Use of Neutral and Adaptive Genetic Variation in the Management and Conservation of Pacific Salmon

Home , Skeena River, Stuart Lake, Thompson River

CM 2002/U:17

Kristina M. Miller*, Terry D. Beacham and Ruth E. Withler

Salmonid species are under pressure generated both by human activities and changing environmental conditions including global warming. Numerous Pacific salmonid populations in North America have declined to low abundance, and some have been listed as endangered. Alternately, many populations appear relatively healthy. In an effort to better understand why some populations are in decline and others not, and to facilitate the regulation of fishing pressure on a stock-specific basis, we have collected extensive genetic databases comprised of over 35,000 samples for each of coho (Oncorhynchus kisutch), chinook (O. tshawytscha) and sockeye (O. nerka) salmon. For each species there has been intensive sampling of populations within British Columbia, and more extensive sampling of populations from throughout the species' range. The genetic data consist of genotypes at both selectively neutral microsatellite loci, which delineate the demographic features of population structure, and major histocompatibility complex (MHC) loci under selection, which resolve complex patterns of adaptive variation among populations. Information gained from both types of loci is currently being used to define ESUs and to perform genetic stock identification (GSI) on mixed- stock multi- and single-fish samples. Because patterns of variation at the neutral and adaptive loci differ, use of both for GSI may be highly effective. GSI applications include in-season stock-based fisheries management (with turn-around times of 9 to 48 hours), tracking of migration patterns, escapement enumeration, identification of country of origin, and forensic identification of confiscated fish samples.

Keywords: salmon, stock, genetic, neutral, adaptive, major histocompatibility complex

All authors: Pacific Biological Station, Fisheries and Oceans, Canada, 3190 Hammond Bay Rd, Nanaimo, B.C. V9R 5K6, fax (250) 756-7053, KM Miller [tel +001 250 756

7155; email [email protected]], TD Beacham [tel +001 250 756 7149, email [email protected]], RE Withler [tel +001 250 756 7148; email [email protected] mpo.gc.ca]

Introduction Of the 9,662 anadromous Pacific salmon (Oncorhynchus) stocks in coastal British Columbia (BC) and the Yukon River drainage, 624 are at high risk, 78 moderate risk, 230 of special concern, and 142 extirpated in this century, primarily as a result of human activities (Slaney et al. 1996). However, while many individual stocks have been adversely affected, overall abundance's of most of the Pacific salmon species have in the recent past remained either stable (chum) or increased (chinook, sockeye and pink salmon). Stock rebuilding efforts and enhancement practices in the Fraser and Skeena rivers, and favourable marine production may be partially responsible for the stabilisation of abundance's in these species (Beamish and Bouillon 1993). However, coho salmon abundance has declined. In 2002, Interior Fraser coho salmon, with spawning locations concentrated in the Thompson River tributary of the Fraser River, was the first Canadian salmon population group to be listed as endangered by COSEWIC (Committee on the Status of Endangered Wildlife in Canada).

Conservation-based management is complicated by the co-existence of healthy and depressed salmonid stocks and their admixture in fishery harvests. The need for stock- specific fisheries management has long been recognised, and a number of natural and applied ‘tags’ have been used for stock identification of Pacific salmonids, including coded wire tags, parasites, scale patterns, and genetic markers. Because of their ubiquitous presence, stability over time, and sensitivity, genetic markers have gradually supplanted others over the last decade. Genetic stock identification (GSI) has evolved from a protein-based methodology to one based on direct analysis of DNA. Current loci

of choice are neutral microsatellite markers, which are sensitive to recent restrictions of gene flow among populations and which have enabled rapid and cost-effective analysis of samples.

