Stock Identification of Fraser River Sockeye Salmon Using Microsatellites and Major Histocompatibility Complex Variation

Transactions of the American Fisheries Society 133:1117 1137, 2004 Copyright by the American Fisheries Society 2004 Stock Identification of Fraser River Sockeye Salmon Using Microsatellites and Major Histocompatibility Complex Variation TERRY D. BEACHAM,* MICHAEL LAPOINTE, 1 JOHN R. CANDY, BRENDA MCINTOSH, CATHY MACCONNACHIE, AMY TABATA, KARIA KAUKINEN, LANGTUO DENG, KRISTINA M. MILLER, AND RUTH E. WITHLER Department of Fisheries and Oceans, Pacific Biological Station, Nanaimo, British Columbia V9T 6N7, Canada Abstract. The utility of DNA-based variation for stock identification was evaluated for Fraser River sockeye salmon Oncorhynchus nerka. For this evaluation, the variation at 14 microsatellite loci and one major histocompatibility complex (MHC) locus was determined from approximately 13,000 fish from 47 populations in the drainage. Genetic differentiation among the populations was observed, the overall F ST value for the 14 microsatellite loci surveyed being 0.054 and that for the MHC locus being 0.215. The variation among regions and populations within regions was approximately 20 times as great as that of the annual variation within populations for the microsatellite loci and 28 times as great for the MHC locus. The power of a microsatellite locus for population-specific identification in simulated mixture samples was positively correlated with the number of observed alleles at the locus. Analysis of simulated mixtures indicated that the mean percentage error of estimated stock compositions was less than 1% per population, with a standard deviation of approximately 3% for a mixture sample size of 150 fish. Estimated stock compositions of a known sample of radio-tagged sockeye salmon indicated that the mean percentage error was 1% per population and 1% per run-time group. The use of DNA variation to estimate stock compositions in the management of the 2002 Fraser River sockeye salmon fisheries indicated the early arrival of the late run in the Fraser River. Stock identification based on DNA-level variation will probably become the preferred method in Pacific salmon applications in the near future. The Fraser River drainage in British Columbia has been considered the greatest single salmonproducing area in the world, as many as 100 million sockeye salmon Oncorhynchus nerka returning to spawn in the watershed in the early 1900s (Ricker 1987). Although not as abundant now, Fraser River sockeye salmon constitute the most valuable individual component of the commercial salmon fishery in British Columbia. Approximately 90 sockeye salmon spawning populations have been identified in the Fraser River drainage, and juveniles rear in some 24 nursery lakes. However, most (about 90%) of the production is centered on fewer than 10 nursery lakes. Juveniles typically rear in freshwater for 1 year after fry emergence, migrate to the Pacific Ocean as smolts, and mature at 4 years of age, returning to spawn in their natal rivers. The populations have characteristic times of return and are broadly classified into four groups for management purposes. The * Corresponding author: beachamt@pac.dfo-mpo.gc.ca 1 Present address: Pacific Salmon Commission, 600-1155 Robson Street, Vancouver, British Columbia V6E 1B5, Canada. Received January 8, 2004; accepted March 11, 2004 early Stuart run has the earliest-returning sockeye salmon; this run is comprised of a number of populations spawning in streams tributary to Takla and Trembleur lakes in the Stuart River drainage (Figure 1). The early summer run is composed of populations with a widespread distribution in the drainage, the Bowron River, Fennell Creek, Raft River, Pitt River, Gates Creek, Scotch Creek, and Seymour River populations being some of the main populations in the run. None of the populations are markedly abundant during their return. The summer run can be quite abundant, with major populations in the Quesnel River system and the Chilko, Stuart, and Stellako rivers. The late run can also be very abundant; populations can be fairly widespread in the drainage, there being major populations in the Lower Adams River (Thompson River drainage) as well as in Weaver Creek and the Harrison River (lower Fraser River drainage). The management of the fisheries targeting Fraser River sockeye salmon has become more complex since 1985, when the Pacific Salmon Treaty (PST) between Canada and the United States was signed. The PST s Pacific Salmon Commission is responsible for ensuring that agreed-upon international and domestic catch allocations, as well as 1117

1118 BEACHAM ET AL. FIGURE 1. Map of the Fraser River drainage showing the sampling locations and the marine approaches to the river. appropriate escapements to spawning populations, are achieved in each year. The Fraser River Panel has the responsibility for managing fisheries that target Fraser River sockeye salmon in the areas defined in the PST. Two main pieces of information are required for the weekly management decisions, namely, the abundance of the sockeye salmon that are present in the river and the stock composition (Woodey 1987). Stock identification is clearly a key part of the management process, and the results are used to assess stock-specific timing and abundance patterns. Daily and weekly estimates of stock composition are used to manage fisheries so that harvest and escapement goals can be achieved. The resulting management decisions concerning fishery openings and closures have a substantial economic impact on the fishing industry. Scale pattern analysis (SPA) incorporating linear discriminant functions has been the key method used in stock identification of Fraser River

