An evaluation of common stock assessment diagnostic tools for choosing among state-space models with multiple random effects processes

State-space models have received increasing attention in fisheries stock assessments given their flexibility to incorporate multiple sources of process errors. Identifying which process errors to include is important because incorrectly including some process errors can induce bias in management quantities. Existing model selection tools commonly applied in traditional statistical catch-at-age models may not perform as well for state-space models. We evaluated the efficacy of common diagnostic tools for correctly identifying the presence, absence, and magnitude of three process errors (survival, selectivity, and natural mortality) in a simulation–estimation experiment. No model diagnostic tools could consistently identify the correct process error structure in all situations. Incorrectly attributing the process error from natural mortality to other processes, or vice versa, led to relatively large bias in management quantities. Furthermore, incorrectly including an additional source of process error in the assessment models exhibited similar performance to the correct model and generally showed unbiased estimates of management quantities; incorrectly excluding a source of process error, however, generated large biases. Thus, despite not having generally reliable model diagnostic tools for state-space assessments, practitioners should err on the side of using overly complex models, except for natural mortality unless there is external corroborating evidence of changing natural mortality.