Evolutionary Applications. 2017;1–16.  | 1wileyonlinelibrary.com/journal/eva

Received:21October2016  |  Accepted:30May2017DOI: 10.1111/eva.12499

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

Strong population structure deduced from genetics, otolith chemistry and parasite abundances explains vulnerability to localized fishery collapse in a large Sciaenid fish, Protonibea diacanthus

Laura Taillebois1,2  | Diane P. Barton1,3 | David A. Crook1 | Thor Saunders3 |  Jonathan Taylor3 | Mark Hearnden3 | Richard J. Saunders4,5 |  Stephen J. Newman6 | Michael J. Travers6 | David J. Welch7 | Alan Greig8 |  Christine Dudgeon9 | Safia Maher9 | Jennifer R. Ovenden9

1ResearchInstitutefortheEnvironmentandLivelihoods,CharlesDarwinUniversity,Darwin,NT,Australia2NorthAustraliaMarineResearchAlliance,ArafuraTimorResearchFacility,Brinkin,NT,Australia3DepartmentofPrimaryIndustryandResources,NorthernTerritoryGovernment,Berrimah,NT,Australia4CentreforSustainableTropicalFisheriesandAquaculture,JamesCookUniversity,Douglas,QLD,Australia5AnimalScience,QueenslandDepartmentofAgricultureandFisheries,Brisbane,QLD,Australia6WesternAustralianFisheriesandMarineResearchLaboratories,DepartmentofFisheries,GovernmentofWesternAustralia,NorthBeach,WA,Australia7C2OFisheries,Cairns,QLD,Australia8SchoolofEarthSciences,TheUniversityofMelbourne,Melbourne,VIC,Australia9MolecularFisheriesLaboratory,SchoolofBiomedicalSciences,TheUniversityofQueensland,St.Lucia,QLD,Australia

ThisisanopenaccessarticleunderthetermsoftheCreativeCommonsAttributionLicense,whichpermitsuse,distributionandreproductioninanymedium,providedtheoriginalworkisproperlycited.©2017TheAuthors.Evolutionary ApplicationspublishedbyJohnWiley&SonsLtd

CorrespondenceLaura Taillebois, Research Institute for the Environment and Livelihoods, Charles Darwin University,Darwin,NT,Australia.Email: laura.taillebois@cdu.edu.au

Funding informationFisheriesResearchandDevelopmentCorporation,Grant/AwardNumber:Project2013/017;NorthAustraliaMarineResearchAlliance

AbstractAspressureoncoastalmarineresourcesisincreasingglobally,theneedtoquantita-tivelyassessvulnerablefishstocks iscrucial inordertoavoidtheecologicalconse-quencesof stockdepletions.SpeciesofSciaenidae (croakers,drums) are importantcomponentsoftropicalandtemperatefisheriesandareespeciallyvulnerabletoex-ploitation.Theblack-spottedcroaker,Protonibea diacanthus,istheonlylargesciaenidincoastalwatersofnorthernAustraliawhereitistargetedbycommercial,recreationalandindigenousfishersdueto itsfoodvalueandpredictableaggregatingbehaviour.Localizeddeclinesintheabundanceofthisspecieshavebeenobserved,highlightingtheurgentrequirementbymanagersforinformationonfine-andbroad-scalepopula-tionconnectivity.ThisstudyexaminedthepopulationstructureofP. diacanthus across north-westernAustraliausingthreecomplementarymethods:geneticvariationinmi-crosatellitemarkers,otolithelementalcompositionandparasiteassemblagecomposi-tion. The genetic analyses demonstrated that there were at least five geneticallydistinctpopulationsacrossthestudyregion,withgeneflowmostlikelyrestrictedbyinshorebiogeographicbarrierssuchastheDampierPeninsula.Theotolithchemistryand parasite analyses also revealed strong spatial variation among locationswithin

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1  | INTRODUCTION

Nearshore coastal ecosystems provide an important source of foodfor humanpopulations, supporting 90%of the globalwild fish har-vest whilst accounting for only 7% of the oceanworldwide (Paulyetal.,2002).Asglobaldemandforseafoodincreases,manyexploitedspecies in these regionshavedeclinedand largenumbersof fisher-iesarecurrently fishedatunsustainable levels (Jacksonetal.,2001;Smithetal.,2010).Sustainablemanagementofexploitedfishpopu-lations requires detailed knowledge of population structure, naturalabundanceandthedegreeofecologically relevantexchangeamongharvestedstocks(Paulyetal.,2002).Theconceptof“stocks”aspop-ulationunitshaslongbeenanchoredinfisheriesscienceandcanbedefinedasgroupsoffishwithinaspeciesthatareself-recruiting;sharesimilar growth rates and rates of natural and non-naturalmortality;andmayreactmoreorlessindependentlytoharvesting(Cadrin,Kerr,&Mariani,2013).

Methods fordelineating stockshaveadvancedconsiderably inrecent years and include genetic techniques, acoustic telemetry,tagging,otolithchemistry,demographicanalysis,otolithshapeandmeristicdata (Hawkinsetal., 2016).Genetic approaches speak toboth inter-and intragenerational timescalesas theytrack the life-history stages of the fish (i.e., from fertilized eggs to adults) thatare interchanged between locations andwhich subsequently par-ticipate insuccessfulspawning.Otolithmulti-elementalsignaturesandparasiteassemblagesareecologicalmarkersrecordingtheen-vironmentthejuvenileandadultfishinhabitandreflectprocessesoccurringwithintheindividuals’lifetime.Theintegrationofmultipletechniquesthatoperateoverdifferent temporalandspatialscalesmakes it possible to overcome many of the limitations of singletechniqueapproachesandgreatlystrengthensthe inferenceavail-able from stock structure studies (Abaunza etal., 2008; Begg &Waldman,1999;Lleonart&Maynou,2003;Waldman,1999;Welchetal.,2009,2015).Asanexample, Izzoetal. (2017)usedan inte-gratedapproachtorevealfourstocksofthesardineSardinops sagax in Australian waters when it was previously considered a semi-continuousmeta-population.

Fishes of the family Sciaenidae, commonly known as croakers,are important components of commercial, recreational and indige-nousfisheriesintropicalandtemperateregionsworldwide(Lenanton&Potter, 1987).They are targeted for their flesh and, increasingly,for their swim bladders, which are sold fresh or dried in South- East Asia(Ghoshetal.,2009;Sadovy&Cheung,2003;Tuuli,2010).ManyspeciesofSciaenidaehavedeclined in recentdecadesduetoover-exploitation,andseveralarenowconsideredthreatened.Theblack-spotted croaker Protonibea diacanthus (Lacapede, 1802) is a largespecies (>1.5mmax. total length; up to 42kgmass) that iswidelydistributed throughout coastalwaters and estuaries of the tropicalIndo-WestPacific (Sasaki,2001). InAustralia, it isdistributedalongthe northern coast from Shark Bay, Western Australia to HerveyBay,Queensland (Bray,2011).The speciesgrows rapidly andhas amaximum recorded age of 13years (Phelan, 2008). Spawning ofP. diacanthusinAustraliaoccursfromAugusttoDecember,andtheyappeartoproducepelagiceggsandhaveapelagiclarvalphase(Froese&Pauly,2016;Leis&Carson-Ewart,2000).Whilst inshoreseasonalbreeding aggregations ofP. diacanthus are thought to occur (Welchetal.,2014),directevidenceofthebehaviourofthespeciesislimited(Phelan,2008)andlittleisknownaboutthesizeandlocationsoftheaggregations.Commercialandrecreationalsectorsbothcontributetototal landings (667 tonnes in2005),butcatcheshave increased re-centlydueprimarilytogrowthinthecommercialsector(from43to250tonsperannumbetween1995and2005;Coleman,2004;Phelan&Elphick,2006). Intensiverecreational fishingontheeastcoastofQueensland(Bowtell,1995,1998)hasalsobeenattributedwithre-ducing fishabundanceand size to thepointwhere thecatchesarealmostexclusivelylimitedtoimmaturefish.Thereisalsoevidenceofover-exploitationofP. diacanthusoutsideofAustralianwaters:over-fishinginIndiainthe1980sledtolocalextirpation(James,1994)andafisherythatoncethrivedinHongKongnolongerexists(Sadovy&Cheung,2003).

ManagementpracticestosupportthesustainableharvestofP. di-acanthusinWesternAustralia,QueenslandandtheNorthernTerritoryarecurrentlyhinderedbya lackofknowledgeof thespecies’popu-lationstructure.Inthisstudy,weexaminedthepopulationstructure

broad-scaleregions,suggestingfine-scalelocationfidelitywithinthelifetimesofindi-vidualfish.Thecomplementarityofthethreetechniqueselucidatedpatternsofcon-nectivityoverarangeofspatialandtemporalscales.Weconcludethatfisheriesstockassessmentsandmanagementarerequiredatfinescales(100sofkm)toaccountfortherestrictedexchangeamongpopulations(stocks)andtopreventlocalizedextirpa-tionsofthisspecies.Realisticmanagementarrangementsmayinvolvethesuccessiveclosureandopeningoffishingareastoreducefishingpressure.

