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Determination of population structure and stock composition of chum salmon (Oncorhynchus keta) in Russia determined with microsatellites.

Abstract--Variation at 14 microsatellite loci was examined in 34 chum salmon (Oncorhynchus keta) populations from Russia and evaluated for its use in the determination of population structure and stock composition in simulated mixed-stock fishery samples. The genetic differentiation index ([F.sub.st]) over all populations and loci was 0.017, and individual locus values ranged from 0.003 to 0.054. Regional population structure was observed, and populations from Primorye, Sakhalin Island, and northeast Russia were the most distinct. Microsatellite variation provided evidence of a more fine-scale population structure than those that had previously been demonstrated with other genetic-based markers. Analysis of simulated mixed-stock samples indicated that accurate and precise regional estimates of stock composition were produced when the microsatellites were used to estimate stock compositions. Microsatellites can be used to determine stock composition in geographically separate Russian coastal chum salmon fisheries and provide a greater resolution of stock composition and population structure than that previously provided with other techniques.

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In Asia, there are two distinct types of chum salmon (Oncorhynchus keta Walbaum). The early-maturing or "summer" chum salmon generally returns to spawn from June through August in streams bordering Kamchatka, the Sea of Okhotsk, the east coast of Sakhalin Island, and the Amur River. Later-maturing or "autumn" chum salmon generally return to spawn from September through November in streams in Japan, the southern Kuril Islands, the west coast of Sahkalin Island, and the Amur River (Sano, 1966). In general, summer chum salmon spawn in areas where egg incubation occurs in subsurface stream flow, whereas autumn chum salmon spawn in areas of groundwater upwelling (Volobuyev et al., 1990). In major river drainages, autumn chum salmon generally migrate further up the drainage to spawn than do summer chum salmon, and are larger, younger, and more fecund than the summer-run fish (Sano, 1966).

Determination of the origin of salmon in mixed-stock fisheries is important for effective management. For chum salmon in Asia, scale pattern variation has provided a technique for the determination of origin of individuals to large geographic areas (Tanaka et al., 1969; Ishida et al., 1989), and in some cases reportedly to a specific river drainage (Nikolayeva and Semenets, 1983). Trace elements in otoliths have also been reported to be effective for stock identification of Korean populations (Sohn et al., 2005). Stock identification techniques based on scale pattern analysis have generally been replaced by applications based on genetic variation, owing to the increased resolution that is possible by applying genetic variation (see example outlined by Wilmot et al. [1998]). Analyses of genetic variation have been demonstrated to be effective in determining salmonid population structure, as well as determining origins of salmon in mixed-stock fisheries. For Russian chum salmon, analyses of allozyme variation have indicated differentiation among populations on the east and west coasts of Kamchatka (Winans et al., 1994), and either marignal (Salmenkova et al., 2007) or some level of differentiation between populations on Sakhalin Island and populations on the mainland Russian coast (Efremov, 2001). Populations in the far northeastern portions of mainland Russia were distinct from populations in western Alaska (Wilmot et al., 1994). Surveys of allozyme variation have generally indicated regional population differentiation among Russian populations.

DNA-level markers have substantially increased the number of polymorphic loci that are available to be included in analyses of genetic variation. Initial surveys of mitochondrial (mt) DNA variation indicated regional differentiation between Sakhalin Island and mainland populations (Ginatulina, 1992). Later analyses of additional mtDNA variation indicated marked differentiation between Japanese and Russian populations (Sato et al., 2004), and some differentiation among Russian populations (Brykov et al., 2003; Polyakova et al., 2006). Limited examinations of minisatellite variation have indicated some level of differentiation between Japanese and Russian populations, but have yielded little evidence of regional structure for Russian populations (Taylor et al., 1994; Beacham, 1996).

Analyses of microsatellite variation have been effective for determining salmonid population structure in local areas (Small et al., 1998; Banks et al., 2000; Beacham et al., 2004), as well as broad-scale differences across the Pacific Rim (Beacham et al., 2005, 2006). Microsatellites have also been of considerable value in estimating stock composition in mixed-stock salmon fisheries, on both a population-specific (Beacham et al., 2003) and regional basis (Beacham et al., 2006). Micro-satellite variation in chum salmon provides the means to examine fine-scale population structure (Chen et al., 2005), as well as the means for fine-scale estimation of stock composition in mixed-stock fisheries (Beacham et al., in press). Analyses of microsatellite variation in Russian chum populations would likely be of value by providing increased resolution of population structure compared with that provided by previous techniques, and would likely aid in increasing accuracy and precision of estimates of stock composition in mixed-stock fishery samples.

