Population Structure and Stock Identification of Chum Salmon from Western Alaska Determined with Microsatellite DNA and Major Histocompatibility Complex Variation

Home , Alagnak River, Mulchatna River, Sheenjek River, Unalakleet River

American Fisheries Society Symposium 70:141–160, 2009

Te r r y D. Be a c h a m *, Kh a i D. Le, Mi c h a e l We tkl o , Br e n d a McIn t o s h , To b i Mi n g , a n d Kr i s t i n a M. Mi ll e r Fisheries and Oceans Canada, Pacific Biological Station 3190 Hammond Bay Road, Nanaimo, British Columbia V9T 6N7, Canada

Abstract.—Microsatellite and major histocompatibility complex (MHC) variation was surveyed to evaluate population structure and the potential for genetic stock identification in chum salmon Oncorhynchus keta populations largely bordering the eastern Bering Sea and northwestern Gulf of Alaska. Variation at 14 microsatellite loci and one MHC locus was surveyed for 59 populations in the study. The genetic

differentiation index (Fst) over all populations and loci was 0.023, with individual locus values ranging from 0.007 to 0.058. At least 10 regional stocks were observed

in the survey area, with populations from Kotzebue Sound (mean Fst value usually

>0.02 in regional comparisons) and the Alaska Peninsula (Fst >0.03) the most distinct of Alaskan populations surveyed. For stock identification applications incorporating DNA variation, chum salmon sampled in rivers’ tributaries to the eastern Ber- ing Sea were classified into the following regions: Kotzebue Sound, Norton Sound, lower/middle Yukon River, Tanana River, Kuskokwim River, Nushagak River, north/ central Bristol Bay, southwest Bristol Bay, northern Alaska Peninsula/Aleutian Is- lands, southwest Alaska Peninsula, and southeast Alaska Peninsula. For simulated single-region mixtures, estimated regional stock compositions were generally above 90% for the previously-listed regions except for the Kuskokwim River, Nushagak and north/central Bristol Bay regions. Incomplete characterization of DNA variation for populations in those regions most likely contributed to the lower accuracies of estimated stock compositions in the simulated mixtures. Estimated regional stock compositions of simulated samples comprising fish from several regions were within 1–3% of actual values provided that no contributions from the three under-represented regions were included in the simulated samples. Microsatellite and MHC variation has the potential to provide accurate estimates of regional stock composition for chum salmon fisheries in the Bering Sea and northern Gulf of Alaska.

*Corresponding author: [email protected]

