Estimation of Stock Composition and Individual Identificationof Chinook Salmon across the Pacific Rim by

Use of Microsatellite Variation

TERRY D. BEACHAM,* JOHN R. CANDY, KIMBERLY L. JONSEN, JANINE SUPERNAULT,MICHAEL WETKLO, LANGTUO DENG, KRISTINA M. MILLER, AND RUTH E. WITHLER

Department of Fisheries and Oceans, Pacific Biological Station,Nanaimo, British Columbia V9T 6N7, Canada

NATALIA VARNAVSKAYA

Kamchatka Fishery and Oceanography Research Institute, 18 Naberezhnaya Street,Petropavlovsk-Kamchatsky 683000, Russia

Abstract.—Variation at 13 microsatellite loci was surveyed for over 52,000 Chinook salmon Oncorhynchus

tshawytscha sampled from 325 localities ranging from Russia to California; the variation was applied to

estimate stock composition in mixed-stock fishery samples. A rapid increase in the accuracy of estimated

stock composition in simulated mixtures with respect to population sample size was observed for sample sizes

of up to about 75 individuals, at which point a 90% accuracy of assignment to population was achieved. The

number of alleles observed at a locus was related to the power of the locus in providing accurate estimates of

the stock composition of single-population mixtures. In analysis of single-population mixtures where the

Pacific Rim baseline was used for estimation of stock identification, 75% accuracy for the average population

was achieved by employing approximately 55 alleles in the analysis. Increasing the accuracy of the estimated

stock composition to 90% for the average population required approximately 350 microsatellite alleles. The

precision of estimated stock composition increased rapidly for approximately the first 100 alleles used;

standard deviations declined from 20.0% to 8.0%. Analysis of known-origin samples indicated that accurate

regional estimates of stock composition were obtained. The accuracy of assigning individuals to a specific

region or river drainage averaged 84% for 54 populations in multipopulation samples. The estimated stock

compositions of mixed-fishery samples from northern and southern locations in British Columbia were quite

different among samples and reflected whether samples were derived from migrating or resident Chinook

salmon. Microsatellites have the ability to provide accurate estimates of stock composition from many

fisheries in the Pacific Rim distribution of Chinook salmon.

The Chinook salmon Oncorhynchus tshawytscha has

a Pacific Rim distribution of spawning populations; the

most abundant Asian populations spawn in rivers on

the Kamchatka Peninsula, and North American pop-

ulations spawn in rivers from western Alaska to

California. The largest river drainages tend to support

the largest runs of Chinook salmon, and Chinook

salmon can spawn in tributaries from the headwaters to

near the mouths of major rivers. Timing of freshwater

return for spawning is variable and may occur during

almost any month of the year (Healy 1991). Chinook

salmon are caught in a variety of fisheries throughout

their Pacific Rim distribution, and considerable effort

can be expended on sampling fisheries to determine the

biological characteristics and origins of fish in the

catch.

Stock identification is a key part of managing

fisheries, and if management of the fisheries is to be

effective, stock composition information from these

fisheries is essential. If stock composition data are used

to guide fishery managers in decisions on fishery

openings and closures, the estimated stock composition

must (1) provide the level of resolution necessary for

management, (2) be timely in its availability, and (3) be

accurate. The traditional method of stock identification

for Chinook salmon made use of coded wire tags

(CWTs; Jefferts et al. 1963) to determine the origin of

individuals. Juveniles of a hatchery population were

tagged with a CWT inserted into the nasal cartilage, the

adipose fin was removed to provide a visual mark

indicating the presence of a CWT, and the juveniles

were released to rear in the ocean. Fisheries were then

sampled to detect the presence of these adipose-clipped

fish. The heads of such fish were removed and sent to

a laboratory for processing, where the CWTs were

recovered and decoded to provide the hatchery of

origin. The number of CWTs recovered were expanded

depending upon the marking rate at the hatchery and

* Corresponding author: beachamt@pac.dfo-mpo.gc.ca

Received October 5, 2005; accepted March 2, 2006Published online June 26, 2006

861

Transactions of the American Fisheries Society 135:861–888, 2006� Copyright by the American Fisheries Society 2006DOI: 10.1577/T05-241.1

[Article]

the sampling rate in the fishery. Estimates of stock

composition were obtained from these expansions.

However, the recent practice in some jurisdictions of

adipose fin clipping all juveniles from hatcheries,

regardless of whether they were implanted with

a CWT, has substantially increased the complexity of

CWT recovery and interpretation of the results. Total

marking of hatchery fish is used to focus harvest on

abundant hatchery stocks through mark-selective

fisheries (release of unmarked wild fish). Thus,

assumptions of equal exploitation of marked and

unmarked fish are violated.

Coded wire tags are physical tags that are implanted

into individual fish, but genetic methods of stock

identification have had a fairly long history of

development and application in management of

Chinook salmon fisheries. Starting in the 1980s,

allozyme variation was used to identify the origin of

Chinook salmon in fisheries (Miller et al. 1983; Milner

et al. 1985; Utter et al. 1987; Shaklee et al. 1999).

Validation of stock composition estimates was con-

ducted through analysis of simulated mixtures of fish

and by comparison with samples of known origin

(Brodziak et al. 1992). Although allozyme-based stock

identification was applied successfully in specific

fisheries, the level of stock resolution provided by

allozymes did not meet the requirements of some

fishery managers and assessment staff, and thus

applications were limited in scope. Since the late

1990s, allozymes have been applied only in a limited

manner to the problem of estimating stock composition

in Chinook salmon fisheries.

The application of DNA-level markers, particularly

microsatellites, has provided much greater resolution

among Chinook salmon populations than was possible

with allozymes (Beacham et al. 1996, 2003b; Banks et

al. 2000). For example, it is possible to discriminate

among Chinook salmon populations from specific

tributaries in the Fraser River drainage in southern

British Columbia with a high degree of accuracy

(Beacham et al. 2003a). Microsatellites can be applied

successfully on a local basis to provide information on

population structure and stock composition. However,

the effectiveness of microsatellites in providing

accurate estimates of stock composition remains to be

demonstrated in application to fishery samples that

potentially incorporate a geographically diverse mix-

ture of Chinook salmon populations.

There are substantial differences among micro-

satellites in terms of the number of alleles observed

at a locus and the range of allele sizes. There has been

some disagreement among laboratories pertaining to

the characteristics of loci to include in surveys of

microsatellite variation. Initial theoretical studies of

locus characteristics to guide selection suggested that

a modest number of independent loci was best, that

each locus should have a modest number of alleles, and

that each allele should be present in modest frequency

(Smouse and Chevillon 1998). In the case of Pacific

salmon Oncorhynchus spp., one could choose loci with

a restricted number of alleles and presumably a re-

stricted size range, a moderate number of alleles as

suggested by Smouse and Chevillon (1998), or a larger

number of alleles. Empirical studies conducted on

sockeye salmon O. nerka indicated that accuracy and

precision of estimated stock composition generally

increased as the number of observed alleles at the loci

increased, regardless of whether the applications were

regional (Beacham et al. 2005b) or Pacific Rim

(Beacham et al. 2005a) in scope. Evaluation of whether

similar patterns exist for Chinook salmon would be

instructive.

Coded wire tags provide the ability to identify the

specific population origin of individuals, but of course

no information is available for those individuals that

are not marked with a CWT. A genetic tag may provide

the ability to assign individuals to specific populations

or regions and has the advantage that all fish in the

sample are naturally marked. Microsatellites have been

demonstrated to provide information on the specific

lake origin of individual sockeye salmon on a Pacific

Rim basis (Beacham et al. 2005a). In Chinook salmon,

microsatellites have been demonstrated to provide

a reliable method for assigning individuals to specific

populations within the Fraser River drainage (Beacham

et al. 2003a). As the technical and cost limitations of

CWT recovery and fishery parameter estimation

become greater, microsatellite analysis may provide

an opportunity to improve estimates from fishery

sampling. Identification of the specific population or

region of origin for individual fish is the most difficult

problem for salmon stock identification, particularly if

there is a widespread array of populations or regions

that could potentially contribute to a sample of

unknown origin.

In the current study, we evaluated the utility of using

the variation at 13 microsatellite loci for stock

identification applications to river- or region-specific

identification of Chinook salmon over their natural

range. This evaluation was conducted by examining the

accuracy and precision of estimated stock composition

for individual loci, combinations of loci, and all loci

combined through analysis of simulated mixtures and

estimation from actual samples collected from fisheries

in the Yukon River drainage and coastal British

Columbia. The mixtures were resolved by use of

a 325-population baseline incorporating populations

from Russia, Alaska, Yukon Territory, British Colum-

862 BEACHAM ET AL.

bia, Washington, Idaho, Oregon, and California. We

demonstrated that sufficient population allele frequen-

cy variation exists at microsatellite loci in Chinook

salmon to enable (1) accurate estimation of stock

composition of mixed-stock samples on a Pacific Rim

basis and (2) accurate assignment of individual fish to

the river or region of origin.

Methods

Collection of DNA samples and laboratory analy-sis.—Genomic DNA was extracted from liver, scales,

operculum punches, or fin clips of Chinook salmon

sampled initially by use of the phenol–chloroform

protocol of Miller et al. (1996) and later by use of

a chelex resin protocol (Withler et al. 2000). Samples

were primarily derived from adults except at some

locations where juveniles were sampled due to the

difficulty of obtaining adults. As outlined by Beacham

et al. (2003a), the initial survey of microsatellite

variation included amplification of products at six

microsatellite loci: Ots100, Ots101, Ots102, Ots104,

Ots107 (Nelson and Beacham 1999), and Ssa197(O’Reilly et al. 1996). The amplified products were

size fractionated on nondenaturing polyacrylamide gels

by staining with 0.5 mg ethidium bromide/mL of water

and illuminating with ultraviolet light. Nelson et al.

(1998) provide a more-complete description of gel

electrophoretic conditions. Beacham and Wood (1999)

give a more-complete description of allele identifica-

tion based on this technology. With the acquisition of

automated sequencers (Applied Biosystems, Inc.; ABI

377) in our laboratory, polymerase chain reaction

products at seven additional loci were size fractionated

on denaturing polyacrylamide gels; these loci were

Ogo2, Ogo4 (Olsen et al. 1998), Oke4 (Buchholz et al.

2001), Omy325 (O’Connell et al. 1997), Oki100 (K. M.

Miller, unpublished data), Ots2, and Ots9 (Banks et al.

1999). Allele sizes were determined with the aid of

Genescan 3.1 and Genotyper 2.5 software (PE

Biosystems, Foster City, California).

Baseline populations.—The baseline survey con-

sisted of analysis of over 52,000 Chinook salmon from

325 populations from Russia, Alaska, British Colum-

bia, Washington, Idaho, Oregon, and California (Figure

1). The sampling sites and populations surveyed in

each geographic region are outlined in Table A.1. The

major geographic regions and river drainages outlined

in Table A.1 are indicated in Figure 1. Information on

regional population structure has been outlined pre-

viously for Fraser River Chinook salmon by Beacham

et al. (2003b). Weir and Cockerham’s (1984) genetic

differentiation index FST

estimate for each locus over

all populations was calculated with FSTAT version

2.9.3.2 (Goudet 1995). Allele frequencies for all

populations surveyed in this study are available at the

Department of Fisheries and Oceans Molecular Genet-

ics Laboratory website (http://www-sci.pac.dfo-mpo.

gc.ca/mgl/default_e.htm).

Conversion of allele sizes between manual andautomated sizing systems.—The six loci previously

analyzed on nondenaturing polyacrylamide gels stained

with ethidium bromide have been analyzed since 1998

on automated DNA sequencers. However, estimated

allele sizes at these loci differed between the two

laboratory techniques. As outlined by Beacham et al.

