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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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FEATURE:
GENETICS
Developmentof a Standardized
DNA Database
for Chinook Salmon
L. W. Seeb,
A.Antonovich,
'•qlr
•
Downloaded by [Oregon State University] at 16:22 19 September 2011
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
Seebisresearch
professor
at theSchoolofAquaticandFisheries
Sciences,
University
of Washington,
Seattle.Shecan
becontacted
at Iseeb@u.washington.edu.
Antonovichisa biometrician,
Decovichisa fishery
biologist,
andTemplinis
a fisheries
geneticist
at theAlaskaDepartment
ofFishandGame,DivisionofCommercial
Fisheries,
Anchorage.
Banks
ismarinefisheries
geneticist
andassociate
professor
andBellingerisa molecular
geneticist
at OregonStateUniversity's
HatfieldMarineScience
Center,Newport.Beacham
isa research
scientist
andSupernault
isa research
technicianat the
Department
ofFisheries
andOceans
Pacific
Biological
Stationin Nanaimo,
BritishColumbia.
Blankenship
wasformerly
with the National Marine FisheriesServiceSouthwestFisheriesScienceCenter in SantaCruz, California, and is now a
geneticist
attheWashington
Department
ofFishandWildlife,Olympia.
Campbell
issenior
fisheries
research
biologist
atthe
IdahoDepartment
ofFishandGame,EagleFishGenetics
Laboratory,
Eagle.Garzaissupervisory
research
biologist,
National
MarineFisheries
ServiceSouthwest
Fisheries
ScienceCenter,SantaCruz,California.Guthrieisa fisheryresearch
biologist
at the NationalMarineFisheries
ServiceAuke BayLaboratories,
TedStevensMarineResearch
Institute,Juneau,Alaska.
Lundrigan
wasformerly
withtheUniversity
ofWashington
SchoolofAquaticandFishery
Sciences,
Seattle,andiscurrently
a fisheries
biologist
at theUniversity
ofVictoria's
CentreforBiomedical
Research,
Victoria,BritishColumbia.
Moranis
a research
geneticist
andTeelissupervisory
fishery
biologist
at theNationalMarineFisheries
Service
Northwest
Fisheries
Science
Center,Seattle,
Washington.
Narumisleadgeneticist
andStephenson
islaboratory
manager
at theColumbia
River
Inter-Tribal
FishCommission,
Hagerman
FishCultureExperiment
Station,Hagerman,
Idaho.Wenburg
isdirectorofgenetics
at the U.S. FishandWildlifeServicein Anchorage,
Alaska.Youngisa fishandwildliferesearch
scientistat the Washington
Department
ofFishandWildlife,Olympia.Smithwasformerly
withtheAlaskaDepartment
ofFishandGameDivisionof
Commercial
Fisheries
andisnowa fisheries
geneticist
at theU.S. FishandWildlifeService,AbernathyFishTechnology
Center,Longview,Washington.
540
Fisheries ß vol 32 No 11 ß NOVEMBER
2007 ß WWW.FISHERIES.ORG
ABSTRACT: An mternauonal
mulu-laboratory
projectwasconducted
to developa standardized
DNA database
forChinook
salmon(Oncorhynchus
tshawytscha).
Thisprojectwasin response
to theneedsof theChinookTechnicalCommitteeofthePacific
SalmonCommission
to identifystockcomposition
of Chinooksalmoncaughtin fisheries
duringtheiroceanicmigrations.
Nine
genetics
laboratories
identified13microsatellite
locithatcouldbereproducibly
assayed
in eachof the laboratories.
To testthat
the lociwerereproducible
amonglaboratories,
blindtestswereconducted
to verifyscoring
consistency
forthenearly500total
alleles.Oncestandardized,
a datasetof over16,000Chinooksaltnonrepresenting
110putativepopulations
wasconstructed
rangingthroughout
the areaof interestof the PacificSalmonCommission
fromSoutheast
Alaskato the Sacramento
River
•n California.The datasetdifferentiates
the majorknowngeneticlineages
of Chinooksaltnonandprovides
a toolfor genetic
stockidentification
of samples
collected
frommixedfisheries.
A diverse
groupof scientists
representing
thedisciplines
of fishery
management,
genetics,
fishery
administration,
population
dynamics,
andsampling
theoryarenowdeveloping
recommendations
forthe integration
of thesegeneticdataintooceansalmonmanagement.
Desarrollo
de una base de datos estandarizada
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de DNA parael salm6nrey
RESUMEN: Se realiz6un proyecto
internacional
conla participaci6n
dediversos
laboratorios
conla finalidaddedesarrollar
unabasededatosestandarizada
deDNA parael salm6nrey(Oncorhynchus
tshawytscha).
Dichoproyecto
surgi6comorespuesta
a
lasnecesidades
delComit6T6cnicoChinookdelaComisi6n
delSalm6ndelPac•fico
paraidentificar
lacomposici6n
poblacional
del salm6nreyqueescapturado
pot la pesquerfa
durantesumigraci6n.
Un totalde nuevelaboratorios
de anfilisis
gen•ticos
tdentificaron
y reprodujeron
cadauno 13 loci microsat61ites.
Con el objetode probarquedichosloci fueranreproducibles
entrelaboratorios,
secondujeron
pruebas
an6nimas
paraverificarla consistencia
de casi500 alelos.Una vezestandarizada,
se
construy6
unabasededatos
construida
coninformaci6n
proveniente
demilsde16,000salmones
querepresentan
110poblaciones
putativas
distribuidas
a lo largodel fireade inter6sde la Comisi6ndelSalm6ndel Pacffico,
delsureste
deAlaskahastael Rfo
Sacramento,
California.La basededatossirvetantoparaidentificar
gen6ticamente
losdistintos
stocks
de salm6nreya partit
demuestras
combinadas
provenientes
dela pesquer•a
comoparadiferenciar
el linajegen6tico
conocido
m•simportante
deesta
especie.
En la actualidad,
un importante
grupodecientfficos
especializados
en disciplinas
comoel manejoy administraci6n
de
pesquerfas,
gen6tica,dinfimicapoblacional
y teor•adel muestreo
estrindesarrollando
recomendaciones
paraqueestabasede
datosgen6ticos
seincorpore
en el manejodelsalm6n.
INTRODUCTION
The modelusescatch,escapement,
coded- fromthe indicatorstockassigned
to them
wiretag(CWT) recovery,
andrecruitment in the model.
The PacificSaltnonTreatywasratified information to forecast relative abundance
In 2004,the PacificSalmonCommission
in treatyfisheries.
conveneda panelof expertsto examine
1985,renegotiated
in 1999,and extended
With the increaseddependence
on limitationsof the CWT prograinin the
to the YukonRiver in 2002.Throughthe CWT recoverydata, concernhas been contextof massmarkingand mark-selectreaty,the twonationsagreedto cooper- raisedregardingthe 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 Chinooksahnon
salmonmigratelongdistances
duringtheir onlyfishcarrying
a CWT hadan adipose (Hankin et al. 2005). The expertpanel
marineperiodand are routinelyinter. clip. However,with the adventof mass- concludedthat alternativetechnologies
ceptedin fisheries
beyondthejurisdiction markingof largenumbersof hatcheryfish couldnot by themselves
replaceCWTs,
of the government
in whosewatersthey usingan adipose
clip only (e.g.,Mobrand but thatgeneticstockidentification
(GSI)
spawn.
The PacificSaltnonTreaty,through et al. 2005), recoveryof CWT fishhas could indeedcomplementthe existing
the PacificSahnonCommission(PSC), beencomplicated.