141 142 Beacham et al. Introduction stock fisheries (Winther and Beacham 2009, this volume). Initial surveys of population Determination of the origin of salmon structure of western Alaska and Yukon Riv- in mixed-stock fisheries is key for effective er chum salmon that incorporated allozyme management of the fishery. For chum salmon variation were described by Wilmot et al. Oncorhynchus keta, scale variation initially (1994). Yukon River fall-run populations provided a technique for determination of or- were generally distinct from summer-run igin of individuals to large geographic areas populations in the same drainage. Northern (Tanaka et al. 1969; Ishida et al. 1989), and Alaska Peninsula populations were generally in some cases reportedly to a specific river distinct from those in Bristol Bay and more drainage (Nikolayeva and Semenets 1983). northern areas in Alaska, but some similiarity Variation in trace elements in otoliths have occurred between Peninsula populations and also been reported to be effective in stock those in Russia. In a more extensive study of identification for Korean populations (Sohn allozyme variation in western Alaska chum et al. 2005). Annual stability of the characters salmon, Seeb and Crane (1999a) surveyed a used to discriminate among stocks is a key number of populations including some from requirement of any technique used in stock Kotzebue Sound, Norton Sound, summer- identification, particularly if the baseline run Yukon River, Kuskokwim River, Kanek- populations cover a wide geographic area. tok River, Goodnews River, and the Alaska Techniques that rely on environmental varia- Peninsula. They found that populations from tion for stock identification, such as scale pat- Norton Sound, lower Yukon River summer- tern analysis or more recently elemental anal- run populations, the Kuskokwim River, and ysis coupled with laser ablation, are prone to Bistol Bay formed a homogenous group with annual variation in distinguishing characters little evidence of geographic differentiation. such that annual sampling of contributing Populations from Kotzebue Sound popula- baseline populations is required. In contrast, tions were reported to display some level genetic characters show a pronounced, well of differentiation from those in other areas, defined stability of allele frequencies- rela but they were still part of a larger cluster of tive to population differentiation (Beacham western Alaska populations. In stock iden- and Wood 1999; Tessier and Bernatchez tification applications, summer-run popula- 1999; Miller et al. 2001). Owing to the rela- tions from Kotzebue Sound, Norton Sound, tive stability of genetic characters, baseline the Yukon River, the Kuskokwim River, the samples collected in one year can be used to Kanektok River, and Bristol Bay were all estimate stock compositions of samples col- grouped into a single unit, with little ability lected in following years, or in years previous to provide more refined estimates of stock to collection of the baseline samples. Stock composition for these groups of populations identification applications in chum salmon (Seeb and Crane 1999b). Surveys of direct based upon environmentally-induced varia- DNA variation through examination of am- tion have generally been replaced by appli- plified fragment length polymorphisms have cations based upon genetic variation, owing also been conducted for Yukon River chum to the stability of genetic characters used in salmon with respect to population structure applications. and potential stock identification applica- Surveys of genetic variation have been tions (Flannery et al. 2007a). Improvement demonstrated to be effective in determin- to existing capability for stock identification ing salmonid population structure, as well of Yukon River chum salmon was limited for as determining origins of salmon in mixed- this application. Western Alaska Chum Salmon Structure and Stock Identification 143 Surveys of microsatellite variation have western Alaska during previous analyses of been effective in determining salmonid popula- genetic variation (Seeb and Crane 1999a), tion structure in local areas (Small et al. 1998; from other studies in the region, from fall-run Banks et al. 2000; Beacham et al. 2004; Utter Yukon River populations in the Canadian por- et al. 2009, this volume), as well as broad-scale tion of the Yukon River drainage, and from differences across the Pacific Rim (Beacham two populations in British Columbia. A total et al. 2005, 2006). Microsatellites have also of 59 populations from western Alaska and been of considerable value in estimating stock adjacent regions were surveyed in the study composition in mixed-stock salmon fisheries, (Table 1; Figure 1). DNA was extracted from on both a population-specific (Beacham et al. the tissue samples using a variety of methods, 2003) and regional basis (Beacham et al. 2006). including a chelex resin protocol outlined by For chum salmon, microsatellite variation pro- Small et al. (1998), a Qiagen 96-well Dneasy vides the potential to examine fine-scale popu- procedure, or a Promega Wizard SV96 Ge- lation structure (Chen et al. 2005), as well as nomic DNA Purification system. Once ge- fine-scale estimation of stock composition in nomic DNA was available, surveys of varia- mixed-stock fisheries (Flannery et al. 2007b). tion at 14 microsatellite loci were conducted: In addition to the microsatellite loci, variation Ots3 (Banks et al. 1999), Oke3 (Buchholz et at major histocompatibility complex (MHC) al. 2001), Oki2 (Smith et al. 1998), Oki100 loci has been demonstrated to be very effective (primer sequence 5’ to 3’ F: GGTGTTT- in salmonid population differentiation (Miller TAATGTTGTTTCCT, R: GTTTCCAGAG- and Withler 1997; Miller et al. 2001; Beacham TAGTCATCTCTG), Ots103 (Nelson and et al. 2001). Analysis of variation at microsat- Beacham 1999), Omm1070 (Rexroad et al. ellite and MHC loci may provide greater reso- 2001), Omy 1011 (Spies et al. 2005), One101, lution among western Alaska chum salmon One102, One104, One111, and One114 (Ol- populations than had previously been observed sen et al. 2000), Ssa419 (Cairney et al. 2000), with allozymes. and OtsG68 (Williamson et al. 2002). The objectives of the present study were In general, PCR DNA amplifications for to analyze the variation at 14 microsatel- microsatellites were conducted using DNA lite loci and three major histocompatibility Engine Cycler Tetrad2 (BioRad, Hercules, complex (MHC) allele lineages to evaluate CA) using the thermal cycling profile for each population structure of western Alaska chum locus outlined in Table 2. PCR fragments salmon populations from Kotzebue Sound to were initially size fractionated in denaturing the Alaska Peninsula, and then evaluate the polyacrylamide gels using an ABI 377 auto- utility of the loci surveyed to the practical mated DNA sequencer, and genotypes were issue of providing accurate and precise esti- scored by Genotyper 2.5 software (Applied mates of stock composition in mixed-stock Biosystems (ABI), Foster City, CA) using an fishery samples. Stock composition analysis internal lane sizing standard (GeneScan 500 was accomplished by the analysis of simu- ROX from ABI). Later in the study, microsat- lated mixtures, and by estimation of stock ellites were size fractionated in an ABI 3730 composition from known-origin samples. capillary DNA sequencer, and genotypes were scored by GeneMapper software 3.0 Methods (Applied Biosystems, Foster City, CA) using an internal lane sizing standard (GeneScan Tissue samples were collected from ma- 500 LIZ from ABI). ture chum salmon at a number of rivers in The MHC locus surveyed was described by Miller et al. (2006) as Exon 3. The three 144 Beacham et al.

Ta b l e 1. Stock identification regional groupings, sampling location, sample collection years, and average number of fish successfully genotyped at each locus (N) for chum salmon surveyed from 59 sites. 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 N

Kotzebue Sound 1) Kelly Lake 1991 92 2) Noatak River 1991 45 3) Inmachuk River 2005 194 4) Kobuk River 1991, 2000 374 5) Agiapuk River 2005 180 6) Koyuk River 2005 44 Norton Sound 7) Niukluk River 2004, 2005 223 8) Pilgrim River 1994, 2004, 2005 474 9) Kwiniuk River 2004, 2005 262 10) Snake River 2004, 2005 394 11) Nome River 2004, 2005 204 12) Eldorado River 2004, 2005 390 13) Unalakleet River 2005 191 14) Shaktoolik River 2005 196 15) Ungalik River 2005 50 Lower Yukon River summer run 16) Pikmiktalik River 2004, 2005 388 17) Andreafsky River 1987, 1993, 2004 313 18) Anvik River 1988, 1993 182 19) Nulato River 1988, 2003 123 20) Gisasa River 1988, 2003 279 21) Jim River 2002 147 22) Henshaw Creek 2003 193 23) Tozitna River 2002, 2003 347 24) Melozitna River 2003, 2004 161 Tanana River fall run 25) Toklat River 1990, 1994 241 Upper Alaska fall run 26) Sheenjek River 1987, 1988, 1989 229 27) Chandalar River 1989, 2001 185 Porcupine River 28) Fishing Branch 1987, 1989, 1992, 395 1994, 1997 White River 29) Kluane River 1987, 1992, 2001 453 Mainstem Yukon River 30) Big Creek 1992, 1995 175 31) Minto Landing 1989, 2002 145

Western Alaska Chum Salmon Structure and Stock Identification 145

Ta b l e 1. Continued.

