Temporal variation in natural mortality (M) has been recognized for having a degrading effect on the performance of stock assessment models when it is not accounted for in the model. However, time-invariant M has remained widely practiced in stock assessments due to the difficulties in estimation. Therefore, in this study we conducted simulation-estimation experiments to (1) evaluate the ability of a size-structured assessment model to reliably estimate time-varying M as well as other population quantities, and (2) quantify the consequences of mis-specifying M in a size-structured assessment model. We modified an existing size-structured assessment model to include three different approaches to estimate time-varying M within the model. A wide range of scenarios regarding the underlying M patterns (variation over time and length) was considered in this study. The performance of estimation methods largely depends upon whether the assumption of length-dependent/-independent M was correctly specified in the assessment model. Among the three methods, estimating time-varying M as a mean (representing an average process) and time-specific deviations using auxiliary information performed best. Estimating time-varying M as a random walk process also performed well except when underlying time-varying M had large year-to-year variation. A temporal pattern in M affected the ability of the assessment model to provide reliable estimates of M, spawning stock biomass and recruitment. Assuming a constant M (i.e., fixing M at the correct value) was quite robust over a range of actual temporal variation in underlying M.

当自然死亡率 (M) 的时间变化没有在模型中考虑时，它被认为会对种群评估模型的性能产生降级影响。然而，由于难以估计，时不变 M 在库存评估中仍然被广泛使用。因此，在本研究中，我们进行了模拟估计实验，以 (1) 评估规模结构化评估模型可靠估计随时间变化的 M 以及其他人口数量的能力，以及 (2) 量化错误指定的后果M 在规模结构的评估模型中。我们修改了现有的规模结构评估模型，以包括三种不同的方法来估计模型中的时变 M。本研究考虑了有关潜在 M 模式（随时间和长度的变化）的广泛情景。估计方法的性能很大程度上取决于是否在评估模型中正确指定了长度相关/独立 M 的假设。在这三种方法中，将随时间变化的 M 估计为平均值（表示平均过程）和使用辅助信息的时间特定偏差表现最好。将时变 M 估计为随机游走过程也表现良好，除非基础时变 M 具有较大的逐年变化。M 中的时间模式影响评估模型提供 M、产卵种群生物量和补充的可靠估计的能力。假设一个常数 M（即，