Microsatellites are highly repetitive neutral DNA elements that mutate rapidly, with alleles that differ in size. They are easily genotyped by running PCR (Polymerase chain reaction)-amplified samples out for sizing on automated DNA sequencers. As very little tissue is necessary for PCR amplification, microsatellite analyses can be performed using non-destructive sampling methods, e.g. using fin clips or scales. Most microsatellite DNA is non-coding and considered selectively neutral, with mutations accumulated through genetic drift. Thus, the temporal and spatial distributions of allele frequencies observed at microsatellite loci are interpreted in terms of the demographic histories of populations (stocks), and provide information on numbers of stocks, levels of intraspecific differentiation, founder effects, and bottlenecks. In addition, parameters that shape the observed structure, such as effective population size, mutation rate and gene flow can all be estimated using information from neutral microsatellite loci.

Whereas neutral microsatellite loci are ideal for delineating the demographic features of stock structure, they do not provide direct information on the fitness of individuals and the evolutionary potential of stocks, which depend upon the levels and distribution of adaptive genetic variation. Phenotypic variation among stocks is often considered a hallmark of adaptation, but it can be difficult to differentiate phenotypic plasticity from phenotypic variation with an underlying genetic basis that is subject to natural selection and therefore contributes to biodiversity. Understanding of adaptive genetic variation among stocks will come from direct observation of loci under selection, such as those of the MHC (major histocompatibility complex).

The MHC contains genes involved in adaptive immunity that play a primary role in the defence against bacterial, viral and parasitic disease. Because MHC genes must recognise a wide variety of foreign peptides, they are highly variable, and are generally under positive selection for heterozygosity (Nei and Hughes 1991). Although the

distribution of MHC alleles is influenced by demographic forces (i.e. postglacial recolonization histories and migration in salmonids), MHC variation within and among salmonid populations is also selectively shaped by the pathogenic environment of each population (Miller et al. 2001). Thus, given similar pathogenic environments, populations may resemble each other at MHC loci more closely than their evolutionary histories would suggest. Alternately, populations with very different pathogenic histories may be highly differentiated at MHC, in spite of a close evolutionary connection. Differences in the spatial distribution of neutral microsatellite variation and adaptive MHC variation provide a higher level of resolution among stocks for GSI applications than is possible with either alone, and data both types of loci form the basis of our stock identification program.

Applied genetic research on population structure conducted in the molecular genetics laboratory at the Pacific Biological Station over the past 10 years has been based on analysis of minisatellite, microsatellite and MHC loci. The work has been focussed on three Pacific salmon species, sockeye (Oncorhynchus nerka), coho (O. kisutch) and chinook salmon (O. tshawytscha), with some effort expended on other salmonid and non- salmonid species. In anticipation of increasing GSI requirements in management and conservation of these three species, we undertook compilation of comprehensive baseline data sets. Analysis of mixed-stock fishery samples necessitated intensive sampling of stocks from throughout BC and more extensive sampling of stocks from throughout each species' range for the baseline data. The geographic coverage extends from California through Alaska in North America and includes Russia and Japan on the Asian Pacific rim. The database for each species currently contains genetic information from over 30,000 fish sampled from between 100 and 225 populations and genotyped at 8 to 14 microsatellite loci and one or two MHC loci (MHC in sockeye and coho salmon only). We have incorporated multi-year sampling of populations to assess the stability of the population structure observed.

Herein, we present an overview of the sockeye salmon database and the GSI capabilities it provides for fisheries management in BC. The sockeye database currently

contains genotypic data for 14 microsatellite loci and the MHC class II DAB locus for over 40,000 fish sampled from 225 stocks. The MHC DAB locus is primarily responsible for the recognition of bacterial and parasitic pathogens. Genotypic data for the MHC class I UBA locus, which responds primarily to viral pathogens, is under collection. Differing selective forces on the MHC class I and II loci, and the resultant differences in distribution of genetic variation within and among populations, will likely further enhance the recognition of individual sockeye salmon stocks in GSI analyses.

