Development of a Standardized DNA Database for Chinook Salmon L

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Fisheries Publication details, including instructions for authors and subscription information: http://www.tandfonline.com/loi/ufsh20 Development of a Standardized DNA Database for Chinook Salmon L. W. Seeb, A. Antonovich, M. A. Banks, T. D. Beacham, M. R. Bellinger, S. M. Blankenship, M. R. Campbell, N. A. Decovich, J. C. Garza, C. M. Guthrie III, T. A. Lundrigan, P. Moran, S. R. Narum, J. J. Stephenson, K. J. Supernault, D. J. Teel, W. D. Templin, J. K. Wenburg, S. F. Young & C. T. Smith Available online: 09 Jan 2011

To cite this article: L. W. Seeb, A. Antonovich, M. A. Banks, T. D. Beacham, M. R. Bellinger, S. M. Blankenship, M. R. Campbell, N. A. Decovich, J. C. Garza, C. M. Guthrie III, T. A. Lundrigan, P. Moran, S. R. Narum, J. J. Stephenson, K. J. Supernault, D. J. Teel, W. D. Templin, J. K. Wenburg, S. F. Young & C. T. Smith (2007): Development of a Standardized DNA Database for Chinook Salmon, Fisheries, 32:11, 540-552 To link to this article: http://dx.doi.org/10.1577/1548-8446(2007)32[540:DOASDD]2.0.CO;2

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Developmentof a StandardizedDNA Database for Chinook Salmon

L. W. Seeb, '•qlr A.Antonovich, • M. A. Banks, T. D. Beacham, M. R. Bellinger, • • S.M. Blankenship,i L M. R. Campbell, N. A. Decovich, J. C. Garza, C.M. Guthrie III, T. A. Lundrigan, • P. Moran, S. R. Narum, J. J. Stephenson, K. J. Supernault, D. J. Teel, W. D. Templin, J.K. Wenburg, '" S. E Young,and C. T. Smith

Seebis research professor at the Schoolof Aquaticand Fisheries Sciences, University of Washington,Seattle. She can becontacted at [email protected] is a biometrician,Decovich is a fisherybiologist, and Templin is a fisheriesgeneticist at theAlaska Department of Fish and Game, Division of CommercialFisheries, Anchorage. Banks Downloaded by [Oregon State University] at 16:22 19 September 2011 ismarine fisheries geneticist and associate professor and Bellinger is a moleculargeneticist at OregonState University's HatfieldMarine Science Center, Newport. Beacham is a researchscientist and Supernault is a researchtechnician at the Departmentof Fisheries and Oceans Pacific Biological Station in Nanaimo,British Columbia. Blankenship was formerly with the National Marine FisheriesService Southwest Fisheries Science Center in SantaCruz, California, and is now a geneticistat theWashington Department of Fishand Wildlife, Olympia. Campbell is senior fisheries research biologist at the IdahoDepartment of Fish and Game, Eagle Fish Genetics Laboratory, Eagle. Garza is supervisory research biologist, National Marine FisheriesService Southwest Fisheries Science Center, Santa Cruz, California. Guthrie is a fisheryresearch biologist at the NationalMarine Fisheries Service Auke BayLaboratories, Ted StevensMarine Research Institute, Juneau, Alaska. Lundriganwas formerly with the University of WashingtonSchool of Aquatic and Fishery Sciences, Seattle, and is currently a fisheriesbiologist at theUniversity of Victoria's Centre for Biomedical Research, Victoria, British Columbia. Moran is a researchgeneticist and Teel is supervisory fishery biologist at theNational Marine Fisheries Service Northwest Fisheries ScienceCenter, Seattle, Washington. Narum is lead geneticist and Stephenson islaboratory manager at theColumbia River Inter-TribalFish Commission, Hagerman Fish Culture Experiment Station, Hagerman, Idaho. Wenburg is director of genetics at the U.S. Fishand Wildlife Service in Anchorage,Alaska. Young is a fishand wildlife research scientist at the Washington Departmentof Fishand Wildlife, Olympia. Smith was formerly with the Alaska Department of Fishand Game Division of CommercialFisheries and is now a fisheriesgeneticist at the U.S. Fishand Wildlife Service, Abernathy Fish Technology Center,Longview, Washington. 540 Fisheries ß vol 32 No 11 ß NOVEMBER2007 ß WWW.FISHERIES.ORG ABSTRACT: An mternauonalmulu-laboratory project was conducted to developa standardizedDNA databasefor Chinook salmon(Oncorhynchus tshawytscha). This project was in responseto theneeds of theChinook Technical Committee of the Pacific SalmonCommission to identifystock composition of Chinooksalmon caught in fisheriesduring their oceanic migrations. Nine geneticslaboratories identified 13 microsatelliteloci that could be reproduciblyassayed in eachof the laboratories.To testthat the lociwere reproducible among laboratories, blind tests were conducted to verifyscoring consistency for the nearly500 total alleles.Once standardized, a dataset of over 16,000Chinook saltnon representing 110 putativepopulations was constructed rangingthroughout the areaof interestof the PacificSalmon Commission from Southeast Alaska to the SacramentoRiver •n California.The datasetdifferentiates the majorknown genetic lineages of Chinooksaltnon and provides a toolfor genetic stockidentification of samplescollected from mixed fisheries. A diversegroup of scientistsrepresenting the disciplines of fishery management,genetics, fishery administration, population dynamics, and sampling theory are now developing recommendations for the integrationof thesegenetic data into ocean salmon management. Desarrollo de una base de datos estandarizada de DNA parael salm6nrey RESUMEN: Se realiz6un proyectointernacional con la participaci6nde diversoslaboratorios con la finalidadde desarrollar unabase de datos estandarizada deDNA parael salm6nrey (Oncorhynchus tshawytscha). Dicho proyecto surgi6 como respuesta a lasnecesidades delComit6 T6cnico Chinook de la Comisi6ndel Salm6n del Pac•fico para identificar la composici6npoblacional del salm6nrey quees capturado pot la pesquerfadurante su migraci6n. Un total de nuevelaboratorios de anfilisisgen•ticos tdentificarony reprodujeroncada uno 13 loci microsat61ites.Con el objetode probarque dichos loci fueranreproducibles entrelaboratorios, se condujeron pruebas an6nimas para verificar la consistenciade casi500 alelos.Una vezestandarizada, se construy6una base de datos construida con informaci6n proveniente de mils de 16,000 salmones que representan 110 poblaciones putativasdistribuidas a lo largodel fireade inter6sde la Comisi6ndel Salm6ndel Pacffico,del surestede Alaska hasta el Rfo Sacramento,California. La basede datossirve tanto para identificar gen6ticamente los distintos stocks de salm6nrey a partit de muestrascombinadas provenientes de la pesquer•acomo para diferenciar el linajegen6tico conocido m•s importante de esta especie.En la actualidad,un importantegrupo de cientfficosespecializados en disciplinas como el manejoy administraci6nde pesquerfas,gen6tica, dinfimica poblacional y teor•adel muestreoestrin desarrollando recomendaciones para que esta base de datosgen6ticos se incorpore en el manejodel salm6n.

