edna metabarcoding as a new surveillance approach for coastal arctic ... - altermatt lab · 2018....
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Ecology and Evolution. 2018;1–15. | 1www.ecolevol.org
Received:20February2018 | Revised:20April2018 | Accepted:23April2018DOI:10.1002/ece3.4213
O R I G I N A L R E S E A R C H
eDNA metabarcoding as a new surveillance approach for coastal Arctic biodiversity
Anaïs Lacoursière-Roussel1 | Kimberly Howland2 | Eric Normandeau3 | Erin K. Grey4 | Philippe Archambault5 | Kristy Deiner6 | David M. Lodge7 | Cecilia Hernandez3 | Noémie Leduc3 | Louis Bernatchez3
ThisisanopenaccessarticleunderthetermsoftheCreativeCommonsAttributionLicense,whichpermitsuse,distributionandreproductioninanymedium,providedtheoriginalworkisproperlycited.©2018TheAuthors.Ecology and EvolutionpublishedbyJohnWiley&SonsLtd.
1St.AndrewsBiologicalStation(SABS),FisheriesandOceansCanada,St.Andrews,NB,Canada2CentralandArcticRegion,FisheriesandOceansCanada,FreshwaterInstitute,Winnipeg,MB,Canada3DepartmentofBiology,InstitutdeBiologieIntégrativeetdesSystèmes(IBIS),UniversitéLaval,Québec,QC,Canada4DivisionofScience,MathematicsandTechnology,GovernorsStateUniversity,UniversityPark,IL,USA5DepartmentofBiology,UniversitéLaval,Québec,QC,Canada6DepartmentofEvolutionaryBiologyandEnvironmentalStudies,UniversityofZurich,Zürich,Switzerland7DepartmentofEcologyandEvolutionaryBiology,CornellUniversity,Ithaca,NY,USA
CorrespondenceAnaïsLacoursière-Roussel,St.AndrewsBiologicalStation(SABS),FisheriesandOceansCanada,St.Andrews,NB,Canada.Email:[email protected]
Funding informationArcticNet;POLARknowledge;NunavutWildlifeManagementBoard;FisheriesandOceansCanadaAquaticInvasiveSpeciesMonitoringProgram
AbstractBecausesignificantglobalchangesarecurrentlyunderwayintheArctic,creatingalarge-scalestandardizeddatabaseforArcticmarinebiodiversityisparticularlypress-ing.ThisstudyevaluatesthepotentialofaquaticenvironmentalDNA(eDNA)meta-barcodingtodetectArcticcoastalbiodiversitychangesandcharacterizesthelocalspatio-temporaldistributionofeDNAintwolocations.WeextractedandamplifiedeDNAusingtwoCOIprimerpairsfrom~80watersamplesthatwerecollectedacrosstwoCanadianArcticports,Churchill and Iqaluitbasedonoptimizedsamplingandpreservationmethodsforremoteregionssurveys.ResultsdemonstratethataquaticeDNAsurveyshavethepotentialtodocumentlarge-scaleArcticbiodiversitychangebyprovidingarapidoverviewofcoastalmetazoanbiodiversity,detectingnonindig-enousspecies,andallowingsamplinginbothopenwaterandundertheicecoverbylocalnorthern-basedcommunities.WeshowthatDNAsequencesof~50%ofknownCanadianArcticspeciesandpotentialinvadersarecurrentlypresentinpublicdata-bases.Asimilarproportionofoperationaltaxonomicunitswasidentifiedatthespe-cies levelwitheDNAmetabarcoding, for a total of181 species identified atbothsites.Despitethecoldandwell-mixedcoastalenvironment,speciescompositionwasverticallyheterogeneous,inpartduetoriverinflowintheestuarineecosystem,anddifferedbetweenthewatercolumnandtidepools.Thus,COI-basedeDNAmetabar-codingmayquickly improve large-scaleArcticbiomonitoringusingeDNA,butwecautionthataquaticeDNAsamplingneedstobestandardizedoverspaceandtimetoaccuratelyevaluatecommunitystructurechanges.
K E Y W O R D S
arctic,coastalbiodiversity,eDNAmetabarcoding,globalchanges,invasion,spatio-temporaldistribution
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1 | INTRODUC TION
IntheArctic,climatechangeandmarineinvasionsareexpectedtoresultinover60%speciesturnoverfrompresentbiodiversitywithsubstantial impactsonmarineecosystems (Cheungetal., 2009).Climate change is opening newwaterways in the ArcticOcean,resulting in greater shipping traffic (ACIA 2004; Arctic Council2009;Guy&Lasserre,2016).Predictedincreasesinshippingfre-quencyandroutes(Eguíluz,Fernández-Gracia,Irigoien,&Duarte,2016;Miller&Ruiz,2014;Smith&Stephenson,2013), increasedinfrastructuredevelopmentinports(Gavrilchuk&Lesage,2014),andassociatedchemical/biologicalpollutionwillplaceothereco-system services at risk. Furthermore, the introductionof nonin-digenous species (NIS)maydisplacenative species, alterhabitatand community structure and increase aquaculture and fishinggearfoulinginestuariesandcoastalzones(Goldsmitetal.,2018;Grosholz, 2002; Parker etal., 1999). Currently, the continuousmonitoringneededtoevaluatelarge-scalechangesincoastalbio-diversityandfaunalassemblagesintheCanadianArcticislimited(Archambaultetal.,2010),hinderingriskmanagementandecosys-temsustainabilityplanning(Larigauderieetal.,2012).
Recentadvancesinthecollectionandanalysisofenvironmen-talDNA(eDNA)provideanewcomplementaryapproachthatcanhelptofillgapsinregionalspeciesdistributiondataleftbylogis-tically difficult traditional methods (e.g., bottom trawl, SCUBAdiving) (Deineretal.,2017),particularly inremoteandotherwisechallenginglocations.eDNAallowsforthedetectionoftracesofDNAinwaterfrommacro-organisms(Thomsen,Kielgast,Iversen,Wiuf, etal., 2012). Collecting water samples for eDNA surveyscould allow rapid sample collection, reduce the cost associatedwith data collection/shipping, and is less destructive because itdoesnotrequirethemanipulationoforganisms(Lodgeetal.,2012;Taberlet,Coissac,Hajibabaei,&Rieseberg,2012).eDNAmetabar-coding (i.e., high-throughput eDNA sequencing) can enable theidentification of millions of DNA fragments/sample, providing apowerfulapproachtosurveyaquaticbiodiversity.RepeatedeDNAsurveyscouldpotentiallybeusedtoevaluatelong-termbiodiver-sity changes such as detecting native species loss and declines,NISintroductionsandrangeexpansions,andcommunitystructurechanges. However, the detection of species using eDNA variesas a function of the population densities (Lacoursière-Roussel,Côté, Leclerc, &Bernatchez, 2016; Lacoursière-Roussel,Dubois,&Bernatchez,2016;Mahonetal.,2013), lifehistorytraits,shed-ding rates (Lacoursière-Roussel, Rosabal, & Bernatchez, 2016;Sassoubre,Yamahara,Gardner,Block,&Boehm,2016)localenvi-ronmentalconditionsandtechnicalapproachessuchassequencingeffortsandprimerbiases(Freeland,2017;Pawluczyketal.,2015).Moreover,majorconcernswitheDNAmetabarcoding,includingitsabilitytoaccuratelyidentifysequencestospecies(Chain,Brown,MacIsaac,&Cristescu,2016)andtheunknownecologicaldynam-ics of eDNA in coastal ecosystems, need to be studied beforemarinebiodiversitycanbecomparedacrossspatialandtemporalscalesusingthismethod.
