Hierarchic modeling of salmon harvest and migration

Using fishery recoveries from a tagged cohort of coho salmon, the ocean spa tial-temporal abundance of the cohort is predicted using a state-space mode l. The model parameters, which reflect spatial distribution, mortality, and movement, vary considerably between different cohorts. To evaluate the eff ect of proposed management plans on a future cohort, uncertainty in the coh ort-specific parameters is accounted for by a hierarchic model. As an appli cation, release-recovery and fishing effort data from several cohorts of a hatchery-reared coho salmon stock originating from Washington state are use d to calculate maximum likelihood estimates of the hyperparameters. Markov chain Monte Carlo is used to approximate the likelihood for the hyperparame ters. The Markov chain simulates the sampling distribution of the state-spa ce model parameters conditional on the data and the estimated hyperparamete rs and provides empirical Bayes estimates as a by-product. Given the estima ted hyperparameters and the hierarchic model, fishery managers can simulate the variation in cohort-specific parameters and variation in the migration and harvest processes to more realistically describe uncertainty in the re sults of any proposed management plan.