Neutral and Adaptive Variation among Sockeye Salmon Stocks Although microsatellite loci evolve at a rapid rate relative to coding loci, microsatellite allele frequencies are sufficiently stable over short time periods for accurate GSI analysis. Microsatellite allele frequency variation among salmonid stocks is generally at least 10 times greater than annual variation within stocks (Beacham and Wood 1999; Tessier and Bernatchez 1999; Beacham et al. 2000). Similarly, in spite of strong historical selective effects apparent at the salmonid class II MHC locus, sockeye salmon MHC allele frequencies within a major watershed are 25 times more variable on a spatial than a temporal basis (Miller et al. 2001). A strong element of regional structure among stocks is of paramount importance in GSI of mixed-stock fishery samples because it eliminates the need to survey all individual stocks that may contribute to a fishery. Demographically (microsatellite allele frequency data only), sockeye salmon cluster into 19 geographically based groups, or regions. Within BC, each of the major river drainages forms a separate region, whereas the populations of small coastal lakes on the BC central coast and Vancouver Island each constitute a distinctive region (Fig 1).

Within each region, there exists a second level of structure based on individual lake systems. Sockeye salmon exploit diverse environments through extensive modification of their typical migratory, spawning and rearing behaviours. Sockeye salmon can spawn in streams or lakes, but generally rear in a lake environment. All types of genetic data examined thus far (allozymes, mitochondrial DNA, minisatellite DNA, microsatellite DNA and MHC) indicate that strong philopatry to nursery lakes limits gene flow among

populations, which, in turn, sets the stage for strong adaptation to local conditions (Wood, 1995; Wood et al. 1994; Withler et al. 2000, Miller et al. 2001).

At the class II MHC locus, the putatively adaptive allele frequency differentiation among lake systems exceeds neutral genetic variation by an order of magnitude. Notable examples occur among sockeye salmon populations of the interior Fraser River drainage. The common postglacial origin of the geographically proximate Stuart and Bowron lake systems is apparent in their similarity at microsatellite loci, but not at the almost completely non-overlapping class II MHC allelic profiles (Fig 1B; Miller et al. 2001). The MHC divergence apparently has arisen from directional selection for different alleles within the two lake systems. The frequency of one allele exceeds 90% in the Stuart Lake system, whereas two different common alleles predominate in the Bowron Lake system. In contrast, sockeye populations in other interior Fraser River lake systems are highly polymorphic at the MHC locus, with 5-10 alleles maintained at fairly even frequencies, presumably by balancing selection. The greater adaptive than neutral differentiation among geographically proximate populations increases the accuracy of classification when identification of samples to nursery lakes within a drainage system is required. The use of both neutral and adaptive loci for GSI exploits both the regional similarities revealed by microsatellite loci and the small scale geographic differentiation apparent at the MHC locus.

Capabilities of the Sockeye Database Mixed stock identification--testing known sample mixtures We evaluated the sockeye salmon genetic baseline dataset by examining the accuracy and precision of estimated stock compositions through analysis of simulated mixtures and samples from fisheries in coastal BC (Beacham et al. 2001). At that time, the dataset contained samples from 188 populations (29,000 individuals) from Japan, Russia, Alaska, BC, and Washington. For the highly polymorphic microsatellite loci, low frequency adjacent alleles were binned to reduce the number of genotypic frequencies to be estimated, whereas no compression of the 12 MHC alleles was instituted. The binning of microsatellite alleles did not significantly affect the ability to discriminate among

populations. For baseline samples, allele frequencies were converted to genotypic frequencies for each population using the assumption of Hardy Weinberg equilibrium for model inputs. Stock composition estimates were obtained using maximum likelihood mixture analysis with the statistical package for the analysis of mixtures software program (SPAM 3.2) (Debevec et al. 2000). Regional estimates were above 90% (mean of 91.5%) when mixtures contained 100% of each of 18 of the 19 defined regions. The only region with an estimate significantly below 90% was Russia, for which most population samples in the database contained fewer than 20 fish. In a similar analysis of the baseline dataset in 2002 (by then expanded to data from 39,000 fish), Withler et al. (in press) identified 11 geographically defined North American “reporting regions” of interest to fishery regulation enforcement, for which the accuracy of fish classification was 90% or higher (mean of 95.3%).