SOCKEYE SALMON STOCK IDENTIFICATION 1119 sockeye salmon because of the ease of collection and rapidity of analysis (Gable and Cox-Rogers 1993). Scale pattern analysis exploits variation among stocks primarily in the freshwater growth zone of the scale, which reflect the different lake environments in which the juveniles rear. Parasites can also be used when additional discrimination is required (Bailey et al. 1988). Although SPA is routinely used to estimate stock composition in sockeye salmon (Cook and Guthrie 1987; Marshall et al. 1987; Gable and Cox-Rogers 1993), annual variation in the characters used requires that there be an annual survey and update of the scale characters used for discrimination. Fishery management decisions require that estimates of stock composition be available before the migrating salmon have returned to the spawning grounds, where scales can be easily collected from specific populations. As an interim measure, this problem is partially rectified by collections of scales from jacks (males maturing at age 3 the previous year) and in some cases by analyzing scales from the same age-class in a previous cycle. After the fishing season, scales are collected from spawning populations and new discriminant function models are developed that incorporate the variation in the year being analyzed. New postseason estimates of stock composition are derived for all fishery samples in which in-season estimates of stock composition were used in management decisions. Stock identification of Fraser River sockeye salmon using SPA has become particularly challenging in recent years. The abundant, late-timing Lower Adams River population sometimes returns much earlier than anticipated and may overlap in timing with the early-summer Scotch Creek and Seymour River populations. Juveniles from all three populations rear in Shuswap Lake. Juveniles from several populations rearing in the same nursery lake will not be discriminated via SPA, as they will have had a common rearing environment. It is, however, of substantial management interest to determine whether the Lower Adams population has returned earlier than expected. Differentiation between two major summer-run populations, the Chilko River and the Horsefly River in the Chilko and Quesnel Lake systems, respectively, has declined over time such that SPA alone is not able to provide reliable discrimination between the two populations. The number of jacks returning to populations in the drainage has also declined, resulting in increased difficulty in constructing appropriate in-season stock identification models. Late-run populations traditionally do not enter the lower Fraser River until September, but beginning in 1996 late-run populations have entered the lower river as much as six weeks earlier than normal. This earlier-than-normal entry has been associated with high levels of mortality prior to reaching the spawning grounds and with higher levels of prespawning mortality at the natal streams. Prespawning mortality is thought to have been caused largely by the parasite Parvicapsula minibicornis and has resulted in fishery closures designed to increase escapement to late-run populations (Cooke et al. 2004). In 2002, the late-run Lower Adams population was expected to comprise a substantial fraction of the returns and the Fraser River Panel adopted a maximum 15% exploitation rate of late-run populations to allow sufficient numbers of fish to reach their natal spawning grounds. Accurate and timely estimation of stock composition was necessary for this policy to be implemented. Concerns over the identification and management of minor stocks within the watershed, the declining abundance of the jacks from which scales are derived for current-year management, and the requirement for increased resolution among stocks that scale pattern analysis has not been able to provide led us to evaluate alternative methods of stock identification for Fraser River sockeye salmon. Genetic techniques incorporating allozyme variation have been successfully applied to identify a number of other salmonid stocks (Utter et al. 1987; Shaklee et al. 1999; Seeb et al. 2000), but the level of population-specific accuracy required for Fraser River sockeye salmon applications was unlikely to be attainable from allozyme variation (Wood et al. 1994). The variation in microsatellite loci had been of substantial value in identifying stocks of sockeye salmon in other watersheds (Beacham and Wood 1999; Beacham et al. 2000a, 2000b). Previous surveys of the microsatellite variation in Fraser River sockeye salmon at six loci indicated differentiation among populations (Withler et al. 2000). In addition, the variation at major histocompatibility complex (MHC) loci has been very effective in salmonid population differentiation (Miller and Withler 1997; Beacham et al. 2001). For Fraser River sockeye salmon, 25% of the variation at the MHC locus DAB- 1 was partitioned among populations, compared with 5% at neutral microsatellite loci (Miller et al. 2001). As the annual variation in allele frequencies at salmonid microsatellite and MHC loci is substantially less than the variation among populations (Beacham and Wood 1999; Tessier and Bernatchez

1120 BEACHAM ET AL. 1999; Beacham et al. 2000a, 2000b; Miller et al. 2001), there is no requirement for annual updating of baseline populations once enough surveys have been conducted to adequately characterize the genetic differentiation among populations. Unlike with SPA, there is no difference in stock composition estimates depending on whether the technique is applied during the fishing season or after the season has ended. Given the limitations of scales and the advantages of DNA markers, we chose to evaluate and apply the variation in microsatellite and MHC loci to Fraser River sockeye salmon stock identification. Previous evaluation of the accuracy of stock composition estimates of Fraser River sockeye salmon of known origin indicated that DNA-based variation is reliable (Beacham et al. 2004a). In the current study, we determine the stability of allele frequencies of DNA-based loci, examine the population structure of Fraser River sockeye salmon, and evaluate the utility of using variation at 1 MHC and 14 microsatellite loci for stock identification of Fraser River sockeye salmon. This evaluation is conducted by examining the relative power of each locus used for stock identification, along with the accuracy and precision of estimated stock compositions, through analysis of simulated mixtures. We then apply the methods to estimate stock composition for a number of fishery samples of Fraser River sockeye salmon collected during 2002. Methods Collection of DNA samples and laboratory analysis Tissue samples were collected from adult fish from sockeye salmon populations in the Fraser River drainage in British Columbia, and DNA was extracted from the samples as described by Withler et al. (2000). For the survey of baseline populations, polymerase chain reaction products at 14 microsatellite loci Ots2, Ots3 (Banks et al. 1999), Ots100, Ots103, Ots107, and Ots108 (Beacham et al. 1998; Nelson and Beacham 1999), Oki1a, Oki1b, Oki6, Oki10, Oki16, and Oki29 (Smith et al. 1998 and unpublished), One8 (Scribner et al. 1996), and Omy77 (Morris et al. 1996) were sizefractionated on denaturing polyacrylamide gels and allele sizes determined with the ABI 377 automated DNA sequencer. Genetic variation at the MHC class II DAB- 1 locus (Miller et al. 2001) was surveyed by means of denaturing gradient gel electrophoresis (DGGE). The 1 alleles were separated by DGGE with the Bio-Rad (Hercules, California) D Gene or D Code electrophoresis systems under conditions determined by the methods of Miller et al. (1999). Fluorescently multiplexed DGGE (Miller et al. 2000) was used in the population survey and analysis of fishery samples. Baseline populations The baseline survey included approximately 13,000 sockeye salmon in 47 populations in the Fraser River drainage. Information on population structure derived from six microsatellites and 30 populations was outlined by Withler et al. (2000) and that from the from the DAB- 1 MHC locus by Miller et al. (2001). The complete survey of populations in the Fraser River drainage at which 14 microsatellite and 1 MHC loci were surveyed included 47 populations from 24 nursery lakes (Table 1). Each population at each locus was tested for departure from Hardy Weinberg equilibrium using the program Genetic Data Analysis (P. O. Lewis and D. Zaykin; available at http://lewis.eeb.uconn.edu/lewishome/software.html.). Annual samples within populations were tested separately, 135 tests being conducted at each locus (Table 1). Linkage disequilibrium between loci in each population was also evaluated using GDA, 135 tests being conducted for each two-locus combination. With 15 loci, there were 105 different two-locus combinations to evaluate. Critical significance levels for simultaneous tests were evaluated using sequential Bonferroni adjustment (Rice 1989). Genetic differentiation index (F ST ) estimates for each locus were calculated with FSTAT (Goudet 1995); the standard deviation of the estimate for each locus was determined by jackknifing over populations and that for all loci combined by bootstrapping over loci. The Cavalli- Sforza and Edwards (1967) chord distance was used to estimate the distances among populations. An unrooted, consensus neighbor-joining tree based on 500 replicate trees was generated with the CONSENSE program from PHYLIP (Felsenstein 1993). Estimation of the variance components of regional differences, population differences within regions, and annual variation within populations was determined with GDA, only those populations being included in the analysis for which two or more years of sampling were available. Seven regions were defined based on the observed population structure: Stuart Stellako, upper mid-fraser, lower mid-fraser, lower Fraser north, lower Fraser south, south Thompson, and north Thompson (Table 1). All annual samples

