Ecology and Evolution. 2018;8:3139–3151.  | 3139www.ecolevol.org

Received:5May2017  |  Revised:29August2017  |  Accepted:7December2017DOI:10.1002/ece3.3765

O R I G I N A L R E S E A R C H

Fresh is best: Accurate SNP genotyping from koala scats

Anthony J. Schultz1,2  | Romane H. Cristescu2 | Bethan L. Littleford-Colquhoun1,2 |  Damian Jaccoud3 | Céline H. Frère2

ThisisanopenaccessarticleunderthetermsoftheCreativeCommonsAttributionLicense,whichpermitsuse,distributionandreproductioninanymedium,providedtheoriginalworkisproperlycited.©2018TheAuthors.Ecology and EvolutionpublishedbyJohnWiley&SonsLtd.

1GeneCologyResearchCentre,UniversityoftheSunshineCoast,MaroochydoreDC,Qld,Australia2GlobalChangeEcologyResearchCentre,UniversityoftheSunshineCoast,MaroochydoreDC,Qld,Australia3DiversityArraysTechnology,UniversityofCanberra,Bruce,ACT,Australia

CorrespondenceAnthonyJ.Schultz,GeneCologyResearchCentre,UniversityoftheSunshineCoast,MaroochydoreDC,Qld,Australia.Email:anthony.schultz@research.usc.edu.au

Funding informationKoalaActionInc;CouncilsoftheSunshineCoast,GympieandFraserCoast;GeneCologyResearchCentre;QueenslandGovernmentDepartmentofTransportandMainRoads

AbstractMaintaininggeneticdiversityisacrucialcomponentinconservingthreatenedspecies.FortheiconicAustraliankoala,thereislittlegeneticinformationonwildpopulationsthat isnoteitherskewedbybiasedsamplingmethods(e.g.,samplingeffortskewedtowardurbanareas)orof limitedusefulnessdue to lownumbersofmicrosatellitesused.TheabilitytogenotypeDNAextractedfromkoalascatsusingnext-generationsequencingtechnologywillnotonlyhelpresolvelocationsamplebiasbutalsoimprovetheaccuracyandscopeofgeneticanalyses(e.g.,neutralvs.adaptivegeneticdiversity,inbreeding,andeffectivepopulationsize).Here,wepresentthesuccessfulSNPgeno-typing(1272SNPloci)ofkoalaDNAextractedfromscat,usingaproprietaryDArTseq™ protocol.Wecomparegenotyperesultsfromtwo-day-oldscatDNAand14-day-oldscatDNAtoabloodDNAtemplate,totestaccuracyofscatgenotyping.WefindthatDNAfromfresherscatresultsinfewerlociwithmissinginformationthanDNAfromolderscat;however,14-day-oldscatcanstillprovideusefulgeneticinformation,de-pendingontheresearchquestion.Wealsofindthatasubsetof209conservedlocicanaccuratelyidentifyindividualkoalas,evenfromolderscatsamples.Inaddition,wefindthatDNAsequencesidentifiedfromscatsamplesthroughtheDArTseq™processcanprovidegeneticidentificationofkoaladietspecies,bacterialandviralpathogens,andparasiticorganisms.

K E Y W O R D S

diet,disease,koala,scat,SNPGenotyping

1  | INTRODUCTION

Challengestotheconservationandmanagementofrare,endangered,orcrypticspeciesarecompoundedinpartbythedifficultyofgatheringbaselinepopulationdata(Boakes,Fuller,McGowan,&Mace,2016).Alackofrobustdataonpopulationsize,distribution,andgeneticdiversity(Phillips,2000;Sherwin,Timms,Wilcken,&Houlden,2000)increasestheuncertaintyassociatedwithmanagementdecisions,andthetrade-offbetweeninvestingresourcesindatacollectionversusappliedman-agement is a complex issue (Grantham,Wilson,Moilanen,Rebelo,&Possingham,2009;Jaramillo-Legorretaetal.,2007;Knightetal.,2008;

Whitten,Holmes,&MacKinnon,2001).Thisisparticularlytrueforrareandendangeredspecies,whicharegenerallycharacterizedbysmall,re-productivelyisolatedpopulationsinfragmentedlandscapes(Channell&Lomolino,2000;Drury,1974;Gaston,1994).Small, isolatedpopu-lationsareknowntoreduceindividualfitnessandheightenextinctionrisk(Lynch&Lande,1993;Willi&Hoffmann,2009;Willi,VanBuskirk,&Hoffmann,2006),asincreasedinbreedingandgeneticdriftdecreasestandinggeneticdiversity (Keller&Waller,2002;Spielman,Brook,&Frankham,2004;Willi,VanBuskirk,Schmid,&Fischer,2007).

As a result, the IUCN (International Union for Conservationof Nature) identifies genetic diversity as one of the key forms

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of biodiversity requiring conservation (McNeely, Miller, Reid,Mittermeier,&Werner, 1990).Traditional conservationplanning formaintainingspeciesbiodiversity requiresknowledgeofhabitat type,speciesassemblages, andecologicalprocesses (Margules&Pressey,2000; Pressey, Cabeza,Watts, Cowling, &Wilson, 2007). Similarly,managementplanningfortheconservationofgeneticdiversityinwildpopulationsrequiresaccuratemeasurementofpopulationgeneticpa-rameters. Specifically, conservation decisionmakers require reliabledataonpatternsof individualgeneticdiversity,dispersal,geneflow,population-leveldiversity, levelsofinbreeding,andeffectivepopula-tionsize(Ne).

Patternsofconnectivityandgeneflowcanbesuccessfullyinves-tigatedusingbothmicrosatellitemarkers(Hodeletal.,2016;Morin,Luikart,&Wayne,2004)andSNP(single-nucleotidepolymorphism)markers (Van Inghelandt, Melchinger, Lebreton, & Stich, 2010).However, measuring inbreeding coefficients and effective popula-tion sizes ismore suited togenome-widemarkers, forwhichSNPsareanincreasinglypopularchoice(e.g.,Bjelland,Weigel,Vukasinovic,&Nkrumah, 2013; Luikart, Ryman,Tallmon, Schwartz,&Allendorf,2010;Sauraetal.,2015).Inaddition,thehigherresolutionprovidedbySNPgenotypingcanalsogivelessbiasedmeasuresofgeneticdi-versitythanmicrosatellites(e.g.,Munshi-South&Kharchenko,2010;Munshi-South,Zolnik,&Harris,2016).Generally,twotothreeSNPsareexpectedtoprovidethesamepowerasonemicrosatellite,acrossa range of analyses (Fernández etal., 2013; Glover etal., 2010;Sellarsetal.,2014).SNPsarealsosignificantlymoreeffectiveinspe-cieswithlowgeneticdiversity(Tokarskaetal.,2009).Itis,however,intheabilitytogenotypethousandsofSNPsacrossthegenomeofatargetspecies,thatthepowerofSNPsovermicrosatelliteslies(Daveyetal.,2011).