INTRODUCTION The modeluses catch, escapement, coded- fromthe indicatorstock assigned to them wiretag (CWT) recovery,and recruitment in the model. The PacificSaltnon Treaty was ratified information to forecast relative abundance In 2004,the PacificSalmonCommission between the United States and Canada in in treatyfisheries. conveneda panelof expertsto examine 1985, renegotiatedin 1999, and extended With the increaseddependence on limitationsof the CWT prograinin the to the YukonRiver in 2002.Through the CWT recoverydata, concernhas been contextof massmarking and mark-selec- treaty,the two nationsagreed to cooper- raisedregarding the qualityof the CWT tive fisheries,as well as to evaluate the ate in the management,research, and data and inferences drown from those capacityof alternativetechnologies to enhancement of Pacific salmon. Pacific data (Hankin et al. 2005). Historically improveassessment of Chinook sahnon salmonmigrate long distances during their onlyfish carrying a CWT hadan adipose (Hankin et al. 2005). The expertpanel marineperiod and are routinelyinter. clip. However,with the adventof mass- concludedthat alternativetechnologies ceptedin fisheriesbeyond the jurisdiction markingof largenumbers of hatcheryfish couldnot by themselvesreplace CWTs, of the governmentin whosewaters they usingan adiposeclip only (e.g.,Mobrand but thatgenetic stock identification (GSI)

Downloaded by [Oregon State University] at 16:22 19 September 2011 spawn.The PacificSaltnon Treaty, through et al. 2005), recoveryof CWT fishhas could indeedcomplement the existing the PacificSahnon Commission (PSC), beencomplicated. Recovery now requires CWT programsor be usedin combina- servesas a meansto coordinatemanage. both handlingmuch largernumbers of tion with othertechniques such as otolith ment of the salmon resourceand conduct individualspaired with electronicscan- thermalmarking to estimatethe stock conservationactions as required. ning of thoseadipose-clipped individu- compositionof a landedcatch in a par- Chinooksalmon (Oncorhynchus tshaw- als to detecttags. As a furtherconcern, ticulartime/area fishery (Findings 11-13) ytscha)are harvestedthroughout the abundance and harvest information is However,they notedlimitations in the yearby commercialand sportfishers in not available for most Chinook saltnon GSI methods due to the lack of coastwide the waters of southeastAlaska, British stocks,so indicator stocks are usedwithin geneticbaselines, which are databasesof Columbia, and the Pacific Northwest. the modelto representboth largergroups genotypesfrom breeding populations. Fisheriestypically harvest highly mixed of hatcheryand wild stocks. The abilityof stocks of Chinook saltnon and are there- thehatchery indicator stocks to accurately Finding 13. ModernGSI methods fore underthe jurisdictionof the Pacific representwild stockshas been generally can be used to estimatethe stockcom- SaltnonTreaty. Quotas specified by the supportedin coho saltnon(O. kisutch; positionof thelanded catch in a par- PSC are dependenton the abundanceof e.g.,Weitkamp and Neely 2002),but is ticular time/areafishery. However, Chinooksaltnon projected by itsChinook still largelyunknown for manystocks of the accuracyand precisionof data TechnicalCommittee (CTC) usingthe Chinooksahnon. The wild stocksmay requiredto estimatestock-age-fishery Chinooksaltnon model (e.g., CTC 2005). differin ancestry,abundance, and timing specificexploitation rates using GSI

Fisherles * vot 32 .o II * NOVEMBER2007 e VV•/VV. FISHERIES,ORG 541 methods•s dependentupon a variety this databasewas comprehensiveand vanousmeeungs of salmomdgenet•c•sts of factors.For example,microsatellite usedby multiple laboratories,a number from 1999 to 2001, but progresswas lim- DNA-basedGSI technologyis not yet of limitations,including the requirements ited (LaHoodet al. 2002).Finally, a work- capableof generatingconsistent, rep- for lethalsampling, cryopreservation, lack shophosted by the CTC in 2003 resulted licableestimates due to thelack of a of laboratoryautomation, and the finite in funding for the databasedescribed coastwidegenetic baseline, the history numberof loci, led researchersto the deci- herein.In the followingyear, a symposium of stocktransfers within and among sionto replacethe allozymebaseline with watersheds,and differencesin meth- a DNA database. on geneticdatabases for fisherymanage- odologiesand mixture separation algo- Markers basedon DNA, suchas mito- ment and conservation was held at the rithms.(Hankin et al. 2005) chondrialDNA (Cronin et al. 1993) and 2004 AmericanFisheries Society (AFS) microsatellites(Banks et al. 2000;see Box Annual Meeting, and groupsstudying Herewe reviewa largemulti-laboratory 1), were alsoin useand shownto be valu- effortto developand standardizea repli- a number of divergentfishery resources cable DNA databasefor Chinook salmon. ablefor resolving Chinook salmon popula- beganstandardization efforts (e.g., Atlanuc tion structure.However, unlike allozymes herring;Mariani et al. 2005).So although where a systemwas developedfor shar- Historyand needfor the advantagesof DNA-based markers, ing and transferringdata amonglabora- coast.wide databases andmicrosatellites in particular,were well tories, microsatellite baselines evolved documentedbeginning in the early 1990s Beginning in the 1980s, consider- for usewithin singlelaboratories (Moran (e.g., Wright and Bentzen1994; Wirg•n able effort and coordination was directed et al. 2006). The largenumber of available microsatellite loci resulted in little andWaldman 1994), coordination among towardsdeveloping standardized allozyme (protein) baselines(e.g., Shaklee and overlapamong researchers' datasets. For laboratorieslagged markedly. Phelps1990; White and Shaklee1991; example,at the beginningof 2003over 60 The presentdatabase was constructed seeBox 1). Standardizationamong labo- loci were in usefor Chinook salmon,but and evaluatedthrough a two-yearcollab- ratoriesinvolves identifying and adopting most(43) wereused in onlya singlelabo- orative effort of an international mulu- a commonsuite of loci (specificpolymor- ratory(Figure la). This wasnot limitedto agencywork group. Guiding principles Chinook salmon,but wasthe normfor fish- phicDNA segmentsor theirproducts) and requiredthat the databasewould: (1) be eriesstudies of manyspecies. Furthermore, adoptingconsistent names for the various subjectto review by scientistsfrom all alleles(see Box 1). Genetic analysesusing evenwhen identical loci werebeing used, interestedagencies, (2) be freelyavailable allozymedatabases were usedextensively differences in chemistries and instrument to estimate the stock contribution of platformsamong laboratoriesproduced to all researchersmanaging or studying Chinook salmon fisheries in the Columbia variable size calls for the same allele from Chinooksalmon, and (3) coverthe range River, coastalWashington, and Strait of the sameindividual, which precluded eas- of Chinooksalmon at a geographicscale Juande Fuca(e.g., Marshallet al. 1991; ily mergingdatasets from multiple regions appropriateto the managementobjectives Shakleeet al. 1999). Collaborativework (Figurelb). As a result,despite collection of the PSC with the understandingthat of a substantial amount of microsatellite duringthe 1990sby multiple state, pro- the databasecould be easilyexpanded to data(e.g., Nelson et al. 2001;Beacham et vincial,and federal agencies was directed includethe entirerange of the species.It towardsenlargement of the database(Teel al. 2003), progressin replacingthe allo- was anticipatedthat the databasewould et al. 1999).The allozymedatabase grew to zyme databasewas slow for multi-juris- •ncludecomprehensive coverage of popu- dictionalfisheries, and multipledatabases allow for a wide varietyof management lations rangingfrom California through proliferated. andresearch applications on all life history Alaska with representativepopulations Concernsregarding these multiple and stagesthroughout the species range in both from Russia(Teel et al. 1999). Although independentdatabases were addressedat freshwater and marine environments. Downloaded by [Oregon State University] at 16:22 19 September 2011

Box 1. Definitionof termscommonly used in geneticstock identification of Pacificsalmon.

