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.

中文翻译：

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对用于在具有多个随机效应过程的状态空间模型中进行选择的普通股评估诊断工具的评估

由于状态空间模型能够灵活地纳入过程误差的多种来源，因此在渔业资源评估中受到越来越多的关注。确定要包含哪些过程错误非常重要，因为错误地包含某些过程错误可能会导致管理数量出现偏差。通常应用于传统统计捕捞年龄模型的现有模型选择工具对于状态空间模型可能表现不佳。我们评估了常见诊断工具在模拟估计实验中正确识别三种过程错误（存活率、选择性和自然死亡率）的存在、不存在和严重程度的有效性。没有模型诊断工具能够在所有情况下一致地识别正确的过程错误结构。将自然死亡率的过程误差错误地归因于其他过程，反之亦然，导致管理数量出现相对较大的偏差。此外，在评估模型中错误地包含额外的过程误差源，表现出与正确模型相似的性能，并且通常显示出对管理数量的无偏估计；然而，错误地排除过程错误的来源会产生很大的偏差。因此，尽管没有普遍可靠的状态空间评估模型诊断工具，但从业者应该错误地使用过于复杂的模型，自然死亡率除外，除非有自然死亡率变化的外部确凿证据。