Stock assessment models often require an external estimate of the natural mortality rate (M) that is usually assumed to be the same for all ages and years in the model. Although the fixed M assumption can be a major oversimplification, model diagnostics (e.g. profile likelihoods) that can help provide an understanding of how the choice of M affects model fit are often not used in practice. In the state-space setting, model diagnostics are especially complicated because of the complex dependencies in the data caused by process errors. To get a better understanding of the effect of broad changes in M across all ages and years on the state-space model fit, we develop new methods that provide profile likelihoods for individual data sources (surveys, landings, age compositions) by decomposing the state-space integrated likelihood. We also use local influence diagnostics to assess the influence of age and year specific changes in M on model fit. We jointly call these methods M diagnostics and apply them to a case study for American plaice (Hippoglossoides platessoides) on the Grand Bank of Newfoundland. The M diagnostics indicate that most input data sources are fit better with a higher M in recent years. We suggest that M diagnostics should be routinely examined when formulating an assessment model.

种群评估模型通常需要对自然死亡率 (M) 进行外部估计，通常假设模型中的所有年龄和年份都相同。尽管固定 M 假设可能是一个主要的过度简化，但在实践中通常不使用有助于理解 M 的选择如何影响模型拟合的模型诊断（例如轮廓可能性）。在状态空间设置中，由于过程错误导致数据中的复杂依赖关系，模型诊断尤其复杂。为了更好地了解 M 在所有年龄和年份的广泛变化对状态空间模型拟合的影响，我们开发了新方法，通过分解状态为单个数据源（调查、着陆、年龄组成）提供轮廓可能性-空间综合似然。我们还使用局部影响诊断来评估 M 的年龄和年份特定变化对模型拟合的影响。我们将这些方法统称为 M 诊断，并将它们应用于美洲鲽的案例研究（Hippoglossoidesplatesoides）在纽芬兰大银行。M 诊断表明，近年来大多数输入数据源更适合更高的 M。我们建议在制定评估模型时应定期检查 M 诊断。