Little iscurrentlyknownabouttheefficacyofeDNAmetabar-coding insurveying long-termvariation inmarinecoastalbiodiver-sity(Limetal.,2016;Portetal.,2016;Thomsen&Willerslev,2015).Relative to freshwaterecosystemswheremore studieshavebeenconducted, eDNA in coastal marine ecosystems is diluted into amuchlargervolumeofwaterandexposedtopronouncedhydrody-namics(e.g.,tides,currents)andvariationinabioticconditions(e.g.,salinity,temperature),whichislikelytoaffecteDNAtransportanddegradation(Footeetal.,2012;Thomsen,Kielgast,Iversen,Møller,etal., 2012). In spite of these challenges, a recent study of hori-zontal spatial eDNAdistribution in thePugetSound (Washington,USA;O’Donnelletal.,2017)wassuccessful in revealing finescaledistributionofspeciesinthesecommunities.InArcticecosystems,highereDNAtransportanddiffusionisexpectedduetoslowerDNAdegradationincold-watertemperatures,butnostudyhasyetchar-acterizedaquaticeDNAdistributioninthisenvironment.ImprovingourunderstandingoftheecologyofeDNA—themyriadof interac-tionsbetweenextraorganismalgeneticmaterialanditsenvironment(Barnes&Turner,2016)—invariousecosystemsisfundamentaltode-termininghoweDNAcanandcannotimprovebiodiversityresearch.
Our objective is to explore the potential of eDNA as a biodi-versitymonitoringapproach toassist in rapiddetectionofcoastalbiodiversityshiftsonlargespatialscaleintwoArcticcoastalareas:Churchill and Iqaluit. These two Arctic commercial ports are ex-pectedtobeparticularlypronetobiodiversitychangesbecausetheyareamongthetopthreeportsintheCanadianArcticwithrespecttovesselarrivalsandassociatedballastand/orhullfoulinginvasionsrisk (Chan,Bailey,Wiley,&MacIsaac,2013).Morespecifically,weestimatetheproportionoftheArcticbiodiversitythatcanbeiden-tifiedatthespecieslevelwitheDNA,andwethencharacterizethespatio-temporaldistributionofeDNAwithrespecttowatercolumndepths,tidepools,andseasons.
2 | MATERIAL S AND METHODS
Thespatio-temporaleDNAdistributionwascharacterizedatthreedifferent depths in thewater column, in tide pools, and betweensummerandfallseasons.Specifically,watersampleswerecollectedin13subtidalsitesatthreedifferentdepths(surface,middepthanddeepwater (i.e.,50cmfromthebottom),12tidepoolsiteswithinthree intertidal areas (N=4 sites/area) and 20 samples were col-lectedatasinglesitefromtheshoreapproximately2mspacedalonga transect (Figure1). For the summer period (without ice cover),ChurchillandIqaluitweresurveyedin2015betweenAugust11–14andAugust17–22,respectively (hereaftercalledS20).Toevaluateseasonaleffects (Iqaluitonly),the20samplesatasinglesitewerecollectedduringfall(November18th,2015)nearshorefromwaterthatrosebetweenicepansathightide(hereaftercalledF20).
Eachsample(250mlwater)wascollectedusingaNiskinbottleandthenrapidlyfilteredinthefieldthrougha0.7μmglassmicrofi-berfilter(WhatmanGF/F,25mm)usingsyringes(BD60ml;KranklinLakes,NJ,USA).Fieldnegativecontrols(i.e.,250mldistilledwater)
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were filtered for every10 samples. Filterswerepreserved at 4°Cin700μlofLongmire’slysis/preservationbufferwithina2mltubeforupto3weeks(Wegleitner,Jerde,Tucker,Chadderton,&Mahon,2015)andthenfrozenat−20°CuntilDNAextraction.Toreduceriskof crosscontamination during sampling and the filtration process,individualsamplingkitswereusedforeachsample(bottlesandfil-terhousingsterilizedwitha10%bleachsolutionandnewsterilizedgloves, syringes, and tweezers).Eachsamplingkitwasexposed toUVfor30min.Toreducetheriskoflaboratorycrosscontamination,procedures for eDNA extraction, PCR preparation, and post-PCRstepswereallperformedindifferentroomsandPCRmanipulationswereperformedinadecontaminatedUVhood.Samplesfromaspe-cificportwerealltreatedtogether,andthebenchspaceandlabo-ratorytoolswerebleachedandexposedtoUVfor30minpriortoprocessing the next port. Siteswithin a portwere processed in arandomizedorder.
2.1 | eDNA extraction, amplification and sequencing
DNA was extracted using a QIAshredder and phenol/chloroformprotocol (seeSupporting InformationAppendixS1).Negativecon-trol extractions (950μl distilled water) were performed for eachsamplebatch(i.e.,oneforeach23samples).TwopairsofuniversalmetazoanmitochondrialcytochromecoxidasesubunitI(COI)prim-ersthathavebeendevelopedandtestedonabroadarrayofmarinespecieswereused to amplify eDNA fromasmanymetazoan taxaaspossible:theforwardmlCOIintF(Lerayetal.,2013)andreversejgHCO2198(Geller,Meyer,Parker,&Hawk,2013)amplifying313bp(hereafter called COI1) and the forward LCO1490 (Folmer, Black,Hoeh,Lutz,&Vrijenhoek,1994)andreverseill_C_R(Shokrallaetal.,2015)amplifying325bp(COI2).
Theperformanceof the twoselectedprimerpairsused in thisstudywaspreviouslytestedon104zooplanktonspeciesandwasval-idatedonmockmetazoancommunitiescollectedinCanadianportsbyZhang (2017).Basedona totalof13COIprimerpairsselectedfromtheliteratureandtested,Zhang(2017)showedtheefficiencyofusingmultipleCOIprimerpairsinasingleIlluminaruntorecoverspe-ciesbymetabarcodinganddetected32%ofspeciesusingCOI1and49%ofspeciesusingCOI2.Here,theDNAamplificationprotocolsforbothprimerpairswereoptimizedinvitrousing12Arcticspeci-mensand12potentialinvaders(i.e.,annealingtemperaturegradientusingDNAextracted from tissue samples; Supporting InformationTableS1).Theprimersequencesandsequencedatabaseswerealsoevaluated in silico for their ability to detect native and potentialnonindigenous Arcticmetazoans. A list of recorded coastal Arcticmetazoans was obtained by pooling all Arctic species databasesthatwehadaccessto(Ntotal=897metazoanidentifiedatthespe-cieslevel;FisheriesandOceansCanadaArcticMarineInvertebrateDatabase (Supporting Information Appendix S2), Archambault un-publisheddata,Cusson,Archambault,andAitken(2007),Goldsmit,2016; Goldsmit, Howland, & Archambault, 2014; K. Howland, P.Archambault,N.SimardandRYoung,unpublisheddata,Piepenburgetal.,2011;Link,Piepenburg,&Archambault,2013;López,Olivier,Grant, & Archambault, 2016; Olivier, SanMartín, & Archambault,2013; Roy, Iken, & Archambault, 2015; Young, Abbott, Therriault,&Adamowicz,2016).PotentialNISinvaders(N=130species)weretargetedbasedon(1)screeninglevelriskassessmentsandpredictivespeciesdistributionmodelsindicatingtheywerehighrisk(Goldsmitetal.,2017), (2)theirpresenceinportsconnectedtotheCanadianArctic,and/or(3)theirpresenceinballastwatersandhullsofshipsbased onmonitoring at CanadianArctic ports (Chan,MacIsaac, &Bailey,2015;Chanetal.,2012).HistoricaldataincludemanyArctic
F IGURE 1 GeographicallocationsofthesamplingportintheCanadianArctic(mapa)andthesitedistributionwithinChurchill(mapb)andIqaluit(mapc).Subtidalareasareshowninwhiteandtheintertidalareasinlightgray.Circlesdepictthewatercolumnsites,trianglesarethetidepoolssitesandthesquaresaretheS20andF20shoresamplingsites
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regions,surveyedmainlyduringtheopenwaterperiod,withfocaltaxavaryingamongsurveys.ComprehensiveportsurveysinChurchillandIqaluitwereonlyconductedonceeveryfewyears (Churchill2007,2011and2015;Iqaluit2012and2015–2016).Ascriptwasusedtodeterminewhethertheprimersequenceswerepresentforthetar-getedspecies(speciespreviouslyrecordedfromtheArticandpoten-tialNIS)availableintheNCBIandBOLDdatabases(September2016;http://www.barcodinglife.org).SearchesforArcticspeciesinthese-quencedatabaseswereperformedwithPythonandBashprograms(developedbyJérômeLarocheattheInstitutdeBiologieIntégrativeetdesSystèmes(IBIS),UniversitéLaval)andanalysesarefreelyavail-ableonBitbucket(https://bitbucket.org/jerlar73/env-dna).
