A Bayesian decision analysis to set escapement goals for Fraser River sockeye salmon (Oncorhynchus nerka)

Abstract

This paper illustrates a complete Bayesian decision analysis for evaluating multistock harvest goals in the fishery on Fraser River sockeye salmon (Oncorhynchus nerka). We identify four key steps necessary to assess a resource production system. Each step entails choices that can alter the perceived consequences of management decisions. A Markov chain Monte Carlo sample captures uncertainty in the population dynamics. The Bayesian formalism then translates this uncertainty into uncertain policy outcomes. We examine a relatively simple control law, designed to protect stocks at low abundance. We restrict our attention to retrospective policy analysis by investigating what might have happened to sockeye stocks if management had proceeded differently during years for which historical data are available. A formal objective function quantifies societal values associated with a range of policy options. To confine the paper to manageable scope, we consider only relatively simple assumptions. Our analytical framework offers an iterative route to policy design, where managers play an active role in formulating policy options and evaluating their consequences.