Allele An alternativeform of a givengene or DNAsequence that differsfrom otheralleles in DNAsequence or phenotype. Allelic ladder A pooledand diluted aliquot of multiplePCR products that originates from multipleindividuals of knowngenotype. With the choiceof appropriateindividuals, a ladderwith all the "rungs"necessary for successfulinterlaboratory allele indexingcan be created. AIIozyme AIlelicform of a proteinenzyme encoded at a givenlocus. AIIozymes are usually distinguished by protein electrophoresisand histochemicalstaining techniques. Locus(loci, plural) The sitethat a particulargene or DNAsequence occupies on a chromosome. Microsatellite DNAsequences containing short (2-5 basepairs) repeats of nucleotides(e.g. GTGTGTGT). Neighborjoining tree A bottom-upclustering method used for the creationof phylogenetictrees that is basedon the distancebetween eachpair of populationsor taxa. PCR Thepolymerase chain reaction or PCRamplifies a singleor few copiesof a pieceof DNAacross several orders of magnitude,generating millions of copiesof the DNA. SNP Singlenucleotide polymorphism; DNA sequence variation occurring when a singlenucleotide (A, T, C, or G)differs betweenmembers of a speciesor withinan individualbetween paired chromosomes.

542 Fisheries ß VOL 32 NO 11 ø NOVEMBER2007 o WWW.F•SHERIES.ORG METHODS AND MATERIALS Figure1. a. Distributionof locianalyzed across laboratories at the beginningof the studyMost markers wereassayed in only a singlelaboratory. Only three loci were used in morethan half of the laboratories. b. Electropherogramsfromthe same individual assayed on two different instruments. Identical size standards Identification of loci andsizing algorithm were used, yet differences insize are seen that reflect differences inamplification chemistfidsand electrophoresis hardware, polymer and running conditions employed in different During the first yearof the study, laboratories.The figure illustrates a significant difference inthe estimated size of thealldes, in addition to a each contributing laboratory analyzed differentrelative size of therepeat unit (i.e., 4.14 versus 4.25 bp; adopted from Moran et al.2006). an identical set of 500 individuals drawn frompopulations ranging from California, USA, to Kamchatka, Russia,for its own microsatellitelocus panel. A total of 60 loci were evaluated,and "sponsorship" documents were created for each locus including description,source, analysis conditions,and locusvariability. A subset of 25 loci drawn from the combined set was chosen based on qualitative selectioncriteria includingthe reliabil- ity of the PCR (polymerasechain reac- Microsatellite marker (N=62) tion; seeBox 1) amplification,and, to a lesserextent, allelic sizerange and number of alleles.Locus diversity across the rangewas generally not a consideration 70 75 since too few individualswere assdyed to reliablycompute statistical measures. Each laboratorytested the 25 loci on a subset of 96 individuals drawn from the original 500 individuals. Scores for these 96 individuals were submitted to the coordinatinglaboratory (Northwest Fisheries Science Center, National Marine FisheriesService). From this set, collaborators selected and tested the 15 most reliable loci for inclusion in the standardized baseline. A curator laboratory was identified for each locus with responsibilityfor receiving,compiling, ^ and distributingallele information. A decision was also made to link alleles to 175.ool I 00.001 tissuesfrom a specificindividual salmon that wouldreside in eachlaboratory. An Fragment size (bp) additional set of locus documentation was prepared designating recognized alleles, assigningallelic nomenclature, Columbia; the second set was collected Constructionof the Database Downloaded by [Oregon State University] at 16:22 19 September 2011 and designatingindividuals for every from southeast Alaska. Allelic scoresfrom recognizedallele at each locus. These eachlaboratory were adjusted to the rec- Baselinepopulations were chosen that master allele lists were termed "curator ognizednomenclature, submitted to the representall previouslyidentified genetic documents." coordinatinglaboratory, and compared lineagesof Chinook salmonfrom the across seven laboratories. Two additional southernend of the speciesrange north Genotypingevaluation laboratories that had not been involved to southeastAlaska with focuson major in locus selection or the first blind test productionareas and likely contributors To assessthe ability of eachlaboratory participatedin the secondblind test.The to PacificSalmon Treaty fisheries (Figure to accuratelyresolve all 15 loci, 2 setsof alleleswere not sequenced,so the abso- 2). Six laboratories(Alaska Department 96 "blind" sampleswere analyzed(first lute lengthwas unknown. Therefore, the of Fish and Game [ADFG]; Department andsecond blind samples).These samples modal score (the most common allele of Fisheriesand Oceans Canada [DFO]; had never been previouslygenotyped in scoredacross the participatinglaborato- Columbia River Inter-Tribal Fish any of the laboratories.In both cases,test ries) wasdefined to be the "correct"score. Commission[CRITFC]; Oregon State fish were drawn from marine fisheries and Concordance of scores across laboratories University [OSU]; SouthwestFisheries assumedto be highly mixed with broad or percent accuracyfor each locus for ScienceCenter, National Marine Fisheries representationof stocks.The samplesfor eachlaboratory was based on this modal Service [SWFSC]; and Washington the first test were collected from British score. Departmentof Fish and Wildlife [WDFW])

Fisherles ß VOL 32 NO 11 ß NOVEMBER2007 ß WWW.FISHERIES.ORG 543 Figure2. Collectionsites for Chinooksalmon included in the DNA baseline.These sites represent popul•ions potentially contributing to mixtures harvestedin PacificSalmon Treaty fisheries. Sites are designatedby regionalgroups from Table3.