The ability to recognise sockeye salmon from Fraser and Skeena River populations in mixtures was further tested by Beacham et al. (2001) with samples obtained from fishery sampling within each drainage. Assuming that fish caught within a drainage originated from populations within that drainage, the estimated stock compositions displayed very little bias, with 98.8% of Fraser-derived fish allocated to Fraser River populations and 96.4% of Skeena-derived fish allocated to Skeena River populations. Additionally, simulated multi-regional mixtures containing fish that might be expected in fishery samples from throughout BC were also analysed. For all mixtures, each estimated regional contribution was within 2 to 3% of the actual contribution from that region. Further testing of the database using fishery samples for which prior knowledge of the stock composition was available yielded similarly precise estimates.

We evaluated the accuracy and precision of allocation to regions within a watershed using SPAM on simulated mixtures of Fraser River sockeye salmon, with the 48 populations sampled from the Fraser River constituting the baseline dataset (Fig 2). Predicted contributions of individual stocks were generally within 2% of the actual contributions, and many were less than 1% (Fig 3A). When stocks were grouped into the eight previously defined regions, accuracy was also within 1-2% (Fig 3B). Highly

accurate stock composition estimates were also obtained when stocks were grouped into four recognised management units defined by time of adult migration through the Fraser River (run time), with all estimated contributions deviating by less than 1% from actual values (Fig 3C).

Because the primary units of stock structure and adaptation in sockeye salmon are the lake systems, accurate classification of sockeye salmon to individual lakes/rivers or smaller regional groupings within major drainages is possible for many areas of BC (Beacham et al. 2001). The extensive baseline coverage of populations in the Fraser and Nass/Skeena drainages enables reliable estimates of stock compositions to be made to local geographic areas and to specific populations. In addition, many of the smaller lake systems in central and northern BC and Alaska are genetically distinctive, and accurate estimation of stock contributions from these primarily coastal lakes is also possible.

Individual Identification Classification of individual genotypes to source has been conducted using the Bayesian option of the GeneClass program (Cornuet et al. 1999). Using the Bayesian leave-one- out 'self-classification' routine on only one third of the baseline dataset (due to program limitations) Withler et al. (in press) found that the accuracy of individual classification was similar to accuracy in the mixed-stock maximum likelihood estimates achieved with SPAM, with a mean level of correct classification over the 11 'reporting regions' of 95.5%.

DNA in Action: GSI Applications for Fisheries Enforcement and Management

GSI applications based on the extensive microsatellite and MHC baseline datasets compiled for chinook, coho and sockeye salmon include the tracking of migration patterns, escapement enumeration, identification of country of origin, forensic identification of confiscated samples, and in-season mixed-stock analysis. Examples of the latter two applications using the sockeye salmon dataset are given below.

Forensic identification of confiscated fish samples The recognition of, and in some cases listings of, specific populations of fish in need of conservation, the resultant fishery restrictions has created a demand for forensic identification of confiscated samples. The genetic methodologies and baselines developed in our laboratory for species and stock identification of Pacific salmon are sufficiently accurate and precise to use in the classification of samples collected as evidence in court proceedings (Withler et al. in press). We conduct forensic species identification of salmonids using species-specific sequence variation of the MHC class II gene (Withler et al. 1997). We conduct GSI analysis on sockeye, chinook and coho salmon using the microsatellite and MHC databases collected for each species and classification of the confiscated samples with SPAM or GeneClass.

Forensic samples analysed have included muscle tissue, fin clips, scales, blood on clothing, canned salmon and even the slime in the bottom of a bucket thought to previously contain fish. Species identification has been required primarily for identification of coho salmon harvested or sold illegally from the restricted fishery for this species in British Columbia since 1998. GSI of sockeye salmon has been conducted for 17 forensic cases. Sixteen cases involved identification of fish sampled from southern BC or Washington State that were thought to have originated illegally from the Fraser River. Analysis of 14 of the 16 forensic samples, which ranged in size from 5 to 144 fish, was consistent with a Fraser River origin (i.e. estimates of 95% or greater Fraser River contribution). In fact, in thirteen of the samples, at least 95% of the fish were allocated to sockeye salmon populations of interior Fraser River and Thompson tributaries, consistent with the postulated location and timing of the catch (in the Fraser River canyon upstream of the lower Fraser tributaries during August). However, in two cases, in which 6 and 56 fish were analysed respectively, GSI analysis substantiated the claim of the suspects that the fish originated from legal fisheries in the Skeena River watershed and not the Fraser River, thereby exonerating the suspects and terminating the investigations (Withler et al. in press).