Inbreeding and heterozygosity analysis comparisons have foundthat the addition of SNPs tomicrosatellite panels can increase ac-curacy, but adding microsatellites to SNP panels has little impact(Santure etal., 2010; Smouse, 2010). Effective population size esti-mationusingSNPshasbeensuccessfulacrossarangeofspecies(TheBovineHapmapConsortium,2009;Corbinetal., 2010;McEachern,Eadie,&VanVuren,2007;Uimari&Tapio,2011).Foruseinpopulationandconservationgeneticstudies,SNPscangenerallyprovidebroadergenomecoverthanmicrosatellitesandmtDNAwithequivalentstatis-ticalpower(Morinetal.,2004).

Koalas (Phascolarctos cinereus) are cryptic, arboreal marsupials,withpatchydistributiondowntheeastcoastofAustralia,andlistedasthreatenedinthenorthernpartsoftheirrange(Commonwealth2012;DSEWPC 2013). Threats to koala populations include habitat lossandfragmentation,dogattacks,carstrikes,wildfires,andChlamydia pecorum-relateddisease(Lunney,Gresser,Mahon,&Matthews,2004;Lunney,Matthews,Moon,&Ferrier,2000;Matthews,Lunney,Gresser,&Maitz, 2007;Melzer, Carrick,Menkhorst, Lunney, & John, 2000;Melzer, Cristescu, Ellis, FitzGibbon, & Manno, 2014; Polkinghorne,Hanger, &Timms, 2013).While there are no national-scale studiesofkoalapopulationstatuses,studiesinthenorthernpartsoftheko-ala’s range suggest that habitat fragmentation has produced small,reproductively isolated populations which exhibit rapid genetic

differentiation(Leeetal.,2010).Populationsmonitoredinthisregionhavedeclinedbyupto80%overthelasttwodecades,addingurgencytoourunderstandingofkoalagenetichealth(Rhodes,Beyer,Preece,&McAlpine,2015).

Geneticstudiesofkoalapopulationshavetraditionallyreliedontissue or blood samples, either collected by capturingwild koalas(e.g.,Fowler,Houlden,Hoeben,&Timms,2000),orcollectingsam-plesfromillorinjuredanimalsboughtintoveterinaryhospitals(e.g.,Dudaniecetal.,2013).Thesestudieshave,forthemostpart,reliedonmicrosatellitesforgenotypingandmeasuringdiversity,usingbe-tween6and15microsatelliteloci(Cristescuetal.,2009;Dennisonetal., 2017; Houlden, England, & Sherwin, 1996; Ruiz-Rodriguez,Ishida,Greenwood,&Roca, 2014).This results in greatly reducedgeneticcomparabilitybetweenstudies,whichforalowdensity,dif-ficult to sample species, is a lost opportunity. Finding, capturing,and samplingwild koalas is costly in both time andmoney,whilesamplingsickorinjuredanimalsmaybiassamplingtowardareasofincreasedhumanpresence.However, the increasinguseofnonin-vasive sampling has allowed for cheaper, easier genetic samplingacross a range of species (e.g., okapi (Okapia johnstoni) (Stantonetal.,2016),wolves(Canis lupus)(Scandura,2005;Stenglein,Waits,Ausband,Zager,&Mack,2011),Spanishimperialeagles(Aquila adal-berti)(Horváth,Martínez-Cruz,Negro,Kalmár,&Godoy,2005)),andmore recently koalas (Wedrowicz, Karsa,Mosse,&Hogan, 2013).Scatsampling inparticular,coupledwithnovelcollectionmethodssuchasdetectiondoguse,allowsforwidespread,unbiasedsamplingofkoalageneticmaterial.Microsatellitegenotypingfromkoalascatisalreadyavailable(Wedrowiczetal.,2013),albeitwithoutatissueDNAsamplewithwhichtocomparethegenotypingaccuracy.WhileDNAisolatedfromtissueorblood isalso likelytohave low levelsoferrorassociatedwiththem,acomparisonoftheerrorratesbe-tweenblood/tissueDNAandscatDNAwouldproveauseful toolforassessingthepracticalityofusingDNAfromscat.ASNPpanelhasbeendevelopedforkoalas(Kjeldsenetal.,2015),althoughthusfar ithasonlybeenapplied to tissue samples.SNPgenotypingofnoninvasivelycollectedsampleshasbeeneffectiveacrossarangeofwildspeciesincludingwolves(Krausetal.,2015),riverotters(Lutra canadensis)(Stetzetal.,2016),andEuropeanwildcats(Felis silvestris silvestris)(Nussberger,Wandeler,&Camenisch,2014).Withregardtokoalas,whileweknowthatDNAcanbeextractedfromscats,wedo not knowwhether this is sufficient to reliably genotype thou-sandsofSNPmarkers,oratwhatscatagesthismightbepossible.

This is important as efficient, unbiased sampling of scat, andsuccessful SNP analysis of theDNA contained thereinwould allowresearchers to gather fine-scale individual, population-level, andlandscape-leveldataaccuratelyandefficiently.Utilizingscatdetectiondogs, as previouslymentioned,wouldbeonewayof accomplishingwidespreadscatsamplingforsuchgeneticanalyses.Thiswillprovideenoughhigh-resolutiongenetic informationtoenableacomprehen-siveevaluationofkoalageneticmeasures,specificallythosementionedabove(Ne,inbreeding,populationdiversity,andinterpopulationdiver-sitypatterns).Withagreaterdepthofgeneticinformation,wewillbefarmoreabletoplanconservationprogrammesandinterventionsto

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bestmaintainkoalageneticdiversity,whichuntilnowhasbeenverydifficulttoassess.

Here,weusedDArTseq™totestthefeasibilityofSNPgenotypingusingDNAextractedfromkoalascats.Inordertoassesstheeffectofscatageongenotypingresults,weextractedDNAfromscatsofdif-ferentages.Inparticular,andinadditiontopreviousstudies,wecom-paredresultsfromfecalDNAsamplestoDNAfrombloodtotestfecalgenotypingaccuracy.DArTseq™technologywaschosenforthisanaly-sisinpartduetoitshighrepeatabilityandstandardizationofSNPlociusedforgenotyping.Multiplesamplesacrossmultipleanalysescanbegenotypedusingthesamecomplexityreductionmethod,allowingformaximumcomparabilityacrossstudiesandindividuals.