SOCKEYE SALMON STOCK IDENTIFICATION 1121 available for a location were combined to estimate population allele frequencies, as recommended by Waples (1990). Allele frequencies for all of the baseline location samples surveyed in this study are available at http://www-sci.pac.dfo-mpo.gc.ca/ mgl/default e.htm. The timing of the adult return of specific populations is indicated in the Appendix. Collection of known samples The accuracy of the estimated stock compositions in relation to two known samples that were independent of the baseline was outlined by Beacham et al. (2004a). These two samples were comprised of operculum punches from (1) sockeye salmon from a number of populations from the spawning grounds that were not used in the development of the baseline standards or (2) radiotagged sockeye salmon that were subsequently tracked to a Fraser River tributary. For the radiotagging study, which was similar in technique to those outlined by Eiler et al. (1992), we assumed that these fish migrated to their correct natal tributary. In 2003, sockeye salmon were again radiotagged in marine approaches to the Fraser River, and 202 of these fish were tracked to known locations on the spawning grounds. We then compared the estimated stock composition of these fish with their known spawning timing and locations. Collection of mixed-stock samples All mixed-stock samples were collected from test fisheries in Johnstone Strait, the Strait of Juan de Fuca, and Whonnock, the latter being within the lower Fraser River. Samples of DNA from the fisheries were obtained from either operculum punches or fin clips preserved in 95% ethanol. The mixed-stock samples were screened at all 14 microsatellite and 1 MHC loci. The samples generally consisted of 96 individuals from both commercial and test fisheries. Test fisheries were conducted at regular intervals throughout the fishing season with purse seines, gill nets, and trolling gear. These test fisheries were designed to take representative samples of the sockeye salmon that were present in a given area at a specific time. For example, a multipanel gill net (with different mesh sizes in each of the panels) was used in the freshwater test fisheries to increase the likelihood that the full size range of sockeye salmon would be caught, and the samples for DNA analysis were collected from the catch in proportion to the number of fish caught in each panel. With respect to the commercial fisheries, unsorted landings from multiple boats were sampled at packers when the origin of the catch could be verified. Otherwise, sockeye salmon landings were sampled from a minimum of three boats. Beginning in 2001, all mixed-stock samples from both test and commercial fisheries were analyzed for stock composition within 9 30 h of delivery to the laboratory, the results being made available to fishery managers for subsequent fishery management decisions. Identification of individuals Identification of individuals to specific populations was done with the program GENECLASS 1.0 (Cornuet et al. 1999). The probability of an individual s belonging to a given population was calculated via a Bayesian approach, and each individual was assigned to the population for which it had the highest marginal probability. The individual to be classified was not included in the baseline population sample during the classification procedure. Individuals not scored at more than two loci were excluded from the analysis. 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; Debevec et al. 2000) was used to estimate the stock composition of each mixture. All loci were considered to be in Hardy Weinberg equilibrium, and expected genotypic frequencies were determined from the observed allele frequencies. We also evaluated a Bayesian procedure for estimating stock composition outlined by Pella and Masuda (2001). The results (not published) with samples of known origin indicated that there was little difference between the results from SPAM and those from the Bayesian analysis in Fraser River sockeye salmon applications. However, there were very marked differences in the amount of time required for computer analyses, SPAM requiring substantially less time. Given the requirement for rapid analysis of the mixed-stock samples for in-season management decisions, we used SPAM exclusively in the analysis of the mixedfishery samples. The reported stock compositions for actual fishery samples are point estimates for each mixture analyzed, variance estimates being derived from 100 bootstrap simulations. Each baseline population and fishery sample was sampled with replacement in order to simulate the random variation involved in the collection of the baseline and fishery samples. The reported stock composition for

