A Bayesian life-cycle model to estimate escapement at maximum sustained yield in salmon based on limited information

Life-cycle models combine several strengths for estimating population parameters and biological reference points of harvested species and are particularly useful for those exhibiting distinct habitat shifts and experiencing contrasting environments. Unfortunately, time series data are often limited to counts of adult abundance and harvest. By incorporating data from other populations and by dynamically linking the life-history stages, Bayesian life-cycle models can be used to estimate stage-specific productivities and capacities as well as abundance of breeders that produce maximum sustained yield (MSY). Using coho salmon (Oncorhynchus kisutch) as our case study, we show that incorporating information on marine survival variability from nearby populations can improve model estimates and affect management parameters such as escapement at MSY. We further show that the expected long-term average yield of a fishery managed for a spawner escapement target that produces MSY strongly depends on the average marine survival. Our results illustrate the usefulness of incorporating information from other sources and highlight the importance of accounting for variation in marine survival when making inferences about the management of Pacific salmon.