Performance of a Bayesian State-Space Model for Stock-Recruitment Data Subject to Measurement Error

Measurement errors in spawner abundance create problems for fish stock assessment scientists. To deal with measurement error, we develop a Bayesian state-space model for stock-recruitment data that contain measurement error in spawner abundance, process error in recruitment, and time series bias. Through extensive simulations across numerous scenarios, we compare the statistical performance of the Bayesian state-space model with that of standard regression for a traditional stock-recruitment model that only considers process error. Performance varies depending on the information content in data determined by stock productivity, types of harvest situations, and the amount of measurement error. Neither model performs best across all scenarios, but the Bayesian state-space model is most frequently best for informative data. However, the traditional model may be used for very low-productivity stocks having a moderate amount of measurement error.