1122 BEACHAM ET AL. TABLE 1. Population, nursery lake, sample collection years, number of fish sampled per year, and total number of fish sampled for seven regional groups of Fraser River sockeye salmon surveyed at 47 sampling sites or populations. The early Stuart and late Stuart Stellako groups were considered to be subgroups of the Stuart Stellako regional group. Population Nursery lake Sampling years Per year Number sampled Early Stuart Kynock Creek Trembleur 1994, 1997 74, 98 172 Gluskie Creek Trembleur 1997 149 149 Forfar Creek Trembleur 1997 148 148 Dust Creek Takla 1988, 1991, 1997 24, 44, 105 173 Porter Creek Takla 2000 15 15 Hudson Bay Creek Takla 2000 18 18 Blackwater Creek Takla 2000 20 20 Late Stuart Stellako Stellako River Stellako 1992, 1995, 1996, 99, 143, 35, 100, 88, 117 582 1998, 1999, 2000 Middle River Trembleur 1993, 1996, 1997, 40, 41, 51, 99, 100, 105 436 1998, 2000, 2001 Nadina River Francois 1986, 1992, 1999, 2000 39, 99, 100, 118 356 Pinchi Creek Stuart 1999 74 74 Tachie River Stuart 1996, 1997, 2000 56, 100, 105 261 Kuzkwa River Tezzeron 2001 105 105 Upper mid-fraser Bowron River Bowron 1999, 2000, 2001 65, 100, 100 265 Chilko River Chilko 1992, 1995, 1996, 99, 60, 148, 119, 100, 122, 100, 110 858 1997, 1998, 1999, 2000, 2001 Chilko Lake (south) Chilko 1996, 1997, 2001 97, 102, 200 399 Horsefly River (mixed) Quesnel 1985, 1986, 1993, 80, 96, 97, 99, 95, 101, 118 686 1996, 1997, 1998, 1999 Lower Horsefly River Quesnel 2001 200 200 Middle Horsefly River Quesnel 2001 200 200 Upper Horsefly River Quesnel 2000, 2001 102, 400 502 Roaring River Quesnel 2001 100 100 Wasko Creek Quesnel 2001 100 100 Blue Lead Creek Quesnel 2001 100 100 McKinley Creek Quesnel 2001 200 200 Mitchell River Quesnel 1993, 1998, 2001 114, 205 319 Lower mid-fraser Portage Creek Seton 1986, 1997, 1998, 1999 98, 115, 72, 47 332 Gates Creek Anderson 1986, 1992, 1995, 91, 49, 60, 103, 100 403 1999, 2000 Nahatlatch River Nahatlatch 1996, 1997 106, 132 238 Lower Fraser (north side) Birkenhead River Lillooet 1992, 1995, 1997, 99, 136, 48, 100, 41, 100 524 1998, 1999, 2001 Weaver Creek Harrison 1982, 1986, 1992, 83, 139, 81, 49, 101, 46, 100, 100 699 1996, 1998, 1999, 2000, 2001 Big Silver Creek Harrison 2000 100 100 Harrison River None 1986, 1995, 2000 132, 50, 100 282 Pitt River Pitt Lake 1986, 2000, 2001 145, 100, 100 345 Lower Fraser (south side) Cultus Lake Cultus 1992, 1995, 1999, 61, 69, 84, 34, 56 304 2000, 2001 Chilliwack River Chilliwack 1996, 2001 59, 100 159 South Thompson River Lower Adams River Shuswap 1982, 1990, 1995, 1996, 1998, 1999 100, 50, 103, 97, 102, 115 567 Total

SOCKEYE SALMON STOCK IDENTIFICATION 1123 TABLE 1. Continued. Population Nursery lake Sampling years Per year Number sampled South Thompson River Upper Adams River Adams 1996, 2000 278, 100 378 Cayenne Creek Adams 2000 100 100 Lower Shuswap Mara 1983, 1986, 1990, 30, 36, 28, 5, 99, 85 283 1996, 1998, 1999 Middle Shuswap Mabel 1986 147 147 Little Shuswap Little Shuswap 1994 81 81 Scotch Creek Shuswap 1994, 1995, 1996, 100, 77, 112, 83, 100 472 1999, 2000 Seymour River Shuswap 1986, 1996, 1999 143, 107, 86 336 Eagle River (early) Shuswap 2000 100 100 Eagle River (late) Shuswap 1990 80 80 North Thompson River Fennell Creek North Barriere 1996, 1999, 2000, 2001 199, 100, 100, 94 493 Raft River Kamloops 1996, 2000, 2001 101, 100, 100 301 Total the simulated mixtures is the bootstrap mean and its standard deviation. Results Variation within Populations All loci surveyed were polymorphic in all populations sampled. The number of observed alleles at each locus ranged from 8 to 40, lower heterozygosity being observed at loci with 15 or fewer alleles (Oki1a, Oki1b, Ots107, and DAB- 1; Table 2). Maximum heterozygosity was observed at Oki10, the locus with the largest observed number of alleles. Heterozygosity varied both among loci and among the populations surveyed. Low population heterozygosities were observed in Cayenne Creek (0.57) in the South Thompson River drainage and Cultus Lake (0.58) in the lower Fraser River, whereas high heterozygosities were observed in Hudson Bay Creek (0.73), Blackwater Creek (0.70), and the Kuzkwa River (0.71) in the Stuart River drainage, Eagle River (0.71) and Little Shuswap Lake (0.72) in the South Thompson River drainage, McKinley Creek (0.71) in the Quesnel River drainage, and Big Silver Creek (0.71) in the lower Fraser River. No association was observed between heterozygosity and geographic region or run timing. The genotypic frequencies at each locus within sampling locations and years generally conformed to those expected under Hardy Weinberg equilib- TABLE 2. Number of alleles, expected heterozygosity (H e ), observed heterozygosity (H o ), population range, percent significant Hardy Weinberg equilibrium tests (HWE; N 135 tests), and F ST among 47 Fraser River sockeye salmon populations (SDs in parentheses) for 14 microsatellite and 1 major histocompatibility complex loci. Locus Alleles H e H o Range HWE F ST Oki1a Oki1b Oki6 Oki10 Oki16 Oki29 Omy77 One8 Ots2 Ots3 Ots100 Ots103 Ots107 Ots108 DAB- 1 All loci 8 8 33 40 25 36 19 23 25 24 32 29 15 28 11 0.38 0.56 0.70 0.91 0.62 0.83 0.81 0.58 0.76 0.62 0.73 0.86 0.38 0.87 0.55 0.38 0.56 0.69 0.90 0.63 0.81 0.81 0.59 0.78 0.62 0.71 0.85 0.38 0.84 0.54 0.02 0.67 0.28 0.78 0.15 0.79 0.78 1.00 0.39 0.77 0.61 0.92 0.52 0.91 0.31 0.84 0.60 0.88 0.46 0.84 0.47 0.94 0.69 0.96 0.14 0.67 0.69 0.91 0.11 0.79 0.0 0.7 0.0 2.2 0.0 1.5 3.0 0.0 0.7 0.7 1.5 1.5 0.0 1.5 3.0 0.062 (0.019) 0.044 (0.006) 0.075 (0.020) 0.030 (0.004) 0.062 (0.011) 0.068 (0.010) 0.055 (0.012) 0.069 (0.018) 0.075 (0.008) 0.063 (0.009) 0.081 (0.010) 0.050 (0.007) 0.076 (0.020) 0.057 (0.006) 0.215 (0.032) 0.064 (0.011)