(2003a), to convert allele sizes between the two

techniques we analyzed approximately 600 fish by

use of both techniques and determined the distributions

of allele frequencies. By inspection of the allele

frequencies, we were able to match specific allele sizes

obtained from the sequencers to specific allele sizes

from the manual gels; we then converted the sizing in

the manual gel data set to match that obtained from the

automated sequencers. Estimated allele sizes from both

systems were very highly correlated; coefficients of

determination r2 exceeded 0.987 for all loci. In general,

for the same allele, sizes from the sequencers were

larger than those estimated from manual gels, and the

differential increased with allele size.

The initial technique used in our laboratory to survey

microsatellite variation, which incorporated four 20-

base-pair (bp) size ladders on a gel, lacked the

resolution to differentiate between alleles differing by

2 bp when allele sizes were greater than about 180 bp.

Once automated DNA sequencers were used to

estimate allele size, resolution of larger-sized alleles

improved considerably. However, in merging the allele

frequency information between the two techniques, it

was necessary to forgo some of the resolution in allele

size provided by the automated sequencers. Although

alleles differing by 2 bp in size were observed with the

automated sequencers at some of the six loci initially

surveyed, adjacent alleles were combined to conform to

the 4-bp resolution obtained during application of the

manual gel technique. The practical effect of merging

the two data sets at these loci was the loss of some

resolution among populations that would have been

provided by the identification of additional alleles.

Estimation of stock composition.—Single-population

mixtures (mixtures containing simulated multilocus

genotypes derived entirely from a single population)

were simulated for populations spanning the Pacific

Rim distribution of Chinook salmon surveyed, and the

entire 325-population baseline was used to estimate the

stock composition of each mixture. Genotypic fre-

quencies were determined for each locus in each

population, and the Statistical Package for the Analysis

of Mixtures (SPAM) software program (version 3.7;

PACIFIC RIM CHINOOK SALMON STOCK IDENTIFICATION 863

Debevec et al. 2000) was used to estimate stock

composition of simulated mixtures. The Rannala and

Mountain (1997) correction to baseline allele frequen-

cies was used in the mixed-sample analysis to avoid the

occurrence of alleles that were not observed in the

baseline samples from a specific population. All loci

were considered to be in Hardy–Weinberg equilibrium

(HWE), and expected genotypic frequencies were

determined from the observed allele frequencies.

Reported stock compositions for simulated fishery

samples are the bootstrap mean estimates of each

mixture of 150 fish analyzed, and mean and variance

estimates were derived from 100 simulations. Each

baseline population or simulated fishery sample was

sampled with replacement to simulate the random

variation involved in the collection of baseline and

fishery samples. Reporting groups for estimated stock

composition were defined upon the basis of known

population structure for Chinook salmon (Utter et al.

1989; Beacham et al. 2003b and in preparation;

Brannon et al. 2004; Waples et al. 2004).

The effect of the number of alleles observed at

a locus on the accuracy of estimated stock composi-

tions was evaluated for each of the 13 microsatellite

loci. The mean accuracy of estimated stock composi-

tion for 27 single-population mixtures spanning

a Pacific Rim distribution was compared with the

number of alleles observed at each microsatellite locus.

Genotypic frequencies at Ots102 were not in HWE in

many of the populations surveyed (Beacham et al.

2003b; authors’ unpublished data), and thus we

evaluated the effect of assuming a HWE distribution

of genotypic frequencies for this locus. Analysis of

subsequent simulated single-population mixtures for

these 27 populations employed 12 microsatellites

(Ots102 was excluded from the suite of loci used) as

well as the full set of 13 microsatellites. The effect of

allele number on the accuracy of estimated stock

compositions of single-population mixtures was eval-

uated by sequentially adding microsatellite loci to the

analysis of the 27 single-population mixtures beginning

with the locus possessing the least number of alleles

(Ots9) and ending with the locus having the greatest

number (Ots102). Six additional simulated multipopu-

lation mixtures were evaluated; the accuracy and

precision of estimated stock compositions were de-

termined on both a population and geographic region

basis.

FIGURE 1.—Map indicating the major Pacific Rim geographic regions from which Chinook salmon were surveyed for

microsatellite variation. The populations sampled in each region are outlined in Table A.1.

864 BEACHAM ET AL.

Analysis of simulated mixtures provided the initial

evaluation of the utility of the baseline for stock

composition analysis. The key assumption in the

simulations was that the baseline used will be

representative of populations present when it is applied

to mixed-stock fishery samples. The next stage in the

evaluation was to estimate stock composition of

known-origin samples that were completely indepen-

dent of the baseline used in the estimation. Samples

from freshwater test fisheries were analyzed for three

major rivers (Yukon, Skeena, and Fraser rivers). These

test fisheries occurred in the lower part of the Skeena

and Fraser rivers and in the Canadian portion of the

Yukon River drainage. It was assumed that all fish

sampled in the test fisheries were native to the drainage

in which the test fishery was conducted. Two

additional known-origin samples based upon analysis

of coded-wire-tagged individuals caught in fisheries in

British Columbia were also evaluated using the Pacific

Rim baseline.

Analysis of the simulated mixtures was conducted

entirely with SPAM software. However, analysis of

actual fishery samples was conducted with a Bayesian

procedure. As outlined by Beacham et al. (2005a), the

BAYES routine of Pella and Masuda (2001) was

modified by our laboratory to a Cþþ-based program

(cBAYES), which is available from our laboratory

website. The BAYES or cBAYES analyses required

substantially more computer analytical time than did

the SPAM software for analysis of an individual

sample. As a large number of simulations were

conducted in the current analysis, it was not practical

to use cBAYES for the simulations. Previous applica-

tions of both SPAM and cBAYES to the same mixed-

stock sample suggested that accuracy was improved

with the cBAYES application (Beacham et al. 2005a;

authors’ unpublished data). Therefore, cBAYES was

used in the estimation of stock composition from actual

fishery samples. In the analysis, eight 20,000-iteration

Monte Carlo Markov chains of estimated stock

composition were produced, and initial starting values

for each chain were set at 0.90 for a particular

population that was different for each chain. Estimated

stock compositions were considered to have converged

when the shrink factor was less than 1.2 for the eight

chains (Pella and Masuda 2001), and thus the starting

values were considered to be irrelevant. Stock

composition estimates converged before 20,000 iter-

ations, and no further improvements in the estimates

were observed in excess of 20,000 iterations. There-

fore, 20,000 iterations were set as the standard in the

analysis. The last 1,000 iterations from each of the

eight chains were then combined, and the mean, mode,

and standard deviation of estimated stock compositions

were determined.

Four marine fishery samples of unknown composi-

tion from different geographic origins were analyzed to

compare the performance of the baseline in estimating

stock composition. One sample was obtained from

a troll fishery during June 2002 off the northwest coast

of the Queen Charlotte Islands, one sample was from

a troll fishery in the Strait of Georgia in February 2004,

one sample was from a troll fishery off the southwest

coast of Vancouver Island, British Columbia, during

October 2004, and a fourth sample was from creel

surveys based in Victoria on the southern tip of

Vancouver Island during January–March 2000. We

expected that divergent estimates of stock composition

would be obtained from these samples given the

seasonal and geographic differentiation of the samples.

The accuracy of assigning individuals to a particular

river or geographic region was evaluated with

cBAYES only. Representative samples were removed

from the baseline data, and these samples provided

a multipopulation mixture sample of known origin that

was independent of the baseline. This method of

creating a mixture of known origin is heavily de-

pendent on geographic population structure to provide

sufficient information for assignment of individuals to

the correct river drainage or geographic region.

Identification of the river or region of origin for

individual fish was conducted with cBAYES, and the

river or region of origin was the one with the highest

probability of assignment. The analysis was restricted

to those individuals scored at nine or more loci in each

of the test populations.

ResultsPopulation Structure

A regional population structure is central to the

application of microsatellites for stock composition

estimation, as a critical assumption in the application is

that the portion of the mixed-stock sample derived

from unsampled populations is allocated to sampled

populations from the same region. This assumption

reduces the cost and complexity of developing

a baseline for stock composition analysis. For Chinook

salmon, major river drainage structure and regional

population structure clearly existed. For example,

populations from the Yukon River were distinct from

other populations sampled in our survey, as were

populations in the transboundary rivers (Alsek, Taku,

and Stikine rivers), the Fraser River drainage, and the

Columbia River drainage. Regional clustering of

populations in smaller river drainages was observed

for populations on the Kamchatka Peninsula in Russia,

the east and west coasts of Vancouver Island,

PACIFIC RIM CHINOOK SALMON STOCK IDENTIFICATION 865

Washington populations in Puget Sound and the outer

coast, and coastal populations in Oregon. Chinook

salmon population structure thus meets the necessary

condition that unsampled populations contributing to

mixed-fishery samples will probably be allocated to

sampled populations in the same region.

Population Sample Size

The effect of baseline population sample size on the

accuracy of estimated stock compositions for single-

population mixtures was evaluated for 60 populations

that covered wide geographic and sample size ranges

and that were representative of the populations in-

cluded in the survey (Table A.1 in the Appendix). A

rapid increase in accuracy of estimated stock compo-

sition coinciding with population sample size increases

was observed for sample sizes of up to about 75

individuals, at which point a population assignment

accuracy of 90% was achieved for most of the single-

population mixtures evaluated (Figure 2A). A 90%

accuracy of assignment to a specific region was

achieved with an approximate population sample size

of 50 individuals, and 75% accuracy was achieved with

a sample size of approximately 25 individuals (Figure

2B). Larger sample sizes were required to obtain the

same level of population-specific accuracy relative to

region-specific accuracy. Although the highest levels

of accuracy were observed in those populations with

the largest sample sizes in the baseline, the increase in

accuracy achieved for sample sizes beyond 150–200

fish was modest in both cases.

Comparisons among Individual Microsatellite Loci

The number of alleles observed at the 13 loci

examined in the Pacific Rim survey of microsatellite

variation ranged from 15 to 60 (Table 1). This range in

the number of alleles observed at a locus allowed an

evaluation of the effect of allele number on estimated

accuracy and precision of stock compositions for 27

test populations located throughout the Pacific Rim

distribution of Chinook salmon (Table 2). The number

of alleles observed at a locus was related to estimated

accuracy of stock composition of the single-population

mixtures (r2 ¼ 0.76, P , 0.01; Figure 3A). Mean

estimated stock compositions of the single-population

mixtures (correct ¼ 100%) were 22.8% accurate for

single loci with less than 20 alleles, 52.4% accurate for

loci with 20–30 alleles, 64.3% accurate for loci with

40–50 alleles, and 67.9% accurate for loci with more

than 50 alleles (Table 1). The number of alleles

observed at a locus also had a marked effect on the

precision of the estimated stock compositions of single-

population mixtures; more-precise estimates (lower

SDs) were derived from loci with larger numbers of

alleles (r2¼0.77, P , 0.01; Figure 3B). Mean standard

deviations of the estimated stock compositions were

20.3% for single loci with fewer than 20 alleles, 20.3%

for loci with 20–30 alleles, 12.4% for loci with 40–50

alleles, and 10.3% for loci with over 50 alleles (Table

1). Loci that displayed more alleles during the survey

of microsatellite variation provided more-accurate and

precise estimates of stock composition of the single-

population mixtures than did loci with fewer observed

alleles.

The number of microsatellite loci and alleles

employed in stock composition estimation had a direct

effect on the accuracy and precision of the estimates.

Accuracy and precision of the estimated stock

composition obtained by employing three loci (Ots9,

Oke4, and Ogo4) with the fewest number of alleles (55

alleles total) were equivalent to those obtained by use

of a single locus (Ots102) with the largest number of

alleles (60 alleles) (Figure 3A, B). The number of

alleles observed at a locus was thus a key characteristic

for predicting the power of a locus to provide accurate

and precise estimates of stock composition.