36b 36a

39 34d 34c 34a 33a ..... 37b, c NorthAmerica 37a, e 26b 27b 37d r31b 26••_27d

31a 26c4d', '•'•27c

30a •b . •a"'•3a• '24c 1•

29a 29e •ld ½4a 16b 20a•lc 10a,1 20b 10b 10c,11b 9b 1314c 9c 18b

6 •'•_.• 7b 5a•5bc31a2d I

KM 0 250 500 1,000 Downloaded by [Oregon State University] at 16:22 19 September 2011 contributed to the construction of the zymebaseline (Teel et al. 1999). In order mixtures of Chinook salmon harvested database. to visualizehow the presentmicrosatel- in treaty fisheries.These simulations lite data might be concordantwith and/or assessedwhether the baseline of microsat- Evaluation of the database improveupon this list, we calculatedpair- ellite allelefrequencies provided sufficient wise chord distances(Cavalli-Sforza and informationto identifyregional groups in Definition of regional groups. The Edwards 1967) between all collections hypotheticalmixtures. databasewas evaluated for itsability to cor- and then usedPHYLIP (Felsenstein2004) Simulationswere performed using the rectly allocateto regionalgroups defined to createa neighborjoining tree (Saitou StatisticalPackage for AnalyzingMixtures basedon a combinationof geneticsimilar- and Nei 1987; seeBox 1). On sucha tree, (SPAM version3.7; Debevecet al. 2000). ity, geographicfeatures, and management geneticallysimilar populationswill clus- Mixture genotypeswere randomly gener- applications.Population estimates are ter together,facilitating the definitionof ated from the baselineallele frequencies combinedinto regionalgroups to provide regionalgroups. assumingHardy-Weinberg equilibrium. the desiredaccuracy and precision of stock Mixture simulation. Simulations were The baselineallele frequencies were para- compositionestimates. The initial list of conductedto evaluate the application metricallyresampled to accountfor sam- regionalgroups for compositionalanalyses of theseregional groups to geneticstock pling variability.Each simulatedmixture wasenlarged from those used for the allo- identification(compositional analyses) of (N = 400) wasentirely composed (100%) 54.4 Fisheries ß VOL 32 No 11 ß NOVEMBER2007 ß WWW.FISHERIES.ORG of the regionalgroup under study wLth the overall concordance for the laborato- DISCUSSION equal contribution of all populations riesat theseloci wasover 99% (Table 2b). wLthin the regionalgroup. Then each Database construction s•mulatedmixture was analyzedagainst Baseline construction the complete baseline.Bootstrap means Our primarygoal of developinga reph- for regionalgroups and 90% confidence Dataprovided by the collaborators were cable set of microsatellite loci for Chinook •ntervals were derived from 1,000 simu- combinedto createa largedatabase suited salmonwas successfully completed. With lationsper group.Regional groups with to GSI compositionalanalyses of fisher- an averageabove 90% correctallocation mean correct estimates of at least 90% are ies managedunder the Pacific Salmon to regional groups,the DNA baseline consideredhighly identifiable in potential Treaty.A total of 220 samplecollections providesan unprecedentedresource for m•xturesfrom treaty fisheries.Regional and 16,394 individual fish representing improvedinformation and management of Chinooksalmon during their oceanand groupswith meancorrect estimates lower 110putative populations were included in than 90% can still be considered iden- freshwaterlife stages.Additional labora- Version1.1 of the baseline(Table 3, Figure toriesemploying various chemistries and nfiable in mixtures, but sourcesof mis- 2, Appendix1). All individualfish were allocation should be considered when hardware will be able to use the database assayedacross 13 loci; however,failures throughthe useof voucherspecimens or LnteFpretingthe results(e.g., Seeb et al. at particularloci sometimesresulted in 2000). byusing the allelic ladders (see Box 1) that incompletemultilocus genotypes. A target arecurrently under construction for all loci wasset of 144 individualsper population RESULTS (e.g.,LaHood et al. 2002).Allelic ladders with at least 120 genotypesper locus.In consistof a collectionof allelescovering Locus selection and evaluation somepopulations, fewer than 120individ- a rangeof knownsizes that canbe usedas ualswere available,so actual samplesizes of microsatellites an internal measurement to standardize werenecessarily below the target. the data. Theseladders hold the promtse The first blind test revealed that data- The number of alleles varied mark- of simplifyingstandardization as laborato- handlingerrors, such as misalignmentof edlyacross loci, ranging from 9 (Ors9)to rieswill not needto analyzevoucher spect- individuals,were the largestsource of dis- 74 (Omm1080;Table 1). Acrossall loci, mensfor everyallele. agreementamong laboratories. In addition 487 alleles were observed.A voucher set All geographicregions and genenc to data-handlingerrors, 2 of the 15 loci of tissuesfrom a singleindividual for each lineageslikely to contributeto fisheries selectedand tested stood out as being poorly of the alleleswere identified and provided of CTC interest are representedin the standardized across3 or more laboratories. to all collaborators.For somealleles, insuf- presentbaseline, and thesedata should These 2 loci were removed,and the final ficient tissuewas available for sharing be appropriatefor complexfishery mtx- groupof 13 lociwas identified for inclusion between all labs, so a secondindividual tures that include diverse populations tn the baseline(Table 1). Beyondthese known to exhibit the same allele was sub- from widespreadlocations. Although the sourcesof conflict, the resultsof the first stituted as the voucher. Additional alleles currentbaseline is broad,it is not compre- bhndcomparison indicated that technical are expectedas coverageof the baseline hensive.Efforts are currentlyunderway to standardizationhad been accomplished for exTpands. increaselocal coverage.Expanded base- greaterthan 90% of the observedalleles line datawill likely improvethe accuracy at each locus.Furtherefore, much of the Simulations of allocationto the regionalgroups, and, remainingvariability among laboratories in at leastsome cases, are likely to provtde wasdue to allelesbeing observed in the Forty-tworegional groups were defined a finer scaleof estimation(e.g., sub-bastns bhndsamples that hadnot beenobserved basedon geneticsimilarity, geographic within major river systems).Fine-scale in the original96 samples. features,and managementapplications. geographicallocation of mixturesand The results of the second blind test potential assignmentof individualfish Downloaded by [Oregon State University] at 16:22 19 September 2011 Simulationswere performedto examine indicated that technical standardization to population-of-originmay alsoprovide the accuracyand precision of thisbaseline importantbiological and life-history infor- had been accomplishedfor more than simulatingmixtures composed of indi- 95% of the observedalleles (Table 2a). mationsuch as migration timing and path- viduals drawn entirely from populations However,a numberof low-concordance waysand age-relatedchanges in habitat of a singleregion. Almost all regionswere valuesfor specificloci indicatederrors in use.Expansion of the baselineto include definedas highly identifiablewith boot- the data from two of the laboratories. One populationsthroughout Alaska, Canada, laboratoryobtained a concordanceof only strapmeans of 1,000 simulatedmixtures and Russiais underway.In addition to 44%for Ogo4, whereas other loci provided above90% to the correctregional group improvedmanagement, the baselinewdl by the samelaboratory showed 98% con- (Table3). Exceptionswere the Deschutes be usedto betterunderstand the biologyof cordanceor more.Another laboratoryhad Fall and Upper Stikine River regions the speciesand provide information about 0% concordance at five loci and 82% at which had 89.5% and 84.5% mean correct effective population size, evolutionary another locus. Data for the other seven allocations,respectively. In addition,the and demographichistory, and population loci fromthis secondlaboratory were close lower bound of the 90% confidence inter- boundaries. to the overallaverage. Cursory checks in val for all regionswas above a 90% thresh- The results of the two blind tests were respectivelaboratories revealed data-han- old, with the exceptionof the Deschutes encouraging,but also signalleda need dlingerrors that explained most of thedis- Fall,Upper Stikine River, Taku River, and for continuedvigilance in errorcheckLng crepancies.After correctionof theseerrors SoutheastAlaska Stikine River groups. anddata manipulation. On the onehand,