1124 BEACHAM ET AL. FIGURE 2. Neighbor-joining dendrogram of chord distances (Cavalli-Sforza and Edwards 1967) for annual samples of Fraser River sockeye salmon assessed at 14 microsatellite loci. The values at the nodes are the percentages of 500 bootstrap trees in which the samples from the populations to the right of the nodes clustered together. See Figure 1 for locations. rium (Table 2). There was no evidence of null or nonamplifying alleles at any locus, nor was there evidence of any admixtures having been sampled on the spawning grounds. A comparison of linkage disequilibrium in all annual baseline samples in all populations indicated that the largest number of significant cases was observed between Ots108 and DAB- 1, 5.2% of the comparisons being significant at the 0.05 level. The next largest number was between Ots108 and Oki10 (4.5%), followed by Oki10 and Oki29 (3.7%). There was also no evidence of any significant linkage between any of the other loci surveyed, and these loci were considered to be unlinked. Population Structure Genetic differentiation among the 47 sockeye salmon populations sampled in our study was clear. The overall F ST value for the 14 microsatellite loci surveyed was 0.054, individual locus values ranging from 0.030 at Oki10 to 0.081 at Ots100 (Table 2). There was no clear relationship between the number of alleles observed at a locus and the F ST value, a larger number of alleles being observed at Oki10 and Ots100 (40 and 32, respectively) but with wide variance in their F ST values. Very substantial differentiation was observed among populations at the MHC locus, with an F ST value of 0.215, which is indicative of marked dif-

SOCKEYE SALMON STOCK IDENTIFICATION 1125 FIGURE 2. Continued. ferentiation in allele frequencies among populations. Pairwise comparisons of population allele frequencies were also used to evaluate differences among the populations surveyed. All comparisons between populations were significant with the exception of some comparisons in the Quesnel River drainage (e.g., the Lower Horsefly, Middle Horsefly, Upper Horsefly, and mixed Horsefly River populations) and some in the early Stuart River group. Generally, the sockeye salmon populations in the Fraser River drainage are genetically distinct. Temporal and regional structuring of population samples was observed in our study. There was a very strong clustering of annual samples within populations based on microsatellite variation, those samples clustering together in excess of 90% of the time in most populations (Figure 2). Regional structuring of populations was also observed and corresponded to the regions defined in Table 1. For example, the Cultus Lake and Chilliwack River populations clustered together 92% of the time, and these populations were considered to be in the lower Fraser River (south side) region. Similarly, the Birkenhead River, Weaver Creek, Harrison River, Big Silver Creek, and Pitt River clustered together 50% of the time in the lower Fraser River (north side) region. South Thompson River populations and those from Portage Creek were similar, clustering together 82% of the time, and the North Thompson River populations clustered together 100% of the time. In pairwise comparisons over all 47 populations, the most distinct group of populations surveyed was that of the lower Fraser River (south side), with a mean F ST value usually exceeding 0.10 in regional comparisons (Table 3). The Cultus Lake

1126 BEACHAM ET AL. TABLE 3. Mean pairwise F ST values averaged over 15 loci for sockeye salmon from seven regional groups in the Fraser River and Thompson River drainages that were sampled at 47 locations. Comparisons were conducted between individual populations in each region. The number of populations in each region is listed in Table 1; SDs are given in parentheses. Values in bold italics are comparisons among populations within regions. Group Stuart Stellako River Upper mid-fraser River Lower mid-fraser River Lower Fraser River (north side) Lower Fraser River (south side) South Thompson River North Thompson River Stuart Stellako Upper mid-fraser 0.011 (0.008) 0.065 (0.020) 0.034 (0.025) Lower mid-fraser 0.084 (0.038) 0.089 (0.044) 0.109 (0.043) and Chilliwack River populations were very distinct from other populations in the Fraser River drainage, and indeed, were quite distinct from each other. Populations in the south Thompson River were on average distinct from those in other regions and were most similar to those in the North Thompson River. Substantial differentiation was observed among populations within the lower mid Fraser River region, probably because of the three populations in the region, one was the very distinctive Gates Creek population (Figure 2) and another was the Portage Creek population, a population that has probably been altered by the transplantation of sockeye salmon from the lower Adams River into the South Thompson. The least amount of differentiation among populations within regions was observed in the Stuart Stellako region, which had only a modest degree of differentiation among the early- and summer-returning populations (Stuart River drainage) as well as among those (Stellako, Nadina) in the Nechako River drainage (Figure 2; Table 3). Modest differentiation was also observed between the Fennell Creek and Raft River populations in the north Thompson River region, which may be expected because the current Fennell Creek population was developed with transplants from the donor Raft River population after 1952 (when dams in the Barriere River system were removed; Aro 1979). Distribution of Genetic Variation Gene diversity analysis of the 15 loci surveyed was used to determine the magnitude of the annual variation within and among sockeye salmon populations (organized into the seven regions in Table 1 and only populations with two or more years of sampling being included in the analysis). The amount of variation within populations ranged from 76% (DAB- 1) to 97% (Oki10), the average for microsatellite loci being 94% and that for the MHC locus 76% (Table 4). The maximum range of sampling times was approximately 20 years for any population, a maximum of 8 years of sampling being recorded for a single population. The variation among sampling years within populations was the smallest source of variation observed, accounting for just 0.34% of all variation. For all microsatellite loci the variation among years within populations accounted for 0.30% of total variation; the corresponding value was 0.83% for the MHC locus. The variation among populations within regions accounted for an average of 3.53% of the variation at microsatellite loci and 7.24% of the variation observed at the MHC locus. For the microsatellite loci variation among regions accounted for only 2.38% of the observed variation, but 16.04% of the variation for the MHC locus was attributed to regional differentiation. Differentiation among regions and populations within regions was approximately 20 times that of the annual variation within populations for the microsatellite loci and 28 times for the MHC locus. For the time period surveyed in our study, the annual variation in allele frequencies was minor relative to the differentiation among populations. Comparison of Individual Loci Determination of the relative power of the individual loci for either population or regional discrimination can be very important in practical stock identification applications, particularly if only a subset of the available loci are being used. In simulations comparing the relative power of the loci to estimate the stock composition of representative single-population samples, the number of alleles observed at a locus was significantly correlated with the power of the locus to provide accurate estimates of stock composition (r 0.85, P 0.01). The power of a locus can sometimes be enhanced when used in combination with other

