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首页> 外文期刊>ICES Journal of Marine Science >Connecting single-stock assessment models through correlated survival
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Connecting single-stock assessment models through correlated survival

机译：通过相关生存率连接单股票评估模型

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摘要

Fisheries management is mainly conducted via single-stock assessment models assuming that fish stocks do not interact, except through assumed natural mortalities. Currently, the main alternative is complex ecosystem models which require extensive data, are difficult to calibrate, and have long run times. We propose a simple alternative. In three case studies each with two stocks, we improve the single-stock models, as measured by Akaike information criterion, by adding correlation in the cohort survival. To limit the number of parameters, the correlations are parameterized through the corresponding partial correlations. We consider six models where the partial correlation matrix between stocks follows a band structure ranging from independent assessments to complex correlation structures. Further, a simulation study illustrates the importance of handling correlated data sufficiently by investigating the coverage of confidence intervals for estimated fishing mortality. The results presented will allow managers to evaluate stock statuses based on a more accurate evaluation of model output uncertainty. The methods are directly implementable for stocks with an analytical assessment and do not require any new data sources.
机译：渔业管理主要通过单种群评估模型进行，假设除假定的自然死亡率外，鱼类种群不发生相互作用。当前，主要的替代方法是复杂的生态系统模型，该模型需要大量数据，难以校准且运行时间长。我们提出了一个简单的替代方案。在三个案例研究中，每个案例有两个股票，我们通过增加队列生存率的相关性，改进了按Akaike信息标准衡量的单股票模型。为了限制参数的数量，通过相应的局部相关对相关进行参数化。我们考虑六个模型，其中股票之间的部分相关矩阵遵循从独立评估到复杂相关结构的范围结构。此外，模拟研究通过调查置信区间的覆盖范围以估计捕捞死亡率来说明充分处理相关数据的重要性。呈现的结果将使管理人员可以根据对模型输出不确定性的更准确评估来评估库存状态。该方法可直接用于具有分析评估的股票，并且不需要任何新的数据源。

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