K E Y W O R D S

croaker,fisheriesmanagement,otolithchemistry,parasites,populationgenetics,stockdiscrimination

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ofthespeciesacrossnorth-westernAustraliawiththeexpectationofspatially distinct stocks,whichwould be consistentwith the obser-vationoflocalizeddepletion.Moreover,weaimedtotesttheexpec-tation that genetics provides informationon a broader spatial scalethanotolithchemistryandparasitedata in thiscoastallydistributedspecies.Todosoandtodemonstratethepowerofintegratingacrosstechniques,wecontrastedpopulationstructureresultsfromthethreemethodsandfocusedononeexamplewheretheassignmentofindi-vidualstoadjacentpopulationswasmethod-specific.Finally,weuseourresultstoaddresstheappropriatespatialscaleforstockassess-mentandsuggest fisheriesmanagementarrangementsforP. diacan-thusacrossnorth-westernAustralia.

2  | MATERIAL AND METHODS

2.1 | Samples

Atotalof298fishwerecollectedfrom11samplinglocationsacrossnorth-western Australia from Roebuck Bay (Western Australia) totheVanderlinIslandsintheGulfofCarpentaria(NorthernTerritory)(Figure1,Table1).Thedeclineof thespecies (Phelan,2002)meantthat samples could not be obtained from north-eastern Australia.The lack of knowledge on the location and size of the aggregationgroundsinP. diacanthusdidnotallowtheexclusivecollectionofma-ture fishonspawninggrounds,andthus, fishofdifferentsizewereincludedintheanalysis.SampleswerecollectedwiththeassistanceofIndigenousmarinerangers,fishingtouroperatorsandrecreationaland

commercialfishers.Thetotallength(TL),standardlength(SL)andsexofeachspecimenwererecorded.Forgeneticanalyses,muscletissueorfinclipsweresampledandimmediatelyplacedintovialscontain-ingmoleculargrade95%ethanolor20%dimethylsulfoxide(DMSO)solutionin5mol/LNaClandstoredat4°Cinthefieldand−20°Cinthe laboratory. The sagittal otolithswere dissected from each fish,cleanedandrinsedthoroughly,dried,andstoredinpaperenvelopespriortopreparationfortraceelementanalysis.Gillsandinternalbodyorganswereremoved,placedintoindividualbagsandfrozenuntilex-amination for parasites. The same individualswere used for otolithmicrochemistry,geneticandparasiteanalyses.

2.2 | Data analysis

Populationstructurewastestedinanhierarchicalfashion,withgenet-icsbeingdeployedat thebroadscale toguidepopulationstructureandecologicalmarkersusedatthefinerscale.Thenatureofotolithmicrochemistry reflects fine-scale spatial variation between envi-ronmentsandisnotusefulforbroad-scalevariation.Indeed, lackofsignificance (i.e.,otolithsimilarity)onabroadspatial scaledoesnotindicateecologicalpopulationhomogeneity.Fine-scalegeneticanal-yseswereused to integrateacross the threemethods for apairoflocationsinNWAustraliaforwhichonepresentedastrikinggeneticheterogeneity.

GenomicDNAfromallsamplesforgenotypingwasextractedusingISOLATEIIGenomicDNAKit(Bioline)followingthemanufacturer’sin-structionstoproduce100μlofelutedDNAfromeachsample.Allthe

F IGURE  1 Locationofthe11samplingsitesacrossnorthernAustraliashowingthetwojurisdictions(WesternAustraliaandNorthernTerritory).ThedetailsforeachofthesamplinglocationsareprovidedinTable1

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DNAextractswerequantifiedusingtheQubitv3(ThermoFisher)flu-orometricmachine.Elevenspecies-specificmicrosatelliteloci(Prd012, Prd023, Prd044, Prd042, Prd018, Prd045, Prd046, Prd020, Prd036, Prd049, Prd024)weregenotypedacrossallthesamplesaspartofmul-tiplexesasdescribedinTailleboisetal.(2016).

The potential for null alleles, large allele dropout and stutteringto interferewith scoring accuracywas checked for eachmicrosatel-lite locus in each sample usingMICROCHECKERversion 2.2.3 (VanOosterhout, Hutchinson,Wills, & Shipley, 2004). Summary statisticsformicrosatellite loci, includingthenumberofalleles,allelicrichness,expected and observed heterozygosity and fixation indexes, wereobtained for each sampling location using GENALEX version 6.502(Peakall&Smouse,2006).Testsofconformanceofgenotypicpropor-tions to Hardy-Weinberg equilibrium expectations were carried outfor each locus at each sample location, and testsof genotypic equi-libriumbetweenpairsofmicrosatellites (linkagedisequilibrium)werecarriedoutforeachsamplelocation,usinganexactprobabilitytestasimplementedinGENEPOPversion4.5(Rousset,2008).TheexacttestwasestimatedusingaMarkovChainthatemployed1,000dememo-rizations,500batchesand1,000 iterationsperbatch.WeaccountedforthelinkagedisequilibriummultipletestingbyapplyingaBonferronicorrection to the p-value.Atestwithap-value<.0045(0.05/numberoflociconsideredineachmultipletest)wasrequiredtobeconsideredsignificant.

Inordertoassesswhetherlocationscouldbetreatedasindepen-dentgeneticallycohesivegroupsonabroadspatialscale,andtotestfortheextentofadmixturewithinthegroups,twocontrastingclus-tering approacheswere used. Population assignment and clusteringwas performedusing theBayesianmodel-based clustering programSTRUCTUREversion2.3.4(Pritchard,Wen,&Falush,2003).Forcom-parativepurposesonthebroadspatial scale,adiscriminantanalysisofprincipalcomponents(DAPC,Jombart,Devillard,&Balloux,2010)available in the adegenetpackage(Jombart,2008)forRversion3.3.1

(RCoreTeam,2017)wasalsoused.Anhierarchicalapproachwassub-sequently taken toexplorepotential substructurewithin thegroupsrevealed by the STRUCTURE broad- scale analysis. The hierarchical analyseswere also performed using STRUCTURE.Methodology forSTRUCTUREandDAPCisdetailedinAppendixA1inAppendixS1.

To test alternate post hocobservationsofgeneticstructurerevealedby our different approaches, we assessed the per cent of geneticvarianceexplainedbythegroupingsdeducedfromSTRUCTUREandDAPC results using an analysis of molecular variance (AMOVA) asimplemented in ARLEQUIN. The significance of any differentiationwasdeterminedbypermutationof22,000replicates.

PairwiseFSTvalueswere calculated (i) to further investigate thepopulationstructurewithinthegroupsrevealedwiththeclusteringap-proachesand(ii)totestwhetherP. diacanthusisconstrainedbyapat-ternofisolationbydistance(IBD).Fixationindices(FST)betweenpairsofsamplelocalitieswereestimatedasimplementedinARLEQUINver-sion3.5.2.2(Excoffier&Lischer,2010).WeperformedaManteltestof FST/(1−FST) (G)versusgeographic (D)distances among locationsusingARLEQUIN.

Toinvestigatethefine-scalepopulationstructureandprovidein-formationonthedispersalof fish, themicrochemistryoftheotolithwasexaminedintwosectionsoftheotoliths:thenearcoreandthemargin. The near core area is located just outside the first opaquezone ~500 μmfromthecoreoftheotolithandrepresentsthe juve-nilephase;andtheotolithmargin(edge)wassampledadjacenttothesulcus acusticustorepresentgrowthintheperiodpriortocapture.Atotalof11traceelements(7Li, 25Mg,23Al,49Ti, 53Cr, 55Mn,60Ni, 63Cu, 66Zn, 88Sr, 138Ba)andtheinternalstandard(43Ca)wereanalysedfromthetwoablationzonesineachotolith.Detailsonthegeneralmethod-ologyfortheotolithpreparation,thelaserablationanddatatransfor-mationarepresentedinAppendixA2inAppendixS1.Forallelements,theratioofelementisotopeintensitytoCaintensitywasusedtoes-timate the element:43Ca ratio. These ratios were converted to molar

TABLE  1 Detailsofthe11sampledlocationsofProtonibea diacanthusacrossnorthernAustralia

Jurisdiction Sampling location Prefix Sample size Collection date Mean TL Mean age

Western Australia

RoebuckBay RB 36 Jul,Aug,Oct,Nov2014–May,Jun,Jul,Aug2015

1,018(720–1,199) 6(2–10)(34)