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Our objectives were to analyze the variation at 14 microsatellite loci to evaluate population structure of Russian chum salmon populations from the far north eastern coast of Russia to the more southern areas of Primorye and Sakhalin Island, and then to evaluate the use of these loci for the practical purpose of providing accurate and precise estimates of stock composition in mixed-stock fishery samples. Stock composition evaluation was accomplished by the analysis of simulated mixed-stock fishery samples.

Materials and methods

Tissue samples were collected from mature chum salmon at a number of rivers during previous analyses of genetic variation (Winans et al., 1994). Additional tissue samples were sent to the Molecular Genetics Laboratory at the Pacific Biological Station. The geographic area of the 34 populations sampled ranged from Primorye in the south to northeastern Russia (Fig. 1) and encompassed eight geographic regions (Table 1). DNA was extracted from the tissue samples by a variety of methods, including that with chelex resin outlined by Small et al. (1998), a Qiagen 96-well Dneasy[R] procedure (Qiagen, Mississauga, Ontario, Canada), or a Promega Wizard SV96 Genomic DNA Purification system (Promega, Madison, WI). Once extracted DNA was available, analyses of variation at 14 microsatellite loci were conducted: Ots3 (Banks et al., 1999), Oke3 (Buchholz et al., 2001), Oki2 (Smith et al., 1998), OkilO0 (primer sequence 5' to 3' F: GGTGTTTTAATGTTGTTTCCT, R: GTTTCCAGAGTAGTCATCTCTG), Omml070 (Rexroad et al., 2001), Omy 1011 (Spies et al., 2005), One101, One102, One104, One111, and One114 (Olsen et al., 2000), Otsl03 (Nelson and Beacham, 1999), Ssa419 (Cairney et al., 2000), and OtsG68 (Williamson et al., 2002).

In general, PCR DNA amplifications were conducted by using DNA Engine Cycler Tetrad2 (BioRad, Hercules, CA) in 6-[micro]L volumes consisting of 0.15 units of Taq polymerase, 1 [micro]L (25-50 ng) of extracted DNA, 1 x PCR buffer (Qiagen, Mississauga, Ontario, Canada), 60 [micro]M each nucleotide, 0.40 [micro]M of each primer, and deionized water. The thermal cycling profile involved one cycle of 15 minutes at 95[degrees]C, followed by 30-40 cycles of 20 seconds at 94[degrees]C, 30-60 seconds at 47-65[degrees]C, and 30-60 seconds at 68-72[degrees]C (depending on the locus). Specific PCR conditions for a particular locus could vary from this general outline. PCR fragments were initially size fractionated in denaturing polyacrylamide gels by using an ABI 377 automated DNA sequencer, and genotypes were scored by Genotyper 2.5 software (Applied Biosystems, Foster City, CA) using an internal lane sizing standard. Later in the study, microsatellites were size fractionated in an ABI 3730 capillary DNA sequencer, and genotypes were scored by GeneMapper software 3.0 (Applied Biosystems, Foster City, CA) by using an internal lane-sizing standard. Allele identification between the two sequencers was standardized by analyzing the same approximately 600 individuals on both platforms and converting the sizing in the gel-based data set to match that obtained from the capillary-based set.

Data analysis

Each population at each locus was tested for departure from Hardy-Weinberg equilibrium (HWE) by using genetic data analysis (GDA). Critical significance levels for simultaneous tests (34 populations, Table 1) were evaluated using Bonferroni adjustment (0.05/34=0.0015) (Rice, 1989). All annual samples available for a location were combined to estimate population allele frequencies, as was recommended by Waples (1990). [F.sub.st] estimates for each locus were calculated with FSTAT (Goudet, 1995), individual locus values were determined by jackknifing over populations, and the overall [F.sub.st] estimate was determined by jackknifing over loci (Goudet, 1995). Inter-regional comparisons of [F.sub.st] estimates were determined by calculation of all appropriate pairwise point estimates of [F.sub.st] values, and then determining the mean and standard deviation of these values. The Cavalli-Sforza and Edwards (CSE) (1967) chord distance was used to estimate distances among populations. An unrooted neighbor-joining tree based upon CSE was generated using NJPLOT (Perriere and Gouy, 1996). Bootstrap support (by sampling loci) for the major nodes in the dendrogram was evaluated with the CONSENSE program from PHYLIP (Univ. Washington, Seattle, WA) and based on 500 replicate trees. Computation of the number of alleles observed per locus, as well as allelic diversity standardized to a common sample size, was carried out with FSTAT.