Recovery
nowrequires CWT programs
or be usedin combinaservesas a meansto coordinatemanage. both handlingmuch largernumbersof tion with othertechniques
suchasotolith
ment of the salmon resourceand conduct individuals
pairedwith electronicscan- thermalmarkingto estimatethe stock
conservation
actionsasrequired.
ning of thoseadipose-clipped
individu- composition
of a landedcatchin a parChinooksalmon(Oncorhynchus
tshaw- als to detecttags.As a furtherconcern, ticulartime/areafishery(Findings11-13)
ytscha)are harvestedthroughoutthe abundance and harvest information is However,they notedlimitationsin the
yearby commercial
and sportfishersin not available for most Chinook saltnon GSI methods due to the lack of coastwide
the waters of southeastAlaska, British stocks,soindicatorstocksare usedwithin geneticbaselines,
which are databases
of
Columbia, and the Pacific Northwest. the modelto represent
bothlargergroups genotypes
frombreeding
populations.
Fisheries
typicallyharvesthighly mixed of hatchery
andwildstocks.
The abilityof
stocks of Chinook saltnon and are thereFinding 13. ModernGSI methods
thehatcheryindicatorstocks
to accurately
can
be used to estimatethe stockcomfore underthe jurisdictionof the Pacific represent
wild stockshasbeengenerally
position
of thelanded
catchin a parSaltnonTreaty.Quotasspecified
by the supportedin coho saltnon(O. kisutch;
ticular time/areafishery. However,
PSC aredependent
on the abundance
of e.g.,Weitkampand Neely 2002),but is
the accuracyand precision
of data
Chinooksaltnon
projected
byitsChinook still largelyunknownfor manystocksof
TechnicalCommittee(CTC) usingthe Chinooksahnon.The wild stocksmay
required
to estimate
stock-age-fishery
Chinooksaltnon
model(e.g.,CTC 2005). differin ancestry,
abundance,
andtiming
specificexploitation
ratesusingGSI
between the United States and Canada in
Fisherles * vot 32 .o II
* NOVEMBER
2007
e VV•/VV. FISHERIES,ORG
541
methods
•s dependent
upona variety
of factors.For example,
microsatellite
DNA-basedGSI technology
isnotyet
capable
of generating
consistent,
replicableestimates
dueto thelackof a
coastwide
geneticbaseline,
thehistory
of stocktransfers
withinand among
watersheds,
and differences
in methodologies
andmixtureseparation
algo-
this databasewas comprehensive
and vanousmeeungsof salmomdgenet•c•sts
usedby multiplelaboratories,
a number from 1999to 2001,but progress
waslimof limitations,includingthe requirements ited (LaHoodet al. 2002).Finally,a workfor lethalsampling,
cryopreservation,
lack shophostedby the CTC in 2003 resulted
of laboratoryautomation,and the finite
in funding for the databasedescribed
numberof loci, led researchers
to the deciherein.In thefollowingyear,a symposium
sionto replacethe allozymebaselinewith
on geneticdatabases
for fisherymanagea DNA database.
rithms.(Hankin et al. 2005)
2004 AmericanFisheriesSociety(AFS)
Annual Meeting, and groupsstudying
a numberof divergentfisheryresources
ableforresolving
Chinooksalmonpopula- beganstandardization
efforts(e.g.,Atlanuc
tion structure.However,unlikeallozymes herring;Marianiet al. 2005).So although
where a systemwas developedfor sharthe advantagesof DNA-based markers,
ing and transferring
data amonglaboraandmicrosatellites
in particular,
werewell
tories, microsatellite baselines evolved
documented
beginning
in
the
early
1990s
for usewithin singlelaboratories
(Moran
(e.g.,
Wright
and
Bentzen
1994;
Wirg•n
et al. 2006). The largenumberof available microsatellite loci resulted in little
andWaldman1994),coordination
among
laggedmarkedly.
overlapamongresearchers'
datasets.For laboratories
example,at the beginning
of 2003over60
The presentdatabase
wasconstructed
loci were in usefor Chinook salmon,but and evaluatedthrougha two-yearcollabmost(43) wereusedin onlya singlelabo- orative effort of an international muluratory(Figurela). Thiswasnot limitedto agencywork group. Guiding principles
Markers basedon DNA, suchas mito-
ment and conservation was held at the
chondrialDNA (Cronin et al. 1993) and
microsatellites
(Bankset al. 2000;seeBox
Herewereviewa largemulti-laboratory
1), were alsoin useand shownto be valu-
effortto developand standardize
a replicable DNA databasefor Chinook salmon.
Historyand needfor
Downloaded by [Oregon State University] at 16:22 19 September 2011
coast.wide databases
Beginning in the 1980s, considerable effort and coordination was directed
towardsdeveloping
standardized
allozyme
(protein) baselines(e.g., Shaklee and
Phelps1990; White and Shaklee1991;
seeBox 1). Standardization
amonglaboratoriesinvolvesidentifyingandadopting
a commonsuiteof loci (specificpolymorphicDNA segments
ortheirproducts)
and Chinook salmon,butwasthe normfor fishFurthermore,
adopting
consistent
namesfor the various eriesstudiesof manyspecies.
even
when
identical
loci
were
beingused,
alleles(seeBox 1). Geneticanalyses
using
differences
in
chemistries
and
instrument
allozymedatabases
were usedextensively
platformsamong laboratoriesproduced
to estimate the stock contribution of
requiredthat the database
would:(1) be
subjectto review by scientistsfrom all
interestedagencies,
(2) be freelyavailable
to all researchers
managingor studying
variable size calls for the same allele from
Chinooksalmon,and (3) coverthe range
Chinook salmon fisheries in the Columbia
the
same
individual,
which
precluded
easof Chinooksalmonat a geographic
scale
River, coastalWashington,and Strait of
ily
merging
datasets
from
multiple
regions
Juande Fuca(e.g.,Marshallet al. 1991;
appropriate
to the management
objectives
Shakleeet al. 1999). Collaborativework (Figurelb). As a result,despitecollection of the PSC with the understanding
that
duringthe 1990sby multiple state,pro- of a substantial amount of microsatellite the database
couldbe easilyexpandedto
et
vincial,andfederalagencies
wasdirected data(e.g.,Nelsonet al. 2001;Beacham
includethe entirerangeof the species.
It
in replacingthe allotowards
enlargement
of the database
(Teel al. 2003), progress
was anticipatedthat the databasewould
et al. 1999).The allozymedatabase
grewto zymedatabasewas slow for multi-jurisand multipledatabases allow for a wide varietyof management
•ncludecomprehensive
coverageof popu- dictionalfisheries,
andresearch
applications
onall lifehistory
lations rangingfrom California through proliferated.
stages
throughout
the
species
rangein both
Concerns
regarding
thesemultipleand
Alaska with representative
populations
freshwater
and
marine
environments.
wereaddressed
at
fromRussia(Teel et al. 1999). Although independentdatabases
Box 1. Definitionof termscommonlyusedin geneticstockidentification
of Pacificsalmon.
Allele
Allelic ladder
An alternative
formof a givengeneor DNAsequence
that differsfromotherallelesin DNAsequence
or phenotype.
A pooledanddilutedaliquotof multiplePCRproducts
thatoriginates
frommultipleindividuals
of knowngenotype.
With the choiceof appropriate
individuals,
a ladderwith all the "rungs"necessary
for successful
interlaboratory
allele
AIIozyme
AIlelicformof a proteinenzymeencodedat a givenlocus.AIIozymes
areusually
distinguished
byprotein
indexingcan be created.
