Hidden Markov models with serial correlation for identifying stock-recruitment regime shifts

Resolving productivity changes is crucial to effective fisheries management as shifts in the stock-recruitment (SR) relationship redefine levels of sustainable removals. Hidden Markov models (HMMs) and state-space models are complementary methodologies for capturing discrete (regime-like) and continuous variations, respectively, in fisheries stock productivity. In this work, we combine the strengths of both approaches by developing HMMs with serial correlation and implementing them efficiently. To account for interactions between SR parameters and other components of stock assessment models, we embed hidden Markov SR models within the broader stock assessment framework. Additionally, we incorporate covariates into the transition probabilities of the HMM to address nonstationarity and substantially reduce model complexity. Simulation and case studies demonstrate the strong performance of this novel methodology.