Estimation of stock composition

Genotypic frequencies were determined for each locus in each population, and the Statistical Package for the Analysis of Mixtures software program (SPAM, vers. 3.7, Debevec et al., 2000) was used to estimate stock composition of simulated mixtures. The Rannala and Mountain (1997) correction to baseline allele frequencies was used in the analysis in order to accommodate the occurrence of fish in the mixed sample that were from a specific population having an allele not observed in the baseline samples from that population. All loci were considered to be in Hardy-Weinberg equilibrium, and expected genotypic frequencies were determined from the observed allele frequencies. Reported stock compositions for simulated fishery samples were the bootstrap mean estimate of each mixture of 150 fish analyzed, and mean and variance estimates were derived from 100 bootstrap simulations. Both the baseline population and the simulated single-population were sampled with replacement in order to simulate random variation involved in the collection of the baseline and fishery samples.

Results

Variation within and among populations

The observed number of alleles observed at a locus ranged from 21 alleles at Oke3 and Oki2 to 138 alleles at One111 (Table 2). Lower expected heterozygosities were generally observed at loci with fewer alleles. The genotypic frequencies observed at the 14 loci generally conformed to those expected under Hardy-Weinberg equilibrium (HWE) after Bonferroni correction. For the Oke3 and OtsG68 loci, a minor HWE nonconformance of genotypic frequencies was observed, and observed heterozygosities were 2-6% less than those expected (Table 2).

Genetic diversity, with respect to the number of alleles observed, was evident among regional groups of chum salmon. Chum salmon populations from Primorye, the northern Sea of Okhotsk, and northeast Russia displayed fewer alleles (mean 320 alleles) than did populations in Magadan, west Kamchatka, and east Kamchatka (mean 370 alleles) (Table 3). Chum salmon from the latter regions displayed approximately 16% more alleles than did those from the former regions. The greatest differentiation in allelic diversity was observed at those loci with greater numbers of alleles, particularly at locus One111.

Population structure

Genetic differentiation was evident among chum salmon populations from the different geographic regions surveyed. The [F.sub.st] value over all 34 populations and 14 loci surveyed was 0.017, and individual locus values ranged from 0.003 (One102) to 0.054 (Ots3) (Table 2). Chum salmon populations from Primorye and the Amur River were well defined compared with other regional populations (Table 4). Populations from the southwestern portion of Russia (Primorye, Amur River, Sakhalin Island) were most distinct from those in more northern and eastern regions (Magadan, Sea of Okhotsk, Kamchatka, northeast Russia).

Regional clustering of population samples was observed in the analysis of population structure. Strong clustering of population samples from the Primorye region was observed; the three population samples included in the analysis clustered together in 100% of the trees examined (Fig. 2). Similarly, strong clustering was observed in most of the population samples from Sakhalin Island, as well as the two population samples from the northern coast of the Sea of Okhotsk. The two population samples from northeast Russia clustered together in 100% of the trees examined, together with the Utka River population sample from west Kamchatka. Although there was a general clustering of population samples from east and west Kamchatka, these regional groupings were not strongly supported in the cluster analysis.

Stock identification

Genetic differentiation observed among the chum salmon in the regions surveyed was evaluated to determine if it was sufficient for a mixed-stock analysis with the objective of obtaining accurate regional stock compositions. Analysis of simulated single-region samples indicated that estimates of the contribution of chum salmon from that region were usually greater than 89% (Table 5), although there was the expectation that errors in estimation for the region in question would be maximized when the single region comprised 100% of the simulated sample. The level of accuracy observed in the estimated stock compositions indicated that accurate regional estimates of stock composition should be obtained in samples containing individuals from multiple regions.

Testing of accuracy of regional estimates of stock composition in multiregion mixtures was conducted by evaluating four simulated fishery samples. Estimated stock composition of a simulated mixture containing chum salmon from Primorye, Sakhalin Island, the Amur River, and Magadan was within 3% of the actual population composition and 4% of the regional composition (Table 6, mixture 1). Similar results were observed for a simulated mixture comprising chum salmon from Sakhalin Island, Magadan, the northern Sea of Okhotsk, and west Kamchatka, with population estimates within 3% of actual composition and regional estimates within 3% (Table 6, mixture 2). Estimated stock compositions of a simulated mixture of chum salmon from west Kamchatka, east Kamchatka, and northeast Russia were within 6% of actual population composition and within 5% for regional contributions (Table 6, mixture 3). Regional estimated stock compositions of a more complex simulated sample of fish from multiple regions were within 3% of actual regional contributions (Table 6, mixture 4). Accurate regional estimates of stock composition should be obtained when the current baseline is applied to mixed-stock samples of chum salmon taken from Russian coastal waters, provided that the individuals in the mixture originate entirely from Russian populations.

