Data-Limited Stock Assessment for Fish Species Devoid of Catch Statistics: Case Studies for Pampus argenteus and Setipinna taty in the Bohai and Yellow Seas

文献类型： 外文期刊

第一作者： Han, Qingpeng

作者： Han, Qingpeng;Shan, Xiujuan;Jin, Xianshi;Yang, Tao;Su, Chengcheng;Han, Qingpeng;Shan, Xiujuan;Jin, Xianshi;Yang, Tao;Shan, Xiujuan;Jin, Xianshi;Yang, Tao;Gorfine, Harry

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关键词： ensemble models; length-based; multimodel averaging; population dynamics; data-limited fisheries

期刊名称：FRONTIERS IN MARINE SCIENCE （ 影响因子：5.247； 五年影响因子：5.72 ）

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年卷期： 2021 年 8 卷

页码：

收录情况： SCI

摘要： For many fish stocks, such as Pampus argenteus and Setipinna taty in China, size composition data are more accessible than catch data. Varied results can arise when different length-based stock assessment models are applied to these data, and fishery managers often need to reconcile conflicting estimates of population status. Superensemble modeling, a relatively recent innovation in fish stock assessments commonly used in other fields, may provide an effective solution to resolving uncertainties among the results from multiple length-based models. To verify potential for this approach to improve estimates of population status, we applied ensemble modeling to fit simulated data of P. argenteus and S. taty in the Bohai and Yellow Seas using predictions from a length-based integrated mixed effects (LIME) and length-based spawning potential ratio (LB-SPR) models as covariables in a superensemble model developed in this study. All simulation modeling of P. argenteus and S. taty in the Bohai and Yellow Seas was conducted using the operating model in the R package LIME. Initially, the LIME and LB-SPR performances were tested separately under three scenarios of fishing mortality and recruitment variability ("equilibrium scenario," "endogenous scenario," and "one-way base scenario"). Then, estimates of spawning potential ratio (SPR) were combined with the superensemble models (a linear model, a support vector machines, a random forest and a boosted regression tree). We trained our superensemble models with 80% of the simulated data and tested them with the remaining 20%. Our results showed that superensemble modeling substantially improved the estimates of SPR, with support vector machines performing the best at estimating population status: precision improved by 12.7% for S. taty and 8% for P. argenteus on average (namely, median absolute proportional error decreased by 0.127 and 0.08 on average) compared to the individual models. This finding has important implications for fisheries management in the context of species for which catch data are unavailable. Applying the size composition survey data, the results from support vector machines superensemble model suggested that neither S. taty nor P. argenteus in the Bohai Sea in 2019 are overfished, but the stock status of P. argenteus warrants vigilant monitoring.

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