Bayesian methods for analysis of stock mixtures from genetic characters

Fisheries that exploit mixed stocks are very common, and their management oftentimes requires assessment of composition of the mixed catches (Begg et al., 1999). Multilocus genotypes of fi sh are a natural tag by which to infer their origins. The unknown proportions from stocks comprising a stock mixture, or its stock composition, can be estimated from genotype counts in a random sample of the stock-mixture individuals if relative frequencies (RFs) of the genotypes vary among the contributing stocks. Larger differences in genotypic RFs among stocks result in more accurate and precise stock composition estimates. The conditional maximum likelihood (CML) method (Fournier et al., 1984; Millar, 1987; Pella and Milner, 1987) has most commonly been used for stock-mixture analysis. Baseline samples drawn from the separate contributors are used in estimating the RFs of the observed stock-mixture genotypes in each stock. The CML stock composition estimate maximizes a likelihood function of the stock-mixture genotypes as if their RFs in the baseline stocks were known without error. The baseline multilocus genotype RFs determine the outcome of a stock-mixture analysis. Larger errors in these estimated RFs result in larger stock composition errors. Usually the variation in CML stock composition estimates from baseline and stock-mixture Bayesian methods for analysis of stock mixtures from genetic characters