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Discussion

Population structure

The range of populations sampled in the study required a concerted sampling effort, and in some locations collection of appropriate samples proved to be difficult. The number of fish sampled from a site or population ranged from 17 to 338 individuals. Estimated population-level allele frequencies will be subject to relatively larger sampling error at smaller population sample sizes, particularly for loci with large numbers of alleles such as One111. Small sample size may have contributed to errors on allele-frequency estimates for some populations. Sampling error may obscure genetic relationships among related populations, or conversely genetic relationships among some populations may be falsely inferred. However, the available evidence indicates that variation in population sample sizes did not obscure relationships among related populations. For example, we analyzed chum salmon from three sampling sites in Primorye with sample sizes ranging between 17 and 49 fish per site,. Although these sample sizes were small, and thus the estimation of allele frequencies would be subject to sampling error, the clustering of these populations was well supported by our bootstrap calculations (100%). Regional clustering of samples or populations is typically observed in chum salmon (Beacham et al., 1987; Winans et al., 1994), and thus it is unlikely that close genetic relationships among these populations from Primorye were inferred incorrectly.

If populations spawn in remote areas, opportunities to collect samples may be limited. In our study, all samples that were available for a specific sampling site or population were combined in order to estimate genetic differentiation among populations. Annual variation in allele frequencies within a population is typically less than the geographic and population differences observed; therefore pooling annual samples over time is a reasonable approach to estimate population-level allele frequencies. Relative annual stability of microsatellite allele frequencies is a general feature of microsatellite loci in salmonids (Tessier and Bernatchez, 1999; Beacham et al., 2006).

The population structure of chum salmon in Russia has been investigated previously. For example, Winans et al. (1994) after examining 35 allozyme loci, indicated that there were four groups of Russian chum salmon populations, and that one group generally comprised populations from west Kamchatka, a second group comprised populations from Magadan, the Sea of Okhotsk, and east Kamchatka, the third group was solely from east Kamchatka, and a fourth group comprised the populations from the Utka River in west Kamchatka and the Anadyr River in northeast Russia. In our study, some similarities were observed between Magadan and the Sea of Okhotsk regional population groups, as well as between the groups from east and west Kamchatka. Winans et al. (1994) did not include populations from Primorye or Sakhalin Island in their survey, but Ginatulina (1992) had previously demonstrated clear differentiation of mitochondrial genotypes between the populations from these two regions. The results of our study revealed regional separation of populations between these two areas. Our analysis supports the concept of regional groups of populations, and generally supports the concordance in patterns of population differentiation derived from analysis of allozymes and mitochondrial DNA variation. Allendorf and Seeb (2000) reported a concordance between results from allozyme and microsatellite analyses of population structure for sockeye salmon (O. nerka).

Genetic differentiation of Russian chum salmon generally follows a regional structure because proximate populations are generally more similar to each other than to more distant populations. However, there were some cases of populations from one region clustering with populations from another region. One example was the Utka River population from west Kamchatka clustering with populations from northeast Russia. An association between the Utka River population and the Anadyr River population was also reported by Winans et al. (1994) in an analysis of allozyme variation. Because Winans et al. (1994) and authors of the present study analyzed the same sample from the Utka River population, concurrence between the allozyme and microsatellite analyses was not unexpected. However, the number of fish sampled for the Utka River population was the fewest for any of the west Kamchatka populations (Table 1), and it may be that an increase in sample size for this population would result in estimated allele frequencies that would be more similar to those of other populations in west Kamchatka. Additionally, the Tugur River population was most closely associated with the population from the Amur River. In recent geologic history, the Tugur River may have been once part of the Amur River drainage, but now flows into Tugur Bay on the Sea of Okhotsk. A common origin between the Amur River and Tugur River populations may account for the current association between the two populations.