Three PCR replicates were performed for each eDNA sampleandeachprimerset.DNAamplificationswereperformedinaone-stepdual-indexedPCRapproachdesignedforIlluminainstrumentsatIBIS.ThefinalreactionvolumeforeachPCRreplicatewas24μl; including12.5.μlQiagenMultiplexMastermix,6.5μldiH20,1μlofeachprimer(10μM),and3.0μlofDNA.Forallsamples,thePCRmix-turewasdenaturedat95°Cfor15min,followedby35cycles(94°Cfor30s,54°Cfor90s(exceptfortheCOI2primers,whichwereat52°Cfor90sand72°Cfor60s)andafinalelongationat72°Cfor10min.Productsofthethreealiquotswerepooledforeachsample.AnegativePCRcontrolwasperformedforeachsampleandprimerset.Allamplificationswerevisualizedona1.5%agarosegelelectro-phoresis.NopositiveamplificationofthePCRnegativecontrolwasobserved.Field andextractionnegative controlswere treatedex-actlythesameasregularsamplesandwerealsosequenced.PooledproductswerepurifiedusingAxygenPCRcleanupkitfollowingthemanufacturer’s recommended protocol. Libraries were quantifiedbyAccuClearUltraHighSensitivitydsDNAQuantificationKitusingtheTECANSpark10MReaderforeachsampleandwerepooledinequalmolarconcentrationstomaximizeequalsequencedepthpersamplelocation(150and37ngpersampleforCOI1andCOI2primersets, respectively, inChurchilland200and300ngpersample forCOI1andCOI2primersets,respectively,inIqaluit).WhenQuant-iTPicoGreen(LifeTechnologies)didnotdetectanyDNA,22.0μlPCRmixturesweremixednonetheless(seeSupportingInformationTableS2fortheconcentrationandvolumeforeachsampleseparately).
Sequencingwas carried out using an IlluminaMiSeq (Illumina,San Diego, CA, USA) using a paired-end MiSeq Reagent Kit V3(Illumina)andfollowingthemanufacturer’sinstructions(SupportingInformationAppendixS1).Eachportwasanalyzedonaseparateruntoensureindependency,butthesampleswithinaportwerepooledwithinasingleIlluminaMiSeqruntoensuretheequalityofsequenc-ingdepthamongsamples.RawsequencesreadsweredepositedinNCBI’sSequenceReadArchive(SRA,http://www.ncbi.nlm.nih.gov/sra)underBioprojectPRJNA388333.
2.2 | Taxonomic identification
Forward and reverse sequences for each sample were trimmedusing Trimmomatic 0.30 (Bolger, Lohse, & Usadel, 2014). FastQCversion v0.11.3 was used to confirm the quality of the trimmed
reads(Andrews,2010).TheFastqqualityscoreswereallwellabove20 for the trimmed reads. Readswere thenmerged using FLASHv1.2.11withaminimumoverlapof30bp(Magoč&Salzberg,2011).“Orphan”readswith<30bpofoverlapbetweenforwardandreversereadswerediscardedandonlymergedreadswereusedintheanaly-ses.COI1andCOI2ampliconsweresplitusingaPythonscriptwhichsearchesfordegenerateprimersatthebeginningandendofeachsequenceandonlykeepssequenceswherethereispositiveidenti-ficationforbothprimers≥270bp.Thesesequenceswerecomparedfor identitywith themetazoan sequences present in theBarcodeofLifeDatabase (BOLD) (Ratnasingham&Hebert,2007;availableontheBOLDSYSTEMS3website,http://www.boldsystems.org,onthe 22ndAugust 2016). Terrestrial species (insects, human, birds,andmammals)andsequencesthatdidnothaveataxonomicnameassigned at the species level were removed from the referencedatabase.
To examine biodiversity at the species level, direct taxonomicassignmentofeachmergedreadwith≥97%identitywasperformedusing theBarque pipeline version 0.9 (see Supporting InformationAppendix S3), an open source and freely availablemetabarcodinganalysis pipeline (www.github.com/enormandeau/barque). Readsmatchingwith equal quality scores tomore thanone species dueto low interspecific divergence were found using usearch. Only156 reads (i.e., 0.02% reads, 17 cases) in total were found withsuchmultiplehits.Foreachcase,thelistofspecieswasscrutinizedand species that were clearly not expected in the Arctic basedon Ocean Biogeographic Information system (OBIS), The WorldPoriferaDatabase,theWorldRegisterofMarineSpecies(WoRMS)database, invasion risk assessments (see references above andSupportingInformationAppendixS2),andexpertknowledgewereremoved fromthesequence referencedatabasementionedabove(seeSupportingInformationTableS3fordetailsaboutthemultiplehitsandactionsmadeforeachspecies).Thepipelinewasrunagaintofindthetophitsonly.Theproportionofmissingspeciesassign-mentsduetoBOLDincompletenesswasfurtherexploredforeachmetazoanphylausingOperationalTaxonomicUnits (OTU)cluster-ing according to 97% similaritywith swarm 2.2.0 (Mahé, Rognes,Quince, De Vargas, & Dunthorn, 2015; see bioinformatic detailsSupportingInformationAppendixS3).OTUsrepresentedbyasingleread(singletons)wereexcludedandtheidentitybetweentherepre-sentativesequencesandtheBOLDdatabasewasperformedusingvsearch (Rognes,Flouri,Nichols,Quince,&Mahé,2016).Foreachphylum,proportionofthebiodiversityassignedtothespecieslevelwasobtainedfromthenumberofOTUsbetween97–100%(similartothresholdusedtoassignspeciesforsequencesintheBOLDda-tabase)relativetothosebetween80–97%(i.e.,belowspecieslevel).
