Improved State-Space Models for Pacific Salmon Stock-Recruitment Analysis

Obtaining reliable parameter estimates for stock-recruitment (SR) relationships is essential for fisheries management. To this end, we developed a state-space model (SSM) for Pacific salmon that assumes a time-varying productivity parameter, observation errors in both spawner abundance and catch, and a model for harvest rates. We also developed a Bayesian  estimation method for the SSM. Using extensive simulations by management strategy evaluations, we found that the state-space model can be helpful for analyzing stock-recruitment data that are often subject to multiple sources of uncertainty. The SSM is robust to the effects of errors in measuring spawner abundance and catch, changes in stock productivity and harvest rates. It reliably estimates optimal spawning abundance and harvest rate over a wide range of conditions. In contrast, a traditional SR model that only considered recruitment variation performed poorly in various conditions. Our findings have the potential to improve the management of Pacific salmon and to promote the adoption of state-space models in ecological applications.