254 
Fishery Bulletin 106(3) 
Analysis of simulated mixed-stock samples of known 
origin is an initial practical method to evaluate the 
potential for applying genetic variation to mixed-stock 
fishery analysis. Our analysis of simulated mixtures 
indicated that microsatellite variation provides accurate 
estimates of regional contributions of chum salmon 
stocks from Russia, and in some cases provides reli- 
able estimates of individual populations in simulated 
mixtures. Microsatellites have previously been reported 
to provide reliable estimates of stock composition in 
mixed-stock chum salmon samples of largely North 
American origin (Beacham et ah, in press), and our 
results from simulated mixtures indicated that mic- 
rosatellites should provide reliable estimates of stock 
composition for chum salmon in coastal waters in Rus- 
sia. However, if Japanese or Korean chum salmon or po- 
tentially North American chum salmon are intercepted 
in coastal or nearshore fisheries in Russia, then clearly 
a larger baseline than the one examined in the current 
study would be required to provide reliable estimates of 
stock composition under these circumstances. 
The application of microsatellites for the determina- 
tion of population structure of Russian chum salmon 
will allow significant regional differentiation among 
these populations to be employed in estimating region- 
al contributions to mixed-stock fishery samples from 
coastal waters. Microsatellites provide similar results 
for other Pacific salmon species (Beacham et al., 2005, 
2006) and are likely to be effective in identifying the 
origin of Russian chum salmon in mixed-stock fisheries 
in nearshore and offshore waters. 
The present analysis of microsatellite variation of 
Russian chum salmon provides evidence of a more fine- 
scale population structure than those that have previ- 
ously been demonstrated with other genetic-based mark- 
ers such as allozymes (Winans et al., 1994; Efremov, 
2001) or mitochondrial based SNPs (Sato et al., 2004). 
This more fine-scale resolution of population structure 
was likely due to the larger number of alleles associ- 
ated with the microsatellite loci than with either the 
allozyme or SNP loci. Because genetic-based markers 
generally exhibit annual stability in allele frequencies, 
they are generally more effective for stock identification 
applications than are techniques that rely on envi- 
ronmentally induced variation to discriminate among 
stocks, such as scale-pattern analysis of trace elements 
in otoliths. Once the baseline has been established for 
genetic applications, annual surveys of contributing 
stocks are not necessary, as is the case with for environ- 
mentally induced variation. Should greater resolution in 
stock composition estimates be required than that pro- 
vided by the 14 microsatellites surveyed in the present 
study, the addition of markers specifically designed to 
provide the required resolution will be necessary. These 
markers could either be additional microsatellites, or 
perhaps single nucleotide polymorphisms (SNPs) (Smith 
et al., 2005). It is likely that a combination of microsat- 
ellites and SNPs can be employed to provide accurate 
population or regional estimates of stock composition of 
mixed-stock samples. 
Acknowledgments 
A significant effort was undertaken to collect samples 
from chum salmon populations analyzed in the study. 
Samples of populations from Primorye and Sakhalin 
Island, as well as some Magadan samples, were intially 
provided by V. V. Efremov to the United States National 
Marine Fisheries Service (NMFS) Auke Bay Laboratory, 
where R. Wilmot provided access to the Molecular Genet- 
ics Laboratory (MGL). G. Winans of the NMFS Mont- 
lake laboratory also provided access to some population 
samples. C. Wallace and J. Candy of the MGL assisted 
in the analysis. Funding was provided by Fisheries and 
Oceans Canada. 
Literature cited 
Allendorf, F. W., and L. W. Seeb. 
2000. Concordance of genetic divergence among sockeye 
salmon populations at allozyme, nuclear DNA, and mito- 
chondrial DNA markers. Evolution 54:640-651. 
Banks, M. A., M. S. Blouin, B. A. Baldwin, V. K. Rashbrook, 
H. A. Fitzgerald, S. M. Blankenship, and D. Hedgecock. 
1999. Isolation and inheritance of novel microsatellites in 
chinook salmon (Oncorhynchus tshawytscha). J. Hered. 
90:281-288. 
Banks, M. A., V. K. Rashbrook, M. J. Calavetta, C. A. Dean, and 
D. Hedgecock. 
2000. Analysis of microsatellite DNA resolves genetic 
structure and diversity of chinook salmon (Oncorhyn- 
chus tshawytscha) in California’s Central Valley. Can. 
J. Fish. Aquat. Sci. 57:915-927. 
Beacham, T. D. 
1996. The use of minisatellite DNA variation for stock 
identification of chum salmon, Oncorhynchus keta. Fish. 
Bull. 94:611-627. 
Beacham, T. D., J. R. Candy, K. L. Jonsen, J. Supernault, 
M. Wetklo, L. Deng, K. M. Miller, R. E. Withler, and N. V. 
Varnavskaya. 
2006. Estimation of stock composition and individual 
identification of Chinook salmon across the Pacific Rim 
by use of microsatellite variation. Trans. Am. Fish. 
Soc. 135:861-888. 
Beacham, T. D., J. R. Candy, B. McIntosh, C. MacConnachie, 
A. Tabata, K. Kaukinen, L. Deng, K. M. Miller, R. E. Withler, 
and N. V. Varnavskaya. 
2005. Estimation of stock composition and individual iden- 
tification of sockeye salmon on a Pacific Rim basis using 
microsatellite and major histocompatibility complex 
variation. Trans. Am. Fish. Soc. 134:1124-1146. 
Beacham, T. D., J. R. Candy, K. J. Supernault, M. Wetklo, 
B. Deagle, K. Labaree, J. R. Irvine, K. M. Miller, R. J. Nelson, 
and R. E. Withler. 
2003. Evaluation and application of microsatellites 
for population identification of Fraser River chinook 
salmon (Oncorhynchus tshawytscha). Fish. Bull. 
101:243-259. 
Beacham, T. D., A. P. Gould, R. E. Withler, C. B. Murray, and 
L. W. Barner. 
1987. Biochemical genetic survey and stock identifica- 
tion of chum salmon (Oncorhynchus keta) in British 
Columbia. Can. J. Fish. Aquat. Sci. 44:1702-1713. 