Fisheries • vol 32 NO 11 * NOVEMBER2007 o WWk•/ FISHER[ES,ORG 545 laboratoneswere able to achievea very Table 1. Microsatelliteloci standardized for Chinooksalmon. Reference, curator agency, high degreeof concordancethrough the and observednumber of allelesare givenfor baselineVersion 1.1. standardizationprocess and vouchersam- Locus Reference Curator agency• Observednumber ple comparisons.Furthermore, two new of alleles laboratoriesthat hadnot beenpreviously Ogo2 Olsenet al. 1998 ADFG 26 Ogo4 Olsenet al. 1998 WDFW 20 involved in locusselection, baseline con- 0ki100 DFOunpublished 2 DFO 48 struction, or the first blind test were able Ornrnl080 Rexroad et al. 2001 SWFSC 74 to achievevery high concordance without Ots2Olb Grieget al. 2003 ADFG 52 Ots208b Grieget al. 2003 CRITFC 54 anysignificant errors. On the otherhand, 0ts211 Grieget al. 2003 ADFG 43 significanterrors in datahandling were stfil 0ts212 Grieget al. 2003 OSU 36 apparentin initial blindsubmissions from 0•213 Grieg et al. 2003 OSU 48 Ots3M Grieg and Banks1999 WDFW 19 two of the original laboratories.These Ors9 Banks et al. 1999 DFO 9 results,although not uniqueto microsatel- OtsG474 Williamson et al. 2002 CRITFC 19 lite data (e.g.,White and Shaklee1991), Ssa408 Cairneyet al. 2000 NWFSC 39 suggestthat ongoingquality control and Laboratoryabbreviations: OSU, Oregon State University; SWFSC, Southwest Fisheries Science Center--National Marine FisheriesService; DFO, Departmentof Fishenesand OceansCanada; NWFSC, Northwest Fisheries Science Center-- errorchecking procedures both internally NationalMarine Fisheries Service; CRITFC, Columbia River Inter-Tribal Fish Commission; ADFG, Alaska Department of within each laboratoryand amonglabo- Fishand Game; WDFW, WasNngton Department of Fishand Wildlife. Personalcommunication, K. Miller,Department of Fisheriesand Oceans Canada, Nanaimo, British Columbia, Canada. ratories are warranted to ensure database integrity.

Table 2. Proportionalgenotyping accuracy by laboratoryand locusfor the secondblind test of 13 microsatelliteloci. Averages across locus and laboratoryare given.A. Resultsas submitted.B. Resultsafter correction for errors(see text).

A. Locus Lab I Lab 2 Lab 3 Lab4 Lab 5 Lab 6 Lab 7 Lab 8 Lab 9 Average Ogo2 0.987 1.000 1.000 1.000 0.993 0.000 1.000 0.993 1.000 0.886 Ogo4 0.439 1.000 1.000 0.995 1.000 0.000 0.995 0.994 0.990 0.824 OkilO0 0.978 1.000 1.000 1.000 1.000 1.000 1.000 0.970 1.000 0.994 Ornrnl080 1.000 1.000 0.995 1.000 1.000 0.000 1.000 0.994 1.000 0.888 Ots2Olb 0.984 1.000 1.000 1.000 1.000 0.993 0.995 0.985 1.000 0.995 Ots208b 0.994 1.000 1.000 1.000 1.000 1.000 0.995 0.970 0.995 0.995 0ts211 1.000 1.000 0.994 1.000 0.993 0.955 0.994 0.985 0.994 0.991 0ts212 0.989 1.000 1.000 1.000 1.000 0.989 0.995 0.994 1.000 0.996 Ors213 0.987 1.000 0.982 1.000 0.985 0.000 1.000 1.000 1.000 0.884 Ots3M 1.000 1.000 0.988 0.994 1.000 0.000 1.000 1.000 0.995 0.886 Ors9 1.000 1.000 1.000 1.000 1.000 0.823 1.000 1.000 1.000 0.980 OtsG474 0.995 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 0.999 Ssa408 0.987 0.929 1.000 1.000 1.000 1.000 1.000 1.000 1.000 0.991 Average 0.949 0.995 0.997 0.999 0.998 0.597 0.998 0.991 0.998 0.947 Downloaded by [Oregon State University] at 16:22 19 September 2011

B. Locus Lab I Lab 2 Lab 3 Lab 4 Lab 5 Lab 6 Lab 7 Lab 8 Lab 9 Average Ogo2 0.987 1.000 1.000 1.000 0.993 0.988 1.000 0.993 1.000 0.996 Ogo4 0.994 1.000 1.000 0.995 1.000 0.968 0.995 0.994 0.990 0.993 0ki100 0.978 1.000 1.000 1.000 1.000 1.000 1.000 0.970 1.000 0.994 Ornrnl080 1.000 1.000 0.995 1.000 1.000 0.938 1.000 0.994 1.000 0.992 Ots2Olb 0.984 1.000 1.000 1.000 1.000 0.993 0.995 0.985 1.000 0.995 Ots208b 0.994 1.000 1.000 1.000 1.000 1.000 0.995 0.970 0.995 0.995 0ts211 1.000 1.000 0.994 1.000 0.993 0.955 0.994 0.985 0.994 0.991 0ts212 0.989 1.000 1.000 1.000 1.000 0.989 0.995 0.994 1.000 0.996 0ts213 0.987 1.000 0.982 1.000 0.985 0.994 1.000 1.000 1.000 0.994 Ots3M 1.000 1.000 0.988 0.994 1.000 0.949 1.000 1.000 0.995 0.992 Ors9 1.000 1.000 1.000 1.000 1.000 0.979 1.000 1.000 1.000 0.998 OtsG474 0.995 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 0.999 Ssa408 0.987 0.929 1.000 1.000 1.000 1.000 1.000 1.000 1.000 0.991 Average 0.992 0.995 0.997 0.999 0.998 0.981 0.998 0.991 0.998 0.994

.546 Fisheries o VOL32 NO 'i 'i "NOVEMBER2007 "WWW.FISHERIES.ORG Table 3. Mean correctallocations from 100% simulationsfor 42 regionalgroups of Chinooksalmon populations. Bootstrap means of 1,000 simulatedmixtures and upperand lower90% bootstrapconfidence intervals are given. Number of populationsper regionis also given.

Region number Region name Number of populations Bootstrap mean 90% confidence interval Lower Upper 1 CentralValley fall 4 0.945 0.916 0.971 2 CentralValley spring 4 0.936 0.908 0.962 3 CentralValley winter 1 0.990 0.980 0.998 4 California Coast 2 0.985 0.975 0.995 5 Klamath River 3 0.984 0.970 0.995 6 North California/SouthOregon Coast 1 0.972 0.956 0.987 7 RogueRiver 2 0.941 0.912 0.968 8 Mid OregonCoast 3 0.944 0.918 0.968 9 North OregonCoast 3 0.957 0.933 0.979 10 LowerColumbia spring 3 0.971 0.952 0.987 11 Lower Columbia fall 3 0.973 0.956 0.987 12 Willamette River 2 0.983 0.968 0.994 13 Mid Columbia Tule fall 1 0.969 0.949 0.986 14 Mid and UpperColumbia spring 5 0.965 0.945 0.985 15 Deschutes fall 1 0.895 0.859 0.931 16 UpperColumbia summer/fall 3 0.963 0.937 0.985 17 Snake River fall 1 0.946 0.915 0.974 18 SnakeRiver spring/summer 5 0.966 0.946 0.984 19 WashingtonCoast 3 0.951 0.928 0.971 20 SouthPuget Sound 2 0.988 0.977 0.997 21 North PugetSound 4 0.971 0.955 0.985 22 Lower Fraser 2 0.984 0.972 0.994 23 LowerThompson River 2 0.983 0.971 0.994 24 SouthThompson River 3 0.976 0.962 0.990 25 NorthThompson River 2 0.978 0.964 0.990 26 Mid Fraser River 4 0.980 0.966 0.991 27 UpperFraser River 4 0.969 0.949 0.986 28 East Vancouver Island 2 0.979 0.966 0.991 29 West Vancouver Island 5 0.990 0.980 0.997 30 South BC Mainland 2 0.975 0.961 0.988 31 Central BC Coast 3 0.960 0.940 0.978 32 Lower Skeena River 2 0.950 0.927 0.971 33 UpperSkeena River 3 0.950 0.925 0.971 34 Nass River 4 0.939 0.913 0.963 35 UpperStikine River 1 0.845 0.793 0.893 36 Taku River 4 0.920 0.884 0.954 37 Southern Southeast Alaska 5 0.969 0.950 0.986 38 SoutheastAlaska, Stikine River 1 0.917 0.884 0.947 39 KingSalmon River 1 0.987 0.977 0.995 40 Chilkat River 2 0.988 0.977 0.996 41 Alsek River 1 0.980 0.967 0.992 42 Situk River 1 0.977 0.963 0.990 Downloaded by [Oregon State University] at 16:22 19 September 2011