2  | MATERIALS AND METHODS

Freshscats(<6hrold)werecollectedfromfivecaptivekoalas(threefemalesandtwomales)bywatchingkoalasastheydefecatedandre-trieving thepellets from the ground. Sampled koalaswere residentatWildlifeHQZooinWoombye,Queensland.Wholebloodsamples(2ml)weretakenfromeachanimalduringregularveterinarianexami-nations.Basedonzoorecords,thesefiveanimalsareallfromdiffer-entareasinQueensland,andtwoindividualsarerelated(afatheranddaughter).

To establish the effectiveness of SNP genotyping from scat ex-tractions,twoscatsperindividualwerestoredontoothpicksstuckinaStyrofoamboardinthelaboratory,underambientlightandtempera-ture (approximately 28°C). Scatswere aged under these conditionsoverthecourseoftwoweeks.ScatswereharvestedforDNAisolationondaytwoandday14postcollection.Atbothsamplingpoints,DNAwasisolatedfromtwoscatsperindividual.Duetokoalassharingen-closurespace,therewasamisidentificationofascatwhichwasonlydiscoveredthroughgenotypingresults.Oneofthetwo-day-oldscatsfromKoala4wasactuallyfromKoala2andwasrenamedassuch.

2.1 | DNA isolation—blood

DNAwas isolatedfromeachkoalabloodsampleusingtheWizard® GenomicDNAPurificationKit (Promega) following themanufactur-er’s “IsolatingGenomicDNAfromWholeBlood (300μl samplevol-ume)”protocol.Isolateswerestoredat−80°C.

2.2 | DNA isolation—scat

Koala DNAwas isolated from intestinal epithelial cells on sampledscats.Epithelialcellsfromthesurfaceofeachscatwerecollectedbyslicingofftheouter-mostlayerofthescatusingascalpel.Thesesur-facesliceswerethenusedtoextractkoalaDNAusingtheQIAampDNAStoolMiniKit(Qiagen),followinganadaptedversionoftheman-ufacturer’s protocol “Isolation ofDNA from Stool forHumanDNAAnalysis,”asfollows:Atcelllysisstage,1.8mlBufferASLwasadded,vortexedforoneminute,andcentrifugedatfullspeedfortwomin-utes.Foreachisolate,2μl(100ng/ml)RNaseA(Qiagen)wasadded

andincubatedat37°Cfor30min.ThequantityoftotalDNAineachscatisolatewasmeasuredusingaThermo-ScientificNanodrop2000Spectrometer.DNAisolateswerestoredat−80°C.

Koala dietary species are known to contain volatile compoundsand phenolics,which are subsequently excreted in scats (Eberhard,Mcnamara, Pearse, & Southwell, 1975). Some of these compounds(including1,8-cineoleand terpinene-4-ol)havebeenshown tocon-tribute to cellmembranedamage (Carson,Hammer,&Riley, 2006).Additionally,phenolicsareknowntocontributetoacceleratedDNAdegradation(Khan&Hadi,1998)andmayalsoinhibitPCRprocesses(Kreader,1996).QIAampDNAStoolMiniKit(Qiagen)specificallyin-cludesInhibitExtabletsspecificallydesignedtoremovesuchPCRin-hibitorsduringDNAextraction.

2.3 | SNP genotyping

Two DNA isolates for each individual, for each sampling point,wereused forSNPgenotyping. SNPgenotypingwas conductedbyDiversityArraysTechnology,Canberra,usingproprietaryDArTseq™ technology. DArTseq™ technology has been tested and used suc-cessfully for a wide range of genomic studies across a variety ofvertebrate species (Melville etal., 2017). Examples of this includeCunningham’sskinks(Egernia cunninghami)(Ofori,Beaumont,&Stow,2017),NorthAmericangreenfrog(Rana clamitans)(Lambert,Skelly,&Ezaz,2016),troutcod(Maccullochella macquariensis)andMurraycod(Maccullochella peelii)(Couch,Unmack,Dyer,&Lintermans,2016),yel-lowfin tuna (Thunnus albacares) (Greweetal., 2015), eastern yellowrobin(Eopsaltria australis)(Moralesetal.,2017),andsouthernfiddlerrays(Trygonrrhina dumerilii)(Donnellanetal.,2015).

DArTseq™representsacombinationofDArTcomplexityreductionmethods and next-generation sequencing platforms (Courtois etal.2013;Cruz, Kilian,&Dierig, 2013;Kilian etal., 2012; Raman etal.,2014). Similar to DArTmethods based on array hybridizations, thetechnologyisoptimizedforthespecificorganismandapplicationbyselectingthemostappropriatecomplexityreductionmethod. Inthisstudy,thecombinationofPstIandSphIrestrictionenzymes(RE)per-formedbetter inpolymorphismdetectionefficiency.Whengenomecomplexityreductionmethodsarecompared,thoseshowingincreasedpercentagesof repetitiveelements, skewed size ranges,ornonidealnumbersoffragmentsareavoided.

DNA samples were processed in digestion/ligation reactions(Kilianetal.,2012),ligatingtwoadaptorscorrespondingtothecom-binationofREoverhangs.ThePstI-compatible adapter includes thebarcode.Thebarcodesareofdifferentlengthvaryingbetween4and8bp,thiswasdesignedtostaggerthesequencingstartposition,simi-lartothemethodreportedbyElshireetal.(2011).ThereverseadaptercontainedtheSphI-compatibleoverhangsequence.

The PstI-SphI fragments were amplified by adapter-mediatedPCRas follows: initial denaturationof94°C for1min, followedby30cyclesofdenaturation(94°Cfor20s),annealing(58°Cfor30s),andextension(72°Cfor45s),withfinalextensionphaseof72°Cfor7min.ThePCRprimersweredesignedtoaddtherequiredsequencesforenablingsequencing inasingle-read Illumina flowcell.Equimolar

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amountsofamplificationproductsfromeachsamplewerebulkedandappliedtoc-Bot(Illumina)bridgePCRfollowedby77cyclesofsingle-readsequencingonIlluminaHiseq2500(Illumina).