Locus(loci,plural)
Microsatellite
Neighborjoiningtree
PCR
SNP
542
electrophoresis
and histochemical
stainingtechniques.
Thesitethat a particular
geneor DNAsequence
occupies
on a chromosome.
DNAsequences
containing
short(2-5 basepairs)repeatsof nucleotides
(e.g.GTGTGTGT).
A bottom-upclustering
methodusedfor the creationof phylogenetic
treesthat isbasedon the distancebetween
eachpairof populations
or taxa.
Thepolymerase
chainreactionor PCRamplifies
a singleor few copiesof a pieceof DNAacross
several
ordersof
magnitude,generatingmillionsof copiesof the DNA.
Singlenucleotide
polymorphism;
DNAsequence
variation
occurring
whena singlenucleotide
(A,T,C, or G)differs
betweenmembers
of a species
or withinan individual
betweenpairedchromosomes.
Fisheries ß VOL 32 NO 11 ø NOVEMBER
2007
o WWW.F•SHERIES.ORG
METHODS
AND
MATERIALS
Figure1. a. Distribution
of locianalyzed
across
laboratories
at the beginning
of thestudyMostmarkers
wereassayed
inonlya singlelaboratory.
Onlythreelociwereusedinmorethanhalfof thelaboratories.
b. Electropherograms
fromthesameindividual
assayed
ontwodifferent
instruments.
Identical
sizestandards
Identificationof loci
andsizing
algorithm
wereused,
yetdifferences
insizeareseenthatreflect
differences
inamplification
chemistfids
andelectrophoresis
hardware,
polymer
andrunning
conditions
employed
indifferent
Thefigureillustrates
a significant
difference
intheestimated
sizeof thealldes,inaddition
to a
During the firstyearof the study, laboratories.
different
relative
sizeoftherepeatunit(i.e.,4.14versus
4.25bp;adopted
fromMoranet al.2006).
each contributing laboratory analyzed
an identical set of 500 individuals drawn
frompopulations
rangingfromCalifornia,
USA, to Kamchatka, Russia,for its own
microsatellitelocuspanel. A total of 60
loci were evaluated,and "sponsorship"
documents were created for each locus
including description,source,analysis
conditions,and locusvariability.A sub-
Downloaded by [Oregon State University] at 16:22 19 September 2011
set of 25 loci drawn from the combined
set was chosen based on qualitative
selectioncriteria includingthe reliability of the PCR (polymerasechain reaction; seeBox 1) amplification,and, to a
lesserextent, allelic sizerangeand number of alleles.Locusdiversityacrossthe
rangewasgenerallynot a consideration
since too few individualswere assdyed
to reliablycomputestatisticalmeasures.
Each laboratorytested the 25 loci on
a subset of 96 individuals
Microsatellite marker (N=62)
70
75
drawn from
the original 500 individuals. Scoresfor
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 labora-
^
tory was identified for each locuswith
responsibilityfor receiving,compiling,
and distributingallele information. A
175.ool
decision was also made to link alleles to
tissuesfrom a specificindividualsalmon
that wouldresidein eachlaboratory.An
additional
I 00.001
Fragment size (bp)
set of locus documentation
was prepared designating recognized
alleles, assigningallelic nomenclature, Columbia; the second set was collected Constructionof the Database
and designatingindividualsfor every from southeast Alaska. Allelic scoresfrom
recognizedallele at each locus. These eachlaboratorywereadjustedto the recBaselinepopulations
werechosenthat
master allele lists were termed "curator
ognizednomenclature,submittedto the represent
all previously
identifiedgenetic
documents."
coordinatinglaboratory,and compared lineagesof Chinook salmonfrom the
across seven laboratories. Two additional
southernend of the speciesrangenorth
laboratories that had not been involved
Genotypingevaluation
to southeastAlaskawith focuson major
in locus selection or the first blind test productionareasand likely contributors
To assess
the ability of eachlaboratory participatedin the secondblind test.The to PacificSalmonTreatyfisheries
(Figure
to accuratelyresolveall 15 loci, 2 setsof alleleswere not sequenced,
so the abso- 2). Six laboratories(Alaska Department
96 "blind" sampleswere analyzed(first lute lengthwasunknown.Therefore,the of Fish and Game [ADFG]; Department
andsecondblindsamples).
Thesesamples modal score (the most common allele of Fisheriesand Oceans Canada [DFO];
River
Inter-Tribal
Fish
had neverbeen previouslygenotypedin scoredacrossthe participatinglaborato- Columbia
any of the laboratories.
In both cases,test ries) wasdefinedto be the "correct"score. Commission[CRITFC]; Oregon State
fish were drawn from marine fisheries and
Concordance of scoresacross laboratories
University [OSU]; SouthwestFisheries
assumedto be highly mixed with broad or percent accuracyfor each locus for ScienceCenter, National Marine Fisheries
representation
of stocks.The samplesfor eachlaboratorywasbasedon this modal Service [SWFSC]; and Washington
the first test were collected from British
score.
DepartmentofFishandWildlife [WDFW])
Fisherles ß VOL32 NO 11 ß NOVEMBER
2007
ß WWW.FISHERIES.ORG
543
Figure2. Collectionsitesfor Chinooksalmonincludedin the DNAbaseline.Thesesitesrepresentpopul•ionspotentially
contributing
to mixtures
harvestedin PacificSalmonTreatyfisheries.Sitesare designatedby regionalgroupsfrom Table3.
36b
36a
.....
35
39
34d 34c 34a
North
America
33a
37b, c
26b 27b
37a, e
37d
26••_27d
r31b
26c4d
', '•'•27c
31a
Downloaded by [Oregon State University] at 16:22 19 September 2011
30a
•b .
•a"'•3a•
'24c
1•
29a
29e
•ld ½4a
16b
20a•lc
10a,1
10b
10c,
11b
20b
9b
9c
1314c
18b
6•'•_.• 7b
5a•5bc
31a2d
I
KM
0
contributed
250
500
to the construction
of the
database.
Evaluation of the database
Definition of regional groups. The
database
wasevaluatedforitsabilityto correctlyallocateto regionalgroupsdefined
basedon a combinationof geneticsimilarity, geographic
features,and management
applications.Population estimatesare
combinedinto regionalgroupsto provide
the desiredaccuracy
andprecisionof stock
composition
estimates.
The initial list of
regionalgroupsfor compositional
analyses
wasenlargedfromthoseusedfor the allo54.4
1,000
zymebaseline(Teel et al. 1999). In order
to visualizehow the presentmicrosatellite data might be concordantwith and/or
improveuponthislist,wecalculated
pair-
mixtures of Chinook
salmon harvested
in treaty fisheries.These simulations
assessedwhether the baseline of microsat-
ellite allelefrequencies
providedsufficient
wise chord distances(Cavalli-Sforzaand informationto identifyregionalgroupsin
Edwards 1967) between all collections hypotheticalmixtures.
and then usedPHYLIP (Felsenstein2004)
Simulations
wereperformedusingthe
to createa neighborjoining tree (Saitou StatisticalPackage
forAnalyzingMixtures
and Nei 1987; seeBox 1). On sucha tree,
(SPAM version3.7; Debevecet al. 2000).
geneticallysimilarpopulationswill clus- Mixture genotypes
wererandomlygenerter together,facilitatingthe definitionof ated from the baselineallele frequencies
regionalgroups.
assumingHardy-Weinbergequilibrium.
