Abstract
Stock composition analysis is specific to fishery science. In many fisheries, catches include fish that are conspecific but that originate in several spawning stocks. Because the population effects of fishing—and thus the choice of suitable management approaches—depend on which stock or stocks are harvested, estimates of stock composition of catches are needed. Data used for such analyses are observations on characteristics of individual specimens; typical characteristics may include morphometrics, meristics, genetic characters, or chemical signatures.. Analyzing characteristics of individual objects in a mixture to estimate the mixture's proportions is a general statistical problem known as finite mixture analysis. Constituent fish stocks in a mixed harvest are just one example of constituent classes or statistical populations of mixed objects; here, the term class is used when describing algorithms generally, and stock when describing fisheries applications. Estimating stock composition is therefore a special case of the general problem of estimating mixing proportions. This chapter introduces some algorithms useful for stock composition analysis. It also discusses issues involved in estimating performance of various algorithms, either in an absolute sense or relative to one another on a particular data set.
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