The resulting sequences generatedwere processed using pro-prietaryDArTanalyticalpipelines.Theprimarypipelinefilteredoutpoorqualitysequences,whileapplyingmorestringentselectioncri-teriatothebarcoderegion.Inthisway,assignmentofsequencestospecific sampleswasvery reliable. Identical sequenceswere thencollapsed into “fastqcol” files for use in secondary pipeline analy-sis, usingDArTPL’sproprietarySNPandSilicoDArT (presence/ab-senceofrestrictionfragmentsinrepresentation)callingalgorithms(DArTsoft14).

For SNP calling, all tags from all libraries included in theDArTsoft14analysisareclusteredusingDArTPL’sC++algorithmatthethresholddistanceof3,followedbyparsingoftheclustersintoseparateSNPlociusingarangeoftechnicalparameters,especiallythebalanceofreadcountsfortheallelicpairs.Additionalselectioncriteriawereadded to thealgorithmbasedonanalysisofapprox-imately 1,000 controlled cross populations.Testing forMendeliandistribution of alleles in these populations facilitated selection oftechnical parameters discriminatingwell true allelic variants fromparalogoussequences.Inaddition,multiplesampleswereprocessedfromDNAtoalleliccallsastechnicalreplicates,andscoringconsis-tencywasusedas themain selectioncriteria forhighquality/lowerrorratemarkers.Callingqualitywasassuredbyhighaveragereaddepth per locus. This process is similar to that used in publishedliteratureusingDArTseq™ SNPs fromanimal genetic samples (e.g.,Couchetal.,2016;Donnellanetal.,2015).

Sequences identified during the DArTseq™ process were runthrough theNationalCenter forBiotechnology Information’s (NCBI)BLAST(basiclocalalignmentsearchtool)(Altschul,Gish,Miller,Myers,& Lipman, 1990) to investigate possible dietary or disease-relatedDNAthatwasincludedinscats.

2.4 | Assessing error, descriptive analysis, and potential sexing loci

Foreachgenotypedsample,percentagemissingdatawerecalculated,andgenotypecomparisonbetweenbloodDNAresultsandscatDNAresultsforbothscatageswasusedtoassessallelicdropoutandfalsealleles.SNPlocioverlapbetweenbloodDNAgenotypesandscatgeno-typeswerecalculated,aswellaslocioverlapacrossallbloodsamples(populationlocioverlap).Inthiscontext,overlapreferstothepercent-age of SNP loci with successful genotype reads across all specifiedsamples. Thus, high overlap between samples suggests that a highpercentageofthe1272SNPlociproducedgenotypereadsacrossallthespecifiedsamplesbeingcompared.Asthesexofallkoalaindividu-alswasknownforthisstudy,putativesex-linkedSNPlociwerealsoidentified.

Sequencingdepths for both reference andSNPalleles, for eachlocus, for each sample were investigated, and average sequencingdepthforbloodDNA,two-day-old,and14-day-oldscatsampleswerecalculated.

2.5 | Genetic analyses and visualization

Analysesofallelicfrequencyandgeneticdistancebetweenallsam-pleswere conducted inGenAlEx6.503 (Peakall&Smouse, 2006,2012). To assess whether individuals could be accurately identi-fiedusinggenotypes fromscatDNAextractions,neighbor-joiningtreeswereconstructedforavarietyoflocisubsets,basedonerrorrates(i.e.,missingdata,scatgenotypedifferenttobloodgenotype),sequencing depth, overlap between two-day-old and 14-day-oldsamples, and excluding homozygous SNP loci. This enabled us toidentifyasuiteofaccurateSNPlociappropriateforsuccessful in-dividual identification from scat samples. Neighbor-joining treeswere constructed using FAMD (Fingerprint AnalysiswithMissingData)software(Schlüter&Harris,2006)andvisualizedinMEGA7(Kumar,Nei,Dudley,&Tamura,2008;Kumar,Stecher,&Tamura,2016).

Tofurthertesttheutilityofthe209SNPpanelselectedforindivid-ualidentification,wecalculatedtheprobabilityofidentityforunrelatedindividuals(PID)andthemoreconservativeprobabilityofidentityforfullsiblings(PIDsibs).Theprobabilityofidentitymeasurestheprobabil-itythattwoindividualsdrawnrandomlyfromthepopulationwillhaveidenticalgenotypesacrossagivenmarkerpanel (Lorenzini,Posillico,Lovari,&Petrella,2004;Mills,Citta,Lair,Schwartz,&Tallmon,2000;Waits, Luikart, & Taberlet, 2001).We used GenAlex 6.503 (Peakall&Smouse, 2006, 2012) and included all samples used in neighbor-joiningtreeanalysis(n =19).

3  | RESULTS

While100%ofthetwo-day-oldscatsamplesweresuccessful,only 70%of 14-day-old samples provided high enough qual-ity DNA for successful library construction. The low-qualitysamples were therefore excluded from subsequent analyses.DNA concentrations from scat samples in this study werefoundtobecomparabletoasimilarstudyinwhichkoalafecalDNA was isolated for microsatellite genotyping (Wedrowiczetal., 2013), suggesting that the DNA isolation methodsutilized in this study provide comparable results to extrac-tion methods used in other studies. The average DNA con-centration for two-day-old scat in this study was similar tothat found in the comparison study (two-day-old-scat aver-age:10.94ng/μl;<30-hr-oldscatincomparisonstudy:11ng/μl), while the average for 14-day-old scat in this study wasfound to be higher than that of 28-day-old scat in the com-parisonstudy(14-day-oldscataverage:3.2ng/μl;28-day-oldscat incomparisonstudy:2.2ng/μl).DNAconcentrations foreachtwo-day-oldand14-day-oldscatsamplesarereportedinTable1.Theestimatedsizerangesfortheamplifiedfragmentswerebetween20bpand700bp,withapeakbetween120bpand200bp.Additionally, having access to the koala genome(whenpublished)willallowforbettermappingandcalculationoffragmentlengths.

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3.1 | SNP loci from blood DNA

DArTseq™technologyidentified1272SNPloci.Askoalasareanon-modelspecies,referenceallelesandSNPallelesforeachlocuswereassigned arbitrarily—inmost cases, reference alleleswere indicatedastheallelethatwasmostfrequentacrossallsamplesforthatlocus.Ofthe1272lociidentified,1247loci(98.0%)werefoundtooverlapacrossbloodDNAsamplesfromallfiveindividuals.Onehundredandsixty-nineloci(13.6%)werefoundtobehomozygousacrossallindi-vidualsandsoareuninformativeforthissamplesize.

