DNA and allozyme markers provide concordant estimates of population differentiation: analyses of U.S. and Canadian populations of Yukon River fall-run chum salmon (Oncorhynchus keta)

Abstract

Although the number of genetic markers available for fisheries research has steadily increased in recent years, there is limited information on their relative utility. In this study, we compared the preformance of different "classes" of genetic markers (mitochondrial DNA (mtDNA), nuclear DNA (nDNA), and allozymes) in terms of estimating levels and partitioning of genetic variation and of the relative accuracy and precision in estimating population allocations to mixed-stock fisheries. Individuals from eight populations of fall-run chum salmon (Oncorhynchus keta) from the Yukon River in Alaska and Canada were assayed at 25 loci. Significant differences in mitochondrial haplotype and nuclear allele frequencies were observed among five drainages. Populations from the U.S.-Canada border region were not clearly distinguishable based on multilocus allele frequencies. Although estimates of total genetic diversities were higher for the DNA loci (Ht = 0.592 and h = 0.647 for nDNA and mtDNA, respectively) compared with protein allozymes (Ht = 0.250), estimates of the extent of population differentiation were highly concordant across marker classes (mean theta = 0.010, 0.011, and 0.016 for allozymes, nDNA, and mtDNA, respectively). Simulations of mixed-stock fisheries composed of varying contributions of U.S. and Canadian populations revealed a consistent bias for overallocation of Canadian stocks when expected Canadian contributions varied from 0 to 40%, due primarily to misallocations among genetically similar border populations. No single marker class is superior for differentiating populations of this species at the spatial scale examined.

Genetic analysis of four wild chum salmonOncorhynchus keta populations in China based on microsatellite markers

“…Mean F ST (0.057) showed that 94.3% of the genetic divergence was within chum salmon populations and only 5.7% was due to the divergence among populations, which is in agreement with the breeding habit of chum salmon. F ST averaged over all loci in this study (0.057) is comparable with chum salmon from the southern Russian Far East (0.052) (Efremov et al 2001) and the Yukon River (0.030) (Scribner et al 1998) and the Yukon River, Alaska (0.021) (Olsen et al 2004). Genetic divergence among populations in China is higher than that of the Yukon River populations.…”

“…However, in our study of chum salmon populations in China, the mean observed heterozygosity (Ho) of four loci (Oke3, Oke4, Oke8 and Oke11) were 0.64, 0.50, 0.75 and 0.45, respectively. Total gene diversity (Ht) (Nei 1973) across the four populations was 0.676, but it was 0.592 in the Yukon River according to microsatellite analysis (Scribner et al 1998). This implies a high level of genetic diversity in Chinese chum salmon populations.…”

Concordance of Genetic Divergence Among Sockeye Salmon Populations at Allozyme, Nuclear Dna, and Mitochondrial Dna Markers

“…In addition, the direction and magnitude of differentiation at mtDNA relative to the nuclear DNA markers is very close to that predicted assuming selective neutrality. Several recent studies with salmonid fishes also have found similar patterns of differentiation at allozymes and nuclear DNA markers (Sánchez et al 1996;Estoup et al 1998;Scribner et al 1998).…”

“…Oncorhynchus keta Scribner et al 1998 VNTRs ϭ introns ϭ proteins water buffalo Bubalus bubalis Barker et al 1997 VNTRs ϭ proteins blue marlin Makaira nigricans Buonaccorsi et al 1999 …”

Phylogeography and conservation genetics of scaleless carp (Gymnocypris przewalskii) of Lake Qinghai, China.

“…This can be accomplished using a genotyping tool that can identify each stock within the 84 lake. Multiple marker systems are often used to identify stocks in fish populations (Beacham et al, 2004;Beacham et al, 2005) and can offer a greater amount of precision in estimating population origins in mixed stock fisheries (Scribner et al, 1998). Most studies used a combination of Mhc variation and microsatellites.…”