2.3 | Statistical analyses
SamplingeffortisanimportantfactortoconsiderinbothtraditionalandeDNAbiodiversitysurveys.Twolevelsofport-specificsamplingeffortwereexplored:numberofuniquespeciesperread(ameasureofsequencingeffort)andthenumberofuniquespeciespersample
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(ameasureofeDNAcollectioneffort).Forwatercolumn (surface,middepth and deep), tide pool and shore (S20 and F20) samplinglocations,weplottedboth readand sample rarefiedaccumulationcurvestovisualizewhetherorwhenaplateauwasreached(whichwould indicate adequate sequencing and sampling effort to char-acterizeallspecies).Wealso inspectedtherelativepositionoftheread curve compared to the sample curve, as read curves lyingabove sample curves typically indicate spatial aggregationof spe-cies(Gotelli&Colwell,2010),orinthiscaseeDNAsequences.ThesesamplingeffortanalyseswereperformedinR3.4.1usingthespecac-cumfunctionintheveganpackage.
All further statistical analyses were performed using R 3.0.3.ThespatialdistributionofeDNAandtheseasonalvariabilityinthecommunity composition was represented using Principal compo-nent analysis (PCoA) and tested using PERMANOVA (Anderson,2001)afterHellingertransformation.Hellingertransformationwasappropriatetodealwiththelargeproportionofzerosandreducesthe importanceof large abundances (Legendre&Legendre, 1998)thatcouldbeduetotheeDNAorigin(e.g.,captureofcellormito-chondriavs.extracellularDNA)ortheamplificationprocess.Speciesthatmostlycontributed to thedissimilarity/similaritybetween thetreatments(depthsandtidepoolsvs.watercolumn)wereidentifiedusingSIMPERanalysisusingthesimper()functionoftheveganpack-age.Shannondiversity indiceswerecalculatedwiththeRpackagevegan.Analysesofvariance (ANOVAs)wereused to testwhetherspecies diversity, richness and log10(reads abundance) varied asa functionof sampling location (i.e.,water columnand tidepools;sitesincludedasarandomvariable)andwaterdepthsforeachport
separatelyusingthe lme()functionoftheNLMEpackage(Pinheiro,Bates,DebRoy,&Sarkar,2017)withsitesincludedasarandomvari-able(interactionsbetweensitesanddepthscouldnotbetestedduetouniquevaluesperdepth).Theseasonaleffectonreadabundance(i.e.,metazoanreads,seesectiontaxonomic identification),ShannondiversityandspeciesrichnesswasevaluatedusingaStudent’sttestcomparingtheS20andF20samplesinIqaluit.SørensenandJaccardnonparametricestimateswerecalculatedforlocationpairsusingtheSimilarityPairfunctionoftheSpadeRpackageinR(Chao,Ma,Hsieh,&Chiu,2016)totestforthelevelofsimilarityinspeciescompositionbetweensamplinglocationandseasons.
3 | RESULTS
Afterbioinformaticsfiltering(SupportingInformationTableS2),weobtained 712,494 aquatic eukaryotic reads in Churchill (200,732reads forCOI1and511,762 reads forCOI2)and178,728 reads inIqaluit(100,139readsforCOI1and78,589readsforCOI2).Noam-plificationwasvisualizedonthegelelectrophoresisforthenegativePCRcontrolsandnosignificanteDNAreadsweresequencedinanyofthenegativeextractionscontrols(Churchill:1–12reads,averageof0.05%oftheeDNAsamplereads;Iqaluit:1–8reads,averageof0.17% of the eDNA samples reads) or the negative field controls(Churchill:2–73reads,0.30%inaverageoftheeDNAsamplereads;Iqaluit:0–54reads,0.75%inaverageoftheeDNAsamplereads).
Cytochrome c oxidase subunit I sequences of 46% and 44%of the known Canadian Arctic native taxa and 63% and 53% of
F IGURE 2 ThenumberofOperationaltaxonomicunits(OTU)identifiedatthespecieslevel(dark:≥97%identity)relativetothoseidentifiedbelowthespecieslevel(lighten:≥85%and<97%identity)foreachphylumandfromtheCOI1(mlCOIintF-jgHCO2198:blackandgray)andCOI2(LCO1490-ill_C_R:blue)primersetsseparatelyforbothArcticsamplingports(ChurchillandIqaluit)
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potentialinvadersarecurrentlyinGenBankorBOLDdatabase,re-spectively.Inparallel,theproportionofOTUsmatchedtoaspeciesin the eDNA surveywas53% inChurchill and50% in Iqaluit (seetheproportionbyphyluminFigure2).Forbothports,thesamplingeffortcouldhavebeenincreasedtorevealadditionalspeciesasthesample and read accumulation curves did not plateau (SupportingInformationFigureS1).However,therewaslittleevidenceforspatialeDNAaggregationwithinalocationassample-basedcurvesfellonlyslightlybelowreadcurves,andwithin95%confidenceintervals,atalllocations.
3.1 | Taxonomic composition in Arctic coastal ports
Atotalof181speciesweredetectedintheeDNAsurvey;140spe-ciesinChurchilland87speciesinIqaluit(seeSupportingInformationFigure S2 for the species list for each primer set and their statusaccordingtopreviousCanadianArcticreports).Forty-eightspecieswereamplifiedwithbothCOIprimersets,116speciesrecordedbytheCOI1 primer set only and17 species by theCOI2 primer set.At thespecies level, theprimersetsdetecteda totalof tenphyla;includingninephylafor theCOI1primerset (44Annelidaspecies,31Arthropoda,35Chordata,17Cnidaria,17Echinodermata,eightMollusca, threeNemertea, fivePoriferaand fourRotifera) and10
fortheCOI2primerset(27Annelidaspecies,tenArthropoda,twoBryozoa, five Chordata, six Cnidaria, one Echinodermata, eightMollusca, twoNemertea, threePoriferaandoneRotifera). Incon-trasttomockmetazoancommunities(seemethodsection),alargernumber of species was identified using COI1 primers than COI2primers,butthelatterdetectedproportionatelymoreAnnelidaandPorifera.
Forbothports,74.0%ofthespeciesdetectedhavebeenpre-viously reported from the Arctic (Churchill: 70.0% and Iqaluit:87.4%;COI1:78.6%andCOI2:61.5%).Thenumberofspeciesde-tectedusingeDNAinChurchillandIqaluitrepresents10.9%and8.5%metazoanspeciesrecordedwithintheoverallArcticspeciesdatabases.Forty-sevenspeciesnotpreviouslyreportedwerede-tected,including15Annelida,fiveArthropoda,twoBryozoa,fourChordata,eightCnidaria,twoEchinodermata,fourMollusca,threeNemerteaandfourRotiferaspecies.Theonlypotential invadersdetected,theArthropodaAcartia tonsa,wasfoundwiththeCOI1primers inChurchill (64 readsaveraging99.4% identitywith thesequence references). This species was previously recorded inballastwater inportsconnectedtoChurchillandisconsideredapotential invader (Chanetal.,2012).However,COIsequences inBOLDassigned toA. tonsaarenotmonophyleticandseveralareindistinguishable from sequences assigned to the nativeA. hud-sonica,suggestingmisidentificationofsomeAcartiaspecimensinBOLD.