Camden Sound CS 20 Sep,Oct2013 647(520–920) 3(3–4)(4)

Wyndham Wy 34 May,Jun2015 1,061(804–1,300) 5(3–8)(26)

Northern Territory

Wadeye Wa 25 Jun,Nov2014 789(540–1,160) 3(2–5)(17)

PeronIslands PI 29 May2015 N/A 4(3–5)(13)

Offshore Darwin OD 17 Oct,Dec2012–Jul,Sep2013–Mar2014 608(395–1,150) 3(2–4)(8)

BathurstIsland BI 28 Nov2013–Sep,Nov2014–Aug,Sep2015

981(387–1,235) 6(4–8)(19)

MelvilleIsland MI 30 Aug2012–Sep,2013–Apr,Aug2015 646(405–1,170) 2(2–5)(26)

Maningrida Ma 30 Aug2014–Jun,Jul2015 746(420–1,210) 3(2–5)(17)

ArafuraSea AS 20 Jul2013 N/A 2(2–3)(10)

VanderlinIslands VI 29 Feb2014 592(440–770) 2(2–3)(25)

Meantotallength(TL)isexpressedinmmandageinyears.Rangeoflengthandageareindicatedinbracketsafterthemean.Numberoffishagedisitali-cizedinbracketsaftertherange.

     |  5TAILLEBOIS ET AL.

ratiosandwereexpressedaselement:Camolarratiosinmmol/molorμmol/mol(AppendixB1inAppendixS2).

Parasiteswerealsousedasbiologicaltagstoinvestigatethefine-scalepopulationstructureofP. diacanthus.Directlifecycleparasitesgenerallyhaveshort-livedlarvalstagesthatareunabletocoverlargedistancesandshouldreflectlocalityoforigin.Indirectlifecyclepar-asites,ontheotherhand,cantravelthroughmultiplehostsandthushaveadistributionpatternthatisultimatelyatthemercyofanum-berofdifferentfactors.Actual lifecyclesofthemajorityofaquaticparasites remainunknown,andwecanonlygeneralizeas tohostswithinthecyclebasedoncurrentknowledge.Initially,analyseswereperformedforexternalparasites(=direct)andlarvalstages(=indirectorpermanentparasites)separately.Bothanalysesshowedsignificantseparation among sites. We subsequently combined the data fordirectand indirectparasites for the finalanalyses,as thisprovidedmore robust and biologically reliable and interpretable results (seebelow).

DetailsonthegeneralmethodologyfortheparasiteextractionandexaminationarepresentedinAppendixA3inAppendixS1.Summarystatisticsoftheparasitesdatawerecompiledforeachlocation,aswellasforallfishexaminedandarepresentedinAppendixB2inAppendixS2.Thisincludedmeanabundance(totalnumberofindividualsofapar-ticularparasitepercollectionlocationdividedbythetotalnumberoffishfromthatlocationexamined,includinguninfectedhosts)andprev-alence(numberofhostsinfectedwithaparticularparasitedividedbythenumberofhostsexamined,expressedasapercentage)foreachoftheparasitespeciesdeemedsuitableforuseintheanalyses,followingtheterminologyofBush,Lafferty,Lotz,andShostak(1997).Onlypar-asiteswithaprevalencegreaterthanorequalto10%inatleastoneofthelocationswereusedintheanalysis(Bush,Aho,&Kennedy,1990);additionalparasiteswereremovedfromanalysesiftheycouldnotbeeasily identified and/or accurately counted (MacKenzie & Abaunza,1998).

The otolith and parasite datawere broken down into three geo-graphic regions for statistical analyses (i) because these ecologicalmarkersarethoughttooperateoversmallerspatialandtemporalscalescomparedtothegenetics;hence,theyrequireasmallerscaleofanal-ysisand(ii)totestofthecurrentjurisdictionalmanagementboundarybetweenWesternAustraliaandtheNorthernTerritoryandtheregionalmanagementunitsacrosstheNorthernTerritory.Eachregionincludedthegeographicallyclosestsamplelocationfromtheadjacentregiontoallowinter-regioncomparisons.TheWesternregionincludesalloftheWesternAustralianlocations(RB,CSandWy)aswellasWa,whichistheclosestNorthernTerritorysamplelocation(Figure1).TheDarwinregionincludesalltheNorthernTerritorylocationsfromWatoMI(Wa,PI,OD,BIandMI).TheArnhem/GulfregionincludesalltheArnhemLandandGulfofCarpentariapopulationsoftheNorthernTerritory(Ma,ASandVI)aswellasMI,whichistheclosestNorthernTerritorysamplelocation.

All data analysis was performed using R (R Core Team, 2017).Pairwisedistributionsofallvariablesusedineachanalysisforthepar-asiteandotolithdatasetswereinspectedforextremeoutliers,unsatis-factorycovariatedistributions,andthepresenceofcollinearity.Wherenecessaryabundancedatawerelog(Y+1)transformed.Homogeneity

ofthecovariancematricesforthegroupingfactorwasassessedusingBox’sM-testbasedonthechi-squareapproximationwiththebiotools packageinR(daSilva,2016).

Fishareknowntoaccumulatelonger-livedparasitespecieswithage(Rohde,1982),whichwillaffectdifferencesinparasitefaunaifsamples contain individuals of different ages. All parasite speciesthatwereselectedonprevalencewereexaminedfor theircorrela-tionwith host length (as an indicator of age) using linearmodels.Wherelengthwassignificantlycorrelatedwithabundance,numbersgreaterthanzerowereadjustedtothemeanhostsize(totallength)usingthemethodsdescribedinMoore,Buckworth,Moss,andLester(2003).

Spatial variation in parasite assemblages and the otolith nearcoreandmarginchemistryamongregionsand locationswithin re-gionswere investigated separately using single-factormultivariateanalysisofvariance (MANOVA). Lineardiscriminant function anal-ysis (LDFA)was conducted toprovide a statistical andvisual indi-cationof thesimilaritiesofeither theparasitecommunitiesor themulti-elementalotolithmicrochemicalsignaturesamongsamplesattheregionalspatialscaleusingtheMASSpackageinR(Venables&Ripley, 2002). LDFA classification success rates and an associatedproportional chance criterion (the expected proportion of correctclassification by chance alone) were calculated (Poulin & Kamiya,2015)foreachlocationwithintheregions.Toshowtheseparationachieved for the groups in the analysis, the first two discriminantfunctionsforeachindividualwereplottedandthe95%confidenceellipses around the centroid means of the first two discriminantfunctionsforeachgroupinthesampleusingtheellipsepackageinR(Murdoch&Chow,2013).

Totesttheeffectofdistancebetween locationsontheparasiteassemblages, and the otolith elemental concentrations in the nearcoreandthemargin,weperformedaManteltestoftheJaccarddis-similarityusingthemeandifferencesinnumberandconcentrations,respectively, between each pair of locationsversus geographic dis-tancesamonglocationsusingtheveganpackageinR(Oksanenetal.,2017).

The individualassignmentoffishwastestedthroughaBayesianapproach across all fish for otoliths andparasites.Classes andpos-terior probabilities were calculated by jackknife cross-validation toassign individuals tomost probable locationswithin each region. Ina couple of locationswhere the genetic structure gave evidenceofgeneticheterogeneitywithinlocations,STRUCTUREwasusedtocal-culateposteriorprobabilitiesandassignindividualstomostprobablelocations. Individual mis-assignments were compared across tech-niquestotestwhethereachtechniqueprovideddifferentinformationornot(i.e.,seewhetherthesamefishweremis-assignedtothesamelocationsbetweentechniques).

3  | RESULTS

Genotypesfrom11microsatellitelociwereobtainedfor284individu-als of P. diacanthus.Thelevelofmissingdatawaslow(2.15%)andas

6  |     TAILLEBOIS ET AL.

suchwas retainedand identifiedasmissingdata in the subsequentanalyses (missingdatawerecalculatedas thenumberofgenotypesthatcouldnotbescoredoverthetotalnumberofgenotypespresentintheidealdataset,11×284).Thenumberofallelesperlocusrangedfrom5 (Prd046) to21 (Prd012) (AppendixB3 inAppendixS2). Thedatawere freeof scoringerrorsasevaluatedbyMICROCHECKER.Genotypeswererescoredandcorrecteduntiltherewasnoevidenceof null alleles at these or other locations and loci. Deviations from Hardy-Weinbergproportions(HWP)weredetected14times(p- value <.05 for Prd044atRB,Prd023atRB,WaandBI,Prd042 at OD, Prd012 atWaandMa,Prd018atCSandMa,Prd020atPI,ODandAS,Prd036 atRBandPrd024atPI)outofthe121testsperformedforeachlocusagainst each location. Therewasno significant test for linkagedis-equilibriumbetweenpairsoflociacrossalllocationsafterBonferronicorrection(p-value<.0045).Theanalysisproceededwithoutremov-inglociorindividualsfromthedataset.Heterozygositywasmoderatetohigh forall loci acrossall locations (0.659±0.179,AppendixB3inAppendixS2)andgenerallysimilartoexpectationsformarinefish(approximately0.7,DeWoody&Avise,2000).