Distinctive groups of populations surveyed were those from the Primorye, Sahkalin Island, the northern Sea of Okhotsk, and northeast Russia, and a strong regional clustering of these populations was observed in the dendrogram analysis. Most of these population groups were characterized by slightly lower genetic variation compared with other populations surveyed in Russia. For other salmonids, populations from regions with reduced genetic variation have formed distinctive clusters in dendrogram analysis (Beacham et al., 2006). Russian chum salmon populations displayed on average less genetic differentiation than did populations from western Alaska and adjacent areas. Despite the

Russian populations being surveyed from a larger geographic area than were populations in western Alaska (Beacham et al., in press), and thus there was greater likelihood of differentiation due to isolation by distance, comparisons of locus-specific [F.sub.st] values between the two groups indicated that Russian populations were less differentiated (lower values in 11 of 14 loci, P=0.057). This result indicates that there may be more straying among Russian populations than among those in western Alaska, possibly as a result of adaptation to harsh enviromental conditions or hatchery development and broodstock transfer in Russia. Alternatively, less differentiation would also be observed if Russian chum salmon had colonized available habitats more recently than had chum salmon in western Alaska.

Stock identification

Accurate, economical, and practical methods of stock identification are required to determine the migration pathways of juvenile and maturing salmon, and to manage fisheries that may intercept salmon during their migration to natal spawning grounds. Effective stock identification techniques are based on characters that display stable differentiation among groups to be discriminated, and these characters must be examined easily in a rapid and cost-effective manner. Allozymes provided the characters for initial genetically based population surveys and stock identification of Russian chum salmon (Winans et al., 1994, 1998). Later, single nucleotide polymorphisms (SNPs) were used to estimate the genetic structure of the population (Sato et al., 2001); therefore estimation of stock composition in mixed-stock samples can proceed (Sato et al., 2004). In an analysis of 30 haplotypes from mtDNA, Sato et al. (2004) were able to indicate some level of regional structure in populations in Russia, but the exact nature of the geographic structure was uncertain. In our analysis of microsatellite variation, clear differentiation was observed among regional groups of populations, and populations from Primorye were the most distinctive.

Accuracy of estimated stock compositions is directly influenced by the baseline used in the estimation procedure, and the level of genetic differentiation among regional groups of populations is a key component. However, sample sizes of populations in the baseline are also an important part because the accuracy of estimation is related to the population sample size (Beacham et al., 2006). Fewer than 60 fish were sampled for many of the populations sampled in our survey, and increasing sample sizes to approximately 150 fish per population would likely lead to all regional estimates of stock composition being in excess of 90% accurate in all simulated single-region mixture samples.

Surveys of genetic variation of salmon populations allow stock identification in mixed-stock fisheries, where the origins of fish contributing to mixed-stock fisheries are determined by comparing the genetic characteristics of fish in the fishery samples to the genetic characteristics of fish from potentially contributing populations. Analysis of simulated mixed-stock samples of known origin is an initial practical method to evaluate the potential for applying genetic variation to mixed-stock fishery analysis. Our analysis of simulated mixtures indicated that microsatellite variation provides accurate estimates of regional contributions of chum salmon stocks from Russia, and in some cases provides reliable estimates of individual populations in simulated mixtures. Microsatellites have previously been reported to provide reliable estimates of stock composition in mixed-stock chum salmon samples of largely North American origin (Beacham et al., in press), and our results from simulated mixtures indicated that microsatellites should provide reliable estimates of stock composition for chum salmon in coastal waters in Russia. However, if Japanese or Korean chum salmon or potentially North American chum salmon are intercepted in coastal or nearshore fisheries in Russia, then clearly a larger baseline than the one examined in the current study would be required to provide reliable estimates of stock composition under these circumstances.

The application of microsatellites for the determination of population structure of Russian chum salmon will allow significant regional differentiation among these populations to be employed in estimating regional contributions to mixed-stock fishery samples from coastal waters. Microsatellites provide similar results for other Pacific salmon species (Beacham et al., 2005, 2006) and are likely to be effective in identifying the origin of Russian chum salmon in mixed-stock fisheries in nearshore and offshore waters.

The present analysis of microsatellite variation of Russian chum salmon provides evidence of a more fine-scale population structure than those that have previously been demonstrated with other genetic-based markers such as allozymes (Winans et al., 1994; Efremov, 2001) or mitochondrial based SNPs (Sato et al., 2004). This more fine-scale resolution of population structure was likely due to the larger number of alleles associated with the microsatellite loci than with either the allozyme or SNP loci. Because genetic-based markers generally exhibit annual stability in allele frequencies, they are generally more effective for stock identification applications than are techniques that rely on environmentally induced variation to discriminate among stocks, such as scale-pattern analysis of trace elements in otoliths. Once the baseline has been established for genetic applications, annual surveys of contributing stocks are not necessary, as is the case with for environmentally induced variation. Should greater resolution in stock composition estimates be required than that provided by the 14 microsatellites surveyed in the present study, the addition of markers specifically designed to provide the required resolution will be necessary. These markers could either be additional microsatellites, or perhaps single nucleotide polymorphisms (SNPs) (Smith et al., 2005). It is likely that a combination of microsatellites and SNPs can be employed to provide accurate population or regional estimates of stock composition of mixed-stock samples.