Power of the database thesepopulations was used in the allozyme singleallozyme group was separated into studyof Guthrieand Wilmot (2004). fall, spring,and winter groups. Our simulationresults suggest that the The neighbor-joiningtree (Figure3) presentdatabase provides greater resolu- suggestedthat somegroups that had not Growth of and accessto the baseline uon than wasprovided by the allozyme been distinct from one another based on baseline.Only the DeschutesFall and the allozymebaseline might be resolv- More detailed analysesof the power UpperStikine regions fell belowour target ablewith the presentmicrosatellite data. of the databaseare ongoing.We expect of 90% accuracy.Additional populations futureanalyses to betterdefine limitations and tests are needed to evaluate whether Overall,37 regionalgroups were resolvable with the allozymedataset (Teel et al. 1999) of the presentdatabase and to identify thesegroups can be accuratelyidentified where the addition of baseline collections or,alternatively, should be combinedwith while42 wererecognized in thismicrosat- adjacentregional groups. The similarity ellite datasetdespite that fact that more and increasedpower via the additionof amongthe Taku and $tikine river trans- populationswere included in the allozyme new markers are most needed. The addi- boundarypopulations was also observed baseline than the microsatellite baseline. tion of singlenucleotide polymorphism w•th the allozymedataset of Teel et al. Gains in resolutionwere most apparent (SNP) markers(see Box 1) which are eas- (1999), and a singlecombined group for in the California Central Valley where a ily standardizedand may reflectselective

Fisheries o VOL 32 r•O 11 o r•OV[r•B[R 2007 o WWW.FISHERIES,ORG 547 F,gure3 Nelghbordolmngtree based on pairwisechord distances between collections of Chinook salmon (Appendix 1) Numbersand vertical hnesonrlght•ndlcatereglonalgroupsasdeslgnatedlnTable3Regional groupswlthonlyaslnglepopulatlonareln bold

FeatherRiver Hatchery Fall Battle Creek Fall StanislausRiver Fall ButteCreek Fall FeatherRiver Hatchery Spring 1,2,3 ButteCreek Spring • Deer Creek Spring • Mill Creek Spring -- , SacramentoWinter I r• LowerDeschutes R•ver 15 I t r• LyonsFerry Hatchery Fall 11 I •l r i- Hanford Reach Creek Summer/Fall I • WellsDam Hatchery Summer/Fall 16 /'--1 --• MethowRiver Summer/Fall SolDuc River Spring 1•9 / • SpringCreek Hatchery Fall 13 / • CowlitzHatchery Spring L • • •Cow•tzHatchery Fall I I I • Lewis •iver I-all I • • SandyRiver Fail I ,u, 11 I r• LewisHatchery Spring • I r Kalama HatcherySpring I '•L •-• McKenzieHatchery Spring • NorthSantiam Hatchery Spring Eel River Fall [-- RussianRiver Fall Chetco River Fall RiverSpring Klamath River Fall I [• TrinityHatchery SpringFall 4 - 9 Cole RiversHatchery Spring ApplegateCreek Fall CoquilleRiver Fall Siuslaw River Fall Nehalem River Fall Alsea River Fall Siletz River Fall

QueersQuillayute/BogachielRiverFall River Fall I 19

Marble-NVlConumaHatchery Hatchery NitinatHatchery h I__ RobertsonSaritaHatcheryHatchery 28,29 Quinsam Hatchery White River HatcherySpring SoosCreek Hatchery Fall NorthFork Nooksack I 20 Suiattle-SkagitRiver Spring Skagit River Summer • NorthFork StilliguamishBigQualicumRiver Summer Hatchery RiverSpringI21 ChilliwackRiverHatchery Fall BirkenheadRiverHatchery SpringI 22 -- TucannonHa..tchery Spring/Summer Carson Hatchery Spring Warm SpringsHatchery Spdng Secesh River Spring/Summer 14, 18 John Day RiverSpring Minam RiverSpring/Summer Rapid River Hatchery Spring/Summer -- Imnaha River Spring/Summer UpperClearwaterYakima RiverHatchery SummerSpring WenatcheeRiverspringLouisRiver Spring I SpiusRiver Hatchery Spring ' Nicola HatcherySpring ChilkoRiver Summer • QuesnelRiver Summer I Li• NechakoRiver Summer • • StuartRiver Summer I I• TorpyRiver Summer 23 - 27 L • r• SalmonRiver Summer ' / r• -- Swift River Summer • • MorkillRiver Summer r• LowerThompson River Fall 1 I LowerMiddleAdams Shuswap River HatcheryHatchery SummerFall Downloaded by [Oregon State University] at 16:22 19 September 2011 itimatHatchery Porteau CoveHatchery 30,31 AtnarkoHatcheryKlinaklini WannockRiver RiverHatchery I Ecstall River 32 KincolithRiver 34 Chickamin River

__ ClearCreek KingCreek 37, 38 -- CrippleCreek -- Andrews Creek -- Little Tahltan River 35 River

Kowatua Creek Tatsatua Creek 4um River BulkleyRiver bine River Sustut River 32 - 34 KwinageessRiver -- Owegee River King Salmon River SitukRiver 39- 42 BigBoulder Creek KlukshuTahini River I 0.01