Population Years N

Kuskokwim River/Bay 32) George River 1996 82 33) Kasigluk River 1990 68 34) Kwethluk River 1989 76 35) Nunsatuk River 1994 83 36) Aniak River 1992 86 37) Kanektok River 1989, 1994 171 Nushagak River 38) Stuyahok River 1992, 1993 74 39) Mulchatna River 1994 82 North/Central Bristol Bay 40) Goodnews River 1991 92 41) Togiak River 1993 75 42) Alagnak River 1992 77 43) Naknek River 1993 64 South Bristol Bay 44) Egegik River 1993 86 45) Meshik River 1989 57 46) Gertrude Creek 1987 97 47) Pumice Creek 92 North Peninsula/Aleutians 48 Moller Bay Creek 1998 93 49) Frosty Creek 1992, 2000 179 50) Joshua Green River 1994 95 Southwest Peninsula 51) Coleman Creek 1996 70 52) Volcano Bay Creek 1992 104 53) Delta Creek 1996 78 54) Westward Creek 1993 79 Southeast Peninsula 55) Stepovak Bay 1993 94 56) Big River 1993 87 57) Alogoshak River 1993 91 BC Central Coast 58) Salmon Bay Creek 2004, 2005 147 Fraser River 59) Chilliwack River 1992, 2004 177 146 Beacham et al.

Fi g u r e 1. Locations of regions defined in the study, as well as the test fishery locations at Pilot Station and Bio Island.

Ta b l e 2. Microsatellite loci and their associated annealing and extension temperatures and times as well as the number of cycles used in PCR amplifications.

