Detecting regime shifts in fish stock dynamics

Abstract

Environmental factors such as water temperature, salinity, and the abundance of zooplankton can have major 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 (Gadus morhua) stocks obtained from RAM legacy database. Our analysis shows that the algorithm performs well when the variability between the regimes is high enough compared with 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.