 3.1 | Prey resources Preydiversityvariedwidelyamonglocalities,withtheShannonindex rangingfrom0.50to0.98,andpreyabundancesvariedamongsamplingsites,rangingfrom63.1to393.2ind./m2(Table1).Chironomidae wasusually the mostabundant taxon, butinsome localitiesBaetis spp.,Ephemerella aurivillii(Bengtsson),andCapniasp.werenumerically dominant(taxarecordedinbenthicinvertebratesamplesaregivenin TableS1). 3.2 | Food resource partitioning Both salmon and bullhead fed mainly on benthic invertebrates (Figure2),butdifferenceswerefoundbetweenthespeciesandamong localitiesinthecontributionsofthedifferentpreytaxatothediet.In general,EphemeropteranymphsandlarvalDipteraandTrichoptera dominatedthedietofbothfishspecies,withabundancevaluesrangingbetween21.3%and94.7%.Surfacepreywasanimportantdietary componentforsalmoninsomelocalities(Figure2)(detailsofstomach contentanalysesaregiveninTableS2). Meandietaryoverlapbetweensalmonandbullheadwas36.8%, butoverlapvariedquitewidelyamongsamplingsites,rangingfrom 11.5%to62.5%(Table1).Amodelthatincludedpreydiversityasthe onlyexplanatoryvariablewasthebestone,havingthelowestAICc value(TableS3),andparametersofthismodelaregiveninTable3. Dietaryoverlapexhibitedasignificantnegativecorrelationwithprey diversity(Figure3a;R=−.726,p = .011),andinclusionofdatafrom theseasonalstudiesgaveasimilarrelationship(Figure3b;R=−.899, p =<.001).Thus,atsamplingsiteswithrelativelyhighpreydiversity, thesalmonandbullheadsegregatedinresourceuseandfoodresourcepartitioningwashigh,whereaswhenpreydiversitywaslow, foodresourcepartitioningwasalsolow.Oursensitivityanalysesdid notaltertheresults,andthebestmodelwasalsothemodelincluding only prey diversity as explanatory variable (AICc=80.1;Table S4).Additionally,themodelremainsthesameusingbootstrapped data, corroborating a significant negative correlation between dietaryoverlapandpreydiversity(R=−.991,p < .001). Variablesotherthanpreydiversitycouldinfluencefoodresource partitioning(seeTable2),andmodelsimulationsafterforwardvariable TABLE3 Summaryofthebestlinearmixed-effectsmodel explainingvariationoffoodresourcepartitioningbetweenAtlantic salmonparrandalpinebullhead.Standarderror=SE Value SE t value p value Intercept 95.02 18.64 5.096 <.001 Preydiversity −75.21 23.75 −3.166 .011 FIGURE3 Relationshipbetweenpreydiversityandfoodresource partitioning(measuredasdietaryoverlap)betweenAtlanticsalmon parrandalpinebullheadat(a)elevensitesinRiverReisa,(b)withdata onseasonalvariationincluded(filledcircles),(c)betweenabundance ofsurfacepreyinthedietofAtlanticsalmonparrandfoodresource partitioning.Bothfoodresourcepartitioningandpreydiversityhave beenestimatedwiththehighesttaxonomicalresolutionoftheprey. Significantlineartrendswith95%confidencelimitsareshown
8 | SÁNCHEZHERNÁNDEZ Et al. selection(seeTableS3)suggestthattheabundanceofsurfacepreyin thedietofsalmonandfishabundance(salmon,bullhead,andArctic charrabundance)mayhavehadsomeinfluenceonfoodresourcepartitioning.Theabundanceofsurfacepreyinthedietofsalmongave a significant negative correlation with dietary overlap between the salmonandbullhead(Figure3c;R=−.671,p = .024). 4 | DISCUSSION Therewasanegativecorrelationbetweenpreydiversityanddietary overlapofsalmonandbullhead,supportingthehypothesisthathigh prey diversity may enhance food resource partitioning between sympatricspeciesandtherebyfacilitatetheircoexistence.Inpreviousstudies,highdietaryoverlapwasobservedbetweensalmonand bullheadatahomogeneousriversitewithalowdiversityofzoobenthos(Gabler&Amundsen,1999),whereasstrongdietarysegregation wasobservedbetweensalmonandEuropeanbullhead(Cottus gobio Linnaeus, 1758) in a more heterogeneous river that had relatively highdiversityofzoobenthos(Gabler,Amundsen,&Herfindal,2001). Thus,resourcepartitioningbetweensalmonandbullheadspeciesmay berelatedtobetween-riverdifferencesinpreydiversityandhabitat characteristics(Gabler&Amundsen,1999;Gableretal.,2001).The presentstudyrevealsthatfoodresourcepartitioningbetweensalmon andbullheadcanvarywithinariversystem,betweensitesatrelatively