Mixture simulation. Simulations were
The baselineallelefrequencies
wereparato accountfor samconductedto evaluate the application metricallyresampled
of theseregionalgroupsto geneticstock pling variability.Eachsimulatedmixture
identification(compositional
analyses)
of (N = 400) wasentirelycomposed
(100%)
Fisheries ß VOL32 No 11 ß NOVEMBER2007
ß WWW.FISHERIES.ORG
of the regionalgroupunderstudywLth the overall concordance for the laboratoequal contribution of all populations riesat theseloci wasover 99% (Table 2b).
wLthin the regionalgroup.Then each
s•mulatedmixture was analyzedagainst Baseline construction
Database
construction
Our primarygoalof developing
a reph-
the completebaseline.Bootstrapmeans
for regionalgroupsand 90% confidence
Dataprovidedbythecollaborators
were
•ntervals were derived from 1,000 simu- combined
to createa largedatabase
suited
lationsper group.Regionalgroupswith to GSI compositionalanalysesof fishermean correct estimates of at least 90% are ies managedunder the Pacific Salmon
considered
highlyidentifiable
in potential Treaty.A total of 220 samplecollections
Downloaded by [Oregon State University] at 16:22 19 September 2011
DISCUSSION
cable set of microsatellite loci for Chinook
salmonwassuccessfully
completed.With
an averageabove90% correctallocation
to regionalgroups,the DNA baseline
providesan unprecedented
resourcefor
improved
information
and
management
m•xturesfrom treaty fisheries.Regional and 16,394 individual fish representing
of Chinooksalmonduringtheir oceanand
groups
with meancorrectestimates
lower 110putativepopulations
wereincludedin
than 90% can still be considered idenfreshwater
life stages.AdditionallaboraVersion1.1 of the baseline(Table3, Figure
toriesemployingvariouschemistries
and
nfiable in mixtures,but sourcesof mis2, Appendix1). All individualfishwere hardware will be able to use the database
allocation should be considered when
assayed
across13 loci; however,failures
throughthe useof voucherspecimens
or
LnteFpreting
the results(e.g., Seebet al.
at particularloci sometimes
resultedin
byusingtheallelicladders
(seeBox1) that
2000).
incompletemultilocusgenotypes.
A target arecurrentlyunderconstruction
forall loci
was
set
of
144
individuals
per
population
RESULTS
(e.g.,LaHoodet al. 2002).Allelicladders
with at least120 genotypes
per locus.In consistof a collectionof allelescovering
some
populations,
fewer
than
120individ- a rangeof knownsizes
Locus selection and evaluation
that canbeusedas
of microsatellites
ualswere available,so actual samplesizes an internal measurement to standardize
werenecessarily
belowthe target.
the data.Theseladdershold the promtse
The number of alleles varied markof simplifying
standardization
aslaboratoedly
across
loci,rangingfrom9 (Ors9)to rieswill not needto analyzevoucherspecthandlingerrors,suchasmisalignment
of
individuals,
werethe largestsourceof dis- 74 (Omm1080;Table 1). Acrossall loci, mensfor everyallele.
All geographicregionsand genenc
agreement
amonglaboratories.
In addition 487 alleles were observed.A voucher set
froma singleindividualfor each lineageslikely to contributeto fisheries
to data-handling
errors,2 of the 15 loci of tissues
selected
andtestedstoodoutasbeingpoorly of the alleleswereidentifiedandprovided of CTC interestare representedin the
standardized across3 or more laboratories. to all collaborators.For somealleles,insuf- presentbaseline,and thesedata should
for complexfisherymtxThese 2 loci were removed,and the final ficient tissuewas available for sharing be appropriate
tures
that
include
diverse populations
between
all
labs,
so
a
second
individual
groupof 13lociwasidentified
forinclusion
locations.Although the
tn the baseline(Table 1). Beyondthese known to exhibit the same allele was sub- from widespread
The first blind test revealed that data-
sourcesof conflict, the resultsof the first
stituted as the voucher. Additional alleles
currentbaselineis broad,it is not compre-
bhndcomparison
indicatedthat technical are expectedas coverageof the baseline hensive.Effortsare currentlyunderwayto
increaselocal coverage.Expandedbasestandardization
hadbeenaccomplished
for exTpands.
line datawill likely improvethe accuracy
greaterthan 90% of the observed
alleles
of allocationto the regionalgroups,and,
at each locus.Furtherefore,much of the Simulations
in at leastsomecases,
arelikelyto provtde
remainingvariabilityamonglaboratories
a
finer
scale
of
estimation
(e.g.,sub-bastns
wasdue to allelesbeingobserved
in the
Forty-tworegionalgroups
weredefined
within
major
river
systems).
Fine-scale
bhndsamples
that hadnot beenobserved basedon geneticsimilarity,geographic
geographic
allocation
of
mixtures
and
in the original96 samples.
features,and managementapplications.
The results of the second blind test
potential assignment
of individualfish
Simulationswere performedto examine
indicated that technical standardization
to population-of-origin
mayalsoprovide
the accuracy
andprecision
of thisbaseline
importantbiological
andlife-history
inforhad been accomplished
for more than
simulatingmixturescomposed
of indimationsuchasmigrationtimingandpath95% of the observedalleles (Table 2a).
vidualsdrawn entirely from populations
waysand age-related
changesin habitat
However,a numberof low-concordance
of a singleregion.Almostall regionswere use.Expansion
of the baselineto include
valuesfor specificloci indicatederrorsin
definedas highly identifiablewith boot- populations
the data from two of the laboratories.One
throughout
Alaska,Canada,
strap
means
of
1,000
simulated
mixtures
and
Russia
is
underway.
In additionto
laboratory
obtaineda concordance
of only
above
90%
to
the
correct
regional
group
improved
management,
the
baselinewdl
44%forOgo4,whereas
otherlociprovided
werethe Deschutes be usedto betterunderstand
the biologyof
by the samelaboratoryshowed98% con- (Table3). Exceptions
andprovideinformationabout
cordanceor more.Anotherlaboratory
had Fall and Upper Stikine River regions the species
0% concordance at five loci and 82% at which had 89.5% and 84.5% mean correct effective population size, evolutionary
allocations,
respectively.
In addition,the anddemographic
another locus. Data for the other seven
history,andpopulation
loci fromthis secondlaboratorywereclose lower bound of the 90% confidence inter-
boundaries.
The results of the two blind tests were
wasabovea 90%threshto the overallaverage.Cursorychecksin val forall regions
old,
with
the
exception
of
the
Deschutes
encouraging,
but alsosignalleda need
respective
laboratories
revealeddata-handlingerrors
thatexplained
mostofthedis- Fall,UpperStikineRiver,TakuRiver,and for continuedvigilancein errorcheckLng
anddatamanipulation.
On the onehand,
AlaskaStikineRiver groups.
crepancies.
After correction
of theseerrors Southeast
Fisheries • vol 32 NO 11 * NOVEMBER
2007
o WWk•/
FISHER[ES,ORG
545
Table 1. Microsatellite
locistandardized
for Chinooksalmon.Reference,curatoragency,
and observednumberof allelesare givenfor baselineVersion1.1.
Locus
Reference
Curator agency•
Observednumber
of alleles
Downloaded by [Oregon State University] at 16:22 19 September 2011
Ogo2
Ogo4
0ki100
Olsenet al. 1998
Olsenet al. 1998
DFOunpublished
2
ADFG
WDFW
DFO
26
20
48
Ornrnl080
Rexroad et al. 2001
SWFSC
74
Ots2Olb
Ots208b
0ts211
0ts212
0•213
Ots3M
Grieget al. 2003
Grieget al. 2003
Grieget al. 2003
Grieget al. 2003
Grieget al. 2003
Griegand Banks1999
ADFG
CRITFC
ADFG
OSU
OSU
WDFW
52
54
43
36
48
19
Ors9
Banks et al. 1999
DFO
9
OtsG474
Williamson et al. 2002
CRITFC
19
Ssa408
Cairneyet al. 2000
NWFSC
39
laboratones
were able to achievea very
high degreeof concordance
throughthe
standardization
process
andvouchersample comparisons.