541l Fisheries o VOL32 NO 11 o NOVEMBER2007 o WWVV.FISHERIES.ORG variation(S•nith et al. 2005; S•nithet al. in press)is presently In May andSeptember of 2007,the PSC convened a workshop underway. focusingon thecurrent and future capabilities, limitations, and use of As partof the collaboration,a web-based application has been geneticstock identification methods in oceansahnon management. developedto allowthe fisheriesand research community to access A diversegroup of scientistsattended representing the disciplinesof the genotypedata, curator documents, and supportingmetadata. fisherymanagement, genetics, administration, population dynamics, The PacificSahnon Commission provided developmental fund- and samplingtheory. Attendees were divided into fourworkgroups: ing to the NorthwestFisheries Science Center, National Marine genetics,manage•nent, logistics, and •nodeling/sampling. Reviews of FisheriesService, Seattle, Washington(www. nwfsc.noaa.gov/ the workshopare available at the PSC website(http://psc.org/). The research/divisions/cbd/standardization.cfm),and a test versionof finalrecommendations will bepublished when completed in 2008. theweb application is now being evaluated. The permanentloca- Asgenetic approaches to fisheries management become more com- tionand support for the database are currently under discussion by monplace,we anticipate the growth of thisand similar types of data- the collaboratingagencies and the PacificSahnon Commission. basesthat are publicly available through web applications. Ukimately, Until the final location of the databaseis established,the data thesedatabases will provide a wealthof informationnot only for fish- are availableat the ADFG website(www. genetics.cf. adfg. state. eriesapplications, but also for researchers from a varietyof disciplines ak.us/publish/data/PSCchinookver1_1 .pdf). investigatingdiverse aspects of sahnonidlife history, population genetics,and evolutionary theory. CONCLUSIONS ACKNOWLEDGEMENTS Herewe described a large collaborative study to developa shared, Thisstudy was result of a collaborationamong three National Marine web-accessibledatabase of DNA markersto allowmultiple agencies FisheriesService laboratories•Northwest Fisheries Science Center, Auke to conductgenetic stock identification studies of Chinooksahnon. BayLaboratory, and Southwest Fisheries Science Center; Washington Replicatescorh•g of the microsatelliteloci wassuccessfully accom- Departmentof Fishand Wildlife; University of Idaho/ColtunbiaRiver plishedamong nine geographically-dispersedlaboratories. However, Inter-tribalFisheries Conmfission/Idaho Department of Fishand Game; thestudy also revealed that ongoing coordination and error-checking Oregon State University; Canadian Department of Fisheriesand Oceans; will berequired to ensurethe integrityof thedatabase as it expands. andthe Alaska Department of Fish and Game. The U.S. Fish and Wildlife Service,Anchorage, Alaska, and Idaho Department of Fishand Game, Growthof the database ispresentl v underwav via the addition of new Eagle,Idaho, participated in the second blind sample. Primary funding for collectionsand additional SNP markers.The databaseis nowbeh•g thestudy was provided by theU.S. Departmentof Commerce,NOAA, usedto estimatecompositional analyses of PSC andother fisheries NMFS,funds appropriated tofund the technical work required for the U.S. around the Pacific Ocean. sectionof thePacific Sahnon Treaty to implementthe Chinook Salmon

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View our latest catalog at www.flo.qta_q.,com, or email us at: salesC•flo.vta.q,COm or call to discussyour custom tagging needs: (8•0) 843-1172 '

Fisheries ß VOL32 •O 11 ß NOVEMBER2007 ß WWW.FISHERIES.ORG $49 Agreementregarding abundance based man- Umvers•tyof Washington,Seattle Avadable hte markersfor rainbow trout (Oncorhynchus agementPartial funding for the U S F•shand at http//evoluuongeneucs washington edu/ myktss)Ammal Genetics 32 317-319 WfidhfeService was provided by the U.S. Yukon phyhp/geune.htmL Saitou,N., andM. Nei. 1987.The neighbor-join- RiverResearch and Management Fund (USRM- Greig,C. A., andM. A. Banks.1999. Five mul- ingmethod: a newmethod for reconstrucung 08-05)and to the Universityof Washingtonby tiplexedmicrosatellite loci for rapid response phylogenetictrees. Molecular Biology and JointInstitute for the Studyof the Atmosphere run identificationof Califomia'sendangered Evolution 4:406-425. andOcean (JISAO) under NOAA Cooperative winter Chinook salmon. Animal Genetics AgreementNA17RJ1232, Contribution 1444. 30(4):318-320. Seeb,L. W., C. Habicht,W. D. Templin,K. E. Significantmatching funds were provided by all Greig, C., D. P. Jacobson,and M. A. Banks. Tarbox, R. Z. Davis, L. K. Bmnnian, and participatinginstitutions. We thank the many 2003. New tetranucleotide microsatellitesfor J. E. Seeb.2000. Genetic diversity of sockeye members of the Chinook Technical Committee fine-scalediscrimination among endangered salmonof Cook Inlet, Alaska, and its apphca- of the PacificSalmon Committee for their vision Chinooksalmon (Oncorhynchus tshawytscha). tionto management ofpopulations affected by and supportfor the studyand five anonymous MolecularEcology Notes 3:376-379. the ExxonValdez oil spill.Transactions of the reviewers for their valuable comments on the Guthrie, C. M., III, and R. L. Wilmot. 2004. AmericanFisheries Society 129:1223-1249 manuscript. Genetic structure of wild Chinook salmon Shaklee,J. B., T. D. Beacham,L. Seeb,and B. populationsof southeastAlaska and northern A. White. 1999.Managing fisheries using REFERENCES BritishColumbia. 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Microsatellites:genetic markersfor the InferencePackage) version 3.6. Distributed by Martin, W. K. Hershberger,and J. Killefer. future.Reviews in FishBiology and Fishenes theauthor, Department of GenomeSciences, 2001. Thirty-fivepolymorphic microsatel- 4:384-388. 550 Fisheries ß vOL32 NO 11 ß NOVE•IBER2007 ß •/I,q/VW,FISHERIES.ORG Appendix1 Chinooksalmon populations analyzed in th•s study and Included in baselineVersion 1 1 Runt•me, hatchery (H) or w•ld(W) ongln,life stage, collection data, andanalys•s laboratory are Dven. Lower case des,gnat•ons w•th•n a reDoncorrespond to locationson F•gure1.

Regionnumber Region name Population Runtime • Origin Lifestage Collectiondate Analysislaboratory 2

1 CentralValley fall BattleCreek (a) Fa W Adult 2002, 2003 SWFSC FeatherHatchery fall (b) Fa H Adult 2003 SWFSC StanislausRiver (c) Fa W Adult 2002 SWFSC TuolumneRiver (d) Fa W Adult 2002 SWFSC 2 CentralValley spring ButteCreek (a) Sp W Adult 2002, 2003 SWFSC DeerCreek spring (b) Sp W Adult 2002 SWFSC FeatherHatchery spring (c) Sp H Adult 2003 SWFSC MillCreek spring (d) Sp W Adult 2002, 2003 SWFSC 3 CentralValley winter SacramentoRiver winter Wi W/H Adult 1992, 1993, 1994, 1995, 1997, SWFSC 1998, 2001, 2003, 2004 4 CaliforniaCoast Eel River(a) Fa W Adult 2000, 2001 SWFSC RussianRiver (b) Fa W Juvenile 2001 SWFSC 5 KlamathRiver KlamathRiver fall (a) Fa W Adult 2004 SWFSC TrinityHatchery fall (b) Fa H Adult 1992 SWFSC TrinityHatchery spring (c) Sp H Adult 1992 SWFSC 6 North California/ Chetco Fa W Adult 2004 OSU SouthOregon Coast 7 RogueRiver Applegate(a) Fa W Adult 2004 OSU ColeRivers Hatchery (b) Sp H Adult 2004 OSU 8 Mid OregonCoast Coquille(a) Fa W Adult 2000 OSU Siuslaw(b) Fa W Adult 2001 OSU Umpqua(c) Sp W Adult 2004 OSU 9 NorthOregon Coast AIsea(a) Fa W Adult 2004 OSU Nehalem(b) Fa W Adult 2000, 2002-1, 2002-2 OSU Siletz(c) Fa W Adult 2000 OSU 10 LowerColumbia spring CowlitzHatchery spring (a) Sp H 2004 CRITFC KalamaHatchery spring (b) Sp H 2004 CRITFC LewisHatchery spring (c) Sp H 2004 CRITFC 11 LowerColumbia fall CowlitzHatchery fall (a) Fa H 2004 CRITFC Lewisfall (b) Fa W Adult 2003 WDFW Sandy(c) Fa W Adult 2002, 2004 OSU 12 WillametteRiver McKenzie(a) Sp H Adult 2002, 2004 OSU NorthSantiam (b) Sp H Adult 2002, 2004-1,2004-2 OSU 13 Mid ColumbiaTule fall SpringCreek Fa H 2001,2002 CRITFC 14 Mid and UpperColumbia spring Carson Hatchery (a) Sp H 2001,2004 CRITFC JohnDay (b) Sp W Juvenile,Adult 2000-1,2000-2, 2000-3, OSU 2000-4, 2000-5, 2000-6, 2004 UpperYakima (c) Sp H Adult, Mixed 1998, 2003 WDFW