Acknowledgments

A significant effort was undertaken to collect samples from chum salmon populations analyzed in the study. Samples of populations from Primorye and Sakhalin Island, as well as some Magadan samples, were initially provided by V. V. Efremov to the United States National Marine Fisheries Service (NMFS) Auke Bay Laboratory, where R. Wilmot provided access to the Molecular Genetics Laboratory (MGL). G. Winans of the NMFS-Montlake laboratory also provided access to some population samples. C. Wallace and J. Candy of the MGL assisted in the analysis. Funding was provided by Fisheries and Oceans Canada.

Manuscript submitted 18 December 2007. Manuscript accepted 25 February 2008.

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Terry D. Beacham (contact author)

Email address: Beachamt@pac.dfo-mpo.gc.ca

Department of Fisheries and Oceans

Pacific Biological Station

Nanaimo, British Columbia, Canada V9T 6N7

Nataly V. Varnavskaya

Kamchatka Fishery and Oceanography Research Institute

18 Naberezhnaya Street

Petropavlovsk-Kamchatsky 683000, Russia

Khai D. Le

Michael H. Wetklo

Department of Fisheries and Oceans

Pacific Biological Station

Nanaimo, British Columbia, Canada V9T 6N7
Table 1
Population, sample collection years, number of fish sampled per
year, and total number of fish sampled for 34 populations of chum
salmon (Oncorhynchus keta) in eight geographic regions from
Russia. Eight regions have been defined, and populations
(numbered in brackets) were sampled in each region listed.
N = population size.
 Annual sample
Region and population Years size N

1 Primorye
 Narva [1] 1994 17 17
 Ryazanovka [2] 1994 49 49
 Avakumovka [3] 1994 35 35

2 Amur River
 Amur River [4] 1994, 2001, 2004 43, 97, 198 338

3 Sakhalin Island
 Tym [5] 1995 55 55
 Naiba [6] 1994, 1995 50, 99 149
 Udarnitsa [7] 1994 50 50
 Kalininka [8] 1994 49 49

4 Magadan
 Tugur [9] 2004 98 98
 Okhota [10] 2004 94 94
 Magadan [11] 1991 79 79
 Tauy [12] 1990 55 55
 Ola [13] 1990, 1992 80, 40 120

5 Northern Sea of Okhotsk
 Oklan [14] 1993 76 76
 Penzhina [15] 1993 43 43

6 West Kamchatka
 Hairusova [16] 1990, 1993 138, 48 186
 Vorovskaya [17] 1991, 1993 79, 170 249
 Kol [18] 1991 79 79
 Pymta [19] 1992, 1993 40, 59 99
 Kikchik [20] 1992, 2005 20, 86 106
 Utka1 [21] 1992 40 40
 Bolshaya [22] 2004 96 96
 Plotnikova [23] 2001 69 69

7 East Kamchatka
 Zhypanova [24] 2004 46 46
 Kamchatka [25] 1990 76 76
 Ivashka [26] 2005 48 48
 Nerpichi [27] 1992 39 39
 Karaga [28] 2005 42 42
 Ossora [29] 1990, 1996, 2005 39, 41, 48 128
 Dranka [30] 2005 44 44
 Apuka [31] 2002 47 47
 Olutorsky Bay [32] 2002 49 49

8 Northeast Russia
 Anadyr [33] 1991, 1992 79, 15 94
 Kanchalan [34] 1991 79 79

Table 2
Number of alleles per locus, [F.sub.st], expected heterozygosity
(He), observed heterozygosity (Ho), and percent significant
Hardy-Weinberg equilibrium tests (HWE) for 14 microsatellite loci
examined in 34 populations of chum salmon (Oncorhynchus keta)
listed in Table 1. Standard deviation of [F.sub.st] is shown in
parentheses.