Four distinct populationswere identified usingBayesianmodel-basedclusteringmethods.Theoptimumnumberofgenetic clustersreturnedby∆K in STRUCTURE was 5 without LOCPRIORand3withLOCPRIOR. The LOCPRIORparameterenhancedthepopulationstruc-turepattern(AppendixB4inAppendixS2);hence,thefollowingre-sults describe the observed pattern using location information as aprior.ForK=3,fourgeographicallydistinctpopulationsorgroupsofpopulationswererevealed.ThewesternpopulationsRBandCSweredistinctfromeachother,andagroupofcentralnorthernpopulationsWy-Wa-PI-OD-BI-MI-Maappeared tobe similar.TherewasalsoaneasterngroupwithlocationsASandVIappearingsimilartoeachother.DespitethesimilaritybetweenAS-VIandCSgenotypeswhenK=3,theywereassumed tobedifferentpopulationsorgroupofpopula-tions because of their geographic distance (Figure2a). Independentinvestigations of hierarchical structure within the northern groupWy-Wa-PI-OD-BI-MI-Ma and within the Gulf locations AS-VI didnot reveal further substructurewithin those twogroups (Figure2a).Overall,thegroupsfavouredbySTRUCTUREwerethefollowingfour:RB,CS,Wy-Wa-PI-OD-BI-MI-Ma,AS-VI.

In successive K-means clustering using find.clusters in R, the Bayesian InformationCriterion (BIC) showedan initial sharpdeclinein values until K=5afterwhichBICvalues increased (Figure2b).Tomatch the number of genetic clusters revealed by find.clusters, the individualswerepartitioned intofivegroupsof locationsthatwouldtake into account the genetic structure previously highlightedwithSTRUCTURE.TheGulfofCarpentarialocationsASandVIwereheresplit basedon their geographic distance.The five groups used as apriorintheDAPCwereasfollows:RB,CS,Wy-Wa-PI-OD-BI-MI-Ma,AS andVI.Theoptimal numberofPCswith thehighestmean suc-cessfulassignmentandthelowestmeansquarederrorwas30.Whenpartitioningtheindividualsintofivegroups,DAPCrevealedoverlap-pingdistributionsofgeographicgroupsontheordinationplotthatin-dicatedalowdegreeofgeneticdifferentiationbetweenthesegroupsorlocations.TheDAPCseparatedRBfromotherlocationsalongthe

firstprincipalcomponentaxis (eigenvalue=47.99) (Figure2c).Alongthesecondcomponentaxis(eigenvalue=35.59),ASwasplotteddis-tant fromtheWesternAustralia locationsRBandCSaswellas theWy-Wa-PI-OD-BI-MI-MagroupbutclosertoVI(Figure2c).

AMOVAwasusedtocomparethedifferentscenariossupportedbySTRUCTUREandDAPC.The4-and5-groupscenariohighlightedbySTRUCTUREandDAPC,respectively,explained2.15%(p-value=.000)and 2.28% (p-value=.000) of the total genetic variation. As bothscenariosexplainedverysimilaramonggroupgeneticvariation,theyarebothlikelytorepresentbiologicalpopulationstructure.However,the 4-group scenario revealed by STRUCTUREwill be used in thediscussion,asitistheresultofunsupervisedanalyses,whichwasnotthecaseforDAPC.

ThepairwiseFST values confirmed some of the results found with the clustering approaches.A pattern of genetic differentiationwithlowbutsignificantpopulation-pairwiseFSTvalues(range0.009–0.054,Table2)wasobserved,andtheoverallFSTvaluewas0.014.Thepat-ternrevealedthattwolocationsinthewesternpartofthesamplingarea (RBandCS)and two locations in theeasternpartof thesam-plingarea(ASandVI)weregeneticallydistinctfromalmostallotherlocationssurveyedinthestudy.Bycontrast,thesevenlocations(Wy,Wa,PI,OD,BIMI,Ma)inbetweentheeasternandwesternlocationsformed an undifferentiated groupwhere only three of 42 pairwiseFST valueswere significant and noneof themwere significant afterBonferronicorrection.

Toevaluatethespatialprocessesthatdrivepopulationstructure,anisolation-by-distancemodelwasexamined.TheMantelcorrelationcoefficient r between thegenetic andgeographicdistancematriceswas equal to0.655 (coefficient of determination r2 =.429) andwasstatistically significant (p-value=.001). There was a linear relation-shipbetweenFST/(1−FST)andgeographicdistances(Figure3).Thus,fishfromnearbylocationstendedtobegeneticallymoresimilarthanexpected by chance and genetic differences increased linearlywithdistance. There was no correlation between the mean elemental otolith composition and the geographic distance between locations(nearcorer=−.225,p-value=.85;marginr=.449,p-value=.05).Thecorrelationcoefficientbetweentheparasitesabundanceandthegeo-graphicdistancebetweenlocationswaslowandmarginallysignificant(r=.434,p-value=.04).

Ecologicalmarkers(i.e.,otolithmicrochemistryandparasiteabun-dances) provided further information about population structure inP. diacanthus and information on the individuals’ life history.Of the11 elements measured, concentrations of 7Li, 25Mg,55Mn,60Ni, 63Cu, 66Zn, 88Sr and 138BainthenearcoresandmarginsofP. diacanthus oto-lithsweremeasuredabovetheLODinatotalof286fish(AppendixB1 inAppendixS2).A totalof291P. diacanthuswereexamined forthe parasitology component of this study. Overall, all fishwere in-fectedwithatleastoneparasiteindividualandatotalof44differentparasites specieswere identified (AppendixB2 inAppendix S2).Ofthese,11wereexcludedfromtheanalysesbasedonprevalenceandafurtherfourgroups(e.g.,Anisakidnematodelarvae,AncyrocephalineandDiplectanidmonogeneans)wereexcludedbasedon issueswithidentificationand/orenumeration.Themeanparasitespeciesrichness

     |  7TAILLEBOIS ET AL.

was5.8(range1–12)perhost,andthemeanabundance57.3(range1–653)parasiteindividualsperhost.

Themulti-elementalsignaturesof thenearcoreandmarginsandtheparasiteabundancesdifferedsignificantlyamongthethreebroad-scale regions (Western, Darwin, Arnhem/Gulf) and among locationswithin each of the three regions (MANOVA, all testsp- value <.001, Table3,Figure4).AverageclassificationsuccessintheLDFAamongalllocationswas54%fortheotolithmargin,31%fortheotolithnearcoreand67%fortheparasites(Table4)andwasmuchhigherthanexpectedbychance (9%for theotolithand10%for theparasites), suggestinga nonrandomdistributionof theotolithmicrochemistry andparasiteabundancevariabilityacrossthesampledrange.Classificationsuccess

forbothmargin(71%fortheWesternregion,57%fortheDarwinre-gionand74%fortheArnhem/Gulfregion)andnearcore(57%fortheWesternregion,44%fortheDarwinregionand38%fortheArnhem/Gulfregion)washigherwhenthedataforeachregionwereanalysedseparately,butthe%classificationsuccessrelativetothe%expectedby chance was higher when all sites were analysed simultaneously(Table4, Figure4). Similar to the otolith chemistry analysis, separa-tionof theanalyses into regions increased reclassificationsuccess inparasite abundances,with81% for theWestern region,56% for theDarwinregionand89%fortheArnhem/Gulfregion(Table4,Figure4).Forbothotolithsandparasites,theclassificationsuccesswasgreaterfortheArnhemGulfandWesternregionsthanfortheDarwinregion.