Furthermore,
two new
laboratories
that hadnot beenpreviously
involved in locusselection,baselineconstruction, or the first blind test were able
Laboratory
abbreviations:
OSU,OregonStateUniversity;
SWFSC,SouthwestFisheries
ScienceCenter--NationalMarine
Fisheries
Service;DFO,Departmentof Fishenes
and OceansCanada;NWFSC,NorthwestFisheries
ScienceCenter-NationalMarineFisheries
Service;
CRITFC,ColumbiaRiverInter-Tribal
FishCommission;
ADFG,AlaskaDepartmentof
FishandGame;WDFW,WasNngtonDepartment
of FishandWildlife.
Personal
communication,
K. Miller,Department
of Fisheries
andOceansCanada,Nanaimo,BritishColumbia,
Canada.
to achieveveryhighconcordance
without
anysignificant
errors.On the otherhand,
significant
errorsin datahandling
werestfil
apparentin initialblindsubmissions
from
two of the original laboratories.
These
results,
althoughnot uniqueto microsatellite data(e.g.,White andShaklee1991),
suggest
that ongoingqualitycontroland
errorcheckingprocedures
bothinternally
within each laboratoryand amonglaboratories are warranted to ensure database
integrity.
Table2. Proportional
genotyping
accuracy
by laboratory
and locusfor the secondblindtestof 13 microsatellite
loci.Averages
across
locus
and laboratory
aregiven.A. Results
assubmitted.B. Results
aftercorrection
for errors(seetext).
A.
Locus
Lab I
Lab 2
Lab 3
Lab4
Lab 5
Lab 6
Lab7
Lab8
Lab9
Average
Ogo2
Ogo4
0.987
0.439
1.000
1.000
1.000
1.000
1.000
0.995
0.993
1.000
0.000
0.000
1.000
0.995
0.993
0.994
1.000
0.990
0.886
0.824
OkilO0
Ornrnl080
Ots2Olb
Ots208b
0ts211
0.978
1.000
0.984
0.994
1.000
1.000
1.000
1.000
1.000
1.000
1.000
0.993
0.955
1.000
1.000
0.995
0.995
0.994
0.970
0.994
0.985
0.970
0.985
1.000
1.000
1.000
0.995
0.994
0.994
0.888
0.995
0.995
0.991
0ts212
0.989
0.996
Ors213
0.987
Ots3M
1.000
1.000
1.000
1.000
0.995
1.000
1.000
1.000
1.000
1.000
1.000
1.000
0.994
1.000
1.000
1.000
1.000
1.000
0.989
0.995
0.994
1.000
1.000
0.982
1.000
0.985
0.000
1.000
1.000
1.000
0.884
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
0.000
0.993
1.000
B.
Locus
Lab I
Lab 2
Lab 3
Lab 4
Lab 5
Lab 6
Lab 7
Lab 8
Lab 9
Average
Ogo2
Ogo4
0.987
0.994
1.000
1.000
1.000
1.000
1.000
0.995
0.993
1.000
0.988
0.968
1.000
0.995
0.993
0.994
1.000
0.990
0.996
0.993
0ki100
0.978
1.000
0.984
0.994
Ornrnl080
Ots2Olb
Ots208b
1.000
1.000
1.000
1.000
1.000
1.000
0.970
1.000
0.994
1.000
0.995
1.000
1.000
0.938
1.000
0.994
1.000
0.992
1.000
1.000
1.000
1.000
1.000
1.000
1.000
1.000
0.993
1.000
0.995
0.995
0.985
0.970
1.000
0.995
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 correctallocationsfrom 100% simulationsfor 42 regionalgroupsof Chinooksalmonpopulations.Bootstrapmeansof 1,000
simulatedmixturesandupperand lower90% bootstrapconfidence
intervals
aregiven.Numberof populations
per regionisalsogiven.
Downloaded by [Oregon State University] at 16:22 19 September 2011
Region number
Number of populations
Region name
Bootstrap mean
interval
Lower
Upper
0.916
0.908
0.980
0.971
0.962
0.998
1
2
3
CentralValleyfall
CentralValleyspring
CentralValleywinter
4
4
1
4
5
California Coast
Klamath River
2
0.985
0.975
3
0.984
0.970
0.995
0.995
6
7
8
9
10
North California/SouthOregonCoast
RogueRiver
Mid OregonCoast
North OregonCoast
LowerColumbiaspring
1
2
3
3
3
0.972
0.941
0.944
0.957
0.971
0.956
0.912
0.918
0.933
0.952
0.987
0.968
0.968
0.979
0.987
11
Lower Columbia fall
3
0.973
0.956
0.987
12
Willamette
2
0.983
0.968
0.994
13
Mid Columbia Tule fall
1
0.969
0.949
0.986
14
Mid andUpperColumbiaspring
5
0.965
0.945
0.985
15
Deschutes fall
1
0.895
0.859
0.931
16
UpperColumbiasummer/fall
3
0.963
0.937
0.985
17
Snake River fall
1
0.946
0.915
0.974
18
19
20
21
SnakeRiverspring/summer
WashingtonCoast
SouthPugetSound
NorthPugetSound
5
3
2
4
0.966
0.951
0.988
0.971
0.946
0.928
0.977
0.955
0.984
0.971
0.997
0.985
22
Lower Fraser
2
0.984
0.972
0.994
23
24
25
LowerThompsonRiver
SouthThompsonRiver
NorthThompsonRiver
2
3
2
0.983
0.976
0.978
0.971
0.962
0.964
0.994
0.990
0.990
26
Mid Fraser River
4
0.980
0.966
0.991
27
UpperFraserRiver
4
0.969
0.949
0.986
28
East Vancouver Island
2
0.979
0.966
0.991
29
30
West Vancouver Island
South BC Mainland
5
2
0.990
0.975
0.980
0.961
0.997
0.988
31
32
Central BC Coast
Lower Skeena River
3
2
0.960
0.950
0.940
0.927
0.978
0.971
33
UpperSkeenaRiver
3
0.950
0.925
0.971
34
Nass River
4
0.939
0.913
0.963
35
UpperStikineRiver
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,StikineRiver
1
0.917
0.884
0.947
39
KingSalmonRiver
1
0.987
0.977
0.995
40
41
42
Chilkat River
Alsek River
Situk River
2
1
1
0.988
0.980
0.977
0.977
0.967
0.963
0.996
0.992
0.990
River
0.945
0.936
0.990
90% confidence
into
thesepopulations
wasusedin the allozyme singleallozymegroupwasseparated
studyof GuthrieandWilmot (2004).
fall, spring,andwintergroups.
Our simulationresultssuggest
that the
The neighbor-joiningtree (Figure3)
presentdatabaseprovidesgreaterresolu- suggested
that somegroupsthat had not Growth of and accessto the baseline
uon than wasprovidedby the allozyme been distinct from one another based on
baseline.Only the DeschutesFall and the allozymebaselinemight be resolvMore detailed analysesof the power
UpperStikineregionsfell belowourtarget
ablewith the presentmicrosatellite
data. of the databaseare ongoing.We expect
of 90% accuracy.