shortdistancesfromeachother,andbetweenseasonsatagivensite, withresourcepartitioningbeingstronglydependentonpreydiversity atdifferentsites.Studiesonotherspecieshavegivenindicationthat highpreydiversitymayenhancefoodresourcepartitioning(see,e.g., Hillebrand&Shurin,2005;Jiangetal.,2008;Martin&Garnett,2013; Zapataetal.,2005)andthatcompetitionforfoodishighwhenprey diversityislow(Barilietal.,2011;Hillebrand&Shurin,2005;Targett, 1981).Ourstudycorroboratesthesefindingsandsupportsthenotion thathighpreydiversitymaypromoteconsumercoexistencethrough foodresourcepartitioning. Itishypothesizedthatpreydiversityasitrelatestocompetition forfoodresourcesandpartitioningcouldhaveaninfluenceonsegregation and species coexistence in consumerswith similar trophic nicherequirements.Theoreticalconsiderationsthataddressrelationships between dietary overlap, competition, and coexistence posit thatcompetitionforcessympatricspeciestodivergeandsegregate inresourceuse(Schoener,1974,1989),theweakerspeciesmaybe excluded(e.g.,Eloranta,Knudsen,&Amundsen,2013;Nakanoetal., 1999;Schoener,1989),orecologicallysimilarsympatricspeciesmay converge and overlap in resource use (e.g., Cucherousset, Aymes, Santoul, & Céréghino, 2007; Keddy, 2001; Paterson etal., 2014; Wiens, 1993). These are seemingly contradictory standpoints. The first consideration encapsulates the competitive exclusion principle (Gause,1934;Hardin,1960)thathasbeenwidelyacceptedbymany inthescientificcommunity,andthesecond,althoughbeingmorecontroversial,hasalsoreceivedsomesupport(see,e.g.,Bengtsson,1991; Grant,1972;terHorst,Miller,&Powell,2010).Wesuggestthatthe apparentcontradictionscanberesolvedifpreydiversityistakeninto account.Ourreasoningisasfollows:Competitionforfoodmayresult FIGURE4 Schematicillustrationof thepotentialinfluenceofpreydiversity onresourcepartitioningbetweentwo stream-dwellingfishspeciesinsympatry (hereAtlanticsalmonparrandalpine bullhead).Forexample,ifpreydiversityis low,itisprobablethattherewillbestrong competitionbecausepreydiversityis insufficienttoallowsympatricconsumers tospecializeandsegregateinpreyuse
| 9 SÁNCHEZHERNÁNDEZ Et al. ineitherhighandlowdietaryoverlapbetweensympatricconsumers dependingonpreydiversity.Ifpreydiversityishigh,thecompeting speciesmaysegregateinresourceusebyspecialization,forexample, exploitationofGlossosoma intermedium(Klapalek1892)bybullhead anduseofsurfacepreyandApatania stigmatella(Zetterstedt1840)by salmon.Underthesecircumstances,competitionresultsinresource segregationthroughalowdegreeofdietaryoverlap,aspredictedby classicnichetheory(e.g.,Jiangetal.,2008;Martin&Garnett,2013; Schoener,1989;Targett,1981).Thestrengthofcompetitionmaybe lowbecausethecompetingspecieshavethepossibilitytominimize negativeeffectsbysegregatingtheiruseoffoodresourcesviaspecialization.Ontheotherhand,ifpreydiversityislow,itisprobablethat therewillbestrongcompetitionbecausepreydiversityisinsufficient toallowsympatricconsumerstospecializeandsegregateinpreyuse (Figure4).Thiscomplieswithasituationthatleadstoatheoretical predictionthatcompetitionwillresultinhighnicheoverlapifthespeciesaresymmetricalintheircompetitiveabilities(Ågren&Fagerstrøm, 1984;Gilbert,2012;Keddy,2001),orifcompetitionisverystrong (Martin & Genner, 2009; Schoener, 1989; Wiens, 1993).Thus, the seeminglycontrastingconsiderationsabouthowcompetitionaffects dietaryoverlapmaynotbetrulycontradictory,butbothmaybevalid dependinguponthescaleofpreydiversity. Inadditiontopreydiversity,preyabundancemightbeafactorthat influencesthestrengthofcompetition(see,e.g.,Triplet,Stillman,& Goss-Custard,1999).Theintuitiveexpectationisthatcompetitionfor foodshouldbehigherwhenresourcesarescarcethanwhentheyare abundant.Althoughpreyabundancewasasignificantpredictorvariableintheforwardstepwiseprocedure,otherexplanatoryvariables were more influential (see Table2). In this regard, fish abundance couldplay aroleingoverningfoodresourcepartitioning.Thisisin agreementwithpreviousworks,demonstratingthatfishabundance canexacerbatecompetitionforfoodinfishassemblages(Elliott,1994; Engelhardetal.,2013).Itisimportanttonotethatthecorrelationbetweendietaryoverlapandfishabundancewasnotsignificant,sofish abundancemayoperatesynergisticallywithpreydiversitytoenhance food resource partitioning (see models including fish abundance in TableS3).Forexample,Barilietal.