3.2 | Spatial eDNA distribution
Forbothports, thecommunitystructuredifferedsignificantlybe-tweenthewatercolumnandthetidepools,buttheproportionofexplainedvariancewasgreater forChurchill than Iqaluit (Figure3,PERMANOVA; Churchill: R2=0.21, p < 0.001; Iqaluit: R2=0.12,p < 0.001; seasonality did not impact analysis of spatial variabilitywhenanalyzed separately). Forbothports, thewater columnwasdominated by Arthropoda (Churchill: 91,219 reads for COI1 and164,080readsforCOI2;Iqaluit:30,550readsforCOI1and16,971reads forCOI2), followedbyAnnelida (Churchill:28,607 reads forCOI1 and 110,643 reads forCOI2; Iqaluit: 11,518 reads forCOI1and2,621readsforCOI2)(Figure4).Molluscaspeciesweremainlydetectedintidepoolsatbothports(91%and23%,respectively,forChurchilland Iqaluit;Figure4),andwerebyfar thedominanttaxainChurchillwiththemajoritybeingLittorina saxatilis forCOI1andCOI2 (95.8% (i.e., 14,219 reads) and100% (i.e., 198,684 reads) ofMolluscareads;cumulativecontributionsforChurchill=62.4%andIqaluit=52.0%);tidepoolsweredominatedbyArthropodaspeciesinIqaluit(Figure4).
The Shannon diversity index was significantly greater in thewatercolumnthantidepoolsinChurchill(ANOVA:p = 0.002),buttherewasnosignificantdifferenceinIqaluit(p = 0.2;Figure5).InChurchill, despite a significantly greater number of reads in tidepoolsthanthewatercolumn(averaging23,276and11,623readsintidepoolsandwatercolumnsamples,respectively;p = 0.06),therewas no significant difference in species richness betweenwater
F IGURE 3 Principalcomponentanalysisdepictingthecommunitystructureatthespecieslevelamongsamplinglocations:watercolumn(surface,middepthanddeepwater),tidepools(i.e.,intertidalzone)andsurfacewatercollectedinasinglesiteinsummer(i.e.,S20)andinfall(F20)forbothArcticsamplingports(ChurchillandIqaluit).Portswereanalyzedseparatelybecauseeachportwastreatedonaseparatesequencingrun
−0.4 −0.2 0.0 0.2
−0.
4−
0.2
0.0
0.2
0.4
axis 1 (21.2%)
axis
2 (
14.2
%)
−0.4 −0.2 0.0 0.2 0.4
−0.
4−
0.2
0.0
0.2
axis 1 (14.0%)
axis
2 (
9.7%
)(a) Churchill
(b) Iqaluit
Surface of the water column
Mid-depth of the water column
Deep of the water column
Tide pool
Surface water at a single site in summer
Surface water at a single site in fall (i.e. under ice cover)
| 7LACOURSIÈRE- ROUSSEL Et AL.
F IGURE 4 eDNAcommunitydifferencesbetweensamplinglocations(i.e.,watercolumn(surface,middepthanddeep),tidepools)andseasons(summerS20andFallF20).Thedifferentlayersrepresentphyla(central),genusandspecies(peripheral)
Arthropoda
Acartia
longiremis
42%
Pseudocalanus
newmani 17%
acus
pes 2
%
10 m
ore
Annelida
Pectinaria
granulata
8%
oculata
7% pelagica 3% Nais bretscheri 3% 19 more
Echinodermata robusta
8%
aculeata 0.9%
Mollusca
saxatilis
4%
Surf
ace
Chordata 1%Cnidaria 0.8%Porifera 0.2%
Rotifera 0.01%Nemertea 0.006%
Bryozoa 0%
Annelida
Pectinaria
granulata 54%
24 more
Arthropoda
Pseudocalanus
newm
ani 19%
minutus 1%
Aca
rtia
long
irem
is
1
1%
Echinodermata aculeata
6%
robusta 3%
pallidus 3%
mid
-dep
th
Arthropoda
balanus 37%
Acartia
28% longiremis
Pseudocalanusnew
mani 12%
Anneli
dagr
oenla
ndica
1
5%
Pectinariagranulata 2%
Echinoderm
ataaculeata 3%
Dee
p
Arthropoda
Pseudocalanus
newm
ani 34%
Acartialongire
mis
17%
bala
nus
5% 10 more
Annelida
Pectinariagranulata 21%
Nais bretscheri 4%
23 more
Cnidaria superciliaris 4%
capillata 3%
octona 2%
6 more
Chordata
leucas 2%
13 more
Echinoderm
ata 1% 0.2%
0.6% 0.2%
Water column S20
Mollusca
saxatilis 91%
Annelida Pectinaria granulata
3%
Harm
othoeim
bricata 2%
20 more
Arthropoda A
cartialongiremis 1%
6 more
Intertidal
(a) Churchill
Mollusca Porifera Rotifera
Nemertea 0.03%Bryozoa 0.02%
Mollusca 0.4%Cnidaria 0.1%Chordata 0.1%Porifera 0.09%
Nemertea 0.006%Bryozoa 0.003%
Rotifera 0%
Mollusca 0.1%Porifera 0.1%
Cnidaria 0.09%Chordata 0.08%
Nemertea 0.007%Rotifera 0.002%
Bryozoa 0%
Cnidaria 0.08%Chordata 0.07%Nemertea 0.04%
Echinodermata 0.04%Porifera 0.03%
Bryozoa 0.003%Rotifera 0%
8 | LACOURSIÈRE- ROUSSEL Et AL.
columnandtidepoolsamples(averaging25.40and30.27speciesin tide pools and water column samples, respectively; p = 0.42;Figure5).Incontrast,inIqaluit,despitethesimilarnumberofreadsin the tide pool andwater column samples (averaging 1,061 and1,716readsintidepoolsandwatercolumnsamples,respectively;p = 0.50), species richnesswas significantly greater in tide poolsthaninthewatercolumn(averaging18.33and13.92speciesintidepool andwater column samples, respectively;p = 0.02;Figure5).InIqaluit,thetidepoolshadestimatedSørensonsimilarityindicesof0.65,0.64,0.62withthesurface,middepthanddeepwater,re-spectively,whereasinChurchill,thetidepoolshadslightlyhigherestimates of 0.67, 0.84, and 0.68 for the surface, middepth anddeepwater,respectively.