In-season stock-specific fisheries management In order for GSI to be useful to in-season fishery managers, the turn-around time of the analysis must be within 9-48 hours of sampling. We began in-season analysis in 2001, focusing primarily on sockeye salmon, but with some applications to coho and chinook salmon fisheries. The following example is a recent GSI application used in management of the 2002 Fraser River sockeye salmon fishery in which conservation of ‘Late-run’ sockeye salmon populations was a prime objective.

The Fraser River is the most valuable source of fish for the commercial sockeye salmon fishery in British Columbia (Fig 2). The 90+ Fraser River sockeye salmon populations have characteristic times of adult migration and spawning, and management of the Fraser River populations is based upon four recognised run-times: Early Stuart, Early Summer, Summer and Late. In recent years, differences in time of migration within the Fraser River among the groups have diminished and it has become more difficult to target exploitation on specific runs For the past six years, Late-run Fraser River sockeye salmon stocks have been entering the Fraser River earlier than usual. Historically, Late- run fish arrived on the British Columbia coast in August and early September, and held in the Strait of Georgia for three to six weeks before entering the Fraser River between mid September and early October. A noticeable shift in behaviour occurred in 1996, when Late-run sockeye entered the river in early September (Pacific Salmon Commission (PSC), 2001, 2002). For every year since, a progressively earlier entry into the Fraser River has been observed and, in both 2000 and 2001, there was little or no delay in river entry for the Late-run sockeye. This resulted in little distinction of in-river migration time between populations comprising the Summer- and Late runs.

Although the change in river migration time is itself of some concern because the cause is not understood, it is an elevated mortality along the migration route and near the natal spawning streams of the early entering Late-run fish that has prompted the conservation concerns. Progressively higher prespawning mortality has mirrored the progressively earlier entry into the Fraser River and, in 2000 and 2001, an estimated 90% of all Late- run sockeye that entered the Fraser River died without spawning (PSC, 2002). The cause

of mortality is thought to be infection by Parvicapsula minibicornis, a myxosporean parasite that affects the kidney, eventually causing in renal failure (St-Hilaire et al. 2002). Recent surveys have detected the parasite in sockeye populations throughout the Fraser drainage but it is associated with pre-spawning mortality only in the Late-run populations, perhaps because of their newly-extended freshwater residency prior to spawning (St-Hilaire et al. 2002).

The Late-run sockeye salmon that spawn in the Lower Adams River of the Thompson River drainage constitute an important fishery resource in spite of undergoing four-year cyclical changes in abundance, with over 2 million sockeye returning in every fourth (peak) year. During the 1998 peak year, prespawning mortalities of Lower Adams fish within the Fraser River reached 36% (700,000) (PSC, 2001). Hence, early migration and associated mortality in 2002 could have pronounced long-term effects on this valuable renewable resource.

Thus, conservation of the Late-run Fraser sockeye in order to ensure a sufficient number of spawners for the Lower Adams and other stocks was a priority for the Pacific Salmon Commission (PSC; one of the organisations responsible for the management of Fraser River sockeye salmon). We implemented a large in-season GSI effort to track the migration and estimate the abundance of the Late-run salmon through marine and freshwater fishing areas. Sockeye salmon return to the Fraser River from two routes, a northerly route, in which they migrate along the eastern side of Vancouver Island, through Johnstone Strait and into the Strait of Georgia before reaching the Fraser River, or a southerly route, in which they migrate along the western side of Vancouver Island and around the southern tip of the island through Juan de Fuca Strait (Fig. 1). In-season GSI was conducted on samples of sockeye salmon collected in Johnstone Strait (JS) and the Strait of Juan de Fuca (SJF), as well as in the major test fishery area of the Lower Fraser river (Whonnock). Over 7,000 fish were sampled throughout July and August 2002, with sample sizes of 70-190 fish collected from each area on each sample date, and with some multiple sampling within areas. Daily genetic analyses often involved from 100 to 400 fish, with a turn around time of 9 to 30 hours. On two occasions, 1,000 fish

were analysed in 30 hours, and in one instance, when the migration of late-season fish was nearing a critical level, 400 fish were analysed in 9 hours.