F IGURE  2 SummaryofBayesianmodel-basedanddiscriminantanalysisofprincipalcomponents(DAPC)approachesforpopulationclusteringofmicrosatellitedatafromProtonibea diacanthus.(a)PlotoftheBayesianmodel-basedpopulationclusteringusingSTRUCTUREforK=3geneticclustersacrosstheentiredataset,forK=2withinthenortherngroup(Wy-Wa-PI-OD-BI-MI-Ma)andthegulflocations(AS-VI).Eachverticallinerepresentsanindividualandtheposteriorprobabilityproportionsofitsgenotypeassignedtothedifferentgeneticclusters.Individualsareplottedfromwesttoeastalongthesamplinggradient,andpopulationsareabbreviatedfollowingTable1.RedarrowspointattheindividualsfromRBthatareassignedtoCS.Theadmixturemodelandpopulationinformationwereusedintheanalysis.(b)ChangesinmeanBayesianInformationCriterion(BIC)valuesinsuccessiveK-meansclustering.(c)OrdinationplotoftheDAPCforthefivepredefinedclusters(RB,CS,Wy-Wa-PI-OD-BI-MI-Ma=NorthernGroup,VIandAS).Individualgenotypesappearasdots,andlocationsaredepictedbycoloursand95%inertiaellipses.Thebottom-leftinsetshowsthenumberofprincipalcomponentretainedandthecumulativevariancetheyexplain;andthebottom-rightinsetshowstheeigenvaluesforthefourprincipalcomponentsinrelativemagnitudewithx-axisandy-axisconstitutingthefirsttwoprinciplecomponents,respectively

RB

CS

NorthernGroup

AS

VI

DA eigenvaluesPCA eigenvalues

0

(a)

(b) (c)

1 2 3 4 5 6 7 8 9 10 11

318

322

326

330

334

338

Number of clusters (K)

BIC

8  |     TAILLEBOIS ET AL.

Classificationsuccesswashigherforthemarginthanthenearcoreacrossalllocations(exceptWaandODintheDarwinregion),reflectingthefactthatthemarginsampleincorporatedgrowthatthesampling

location immediatelyprior to collection (i.e., samples collected fromthesamelocationinthesameyear).Incontrast,thenearcore(juve-nilephase)samplesincorporatedgrowthfromdifferentyearsbecausefish ineach locationwereofvariableagesandsourcesmayvarybyageandcohort.Individualmis-assignmentswerecomparedbetweennear core andmargin, and therewas no evidenceofmatchingpat-ternofmis-assignment(AppendixB5inAppendixS2).Fishthatweremis-assignedusing thenear corewerenotnecessarilymis-assignedusing the margin; and if they were, the assigned location was notnecessarily thesame.ClassificationsuccesswasgenerallyhigherfortheparasitesthantheotolithexceptBIandWywherebothmarginsandnearcoreshadhigherclassification rates thanparasites;andPIandASwhere only themargins had higher classification rates thanthe parasites. Individual mis-assignments were compared betweenparasitesandmargin,andtherewasnoevidenceofmatchingpatternofmis-assignment(AppendixB5inAppendixS2).Fishthatweremis-assignedusingtheparasiteswerenotnecessarilymis-assignedusingthemargin,andiftheywere,theassignedlocationwasnotnecessarilythe same.

Theresults fromthegeneticSTRUCTUREanalysesshowedthatRBisaheterogeneouscollectionofgenotypescomparedtoallother

F IGURE  3  Isolation-by-distanceanalysisgeneratedfrom1,000Mantelrandomizations.GeneticdistanceFST/(1−FST)againstgeographicdistance(km)andcorrespondingvalues

0

0.01

0.02

0.03

0.04

0.05

0.06

0 500 1000 1500 2000 2500 3000

Gen

etic

dis

tanc

e Fs

t/(1-

Fst)

Geographic distance (km)

2r = 0.655 r = 0.429 p = 0.001 slope = 0.00002

TABLE  3 Resultsofthemultivariateanalysisofvariance(MANOVA)investigatingthespatialvariabilityinparasiteassemblageandotolithnearcoreandmarginmicrochemistryofProtonibea diacanthusamongandwithineachregion

Source

Source, error df Pillai’s trace F

Otolith Parasite Margin Near Core Parasite Margin Near Core Parasite

Amongregion 80, 2, 200 10, 278 1.80 1.04 4.24 7.973*** 4.101*** 6.58***

AmonglocationswithintheWesternregion 24, 291 3,111 1.11 0.88 2.07 7.120*** 5.006*** 8.748***

AmonglocationswithintheDarwinregion 32,468 4, 117 1.08 0.72 1.61 5.392*** 3.203*** 3.03***

AmonglocationswithintheArnhem/Gulfregion

24, 294 3,103 1.27 0.59 2.28 8.992*** 2.997*** 12.807***

dfisthedegreeoffreedom.***Indicates a p<.001.

TABLE  2 PairwiseFST estimates based on 11 microsatellite data from 284 individuals of Protonibea diancanthusamong(a)11samplinglocations

RB CS Wy Wa PI OD BI MI Ma AS VI

RB 0.000 0.000 0.000 0.000 0.001 0.000 0.000 0.000 0.000 0.000

CS 0.033 0.096 0.089 0.000 0.064 0.014 0.006 0.008 0.001 0.012

Wy 0.029 0.006 0.542 0.008 0.974 0.008 0.817 0.532 0.000 0.000

Wa 0.033 0.009 −0.001 0.385 0.991 0.743 0.984 0.806 0.002 0.069

PI 0.035 0.029 0.009 0.000 0.544 0.009 0.229 0.265 0.000 0.000

OD 0.030 0.014 −0.009 −0.012 −0.002 0.822 0.984 0.975 0.138 0.515

BI 0.035 0.016 0.010 −0.003 0.011 −0.005 0.574 0.091 0.000 0.007

MI 0.033 0.014 −0.004 −0.009 0.001 −0.012 −0.002 0.695 0.001 0.095

Ma 0.034 0.019 −0.001 −0.004 0.002 −0.011 0.007 −0.004 0.039 0.044

AS 0.054 0.021 0.022 0.018 0.030 0.007 0.031 0.017 0.011 0.020

VI 0.048 0.014 0.015 0.007 0.016 −0.001 0.013 0.004 0.009 0.011

Lowerdiagonal,FSTestimates;upperdiagonal,p-valuesofthecorrespondingFSTestimate,thecomparisonsthatdifferedsignificantlyfromzero(p<.05)areshadedingrey,theonesthatdifferedafterBonferronicorrection(p<.0045)areinbold.LocationprefixesfollowTable1.

     |  9TAILLEBOIS ET AL.

locationswithindividualspresentingmatchinggenotypeproportionstoindividualsfromCSlocation(Figure2a).FurtherfullBayesianassign-menttestsbetweenRBandCSrevealedthatindividualshavingoutliergeneticsignaturedidnotnecessarilyhavedistinctotolithorparasitecharacteristics.Ofthethree individualsthatweremis-assignedwiththe genetics, fish3167 and3540were alsomis-assignedusing thenearcoreandtheparasites,respectively(Table5).Assumingparasitesand otolith margins provide information about postlarval develop-ment;andnearcoreandgeneticprovideinformationabouttheloca-tionofearlystagedevelopment,wetestedthehypothesesthatotolithnearcorewouldcorrespond to thegenetics; and thatparasitesand

otolithmarginwouldprovide similar assignments too.Off theeightfishthatweremis-assignedusingthenearcore,onlyonewassimilarlymis-assignedforthegenetics (fish3167).Ofthefourfishthatweremis-assignedfor themarginandthetwofor theparasites,onlyonewasmis-assignedforbothparasitesandmargin(fish3518).

4  | DISCUSSION

In this study, the combined application of genetic markers, otolithchemistry and parasites across the same specimens provided three

F IGURE  4 Plotsofthefirsttwodiscriminantfunctionscoresshowingspatialvariationintheparasiteassemblageandthemulti-elementalotolithnearcoreandmarginsignaturesofProtonibea diacanthuscollectedfrom11locationswithinthreeregions:Westernregion,DarwinregionandArnhem/Gulfregion.Ellipsesare95%confidenceintervalsaroundthegroupcentroidforeachlocationwithineachregion,anddatapointsrepresentindividualfish

10  |     TAILLEBOIS ET AL.

independentlinesofevidenceandconsiderablepowertodetectthehighlycomplexpopulationstructureofP. diacanthus across northern Australia. Relatively few studies to date have used this integratedapproachtoinvestigatepopulationstructureandconnectivityorforstock identification (Abuanza etal., 2008; Welch etal., 2015; Izzoetal.,2017).Thesestudiesalsoconcludedthattheintegrateduseofmultipletechniquesconsiderablyimprovedconfidenceinstockiden-tificationusingindexofstockdifferences(ISD)thresholdsdefinition(Izzoetal.,2017;Welchetal.,2015)andwereparticularlyeffectivefor specieswith broadly dispersing larvae or adults that exhibit lit-tleornogeneticstructureacrosstheirrange(Waldman,1999).Asacaseinpoint,ourstudyrevealedtheexistenceofmajorgeneticbreaksalongthenorth-westernAustraliancoastlineandthusthepresenceofseveraldiscretebreedingpopulationsofP. diacanthus.Priortosettle-ment,geneticresultssuggestthatcurrentsandgeographicbarrierstodispersalmaylimitexchangeamongcollectionlocations.Theecologi-calmarkers(otolithsandparasites)stronglysuggestthatP. diacanthus isgenerallysedentaryfollowinglarvalsettlement.