The community structure differed significantly among thewaterdepths,buttheproportionofexplainedvariancewasgreaterfor Churchill than Iqaluit (Figure3, Churchill: R2=0.13, p < 0.001;
Iqaluit:R2=0.08, p = 0.04), The CrustaceanBalanus balanus dom-inated thedeepwaterofbothports (cumulative contributions forChurchill=80.0%middepth vs. deepwater and 67.1% surface vs.deep water; Iqaluit=62.3% middepth vs. deep water and 65.5%surfacevs.deepwater)andNemerteawasfoundonly inmiddepthin Iqaluit (Figure5). In Iqaluit, theShannon index,species richnessand number of reads did not differ significantly among the depthlayers (ANOVA shannon: p = 0.1; species richness: p = 0.3; readsabundance: p = 0.1). In contrast, in Churchill, the Shannon indexdiffered significantly among the depth layers (p ≤ 0.001). Higherspecies richnesswas foundat the surface (p = 0.02),whichgener-allycorrespondedtowheretherearemorefreshwaterinputsfromtheChurchillRiver (Figure6).Speciesdetectedonlyatthesurfaceincluded52.4%and19.0%freshwaterandbrackishspecies,respec-tively.Themiddepthsimilarityindexwasthehighestamongallwaterdepthcomparisons(SørensenandJaccardnonparametricestimates:
Annelida
Harm
othoe
imbricata 47%
cincinnatus 21%
groenlandica 3%
11 more
Arth
ropo
da longiremis 11%
Pseud
o-ca
lanus
acus
pes
10%
Gam
marus
Chordata
saida 3%
crinicaudatus
2%
Arthropoda
Pseudocalanus
acuspes 48%
moultoni 5%
min
utus
1%
longiremis 9%
Gammaru
s crinica
udatus 1
%
Annelida
cincinnatus 17%
Nemert
ea
linearis 13%
Mollusca
tenuis 2%
Cchordata
saida 2%
Arthropoda
balanus 48%
Pseudocalanus
newmani 23%
acuspes 18%
moultoni
Annelida cincinnatus 2%
Chordata
saida 2%
3%
Arthropodalongiremis
37%
Pseudo-calanus
acuspes 10%
litoralis 2%G
amm
arus 1%
Annelida
cirrata 36%
15 more
Mollusca saxatilis 4%
Chordata
saida 3%
groenlandicus 1%
Echinoderm
ata 2%
Annelida
inflatum 77%
praetermissa 5%
15 more
Arth-
ropod
a
Pseudo-
calan
us
acuspes 10%
moultoni 1%
Gam
marus
crinicaudatus
Chordata
saida 2%
Mollusca 1%
Arthropoda
Gam
marus
oceanicus 33%
longiremis 16% Pseudo-
calanus
acuspes 10%
rasc
hii 2
%
balan
oides
0.9%
Mollusca
saxatilis 23% C
nidarialisbethae
7%
annelida 2%
Nem
ertealinearis 2%
Chordata saida 1%
Surf
ace
mid
-dep
thD
eep
S20(b) Iqaluit Water column F20
Intertidal
Mollusca 0.8%Echinodermata 0.5%
Nemertea 0.01%
Porifera 0.007%Rotifera 0%
Cnidaria 0.007%Cnidaria 0.2%
Porifera 0%Rotifera 0%
Nemertea 0.07%Echinodermata 0.1%
Cnidaria 0.03%Porifera 0.02%Rotifera 0.01%
Nemertea 0.001%
Echinodermata 0.4%Porifera 0.05%Cnidaria 0.01%
Rotifera 0%
Echinodermata 0.8%Mollusca 0.6%Cnidaria 0.04%
Nemertea 0.006%Porifera 0.006%
Rotifera 0%
Echinodermata 0.6%Porifera 0%Rotifera 0%
F IGURE 4 . continued
| 9LACOURSIÈRE- ROUSSEL Et AL.
1.0forIqaluitand0.92forChurchill),butnotsignificantlysorelativetotheIqaluitsurface-deepandtheChurchillintertidal-mid,surface-mid,andsurface-deepcomparisons.
3.3 | Seasonal variation
Thecommunitystructurevariedsignificantlybetweenthesummerand fall sampling (Figure3, PERMANOVA; R2=0.30, p < 0.001);Arthropods dominated the summer samples, whereas Annelidsdominated in fall (Figure4)with a total of54.1% shared species.Speciesrichnesswasgreaterundericecoverthaninsummer(rich-ness: t = 2.3, p = 0.02; Shannon index: t=−2.6, p = 0.01), averag-ing 21 and 17 species in fall and summer samples, respectively(Figure5).
4 | DISCUSSION
Improved biodiversity monitoring programs are crucial for main-taining the integrity of coastalmarine ecosystems. Evaluating thepotentialofeDNAtoidentifyArcticspeciesandunderstandingthedynamics of eDNA distribution in coastal environments are bothtimely and important goals for improving biodiversity monitor-ing.Here,wepresentevidence thateDNAmaybeused toassessArcticbiodiversityandshowthat,despitethecoldandwell-mixed
F IGURE 6 RelationshipbetweenthespeciesrichnessdetectedusingeDNAmetabarcodingandthesalinityofthewatercollectedforthesurfacelayer(R2=0.85,black;circles:samplingwatercolumnandS20:triangles)andmiddepthsamples(R2=0.44,graysquares)anddeepwater(graycross)
F IGURE 5 BoxplotscomparingShannonindices,speciesrichness,andreadabundancesdetectedusingeDNAmetabarcodingforeachsamplinglocation(i.e.,watercolumn(surface,middepthanddeep),tidepoolsandS20andFall20)inChurchillandIqaluit.Thelinesinsidetheboxesrepresentsthemedianvalues,thetopandbottomoftheboxesrepresentthe75%and25%quartilesandoutliersareshownusingemptycircles(i.e.,anydatabeyond1.5*IQR)
Surface Mid-depth Deep Intertidal
0.0
0.5
1.0
1.5
2.0
2.5
Shan
non
inde
x
2030
4050
60
Spec
ies
rich
ness
Fall20S20 Surface Mid-depth Deep Intertidal S20
Churchill Iqaluit
Water column Water column
Log
(R
eads
abu
ndan
ce)
0.0
0.5
1.0
1.5
2.0
2.5
510
1520
2530
1.0
2.0
3.0
4.0
2.5
3.0
3.5
4.0
4.5
10(a) (b)
10 | LACOURSIÈRE- ROUSSEL Et AL.
environment,standardizedeDNAapproachestobiodiversitymoni-toringwillneedtoconsiderlocalspatio-temporalvariation.
4.1 | Taxonomic assignment challenges
ThehighcongruencebetweenhistoricalArcticdataandeDNAsamples(74.0%)supportstheefficacyofaquaticeDNAmetabar-coding forevaluatingArctic coastalbiodiversityat the specieslevel.ThespeciesdetectedwitheDNAthatwerenotpreviouslyknownfromtheCanadianArctic(42speciesinChurchilland11speciesinIqaluit)maybenewspeciesrecords,unexpectedNISorArctic species thatarenotyet represented in thesequencereference databases that instead matched a closely relatednon-Arcticspeciessequence.About3,894–4,674(4,284±390)macro- and megabenthic species are estimated to inhabit theArcticshelfregions(Piepenburgetal.,2011).However,Goldsmitetal.(2014)showedthatapproximately15%ofthetaxaidenti-fiedinArcticportswereconsiderednewrecordswithinthere-gionssurveyedandapproximately8%withinthemoreextensiveadjacentsurroundingregions.Piepenburgetal.(2011)suggestedthatfurthertraditionalsamplinginthecoastalArcticwouldin-creasethenumberofMollusca,ArthropodaandEchinodermataspeciesby26–52%,indicatingthatbetweenaboutafifthandathirdoftheexpectedMollusca-Arthropoda-Echinodermataspe-ciespool is still unknown.Given theseestimatedbiases in thehistoricaldata,itisthereforenotsurprisingthatthecongruencebetweenspeciesdetectedbymetabarcodingandhistoricaldataisnot100%.