In SJF, a high percentage of the fish originated from Washington State stocks early in July, but the Washington contribution tapered off by the end of July (Figs 4A and B). As well, 20% of the initial July 8th sample was comprised of sockeye salmon from the Early Stuart run, but its contribution had diminished by the next sample taken on July 14. Summer-run stocks were a major component of the sockeye sampled throughout most of July and into August. Most importantly, the Late-run fish, which were present in small numbers at the beginning of July, significantly increased in abundance in SJF by mid- August, comprising up to 75% of the fish in the August 12 sample. The PSC estimated that 50% of the Late-run fish had arrived in Georgia Strait by August 12, eight days earlier than normal (PSC news release (www.psc.org/NewsRel/Index.htm), August 12, 2002)

Although the arrival of Late-run sockeye salmon to the Juan de Fuca channel was about a week earlier than the historical norm, their entry into the Fraser River was shifted by a month, as in 2000 and 2001, with no delay in the migration of at least a portion of the run from the Strait of Georgia into the Fraser River (PSC news release, Aug 12, 2002). Late- run fish were first detected in the Fraser River by the end of July (estimates of up to 10%), with a large pulse of fish coming through on August 15th (comprising over 60% of the mixture). This was two days after detection of the largest proportion in SJF and was consistent with the known minimum two-day migration time between SJF and the Lower Fraser River. The estimated percentage of Late-run fish in the Fraser River diminished to less than 20% in the next two samples, but increased to over 30% in the August 22 and 24 samples. By August 23, the PSC estimated that over 2 million Late-run sockeye salmon were delaying in the lower Strait of Georgia, while 0.8 million had migrated into the Fraser River.

Late-run fish were also migrating through JS in July and increased in abundance in August, reaching almost 80% on August 12, the same date that the main pulse of Late-

run fish was observed in Juan de Fuca. The proportion of Late-run fish in the JS remained high (over 50%) throughout August.

The stock-specific GSI analysis confirmed that the Lower Adams stock was the largest component of the 2002 Late-run sockeye salmon. However, the shift to early migration was not restricted to the Lower Adams stock, but was observed for all Late-run stocks (data not shown).

The management goal of the Pacific Salmon Commission and other management groups was to maximise the fishing opportunities on the large Summer-run populations (which are the most abundant in the Fraser River, second to Late-run, at least historically) while minimising the exploitation of Late-run fish. A 15% exploitation rate ceiling on Late-run sockeye salmon was adopted in 2002 as a precautionary measure (PSC news release, Aug 2, 2002). The lateness in the timing of the Summer-run sockeye in conjunction with the early arrival of the Late-run sockeye resulted in only a small difference in timing between the runs (approximately 4 days). Historically, this difference is approximately 2 weeks (PSC news release, Aug 12, 2002). The extensive overlap made it near impossible to harvest Summer-run only, and by August 6, when the proportion of Late-run fish in Johnstone Strait reached 50%, and in the Juan de Fuca was around 30%, the gillnet fishery was closed in both areas, at least a week earlier than expected, while the fishery in the Fraser River, where less than 20% Late-run fish were observe, remained open. Due to higher than expected forecasts of Late-run sockeye salmon abundance (Aug 23 forecasted abundance of 5.8 million was 184% of preseason forecast, Pacific Salmon Commission news release) and higher than expected diversion rates into the JS, the fishery on the JS was opened again for 6 hours on Aug 12 and for two 2 days on Aug 21- 22.

