Patterns of Genetic Diversity in Alaskan Coho Salmon

Genetic baselines can be used to reveal population structure, perform mixed-stock analysis (MSA), infer the population origin of individuals and characterize patterns of ocean migration in North American and Asian salmon. Extensive genetic baselines have been developed for chinook (Oncorhynchus tshawytscha), chum (O. keta), and sockeye salmon (O. nerka) using allozyme loci. Unfortunately, allozymes show little variation in coho salmon (O. kisutch), particularly in Alaska. In this study, we used nine microsatellite loci to genotype 32 putative coho salmon populations (N = 2,581) from seven regions of Alaska. The objectives were to: 1) estimate the degree and spatial distribution of neutral genetic diversity in Alaskan coho salmon; 2) evaluate the potential for regional-level mixedstocked analysis and individual assignment. Fin tissue samples for microsatellite analysis were obtained from 32 collections of coho salmon representing seven major regions of Alaska (Fig. 1): Northwest Alaska (N = 4); Southwest Alaska (N = 3); Aleutian Islands/Alaska peninsula (N = 3); Kodiak Archipelago (N = 8); Cook Inlet (N = 8); Prince William Sound (N = 3); Southeast Alaska (N = 3). Nine microsatellite loci were used to estimate genetic variation in each population (Oke2, Oke3, and Oke4, Buchholz et al. 2001; Oki1, Oki3, and Oki11, Smith et al. 1998; One3, Scribner et al. 1996; Ots3.1, Banks et al. 1999; Ots105, Nelson and Beacham 1999). Two of these loci (Oki1, Ots3.1,) have also been used for MSA of coho salmon in British Columbia (Beacham et al. 2001). Total genomic DNA was isolated from approximately 50–100 mg of fin tissue for use in PCR. A description of the microsatellite loci, PCR chemistry, gel electrophoresis and microsatellite allele scoring is given in Olsen et al. (2003). Three analyses were performed. First, an analysis of molecular variation for diploid data (AMOVA, Michalakis and Excoffier 1996) was used to test for genetic structure within and among the seven regions. AMOVA was performed using ARLEQUIN version 2.0 (Schneider et al. 2000) and the degree of population divergence, FST, was estimated according to Weir and Cockerham (1984). The estimate of FST was partitioned into within-region (FSR) and between-region (FRT) genetic variation. Randomization tests were used to test if the estimates of FST, FSR, and FRT were significantly greater than zero. Second, MSA simulations were performed using the direct maximum likelihood method implemented in the program SPAM 3.7 (Debevec et al. 2000). Parametric bootstrap resampling of both the baseline and mixture was carried out 1,000 times to derive the mean allocation estimate and to evaluate precision. Artificially simulated mixtures (N = 400) representing 100% of each individual population were subjected to MSA as a test of baseline performance. Mean allocations to individual populations were then summed for geographically defined regions. The allele frequencies for the baseline samples were estimated using the Bayesian modeling approach of Rannala and Mountain (1997). Finally, the program GENECLASS version 1.0. 02 (Cornuet et al. 1999) was used to conduct a (re)assignment test by the partial Bayesian method with leave-one-out classification. AP1, AP2