3.2 | Potential sexing loci

Of the1272 loci identified,26potentially sex-linkedcandidate lociwerefound.Thatis,lociwhichwerepresentacrossallindividuals,andvariedconsistentlyingenotypebetweenmalesandfemales.Forex-ample, locus ID#12495936(AppendixS2:TableS1)showedallelesTGforbothmaleindividualsandallelesTTforallfemaleindividualsduringgenotypecalling.

3.3 | Blood genotyping to scat genotyping analysis

In comparisonwithDNA extracted from blood, two-day-old scatDNAhadhigher,andmoreconsistentSNPlocioverlap (maximumoverlap:99.8%;minimumoverlap:13.3%;medianoverlap:95.0%)than 14-day-old scat DNA samples (maximum overlap: 99.8%;minimum overlap: 15.6%, median overlap: 72.3%) (Table1). Highoverlappercentagemeansmore lociweresuccessfullygenotypedinbothbloodDNAandscatDNA,suggestingthatfresherscatspro-videdanaveragegenotypingpicturecloser to thatofbloodDNAthanolderscats.

Whencomparingerrorratesandtypesbetweenfresherandolderscats, two-day-old scat DNA samples had on average less missingdata, and lessvariability inmissingdata, than14-day-old scatDNAsamples (Figure1,Table1). Interestingly,among loci thatdoprovidedata,thedifferenceinreaderrorratesornullalleleratebetweentwo-day-oldand14-day-oldscatsampleswaslow,suggestingthatdiffer-encesingenotypingresultsbetweenscatagesweredrivenbymissingdataoverothererrortypes.

TABLE  1 SNPlocioverlapbetweenscatDNAandbloodDNAsamples,percentageofmissinggenotypedataforallsamples,percentageofnullallelesreadinscatsamplesincomparisonwithbloodDNAtemplate,percentageofincorrectgenotypereadsinscatsamplesincomparisonwithbloodDNAreads,andtotalDNAconcentrationsfromscatextractionreactions.Nullallelepercentagesandincorrectgenotypereadpercentagesareapercentageoftotallociwithnomissingdata.DNAconcentrationreadingsincludeallDNAextractedduringreactionandsowillalsoincludenontargetDNA

Sample Sample type

Loci overlap with blood DNA sample (%) Missing data (%)

Null alleles in scat samples (%)

Incorrect genotype reads in scat samples (%)

Total DNA concentration (ng/μl)

Koala 1 Blood 0.24

Koala 2 Blood 0.31

Koala 3 Blood 0.16

Koala4 Blood 0.08

Koala 5 Blood 1.89

Koala1Day2a Scat 94.6 8.81 10.17 5.95 5.65

Koala1Day2b Scat 93.6 24.92 11.31 7.75 30.3

Koala1Day14a Scat 72.3 27.75 21.98 1.63 9.6

Koala1Day14b Scat 34.5 65.64 27 2.29 7.25

Koala2Day2a Scat 99.5 49.21 23.53 1.39 16.3

Koala2Day2b Scat 13.3 8.88 17.26 0.78 6.85

Koala2Day2c Scat 99.5 0.47 18.88 26.22 8.35

Koala3Day2a Scat 94.3 5.66 10.67 1.17 5.7

Koala3Day2b Scat 75.2 24.84 19.77 2.62 6.9

Koala3Day14a Scat 50.8 0.47 4.42 1.58 0.55

Koala3Day14b Scat 91.1 86.71 26.04 14.2 0.25

Koala4Day2a Scat 99.8 0.16 2.91 1.65 5.6

Koala4Day14a Scat 97.5 2.52 9.11 1.69 2.25

Koala4Day14b Scat 99.8 0.24 2.99 1.81 1.93

Koala5Day2a Scat 95.4 4.64 7.75 2.97 13.4

Koala5Day2b Scat 97.3 2.67 5.49 4.04 10.35

Koala5Day14a Scat 15.6 84.43 19.7 11.11 0.6

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3.4 | Average read depths

ReaddepthreferstothenumberoftimesaSNPlocushasbeense-quencedandmappedduringthegenotypingprocess(Fumagalli,2013).Genotypesarethencalledfromthesereads,wheresiteswithhighernumbersofreadsarelikelytohavehigheraccuracyingenotypecall-ing.Conversely,lociwithlowernumbersofreadsarelikelytoexhibitnon-negligibleerrorsingenotypecalling(Crawford&Lazzaro,2012).ReaddepthisthenanimportantmeasureofSNPqualitywhenassess-ingthelikelyaccuracyofgenotypecalling.Theaveragereaddepthforall1272 locidifferedgreatlybetweenbloodDNAsamplesandscatDNAsamples(Figure2),withbloodsampleshavingonaverageninetimesgreaterreaddepthperlocus.Fourteen-day-oldsamplesshowedonaverageslightlyhigher (Referenceallele—6.1X;SNPallele—3.8X)read depths than two-day-old (Reference allele—4.3X; SNP allele—3.2X)samples.However,forall(n =7)14-day-oldsamples,therewere206locipresentwhichdidnotprovidegenotypereadsinanysamples.Incomparison,fortwo-day-oldscatsamples,therewereonlyninelociwhichcontainedmissinginformationacrossallsamples.

3.5 | Allele frequency

For1272SNPlociacrossfiveindividuals,559loci(44%)hadaminorallelefrequencyofeither0%or10%.Thismaybeanartifactofoursmallsamplesize(AppendixS1:FigureS1).

3.6 | Genetic distance

ToidentifyapanelofSNPlociusefulforidentifyingindividualkoalasfromscat-extractedDNA,lociwereselectedbasedonhighsequenc-ingdepth,lowerrorrates(i.e.,missingdata,nullalleles,andfalseallelereads), loci overlapbetween two-day-old and14-day-old scat sam-ples,andhomozygousloci.Fortwo-day-oldscatsamples(n =10)and

14-day-oldscatsamples(n =7),SNPlociwereexcludedifgenotypeswerehomozygousacrosssamples,sequencingdepthforreferenceal-lelewas<5X,missingdatawere found inmore than threesamples,and if scat genotype did not match blood genotype in more thanthree samples.These subsetsof lociwere then comparedbetweenscatages,andonlythoselocicommontobothscatagesubsetswereincludedintheneighbor-joiningtree.Furthermore,scatDNAsamplesmissingmorethan50%dataacrosstheselected209 lociwerealsoexcluded,regardlessofage.