A major shortcoming of metabarcoding is the incompletestate of reference sequence databases. Despite considerablebarcodingefforts,referencesequencesarestillverylimitedforcoastal benthic species, especially for remote regions such astheArctic.Resultsshowedthat~50%ofknownArcticspeciesare actually present in sequence databases and that a similarproportion of the eDNA sequenceswere assigned to species,indicatingthatreferencedatabaseomissionsarelimitingeDNAmetabarcodingsurveysatthistimeandthatCOIsequencingef-fortscanrapidlyimproveArcticbiomonitoring.AsshownbythelowproportionofOTUsidentifiedatthespecieslevel,PoriferaandRotiferawere less likely tobedetectedthanothergroupssuch as Annelida (Figure2). The use of eDNA metabarcodingmay thusbecomeapowerful approach to guide referenceda-tabase improvement (e.g.,97%RotiferaOTUswerenot identi-fiedatthespecieslevel).Moreover,groupssuchasBryozoans,NemerteansandRotiferaarecurrentlynot includedinthehis-torical Arctic Canada species records that we compiled, butthey are important to coastal ecosystems and could be goodindicators of biodiversity shifts caused by ice cover changes.TheeDNAmetabarcodingmethodmight thusbe a goodprac-ticalapproachtoevaluatethecommunitychangesofsuchspe-cies groups, evenwhen poorly identified at the species level.The better our knowledge of local species richness, potentialinvaders,andtheircorrespondinggeneticinformation,themore
accurate our eDNA biodiversity monitoring methods will be-come.However,evenwhennotassignedtospecies,theeDNAsequencesdetectedhereprovideasequencereferencebaselinethatcanbeusedtoevaluatefuturespeciesloss,newinvasions,orotherchangesincommunitystructure.
Oncea taxonhasbeen firmly identifiedby taxonomicexpertsanditsbarcodesequencehasbeendepositedinGenBankorBOLD,eDNAmighteventuallyreducetheneedfor largeteamsofexperttaxonomists to carry out routine biodiversitymonitoring. Yet, theroutineapplicationofmetabarcodingforArcticmonitoringrequiresovercoming various limitations. For example, here the eDNAme-tabarcoding identified Acartia tonsa, a potential invader that hasbeenpreviously recorded in theecoregionsofportsconnectedtoChurchill (Chan etal., 2012). However, the current available COIsequences forAcartia tonsa form several distinct clades, some ofwhichclusterwithAcartia hudsonica,raisingthepossibilitythattheeDNAsequencesassignedtoA. tonsaactuallybelongtothenativeA. hudsonica.Thus,taxonomicexpertiseremainscrucialforreducingbiasesofspeciesdistributionsrelatedtoincreasinguseoflarge-scaleeDNAmetabarcoding.
UsingtwoCOIprimerpairs,weincreasedthelevelofgeneticpolymorphism recorded at the species level, thereby increasingtheresolutionofthemethodforbiodiversitymonitoring(Deagle,Jarman, Coissac, Pompanon, & Taberlet, 2014). In addition toincreasing the number of species detected, combining multipleprimersmayalsoreducebiasofeDNAdominanceamongspeciesgroups(e.g.,dominanceshiftbetweenArthropodaandAnnelida;Figure2).DespitethefactthattheamplificationofCOI isoftendesirable to differentiate species using DNA barcoding proce-dures(Cheetal.,2012),thedegreeofuniversalityforCOIprimersis relatively low and so combiningmultiple COI primer pairs aswedidenabledmonitoringagreaterproportionofthediversity.Further studiesare,however,needed toevaluatehowthecom-binationoftheprimersetsmaydepictlocalspeciesdiversity.Ontheotherhand,targetinggeneswithlowertaxonomicspecificity(e.g., 18S) could improve the detection of biodiversity shifts athigher levels (e.g.,phyla level; seeBiketal.,2012;Deagleetal.,2014;Elbrecht&Leese,2015).
Characterization of biodiversity with metabarcoding is bi-ased at the amplification step (seeDeiner etal., 2017; Freeland,2017; Kelly etal., 2017 and Pawluczyk etal., 2015). Evaluatingthe primer bias of eDNA metabarcoding among primer pairs iscurrentlylimitedduetotheunknownnatureofeDNAandactualtechnologyusedtocharacterizeeDNA.Ourselectedprimerpairswerepreviouslytestedon104zooplanktonspeciesandvalidatedon mock metazoan communities collected in Canadian ports byZhang(2017).However,eventheseinsitumockcommunitiesarenotrepresentativeofthecomplexmixtureofeDNAinrealbiolog-ical samples, as theyconsistedofpurifiedDNAadded inequim-olar concentrations.Thus, future researchevaluating theeffectsofprimerbias isneeded.Nevertheless, the results fromourcur-rent comparisons show that there are important differences ineDNAcommunitycompositionacrossspaceand time insamples
| 11LACOURSIÈRE- ROUSSEL Et AL.
collected using the same sampling and sequencingmethod. Thelargenumberofspeciesdetectedinthisstudydoesallowfores-tablishing a baseline for detecting species from their eDNA andmeasuringArcticcommunitystructurechanges.Thecurrent lackofknowledgeonprimerbiasdoeslimitcomparisonsofspecieslistsandcommunitystructurebetweenstudiesusingdifferentprimersetsandgeneticloci,however.
4.2 | Spatio- temporal eDNA variation
Our results clearly show that metazoan eDNA distribution inArcticcoastalenvironmentshassignificanttemporalandspatialvariation. The transport of eDNA may be substantially highercompared to southern regions due to the limited degradationfromcoldwaterandthelimitedUVexposureduringmuchoftheyear.AlthougheDNA isexpected tobehighlydispersed incoldenvironments, results here show clear horizontal and verticaleDNAheterogeneity in theArctic. Theobservedheterogeneityof eDNAwithin and between samples suggests that, based onthesummerandfallsamplerarefactioncurves,collectingatleast15samplesacrossasmanysitesaspossible isoptimal forcom-prehensive estimates of biodiversity variation (see SupportingInformationFigureS1);animportantmetricfordetectingeffectsofclimateandshippingtrafficchange.Abetterunderstandingofthespatio-temporalvariationineDNAduetolocalbioticandabi-oticconditionswillbeimportantinstandardizingcomparisonsofeDNAsamplesacrossspatialandtemporalgradientsintheArcticmarineenvironment.
Vertical eDNAdistribution in thewater columnmayvary as afunctionof the life cycleof species, transport and settling advec-tion(Turner,Uy,&Everhart,2015)andcomplexhydrodynamicpro-cesses.InadditiontowaveactiononeDNAmixing(O’Donnelletal.,2017;Portetal.,2016),ourdatasupporttheideathatinestuarineconditions, such as in Churchill, the freshwater flowing from theriveroverlongdistancesmaycontributetoincreasingthediversityinthesurfacewaterlayer(e.g.,Deiner&Altermatt,2014;Janeetal.,2015).CommunitychangesrelatedtoeDNAcompositionthusneedtointegrateinformationontemporalvariationinriverdischarge.ThevariabilityintheeDNAcapturezoneshouldthereforecombinecom-plex interactions between community changes and hydrodynamicmodels.
ThedominanceofMolluscareadsintidepoolsisconsistentwiththeobservedspeciescompositioninthesehabitats(e.g.,Goldsmit,2016).However,ourresultssupportthehypothesisthattidesmaymodifydifferencesineDNAcompositionbetweenthewatercolumnandtidepools.Atthelocalscale,theeDNAdistributionvariedbe-tweenhabitatsatbothports(i.e.,watercolumnandtidepools),butthis patternwasmoredistinct inChurchill. The large tidal area inIqaluit increases thewater admixturebetween tidepools and theopenocean(11.72mmaximumtideinIqaluitand4.93minChurchill(Tide-forecast2017)),whichmayexplaintherelatively lowercom-munitydifferentiationbetweentidepoolandwatercolumnsitesinIqaluitcomparedtoChurchill.