Individual sockeye salmon were radio-tagged in 2002 to monitor migration route, time of entry to the Fraser River and en route mortality in an effort to gain more detailed migratory information and to examine the relationship between time in freshwater and

spawning success. Tagging took place in SJF and JS both early in migration of the Late- run stocks (Aug 3-7), during the peak of the Late-run migration (Aug 13-15), and towards the end of the Late-run migration (Aug 21-24). As fish were tagged, fin clips were removed for GSI of individuals using GeneClass. In the early tagging period, 188 sockeye salmon were tagged in each of SJF and JS, of which 26% and 23% were identified as belonging to Late-run stocks, respectively. By Aug 12-14, six fish tagged in SJF and identified as Late-run Thompson fish had reached the confluence of the Thompson and Fraser River, indicating no delay of freshwater entry. By August 22, 153 of the radio-tagged sockeye had been detected or recovered in fisheries, and 23 of the tagged Late-run sockeye were in various locations within the Fraser River. One hundred fish were tagged in the Juan de Fuca between Aug 13-15, 58% of which were allocated to Late-run stocks by GSI analysis.

Additional studies on the distribution of Parvicapsula among Fraser River sockeye salmon populations in the marine and freshwater environments are also taking advantage of the individual identification capabilities. A new project evaluating the use of gene expression profiles (DNA microarrays) to determine the physiological state of sockeye salmon in the wild may aid in our understanding of the factors influencing the early migration of Late-run sockeye salmon, and changes in the physiological state of these fish that occur in freshwater. By combining the GSI classification of individuals to source population with emerging technologies such as DNA microarrays and quantitative PCR, we hope to expand our knowledge of the factors affecting fitness in wild fish and develop predictions on the response of fish to changing environmental conditions.

REFERENCES: Beacham, T.D., and Wood, C.C. 1999. Application of microsatellite DNA variation to estimation of stock composition and escapement of Nass River sockeye salmon (Oncorhynchus nerka). Can. J. Fish. Aquat. Sci. 56: 1-14. Beacham, T.D., Le, K.D., Raap, M.R., Hyatt, K., Luedke, W., and Wither, R.E. 2000a. Microsatellite DNA variation and estimation of stock composition of sockeye salmon, Oncorhynchus nerka, in Barkley Sound, British Columbia. Fish. Bull. 98: 14-24. Beacham, T.D., Wood, C.C., Withler, R.E., and Miller, K.M. 2000b. Application of microsatellite DNA variation to estimation of stock composition and escapement of Skeena River sockeye salmon (Oncorhynchus nerka). N. Pac. Anad. Fish. Comm. Bull. 2: 263-276. Beacham, T.D., Candy, J.R., Supernault, K.J., Ming, T., Deagle, B., Schultz, A., Tuck, D., Kaukinen, K., Irvine, J.R., Miller, K.M. and Withler, R.E. 2001. Evaluation and application of microsatellite and major histocompatibility complex variation for stock identification of coho salmon in British Columbia. Trans. Am. Fish. Soc. 130: 1116-1155. Beamish, R.J. and D.R. Boullion. 1993. Pacific salmon production trends in relation to climate. Can. J. Fish. Aquat. Sci. 50: 1002-1016. Cornuet, J. M., S. Piry, G. Luikart, A. Estoup, M. Solignac. 1999. Comparison of methods employing multilocus genotypes to select or exclude populations as origins of individuals. Genetics 153: 1989-2000. Debevec, E.M., R. B. Gates, R.B., Masuda, M., Pella, J., Reynolds, J.M. Seeb and L.W. Seeb. 2000. SPAM (Version 3.2): Statistics program for analyzing mixtures. J. Hered. 91: 509-510. Gable, J and Cox-Rogers, S. 1993. Stock identification of Fraser River sockeye salmon: Methodology and management application. Pac. Sal. Comm. Tech. Rep. 5: 36p. Miller, K.M., Kaukinen, K.H., Beacham, T.D., and Withler, R.E. 2001. Geographic heterogeneity in natural selection of an MHC locus in sockeye salmon. Genetica 111: 237-257.