Downloaded by [Oregon State University] at 16:22 19 September 2011 WarmSprings Hatchery (d) Sp H 2002, 2003 C RITFC Wenatcheespring (e) $p W Adult 1993, 1998, 2000 WDFW 15 Deschutesfall Lower DeschutesRiver Fa W 1999-1, 1999-2, 2001,2002 CRITFC 16 UpperColumbia summer/fall HanfordReach (a) Su/Fa W 1999,2000-1,2000-2, 2000-3, CRITFC 2001-1, 2001-2, 2001-3 Methow Riversummer (b) Su/Fa W 1992, 1993, 1994 C RITFC Wells Dam (c) Su/Fa H 1993-1, 1993-2 CRITFC 17 SnakeRiver fall LyonsFerry Fa W Adult 2002-1,2002-2, 2003-1, 2003-2 WDFVV 18 SnakeRiver spring/summer ImnahaRiver (a) Sp/Su W 1998,2002, 2003 CRITFC MinamRiver (b) Sp/Su W 1994,2002, 2003 CRITFC RapidRiver Hatchery (c) Sp/Su H 1997, 1999,2002 CRITFC SesechRiver (d) Sp/Su W 2001,2002,2003 CRITFC Tucannon(e) Sp/Su H Adult 2003-1,2003-2,2003-2 WDFW 19 WashingtonCoast Queets(a) Fa W Adult 1996, 1997 WDFW Quillayute/Bogachiel(b) Fa W Adult 1995-1, 1995-2, 1995-3, WDFW 1996-1, 1996-2 SolDuc (c) Sp H Adult 2003 WDFW 20 SouthPuget Sound SoosCreek (a) Fa H Adult 1998-1, 1998-2,2004 WDFW WhiteRiver (b) Sp H adult 1998-1,1998-2, 2002 WDFW Fisheries ß VOL32 NO 11 ß NOVEr•BER2007 ß WWW.FISHERIES.ORG 55• 21 NorthPuget Sound NF Nooksack(a) Sp HNV adult 1999 WDFW NF Stilliguamish(b) Su HNV adult 1996 2001-1, 2001-2 WDFW Skagitsummer (c) Su W adult 1994 1995 WDFW Suiattle(Skagit) (d) Sp W adult 1989 1998, 1999 WDFW 22 LowerFraser BirkenheadRiver (a) Sp H Adult 1996 1997, 1999, 2001, SWFSC 2002 2003 Chilliwack(b) Fa H Adult 1998 1999 DFO 23 LowerThompson River Nicola(a) Sp H 1998 1999 OSU SpiusRiver (b) Sp H Adult 1996 1997, 1998 SWFSC 24 SouthThompson River LowerAdams (a) Fa H Adult 1996 DFO LowerThompson (b) Fa W Adult 2001 DFO MiddleShuswap (c) $u H Adult 1997 DFO 25 NorthThompson River Clearwater(a) Su W Adult 1997 DFO LouisRiver (b) Sp W Adult 2001 DFO 26 Mid FraserRiver Chilko(a) Su W Adult 1995, 1996, 1999, 2002 DFO Nechako(b) Su W Adult 1996 DFO Quesnel(c) Su W Adult 1996 DFO Stuart(d) Su W Adult 1996 DFO 27 UpperFraser River MorkillRiver (a) Su W Adult 2001 DFO SalmonRiver (Fraser) (b) Su W Adult 1997 SWFSC Swift(c) Su W Adult 1996 DFO TorpyRiver (d) Su W Adult 2001 DFO 28 EastVancouver Island BigQualicum (a) H Adult 1996 DFO Quinsam(b) H Adult 1996, 1998 DFO 29 WestVancouver Island Conuma (a) H Adult 1997, 1998 DFO Marbleat NVI(b) H Adult 1996, 1999, 2000 DFO Nitinat(c) H Adult 1996 DFO Robertson(d) H Adult 1996, 2003 DFO Sarita(e) H Adult 1997, 2001 DFO 30 SouthBC Mainland Klinaklini(a) W Adult 1997 DFO PorteauCove (b) H Adult 2003 DFO 31 CentralBC Coast Atnarko(a) H Adult 1996 DFO Kitimat(b) H Adult 1997 DFO Wannock(c) H Adult 1996 DFO 32 Lower$keena River Ecstall(a) W Adult 2000, 2001, 2002 DFO Lower Kalum (b) W Adult 2001 DFO 33 Upper$keena River Babine(a) H Adult 1996 DFO Bulkley(b) W Adult 1999 DFO Sustut(c) W Adult 2001 DFO 34 NassRiver Damdochax(a) W Adult 1996 DFO Kincolith(b) W Adult 1996 DFO Kwinageese(c) W Adult 1996 DFO Owegee(d) W Adult 1996 DFO 35 UpperStikine River LittleTahltan River W Adult 1989 1990 OSU

Downloaded by [Oregon State University] at 16:22 19 September 2011 36 TakuRiver KowatuaCreek (a) W Adult 1989 1990 ADFG NakinaRiver (b) W Adult 1989 1990 ADFG TatsatuaCreek (c) Adult 1989 1990 ADFG UpperNahlin River (d) W Adult 1989 1990, 2004 ADFG 37 SouthernSoutheast Alaska ChickaminRiver (a) W Adult 1990 1993 ADFG ClearCreek (b) W Adult 1989 2003, 2004 ADFG CrippleCreek (c) W Adult 1988 2003 ADFG Keta River(d) W Adult 1989 2003 ADFG KingCreek (e) W Adult 2003 ADFG 38 SoutheastAlaska, Stikine River AndrewsCreek W Adult 1989, 2004 ADFG 39 KingSalmon River KingSalmon River W Adult 1989, 1990, 1993 ADFG 40 ChilkatRiver BigBoulder Creek (a) W Adult 1992, 1995, 2004 ADFG TahiniRiver (b) W Adult 1992, 2004 ADFG 41 AlsekRiver KlukshuRiver W Adult 1989, 1990 ADFG 42 SitukRiver SitukRiver W Adult 1988, 1990, 1991, 1992 ADFG

Runtime abbreviations: spring (Sp), summer (Su), fall (Fa), and winter (Wi) 2Laboratoryabbreviations: OSU, Oregon State University; SWFSC, Southwest Fisheries Science Center NationalMarine Fisheries Service; DFO, Department of Fisheriesand Oceans Canada; CRITFC, Columbia River Inter-TribalFish Commission; ADFG, Alaska Department of Fish& Game;WDFW, Washington Depadment of Fish& Wildlife.

552 Fisheries. vol 32 NO 11 . NOVEMBER2007. WWW.FISHERIES.ORG