Identification and performance of environmentally driven recruitment relationships in state space assessment models

Environmentally driven recruitment relationships are important for understanding fisheries’ responses to climate change; however, they are difficult to estimate due in part to large recruitment variability. State space models provide a promising way forward in allowing the characterization of multiple sources of stochastic variability. We conducted a large simulation–estimation study using environmentally driven recruitment relationships to evaluate the effects of operating model characteristics on state space assessment model performance. We find low parameter and assessment bias across operating and estimating model combinations. Some assessment bias is present under conditions of high recruitment variability and low spawning stock biomass contrast. Parameters of the stock recruitment function showed nonzero bias, on average. Model identifiability for the stock recruit relationship was poor; however, assessment error was robust to model misspecification. Projections were insensitive to assumed values of the environmental driver. We recommend the use of random effects on recruitment in state space assessments and caution against explicit stock–recruitment relationships. Further work on environmental nonstationarity is recommended as exploited fish stocks experience accelerating rates of environmental change.