Nei, M, and A.L. Hughes. 1991. Polymorphism and the evolution of the major histocompatibility complex loci an mammals. In: Evolution a the Molecular Level (eds RK Selander, AG Clark and TS Whittam) Sinauer Associates Inc., Sunderland, Massachusettes 01375, p. 222-247. Pacific Salmon Commission (2001). Migration behaviour and mortality of Late-run Fraser River sockeye salmon. Pacific Salmon Commission (2002). Migration behaviour and mortality of Late-run Fraser River sockeye salmon. June 2002 PSC update. Slaney, T.L., K.D. Hyatt, T.G. Northcote and R.J. Fielden. Status of anadromous salmon and trout in British Columbia and Yukon. Fisheries 21(10): 20-35. St-Hilaire, S., M. Boichuk, D. Barnes, M. Higgins, R. Devlin, R. Withler, J. Khattra, S. Jones and D. Kieser. 2002. Epizootiology of Parvicapsula minibicornis in Fraser River sockeye salmon, Oncorhynchus nerka (Walbaum). J. Fish Dis. 25: 107-120. Tessier, N., and Bernatchez, L. 1999. Stability of population structure and genetic diversity across generations assessed by microsatellites among sympatric populations of landlocked Atlantic salmon (Salmo salar L.). Mol. Ecol. 8: 169- 179. Withler, R.E., T.D. Beacham, T.J. Ming and K.M. Miller. 1997. Species identification of Pacific salmon by means of a major histocompatibility gene. N. Amer. J. Fish. Manage. 17: 929-938. Withler, R.E, Le, K.D., Nelson, R.J., Miller, K.M., and Beacham, T.D. 2000. Intact genetic structure and high levels of genetic diversity in bottlenecked sockeye salmon, Oncorhynchus nerka, populations of the Fraser River, British Columbia, Canada. Can. J. Fish. Aquat. Sci. 57: 1985-1998. Wood, C.C. 1995. Life history variation and population structure in sockeye salmon. American Fisheries Society Symposium 17: 195-216. Wood, C.C., B.E. Riddell, D.T. Rutherford, and R.E. Withler. 1994. Biochemical genetic survey of sockeye salmon (Oncorhynchus nerka) in Canada. Can. J. Fish. Aquat. Sci. 51(Suppl. 1): 114-131.

Alsek R

Taku

Stikine

Unuk R

Nass R

Skeena R

Queen Charlotte Islands Central Coast Johnstone Fraser R Strait

Vancouver Columbia R Island Juan de Fuca Washington

Figure 1. Map of BC coast, with the genetically distinct regions of the coast labelled in bold. The Juan de Fuca and Johnstone Strait In-season collection sites are also indicated in italics, with the names in boxes.

Fraser River Sockeye Salmon

Stuart/Stellako

• Bowron

Upper Mid Fraser

North

South Lower Mid Fraser Thompson

North Lower Fraser South Figure 2. Map of the Fraser River, with sockeye salmon stocks indicated in black and Regions in bold. The Late-run lower Adams stock from the Thompson River drainage is boxed. 18

2B--Simulations By Region

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N Thompson 60% South Thompson Lower Fraser S 50% Lower Fraser N Lower Mid Fraser 40% Upper Mid Fraser Late Stuart 30% Early Stuart

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2C--Simulations By Run-Time

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Figure 3. Simulations of mixed Fraser River stock compositions, and maximum likelihood estimates of the simulated mixtures obtained using the Fraser baseline. A. compositions on the basis of individual stocks, B. compositions on the basis of Fraser River regions, C. compositions on the basis of run-time.

3C--Johnstone Strait by Run-Time

100% 90% 80% 70% 60% Late 50% Summer Early Summer 40% Early Stuart 30% 20% 10% 0% 21-Jul 24-Jul 28-Jul 29-Jul 31-Jul 6-Aug 9-Aug 12- 13- 13- 16- 21- 24- 27- Aug Aug Aug Aug Aug Aug Aug

Figure 4. Maximum likelihood estimates of Fraser River sockeye salmon Run-time- based stock compositions in the A) Juan de Fuca, B) Lower Fraser River (Whonnock), and C) Johnstone Strait.