Locus Annealing Extension Cycles

Oke3 48oC/45s 72oC/45s 35 Oki100 50oC/60s 72oC/60s 33 Oki2 47oC/60s 72oC/60s 33 Omm1070 65oC/60s 72oC/60s 40 Omy1011 50oC/30s 72oC/30s 35 One101 52oC/60s 68oC/60s 30 One102 52oC/60s 72oC/60s 35 One104 52oC/30s 70oC/30s 40 One111 52oC/30s 68oC/60s 30 One114 47oC/30s 68oC/60s 30 Ots103 49oC/60s 72oC/60s 30 Ots3 48oC/60s 72oC/60s 40 OtsG68 50oC/60s 72oC/60s 37 Ssa419 57oC/30s 68oC/60s 35 Western Alaska Chum Salmon Structure and Stock Identification 147 lineages of alleles observed at the locus were the capillary sequencer, and then convert the the A (L-I of Miller et al. (2006)), UA (L-II), sizing in the gel-based data set to match that and B (L-III). The A lineage of alleles was obtained from the capillary-based set. Esti- amplified with a FAM-labeled sense primer mated allele sizes from both systems were 5’ AAGGGTATGATGGAGAGGATTTC very highly correlated, with over 99.5% con- and an antisense primer 5’ GATACTTCT- currence in allele identification for all loci. TAAGCCAATCAATGCA. The B lineage of alleles was amplified with a sense primer Data Analysis NED-labeled 5’ TCCTAAACAGCAAGCT- GAGA and an antisense primer 5’ GATACT- All annual samples available for a loca- TCTTTAGCCACTCAACGCA. The UA tion were combined to estimate population lineage of alleles was amplified with a VIC- allele frequencies, as was recommended by labeled sense primer 5’ ACTTTCAGTATG- Waples (1990). FST estimates for each lo- GTTATGATGGAG and an antisense primer cus were calculated with FSTAT (Goudet 5’ CTTCTTTAGCCACTCAACGC. PCRs 1995). Each population at each locus was were conducted in 6μ total volumes, with the tested for departure from Hardy-Weinberg UA and B lineage PCRs multiplexed. The equilibrium (HWE) using GDA (Lewis and thermal cycling profile for the UA/B multi- Zaykin 2001). Critical significance levels for plex and A lineages involved one cycle of 15 simultaneous tests (59 populations, Table 1) min at 95°C, followed by 33–35 cycles of 60 were evaluated using sequential Bonferroni s at 95°C, 60 s at 50–52°C and 60 s at 72°C adjustment (0.05/59 = 0.0008) (Rice 1989). , and final maintenance at 4°C . The ampli- Cavalli-Sforza and Edwards’ (CSE) (1967) fied products were then analyzed with the chord distance was used to estimate distances ABI 3730 sequencer, with the lineage scored among populations. An unrooted neighbor- as either present or absent in individual fish. joining tree based upon CSE was generated With three possible lineages, six genotypes using NJPLOT (Perriere and Gouy 1996). were possible (A/A, A/UA, A/B, UA/B, UA/ Bootstrap support for the major nodes in the UA, and B/B), which would be equivalent to tree was evaluated with the CONSENSE pro- a locus with three observed alleles. gram from PHYLIP based upon 1000 repli- cate trees (Felsenstein 1993). Computation Conversion of allele sizes between gel- of the number of alleles observed per locus based and capillary sequencers was carried out with GDA (Lewis and Zaykin 2001). Allele frequencies for all location Changing automated sequencers dur- samples surveyed in this study are available ing the course of the study required the ca- at http://www-sci.pac.dfo-mpo.gc.ca/mgl/ pability to convert alleles sizes estimated on Default_e.htm. the gel-based sequencer (ABI 377) to those estimated on the capillary-based sequencer Estimation of stock composition (ABI 3730). In order to convert allele sizes between the two techniques, we analyzed Allele frequencies were determined for approximately the same 600 fish using both each locus in each population and the Statisti- techniques and determined the distributions cal Package for the Analysis of Mixtures soft- of allele frequencies. By inspection of the ware program (SPAM version 3.7) (Debevec allele frequencies, we were able to match et al. 2000) was used to estimate stock compo- specific allele sizes obtained from the gel- sition of simulated mixtures. The Rannala and based sequencer to specific allele sizes from 148 Beacham et al. Mountain (1997) correction to baseline allele by Pella and Masuda (2001) formed the ba- frequencies was used in the analysis in order to sis of the estimation of stock composition of avoid the occurrence of fish in the mixed sam- the known-origin samples. A modified ver- ple from a specific population having an allele sion of the program was developed by the not observed in the baseline samples from that Molecular Genetics Laboratory as a C-based population. All microsatellite loci were con- program (cBAYES), which is available from sidered to be in Hardy-Weinberg equilibrium, Molecular Genetics Laboratory laboratory and expected genotypic frequencies were de- website (http://www-sci.pac.dfo-mpo.gc.ca/ termined from the observed allele frequencies. mgl/data_e.htm). In the analysis, ten 20,000- Observed genotypic frequencies for the MHC iteration Monte Carlo Markov chains of es- alpha2 locus in each population were used in timated stock compositions were produced, estimation of stock compositions. Reported with initial starting values for each chain stock compositions for simulated fishery sam- set at 0.90 for a particular population, which ples are the bootstrap mean estimate of each was different for each chain. Estimated stock mixture of 150 fish analyzed, with mean and compositions were considered to have con- variance estimates derived from 100 boot- verged when the shrink factor was <1.2 for strap simulations. Each baseline population the ten chains (Pella and Masuda 2001). The and simulated single-population sample was last 1,000 iterations from each of the ten sampled with replacement in order to simulate chains were then combined, and the mean random variation involved in the collection of and standard deviations of estimated stock the baseline and fishery samples. Analysis of compositions determined. As only the last simulated mixtures can be regarded as the ini- 1,000 iterations of each chain were used, and tial step in evaluating the power of a set of loci each chain converged to an equivalent an- for stock composition estimation. Analysis of swer, the initial starting values for each chain single-region mixtures provided some initial were irrelevant. evaluation of the capability of the set of genet- Additional samples were obtained from ic markers employed to provide accurate and test fisheries at Pilot Station in the lower Yu- precise estimates of stock composition, with a kon River drainage and Bio Island in the up- target of at least 90% accuracy anticipated for per drainage in the Yukon Territory adjacent accurate estimation of stock composition for to the border between Alaska and the Yukon the region. Multi-region mixtures were also Territory (Figure 1). Fish sampled in these simulated, including those regions well esti- test fisheries were assumed to be of Yukon mated in the single region analyses to those River origin. A sample of 498 fish collected inadequately estimated. between 19 July to 11 August 2004 at Pi- lot Station was surveyed at 12 microsatel- Analysis of known-origin samples lite loci only (Ots103, Omm1070, MHC not surveyed). A sample of 268 fish collected A known-origin mixture of 200 fish was between 9 August to 4 October 2004 at Bio constructed by removing five populations Island was surveyed for 12 microsatellite loci from the baseline and randomly selecting 50 only, and a sample of 494 fish collected be- fish from the Egegik River population, 40 tween 30 July to 8 October, 2005 was sur- fish from Frosty Creek, 50 fish from Kelly veyed for 14 microsatellite loci (no MHC Lake, 30 fish from Nome River, and 30 fish locus). Stock compositions of these samples from Henshaw Creek. The baseline was thus were estimated with cBAYES incorporating reduced to 54 populations for the analysis. the 59-population baseline. A Bayesian procedure (BAYES) as outlined Western Alaska Chum Salmon Structure and Stock Identification 149 Results In pairwise comparisons of over 59 populations, the two populations from British Loci surveyed Columbia were the most distinctive group