SOCKEYE SALMON STOCK IDENTIFICATION 1127 TABLE 3. Extended. Group Lower Fraser (north) Lower Fraser (south) South Thompson North Thompson Stuart Stellako River Upper mid-fraser River Lower mid-fraser River Lower Fraser River (north side) Lower Fraser River (south side) South Thompson River North Thompson River 0.059 (0.025) 0.073 (0.016) 0.102 (0.037) 0.043 (0.008) 0.136 (0.027) 0.121 (0.018) 0.138 (0.039) 0.102 (0.017) 0.107 0.084 (0.038) 0.065 (0.027) 0.082 (0.053) 0.072 (0.021) 0.122 (0.031) 0.037 (0.031) 0.138 (0.008) 0.044 (0.018) 0.078 (0.049) 0.078 (0.006) 0.114 (0.023) 0.054 (0.023) 0.014 loci. The mean accuracy of estimated stock compositions was lower for Oki1a, Oki1b, and Ots107 than for the other loci surveyed (Table 5), and these were the loci with the fewest number of observed alleles (Table 2). In general, the more alleles that were present at a locus, the greater the power of the locus for population-specific identification in simulated-mixture samples. The MHC locus had the greatest differentiation among populations as determined by F ST (Table 2), but it was not the most effective in terms of identifying individual populations (Table 5). There was little differentiation among populations in the Stuart and Stellako region at this locus, and thus there was little ability to discriminate among specific populations from this geographic area. For example, when either the Stellako River or Middle River population alone comprised a sample, less than 25% of the sample was correctly attributed to the specific population (Table 5). The power of the MHC locus in stock identification applications can probably be used most effectively when combined with microsatellites in mixed-stock analysis. The power of an individual locus to provide accurate estimates of stock composition varied considerably among loci and populations. For example, the Middle River population was the most poorly estimated in the single-locus simulations, with an average accuracy of estimation of less than 50%, whereas the average accuracy of estimation was more than 90% for the Gates Creek population (Table 5). Not all of the loci were equally effective in stock identification applications, and the utility of the loci varied among populations. TABLE 4. Hierarchical gene diversity analysis of Fraser River sockeye salmon based on 14 microsatellite and 1 major histocompatibility complex loci. The proportion of variation attributable to temporal and geographic variables is indicated. Regions, populations within regions, and years within populations are outlined in Table 1. The last column shows the ratio of the sum of columns (3) and (4) to column (2). Only populations with two or more years of sampling were included in the analysis. Locus (1) Within populations (2) Among years within populations (3) Among populations within regions (4) Among regions (3) (4) (2) Oki1a Oki1b Oki6 Oki10 Oki16 Oki29 Omy77 One8 Ots2 Ots3 Ots100 Ots103 Ots107 Ots108 All microsatellites DAB- 1 All loci 0.9279 0.9559 0.9282 0.9707 0.9320 0.9309 0.9356 0.9344 0.9275 0.9337 0.9197 0.9522 0.9218 0.9409 0.9379 0.7589 0.9256 0.0020 0.0023 0.0043 0.0012 0.0065 0.0031 0.0053 0.0012 0.0013 0.0046 0.0030 0.0024 0.0029 0.0026 0.0030 0.0083 0.0034 0.0519 0.0292 0.0443 0.0211 0.0344 0.0417 0.0370 0.0445 0.0279 0.0382 0.0439 0.0339 0.0296 0.0275 0.0353 0.0724 0.0378 0.0181 0.0127 0.0231 0.0071 0.0271 0.0243 0.0221 0.0199 0.0433 0.0236 0.0334 0.0115 0.0457 0.0290 0.0238 0.1604 0.0332 35.0 18.2 15.7 23.5 9.5 21.3 11.2 53.7 54.8 13.4 25.8 18.9 26.0 21.7 19.7 28.0 20.9

1128 BEACHAM ET AL. TABLE 5. Mean estimated percentage stock compositions of single population mixtures (correct 100%) for 15 representative populations of sockeye salmon calculated with single-individual loci for 14 microsatellites and 1 major histocompatibility complex locus. Simulations were conducted using a 47-population baseline with 200 fish in the mixture sample and 500 resamplings in the mixture and baseline samples. Population Oki1b Oki1a Ots107 Ots3 One8 Oki6 Lower Adams Chilko Horsefly Birkenhead Weaver Stellako Bowron Scotch Fennell Middle Gates Nahatlatch Harrison Pitt Cultus Mean 2.9 29.7 55.0 14.3 13.8 3.5 68.0 6.4 82.5 21.7 82.4 15.7 16.4 48.8 90.0 36.7 12.5 10.6 75.7 16.1 16.6 7.3 74.7 9.1 62.3 25.3 91.2 51.8 10.9 42.4 79.3 39.0 6.4 71.1 86.9 55.4 28.8 48.1 54.0 36.3 72.9 48.7 91.9 84.0 64.6 85.7 75.6 60.7 44.6 17.1 66.5 77.8 81.0 66.0 86.0 40.0 74.8 42.2 90.2 60.5 59.8 61.4 86.1 63.6 24.5 73.2 82.3 69.1 53.1 77.4 59.9 57.4 80.3 34.4 96.6 93.4 81.0 91.3 95.4 71.3 26.2 62.4 73.2 91.1 75.6 68.5 75.7 77.0 87.4 46.8 94.2 83.1 86.2 73.0 96.6 74.5 DAB- 1 63.7 87.1 83.4 77.0 83.6 21.1 98.5 73.7 79.5 15.0 72.0 96.9 96.0 94.6 88.1 75.3 Omy77 60.9 65.7 63.1 77.3 91.3 70.5 89.0 54.5 78.6 43.0 94.1 91.1 87.3 82.9 97.9 76.5 Analysis of Simulated Mixtures We evaluated whether the degree of differentiation observed among Fraser River populations was sufficient for mixed-stock analysis in which the objective was to identify specific population contributions in mixed-fishery samples. Four simulated mixed-fishery samples were evaluated, with six or seven populations in each mixture (Table 6). The mean percentage error of the estimates over all four mixtures was less than 1% per population when estimated with a 47-population baseline. The standard deviation of the estimates was approximately 3% with a mixture sample size of 150 fish. Accurate estimates of the contributions of specific populations should be obtainable when the genetic data outlined in our study are applied to actual mixed-population samples. Accuracy of Estimated Stock Composition The accuracy of estimated stock composition was evaluated by comparing the known destination of 202 radio-tagged sockeye salmon recovered in 2003 (under the assumption that these fish returned to their natal tributaries) with the estimated stock composition of these fish derived from the genetic analyses. The mean estimated error of the nine stocks present in the sample was less than 1% of the actual contribution, with a maximum error of about 2% (Figure 3A). Similar results were observed with respect to run timing (Figure 3B). Results from a known mixture of sockeye salmon indicated that a high degree of accuracy was achieved in the estimated stock composition of the sample. Identification of Individuals Variation in DNA was used to classify individuals from the 47 populations in the Fraser River drainage with respect to population, lake, region, and run-time origin. Overall, 60% of individuals were correctly classified to a specific population, 79% to the correct lake, 92% to the correct region, and 90% to the correct run time (Table 7). The classification success rate varied considerably among populations, however. Populations in the lower Fraser River were quite distinct, over 92% of individuals being correctly classified to their lake of origin. Within Quesnel Lake, the accuracy of classifying individuals to specific populations was only about 25%, although approximately 90% of all individuals were correctly identified as having originated from Quesnel Lake. In Quesnel Lake, the same population was probably sampled at more than one sampling site. The success rate of classifying individuals to specific lakes was indicative of the genetic distinctiveness of the populations within each lake. For example, approximately 95% of the individuals from the lower Fraser River, Gates Creek, and Bowron Lake were correctly assigned to their lake of origin, and the sockeye salmon in these lakes were genetically quite distinct. Analysis of Fishery Samples Identification of the four run-time groups is central to the management of the fishery for Fraser