The resulting neighbor-joining tree (Figure3) showed greatergenetic difference between individuals thanwithin individuals.Thissuggests that the 209 loci panel identified could be used to differ-entiatebetweenindividualkoalasatageneticlevel,evenwhenscatsare14daysoldpriortosampling.Interestingly,Koala5isthedaugh-terofKoala3,whichclustertogetheronthejoiningtree,suggestingthatfirst-degreerelatednessbetweenindividualsmayalsobeidenti-fiablefromusingSNPmarkersonDNAfromscat.Thespecificlociin-cludedinthispanelareidentifiedintheDRYADonlinedatarepositorysubmission.

AveragePIDandPIDsibsmeasureswerecalculatedforthe19sam-plesusedinthefinalneighbor-joiningtree,usingthe209locipanelse-lectedforindividualidentification.AveragePIDwas3.5×10

−52,whilethemoreconservativePIDsibswas1.3×10

−26.Thesevaluesareconsid-eredlowforprobabilityofidentitycalculations(<0.0001)andsuggestaverylowprobabilityoftwoindividualswithidenticalmultilocusgen-otypesbeingdrawnrandomlyfromthepopulation.Conversely,then,individualswithidenticalgenotypesfoundinthepopulationwouldbeassumedtoberesamplingofthesameindividual.UsingthesubsettedSNPmarkerpanelof209lociforPID,werequireonlytenlocitoreacha1in100chanceofrandomlydrawingtwoindividualswiththesamegenotype by chance, and 20 loci to reach a 1 in 10,000 chance ofdrawingthesame.ForthemoreconservativePIDsibsmeasure,were-quiretwentylociandthirty-nineloci,respectively.Hence,weexpect

F IGURE  1 Two-day-oldversus14-day-oldscatDNA(a)missingdataand(b)genotypingerror,whencomparedtotemplategenotypefromblood.Missingdata(a)showspercentageofloci(n =1272)whichdonotprovideanyreaddatainscatsamples.Readerrorandnullallele(b)showpercentageofremaininglociwhichdonotmatchbloodtemplategenotypeduetoincorrectreadorallelicdropout.Samplesize:two-day-oldsamples:n =10;14-day-oldsamples:n = 7

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our209locimarkersettohaveadequatediscriminatorypowerinac-curatelyidentifyingindividuals.

3.7 | BLAST results

Running DNA sequences identified during the DArTseq™ pro-cess throughBLAST revealed dietary and disease information (seeAppendix S2: Table S2). In relation to diet, we identified multipleBLASThitsforEucalyptus grandis(41predictiveBLASThits)inscats(acommonfoodtreeknowntobeprovidedbyzoostaffforkoalas,J.Schenk,WildlifeHQCEO,pers.comm.2017).Thisisaknownkoalafoodtree(Lunneyetal.,2000)andsuggeststhatindividual-specificdietary information may be accessible through genetic analysis ofscats.Fromadiseaseperspective,BLASTresultsturnedupmultiplecompletesequencesofkoalaretrovirus(KoRV)isolates(fourBLASThits). EvidenceofKoRVwasmost noticeable in blood samples, al-though there were also positive hits in scat samples. In addition,therewereBLASThitsfortheparasiticnematodeParastrongyloides trichosuri(fourBLASThits),whosenaturalhostsarepossumsoftheTrichosurus genus (Grant etal., 2006).We also found evidence ofPseudomonas aeruginosa bacteria (13 BLAST hits). This is a knownpathogenwhichhasbeenassociatedwithpneumoniainwildkoalas(McKenzie,1981).Theseresults indicate that theprocessofgeno-typingkoalasfromscatDNAmayalsoallowformuchgreaterinfor-mationondiet anddisease (bacterial, viral, andparasitic) presencethanpreviouslythought.

4  | DISCUSSION

DNA extracted from a single koala scat can provide enough high-quality DNA to successfully genotype individuals using 1272 SNPmarkers,withoutthemultitubeapproachrequiredinmanynoninva-sivestudies(Regnaut,Lucas,&Fumagalli,2006).Additionally,thisisthefirsttimethatkoalafecalDNAhasbeencomparedtobloodDNAto test genotyping accuracy.We demonstrate that powerful next-generationpopulationgeneticsapproachesarepossibleforkoalafecalDNA,allowingforagreatervarietyofgeneticanalysesbasedonnon-invasivesamplestakenfromwildkoalas.

Whilegenotypingerrors,mostlydue tomissingdataatunder-performingloci,variedgreatlybetweentwo-day-oldand14-day-oldscat, average sequencing depth did not. Sequencing depth fromfecal DNAwas greatly reducedwhen compared to that of bloodDNA samples, but average depth across all scat sampleswas still4.6X(referencealleleaverage:5.6X;SNPalleleaverage:3.6X).Next-generationsequencingdatasimulationbyFumagalli(2013)suggestthathighlyprecisedetectionofpolymorphicsitescanbeachievedbygenotypingsmallsamplesizesathighsequencingdepth(n =20,depth=50X, precision =1). However, genotyping larger samplesizes at lower sequencing depths can provide comparable results(>75% precision). For example, a sample size of 500 individualssequenced at 2X depth canmaintain precision of 0.778±0.0641,similartoasamplesizeof100individualssequencedat10Xdepth(precision =0.779±0.0441).

F IGURE  2 Distributionofreferenceallelesequencingdepthsof1272SNPlociforbloodDNAextractions,two-day-oldscatDNAextractions,and14-day-oldscatDNAextractions.Averagesequencedepthacrossallloci:Blood:Refallele—49X,SNPallele—31X;Two-day-oldscat:Refallele—4.3X,SNPallele—3.2X;14-day-oldscat:Refallele—6.1X,SNPallele—3.8X

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Sampling larger sample sizes at lowerdepthmaybeparticularlywellsuitedtoscatDNAanalysis.Forexample,detectiondogscatsam-plingallowstogreatlyincreaseoursamplesizeacrossthetargetland-scape(Cristescuetal.,2015),whereintheloweraveragesequencingdepthsweseeinfecalDNAanalysescanstillprovideprecisepolymor-phicreads.Foranalysesinvestigatingpopulation-levelgenetictrends(e.g.,populationstructure,interpopulationgeneticdiversity,andgeneflow),wecanthereforeutilizeall1272 loci identifiedhere,as largersamplesizeswillbalanceoutlowersequencingdepths.

For analyseswhich require accurate individual identification,wecan then focus on the smaller sample sizes and higher sequencingdepthsrecommendedbyFumagalli(2013).