of populations in the survey, with a mean Fst The number of alleles observed at the 14 value usually >0.03 in regional comparisons microsatellite loci surveyed ranged from 20 (Table 4). Significant genetic differentiation was also observed between Alaska Peninsula to 136 (Table 3). Fst values for individual loci ranged from 0.01 to 0.06, with larger values populations and those from western Alaska typically associated with loci with fewer al- (Table 4). Higher within-region pairwise Fst leles. The genotypic frequencies observed values were observed in some regional groups at the 14 loci generally conformed to those defined (Yukon River fall run, Alaska Penin- expected under Hardy-Weinberg equilibrium sula, British Columbia) as a result of inclusion (HWE), with the possible exception of Oke3. of genetically distinct groups of populations For this locus, observed heterozygosity was within each of these three regional groups. an average of 4% less than that expected (Ta- Higher observed variation within Kotzebue ble 3). Sound was due to comparisons including the distinctive Imnachuk River population. The Population structure overall level of genetic differentiation among regional groups was indicative of only mod- Cluster analysis of variation at gene loci est differentiation among groups. generally grouped populations based upon geography (Figure 2). For example, regional Analysis of simulated single-region mix- structure was observed for populations from tures the Yukon River, South Bristol Bay and the Alaska Peninsula. Yukon River populations For Kotzebue Sound, analysis of simu- were differentiated from those further south lated single-region mixtures (Agiapuk River, (Kuskokwim River, northern Bristol Bay), Imnachuk River, Kobuk River, and Kelly and those further north (Norton Sound, Kot- Lake in equal proportions in the mixtures) zebue Sound). The Pikmiktalik River popula- indicated that accurate estimates (90% ac- tion was similar to lower Yukon River popla- curacy) of stock composition for this region tions (Figure 2), and was considered to be should be possible (Figure 3). Norton Sound part of the lower Yukon River complex for simulated mixtures (Eldorado River, Niuk- stock identification applications. Similarly luk River, Nome River, Pilgrim River, Snake the Agiapuk River and Koyuk River popula- River, Unalakleet River in equal proportions) tions were clustered with other populations were also estimated with acceptable levels of from Kotzebue Sound and were considered accuracy. The lower Yukon River summer- to be part of the Kotzebue Sound complex of run component (Andreafsky River, Anvik populations (Table 1). The Kanektok River River, Gisasa River, Henshaw Creek, Jim population was considered to be part of the River, Tozitna River) was identifiable as a Kuskokwim River complex of populations. distinct component (Figure 3). The Tanana The results from the chum salmon popula- River fall component (Toklat River) was esti- tions surveyed to date suggest that unsampled mated with an accuracy approaching 95%, as populations contributing to mixed-fishery was the Porcupine River component (Fishing samples will probably be allocated to sam- Branch). pled populations in the same region. Simulated samples incorporating only Kuskokwim River populations (Aniak Riv- 150 Beacham et al.

a b l e T 3. Number of alleles per locus, genetic differentiation index (Fst), expected heterozygosity (He), observed heterozygosity (Ho), and number of significant Hardy-Weinberg equilibrium genotypic fre-

quency tests (HWE) for 59 populations listed in Table 1. Standard deviation of Fst is in parenthesis.

Locus Number Fst He Ho HWE of alleles

Oke3 20 0.058 (0.012) 0.69 0.65 5 Ots3 27 0.046 (0.008) 0.75 0.75 3 Oki2 28 0.049 (0.011) 0.86 0.86 2 Ssa419 28 0.016 (0.003) 0.84 0.83 1 Oki100 30 0.023 (0.006) 0.88 0.88 1 One104 34 0.014 (0.003) 0.93 0.93 1 Omy1011 35 0.020 (0.004) 0.92 0.91 0 Omm1070 46 0.007 (0.001) 0.96 0.95 1 Ots103 46 0.015 (0.003) 0.94 0.94 0 One101 47 0.035 (0.006) 0.87 0.86 1 One102 48 0.009 (0.002) 0.91 0.89 1 OtsG68 53 0.013 (0.003) 0.94 0.93 0 One114 54 0.012 (0.003) 0.92 0.91 0 One111 136 0.026 (0.004) 0.93 0.92 1 Average 0.023 (0.004)

er, Kasigluk River, Kwethluk River, and Peninsula (Volcano Bay, Delta Creek, Cole- Nunsatuk River) were poorly resolved, with man Creek, Westward Creek) and southeast only about 60% of a sample identifiable as Alaska Peninsula (Alagoshak River, Ste- originating from Kuskokwim River drainage povak Bay, Big River) (Figure 3). Analysis populations (Kanektok River included in the of all simulated mixtures indicated that the Kuskokwim River region) (Figure 3). Simi- genetic markers surveyed provided reliable larly, Nushagak River populations of the estimates of stock composition for Kotebue Bristol Bay region (Stuyahok River, Mulch- Sound, Norton Sound, the Yukon River, poor nata River) were also poorly resolved, with resolution of regions from the Kuskokwim average accuracy of estimated stock com- River south to central Bristol Bay, and reli- positions for this region being <65%. The able estimation of stock composition from North/Central Bristol Bay region (Good- south Bristol Bay to the Alaska Peninsula. news River included in the region) was also These results indicated a mixed success in poorly resolved, with an average accuracy the completion of the initial step of the eval- of about 75% for simulated mixtures (Good- uation of the microsatellites and MHC loci news River, Togiak River) (Figure 3). for stock identification applications. -How Higher accuracies of estimated stock ever, if high accuracy of estimated stock composition for simulated single-region compositions of simulated single-region mixtures were observed for Southwest Bris- mixtures are obtained, then accurate esti- tol Bay (Egegik River, Gertrude Creek, Me- mates of stock composition should be pos- shik Creek, Pumice Creek,), North Penin- sible when multi-region simulated mixtures sula/Aleutians (Frosty Creek, Joshua Green are evaluated. River, Moller Bay Creek), southwest Alaska Western Alaska Chum Salmon Structure and Stock Identification 151

Fi g u r e 2. Neighbor-joining dendrogram of Cavalli-Sforza and Edwards (1967) chord distance for 59 populations of chum salmon surveyed at 14 microsatellite loci and one MHC locus. Bootstrap values at major tree nodes indicate the percentage of 1,000 trees where populations beyond the node clustered together, and only values > 50% are listed. 152 Beacham et al.