SOCKEYE SALMON STOCK IDENTIFICATION 1129 TABLE 5. Extended. Population Oki16 Ots103 Oki10 Ots2 Ots100 Oki29 Ots108 Mean Lower Adams Chilko Horsefly Birkenhead Weaver Stellako Bowron Scotch Fennell Middle Gates Nahatlatch Harrison Pitt Cultus Mean 59.3 86.0 76.7 81.2 90.8 73.6 82.0 61.3 90.4 44.8 91.2 93.5 90.2 91.4 68.6 78.7 44.7 55.1 90.3 86.4 91.5 67.1 90.7 89.0 86.7 69.1 94.5 90.7 65.6 93.5 94.1 80.6 61.7 75.4 91.5 80.6 87.9 74.4 85.7 84.6 93.5 63.7 96.1 93.5 76.2 74.4 80.1 81.3 55.2 93.7 83.7 83.8 87.5 81.6 93.5 85.3 90.1 42.9 94.4 93.3 88.2 90.6 96.4 84.0 56.6 81.9 89.1 81.3 84.7 81.1 84.8 82.6 91.0 56.4 96.8 94.5 86.8 93.6 98.3 84.0 54.9 92.0 87.1 91.8 84.9 88.8 88.1 89.0 90.1 73.5 93.9 94.7 82.1 86.0 97.1 86.3 70.7 88.1 93.2 93.2 95.7 86.2 91.6 88.4 81.1 76.8 95.6 92.6 83.4 91.4 97.3 88.4 43.0 65.9 79.8 71.7 71.1 61.0 81.5 62.3 82.7 46.9 91.7 82.0 71.6 80.1 89.4 All loci 92.9 98.3 99.1 97.2 98.2 95.6 95.0 93.6 96.8 90.8 96.2 96.3 95.1 97.8 95.6 River sockeye salmon. In 2002, SPA was inadequate for stock identification due to considerable overlap in the scale characters of the summer- and late-run populations, and the decision was made to use DNA-based stock identification for realtime management of the fisheries. In the southern approach to the Fraser River through the Strait of Juan de Fuca, sockeye salmon from Washington State dominated the returns in early July but had finished their migration by late July (Figure 4B). The earliest timed run of the Fraser River, the early Stuart run, comprised 22% of the catch in early July but was virtually absent in the catch past mid- July. The peak abundance of the early-summer run was observed in late July, the traditional time of return. The abundant summer run dominated the TABLE 6. Estimated percentage composition of four simulated mixtures of Fraser River sockeye salmon populations incorporating variation at 14 microsatellite loci and one MHC locus. A 47-population baseline was used to estimate stock compositions, and each mixture of 150 fish was generated 100 times with replacement. Standard deviations are shown in parentheses. Simulation 1 Simulation 2 Simulation 3 Simulation 4 Population True Estimated True Estimated True Estimated True Estimated Gates Chilko Horsefly Stellako Weaver Lower Adams Bowron Nahatlatch Birkenhead Cultus Fennell Kynock Nadina Tachie Chilko (south) McKinley Mitchell Pitt Chilliwack Harrison Raft Upper Adams 10 20 25 10 10 25 9.8 (2.4) 19.7 (3.7) 24.1 (4.2) 9.8 (2.6) 10.1 (2.6) 22.2 (3.6) 20 15 15 20 20 10 20.0 (3.4) 14.7 (3.0) 14.8 (3.1) 19.8 (3.2) 19.5 (3.1) 9.8 (2.4) 10 15 10 10 10 25 20 15 15 15.5 (3.2) 14.0 (3.1) 7.4 (2.7) 14.7 (3.1) 10 9.8 (2.5) 9.1 (3.2) 7.4 (2.9) 9.4 (2.5) 23.3 (3.5) 19.1 (3.0) 15 14.7 (3.0) 15 14.4 (3.1) 20 18.8 (3.9) 10 9.3 (2.3)