Additionalpopulations
to betterdefinelimitations
Overall,37 regionalgroups
wereresolvable futureanalyses
and tests are needed to evaluate whether
of
the
present
databaseand to identify
withtheallozyme
dataset(Teelet al. 1999)
thesegroupscan be accurately
identified
where
the
addition
of baseline collections
in thismicrosator,alternatively,
shouldbe combinedwith while42 wererecognized
powervia the additionof
adjacentregionalgroups.The similarity ellite datasetdespitethat fact that more and increased
new markers are most needed. The addipopulations
were
included
in
the
allozyme
amongthe Taku and $tikine river transboundarypopulations
wasalsoobserved baseline than the microsatellite baseline. tion of singlenucleotidepolymorphism
w•th the allozymedatasetof Teel et al. Gains in resolutionwere most apparent (SNP) markers(seeBox 1) which areeasand may reflectselective
(1999), and a singlecombinedgroupfor in the California Central Valley where a ily standardized
Power of the database
Fisheries o VOL 32 r•O 11 o r•OV[r•B[R 2007
o WWW.FISHERIES,ORG
547
F,gure3 Nelghbordolmng
treebasedonpairwise
chorddistances
between
collections
of Chinook
salmon
(Appendix
1) Numbers
andvertical
hnesonrlght•ndlcatereglonalgroupsasdeslgnatedlnTable3
Regional
groupswlthonlyaslnglepopulatlonareln
bold
FeatherRiverHatcheryFall
Battle Creek Fall
StanislausRiver Fall
ButteCreekFall
1,2,3
FeatherRiverHatchery
Spring
•
•
I
I
ButteCreek Spring
Deer Creek Spring
Mill Creek Spring
-r•
t r•
I
•l ri-
I
/'--1
/
•
•
--
/
•
L
•
•
,
LowerDeschutes
R•ver
SacramentoWinter
15
LyonsFerryHatchery
Fall
11
Hanford Reach Creek Summer/Fall
WellsDamHatchery
Summer/Fall
Methow
River
Summer/Fall
SolDucRiverSpring
•
I
I •
I
•
r•
I
1•9
16
SpringCreekHatcheryFall 13
Cowlitz
Hatchery
Spring
•
•
•Cow•tzHatchery
Fall
I
Lewis •iver I-all
I
Sandy
River
Fail
r
I ,u, 11
LewisHatcherySpring
Kalama HatcherySpring
'•L
•-•
[--
I
McKenzieHatcherySpring
•
NorthSantiam
Hatchery
Spring
Eel River Fall
Russian
River
Fall
Chetco River Fall
RiverSpring
Klamath River Fall
I
HatcheryFall
[• Trinity
Trinity
Hatchery
Spring
4-9
Downloaded by [Oregon State University] at 16:22 19 September 2011
Cole RiversHatcherySpring
ApplegateCreekFall
CoquilleRiver Fall
Siuslaw River Fall
Nehalem River Fall
Alsea River Fall
Siletz River Fall
h
Queers
River
FallRiverFall I 19
Quillayute/Bogachiel
Marble-NVl
Hatchery
ConumaHatchery
I__
Robertson
Hatchery
Sarita
Hatchery
NitinatHatchery
28,29
Quinsam Hatchery
White River HatcherySpring
•
Big
Qualicum
Hatchery
River
Spring
I21
I 20
Soos
Creek
Hatchery
FallNorth
Fork
Nooksack
Suiattle-SkagitRiver Spring
Skagit River Summer
NorthForkStilliguamishRiverSummer
Birkenhead
River
Hatchery
Spring
I 22
Chilliwack
River
Hatchery
Fall
--
TucannonHa..tchery
Spring/Summer
Upper
Yakima
Hatchery
Spring
Wenatche
River
spring
Carson HatcherySpring
Warm SpringsHatcherySpdng
Secesh River Spring/Summer
John Day RiverSpring
Minam RiverSpring/Summer
Rapid River HatcherySpring/Summer
-Imnaha River Spring/Summer
Clearwater
I
I Li•
•
I
L •
•
Louis
River
Spring
SpiusRiverHatchery
Spring
'
Nicola HatcherySpring
ChilkoRiverSummer
•
•
River Summer
14, 18
Quesnel
River
Summer
I•
'
r•
1
NechakoRiverSummer
StuartRiverSummer
23 - 27
TorpyRiverSummer
r•
SalmonRiverSummer
r• -Swift River Summer
•
MorkillRiverSummer
/
LowerThompsonRiver Fall
I
Middle
Shuswap
Hatchery
Summer
LowerAdamsRiverHatcheryFall
Klinaklini
River I
itimat
HatcheryPorteau
Cove
Hatchery
30,
31
Atnarko
Hatchery
Wannock
River
Hatchery
Ecstall River
32
Kincolith
River
Chickamin River
34
__ Clear
Creek King
Creek
--
37, 38
CrippleCreek
---
Andrews Creek
Little Tahltan River
River
35
Kowatua Creek
Tatsatua Creek
4um River
bine River
BulkleyRiver
Sustut River
32 - 34
--
KwinageessRiver
Owegee River
0.01
541l
King Salmon River
Klukshu
River I
SitukRiver
BigBoulder
Creek
Tahini River
39- 42
Fisheries o VOL32 NO 11 o NOVEMBER
2007 o WWVV.FISHERIES.ORG
variation(S•nithet al. 2005;S•nithet al. in press)is presently
In MayandSeptember
of 2007,thePSCconvened
a workshop
focusing
onthecurrentandfuturecapabilities,
limitations,
anduseof
underway.
stockidentification
methods
in oceansahnon
management.
As partof thecollaboration,
a web-based
application
hasbeen genetic
groupof scientists
attended
representing
thedisciplines
of
developed
to allowthefisheries
andresearch
community
to access A diverse
genetics,
administration,
population
dynamics,
the genotypedata,curatordocuments,
andsupporting
metadata. fisherymanagement,
theory.Attendees
weredividedinto fourworkgroups:
The PacificSahnonCommission
provideddevelopmental
fund- andsampling
manage•nent,
logistics,
and•nodeling/sampling.
Reviews
of
ingto theNorthwestFisheries
ScienceCenter,NationalMarine genetics,
areavailable
at the PSCwebsite(http://psc.org/).
The
FisheriesService, Seattle, Washington(www.nwfsc.noaa.gov/ the workshop
willbepublished
whencompleted
in 2008.
research/divisions/cbd/standardization.cfm),
and a test versionof finalrecommendations
Asgenetic
approaches
to fisheries
management
become
morecomthewebapplication
isnowbeingevaluated.
The permanent
locaweanticipate
thegrowthof thisandsimilartypesof datationandsupport
forthedatabase
arecurrentlyunderdiscussion
by monplace,
thatarepublicly
available
through
webapplications.
Ukimately,
the collaborating
agencies
and the PacificSahnonCommission. bases
willprovide
a wealthof information
notonlyforfishUntil the final location of the databaseis established,the data thesedatabases
butalsoforresearchers
froma varietyofdisciplines
are availableat the ADFG website(www.genetics.cf.
adfg.state. eriesapplications,
investigating
diverse
aspects
of
sahnonid
life
history,
population
genetak.us/publish/data/PSCchinookver
1_1.pdf).
ics,andevolutionary
theory.