Here,wehaveexcludedSNPlociwithlowsequencingdepthsandhigherrorrates,toidentifyasuiteoflocithatperformwellonscatsup to14daysold, allowing for accurate individual-level analysis forsamplesthatmayhavepartiallydeteriorated.Thispanelof209SNPlocicanbeusedin individual-basedgeneticanalyses,suchasdeter-mining inbreedingcoefficientsandeffectivepopulation sizes,whichareofparticularimportancetotheconservationofgeneticdiversity.Additionally,theuseofSNPgenotypinginrepeatedlyidentifyingindi-vidualanimalsopensthedoorformark–recapturestudiestoestimatekoala population sizes—one of themost difficult ecological metricstoassessinkoalas,andoneofthemostcrucialformakinginformedconservation decisions (Lurz, 2008; Phillips, 2000; Shaffer, 1981).Usingthispanel,weareabletoconfirmthefirst-degreerelatedness

oftwokoalasinthisstudy,identifyingKoala3asthefatherofKoala5.Additionally,byremovingsampleswithhighlevelsofmissingdata(higherthan50%missingdata,invariably14-day-oldsamples),wecanensure that the individual identification results are accurate acrossallindividuals.ByutilizingbloodDNAasatemplateinthisstudy,wecouldassesshowagemayinfluencetheeffectivenessofgenotypingandalsoestablishedathresholdforexcludingsamplesfromanalysesthatrequireindividual-levelaccuracy.Furthermore,theutilityofthis209locimarkerpanelwasreinforcedbythePIDandPIDsibsresults.Theverylowprobability(<0.0001)ofincorrectlyidentifyingtwoindepen-dentindividualsasthesameindividualusingthismarkerpanelatteststo its strong discriminatory power. Given that only thirty-nine lociwereneededtoachievesatisfactorydiscriminationbetweenindividualsamples(i.e.,<0.0001)forthePIDsibsmeasure,wefeelconfidentthatthispanelcanreliablyidentifyindividualsinthetypicallylargersamplesizesusedinanalysesofwildpopulations.

Withregardtootherinformationcapturedduringthegenotypingprocess,thepresenceofdietaryinformation(E. grandis)providesevi-dencethatindividualkoaladietcouldbeassessedalongsidegenotyp-ing.Askoalasareknowntospendtimeinnonfoodtrees(Briscoeetal.,2014),simplepresenceinatreeisnotalwaysindicativeofdiet,andresearcherscurrentlyhavetorelyontime-consumingleafcuticleanal-yses(Melzeretal.,2014).Atailoredapproachtoidentifyingthefoodtreepreferencesofindividualkoalasacrossalandscapecouldprovidelarge-scaleecologicalinformationcurrentlyunavailabletoresearchers.

F IGURE  3 Neighbor-joiningtreeofgeneticdistancesusing209highlyconservedSNPlociforbloodandscatDNAsamples.Lociselectedforgeneticdistancecalculationwasbasedonsortingforsequencingdepth,errorrates,andhomozygousloci.ScatDNAsampleswithmissinginformationatmorethan50%oflociwereexcludedfromthisanalysis

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Testingofthesensitivityofgeneticapproachestochangesindietmaybethenextstepinthisresearch,buttheseresultsarethefirstevidenceweknowof,ofkoaladietaryindicatorsbeinggeneticallyidentifiedinscat.Furthermore,theadditionofinformationondiseasepresenceforbacteria,viruses,andparasiticinvertebratesaddsyetanotherlayerofinformationonkoalahealththatiscurrentlydifficultandcostlytoas-sess.Obviously,BLASTsearcheswillonlyregistersequencesalreadyin theNCBI databases, and so theBLAST hits forParastrongyloides trichosuri, the parasitic possum nematode, are possibly identifyinga koala-specific nematode from the same genus,which has notyetbeendescribed.It is interestingtonotethatthereisnoevidenceofC. pecorum inanyextractedDNA,butgiventhatthefiveindividualsassessed inthisstudyareanimalsbred incaptivity, itshouldnotbesurprisingthattheyareC. pecorum-free.ThatmultipleBLASThitsforeachoftheseorganismsweredetectedaddsstrengthtoourproposalthattheseareaccurateidentifications,supportedbybiologicalratio-nale for theirpresence.Further study into the relationshipbetweenthepresenceofsuchpathogensinbloodandscatandthehealthoftheindividualkoalaisobviouslyrequired.However,thefactthatsuchwide-rangingbacterial,viral,andparasiticorganismscanbedetectedthroughtheDArTseq™processisencouragingforassessingthehealthofwildkoalas.

Whilethereisnodoubtthatfreshisbestwhenitcomestononinva-sivescatsamplingforgeneticanalyses,thelimitationsofcollectingscatfromwildpopulations,evenwiththeadvances inspeedandaccuracyintroducedbydetectiondogs,meansthatitmaynotalwaysbepossibletosamplescatswithinthefirsttwodays.Ourresearch,however,showsthatolderscatscanstillbeuseful,dependingontheresearchquestionandprojectdesign. It is also important to remember thatwhile some14-day-oldscatsprovidedenoughhigh-qualityDNAforindividualiden-tificationinthisstudy,scatswereagedunderlaboratoryconditions,andsoanupperlimitof14daysmaynotberealisticforscatscollectedfromwildkoalas.Ultravioletlight,rain,groundcovervegetation,andpheno-licsandvolatileorganiccompounds released fromkoalascatsas theydecomposemayallleadtorapidkoalafecalDNAdegradation.Indeed,thismayresult in fasterDNAdegradation inkoalascats than isoftenfoundinothernoninvasivelysampledspecies,andsounderidealcircum-stances,thefreshestscatshouldbesoughtwhereverpossible(Cristescu,Goethals,Banks,Carrick,&Frère,2012;Wedrowiczetal.,2013).Whileveryfreshkoalascatisobviouslyidealforgenotyping,thereismostlikelya good compromise in practicality of sampling and quality of resultssomewherebetweentwo-day-oldscatand14-day-oldscat.Fortunately,koalascatagecanbeestimatedbysightwithadegreeofaccuracy,withpellets<14daysoldrecognizablebytheirshiny,uncrackedpatina,andstrongeucalyptsmell(Sullivan,Norris,&Baxter,2002).