Analysis of Simulated Single-Population Mixtures

The analytical procedures for stock composition

estimation assume a Hardy–Weinberg distribution of

genotypic frequencies for all loci in all populations in

the baseline. As genotypic frequencies of Ots102 were

not in HWE in all populations, we evaluated whether

inclusion of Ots102 in the suite of loci used in stock

composition estimation produced estimates that were

less accurate or less precise than those generated when

this locus was excluded. Twenty-seven populations

were selected for analysis to encompass a wide geo-

graphic range and were considered to be representative

for Chinook salmon. Accuracy of estimated stock

composition was greater when Ots102 was included in

the analysis than when it was removed (sign test

analysis: P , 0.01) (Table 2). Estimated stock

composition was also more precise when Ots102 was

included in the analysis than when it was removed (23

of 27 cases; P , 0.01). Therefore, Ots102 was

incorporated in subsequent analyses of stock compo-

sition.

Analysis of simulated mixtures can be regarded as

the initial step in evaluating the power of a set of loci

for stock composition estimation. For 27 populations

with baseline sample sizes generally greater than 100

individuals, the estimated stock compositions of single-

population mixtures derived with a 325-population

baseline were over 90% accurate in all but three

populations; one of these three populations was that of

the Middle Fork John Day River, where only 40 fish

had been surveyed (Table 2). Regional estimates of

866 BEACHAM ET AL.

stock composition were over 90% accurate for all

populations except for the John Day River population

(88%). The set of microsatellite loci examined in this

study provided accurate estimates of stock composition

for simulated single-population mixtures on a popula-

tion basis and a regional basis, as long as a sufficient

number of individuals had been surveyed in the

population (Table 3; Figure 2A, B).

FIGURE 2.—(A) Relationship between the number of Chinook salmon surveyed in a specific population and the accuracy

obtained for the same population and (B) relationship between sample size and regional assignment accuracy during stock

composition estimation of simulated single-population mixtures employing a baseline of 325 populations. Single-population

mixtures were simulated for the 60 populations outlined in Tables 2 and 3. Regions and populations within regions are outlined

in Table A.1.

PACIFIC RIM CHINOOK SALMON STOCK IDENTIFICATION 867

The number of alleles observed at a locus was

previously demonstrated to be a good predictor of the

power of a locus in stock identification applications.

However, the FST

value observed at a locus was a poor

predictor of the locus’ power to accurately estimate

stock composition (Figure 4). In particular, loci with

lower FST

values (but higher numbers of alleles) were

more valuable for providing accurate estimates of stock

composition than were loci with higher FST

values.

Loci with larger numbers of alleles will have lower FST

values, but the number of alleles observed is better than

FST

for predicting locus power for stock identification

applications. A rapid per-allele increase in accuracy of

estimated stock composition was observed for appli-

cations employing 55 alleles (approximately 1.4%

increase in accuracy for each allele used), but the rate

of increase in accuracy per allele diminished sub-

stantially beyond the use of 55 alleles (Figure 5A). As

the accuracy of estimated stock compositions was 75–

80% at this level, the scope for increased improvement

was a maximum 20–25% and thus a reduction in the

effectiveness of each additional allele was expected.

The addition of loci and alleles for stock composition

estimation always produced more-accurate results on

average. There was no indication of any decline in

accuracy with increasing numbers of loci or alleles

used in the estimation. The precision of estimated stock

TABLE 1.—Number of alleles per locus, genetic differenti-

ation index (FST

), mean accuracy (%), and mean standard

deviation (%) for estimated percentage compositions of single-

population mixtures (correct ¼ 100%) for 27 representative

populations of Chinook salmon from the species’ Pacific Rim

distribution. The number of alleles and FST

were calculated

from the entire baseline. Populations are listed in Table 2.

LocusNumber of

alleles FST

Mean accuracy(%)

Mean SD(%)

Ots9 15 0.094 19.5 20.0Oke4 17 0.117 26.1 20.5Ogo4 23 0.113 62.8 18.4Ots2 28 0.110 45.4 21.3Ogo2 30 0.095 49.0 21.1Omy325 43 0.130 59.2 17.6Ssa197 45 0.036 66.2 12.0Ots104 45 0.030 65.2 11.1Oki100 47 0.031 63.0 11.4Ots107 47 0.051 71.6 10.3Ots101 50 0.036 60.8 11.7Ots100 58 0.026 60.2 12.1Ots102 60 0.045 75.5 8.4

TABLE 2.—Mean (SD) estimated percentage compositions of single-population mixtures (correct ¼ 100%) for 27

representative populations of Chinook salmon from the species’ Pacific Rim distribution. Estimates were determined without

Ots102 (12 loci) and with Ots102 (13 loci) in the suite of loci used to estimate stock compositions. The region designation

includes the sum of percentage allocations to all populations in the region. Simulations were conducted with a 325-population

baseline, 150 fish in the mixture sample, and 100 resamplings in the mixture sample and baseline samples.

Population

Population assignment

Region

Regional assignment

12 loci 13 loci (13 loci)

Bistraya River 80.2 (5.6) 81.4 (6.1) Russia 97.9 (1.3)Takhini River 95.6 (2.0) 95.9 (2.0) Upper Yukon River 96.2 (2.0)Blind Creek 91.2 (3.4) 91.3 (3.5) Pelly River 94.9 (2.6)Mayo River 88.8 (4.4) 91.4 (4.0) Stewart River 93.5 (3.1)Gisasa River 91.9 (3.9) 93.4 (3.2) Koyukuk River 93.5 (3.2)Klukshu River 93.1 (4.3) 94.1 (3.4) Alsek River 99.2 (0.9)Little Tatsamenie River 90.8 (4.1) 93.1 (3.2) Taku River 94.6 (2.8)Verrett River 95.0 (2.5) 96.6 (2.1) Stikine River 98.4 (1.4)Kwinageese River 90.9 (3.8) 92.2 (3.1) Nass River 97.7 (1.4)Bulkley River (main stem) 98.6 (1.2) 98.9 (1.0) Bulkley River 99.0 (1.0)Wannock River 98.7 (1.1) 98.8 (1.0) Central coast British Columbia 99.0 (1.0)Porteau Cove 95.6 (2.7) 96.7 (2.3) South coast British Columbia 99.0 (1.0)Quinsam River 98.0 (1.3) 98.5 (1.1) ECVIa 99.4 (0.7)Conuma River 96.9 (2.3) 97.4 (1.7) WCVIb 99.7 (0.5)Swift River 96.8 (2.1) 97.0 (1.8) Upper Fraser River 99.2 (0.8)Chilko River 94.4 (2.6) 94.7 (2.8) Middle Fraser River 98.4 (1.2)Maria Slough 98.3 (1.2) 98.4 (1.2) Lower Fraser River 98.4 (1.2)Raft River 92.2 (3.2) 93.6 (2.8) North Thompson River 95.1 (2.3)Salmon River 96.1 (2.1) 96.6 (1.9) South Thompson River 98.3 (1.3)Deadman River 92.4 (4.0) 92.7 (3.7) Lower Thompson River 99.1 (1.0)Skagit River 96.7 (2.1) 97.0 (1.7) Puget Sound 97.9 (1.4)Solduc River 95.1 (2.1) 95.5 (2.0) Coastal Washington 97.0 (1.7)Abernathy River 94.7 (2.1) 95.6 (2.0) Lower Columbia River 96.0 (2.0)John Day River (middle) 85.8 (3.7) 87.0 (3.1) Middle Columbia River 87.8 (2.8)Twisp River 94.3 (2.4) 94.4 (2.5) Snake River spring 96.8 (1.7)Pistol River 90.8 (3.6) 92.0 (3.1) Oregon coastal 95.4 (2.1)Feather River (fall) 79.7 (6.2) 82.7 (4.9) California Central Valley 97.8 (1.5)

a East Coast Vancouver Island.b West Coast Vancouver Island.

868 BEACHAM ET AL.

FIGURE 3.—(A) Relationship between the number of alleles observed at a microsatellite locus and the average percentage

accuracy of assignment to Chinook salmon population and (B) relationship between number of alleles and the standard deviation

obtained for single-population mixtures based on only a single locus and the 325-population Pacific Rim baseline for the 27

populations outlined in Table 2. Loci are numbered as in Table 1.

PACIFIC RIM CHINOOK SALMON STOCK IDENTIFICATION 869

composition increased rapidly for approximately the

first 100 alleles used, and standard deviations declined

from 20.0% to 8.0% (Figure 5B). Relatively significant

increases in precision were observed with increasing

numbers of alleles; we found a nearly 50% reduction in

standard deviation to 4.2% with a 2.5-fold increase in

allele number. The addition of loci always produced,

on average, more precise estimates of stock composi-

tion. In summary, the number of alleles employed in

the estimation of stock composition directly influenced

the accuracy and precision of the estimates, and higher

accuracy and precision were obtained by employing the

maximum number of alleles.

Analysis of Simulated Multipopulation Mixtures

The accuracy and precision of six multipopulation,

multiregion simulated fishery samples were estimated

for both population and regional components. The

estimated stock composition of a simulated mixture

containing fish from Russia and the Yukon River was

usually within 1% of the specific site or population and

within 1% of the specific region of origin (Table 4,

mixture 1). A simulated mixture containing trans-

boundary river (Alsek, Taku, and Stikine rivers) and

southeast Alaska populations was estimated at an

accuracy within 2% of actual values for both

populations and regions (mixture 2). Similar results

were observed for a mixture containing northern

British Columbia populations from the Nass River,

Skeena River, and the coast (mixture 3); a mixture

containing southern British Columbia populations from

the Fraser River and Vancouver Island (mixture 4); and

a mixture containing populations from Washington, the

Columbia River, Oregon, and California (mixture 5).

The estimated stock composition of a Pacific Rim

distribution of populations in the mixture was usually

accurate within 1% for specific populations and less

than 1% for regional contributions (mixture 6).

Analysis of all simulated mixtures indicated that the

surveyed microsatellite loci provided reliable estimates

of stock composition, which indicated a successful

completion of the initial step in the evaluation of the

utility of these loci for stock identification applications.

TABLE 3.—Mean (SD) estimated percentage compositions of single-population mixtures (correct¼ 100%)

for populations of Chinook salmon from the species’ Pacific Rim distribution. Populations were selected from

those in the baseline with a sample size of less than 80 fish. The region designation includes the sum of

percentage allocations to all populations in the region. Simulations were conducted with a 325-population

baseline, 150 fish in the mixture sample, and 100 resamplings in the mixture sample and baseline samples.