4.1 | Site fidelity and dispersal

Whilsttheresultsofthisstudysuggesthighlevelsofsitefidelityafterlarval settlement for P. diacanthusbasedonthefine-scalepopulationstructure,itshouldbenotedthattherewasoverlapinthemultivariateanalysesofparasiteassemblagecompositionsandotolithelementalsignaturesbetweensome locations thatpotentially reflectdispersalbyaproportionof juvenileor adult fish. Similar to the IBDpatternobservedinthegeneticanalyses,thecorrelationbetweengeographicdistance andparasite assemblagediversity showedadecline in thesimilarity of parasite assemblageswith increasing distance. The ge-neticIBDpatternindicatesthatreproductionismorelikelytooccurbetween individuals in the same location. The positive correlationbetweendistanceandparasiteassemblagedissimilaritycouldbe in-terpreted in a similar manner, suggesting that parasites are sharedbythedispersaloffishwithinandamongneighbouringpopulations,whichmayberesponsiblefortherelativesimilarityofneighbouringlocations. Alternatively, itmay be that similarity in parasite assem-blagesamongneighbouringsitesisdrivenby(i)dispersalofparasitesindependentofP. diacanthusmovement(e.g.,viaotherhosts);(ii)thespatial distributions of the endemic areas of the different parasitespecies(MacKenzie&Abaunza,1998);or(iii)habitatpreferencesoftheparasite species,withneighbouring locationshavingmore simi-larhabitatconditionsthandistant locations (MacKenzie&Abaunza,1998).Underthesethreescenarios,dispersaloffishamonglocationswouldnotbenecessarytoexplaintherelationshipbetweenparasiteassemblagesimilarityandgeographicdistance.

In the case of otolith chemistry, overlap inmulti-elemental sig-natures among locations is likely to be driven by local geochemicalcharacteristics, including the underlying seabed and nearby rivercatchmentgeology,aswellasphysicochemicalfactorssuchaswatertemperatureandsalinityandtheireffectsonfishphysiologyandlocalecologicalprocesses(Campana&Thorrold,2001;Elsdon&Gillanders,2002,2004).Whilstsuchfactorscouldpotentiallyvarysystematicallyovergeographicgradients,wefoundnoevidenceofarelationshipbe-tween multi-elemental signatures and geographic distance. Indeed,severalverydistantsites(e.g.,CSandMI)showedhighlevelsofover-lapinotolithchemicalsignatures,butitisextremelyunlikelythattherewasanyexchangeof individualfishbetweenthesedistant locationsgiven theweightofevidenceacross theother techniques (parasitesandgenetics)oflocationfidelityandlimiteddispersal.Rather,themis-classificationofindividualstodistantlocationsappearstobeduetothe high number of locations relative to the limited number of ele-mentsthatcontributedtolocationseparationinthediscriminantfunc-tionanalysis.Similaroverlapandmisclassificationinotolithchemicalsignatures among locations have been reported previously both infreshwater (e.g.,Macdonald&Crook,2014)andmarine (e.g.,Miller,Banks,Gomez-Uchida,&Shanks,2005)studies.

Aspreviouslymentioned,thegenerallylowerclassificationsuccessinthenearcorecomparedtothemargincan,at least inpart,beat-tributedtothefactthatthenearcoresamples incorporatedgrowthfromdifferentyears (i.e.,differentagecohorts)withineach location(seealsoGillanders,2002). It isalsopossiblethatmovementbyfish

TABLE  4  Jackknifereclassificationsuccessofthelineardiscriminantfunctionanalysis(DFA)fortheoverallotolithnearcoreandmarginchemistryandparasiteassemblageofProtonibea diacanthus

nMargin Near core Parasites% Correct % Correct % Correct

(a)Amongregions

AllLocations 286 54(9) 31(9) 67(10)

(b)WithintheWesternregion

RoebuckBay 34 76 50 86

Camden Sound 18 67 61 95

Wyndham 30 77 73 68

Wadeye 24 58 42 80

Total 106 71(26) 57(26) 81(26)

(c)WithintheDarwinregion

Wadeye 24 33 38 56

PeronIslands 29 93 62 73

Offshore Darwin

17 12 18 65

BathurstIsland 27 63 48 43

MelvilleIsland 29 62 41 50

Total 126 57(21) 44(21) 56(21)

(d)WithintheArnhem/Gulfregion

MelvilleIsland 29 79 41 80

Maningrida 30 47 40 90

ArafuraSea 20 95 25 90

VanderlinIslands

28 82 43 97

Total 107 74(26) 38(26) 89(26)

Dataarepresentedasthe%ofindividualsthatreclassifytotheircollectionlocation. Poulin and Kamiya’s (2015) proportional chance criterion isshowninbracketafterthetotalclassificationsuccesswithineachregion.

     |  11TAILLEBOIS ET AL.

TABLE  5 AssignmentsofProtonibea diacanthusindividualsbetweenRoebuckBay(RB)andCamdenSound(CS)locationsbasedonaBayesianapproachforthegenetics,otolithnearcoreandmarginandparasites

Sample Size (mm) Sampling locationGenetics Near Core Margin ParasitesAssigned location Assigned location Assigned location Assigned location

2504 623 CS CS RB CS CS

2505 580 CS CS CS CS RB

2506 827 CS CS RB CS CS

2508 594 CS CS CS CS CS

2509 638 CS CS CS CS CS

2510 524 CS CS CS CS CS

2511 539 CS CS CS CS RB

2512 920 CS CS RB CS CS

2513 905 CS CS CS CS RB

2514 595 CS CS CS CS CS

2516 565 CS CS CS CS CS

2517 631 CS CS CS CS CS

2518 649 CS CS RB CS CS

2519 671 CS CS RB CS CS

2520 601 CS CS RB CS CS

2521 634 CS CS RB CS CS

2522 673 CS CS CS CS CS

2523 520 CS CS CS CS CS

2507 650 CS NA NA NA CS

2515 595 CS NA NA NA CS

3518 720 RB RB RB CS CS

3539 1,114 RB RB CS CS RB

3540 1,140 RB CS RB CS RB

3545 1,019 RB RB RB CS RB

3521 1,057 RB RB NA NA RB

3523 835 RB RB NA NA RB

3530 1,030 RB RB RB NA RB

3167 1,040 RB CS CS RB RB

3168 1,009 RB RB RB RB RB

3169 1,005 RB CS RB RB RB

3170 1,010 RB RB CS RB RB

3171 1,055 RB NA RB RB RB

3172 975 RB NA RB RB RB

3173 1,100 RB NA RB RB RB

3174 1,000 RB NA RB RB RB

3519 910 RB RB RB RB RB

3520 1,050 RB RB RB RB RB

3522 1,104 RB RB RB RB RB

3524 892 RB RB RB RB RB

3525 1,035 RB RB CS RB RB

3526 1,090 RB RB RB RB RB

3527 1,135 RB RB RB RB RB

3528 1,136 RB RB RB RB RB

3529 810 RB RB RB RB CS

(Continues)

12  |     TAILLEBOIS ET AL.

afterthejuvenilephaseaccountsforsomethereducedclassificationsuccess inthenearcoreanalyses.Yetdespitethepotential forvari-ability among fish of different ageswithin a location, our analysesconsistently revealed highly significant spatial differences in otolithchemistry signatures among locations. This finding agreeswith ourconclusionoflocation-levelpopulationstructurebasedonparasiteas-semblagesand,further,suggeststhatatleastsomespatialstructuringismaintainedfromtheearlystagesofthelifehistoryofP. diacanthus. Eventhoughageswereavailableforoursamples,thelownumberofsamplesperlocationcombinedwiththewiderangeofagesresultedinlownumberspercohort.Hence,cohortscouldnotbecomparedinthisstudy,andfurtherresearchisrequiredtodeterminetheextenttowhichmis-classificationoffishcanbeattributedtotemporalvariationinotolithchemicalsignaturesversusotherpotentialcauses(e.g.,immi-grationfromotherlocations).

4.2 | Population structure and connectivity

Atbroaderspatialscales,thegeneticanalysesrevealedtheexistenceofmajordiscontinuitiesalongthenorth-westernAustraliancoastline,resulting inseveralgeneticallydiscretepopulationsofP. diacanthus. This finding is based onmixed age samples, andwewould predictstrongerspatialstructuring ifmature fishwerecollectedonspawn-ing grounds.Major genetic breaks distinguished populations at RB,CS,ahomogenousgroupoflocationsintheNorthernTerritory(fromWytoMa), theoffshoreregionof theASandtheVI intheGulfofCarpentaria.ThedeepestgeneticstructureobservedwasbetweenRBinWesternAustraliaandtherestofthesamplingrange.ThedistancebetweenRBandthenearestlocationCS(~400km)maypartlyexplainthispronouncedgeneticbreak.However,similarorgreaterdistancesbetweenotherlocations(e.g.,425kmbetweenMaandMIor630kmbetweentheASandVI)didnotresultinsuchgeneticdiscontinuity.