CoastalbiodiversitymonitoringintheArcticusingtraditionalsampling approaches is generally limited to summer. In contrasttotraditionalsurveys,thequalityofeDNAsurveysmightactuallyimproveundertheicecoverduetothelimitedUVexposureandcoldwatertemperature,hencepromotingeDNApreservationanddetection(Barnesetal.,2014).Ontheotherhand,coldtempera-tures areexpected to reduce themetabolismof species andas-sociated eDNA release/detection (Lacoursière-Roussel, Rosabal,etal.,2016).Here,eDNAmetabarcodingofwatercollectedunderice cover detected greater species richness than summer watercollections.ThisisparticularlyrelevantbecausetheuseofeDNAcould expand the timewindow to survey coastal biodiversity inthe Arctic. The observed species dominance changes betweenboth seasonsmayalso reflect lifehistory (e.g., lateAnnelida re-production; P. Archambault unpublished data). Here our surveyis limited to two sampling periods, and thus further studies areneededtodifferentiaterelativeeffectsofspeciesandeDNAecol-ogiesbetweenseasons(Hulbert,1984).
4.3 | Arctic conservation biology
As contributions of sequences from identified specimens in-creasetodatabasessuchasBOLD,sotoowilltheabilitytotrackbiodiversitychangesovertimeatthespecieslevelwithpowerfulmethods such as eDNAmetabarcoding (Gibson etal., 2014; Ji,Ashton,&Pedley,2013;Taylor&Harris,2012).IntheArctic,thedevelopment of cost-effectivemonitoringmethods is essentialforbetterprotectingtheintegrityofimportantnaturalenviron-mentsandendangeredspeciesandtoensuresustainablesubsist-enceharvestingbyaboriginalpeople,aswellasrecreationalandcommercial harvest by non-Aboriginals. Applying eDNA meta-barcodingtoassessbiodiversityinremotecoastalregionsoffersseveral advantages toward increasing the speed and accuracywithwhichwe can amass biodiversity data. As part of this re-searchproject, localcommunitymembersandpermanentlysta-tioned northern research staffwere trained in eDNA samplingtechniqueswith the goal of enabling a network of community-basedmonitoring.Inthiscontext,weoptimizedeDNAstrategiesforremoteregions.Wefirstusedasyringemethodforfilteringsamples(Deiner&Altermatt,2014),whichallowsforsamplingatmultiplesitessimultaneouslyandlimitscross-contaminationbe-tweensamplesaseachsamplecanbeprocessedwithindepend-entequipment.Moreover,thesimplicityofthisapproachallowsinexperienced collaborators to collectmore eDNA samples perunit of time relative to standard practices of using an electricpump.Second,asstoringandshippingfrozensamplesinremoteregions is risky and often not possible, we used methods thatallowed for DNA preservation at room temperature (Renshaw,Olds,Jerde,McVeigh,&Lodge,2014).Lastly,thecost-effectiveextractionmethodincreasestheabilitytoprocesslargenumberofsamples.
By overcoming methodological issues and improving knowl-edge about the ecology of eDNA in coastal area, this project
12 | LACOURSIÈRE- ROUSSEL Et AL.
createstheopportunityforfuturemonitoringofmetazoancoastaldiversityinhighlyvulnerableecosystemssuchasArcticcommer-cialports.Thecombinedbenefitsofbeingable to identify largenumbersofspeciesincludinglocalspeciesandpotentialinvaders,assess a large number of phyla, the local habitat variability andtogetherwith the effectiveness of the eDNAmethod under icecover,are likely tomakeeDNAmetabarcodinganefficientcom-plementaryapproachtodetectlarge-scaleArcticcoastalbiodiver-sitychanges.AstheeDNAmethodprogresses,theuseofeDNAislikelytoexpandandbecomeacatalystforincreasedresearchoncoastalbiodiversity,ecosystemservices,andsustainability,partic-ularlyinremoteregionsoftheworldsuchastheCanadianArctic.However, spatio-temporal dimensions need to be considered instandardizingandoptimizing theassessmentofmarinebiodiver-sityusingeDNA.
ACKNOWLEDG MENTS
This project was funded by ArcticNet, POLAR knowledge,Nunavut Wildlife Management Board and the Fisheries andOceans Canada Aquatic Invasive SpeciesMonitoring Program.LogisticsupportwasprovidedbytheChurchillNorthernStudiesCenter and Fisheries and Oceans Canada Inuvik area office.WethankMelaniaCristescuandGuangZhangfortheirhelp inchoosingprimersandFredericChainandYiyuanLi forsugges-tions about the development of the Barque analysis pipelineanddata interpretation,CathrynAbbott forsharingspecimenstotest invitroprimers,RobYoungforsharingsequencesfromArcticspeciesandJérômeLarocheforhishelpwiththebioinfor-maticsanalyses.WearegratefultoBrianBoyle,MaëlleSevellec,CharlesBabin,EricParent,JésicaGoldsmit,RobYoung,NathalieSimard,FarrahChanandGuillaumeCôtéfortheirsupportinvar-iousareasofthiswork.Wealsothankthefollowingindividualsfor field assistance, participating in training and assistingwithcoordinationoffieldlogistics:ValérieCypihot,FrédéricHartog,Christopher W. Mckindsey, LeeAnn Fishback, Daniel Gibson,DickHunter, AustinMacPherson, Kimberly Thomson,HeatherClark,ColinGallagher,ZoyaMartin,ChrisLewis,JeremiahYoung,Richard Moore, Thomas Whittle, Rory McDonald, FredericLemire, Levi tikivik, Fred Lemire, ConnerMcCormack and stu-dentsfromtheNunavutArcticcollege.Wearealsograteful totheManagingEditorKarenChambers,theSubjectEditorAurélieBoninandthreeanonymousrefereesfortheirconstructivecom-mentsonapreviousversionofthemanuscript.
CONFLIC T OF INTERE S T
Nonedeclared.
AUTHOR CONTRIBUTIONS
Anaïs Lacoursière-Roussel is a conservationecologist evaluatinganthropogenicimpactsonthedynamicofaquaticcommunities.All
authorsofthismanuscriptareinteresteddevelopingandcalibrat-ingtheeDNAmethodintheapplicationofaquaticspeciesdistri-bution to improve the efficiency of conservation planning.ALR,LB,KH,PA,EG,andDLconceivedtheidea,ALR,LB,KH,KD,PA,andENstructuredandeditedthemanuscript,KHandALRdevel-opedthestudydesignandparticipatedinfieldcollections.KHandPAarespecializedintheArcticcoastalsurveillance,CH,ALR,KD,andNLdevelopedlaboratorymethodologyandENdevelopedtheanalysispipeline.
DATA ACCE SSIBILIT Y
Raw sequences reads were deposited in NCBI’s Sequence ReadArchive (SRA, http://www.ncbi.nlm.nih.gov/sra) under BioprojectPRJNA388333.
ORCID
Anaïs Lacoursière-Roussel http://orcid.org/0000-0002-9345-5682
R E FE R E N C E S
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SUPPORTING INFORMATION
Additional supporting information may be found online in theSupportingInformationsectionattheendofthearticle.
How to cite this article:Lacoursière-RousselA,HowlandK,NormandeauE,etal.eDNAmetabarcodingasanewsurveillanceapproachforcoastalArcticbiodiversity.Ecol Evol. 2018;00:1–15. https://doi.org/10.1002/ece3.4213