Acrossdifferentscatages,itisalsoimportanttoconsiderthetwopossible causes of poor genotyping results. Firstly, that insufficienthigh-qualityDNAisextractedfromscatsamplestoallowfor libraryconstruction. In these cases, as seenwith 30%of 14-day -old scatsamples in this study, no information can be produced from suchsamples.Whenthisoccurs,optimizationoftheDNAextractionpro-cess,andinclusionofPCRfacilitatorssuchasBSA(bovineserumal-bumin),mayyieldimprovedresults.Otheralternativesmightinclude

extracting DNA from replicate scats for older samples, to ensurehigherDNAyield.Despitethis,ourstudyshowsthat70%of14dayoldscatscontainedsufficientDNAtoconstructlibrariesforDArTseq™ SNPgenotyping, thusvalidatingtheDArTseq™ technologyforuse inthisapplication.

ThesecondproblemmayarisewherebyextractedDNAisalreadydegraded(duetoenvironmentalfactors,scatcontents,volatilecom-poundsetc.).Thiscanresult inthepresenceofmissingdata(nullal-leles andallelicdropout), asevidenced in the successfully amplified14-day-oldscatsamplesinthisstudy.ThatthismissinggenotypedataisduetoDNAdegradationratherthaninefficienciesintheextractionprocessisfurthersupportedbythefavorablecomparisonofDNAcon-centrationsbetweenthe14-day-oldsamples inthisstudyandoldersamplesinasimilarstudy(Wedrowiczetal.,2013).Furthermore,theseDNAconcentrationresultspointtotheutilityoftheDNAextractionprocessusedinthisstudy,suggestingmosterrorsareduetodegradedDNA.AsthisDNAdegradationwillmostlikelyhaveoccurredpriortoextraction,optimizationoftheamplificationandgenotypingprocessesmaybenecessarytoachieveoptimalresults.Possibleoptionsherein-clude targeting smaller fragments from amplification. Further studyintowhichfactorsaremostlikelytointroduceerrorintogenotypingresults,andhowtospecificallytargetthem,wouldalsoallowforbetterfutureprojectdesign.

With regard to collecting scat fromwild populations, detectiondogs are increasingly used in koala conservation (Cristescu etal.,2015).Whiledogstrainedtofindscatofallagesareusefulforidenti-fyingkoalahabitat,scatsolderthantwoweeksarenotassuitableforgeneticanalysisusingthemethodsoutlinedinthisstudy.Thus,dogstrained specifically to find fresher scatmay be a useful addition toconservation researchandcouldgreatly increase thenumberofge-neticsamplescollectedfromwildkoalapopulations.Tothisend,theauthorsarecurrentlytrainingadetectiondogtoprioritizefindingfresh(<1-week-old)koalascat.This, coupledwithgrowingcitizenscienceprograms whereby members of the public collect and freeze freshscat for researchers,canprovidehigh-qualityDNAsamples forSNPgenotypingandsubsequentanalysis.Thesenovelsourcesofgeneticsamplescanallowfor largeenoughsamplesizestostudy importantaspectsofwildkoalapopulationgenetics,whichhavebeenpreviouslyunavailable to researchers.Additionally, the potential to gather notonly koala genetic information, but also dietary and disease infor-mationusingthissameprocessmakestheuseofkoalascatfornext-generationgeneticanalysesanincreasinglypowerfultool.

Whenitcomestotestingnovelapplicationsofgenotypingmeth-ods,thequestionofsamplesizeisalwaysanimportantconsideration.Whilemore is invariablybetter, in this case, the sample sizeof fiveindividuals is sufficient as a proof of concept for the application ofthismethodology.Thereareanumberofreasonsforthis:Firstly,theDArTseq™protocolutilizedinthisstudyisawell-documentedmeth-odology. It hasbeenusedeffectively across a rangeof species andspecificallyrecommendedforvertebratestudies(Melvilleetal.,2017).In particular, the standardization of loci genotyped across samples,and the repeatablecomplexity reductionmethodsprovidea reliableand widely applicable methodology. This holds true regardless of

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samplessize.Secondly, thehighlyconserved209 lociused for indi-vidual identification perform well for both individual identificationanalysesconducted.Inneighbor-joiningtreeanalysis,allsamplesfromthesameindividualgrouptogetherinneighbor-joiningtreeanalyses.Furthermore, therewas accurate discrimination between scat DNAsamplesfromthefather–daughterpairing.Theprobabilityofidentityanalyses runs in this study also support these results.Thus,we areconfidentofthepowerofthe209SNPlocipaneltodetermineidentityinlargerpopulations.

Aswithapplyingpublishedmethodologiestoanynewcontext,itisalwaysvaluabletoconsiderthepossiblelimitationsoftheapplica-tion and the conditions underwhich it has been tested.Regardlessofthis,thisstudyprovidessufficientevidencethathigh-qualitykoalaDNAcanbeextractedfromscatstofacilitateSNPgenotypingusingtheDArTseq™methodology.TheincreasedpowerprovidedbySNPge-notypingforgeneticanalysisensuresthatimportantaspectsofkoalapopulationecologyandgeneticscanbeadequatelyassessedbeforeconservationdecisionsaremade,allowingformoreaccurateinterven-tionsandmanagementstrategies.

ACKNOWLEDGMENTS

WewouldliketogratefullyacknowledgeJarrodSchenkandWildlifeHQZoo,Woombye,QLD,forprovidingbloodandscatsamplesfromtheir captive koalas. Sampling was conducted under University ofthe Sunshine Coast Animal Ethics Committee approval, applicationnumber AN/A/16/113.We would also like to thank Koala ActionInc. and the Councils of the Sunshine Coast, Gympie and FraserCoastforthefundingofthisproject,andtheGeneCologyResearchCentre for supporting this research.Wealso thankDonPitt,AdamWhittaker,Madeleine Page, Toni Ryan, Marie Millard, SharynJocumsen,andLeniceJarlingfromtheDepartmentofTransportandMainRoadsforlogisticalsupport.

DATA ACCESSIBILITY

BloodDNA and scatDNA genotypes for koalas used in this studyhave been uploaded to Dryad repository. https://doi.org/10.5061/dryad.mq0gb.

CONFLICT OF INTEREST

Nonedeclared.

AUTHOR CONTRIBUTIONS

AS,CF,andRCdesignedthisresearchproject.ASandBL-Cperformedlaboratorywork.AS,DJ,andCFperformeddataanalysis.AS,CF,RC,andDJwrotethemanuscript.

ORCID

Anthony J. Schultz http://orcid.org/0000-0003-2763-8461

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How to cite this article:SchultzAJ,CristescuRH,Littleford-ColquhounBL,JaccoudD,FrèreCH.Freshisbest:AccurateSNPgenotypingfromkoalascats.Ecol Evol. 2018;8:3139–3151. https://doi.org/10.1002/ece3.3765