CONCLUSIONS
Downloaded by [Oregon State University] at 16:22 19 September 2011
ACKNOWLEDGEMENTS
Herewedescribed
a largecollaborative
studyto developa shared,
Thisstudy
wasresult
ofa collaboration
among
threeNational
Marine
web-accessible
database
of DNA markers
to allowmultipleagencies Fisheries
Servicelaboratories•Northwest
Fisheries
ScienceCenter,Auke
to conduct
geneticstockidentification
studies
of Chinooksahnon. BayLaboratory,
andSouthwest
Fisheries
Science
Center;Washington
Replicate
scorh•g
of the microsatellite
lociwassuccessfully
accom- Department
of FishandWildlife;University
of Idaho/Coltunbia
River
Fisheries
Conmfission/Idaho
Department
of FishandGame;
plishedamongninegeographically-dispersed
laboratories.
However, Inter-tribal
StateUniversity;
Canadian
Department
ofFisheries
andOceans;
thestudyalsorevealed
thatongoing
coordination
anderror-checkingOregon
and
the
Alaska
Department
of
Fish
and
Game.
The
U.S.
Fish
andWildlife
willberequired
to ensure
theintegrity
ofthedatabase
asit expands.
Service,
Anchorage,
Alaska,andIdahoDepartment
of FishandGame,
Growthofthedatabase
ispresentl
v underwav
viatheaddition
ofnew Eagle,
Idaho,participated
in thesecond
blindsample.
Primary
funding
for
collections
andadditional
SNP markers.
The database
is nowbeh•g thestudy
wasprovided
bytheU.S.Department
of Commerce,
NOAA,
usedto estimate
compositional
analyses
of PSC andotherfisheries NMFS,funds
appropriated
tofundthetechnical
workrequired
fortheU.S.
around the Pacific Ocean.
section
ofthePacific
Sahnon
Treatyto implement
theChinook
Salmon
Y'our' ,,-:,'.,
.y
uu.:an.;:,;,reThe
View our latest catalog at www.flo.qta_q.,com, or email us at: salesC•flo.vta.q,COm
or call to discuss
yourcustomtaggingneeds:(8•0) 843-1172 '
Fisheries ß VOL32 •O 11 ß NOVEMBER
2007 ß WWW.FISHERIES.ORG
$49
Agreementregarding
abundance
basedmanUmvers•ty
ofWashington,
SeattleAvadable htemarkers
forrainbowtrout(Oncorhynchus
agementPartialfundingfor the U S F•shand
at http//evoluuon
geneucs
washington
edu/
myktss)
AmmalGenetics
32 317-319
WfidhfeService
wasprovided
bytheU.S.Yukon phyhp/geune.htmL
Saitou,
N., andM. Nei. 1987.Theneighbor-joinRiverResearch
andManagement
Fund(USRM- Greig,C. A., andM. A. Banks.1999.Fivemulingmethod:
a newmethod
forreconstrucung
08-05)andto theUniversity
of Washington
by
tiplexed
microsatellite
lociforrapidresponse phylogenetic
trees.MolecularBiologyand
JointInstituteforthe Studyof theAtmosphere runidentification
of Califomia's
endangered Evolution 4:406-425.
andOcean(JISAO)underNOAA Cooperative winter Chinook salmon. Animal Genetics
Seeb,L. W., C. Habicht,W. D. Templin,K. E.
AgreementNA17RJ1232,Contribution1444.
30(4):318-320.
Tarbox,R. Z. Davis, L. K. Bmnnian,and
Significant
matching
fundswereprovided
byall Greig, C., D. P. Jacobson,
and M. A. Banks.
J. E. Seeb.2000.Geneticdiversity
ofsockeye
2003. New tetranucleotide microsatellitesfor
participating
institutions.
We thank the many
members of the Chinook Technical Committee
ofCookInlet,Alaska,
anditsapphcafine-scale
discrimination
amongendangered salmon
of the PacificSalmon Committee for their vision
ofpopulations
affected
by
Chinooksalmon(Oncorhynchus
tshawytscha). tiontomanagement
and support
for the studyand five anonymous Molecular
the ExxonValdez
oil spill.Transactions
of the
Ecology
Notes3:376-379.
reviewers for their valuable comments on the Guthrie, C. M., III, and R. L. Wilmot. 2004.
American
Fisheries
Society
129:1223-1249
Genetic structure of wild Chinook salmon Shaklee,
manuscript.
J. B., T. D. Beacham,
L. Seeb,andB.
populations
ofsoutheast
Alaskaandnorthern A. White. 1999.Managing
fisheries
using
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Wright, J. M., and P. Bentzen. 1994
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and model calibration, TCCHINOOK
550
Fisheries ß vOL32 NO11 ß NOVE•IBER
2007 ß •/I,q/VW,
FISHERIES.ORG
Appendix1 Chinooksalmonpopulations
analyzed
inth•sstudyandIncluded
in baseline
Version1 1 Runt•me,hatchery
(H)orw•ld(W)ongln,lifestage,collection
data,
andanalys•s
laboratory
areDven.Lowercasedes,gnat•ons
w•th•na reDoncorrespond
to locations
on F•gure1.
RegionnumberRegionname
Population
Runtime• OriginLifestage
Collection
date
Analysislaboratory
2
1
CentralValleyfall
BattleCreek(a)
FeatherHatchery
fall(b)
Stanislaus
River(c)
TuolumneRiver(d)
Fa
Fa
Fa
Fa
SWFSC
SWFSC
SWFSC
SWFSC
2
CentralValleyspring
3
4
W
H
W
W
Adult
Adult
Adult
Adult
2002, 2003
2003
2002
2002
CentralValleywinter
ButteCreek(a)
Sp
DeerCreekspring(b)
Sp
FeatherHatchery
spring(c) Sp
MillCreekspring(d)
Sp
Sacramento
Riverwinter Wi
W
W
H
W
W/H
Adult
Adult
Adult
Adult
Adult
2002, 2003
SWFSC
2002
SWFSC
2003
SWFSC
2002,2003
SWFSC
1992, 1993, 1994, 1995, 1997, SWFSC
CaliforniaCoast
EelRiver(a)
Fa
W
Adult
2000, 2001
5
KlamathRiver
RussianRiver(b)
KlamathRiverfall (a)
TrinityHatcheryfall (b)
Fa
Fa
Fa
W
W
H
Juvenile
Adult
Adult
2001
2004
1992
6
North California/
Downloaded by [Oregon State University] at 16:22 19 September 2011
1998, 2001, 2003, 2004
SWFSC
SWFSC
SWFSC
SWFSC
TrinityHatchery
spring(c) Sp
H
Adult
1992
SWFSC
Chetco
