Abstract Natural mortality (M) is often considered to be one of the most important parameters in a fish stock assessment and affects productivity estimates for the population. However, it is also among the most difficult parameters to estimate using commonly available data. The magnitude of error (both bias and variance) when estimating this parameter can be substantial and can be affected by ignoring its variation over time, space, age, and length. In this study we explore the implications of errors in M on estimation and management performance using simulations and illustrative examples. The error in management reference points such as F35% and FMSY is related directly to the error associated with M. Estimates of biomass are expected to be positively biased when M is over-estimated and vice versa. Use of feedback management strategies reduces the impact of errors in M, but performance in meeting management objectives is compromised when M is in error. Estimating M was found to perform better than pre-specifying M in closed-loop simulations. Also, we found that the consequences of setting M to an incorrect value were reduced if stock-recruitment steepness was estimated. Based on our study and a review of related work, we advocate estimating M within an assessment, ideally with a prior for M tailored to the stock concerned.

中文翻译：

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自然死亡率误差的后果及其在种群评估模型中的估计

摘要 自然死亡率 (M) 通常被认为是鱼类种群评估中最重要的参数之一，并影响种群的生产力估计。然而，它也是使用常用数据估计最困难的参数之一。估计此参数时的误差幅度（偏差和方差）可能很大，并且可能会因忽略其随时间、空间、年龄和长度的变化而受到影响。在这项研究中，我们使用模拟和说明性示例探讨 M 中的错误对估计和管理绩效的影响。F35% 和 FMSY 等管理参考点的误差与 M 相关的误差直接相关。当 M 被高估时，预计生物量估计会出现正偏差，反之亦然。反馈管理策略的使用减少了 M 中错误的影响，但是当 M 出错时，实现管理目标的性能会受到影响。发现在闭环模拟中估计 M 比预先指定 M 的性能更好。此外，我们发现如果估计库存招聘陡峭程度，则将 M 设置为错误值的后果会减少。根据我们的研究和对相关工作的回顾，我们主张在评估中估计 M，最好是针对相关股票量身定制 M 的先验。我们发现，如果估计库存招聘陡峭程度，则将 M 设置为错误值的后果会减少。根据我们的研究和对相关工作的回顾，我们主张在评估中估计 M，最好是针对相关股票量身定制 M 的先验。我们发现，如果估计库存招聘陡峭程度，则将 M 设置为错误值的后果会减少。根据我们的研究和对相关工作的回顾，我们主张在评估中估计 M，最好是针对相关股票量身定制 M 的先验。