Population N Population % Region Region %

Big Kalzas River 24 63.6 (4.9) Pelly River 80.6 (4.1)Glenlyon River 24 68.8 (4.6) Pelly River 79.8 (4.3)Little Kalzas River 40 80.0 (4.4) Pelly River 88.5 (3.6)Earn River 55 77.3 (5.3) Pelly River 88.9 (3.4)Morley River 29 67.7 (6.0) Teslin River 73.5 (5.0)Nisutlin River 55 85.5 (4.6) Teslin River 86.4 (4.6)Minto River 11 49.9 (5.2) Main-stem Yukon River 50.7 (5.2)Yukon River main stem 27 66.3 (6.3) Main-stem Yukon River 66.6 (6.3)Chatanika River 19 63.0 (5.7) Tanana River 73.5 (5.2)Salcha River 52 80.2 (4.4) Tanana River 86.7 (3.7)Kateel River 19 61.4 (5.6) Koyukuk River 76.6 (4.9)Koyukuk River (south fork) 55 83.5 (3.9) Koyukuk River 86.5 (3.5)Tatshenshini River 24 51.7 (6.3) Alsek River 88.5 (3.5)Johnny Tashoots Creek 24 70.7 (5.1) Stikine River 89.7 (3.2)Teigen River 31 72.6 (4.9) Nass River 90.5 (3.1)Snowbank River 54 68.8 (5.0) Nass River 89.5 (3.3)Slamgeesh River 34 77.5 (4.5) Upper Skeena River 77.9 (4.6)Harold Price Creek 13 60.3 (5.0) Bulkley River 61.1 (4.9)Sweetin River 46 75.6 (5.3) Middle Skeena River 88.0 (3.9)Thomas Creek 21 72.3 (4.8) Lower Skeena River 77.8 (4.5)Gitnadoix River 42 84.8 (3.5) Lower Skeena River 87.2 (3.5)Dean River 38 76.3 (4.4) North-central British Columbia 91.4 (2.9)Upper Dean River 51 83.2 (4.7) North-central British Columbia 92.4 (3.0)Small Creek 18 48.8 (5.9) Upper Fraser River 76.9 (5.2)Holiday Creek 21 51.9 (6.5) Upper Fraser River 84.5 (4.6)Walker Creek 42 77.7 (5.1) Upper Fraser River 94.5 (2.6)Chilako 45 79.7 (4.6) Middle Fraser River 92.1 (2.6)Eagle River 36 83.9 (3.8) South Thompson River 95.8 (2.0)Hoh River 59 91.4 (3.0) Coastal Washington 94.9 (2.2)Coweeman River 77 94.1 (2.3) Lower Columbia River 94.3 (2.2)Entiat River 64 92.1 (2.9) Upper Columbia (spring) 93.1 (2.5)Euchre Creek 57 90.9 (3.3) Coastal Oregon 95.4 (2.1)Battle Creek 40 76.7 (4.7) California Central Valley 97.0 (1.6)

870 BEACHAM ET AL.

Analysis of Known-Origin Mixtures

Analysis of simulated samples provides an evalua-

tion of the effectiveness of the baseline for stock

composition analysis under the assumption that the

baseline will be representative of all populations

contributing to a sample of unknown origin. This

assumption can be directly tested by estimating the

stock composition of known-origin samples that are

completely independent of the baseline. Such analyses

constitute the second stage of evaluation of the power

of a set of loci and baseline populations for estimating

stock composition. Analysis of simulated mixtures

containing Yukon, Skeena, and Fraser River popula-

tions suggested that accurate estimates of stock

composition by drainage are possible. The Yukon

River test fishery was conducted in the Yukon

Territory near the border with Alaska, so only

Canadian-origin fish would be expected to be sampled

in this fishery. Analysis of the 2003 test fishery

samples with a baseline incorporating a Pacific Rim

distribution of populations indicated that only fish of

Yukon River, Canada, origin were estimated to have

comprised the sample, which was completely in line

with expectations (Table 5). Similarly, the estimated

stock composition of a sample derived from a test

fishery conducted in the lower Skeena River in

northern British Columbia was entirely of Skeena

River�origin populations and regions (Table 5). The

last sample was derived from a test fishery on the lower

Fraser River in southern British Columbia, and the

estimated stock composition included virtually all of

the Fraser River populations and regions (Table 5).

Analysis of actual fishery samples from these three

rivers supported the results derived from the analysis of

simulated mixtures.

Two samples of Chinook salmon marked with

CWTs were also analyzed to evaluate the accuracy of

estimated stock composition. A composite sample

derived from marked Chinook salmon sampled from

a range of British Columbia fisheries in 1997 included

tagged fish from 17 regions. Estimated regional stock

composition was usually within 2% of actual regional

stock composition (Figure 6). A second sample of

marked Chinook salmon was obtained from a fishery

conducted off the west coast of Vancouver Island in

2000. Although fish from seven regions were present in

the sample of marked fish, the sample was dominated

by individuals of Puget Sound and Columbia River

origin (Figure 7). Estimated stock compositions were

within about 3% of actual values for all regions.

Analysis of the two samples of fish with CWTs

indicated that reasonably accurate regional estimates of

stock composition were obtained, even though many of

the individual populations from which CWTs originat-

ed were not included in the baseline used to obtain the

estimates.

FIGURE 4.—Relationship between the mean accuracy of population assignment and the genetic differentiation index FST

for 13

microsatellite loci used to estimate the percentage compositions of single-population mixtures (correct ¼ 100%) for 27

representative Pacific Rim populations of Chinook salmon. Loci are numbered as in Table 1.

PACIFIC RIM CHINOOK SALMON STOCK IDENTIFICATION 871

Identification of Individuals

The assignment of individual fish to a specific

population or region is one of the most demanding

stock identification applications. The wide distribution

of populations sampled in the study allowed an

evaluation of the accuracy of assigning individual fish

to region of origin. Individual populations were

removed from the baseline and used in mixtures of

known origin to allocate individuals to region of origin.

The accuracy of identifying an individual’s specific

region or river drainage averaged 84% for 54

populations in the sample (Table 6). Individuals from

FIGURE 5.—(A) Relationship between the number of microsatellite alleles used in estimating Chinook salmon stock

composition and the average percentage accuracy of assignment to population and (B) relationship between the number of alleles

and the standard deviations obtained for the 27 single-population mixtures outlined in Table 2.

872 BEACHAM ET AL.

TABLE 4.—Estimated percentage stock compositions (SD) of simulated mixtures of Chinook salmon as may be encountered in

marine samples. Each mixture of 150 fish was generated 100 times with replacement, and stock compositions of the mixtures

were estimated by resampling each of the 325 baseline populations with replacement to obtain a new distribution of allele

frequencies. Expected regional composition is obtained by adding true population components, and estimated regional

composition is listed in the region column for each mixture.

Population (region) True

Estimated

Population Region

Mixture 1Whitehorse (upper Yukon River) 10 10.1 (2.8) 10.3 (2.8)Nisutlin River (Teslin River) 10 8.0 (3.1) 8.1 (3.1)Pelly River main stem (Pelly River) 15 13.1 (3.1) 13.8 (3.1)Stewart River main stem (Stewart River) 5 4.0 (2.3) 5.1 (2.6)Cheena River (Tanana River) 10 9.0 (2.7) 9.1 (2.8)Gisasa River (Koyukuk River) 15 14.4 (4.2) 14.6 (4.2)Tozitna River (middle Yukon River) 10 10.0 (2.9) 10.0 (2.9)Pymta River (Russia) 15 13.0 (3.8)Kol River (Russia) 5 3.3 (2.0)Palana River (Russia) 5 4.0 (2.2) 24.1 (4.1)

Mixture 2Blanchard River (Alsek River) 15 14.3 (3.8)Klukshu River (Alsek River) 15 13.9 (4.3) 29.5 (4.6)Little Tatsamenie (Taku River) 10 8.4 (3.2)Dudidontu River (Taku River) 10 8.2 (2.7) 17.6 (3.7)Verrett River (Stikine River) 15 14.5 (3.8)Little Tahltan River (Stikine River) 10 11.8 (3.9)Nakina River (Stikine River) 5 3.3 (2.2) 31.0 (5.3)Unuk River (Southeast Alaska Unuk River) 10 9.7 (3.1)Chickamin River (Southeast Alaska Unuk River) 10 9.9 (2.8) 19.6 (4.0)

Mixture 3Kwinageese River (Nass River) 15 13.3 (3.6)Oweegee River (Nass River) 10 8.2 (2.9) 23.3 (3.9)Sustut River (upper Skeena River) 15 14.8 (3.5) 15.1 (3.4)Bulkley River main stem (Bulkley River) 10 9.9 (2.8) 10.0 (2.8)Kitwanga River (middle Skeena River) 10 8.7 (3.2) 9.2 (3.1)Ecstall River (lower Skeena River) 15 14.3 (3.8) 15.1 (3.7)Chuckwalla River (north-central British Columbia) 15 14.1 (3.3)Kitimat River (north-central British Columbia) 10 9.0 (3.2) 25.4 (4.2)

Mixture 4Cowichan River (East Coast Vancouver Island) 15 13.7 (3.7) 14.9 (3.2)Sarita River (West Coast Vancouver Island) 15 14.2 (3.9) 14.7 (3.8)Holmes Creek (upper Fraser River) 10 7.6 (2.9) 10.0 (2.9)Quesnel River (middle Fraser River) 5 4.8 (2.1) 5.8 (2.3)Harrison River (lower Fraser River) 15 14.1 (3.3) 14.9 (3.5)Finn Creek (North Thompson River) 10 9.0 (2.7) 9.4 (2.8)Bessette Creek (South Thompson River) 10 7.1 (2.8) 9.7 (2.6)Bonaparte River (lower Thompson River) 20 18.9 (4.2) 20.0 (4.2)

Mixture 5Skagit River (Puget Sound) 15 15.4 (3.4) 15.7 (3.4)Quinault River (Washington coast) 5 4.4 (2.4) 4.6 (2.5)Sandy River (lower Columbia River) 10 9.5 (2.8) 9.5 (2.8)Twisp River (upper Columbia Spring) 15 13.4 (3.1) 14.0 (3.1)Handford Reach (upper Columbia River, summer–fall) 15 13.2 (3.3) 14.1 (3.1)Tucannon River (Snake River, spring–summer) 15 14.4 (3.7) 14.6 (3.7)Hunter Creek (Oregon coast) 15 14.0 (3.4) 14.6 (3.4)Feather River (Central Valley, fall) 10 7.6 (3.0) 9.4 (2.9)

Mixture 6Pymta River (Russia) 5 3.9 (1.9) 5.0 (1.9)Cheena River (Tanana River) 10 9.4 (2.9) 9.5 (2.9)Blanchard River (Alsek River) 15 13.6 (3.5) 14.6 (3.4)Sustut River (upper Skeena River) 10 9.2 (2.8) 9.2 (2.8)Quinsam River (East Coast Vancouver Island) 20 19.8 (3.7) 20.4 (3.7)Salmon River (South Thompson River) 10 10.0 (2.8) 10.4 (2.8)Abernathy Creek (lower Columbia River) 15 14.4 (3.3) 14.5 (3.3)Merced River (Central Valley, fall) 15 12.7 (3.9) 14.3 (3.8)

PACIFIC RIM CHINOOK SALMON STOCK IDENTIFICATION 873

populations with the lowest regional or timing accuracy

of identification (Kispiox and Lower Kitsumkalum

rivers above the canyon in the Skeena River drainage;

Butte Creek [spring] in California’s Central Valley)

were correctly assigned with over 95% accuracy to

major river drainage. Below-average accuracy of

identification was also observed for populations from

three rivers in northern British Columbia (Taku,

Stikine, and Nass rivers), whereas the accuracy of

identification for individuals from most other popula-

tions was usually above 90%. Microsatellites provided

the ability to assign individuals to specific regions or

rivers with a fairly high degree of accuracy.

The two CWT samples provided an opportunity to

evaluate the accuracy of assigning individual fish to

a specific population. The analysis was restricted to

tags of Canadian origin, as most American tag–release

locations were not in the baseline. Accuracy of

individual allocation to specific rivers (or specific

tributaries for large drainages like the Skeena and

Fraser rivers) in British Columbia was quite variable,

ranging from 0% to 100% (Table 7). In the Skeena

River drainage, Chinook salmon from the Bulkley

River were quite distinctive, whereas only 2 of 13

Babine River Chinook salmon were assigned correctly

to population. Higher rates of accuracy were observed

for west coast Vancouver Island populations than for

east coast populations. Fraser River populations were

identified with a reasonable rate of accuracy, provided

that transplanted and donor populations were combined

in the analysis. The potential exists for assigning

individuals to specific populations, but it may require

more resolution than that provided by these 13

microsatellites if high accuracy is required over a broad

spectrum of populations.

