Detecting Regime Shifts in Fish Stock Dynamics

Environmental factors such as the water temperature, salinity and the abundance of zooplankton can have significant effects on certain fish stocks’ ability to produce juveniles and, thus, stock renewal ability. This variability in stock productivity manifests itself as different productivity regimes. Here, we detect productivity regime shifts by analyzing recruit-per-spawner time series with Bayesian online change point detection algorithm. The algorithm infers the time since the last regime shift (change in mean or variance or both) as well as the parameters of the data generating process for the current regime sequentially. We demonstrate the algorithm’s performance using simulated recruitment data from an individual-based model, and further apply the algorithm to stock assessment estimates for four Atlantic cod stocks obtained from RAM legacy data base. Our analysis shows that the algorithm performs well when the variability between the regimes is high enough compared to the variability within the regimes. The algorithm found several productivity regimes for all four cod stocks, and the findings suggest that the stocks are currently in low productivity regimes, which have started during the 1990s and 2000s.The accepted manuscript in pdf format is listed with the files at the bottom of this page. The presentation of the authors' names and (or) special characters in the title of the manuscript may differ slightly between what is listed on this page and what is listed in the pdf file of the accepted manuscript; that in the pdf file of the accepted manuscript is what was submitted by the author