Fa
W
Adult
2004
OSU
Applegate
(a)
ColeRivers
Hatchery
(b)
Coquille(a)
Fa
Sp
Fa
W
H
W
Adult
Adult
Adult
2004
2004
2000
OSU
OSU
OSU
Siuslaw(b)
Fa
W
Adult
2001
OSU
Umpqua(c)
Sp
W
Adult
2004
OSU
AIsea(a)
Nehalem(b)
Fa
Fa
W
W
Adult
Adult
2004
2000, 2002-1, 2002-2
Siletz(c)
Fa
W
Adult
2000
OSU
OSU
OSU
2004
2004
2004
2004
CRITFC
CRITFC
CRITFC
CRITFC
2003
2002, 2004
WDFW
OSU
SouthOregonCoast
7
RogueRiver
8
Mid OregonCoast
9
NorthOregonCoast
10
LowerColumbia
spring
11
LowerColumbiafall
12
13
14
CowlitzHatchery
spring(a)Sp
KalamaHatchery
spring(b)Sp
LewisHatchery
spring(c) Sp
CowlitzHatchery
fall (a) Fa
H
H
H
H
Lewisfall (b)
Sandy(c)
Fa
Fa
W
W
Adult
Adult
Sp
Sp
Fa
Sp
Sp
H
H
H
H
W
Adult
Adult
UpperYakima(c)
Sp
WarmSprings
Hatchery
(d) Sp
Wenatcheespring(e)
$p
H
H
W
Adult, Mixed
1993, 1998, 2000
WDFW
C RITFC
WDFW
LowerDeschutesRiver
Fa
W
1999-1, 1999-2, 2001,2002
CRITFC
Su/Fa
W
1999,2000-1,2000-2,2000-3,CRITFC
W
H
2001-1, 2001-2, 2001-3
1992, 1993, 1994
1993-1, 1993-2
WillametteRiver
McKenzie
(a)
NorthSantiam(b)
Mid Columbia
Tulefall
SpringCreek
Mid and UpperColumbiaspringCarsonHatchery(a)
JohnDay(b)
2002, 2004
2002, 2004-1,2004-2
2001,2002
2001,2004
Juvenile,
Adult 2000-1,2000-2,2000-3,
OSU
OSU
CRITFC
CRITFC
OSU
2000-4, 2000-5, 2000-6, 2004
15
Deschutesfall
16
UpperColumbia
summer/fall HanfordReach(a)
Methow Riversummer(b) Su/Fa
Wells Dam (c)
Su/Fa
17
18
SnakeRiverfall
LyonsFerry
SnakeRiverspring/summer ImnahaRiver(a)
MinamRiver(b)
RapidRiverHatchery
(c)
Sesech
River(d)
Tucannon
(e)
19
Washington
Coast
Queets(a)
Quillayute/Bogachiel
(b)
Fa
Sp/Su
Sp/Su
Sp/Su
Sp/Su
Sp/Su
Fa
Fa
W
W
W
H
W
H
W
W
1998, 2003
2002, 2003
Adult
Adult
Adult
Adult
Adult
CRITFC
CRITFC
2002-1,2002-2, 2003-1, 2003-2 WDFVV
1998,2002,2003
CRITFC
1994,2002,2003
CRITFC
1997, 1999,2002
CRITFC
2001,2002,2003
2003-1,2003-2,2003-2
1996, 1997
1995-1, 1995-2, 1995-3,
CRITFC
WDFW
WDFW
WDFW
1996-1, 1996-2
20
SouthPugetSound
SolDuc(c)
SoosCreek(a)
WhiteRiver(b)
Sp
Fa
Sp
Fisheries ß VOL32 NO 11 ß NOVEr•BER
2007 ß WWW.FISHERIES.ORG
H
H
H
Adult
Adult
adult
2003
1998-1, 1998-2,2004
1998-1,1998-2,2002
WDFW
WDFW
WDFW
55•
21
NorthPugetSound
22
LowerFraser
NFNooksack
(a)
NFStilliguamish
(b)
Skagitsummer(c)
Suiattle(Skagit)(d)
Birkenhead
River(a)
Sp
Su
Su
Sp
Sp
HNV
HNV
W
W
H
adult
adult
adult
adult
Adult
Chilliwack(b)
Fa
H
Adult
Nicola
(a)
Sp
H
SpiusRiver(b)
Sp
H
LowerAdams(a)
LowerThompson
(b)
MiddleShuswap
(c)
Clearwater(a)
LouisRiver(b)
Fa
Fa
$u
Su
Sp
Chilko(a)
Nechako(b)
Quesnel(c)
Stuart(d)
MorkillRiver(a)
SalmonRiver(Fraser)(b)
Swift(c)
TorpyRiver(d)
BigQualicum(a)
Quinsam(b)
Conuma (a)
Marbleat NVI(b)
Nitinat(c)
Robertson
(d)
Sarita(e)
Klinaklini
(a)
PorteauCove(b)
Atnarko(a)
Kitimat(b)
Wannock(c)
Ecstall(a)
Su
Su
Su
Su
Su
Su
Su
Su
1999
1996 2001-1, 2001-2
1994 1995
1989 1998, 1999
1996 1997, 1999, 2001,
2002
Downloaded by [Oregon State University] at 16:22 19 September 2011
23
LowerThompson
River
24
SouthThompson
River
25
NorthThompson
River
26
Mid FraserRiver
27
UpperFraserRiver
28
EastVancouver
Island
29
WestVancouverIsland
30
SouthBCMainland
31
CentralBCCoast
32
Lower$keenaRiver
33
Upper$keenaRiver
34
NassRiver
35
36
UpperStikineRiver
TakuRiver
WDFW
WDFW
WDFW
WDFW
SWFSC
2003
1998 1999
DFO
1998 1999
OSU
Adult
1996 1997, 1998
SWFSC
H
W
H
W
W
Adult
Adult
Adult
Adult
Adult
1996
2001
1997
1997
2001
DFO
DFO
DFO
DFO
DFO
W
W
W
W
W
W
W
W
H
H
H
H
H
H
H
W
H
H
H
H
W
Adult
Adult
Adult
Adult
Adult
Adult
Adult
Adult
Adult
Adult
Adult
Adult
Adult
Adult
Adult
Adult
Adult
Adult
Adult
Adult
Adult
1995, 1996, 1999, 2002
1996
1996
1996
2001
1997
1996
2001
1996
1996, 1998
1997, 1998
1996, 1999, 2000
1996
1996,2003
1997, 2001
1997
2003
1996
1997
1996
2000, 2001, 2002
DFO
DFO
DFO
DFO
DFO
SWFSC
DFO
DFO
DFO
DFO
DFO
DFO
DFO
DFO
DFO
DFO
DFO
DFO
DFO
DFO
DFO
LowerKalum(b)
W
Adult
2001
DFO
Babine(a)
Bulkley
(b)
H
W
Adult
Adult
1996
1999
DFO
DFO
Sustut(c)
Damdochax(a)
Kincolith(b)
Kwinageese
(c)
Owegee(d)
W
W
W
W
W
Adult
Adult
Adult
Adult
Adult
2001
1996
1996
1996
1996
DFO
DFO
DFO
DFO
DFO
LittleTahltanRiver
KowatuaCreek(a)
NakinaRiver(b)
W
W
W
Adult
Adult
Adult
1989 1990
1989 1990
1989 1990
OSU
ADFG
ADFG
ADFG
ADFG
ADFG
ADFG
ADFG
ADFG
ADFG
ADFG
ADFG
ADFG
ADFG
ADFG
ADFG
TatsatuaCreek(c)
UpperNahlinRiver(d)
Chickamin
River(a)
ClearCreek(b)
CrippleCreek(c)
Adult
1989 1990
W
W
W
W
Adult
Adult
Adult
Adult
1989 1990,2004
1990 1993
1989 2003, 2004
1988 2003
37
SouthernSoutheast
Alaska
38
39
Keta River(d)
KingCreek(e)
SoutheastAlaska,StikineRiver AndrewsCreek
KingSalmonRiver
KingSalmonRiver
W
W
W
W
Adult
Adult
Adult
Adult
1989 2003
2003
1989, 2004
1989, 1990, 1993
40
ChilkatRiver
41
42
AlsekRiver
SitukRiver
W
W
W
W
Adult
Adult
Adult
Adult
1992, 1995,2004
1992, 2004
1989, 1990
1988, 1990, 1991, 1992
BigBoulderCreek(a)
TahiniRiver(b)
KlukshuRiver
SitukRiver
Runtimeabbreviations:
spring(Sp),summer(Su),fall(Fa),andwinter(Wi)
2Laboratory
abbreviations:
OSU,OregonStateUniversity;
SWFSC,
Southwest
Fisheries
Science
Center NationalMarineFisheries
Service;
DFO,Department
of Fisheries
andOceansCanada;CRITFC,
Columbia
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