Analysis of Marine Samples

The final stage of evaluation of a stock identification

technique is to apply the technique to estimate stock

compositions from widely divergent samples and

evaluate whether the estimated stock compositions

are within expectations. The actual stock composition

of the samples tested are unknown, and stock

composition estimation of these samples of unknown

origin simply involves evaluating whether the estimat-

ed stock compositions are within the range that would

be expected given the location and timing of sample

collection. We tested the technique by analyzing four

mixed-stock samples of Chinook salmon with di-

vergent geographic and temporal origins. Chinook

salmon of Thompson River (the main Fraser River

tributary) origin comprised 52% of the sample obtained

from a June troll fishery off the northwest coast of the

Queen Charlotte Islands, a known migration corridor

for Chinook salmon. Columbia River Chinook salmon

comprised 20% of the sample; 11% of the sample

contained Oregon-origin Chinook salmon (Table 8).

Northern British Columbia stocks were also observed

in the sample. Skeena River�origin Chinook salmon

were estimated to comprise 3% of the sample, and the

Stikine River stock was estimated to comprise 1% of

the sample.

The troll fishery off the southwest coast of

Vancouver Island also intercepted migrating Chinook

salmon from a wide geographic area; Columbia River

(48%), Puget Sound (18%), and Oregon (12%)

Chinook salmon dominated the sample. Fraser Riv-

er�origin salmon were also observed in the sample

(10%), as well as salmon from the east coast of

Vancouver Island (9%) (Table 8). No salmon were

estimated to have originated from any areas north of

the east coast of Vancouver Island or the Fraser River.

The winter sport fishery conducted near Victoria,

British Columbia, in the Strait of Juan de Fuca (the

body of water separating Vancouver Island and

Washington) largely targets Chinook salmon that are

resident in the area; local stocks would thus be

expected to constitute the catch. Samples analyzed

TABLE 5.—Estimated percentage stock compositions (SD) of Yukon River (sampled in 2003, N¼375), Skeena River (2003, N¼ 191), and Fraser River (2001, N¼ 202) Chinook salmon obtained from test fisheries within each river system and estimated

with a 325-population baseline incorporating the variation at 13 microsatellite loci. Estimated stock compositions were derived

from cBAYES (see text).

Yukon River Skeena River Fraser River

Region Estimate Region Estimate Region Estimate

Upper Yukon River 3.2 (1.0) Upper Skeena River 9.9 (4.5) Upper Fraser River 58.6 (4.8)Teslin River 3.9 (1.3) Bulkley River 25.0 (4.6) Middle Fraser River 23.4 (4.4)Yukon River–Carmacks River 41.3 (3.8) Middle Skeena River 47.2 (5.6) South Thompson River 4.6 (1.5)Pelly River 20.2 (3.9) Lower Skeena River 17.9 (3.7) Lower Thompson River 13.2 (2.4)Stewart River 20.9 (3.8) Total Skeena River 100.0 (0.3) Total Fraser River 99.8 (0.5)Lower Yukon River (Canada) 9.8 (1.7)Kluane River 0.7 (0.6)Total Yukon River 100.0 (0.3)

874 BEACHAM ET AL.

FIGURE 6.—Estimated percentage stock composition of 306 Chinook salmon marked with coded wire tags and sampled from

fisheries in British Columbia during 1997. The baseline used for the stock composition analysis consisted of approximately

52,000 Chinook salmon surveyed for the variation at 13 microsatellite loci from 325 populations across the species’ Pacific Rim

distribution. Actual percentages are in white, and estimated percentages (6SD) are in black.

PACIFIC RIM CHINOOK SALMON STOCK IDENTIFICATION 875

FIGURE 7.—Estimated percentage stock composition of 297 Chinook salmon marked with coded wire tags and sampled from

a troll fishery off the southwest coast of Vancouver Island, British Columbia, during 2001. Actual percentages are in white, and

estimated percentages (6SD) are in black.

876 BEACHAM ET AL.

from the creel survey of this fishery indicated that 97%

of the fish sampled were of Puget Sound origin, and

2% of the sample was estimated to be derived from the

east coast of Vancouver Island (Table 8).

The final sample analyzed was from a winter fishery

in the Strait of Georgia (the body of water separating

the southeast coast of Vancouver Island and mainland

British Columbia). We estimated that 66% of the

TABLE 6.—Percentage of individual Chinook salmon that were correctly assigned to their region of origin for 54 populations

ranging from Kamchatka to California based on the variation at 13 microsatellite loci. These individual populations were

removed from the baseline and used as three mixtures of known origin to assign individuals to region. Individuals had to be

scored for at least 9 loci for inclusion in the analysis; N is the number of fish analyzed. The mixture number for analysis is

indicated in parentheses after the sampling site.

Region of origin Sampling site N % Correct

Russia Palana River (1) 49 98.0Vorovskaya River (2) 42 97.6

Upper Yukon River (Yukon, Canada) Michie Creek (2) 38 92.1Teslin River (Yukon, Canada) Morley River (2) 28 89.3Pelly River (Yukon, Canada) Earn River (1) 54 94.4Stewart River (Yukon, Canada) Stewart River (2) 109 86.2Lower Yukon River (Yukon, Canada) Klondike River (1) 111 96.4Tanana River (Yukon, USA) Salcha River (2) 36 86.1Koyukuk River (Yukon, USA) Koyukuk River, south (2) 50 88.0Lower Yukon River (Yukon, USA) Andreafsky River, east fork (2) 24 95.8Alsek River Takhanne River (1) 188 98.9

Tatshenshini River (2) 23 100.0Taku River Little Trapper River (1) 70 65.7Stikine River Craig River (1) 113 80.5

Johnny Tashoots Creek (2) 18 83.3Nass River Snowbank River (1) 48 72.9Upper Skeena River Slamgeesh River (2) 34 52.9Middle Skeena River Kispiox River (3) 133 0.0a

Lower Skeena River Lower Kitsumkalum River (above canyon) (3) 181 0.0b

North-central British Columbia Saloompt River (1) 89 94.4Ashlulm River (2) 42 100.0

Upper Fraser River Goat Creek (1) 76 81.6Middle Fraser River Chilcotin River (mixed) (2) 46 84.8

Westroad River (2) 29 89.7Lower Fraser River Upper Pitt River (3) 67 98.5North Thompson River Blue River (2) 42 95.2South Thompson River Bessette Creek (2) 48 95.8Lower Thompson River Spius Creek (2) 133 96.2West Coast Vancouver Island Kennedy River (1) 49 89.8

Tlupana River (2) 60 100.0East Coast Vancouver Island Nanaimo River (spring) (1) 98 100.0

Quatse River (2) 26 76.9Southern British Columbia mainland Shovelnose Creek (2) 18 83.3

Mamquam River (2) 18 100.0Puget Sound Stillaguamish River (1) 87 95.4

Skykomish River (2) 56 91.1Washington coast Quinault River (1) 62 91.9

Queets River (2) 32 90.6Lower Columbia River Coweeman River (3) 59 81.4Willamette River North Fork Clackamas River (3) 77 98.7Upper Columbia River (spring) Chewuch River (2) 98 88.9Upper Columbia River (summer–fall) Similkameen River (2) 178 97.8Middle Columbia River Granite Creek (2) 20 95.0Snake River (spring–summer) Frenchman Creek (1) 60 100.0

Wenaha River (2) 39 46.2North-central Oregon coast Siuslaw River (2) 35 40.0

Trask River Hatchery (fall) (3) 86 98.8South Oregon coast Lobster Creek (1) 47 80.9

Hunter Creek (3) 94 97.9Klamath–Trinity rivers Trinity River fall (3) 86 100.0California Central Valley (spring) Butte Creek (3) 42 0.0c

California Central Valley (fall) American River (1) 62 100.0Stanislaus River (2) 24 100.0Yuba River (2) 45 97.8

a Identification to Skeena River drainage was 98.5%.b Identification to Skeena River drainage was 96.1%.c Identification to California Central Valley was 100%.

PACIFIC RIM CHINOOK SALMON STOCK IDENTIFICATION 877

sample was derived from the east coast of Vancouver

Island, 33% was from Puget Sound, and 1% was from

the Fraser River (Table 8). All sampled fish were

estimated to have originated from areas adjacent to the

Strait of Georgia. When all four samples were

considered, samples derived from migrating stocks

were more geographically diverse than samples from

winter fisheries.

Discussion

Loci used in stock composition estimation are

assumed to be in HWE in the baseline populations

(Debevec et al. 2000). In our survey, the Ots102 locus

was not in HWE in all populations. The key question is

whether including a locus that is not in HWE in all

populations in the baseline for stock composition

analysis resulted in stock composition estimates that

were less accurate or less precise than those that would

have resulted from the locus’ exclusion. Our results

indicated that accuracy and precision of stock compo-

sitions were in fact improved by including Ots102 in

the set of loci for stock composition analysis, and this

was true for populations with a widespread geo-

graphical distribution. Beacham et al. (2001) illustrat-

ed that the accuracy of stock composition estimates for

coho salmon O. kisutch was enhanced by assuming

HWE distribution of genotypic frequencies for loci at

which observed genotypic frequencies did not conform

to those expected under HWE. Accurate estimates of

stock composition were also obtained for sockeye

salmon, including a locus where genotypic frequencies

were not in HWE in all populations (Beacham et al.

2005a). We concluded that conformance to HWE in all

populations was not essential for use of loci in stock

composition analysis.

An issue of some interest is the appropriate target

number of fish to survey in a population, and if the

target is not met the minimum number of fish that

should be sampled before a population can be included

in a baseline for stock identification applications. The

TABLE 7.—Percentage of individual Chinook salmon from

coded-wire-tagged samples that were correctly identified to

their river of origin for some British Columbia populations

based on the variation at 13 microsatellite loci; N is the

number of fish analyzed.

Geographic area Population N % Correct

Skeena River–Babine River Babine River 13 15.4Skeena River–Bulkley River Bulkley River 6 100.0Skeena River, lower

drainageKitsumkalum River 15 66.7

North-central BritishColumbia

Atnarko River 33 81.8

Kitimat River 3 0.0East Coast Vancouver Island Quinsam River 4 25.0

Cowichan River 5 0.0West Coast Vancouver Island Nitinat River 5 100.0

Robertson Creek 10 70.0Conuma River 7 85.7

Fraser River, middledrainage

Quesnel River 13 46.2

South Thompson River Shuswap River 17 88.2Adams Rivera 10 80.0

Fraser River, lower drainage Chehalis–Chilliwackrivers

27 88.9

a As the Adams River population has been transplanted from the

Shuswap River, identification of either Adams River or Shuswap River

was considered correct.

TABLE 8.—Estimated stock compositions (percentage; SD in parentheses) of four mixed-stock samples of Chinook salmon

obtained from a troll fishery off the west coast of the Queen Charlotte Islands (QCI; N¼ 140; June 2002), from a troll fishery off

the west coast of Vancouver Island (N ¼ 76; October 2004), from a creel survey based in Victoria, British Columbia (WCVI

Strait of Juan de Fuca [SJF]; N ¼ 406; January–March 2000), and from a troll fishery in the Strait of Georgia (SG; N ¼ 38;

February 2004).