 Number of
Locus alleles [F.sub.st] He Ho HWE

Oke3 21 0.050 (0.022) 0.80 0.74 8.6
Oki2 21 0.027 (0.005) 0.87 0.87 2.9
Ots3 26 0.054 (0.014) 0.78 0.78 0.0
Oki100 29 0.015 (0.014) 0.91 0.91 0.0
Ssa419 30 0.010 (0.003) 0.87 0.87 0.0
One102 33 0.003 (0.001) 0.91 0.91 0.0
Omy1011 34 0.010 (0.003) 0.93 0.92 0.0
One104 41 0.013 (0.003) 0.94 0.94 0.0
One101 46 0.052 (0.009) 0.84 0.84 0.0
One114 46 0.015 (0.004) 0.93 0.93 5.7
Omm1070 50 0.005 (0.001) 0.95 0.95 0.0
Ots103 51 0.008 (0.002) 0.95 0.94 0.0
OtsG68 53 0.005 (0.001) 0.95 0.93 8.6
One111 138 0.007 (0.001) 0.98 0.98 0.0
Total 0.017 (0.004)

Table 3
Mean number of alleles observed per locus at 14 microsatellite
loci for 34 chum salmon (Oncorhynchus keta) populations from
eight regions as outlined in Table 1. Regions were the following:
1) Primorye, 2) Amur River, 3) Sakhalin Island, 4) Magadan, 5)
Northern Sea of Okhotsk, 6) West Kamchatka, 7) East Kamchatka,
and 8) Northeast Russia. Allele numbers have been standardized
to a sample size of 79 fish per region.

 1 2 3 4 5 6 7 8

Oke3 9.0 8.5 9.9 8.7 7.9 8.7 9.7 7.0
Oki100 16.0 18.5 22.3 21.4 17.7 19.6 21.4 19.2
Oki2 14.0 12.1 15.1 14.4 14.7 13.8 16.3 14.5
Omm1070 30.1 26.6 29.0 33.3 27.7 32.3 32.9 29.5
Omy1011 22.0 20.0 22.6 23.7 25.2 22.7 21.8 23.6
One101 20.7 28.3 21.9 25.0 16.6 22.8 23.3 23.1
One102 24.1 19.3 20.1 17.7 16.8 18.5 18.9 14.4
One104 23.8 27.6 23.5 23.9 23.4 24.6 24.9 22.7
One111 51.7 50.8 55.8 71.8 57.9 75.1 71.8 60.7
One114 23.1 24.6 24.0 28.6 19.2 27.3 26.4 25.5
Ots3 12.0 14.1 14.3 14.0 9.7 14.7 16.1 12.9
Ots103 30.0 35.3 38.4 34.4 27.3 35.1 34.4 29.1
OtsG68 32.9 34.2 39.3 35.6 28.7 36.3 36.6 32.7
Ssa419 16.5 14.8 16.0 16.7 14.4 18.7 16.2 11.9
Total 325.9 334.7 352.2 369.2 307.2 370.2 370.7 326.8

Table 4
Mean pairwise population F.st values observed over 14
microsatellite loci for 34 chum salmon (Oncorhynchus keta)
populations from eight regions. Regions were the following: 1)
Primorye, 2) Amur River, 3) Sakhalin Island, 4) Magadan, 5)
Northern Sea of Okhotsk, 6) West Kamchatka, 7) East Kamchatka,
and 8) Northeast Russia. Boldface font indicates significant
difference (P<0.05). A dash indicates that it was not possible to
determine within-region pairwise population ft values for the
Amur River because only a single population was sampled.

Region 1 2 3 4

1 0.012
2 0.032# --
3 0.031# 0.035# 0.020
4 0.026# 0.021# 0.031# 0.004
5 0.036# 0.036# 0.046# 0.010#
6 0.028# 0.028# 0.027 0.009#
7 0.026# 0.024# 0.029 0.006
8 0.033# 0.033# 0.041# 0.014#

Region 5 6 7 8

1
2
3
4
5 0.000
6 0.014# 0.006
7 0.011# 0.006 0.004
8 0.009# 0.016# 0.014# 0.000

Note: Significant difference (P<0.05) indicated with #.

Table 5
Mean estimated contribution (%) of populations of chum salmon
(Oncorhynchus keta) in Russia for simulated mixtures. Estimates
are for only a single region (correct regional estimate = 100%)
and were calculated from the populations listed for each mixture,
with each population comprising 50% (100% for Amur River) of each
simulated regional mixture. Estimates were determined from 14
microsatellite loci for eight separate regional stocks. The
region designation includes percentages allocated to all
populations within a region. Simulations were conducted with a
34-population baseline, 150 fish in the mixture sample, and 100
resamplings in the mixture sample and baseline samples. Standard
deviations are shown in parentheses.