a b l e T 4. Mean pairwise population Fst values observed over 14 microsatellite loci for 59 chum salmon populations from nine regions. Regions were: 1) Kotzebue Sound 2) Norton Sound, 3) Yukon River summer run, 4) Yukon River fall run, 5) Kuskokwim River, 6) Bristol Bay, 7) Nushagak River, 8) Alaska Peninsula, and 9) British Columbia. The Tanana River, Upper Alaska, Porcupine River, White River, and mainstem Yukon River regions were combined into a single Yukon River fall run region. Similarly, the northern/central and southern Bristol Bay regions were combined into a single Bristol Bay region, the North Peninsula, Southwest Peninsula, and Southeast Peninsula regions were combined into a single Alaska Peninsula region, and the British Columbia central coast and Fraser River regions were combined into a single British Columbia region. Boldface font indicates significant dif- ference (P < 0.05).

1 2 3 4 5 6 7 8 9

1 0.029 0.019 0.023 0.047 0.021 0.027 0.022 0.047 0.045 2 0.001 0.004 0.026 0.002 0.010 0.003 0.034 0.035 3 0.003 0.019 0.003 0.014 0.004 0.039 0.040 4 0.011 0.025 0.042 0.028 0.066 0.070 5 0.000 0.009 0.000 0.035 0.036 6 0.007 0.008 0.029 0.029 7 0.002 0.035 0.036 8 0.015 0.029 9 0.020

100 90 80 70 60 50 40 30

Estimated percentage Estimated 20 10 0

d m k y y s a a

Porcupine Nushaga Tanana fall Kuskokwi Norton Soun Kotzebue Sound South Bristol Ba uthwest Peninsul Southeast Peninsul Lower Yukon summer So North/Central Bristol Ba North Peninsula/Aleutian

Fi g u r e 3. Mean estimated percentage compositions (plus standard deviation) of single-region mixtures of chum salmon (correct = 100%). The region designation includes the sum of percentage allocations to all populations in the region. Simulations were conducted with a 59-population baseline, 150 fish in the mixture sample, and 100 resamplings in the mixture sample and baseline samples. Western Alaska Chum Salmon Structure and Stock Identification 153 Analysis of simulated multi-region mix- population structure has been observed, then tures the contributions of populations in the mixture but not in the baseline should be allo- Simulated mixtures containing six re- cated to other appropriate regional popula- gional components were evaluated, with the tions in the baseline. This assumption can regional contributions of Kotzebue Sound be directly tested by estimating stock com- (Agiapuk River 10%, Imnachuk River 10%, positions of known-origin samples that are Kelly Lake 5% contributions), Norton Sound completely independent of the baseline used (Shaktoolik River 10%, Unalakleet River for analysis, and in which contributing popu- 10%), the Lower Yukon River (Andreafsky lations have been observed to display a re- River 10%, Anvik River 10%, Tozitna River gional structure. Estimated composition of a 10%), Southwest Bristol Bay (Egegik River mixed-stock sample comprising individuals 10%), North Peninsula/Aleutians (Frosty from Kotzebue Sound, Norton Sound, lower Creek 10%), and Southwest Alaska Penin- Yukon River summer-run, South Bristol Bay, sula (Coleman Creek 5%) estimated with a and North Peninsula/Aleutians populations maximum 3% error for a regional contribu- was generally within 3% of the actual region- tion (Figure 4a). However, including indi- al value (Figure 5a). viduals from a poorly estimated region in the Analysis of simulated single-region Yu- simulated mixtures resulted in higher errors kon River samples suggested that the Yukon in estimated stock compostions. If the South- River component in mixed-stock samples west Bristol Bay, North Peninsula/Aleutians, should be identified with a reasonable- de and Southwest Alaska Peninsula components gree of accuracy. Analysis of the test fishery were replaced with a Kuskokwim River com- samples collected at Pilot Station in the lower ponent (Aniak River 10%, Kasigluk River river and Bio Island in the Yukon Territory 10%, Kwethluk River 5%), the maximum indicated that the samples were estimated to error for regional estimated stock composi- be comprised almost entirely of Yukon River- tions increased to 9%, with an underestima- origin fish as expected (Figure 5b). Summer- tion of the Kuskokwim River component and run populations were estimated to have com- overestimation of the Yukon River and Nor- prised only 9.7% of the catch at Pilot Station ton Sound components (Figure 4b). Accurate during a period when fall-run fish were to be regional estimates of stock composition for expected (19 July–11 August), with fall-run multi-region mixtures were dependent upon populations estimated at 86.4% of the catch. being able to identify accurately all individu- At Bio Island, 99.5% of the 2004 catch and al regional components. 99.8% of the 2005 catch were estimated to have been derived from fall-run populations. Analysis of known-origin samples Discussion Analysis of simulated mixed-stock samples evaluates the effectiveness of the Reliable, accurate, effective, and practi- baseline for stock composition analysis un- cal methods of stock identification are key der the assumption that the baseline will be requirements in the determination of migra- representative of all populations contributing tion pathways for juvenile chum salmon, to a sample of unknown origin. Mixed-stock assessment of the status of juvenile and im- samples frequently contain individuals from mature chum salmon in marine feeding areas, populations not in the baseline. If regional and management of fisheries that target chum 154 Beacham et al.