Region QCI WCVI SJF SG

Stikine River 1.2 (1.3) 0.0 (0.4) 0.0 (0.1) 0.0 (0.4)Nass River 0.6 (0.8) 0.0 (0.3) 0.0 (0.1) 0.0 (0.6)Upper Skeena River 3.2 (1.7) 0.0 (0.3) 0.0 (0.0) 0.0 (0.3)Northern British Columbia mainland 3.1 (1.7) 0.0 (0.7) 0.0 (0.1) 0.0 (0.6)Upper Fraser River 2.2 (1.5) 0.0 (0.4) 0.0 (0.2) 0.0 (0.7)Middle Fraser River 1.2 (1.3) 0.0 (0.8) 0.2 (0.3) 0.0 (0.6)Lower Fraser River 0.0 (0.1) 8.6 (3.3) 0.2 (0.3) 0.5 (2.2)North Thompson River 8.0 (2.3) 0.0 (1.8) 0.0 (0.0) 0.0 (0.5)South Thompson River 43.9 (4.3) 1.5 (2.0) 0.0 (0.1) 0.0 (0.7)East Coast Vancouver Island 1.3 (1.0) 8.7 (3.3) 2.0 (0.9) 66.1 (8.5)West Coast Vancouver Island 4.3 (1.7) 0.0 (0.3) 0.0 (0.1) 0.0 (0.8)Puget Sound 0.1 (0.8) 18.1 (4.5) 97.3 (1.0) 33.4 (8.3)Coastal Washington 0.0 (0.6) 2.9 (2.9) 0.0 (0.0) 0.0 (0.3)Lower Columbia River 0.0 (1.0) 43.9 (5.8) 0.0 (0.2) 0.0 (0.2)Upper Columbia River (summer–fall) 17.7 (3.3) 3.7 (2.3) 0.0 (0.1) 0.0 (1.1)Snake River (spring–summer) 2.5 (1.5) 0.0 (0.9) 0.0 (0.1) 0.0 (1.7)North-central Oregon 10.8 (2.7) 11.5 (4.4) 0.0 (0.1) 0.0 (0.3)California Central Valley (fall) 0.0 (0.1) 0.0 (0.3) 0.3 (0.3) 0.0 (0.7)California Central Valley (spring) 0.0 (0.1) 1.0 (1.3) 0.0 (0.1) 0.0 (0.2)

878 BEACHAM ET AL.

number of fish surveyed in a population was variable,

from fewer than 20 individuals to over 600 individuals

surveyed in a population (Table A.1), and this variation

allowed an evaluation of the effect of sample size on

accuracy of estimated stock composition. Estimation of

allele frequencies was possibly subject to sampling

error for populations with smaller numbers of fish

surveyed, particularly for those loci with larger

numbers of alleles. However, if analyses of population

structure indicate that populations with small sample

sizes are grouped with geographically proximate

neighbors, it is unlikely that sampling errors in allele

frequencies have obscured genetic relationships. If

baseline population sample sizes of 20–30 fish were

adequate to provide expected population structure, they

were included in a baseline for stock identification

applications. These populations provided regional

stock composition estimates of a single-population

simulated mixture that were generally in excess of 75%

accuracy, which we judged to be satisfactory for this

application.

A key characteristic of a locus is the number of

alleles that are present when a survey of variation

among populations is conducted. There has been a lack

of consensus about the choice of loci with the

appropriate number of alleles for surveys of population

differentiation (Smouse and Chevillon 1998; Ber-

natchez and Duchesne 2000; Kalinowski 2002).

Kalinowski (2002) suggested that equivalent informa-

tion can be obtained by examining either a few loci

with many alleles or more loci with more moderate

numbers of alleles. In essence, hypervariable loci were

suggested to provide more information on a per-locus

basis than loci with only a modest number of alleles

(Kalinowski 2004). In contrast, O’Reilly et al. (2004)

reported that a measure of genetic differentiation

among populations declined as the number of alleles

observed at the locus increased and that this resulted in

a reduced ability to discriminate among samples.

Studies conducted in our laboratory on sockeye salmon

(Beacham et al. 2002, 2005a) and Chinook salmon

(this study) stock identification have consistently

indicated that the number of alleles observed at

a microsatellite locus is related to the power of the

locus in providing accurate estimates of stock compo-

sition of single-population mixtures when the baseline

populations have a Pacific Rim distribution, analogous

to the results predicted by Kalinowski (2002, 2004) in

analyses of simulated mixtures. Loci with larger

numbers of alleles were more effective in providing

more accurate and precise estimates of stock compo-

sition than were loci with smaller numbers of alleles.

The cumulative number of alleles used in mixed-

stock analysis directly influenced the accuracy of the

estimated stock compositions. Beacham et al. (2005a)

reported that the mean accuracy of estimated stock

compositions for single-population mixtures of sock-

eye salmon obtained by employing a locus with

approximately 80 alleles (72%) was similar to the

results obtained (78%) by employing five loci with 79

alleles total. Very similar results were observed in the

current study on Chinook salmon stock identification,

where the accuracy and precision of the estimated stock

compositions obtained by employing three loci with 55

alleles were equivalent to those obtained from a single

locus with 60 alleles. Both studies indicated that there

was rapid improvement in the accuracy of the

estimated stock compositions until 80–100 alleles were

employed in the analysis. The use of more than 100

alleles for stock composition estimation resulted in

diminishing returns for per-allele accuracy, whereas the

variance of the estimates continued to decline. The

number of alleles or loci to employ in stock

identification applications is dependent upon (1) the

level of accuracy and precision required for the

estimated composition, (2) whether individuals in the

sample must be assigned to specific populations or

regions, and (3) the cost of the analysis.

Estimation of the stock composition of known-origin

samples is a key step in the evaluation of a technique

and a baseline in providing accurate estimates of stock

composition. The three freshwater test fishery samples

all provided virtually 100% accuracy of stock compo-

sition estimates that were appropriate to the region of

origin for fish in the samples. Although the actual stock

composition was unknown for the Yukon River

sample, we would expect that no Chinook salmon of

U.S. origin would be present in the sample, as these

individuals should have returned to natal spawning

grounds further downstream in the Yukon River

drainage. The estimated stock compositions were in

concurrence with this observation. The sample from the

Fraser River test fishery was derived from the early part

of the run, where upper and middle Fraser River

populations would be expected to be present in high

proportions (Beacham et al. 2003a). Estimated stock

compositions again concurred with expectations.

Estimated regional stock compositions of the two

CWT samples were within 2–3% of actual values,

which is an acceptable accuracy for management

applications.

The epitome of stock identification applications

would be the ability to identify individual fish to

specific natal spawning rivers—or in large river

drainages, to specific tributaries in the drainage from

a Pacific Rim distribution of possibilities. The baseline

required for this application would obviously be

complex and extensive, but it should be possible to

PACIFIC RIM CHINOOK SALMON STOCK IDENTIFICATION 879

provide accurate estimates of the origin of individual

fish to discrete geographical locations or river drain-

ages. For example, it is possible to assign individual

sockeye salmon to a specific lake of origin with a high

degree of accuracy, even with a potential Pacific Rim

distribution of possibilities (Beacham et al. 2005a). Of

the 54 geographically diverse populations that we

tested, we were able to assign individual Chinook

salmon to a specific region or major river drainage with

a high degree of accuracy (except for some rivers in

northern British Columbia). Analysis of the CWT

samples indicated that the accuracy of assignment to

a specific population was variable. Some populations

were quite distinctive, and the level of resolution

provided by the 13 microsatellites surveyed in the

study was sufficient for accurate individual identifica-

tion. Other populations, however, required greater

resolution than that available from these 13 micro-

satellites, and additional microsatellites or perhaps

single nucleotide polymorphisms (SNPs; Smith et al.

2005) may be required to achieve high rates of

accuracy for individual identification across geograph-

ically proximate and diverse populations.

Coded wire tags provide identification of the specific

release location and age information for individual fish.

However, no information is provided for fish that lack

a CWT; this is a significant limitation, as typically only

a very small portion of a sample is made up of marked

fish. Genetic variation is inherent in all individuals, and

as such all salmon in a mixed-stock sample potentially

carry a mark that can provide a population-specific

signature. The key then is to discover the markers that,

when used in combination, will provide population-

specific signatures. If genetic markers are to enhance

the information traditionally provided by CWTs, the

challenge for Chinook salmon applications is to

employ a sufficient number of high-quality markers

to allow for identification of individuals to the smallest

geographical unit at a cost that is affordable to

management agencies. Given the current surveys of

microsatellite and SNP variation in Chinook salmon,

we anticipate that individual identification to specific

populations should be generally available within the

next few years.

Evaluation of a technique for stock composition

estimation initially involves analysis of simulated

mixtures to evaluate the accuracy of estimated stock

compositions. Should the technique appear promising,

analysis of known-origin samples independent of the

baseline used for stock composition estimation is

usually conducted. In our study, analysis of simulated

mixed-stock fishery samples and samples of known

origin indicated that reliable estimates of stock

composition were obtained. However, even if reliable

estimates have been obtained from both simulated and

known-origin samples, there is still a potential for

inaccurate stock composition estimates in real fisheries

applications if a significant portion of the mixed-stock

sample has been derived from populations or regions

that are inadequately represented in the baseline.

The final stage of evaluation of a stock composition

analysis technique is to apply the technique to actual

mixed-stock fishery samples and evaluate whether the

results are biologically reasonable. In our study, we

estimated stock compositions from four geographic and

temporally diverse locations in British Columbia. The

sample from the Queen Charlotte Islands troll fishery

was dominated by salmon of South Thompson River

origin along with sizeable contributions from Columbia

River and coastal Oregon populations. Escapement to

the South Thompson River in 2002, the year of the

fishery sampling, was the largest on record, and this

was reflected by the large contribution of these

populations to the sample. The level of CWT marking

of South Thompson River populations is quite modest,

so the contribution of populations from this region

would probably have been underestimated if CWTs

had been employed for stock composition analysis.

Stocks of northern origin (Stikine and Skeena rivers)

were also estimated to be present. No contribution was

estimated from any region north of the Stikine River;

this result would be expected given the location and

timing of the sample. Based on CWT analysis,

contributions from Columbia River and coastal pop-

ulations would be expected (Anonymous 2004), and

this was observed in the sample. The sample from the

troll fishery off the southwest coast of Vancouver

Island would be expected to be dominated by

populations of Puget Sound, Columbia River, and

coastal Oregon origin (Anonymous 2004); this was

indeed observed in the sample. No contribution from

any region north of the Fraser River or the east coast of

Vancouver Island was observed, which was entirely

expected given the location of the sample. Finally,

samples from winter fisheries in the Strait of Juan de

Fuca and Georgia Strait were composed entirely of

local stocks, which again would be the expectation.

In summary, microsatellites provided reliable esti-

mates of stock composition in local fishery samples

even when there was a Pacific Rim distribution of

populations potentially contributing to the sample.

They also provided the capability of assigning

individuals to regions or major river drainages and in

some cases, specific populations. The power of micro-

satellites for stock identification provides fishery

managers with the capability of structuring fisheries

so that exploitation on stocks of conservation concern

can be reduced, while at the same time providing

880 BEACHAM ET AL.

opportunities for harvest of abundant stocks. We

expect that microsatellites will be increasingly em-

ployed in many fisheries management and assessment

applications.

Acknowledgments

A very substantial effort was undertaken to obtain

samples from Chinook salmon for this study. Starting

from the south, we thank C. Garza of the National

Marine Fisheries Service (NMFS) Southwest Fisheries

Center for samples from some California populations.

D. Teel of the NMFS Northwest Fisheries Science

Center provided samples from California, Oregon, and

the Columbia River. J. B. Shaklee of the Washington

Department of Fish and Wildlife provided samples

from Washington and the Columbia River. In southern

British Columbia, we thank various Department of

Fisheries and Oceans (DFO) field staff and First

Nations staff for baseline sample collection. In northern

British Columbia and the central coast, the Kitasoo

Fisheries Program is acknowledged for some central

coast populations. We would like to thank northern

DFO staff who collected and supervised collections in

the Skeena River and central coast drainages. We also

acknowledge the various agencies, organizations, and

companies who collected samples in British Columbia.