Thegeomorphologyandlocalhydrodynamicregimeofthenorth-ern coastline of Western Australia may influence the population

geneticstructureofP. diacanthusacrossitsrange.Thetidalregime,thecompositionofthesubstrateandtheextentoftheriverinenetworkinfluence the size of themangrove communities in this area.Thus,the Kimberley and Canning bioregions separated by the DampierPeninsula (Figure1) (Semeniuk,1996;Thackway&Cresswell,1998)differsignificantlyinmangrovecharacteristics.ThetipoftheDampierPeninsulahasbeenidentifiedasanimportantbiogeographicbreakformarine species (Hutchins,2001;Travers,Potter,Clarke,Newman,&Hutchins,2010;Wilson,2013)andcoincidewithapronouncedshiftintheunderlyinggeology(e.g.,fromsedimentarysandstoneinthenorthtounconsolidatedsandandsiltinthesouth)andassociateddominantbenthichabitats (e.g., fromcoralreefs inthenorthtosoftsubstratecommunitiesinthesouth).Thisbarrieralsocoincideswithaprominentincrease in tidal currents and associated water turbidity to the north. The Kimberley-Canning border has the largest tropical tidal range(~12m)andsomeofthefastesttidalcurrentsintheworld(2.5m/s),andexperiencesmassivevolumesoffreshwaterinahighlyturbidplumefromtheFitzroyRiverduringthemonsoonalwetseason(Wolanski&Spagnol,2003).ThediscontinuitybetweentheKimberleyandCanninggeomorphology(macrotidal,jagged“ria”coastlinewithextensiveman-groveforestsvssmoothercoastlinewithnorivers)createscontrastedmarinecoastalhabitats,inhibitinglarvaldispersalandreinforcingspa-tialpopulationstructure.Likewise,quantitativeanalysesoffishassem-blageshaverevealedtwomainbiogeographicregions,theKimberleyand Canning coasts (Hutchins, 2001; Travers, Newman, & Potter,2006;Travers,Potter,Clarke,&Newman,2012;Traversetal.,2010;Wilson,2013),matchingthegeneticgroupsreportedhere.OurstudyisthefirsttoidentifyacleargeneticbreakbetweentheKimberleyandtheCanningbioregionsincoastalfish.Acrossthesamebroaderarea,studiesonthreadfinspecies(E. tetradactylum and Polydactylus macro-chir)alsoreportedastrongeast-westphylogeographicbreakbetweenRBandpopulationsofVanDiemenGulforintheGulfofCarpentaria(Horne, Momigliano, Welch, Newman, & van Herwerden, 2011;Horne,Momigliano,Welch,Newman,&VanHerwerden,2012),but

Sample Size (mm) Sampling locationGenetics Near Core Margin ParasitesAssigned location Assigned location Assigned location Assigned location

3531 1,095 RB RB RB RB RB

3532 1,100 RB RB RB RB RB

3533 1,182 RB RB RB RB RB

3534 899 RB RB RB RB RB

3535 980 RB RB CS RB RB

3536 930 RB RB RB RB RB

3537 988 RB RB RB RB RB

3538 1,199 RB RB CS RB RB

3541 984 RB RB RB RB RB

3542 1,012 RB RB CS RB RB

3543 870 RB RB RB RB RB

3544 1,152 RB RB CS RB RB

TABLE  5  (Continued)

     |  13TAILLEBOIS ET AL.

lackedintermediatesamplingbetweentheselocationsthatmayhaveidentified a Kimberley-Canning break. Similarly, a genetic study ongreymackerel (Scomberomorus semifasciatus) highlighted the distinc-tivenessbetweenWesternAustralia(PilbararegiontothesouthoftheCanning) and theNorthernTerritory, but again lacked intermediatesampling(Brodericketal.,2011).

The samples from ofWy at the mouth of the Cambridge Gulfwithin thebroaderJosephBonaparteGulf (JBG)weredistinct fromCS in theKimberley region.Thehydrodynamics of theJBG, in par-ticular the effect of the seasonal current flowand thehighbottomstress,arelikelytobefactorslinkedtotheisolationofWyfromtherest of theKimberley area (Condie, 2011).The geographic distancebetweenCSandWymaybeatleastinpartresponsibleforthisgeneticgap,recognizingthatintermediatesamplingbetweentheselocationsmayhave identifiedadditionalpopulationsubdivision.Wy ispartofageneticallyhomogenousgroup thatspans thewesternsideof theNorthernTerritory.Anexplanation for the isolationof theNorthernTerritorygroupistheyear-roundpersistentArnhemCurrent(Condie,2011)thateffectivelyisolateswatersacrosstheNorthernTerritory.

TheVIaresituatedatthewesternbaseoftheGulfofCarpentaria,anotherareadominatedbyspecificcirculationflows.TheGulfisashal-lowsemi-enclosedseawithverylongflushingtimesandlongresidencetimesforparticles (Condie,2011).Thewatercirculation intheGulf istriggeredbyaseasonalchangeinthewinddirection.TransportinandoutoftheGulfofCarpentariaisinfluencedbyseasonalwindvariationsaswellasthewestwardArnhemCurrentthatoffsetstransportintotheGulfduringthesummermonsoonseason(Condie,2011).Moreover,thesouthernpartoftheGulfofCarpentariaisevenmoreisolatedfromtheArafuraandTimorseasthanthenorthernsectionaseachpartoftheGulfexhibitsitsownseasonalcurrentpattern.Thecurrentflow,throughthetransportationofpresettlementlifestagessuchaseggsandlarvae,couldaccountfortheseparationoftheVIfromtheremainingNorthernTerritory locations. Conversely, in the study of S. semifasciatus, individ-ualsfromtheVIweregroupedwithlocationsontheNorthernTerritorycoastandnotwiththeothereasternGulfofCarpentarialocationsintheQueenslandjurisdiction(Brodericketal.,2011).

TheoffshorepopulationoftheASoffthecoastofArnhemLandandclosetotheWesselIslandswasageneticallyisolatedpopulation.Thedistancefromtheshoreandthegreaterdepthsmaypreventthispopulationfromexchangingindividualswithcoastallocations,whichwouldexplainthedistinctivenessofthatpopulation.However,thereisasignificant informationgapaboutthispopulationand itsdegreeofdependenceuponotherinshorepopulationsofAustraliaorPapua,whichrequiresfurtherinvestigation.

4.3 | Integration across methods: example of RB and CS locations

Despitetheoverallevidenceofastronggeneticbreak,Bayesiananal-ysisat the individual levelshowedthatRBconsistedofanadmixedpopulation,withsomefishhavinggenotypessimilartoCSindividuals(Table5).Ocean currents cangreatly influence recruitmentpatternsoforganisms through thedispersalofpropagules (e.g., Feutryetal.,

2013). Modelling of water currents along the northern Australiancoasthasshownthat the“IndonesianThroughFlow” influences themajorseasonalsouthwardsurfacecurrentalongthenorthernWesternAustraliacoast(knownasthe“HollowayCurrent”)(D’Adamo,Fandry,Buchan, & Domingues, 2009). This narrow boundary current, thatmainlyinfluencesthecontinentalshelfintheCanningandKimberleybioregionsandpredominantly flowssouthwestthroughouttheyear,albeitwithseasonalvariations (Bahmanpour,Pattiaratchi,Wijeratne,Steinberg,&D’Adamo,2016), likelyfavoursthesouthwarddispersaloflarvaefromCStoRB(Traversetal.,2010).However,seasonalvari-ationisalsoobserved,withanorthwardflowofcurrentduringsum-mer(January-March)thatcontributestowaterbuild-upintheGulfofCarpentaria.Duringthesummerspawningmonths,P. diacanthus lar-vae from CS would mainly drift northward but could still drift south-ward to RB.The seasonality and direction of theHolloway currentflowmayexplainthegeneticheterogeneityofRBsamples.AlthoughmostoftheindividualsfromRBareclosetomaturityandpossiblytheoldestfishamongoursamples(meantotallength1,018mm),Wyalsohaslargerfish(meantotallength1,061mm)anddonotpresentsuchageneticdistinctiveness.ThesegregationofgenotypesintheRBpopu-lationdidnotseemtodependontheyearofsamplingnortobelinkedto the fish ageor cohort, although the limited temporalwindowofoursamplingregimedidnotallowstatisticaltestingofthishypothesis.