35 Actual 30 Estimated 25

Percentage 15

d d y a mmer

Norton Soun Kotzebue Soun

Lower Yukon su Southwest Bristol Ba Southwest Peninsul North Peninsula/Aleutians

35 Actual Estimated 30

Percentage 15

0 Kotzebue SoundNorton SoundLower Yukon Kuskokwim summer

Fi g u r e 4. (A) Estimated percentage regional stock compositions of simulated mixtures of chum 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 59 baseline populations with replacement to obtain a new distribution of allele frequencies. (B) Similar mixtures as in (A) with the 30% Southwest Bristol Bay, North Peninsula/Aleutians, Southwest Alaska Peninsula component replaced with a 30% Kuskokwim River component. Western Alaska Chum Salmon Structure and Stock Identification 155

35 Actual 30 Estimated 25

15 Percentage 10

y s

Norton Sound Kotzebue Sound South Bristol Ba

Lower Yukon summer North Peninsula/Aleutian

100

Actual

98 Estimated

Percentage 94

90 Pilot Station 2004 Bio Island 2004 Bio Island 2005

Fi g u r e 5. (A) Estimated percentage stock composition (plus standard deviation) of a known-origin sample of 200 chum salmon derived from five rivers in western Alaska, Bristol Bay, or the Alaska Peninsula and estimated with a 54-population baseline incorporating variation at 14 microsatellite loci and one MHC locus. Estimated stock composition was derived from cBAYES (see text). (B) Estimated percentage Yukon River origin stock composition of mixed-stock samples taken from test fisheries in the lower river at Pilot Station and in the upper river at Bio Island and estimated with a 59-population baseline incorporating variation at 12–14 microsatellite loci. 156 Beacham et al. salmon during their spawning migration. The Norton Sound, with the Pikmiktalik River be- most effective stock identification techniques ing adjacent to the mouth of the Yukon River, for chum salmon are those that provide reli- were more similar to Yukon River summer- able discrimination among regional groups of run populations than they were to other popu- populations. Ideal methods for mixed-stock lations in Norton Sound. South of the Yukon analysis are those based on biological varia- River, the Kanektok River population was tion in characters, which differ substantially grouped with populations in the adjacent among populations, show little temporal or Kuskokwim River drainage, and the Good- annual variation within populations relative news River population was grouped with the to population differences, and can be screened Togiak River population adjacent to Bristol in a rapid, nonlethal, and cost-effective man- Bay. Although the Goodnews River is not tra- ner for both baseline and mixed-population ditionally considered part of the Bristol Bay samples. The survey of microsatellite and region, it was included with other Bristol Bay MHC loci meet these criteria, and regional populations in the regional group. population differentiation can be readily used Estimation of stock composition of sim- for stock composition analysis. ulated single-region mixtures provided good Regional population structure was gener- levels of accuracy for Kotzebue Sound, Nor- ally observed in the chum salmon populations ton Sound, and Yukon River regions: Lower surveyed in this study. A regional population Yukon River summer-run, Tanana River fall- structure is important to the application of run, and the Porcupine River, which is a fall- genetic variation for stock composition esti- run population. Genetic differentiation has mation, as a critical assumption in the appli- been observed previously between summer- cation is that the portion of the mixed-stock run and fall-run populations in the Yukon sample derived from unsampled populations River based upon variation at allozyme loci is allocated to sampled populations from the (Seeb and Crane 1999a), so accurate discrim- same region. This assumption reduces the ination between the two run-time groups is cost and complexity of developing a baseline expected. Poor accuracy of estimated stock for stock composition analysis. However, compositions were observed for the Kuskok- some grouping of populations outside of tra- wim River, Nushagak River, and North/Cen- ditional designations was required for stock tral Bristol Bay regions. Although little dif- identification applications. For example, the ferentiation among populations in these three Kotzebue Sound region included two popula- regions was observed previously in a sur- tions outside of Kotzebue Sound proper. The vey of allozyme variation (Seeb and Crane Agiapuk River population, a later-run popu- 1999a), the population structure observed in lation (compared with other Norton Sound the current study suggested that the North populations) situated on the southwest coast Bristol Bay region should be identifiable in of the Seward Peninsula northwest of Norton mixed-stock analysis. Sound, clustered with Kotzebue Sound popu- Accurate estimation of stock composi- lations. Similarly, the Koyuk River population in mixed-stock samples is dependent tion, a later-run population (compared with upon both the use of genetic markers that other Norton Sound populations) in Norton display adequate genetic differentiation Sound, clustered with Kotzebue Sound popu- among regions to be discriminated, as well lations. Some grouping of populations geo- as sufficient sampling within regions to en- graphically adjacent to a regional stock was able genetic variation present in populations suggested by cluster analysis. For example, in the region to be characterized adequately. chum salmon from the Pikmiktalik River in Average population sample size used in re- Western Alaska Chum Salmon Structure and Stock Identification 157 gions with accurate estimated stock com- Should results appear promising, analysis of positions (Figure 3) from Kotzebue Sound known-origin samples, independent of the to the North Alaska Peninsula were: Kotze- baseline used for estimation of stock com- bue Sound 155 fish (5 populations), Norton positions is usually conducted. In our study, Sound 265 fish (9 populations), Lower Yukon analysis of simulated mixed-stock fishery River summer-run 237 fish (9 