These included LGL Ltd. Environmental Research

Associates for the Nass River and the Gitxsan

Watershed Authority for the Skeena River drainage.

We are also highly appreciative to W. Heard of the

NMFS Auke Bay Laboratory for providing samples

from southeast Alaska. S. Johnston and P. Milligan of

the DFO Whitehorse office supervised collections of

the Canadian portion of the Yukon River drainage, and

P. Etherton and I. Boyce supervised collections in the

transboundary rivers. J. Wenburg of the U.S. Fish and

Wildlife Service’s Anchorage genetics laboratory pro-

vided samples from the Alaskan portion of the Yukon

River drainage. L. Fitzpatrick drafted the map. C.

Wallace assisted in the analysis. P. Moran of the

NMFS Seattle laboratory and an anonymous reviewer

provided many suggestions for improvements to the

manuscript. Funding for the study was provided by the

DFO.

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Appendix follows

882 BEACHAM ET AL.

Appendix: Chinook Salmon Samples

TABLE A.1.—Region, sampling location, sample collection years, annual sample sizes, and total number of fish sampled (N)

for over 52,000 Chinook salmon surveyed from 325 sites in the Pacific Rim. Allele frequencies for all location samples surveyed

in this study are available at http://www-sci.pac.dfo-mpo.gc.ca/mgl/default_e.htm.

Population Years Annual sample size(s) N

Russia1. Pahacha River 2001, 2002 12, 51 632. Bistraya River 2001 110 1103. Bolshaya River 2002 150 1504. Vorovskaya River 2003 49 495. Tigil River 2002 50 506. Pymta River 2002 141 1417. Kamchatka River 2003 45 458. Palana River 2002 50 509. Olijutarchie River 2002 19 1910. Karymay River 2002 35 3511. Kol River 2003 49 4912. Kikchik River 2003 34 3413. Avacha River 2003 34 34

Upper Yukon River(Yukon River, Canada)14. Whitehorse Hatchery 1985, 1987, 1997 39, 89, 113 24115. Takhini River 1997, 2002, 2003 63, 67, 38 16816. Michie Creek 1994 47 4717. Wolf Creek 1995, 1999, 2003 49, 5, 4 58

Teslin River drainage(Yukon River, Canada)18. Nisutlin River 1987, 1997 17, 38 5519. Morley River 1997, 2002, 2003 9, 8, 12 29

Yukon–Carmacks region(Yukon River, Canada)20. Big Salmon River 1987, 1997 76, 35 11121. Tatchun Creek 1987, 1996, 1997, 2002, 2003 27, 200, 58, 36, 48 36922. Little Salmon River 1987, 1997 20, 74 94

Yukon main stem–Nordenskiold region(Yukon River, Canada)23. Yukon River (main stem) 1987, 2002 8, 19 2724. Minto River 1997 11 1125. Nordenskiold River 2003 106 106

Pelly River drainage(Yukon River, Canada)26. Blind Creek 2003, 2004 138, 23 16127. Pelly River 1996, 1997 39, 113 15228. Earn River 2003, 2004 36, 19 5529. Little Kalzas River 2003, 2004 33, 7 4030. Big Kalzas River 2003 24 2431. Glenlyon River 2003 24 24

Stewart River drainage(Yukon River, Canada)32. Mayo River 1992, 1997, 2003 129, 32, 38 19933. Stewart River 1996, 1997 13, 99 112

Yukon River, lower Canadian(Yukon River, Canada)34. Klondike River 1995, 1999, 2001, 2002, 2003 5, 7, 10, 21, 70 11335. Chandindu River 1998, 2001, 2003, 2004 123, 158, 85, 201 567

Kluane River drainage(Yukon River, Canada)36. Tincup Creek 2003 32 32

Yukon River, upper Alaska(Yukon River, USA)37. Beaver Creek 1997 91 9138. Chandalar River 2002, 2003 4, 112 116

Tanana River drainage(Yukon River, USA)39. Chena River 2001 180 18040. Chatanika River 2001 19 1941. Salcha River 2003 52 52

Yukon River, middle Alaska(Yukon River, USA)42. Tozitna River 2002, 2003 197, 250 44743. Melozitna River 2003 27 27

PACIFIC RIM CHINOOK SALMON STOCK IDENTIFICATION 883

TABLE A.1.—Continued.

Population Years Annual sample size(s) N

Koyukuk River (Yukon River, USA)44. Henshaw Creek 2001 147 14745. Gisasa River 2001 196 19646. Koyukuk River (south fork) 2003 55 5547. Kateel River 2002 19 19

Lower Yukon River, Alaska(Yukon River, USA)48. Anvik River 2002, 2003 75, 38 11349. Andreafsky River 2003 207 20750. Andreafsky River (east fork) 2002 28 2851. Archuelingik River 2002 17 17

Alsek River drainage52. Blanchard River 2000, 2001, 2002, 2003 86, 114, 116, 60 37653. Klukshu River 2000, 2001 238, 194 43254. Takhanne River 2000, 2001, 2002, 2003 14, 54, 72, 48 18855. Tatshenshini River 2001 24 24

Taku River56. Little Tatsamenie River 1999 204 20457. Nahlin River 1999, 2004 13, 119 13258. Little Trapper River 1999 72 7259. Nakina River 2004 110 11060. Dudidontu River 2002, 2004 28, 76 104

Stikine River61. Little Tahltan River 1999, 2001, 2004 200, 213, 193 60662. Andrew Creek 2000 145 14563. Christina Lake 2000, 2001, 2002 69, 77, 92 23864. Verrett River 2000, 2002, 2003 224, 161, 82 46765. Shakes Creek 2000, 2001, 2002 42, 99, 18 15966. Craig River 2001 114 11467. Johnny Tashoots Creek 2001, 2004 5, 19 24

Southeast Alaska, Unuk River68. Unuk River 1999 192 19269. King Salmon River 1999 57 5770. Chickamin River 1999 116 116

Queen Charlotte Islands71. Yakoun River 1987, 1989, 1996, 2001 27, 59, 80, 35 201

Nass River72. Kwinageese River 1991, 1995, 1996, 1997 14, 35, 87, 163 29973. Damdochax Lake 1995, 1996, 1997 64, 98, 86 24874. Meziadin Lake 1995, 1996, 1997 50, 111, 34 19575. Owegee River 1995, 1996, 1997 53, 128, 39 22076. Seaskinnish River 1995, 1996, 1997 40, 53, 6 9977. Tseax River 1995, 1996, 2002 33, 54, 93 18078. Cranberry River 1995, 1996, 1997 3, 103, 58 16479. Snowbank River 1996 54 5480. Kincolith River 1996, 1999 239, 48 28781. Teigen River 1996, 1997 24, 7 31

Skeena River, upper drainge82. Bear River 1991, 1995, 1996 99, 25, 53 17783. Sustut River 1995, 1996, 1999, 2001, 2002 38, 41, 90, 200, 47 41684. Slamgeesh River 2004 34 34

Skeena River–Babine River drainage85. Babine River 1994, 1995, 1996 27, 47, 192 266

Skeena River–Bulkley River drainage86. Bulkley River 1991, 1996, 1998, 1999 112, 112, 213, 148 58587. Morice River 1991, 1995, 1996 100, 50, 77 22788. Harold Price Creek 2004 13 13

Skeena River, middle drainage89. Kitwanga River 1991, 1996, 2002, 2003 99, 19, 71, 99 28890. Kispiox River 1985, 1989, 1991, 1995, 2004 31, 24, 21, 25, 62 16391. Sweetin River 2004 46 46

Skeena River, lower drainge92. Ecstall River 1995, 2000, 2001, 2002, 2003 17, 43, 66, 61, 106 29393. Lower Kitsumkalum River 1991, 1995, 1996, 1998, 2001 111, 25, 42, 83, 196 45794. Lower Kitsumkalum

(above canyon)1991, 1998, 2001 70, 95, 25 190

95. Cedar River 1996 116 11696. Gitnadoix River 2002, 2003 22, 20 4297. Thomas Creek 2004 21 21

884 BEACHAM ET AL.

TABLE A.1.—Continued.

Population Years Annual sample size(s) N

North-central British Columbia98. Kitimat River 1996, 1997, 1998 260, 147, 75 48299. Wannock River 1991, 1996, 1997, 2000 51, 216, 69, 171 507100. Atnarko River 1991, 1996 56, 219 275101. Upper Atnarko River 1996 155 155102. Kilbella River 1996, 1998, 2000, 2001 49, 22, 40, 46 157103. Chuckwalla River 1996, 1998, 1999, 2000, 2001 94, 45, 83, 8, 49 279104. Kildala River 1996, 1997, 1998, 1999, 2000 112, 90, 59, 86, 94 441105. Nusatsum River 1996 43 43106. Saloompt River 1996 96 96107. Hirsch River 1998, 1999, 2000 136, 157, 181 474108. Neechanze River 2000, 2002, 2003 28, 13, 16 57109. Ashlulm River 2000, 2002, 2003 27, 18, 19 64110. Sheemahant River 2003 17 17111. Kwinamass River 2000, 2001, 2002 3, 135, 137 275112. Kloiya River 2001 46 46113. Upper Dean River 2001, 2002, 2003, 2004 31, 9, 11, 31 82114. Dean River 2002, 2003 13, 25 38115. Docee River 2002 49 49116. Takia River 2002, 2003 9, 21 30117. Kitlope River 2004 120 120118. Kateen River 2004 74 74119. Ishkheenickh River 2004 88 88

Southern British Columbia120. Squamish River 1990, 1996, 1997 54, 18, 85 157121. Mamquam River 1996 20 20122. Porteau Cove 1996, 2003 158, 199 357123. Shovelnose Creek 1996, 2002 18, 2 20124. Bute River 1991 67 67125. Klinaklini River 1997, 1998, 2002 213, 42, 147 402126. Devereux River 1997, 1998, 2000 214, 89, 26 329127. Homathko River 1997, 1998 20, 32 52128. Phillips River 2000 26 26129. Capilano River 1999 126 126

East Coast Vancouver Island130. Little Qualicum River 1996, 1998 166, 43 209131. Big Qualicum River 1988, 1992, 1996, 1997 49, 41, 149, 135 374132. Big Qualicum–Lang Creek 1998, 2000 138, 155 293133. Quinsam River 1988, 1992, 1996, 1997, 1998 96, 42, 152, 102, 65 457134. Nanaimo River (spring) 1998 99 99135. Nanaimo River (summer) 1988, 1996, 2002 54, 137, 88 279136. Nanaimo River (fall) 1996, 1997, 1998, 1999, 2002 150, 71, 146, 99, 80 546137. Nanaimo River (upper) 2003, 2004 24, 94 118138. Cowichan River 1988, 1996, 1999, 2000 40, 147, 349, 148 684139. Chemainus River 1996, 1999 159, 103 262140. Nimpkish River 1996 57 57141. Puntledge River (summer) 1988, 1996, 1997, 1998, 2000 131, 196, 209, 164, 201 901142. Puntledge River (fall) 1996, 1997, 2000, 2001 60, 127, 194, 195 576143. Quatse River 1996, 2000 27, 11 38144. Woss Lake 2001 31 31145. Goldstream River 1998 22 22

West Coast Vancouver Island146. Robertson Creek 1988, 1996, 2003 48, 155, 183 386147. Stamp River 1973, 1996 155, 148 303148. Conuma River 1988, 1996, 1997, 1998 46, 215, 143, 52 456149. Nitinat River 1989, 1996, 2003 53, 153, 140 346150. Kennedy River 1992 49 49151. Thornton Creek 1992, 1999, 2000, 2001 37, 147, 150, 184 518152. Marble River 1994, 1996, 1999, 2000 58, 98, 149, 192 497153. Sarita River 1996, 1997, 2001 113, 157, 145 415154. Nahmint River 1996, 2001, 2002, 2003, 2004 27, 56, 51, 124, 40 298155. Tranquille River 1996, 1999 209, 133 342156. San Juan River 2001, 2002 80, 116 196157. Burman River 1985, 1989, 1990, 1991, 1992, 2000, 2002, 2003 20, 35, 19, 56, 35, 34, 51, 13 263158. Toquart River 1999, 2000 71, 16 87159. Robertson Creek–Muchalat 2002 33 33160. Robertson Creek–Gold River 1987, 1992, 1999, 2002 58, 82, 44, 42 226161. Gold River 1983, 1985, 1986 6, 13, 71 90162. Colonial Creek 1999, 2004 40, 19 59

PACIFIC RIM CHINOOK SALMON STOCK IDENTIFICATION 885

TABLE A.1.—Continued.