Usingallthreetechniquestodetectmigrantsthroughmis-assignmentstestsforcontemporaryconnectivitybetweenlocations.Neithertheoto-lith or parasite data showed evidence ofmatchingmis-assignment togenotypesattheindividuallevelbetweenRBandCSlocations.Ifthemi-grantsarrivedasjuveniles,theywouldhavethegeneticsignatureofthesourcepopulation,butastheymature inthenewlocation,theywouldacquire the local parasite andotolith signature. If themigrants arrivedasadults,theywouldhavethegeneticandnearcoresignaturesofthesourcepopulation,andpossiblyintermediateparasitefaunacomposition.Alternatively, the genetically distinct immigrants could be the descen-dantsoftrueimmigrants—hence,theywouldbegeneticallydistinct,buthavethelocalotolith(nearcoreandmargin)andparasitesignatures.Theabsenceofamatchingpatternbetweengeneticsandecologicalmethodsinindividualmis-assignmentrevealsthatthegeneticallymis-assignedin-dividualsfromRBmay(i)notbecontemporaryimmigrantsbutratherageneticsignalofancestralimmigrantsor(ii)becontemporaryimmigrantsfromsitesnotsampled in thestudy.Fine-scalesamplingandtemporalseriescollectionwouldberequiredtofurtherinvestigatetheprocessesthatdrivethegeneticcompositionoffishintheRBpopulation.

4.4 | Management implications

ThehighlycomplexpopulationstructureofP. diacanthus shown by this studyacrossnorthernAustraliaemphasizesthelikelyvulnerabilityofthespecies tooverfishing, and importantly, guides theappropriate spatialscaleforstockassessmentandfisheriesmanagement.Inparticular,thepopulation genetics andotolith andparasites data suggest that stockassessmentsandfisheriesmanagementonaspatialscaleofaslittleas100s of kmmay be justified. Given genetic breaks among broad re-gions,itisunlikelythatimmigrationofrecruitsfromotherregionswould

14  |     TAILLEBOIS ET AL.

contributetopopulationrecoveryintheshorttermshouldoneormoreregionssufferseverepopulationdeclines.Atafinerscale,theparasiteandotolithchemistryanalysessuggestthatmovementofrecruitsamongsites(e.g.,reefcomplexes)isgenerallylimitedandthatheavilydepletedsitesmaybe highly reliant on self-recruitment for populationmainte-nance and, therefore,may be slow to recover once depleted.WithintheWesternAustralia fisheries management jurisdiction, RB, CS andWyweregeneticallydistinctfromeachother, indicatingthat location-specificstockassessmentsandlocation-specificmanagementarrange-ments for P. diacanthusneedtobeconsideredbyfisheriesmanagers,oralternativelythatthelocation-specific“stocks”identifiedareconsideredwithinassessment,monitoringandmanagementframeworks.

Protonibea diacanthus is targetedbycommercial, recreationalandindigenous fisheries, which makes population modelling challengingoverlimitedspatialscalesof100sofkms.Thedifficultywithmodellingoverthisspatialscaleisthelogisticalchallengesassociatedwithcollect-ingmatching stock-specificdata suchas reproduction and spawningdynamics,growthrates,exploitationratesandnaturalmortality.Thislogisticalchallengecomeswithasignificantcostburden.Thesefactorsindicate that fine-scalemanagement is problematical once the addi-tionalcostofcompliance,regulationandenforcementisadded.Withinthatframework,considerationneedstobegiventoarangeofotheroptionsthatlimitfishingeffort,includinglimitedentry,limitingfishingeffort, consideration of targeted fisheries spatial closures, rotationalharvestsoracombinationoftheseapproacheswithothertraditionalfisheriesmanagementarrangements(e.g.,co-operativemanagementoflocalfisherieswithIndigenouspeople).Thisapproachhasbeenusedinthe Northern Territory where a series of reef- scale closure areas have beenimplementedwhichhassubsequentlyshowntoaidtherecoveryof P. diacanthus and Lutjanus johnii (NTG2016).Consideration is alsobeinggiventodevelopingfine-scalearealcatchlimitsforP. diacanthus basedoninformationfromthisstudy(ThorSaunderspers.com)demon-stratingthatmanagementatthescaleofpopulationsisachievable.

5  | CONCLUSIONS

SeverallinesofevidencesupportamodeloflowdispersalinP. diacanthus. Significant spatial geneticpopulation structurewas supportedby finerspatial-scale parasite assemblage and otolith chemistry data. There isevidenceofasmallamountofmigration—mostlikelyinthepelagiclarvalphase—fromadjacentpopulationscorrespondingtoprevailingcurrents,whichwasinferredfromintegrationacrossdatafromthreesources.

OurfindingthatP. diacanthus ishighlystructuredacrossnorthernAustraliafromRBtotheVIhasdemonstratedthatthecombinationofecologicalandgeneticmarkersprovidesamoreholisticviewofpop-ulationstructurewhendeployedtogetheronthesamesetofindivid-uals.Geneticsprovides theoverall framework forapplyingecologicalmethodsandprovidesalso considerable informationaboutexchangebetween populations. Sensitive ecological methods, such as otolithchemistryandparasites,provideresolutionofthefine-scalespatialsep-arationwithinandbetweencollectionlocations,butareunabletopro-videbroaderscale informationover intergenerational timescales.Our

resultscollectivelydemonstratelocation-specificfidelityinthejuvenileandadultphasesandsuggestthatdispersalislikelytooccurprimarilyduringtheveryearlylifehistoryaseggsand/orpelagiclarvae.Furtherresearch(e.g.,acoustictelemetry)wouldberequiredtodirectlyquantifytheextentofsiteattachmentacrossthelifehistoryofP. diacanthus.

ThefindingthatP. diacanthusacrossnorth-westernAustraliacon-sistsofasetofspatiallystructuredpopulationswithlowconnectivitysetsaframeworktodevelopapproachesfortheassessmentandspa-tialmanagementofthespecies.ThecollapseoftheNorthQueenslandstock(Phelan,Gribble,&Garrett,2008)andstocksinIndiaandChina(James,1994;Sadovy&Cheung,2003)demonstratesthatP. diacan-thusispronetolocaldepletionandourfindingssuggestthatthisvul-nerability is likelydue to its lowdispersalandhigh location-specificfidelity.Stockassessmentmodelsforthisspeciescannotassumefreeandfrequentmovementofindividualsamongthegeneticpopulationsstockboundaries.Becausepopulationrecoverywillmainlydependonlimitedinterchangeofearlylife-stagessuchaseggsorlarvaebetweenneighbouringpopulations(stocks),itmaytakemultiplegenerationstoreplenishdepletedstocksandthereispotentialthatsomeheavilyde-pletedstocksmaynotrecoveratall.

ACKNOWLEDGEMENTS

This research was supported by the Fisheries Research andDevelopment Corporation (Project 2013/017); LT received addi-tional funding support from the North Australia Marine ResearchAlliance (NAMRA—AIMS/ANU/CDU/NT Government) PostdoctoralFellowship.TheauthorsthankstafffromtheDepartmentofFisheries(Government of Western Australia) and the Northern TerritoryDepartment of Primary Industries as well as the Indigenous ma-rine rangers from Borroloola, Maningrida, Wadeye, Larrakia andDambimangari for the collection of specimens. We would like tothankJeffPrunster andMarkDouglas for kindlyproviding samplesfrom Roebuck Bay and Wyndham. We thank Samuel Williams attheMolecular Fisheries Laboratory and Sean Corley at theAnimalGeneticsLaboratoryfortheirassistanceinthelaboratory.Wethankthefollowingpeoplefortheiridentificationandassistancewithmanyof the parasites included within this study: Frank Moravec, GeoffBoxshall,IanBeveridge,TerryMiller,JoseLluque,JessMorgan,DelaneKritsky,LesleySmalesandShokoofehShamsi.

DATA ARCHIVING STATEMENT

Data files with individual information (genotypes, parasites assem-blageandabundanceandotolithsmicrochemistry)areavailablefromDRYAD(https://doi.org/10.5061/dryad.9rs5d).

ETHICS APPROVAL

This work was conducted under Charles Darwin University AnimalEthicspermitA13014.VoucherspecimensofparasiteswillbedepositedwithinthecollectionsoftheMuseumandArtGalleryoftheNorthernTerritory(MAGNT),theAustralianHelminthologicalCollection(AHC)andMarine Invertebrates section of the South AustralianMuseum

     |  15TAILLEBOIS ET AL.

(SAM),theParasitologyCollectionoftheQueenslandMuseum(QM),andtheNaturalHistoryMuseumofLondon(BMNH).

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SUPPORTING INFORMATION

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How to cite this article:TailleboisL,BartonDP,CrookDA,etal.Strongpopulationstructurededucedfromgenetics,otolithchemistryandparasiteabundancesexplainsvulnerabilitytolocalizedfisherycollapseinalargeSciaenidfish, Protonibea diacanthus. Evol Appl. 2017;00:1–16. https://doi.org/10.1111/eva.12499