1130 BEACHAM ET AL. revealed that 95% of the catch was of late-run origin; this was higher than that observed in purse seine sampling (Figure 4A), suggesting that laterun stocks were more vulnerable to harvest by troll gear. The timing of entry into freshwater in 2002 was evaluated with a gill-net test fishery at Cottonwood and Whonnock in the lower Fraser River. The summer run dominated the sockeye salmon return in the lower river through August, but by September the traditional time of passage of laterun fish in the lower Fraser River the late run dominated the returns (Figure 4C). FIGURE 3. Actual and estimated stock compositions of a sample of 202 Fraser River sockeye salmon that were radio-tagged in Johnstone Strait or the Strait of Juan de Fuca in 2003 and tracked to their last known locations in a Fraser River tributary. Panel (A) shows percentages by stock, panel (B) percentages by run time (E early). catch from late July through early August, the traditional return time. Given the abnormally early river entry of the late run since 1996, in 2002 the Fraser River Panel adopted a maximum 15% exploitation rate of late-run populations in order to allow sufficient numbers of fish to spawn successfully. The timing and abundance of the late run was a key management issue. The late run was observed in the Strait of Juan de Fuca in July, which is earlier than normal but consistent with recent history (Figure 4B). By mid-august, the late run dominated the catch, comprising at least 50% of the total catch of sockeye salmon. In the northern approach to the Fraser River through Johnstone Strait, the summer run comprised the majority of the returns until early to mid-august, after which the late run was the dominant component (Figure 4A). In fact, samples from a troll fishery conducted at the end of August Discussion Population Structure Prior to 1913, up to 100 million sockeye salmon returned to the Fraser River in dominant years (Ricker 1987). A significant event in the history of Fraser River sockeye salmon occurred in 1913 and 1914, when their abundance was reduced dramatically as a result of rock dumping associated with railway construction and rock slides at Hell s Gate in the Fraser River canyon. The resulting obstruction prevented the passage of most mature salmon, skewed the sex ratio of those salmon that passed the obstruction largely in favor of males, and dramatically reduced the condition of the fish that returned to upriver spawning grounds (Ricker 1987). Although there was an extreme reduction in the abundance of upriver populations over a very short time, there was little evidence ofa lasting genetic impact (Withler et al. 2000). Our results support this conclusion: we found no difference in heterozygosity between populations spawning upstream of Hell s Gate and those spawning downstream or in allelic diversity (Withler et al. 2000), which is a sensitive indicator of populations in which the effective size has been recently reduced. Regional structure was apparent for Fraser River sockeye salmon, but one inconsistency in the grouping of the populations was observed, namely, that the Portage Creek population in the lower mid- Fraser group clustered with those of the South Thompson River region. In 1950, 300,000 eyed eggs from the lower Adams River were transplanted to Portage Creek and 193,000 lower Adams River juveniles were released into Anderson Lake, which is upstream of Portage Creek (Aro 1979). In the dendrogram analysis, the Portage Creek population clustered most closely with the lower Adams River population, suggesting that these transplants had some measure of success. Other

SOCKEYE SALMON STOCK IDENTIFICATION 1131 transplants that were probably successful include those from the Raft River into Fennell Creek in the North Thompson River drainage and those from the Seymour River and Cayenne Creek into the upper Adams River in the South Thompson River drainage. As the latter two transfers were both within the same river drainage, regional genetic differences were maintained and the impact on subsequent stock identification applications was minor. The sockeye salmon populations in the Chilliwack River drainage (the Chilliwack River and Cultus Lake; lower Fraser River) were quite distinct from other populations in the Fraser River drainage, as (generally) were populations in the South Thompson River. A comparison of genetic differentiation with geographic distance indicated that there was no strict isolation distance relationship for populations within the Fraser River drainage (Withler et al. 2000). Rather, the distribution of genetic variation was consistent with the upper and lower river drainages colonized by sockeye salmon from different glacial refugia. Upper Fraser River and Thompson River populations are distinct at allozyme loci and mitochondrial DNA (Wood et al. 1994; Bickham et al. 1995), which is consistent with colonization of the Fraser River from different refugia. Upper and lower Fraser River populations are also distinct for Chinook salmon O. tshawytscha (Beacham et al. 2003b), coho salmon O. kisutch (Small et al. 1998), and steelhead O. mykiss (Beacham et al. 2004b), which may also reflect independent colonization of the upper and lower drainages. Among the lower Fraser River populations, substantial differentiation was also observed between those in the Chilliwack River and those in tributaries on the north side of the river, perhaps reflecting either multiple founder effects or genetic drift (Wood et al. 1994; Wood 1995). Annual Variation An issue of key concern for stock identification applications is the consistency of the baseline used to estimate stock compositions in fishery samples. For the microsatellite loci, the variation among populations and regions was on average 20 times as large as the annual variation in allele frequencies within populations. For the MHC locus, the variation among populations and regions was 28 times as large as the annual variation within populations, possibly as a result of natural selection (Miller et al. 2001). There was some evidence to suggest that the annual variation in allele frequencies at this locus was larger than that observed at microsatellite loci (0.83% of the total variation versus 0.30%). However, relative to population differentiation, the annual variation in allele frequencies at the MHC locus was minor. This pronounced, well-defined stability of allele frequencies relative to population differentiation is a key characteristic of microsatellite and MHC loci (Beacham and Wood 1999; Tessier and Bernatchez 1999; Miller et al. 2001) and is in sharp contrast to scale pattern analysis, where annual variability in the scale patterns used in stock identification requires annual sampling of the baseline. Owing to the relative stability of the microsatellite and MHC allele frequencies, baseline samples collected in one year can be used to estimate the stock composition of samples collected in following years or indeed, in earlier years. Annual stability of the characters used to discriminate among stocks is a key attribute of any technique used in stock identification, particularly if the baseline populations cover a wide geographic area. Techniques that rely on environmental variation for stock identification, such as SPA or (more recently) elemental analysis coupled with laser ablation, are prone to annual variation in distinguishing characters (Campana et al. 2000), such that annual sampling of contributing baseline populations is required. If baseline populations originate from a large geographic region, as is typical for marine mixed-stock salmon samples, stock identification techniques requiring annual baseline sampling will either be limited or expensive in their application. Stock Identification Several methods of stock identification currently exist for sockeye salmon, with scale pattern analysis (Cook and Guthrie 1987), parasites (Margolis 1963), allozymes (Seeb et al. 2000), minisatellites (Beacham et al. 1995), microsatellites (Beacham and Wood 1999), and MHC variation (Miller et al. 2001) all potentially available for application to specific problems. How does one choose which method or methods to use in fishery management and assessment applications? We suggest that the method to use depends on the method of stock identification currently in use, the levels of accuracy and precision of the stock composition estimates required for management and assessment, the ease of obtaining those estimates, and the cost of the analysis. The decision as to whether to switch techniques depends on how well the current technique is performing relative to management expectations and the levels of accuracy and pre-