Population Years Annual sample size(s) N

163. Tahsis River 1996, 1999, 2002, 2003 72, 87, 104, 47 310164. Tlupana River 2002, 2003 34, 32 66

Fraser River, upper drainage165. James Creek 1984, 1988 48, 9 57166. Dome Creek 1991, 1994, 1995, 1996, 2000 34, 51, 94, 148, 25 352167. Salmon River near Prince George 1996, 1997 109, 131 240168. Tete Jaune 1993, 1994, 1995, 2001 66, 94, 88, 205 453169. Chilliwack River (red fleshed) 1994, 1999 30, 133 163170. Chehalis River (red fleshed) 1994, 1999 42, 84 126171. Bowron River 1995, 1997, 1998, 2001 57, 39, 78, 2 176172. Horsey Creek 1995, 1997, 2000, 2001, 2002 13, 11, 3, 3, 5 35173. Goat River 1995, 1997, 2000, 2001, 2002 12, 12, 3, 35, 8 70174. Holmes River 1995, 1996, 1999, 2000, 2001, 2002 43, 54, 14, 20, 8, 65 204175. Swift Creek 1995, 1996, 2000, 2001 63, 164, 38, 113 378176. Slim Creek 1995, 1996, 1998, 2001 65, 6, 40, 86 197177. Indian Point Creek 1995 42 42178. Willow River 1995, 1996, 1997, 2000, 2002 62, 9, 11, 1, 2 85179. Fontoniko Creek 1996 57 57180. Holliday Creek 2001, 2002 4, 17 21181. McGregor River 1997 119 119182. Small Creek 1998, 2000, 2001, 2002 10, 2, 1, 5 18183. Nevin Creek 2001, 2002 3, 26 29184. Kenneth Creek 2001, 2002 17, 61 78185. Ptarmigan Creek 2000, 2002 14, 7 21186. Walker Creek 2000, 2001 3, 39 42187. Morkill River 2001 208 208188. Torpy River 2001 170 170189. Robson River 2000, 2002 1, 21 22

Fraser River, middle drainage190. Nazko River 1983, 1984, 1985 120, 24, 50 194191. Baezaeko River 1984, 1985 45, 37 82192. Quesnel River 1990, 1994, 1995, 1996, 1997 20, 77, 100, 276, 95 568193. Stuart River 1991, 1992, 1994, 1995, 1996 95, 67, 109, 108, 175 554194. Nechako River 1991, 1992, 1994, 1995, 1996 81, 120, 84, 101,198 584195. Chilko River 1994, 1995, 1999, 2001, 2002 43, 78, 14, 35, 50 220196. Bridge River 1994, 1995, 1996 23, 35, 326 384197. Cottonwood River 1995 53 53198. Elkin Creek 1995, 1996 19, 216 235199. Upper Chilcotin River 1995, 1996, 1997, 1998, 2001 10, 12, 5, 19, 230 276200. Chilcotin River (mixed) 1997 47 47201. Portage Creek 1995, 1996, 2001, 2002 4, 27, 14, 176 221202. Horsefly River 1996, 1997 14, 15 29203. Lower Cariboo River 1996, 1998 12, 10 22204. Upper Cariboo River 2001 171 171205. Lower Chilcotin River 1996, 2000, 2001 74, 34, 102 210206. Westroad River 1996, 1997 2, 31 33207. Endako River 1996, 1997, 1998, 2000 4, 25, 32, 24 85208. Taseko River 1997, 1998, 2001, 2002 37, 27, 18, 97 179209. Chilako River 1998 45 45

Fraser River, lower drainage210. Big Silver Creek 1996, 2002, 2003 16, 71, 26 113211. Birkenhead River 1993, 1994, 1996, 1997, 1998, 1999,

2000, 2001, 2002, 200343, 3, 31, 22, 27, 19, 31, 28, 20, 27 251

212. Harrison River 1988, 1992, 1994, 1999 134, 99, 100, 215 548213. Upper Pitt River 2002, 2003, 2004 30, 58, 16 104214. Maria Slough 1999, 2000, 2001, 2002 31, 28, 154, 89 302215. Chilliwack River (fall) 1994, 1995, 1998, 1999, 2002 83, 89, 132, 139, 9 452216. Stave River–Chilliwack River 1999, 2000, 2001, 2002 48, 23, 184, 124 379

North Thompson River217. Raft River 1995, 1996, 2002 14, 115, 62 191218. Mahood River 1995 19 19219. Finn Creel 1996, 1998, 2002 101, 35, 24 160220. Clearwater River 1997, 1998 257, 5 262221. Barriere River 2000, 2001, 2002 18, 25, 12 55222. Blue River 2000, 2001, 2002 8, 6, 38 52223. Lemieux Creek 2000, 2001, 2002 2, 32, 61 95224. North Thompson main stem 2001 115 115

South Thompson River225. Lower Shuswap River 1994, 1995, 1996, 1997 130, 73, 90, 42 335

886 BEACHAM ET AL.

TABLE A.1.—Continued.

Population Years Annual sample size(s) N

226. Middle Shuswap River 1994, 1995, 1997, 2001 109, 86, 118, 53 366227. Eagle River 1995, 2001 36, 3 39228. Salmon River (Salmon Arm) 1995, 1996, 1997, 1998, 1999 9, 72, 56, 49, 35 221229. Lower Adams River 1996, 2001, 2002 103, 39, 42 184230. South Thompson River 1996, 2000, 2001 201, 21, 44 266231. Little River 1996, 2001 53, 72 125232. Bessette Creek 1998, 2001, 2002 17, 22, 18 57233. Lower Shuswap–upper Adams River 1993, 1997 24, 21 45234. Duteau Creek 2001, 2002 42, 6 48235. Seymour River near Thompson 2002 13 13

Lower Thompson River236. Lower Thompson River 2001 176 176237. Nicola River 1992, 1994, 1995, 1997, 1998, 1999 54, 73, 75, 49, 77, 92 420238. Coldwater River 1994, 1995, 1996, 1997, 1998, 1999 27, 31, 75, 43, 26, 32 234239. Spius Creek 1996, 1998, 1999 58, 42, 34 134240. Deadman River 1996, 1997, 1998, 1999 132, 61, 53, 45 291241. Bonaparte River 1996 306 306242. Louis Creek 1996, 1997, 1999, 2000, 2001 32, 107, 183, 31, 200 553243. Coldwater River (upper; spring) 2001 141 141244. Spius Creek (upper; spring) 2001, 2002 116, 15 131

Boundary Bay245. Little Campbell River 2002 91 91246. Serpentine River 2002 46 46

Puget Sound247. Skagit River (summer) 1994, 1995, 1996 90, 92, 100 282248. White River (fall) 1994 100 100249. Nooksack River at Kendall Hatchery 1998 100 100250. Green Rvier at Soos Hatchery 1998 100 100251. Green River at Kendall Hatchery 1998 50 50252. Skykomish River (summer) 1996 75 75253. Stillaguamish River 1996 88 88

Strait of Juan de Fuca254. Elwha River (fall) 1996 100 100

Coastal Washington255. Solduc River (fall) 1995 98 98256. Quinault River (fall) 1995, 1997 47, 17 64257. Hoh River (spring) 1995, 1996, 1997 18, 30, 11 59258. Queets River 1997 59 59

Lower Columbia River259. Abernathy Creek (fall) 1995 100 100260. Coweeman River 1996 77 77

Willamette River261. Sandy River (spring)a 1997 92 92262. North Santiam River 1997 99 99263. McKenzie River 1997 12 12264. North Fork Clackamas River 1997 80 80

Middle Columbia River265. Naches River (spring) 1993 31 31266. Granite Creek 2000 20 20267. Middle Fork John Day River 2000 40 40268. North Fork John Day River 2000 40 40269. John Day River (main stem) 2000 36 36

Upper Columbia River (spring)270. Chewuch River 1993 100 100271. Twisp River 1995 100 100272. Chiwawa River 1993 100 100273. Entiat River 2002 64 64

Upper Columbia River (summer–fall)274. Similkameen River 1993 100 100275. Wenatchee River 1993 100 100276. Hanford Reach 1998 100 100277. Deschutes River 1998 100 100278. Okanagan River 2003 13 13

Snake River (spring–summer)279. Tucannon River 1995 100 100280. McCall Hatchery 1989 41 41281. Valley Creek 1989 43 43282. Imnaha River 1999 99 99283. Rapid River 1997 80 80

PACIFIC RIM CHINOOK SALMON STOCK IDENTIFICATION 887

TABLE A.1.—Continued.

Population Years Annual sample size(s) N

284. Valley Creek (upper) 1998 78 78285. Wenaha River 1998 43 43286. Marsh Creek 1989, 1991, 1998, 1999 59, 39, 52, 70 220287. South Fork Salmon River 1997 32 32288. Upper Salmon River 1989, 1992, 1993 50, 60, 55 165289. East Fork Salmon River 1999 53 53290. Frenchman Creek 1992 60 60291. Decker Flat 2000 17 17292. Snake River (unknown timing) 1993 51 51

Snake River (Fall)293. Lyon’s Ferry 1993, 1998 91, 20 111

Northern and central Oregon coast294. Trask River Hatchery (spring) 1997 48 48295. Trask River Hatchery (fall) 1997 100 100296. Euchre Creek (fall) 1996 57 57297. Umpqua River (fall) 1997, 1998 23, 70 93298. Elk River (fall) 1995 69 69299. Nehalem River (summer) 1996 53 53300. Siuslaw River (fall) 1995 37 37

Southern Oregon coast301. Cole Rivers Hatchery (spring) 1995 50 50302. Hunter Creek (fall) 1995 100 100303. Winchuck River (fall) 1995 80 80304. Lobster Creek (fall) 1998 48 48305. Pistol River (fall) 1995 100 100

Klamath River–Trinity River306. Blue Creek (fall) 1999 94 94307. Salmon River (spring) 1998 29 29308. Trinity River (spring) 1998 100 100309. Trinity River (fall) 1998 100 100310. South Fork Trinity River 1997 15 15

California Central Valley (spring)311. Butte Creek 2000 44 44312. Feather River 1999, 2000 30, 52 82313. Yuba River 2000 32 32314. Deer Creek 2000 15 15

California Central Valley (fall)315. Sacramento River 1993, 1995 40, 96 136316. Sacramento River(late fall)

1995 96 96

317. Mokelumne River 1995 96 96318. Tuolumne River 1998 35 35319. Merced River 1998, 1999 120, 80 200320. Yuba River 2000 51 51321. Stanislaus River 1998 25 25322. American River 1999 69 69323. Feather River 1999, 2000 80, 48 128324. Battle Creek 1999 40 40325. Butte Creek 2000 49 49

a Chinook salmon have been transplanted from the Willamette River spring run to the Sandy River spring run, and given the genetic similarity

between the Willamette and Sandy River populations, the spring-run Sandy River population was grouped with the Willamette River drainage.

888 BEACHAM ET AL.