Sharks (class Chondrichthyes, subclass Elasmobranchii) typically have a long lifespan, slow growth rate and low fecundity, leading to low productivity and hence relatively high vulnerability to fishing. Information for managing fisheries catches of elasmobranchs is often lacking, because elasmobranchs are usually non-target species, often with low abundance. It is more feasible to develop management reference points for elasmobranchs based on their life-history information than through traditional stock assessments. The natural mortality rate (M) is the leading life-history parameter (LHP) required by many methods for developing reference points and is itself often derived indirectly from other LHPs. In this paper, we evaluate nine M estimators, using 15 shark stocks in the Western and Central Pacific Ocean as examples. We then compare four methods for developing fishing mortality reference points F_msy, including empirical modelling, the Euler-Lotka equation, an M-productivity-based method and the spawning potential rate converted method. Our analyses show that popular M estimators developed mainly from teleost data resulted in large deviations from the average and were not suitable for elasmobranchs. All four methods for estimating F_msy performed similarly. However, the empirical method is very simple and cost-effective and tended to produce smaller deviations from the average than the other three methods. Nonetheless, it is recommended that multiple methods should be used to minimize possible bias and reflect uncertainty, if the required LHPs are available.

中文翻译：

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使用生活史参数评估鲨鱼自然死亡率和管理参考点的方法

鲨鱼（软骨鱼纲，Elasmobranchii 亚纲）通常寿命长、生长速度慢和繁殖力低，导致生产力低下，因此相对容易捕捞。经常缺乏管理弹性鳃鱼渔获量的信息，因为弹性鳃鱼通常是非目标物种，通常数量很少。与通过传统的种群评估相比，基于它们的生活史信息来开发弹性枝条的管理参考点更为可行。自然死亡率 ( M ) 是许多用于开发参考点的方法所需的主要生命史参数 (LHP)，并且其本身通常间接地从其他 LHP 导出。在本文中，我们评估了 9 个M估计，以西太平洋和中太平洋的 15 只鲨鱼种群为例。然后，我们比较了四种开发捕捞死亡率参考点F _msy的方法，包括经验建模、Euler-Lotka 方程、基于M生产力的方法和产卵潜力率转换方法。我们的分析表明，主要从硬骨鱼数据开发的流行M估计器导致与平均值的较大偏差，不适合于 elasmobranchs。估算F _msy的所有四种方法进行了类似的操作。然而，经验方法非常简单且具有成本效益，并且与其他三种方法相比，与平均值的偏差往往更小。尽管如此，如果所需的 LHP 可用，建议应使用多种方法来最小化可能的偏差并反映不确定性。