(2011)reportedthathighfishabundanceanddiversitycanpromotetrophicspecializationinsympatric species,therebyenhancingfoodresourcepartitioning,anddensity- dependentforagingbehaviormayoccurwhenresourcesarelimited (Sánchez-Hernández&Cobo,2013). Noteworthy, ourinterferences regarding the influence of density dependence on the competition forfoodshouldbetakenwithsomecautionbecauseouranalysesincludedtheoverallfishdensityregardlessoffishsize.Nevertheless,it isreasonabletopositthatfishabundancemayimpactonthemechanismsinvolvedinfoodresourcepartitioningasdiscussedearlier. Our study revealed that surface prey were strongly representedinthedietofsalmonatsiteswheredietaryoverlapbetween salmonandbullheadwaslowest,suggestingthatsurfacefeeding maybeacontributingfactorthatdrivesfoodresourcepartitioningbetweenstream-dwellingfishspecies(see,e.g.,Dineenetal., 2007;Sánchez-Hernández,Gabler, &Amundsen, 2016;Sánchez- Hernández,Servia,Vieira-Lanero,&Cobo,2013).Althoughsalmon and bullhead usually feed primarily on benthic invertebrates (Amundsen&Gabler,2008;Gabler&Amundsen,1999,2010),our studyclearlydemonstratesthatbullheadfeedlessonsurfaceprey thandosalmon,withtheproportionofsurface-driftforagersbeing substantiallyhigherinsalmonthaninbullhead(Sánchez-Hernández etal.,2016).Thus,thisstudycorroboratestheflexibilityofsalmon adoptingitsforagingmodesinrelationtobullhead.Theinference isthatbullheadhasapreferenceforforagingclosetothebottom, whereassalmonmayforagethroughoutthewatercolumnandcan adoptdifferentforagingmodestoovercomecompetitionwiththe co-occurringspecies,butthelackofdriftsamplingdidnotallowus toassesswhetherornotthisfeedingbehavioradoptedbysalmon ismotivatedbydriftavailabilityorfoodcompetitionwithbullhead. However,thisflexibilityislikelytobeinfluencedbypreyavailabilities(e.g.,Nakanoetal.,1999;Sánchez-Hernández&Cobo,2013), leadingtorelationshipsthatareinfluencedbybenthicinvertebrate diversityandtheavailabilityofsurfaceprey.Dielpatternsoffeeding andhabitatutilizationhavethepotentialtoinfluencefoodresource partitioning between sympatric species (e.g., Crow etal., 2010; Kronfeld-Schor&Dayan,2003;Sánchez-Hernándezetal.,2011). However,previousstudiesinRiverReisarevealednostrongsegregationinforagingtime(dielfeedingrhythms)andspace(habitat) betweensalmonandbullhead(Amundsen&Gabler,2008;Gabler& Amundsen,1999,2010),whichsupportsourmainconclusionthat preydiversityisthemaindriverofresourcepartitioninginthese twospecies.Still,thecapacitytoforageatthewatersurface(surface feeding) by salmon needs to be acknowledged as a spatial segregationinfeedingcontributingtotheobservedresourcepartitioningbetweenthetwomodelspecies. Preydiversityemergedasthestrongestpredictorofresourcepartitioningbetweensalmonandbullhead,althoughresourcepartitioning wasalsoinfluencedtosomeextentbysurfacepreyuse,andfishand preyabundances.Preydiversityandsurfacepreymayhaveoperated synergisticallytoenhancefoodresourcepartitioningbetweensalmon andbullhead.Additionalworkwillbeneededtoexploreandenhance ourunderstandingofhowtheinterfacebetweenaquaticandterrestrial ecosystems influences ecological processes, such as resource partitioning. ACKNOWLEDGEMENTS ThanksareduetoR.GuttormsenandS.Sandringforassistanceduring field work. We appreciate constructive comments from Dr. M. Jobling,whichconsiderablyimprovedthequalityofthemanuscript. We also thank two anonymous reviewers for valuable comments thathelpedimprovethemanuscript.Financialsupportwasprovided bytheNorwegianResearchCouncil.J.Sánchez-HernándezwassupportedbyapostdoctoralgrantfromtheGalicianPlanforResearch, Innovation,andGrowth2011–2015(PlanI2C,XuntadeGalicia). CONFLICT OF INTEREST Nonedeclared.