 Regional Population
Mixture Region estimate Population estimate

1 Primorye 89.5 (2.5) Avakumovka 43.9 (4.5)
 Ryazanovka 45.5 (4.5)
2 Amur River 98.2 (1.1) Amur 98.2 (1.1)
3 Sakhalin Island 94.8 (1.8) Naiba 55.1 (4.8)
 Tym 39.6 (4.7)
4 Magadan 90.0 (3.0) Ola 44.3 (4.5)
 Okhota 44.0 (4.1)
5 Northern Sea of 86.9 (3.1) Oklan 47.8 (4.7)
 Okhotsk Penzhina 39.1 (4.3)
6 West Kamchatka 95.9 (2.2) Vorovskaya 49.2 (5.4)
 Hairusova 43.0 (5.2)
7 East Kamchatka 86.1 (3.2) Ossora 43.9 (4.9)
 Kamchatka 41.2 (4.7)
8 Northeast Russia 89.8 (2.9) Anadyr 46.5 (4.9)
 Kanchalan 43.3 (3.7)

Table 6
Estimated contribution (%) of individual populations and regional
estimates of chum chum salmon (Oncorhynchus keta) in Russia in
simulated mixed-population samples. Each mixture of 150 fish was
generated 100 times from baseline allele frequencies, and stock
compositions of the mixtures were estimated with parametric
resampling of each of the 34 baseline populations to obtain a new
distribution of allele frequencies on each iteration. Actual =
100% accurate. Standard deviations are shown in parentheses.
Fourteen microsatellites were used to estimate stock compositions
of the simulated mixtures.

Populations Actual % Estimated %

Mixture 1
 Amur 30 30.5 (4.0)
 Avakumovka 10 8.7 (2.0)
 Ryazanovka 10 9.1 (2.4)
 Naiba 20 21.0 (3.3)
 Udarnitsa 10 7.6 (2.2)
 Tauy 10 7.1 (2.5)
 Ola 10 8.9 (2.5)
 Regions
 Amur 30 30.5 (4.0)
 Primorye 20 17.9 (3.2)
 Sakhalin Island 30 28.7 (3.8)
 Magadan 20 16.5 (3.6)
 West Kamchatka 0 4.0 (1.9)
 East Kamchatka 0 1.6 (1.2)
Mixture 2
 Naiba 20 19.0 (3.2)
 Magadan 10 7.8 (2.6)
 Ola 10 9.7 (2.8)
 Okhota 10 9.0 (2.5)
 Oklan 10 7.8 (2.5)
 Bolshaya 20 16.5 (3.3)
 Hairusova 10 10.0 (3.0)
 Kikchik 10 7.5 (2.6)
 Regions
 Sakhalin Island 20 19.2 (3.3)
 Magadan 30 27.1 (4.0)
 Northern Sea of Okhotsk 10 8.0 (2.6)
 West Kamchatka 40 42.1 (4.7)
 East Kamchatka 0 2.9 (1.6)
Mixture 3
 Plotnikova 20 14.0 (3.3)
 Kol 10 6.7 (2.5)
 Utka 10 5.5 (1.9)
 Kamchatka 10 8.2 (2.4)
 Ossora 10 10.6 (2.8)
 Olutorsky Bay 10 6.0 (2.6)
 Anadyr 20 17.9 (3.4)
 Kanchalan 10 12.3 (3.1)
 Regions
 West Kamchatka 40 40.8 (4.7)
 East Kamchatka 30 25.4 (3.9)
 Northeast Russia 30 30.2 (4.1)
 Magadan 0 1.4 (1.2)
 Northern Sea of Okhotsk 0 1.1 (0.8)
Mixture 4
 Avakumovka 10 8.2 (2.3)
 Naiba 10 10.1 (2.3)
 Tym 10 8.1 (2.5)
 Ola 15 13.0 (2.8)
 Okhota 10 8.7 (2.6)
 Hairusova 15 13.7 (3.1)
 Utka 10 5.5 (2.2)
 Kamchatka 10 8.1 (2.5)
 Kanchalan 10 11.6 (2.6)
 Regions
 Primorye 10 8.3 (2.3)
 Sakhalin Island 20 18.3 (3.1)
 Magadan 25 22.7 (4.0)
 West Kamchatka 25 26.6 (3.8)
 East Kamchatka 10 10.9 (2.9)
 Northeast Russia 10 12.2 (2.7)
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Author:Beacham, Terry D.; Varnavskaya, Nataly V.; Le, Khai D.; Wetklo, Michael H.
Publication:Fishery Bulletin
Article Type:Report
Geographic Code:4EXRU
Date:Jul 1, 2008
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