populations), samples, as well as analysis of samples of Tanana River fall run 241 fish (1 population), known origin, indicated that reliable esti- South Bristol Bay 83 fish (4 populations), mates of stock composition were obtained. and North Alaska Peninsula 122 fish (3 popu- For example, analysis of the 2004 Pilot Sta- lations). Regions with lower accuracy in es- tion samples was also conducted with a dif- timated stock compositions were generally ferent set of microsatellites by Flannery et characterized by fewer fish surveyed for ge- al. (2007b). Summer-run populations in that netic variation: Kuskokwim River 94 fish (5 study were estimated at 13% of the seasonal populations, Nushagak River 78 fish (2 popu- sample, similar to the 10% value estimated in lations), North/Central Bristol Bay 78 fish (4 the current study. However, accurate results populations). Increasing the number of fish were only achieved in larger-scale potential surveyed, as well as increasing the number applications with no contribution of Kuskok- of populations included in the region, would wim River, Nushagak River, or North/Cen- likely increase the accuracy of estimated tral Bristol Bay populations to the mixed- stock compositions of the North/Central re- stock samples. Even if reliable estimates of gion, perhaps allowing the splitting of the re- stock composition have been obtained from gion into a northern component and a central both simulated and known-origin samples, a component as suggested by the dendrogram potential still exists for inaccurate estimates analysis (Figure 3). While an increase in the of stock composition in real fisheries appli- number of fish surveyed in the Kuskokwim cations if a significant portion of the mixed- River and Nushagak River regions is neces- stock sample has been derived from popula- sary to enable genetic variation to be charac- tions or regions inadequately represented in terized adequately, an increase in sample size the baseline. alone will likely not be sufficient to enable Genetic variation based upon allozymes accurate estimates of stock composition to has been used to estimate stock composition of be obtained for these two regions using the mixed-stock chum salmon samples obtained 15 loci surveyed in this study. The addition from fisheries in the northern Gulf of Alaska of markers specifically designed to separate (Seeb and Crane 199b) and in the eastern Ber- these two regions both from each other and ing Sea (Wilmot et al. 1998). In these studies, from other regions in western Alaska will it was not possible to determine fine-scale be required. These markers could either be regional contributions of summer-run chum additional microsatellites, or perhaps single salmon in western Alaska. The application of nucleotide polymorphisms (SNPs) (Smith et DNA-level markers such as microsatellites al. 2005). It is likely that a combination of has substantially improved the resolution that microsatellites and SNPs can be employed to is possible for identifying summer-run chum provide accurate regional estimates of stock salmon in western Alaska in mixed-stock fish- composition of mixed-stock samples. ery samples, such that regional contributions Evaluating accuracy of stock composi- from Kotzebue Sound, Norton Sound, and tion estimates typically initially involves lower Yukon River populations may be esti- analysis of simulated mixtures to evaluate the mated. The increased resolution observed in accuracy of estimated stock compositions. microsatellite-derived stock composition esti- 158 Beacham et al. mates in chum salmon in the current study has microsatellite DNA resolves genetic structure also been observed in sockeye Oncorhynchus and diversity of chinook salmon (Oncorhynchus tshawytscha) in California’s Central Valley. Ca- nerka (Beacham et al. 2005) and Chinook nadian Journal of Fisheries and Aquatic Sciences salmon O. tshawytscha (Beacham et al. 2006) 57:915–927. on a Pacific Rim wide basis. We expect that Beacham, T. D., J. R. Candy, K. J. Supernault, T. Ming, similar results will also be observed for chum B. Deagle, A. Schultz, D. Tuck, K. Kaukinen, J. R. Irvine, K. M. Miller, and R. E. Withler, R. E. salmon on a Pacific Rim basis, with microsat- 2001. Evaluation and application of microsatellite ellite variation perhaps augmented with MHC and major histocompatibility complex variation or other DNA-based variation such as SNPs for stock identification of coho salmon in British providing resolution in stock composition Columbia. Transactions of the American Fisheries Society 130:1116–1155. estimates not observed previously with other Beacham, T. D., J. R. Candy, K. J. Supernault, M. techniques. Wetklo, B. Deagle, K. Labaree, J. R. Irvine, K. M. Miller, R. J. Nelson, and R. E. Withler. 2003. Acknowledgments Evaluation and application of microsatellites for population identification of Fraser River chinook salmon (Oncorhynchus tshawytscha). Fishery A very substantial effort was undertaken Bulletin 101:243–259. to obtain samples from chum salmon sampled Beacham, T. D., J. R. Candy, B. McIntosh, C. Mac- in this study. S. Johnston and P. Milligan of Connachie, A. Tabata, K. Kaukinen, L. Deng, K. M. Miller, R. E. Withler, and N. V. Varnavskaya. the DFO Whitehorse office supervised col- 2005. Estimation of stock composition and indi- lections of the Canadian portion of the Yukon vidual identification of sockeye salmon on a Pacif- River drainage. J. Wenburg of the U.S. Fish ic Rim basis using microsatellite and major histo- and Wildlife Service Conservation Genetics compatibility complex variation. Transactions of the American Fisheries Society 134:1124–1146. Laboratory in Anchorage provided samples Beacham, T. D., J. R. Candy, K. L. Jonsen, J. Super- from the Alaskan portion of the Yukon River nault, M. Wetklo, L. Deng, K. M. Miller, R. E. drainage, as well as from many Norton Sound Withler, and N. Varnavskaya. 2006. Estimation of and Kotzebue Sound populations. L. Seeb of stock composition and individual identification of Chinook salmon across the Pacific Rim using mic- the Alaska Department of Fish and Game rosatellite variation. 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