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首页> 外文期刊>ICES Journal of Marine Science >Comparative performance of data-poor CMSY and data-moderate SPiCT stock assessment methods when applied to data-rich, real-world stocks
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Comparative performance of data-poor CMSY and data-moderate SPiCT stock assessment methods when applied to data-rich, real-world stocks

机译:数据差的CMSY和数据中等幼虫股票评估方法的比较表现适用于数据丰富的现实世界股票

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摘要

All fish stocks should be managed sustainably, yet for the majority of stocks, data are often limited and different stock assessment methods are required. Two popular and widely used methods are Catch-MSY (CMSY) and Surplus Production Model in Continuous Time (SPiCT). We apply these methods to 17 data-rich stocks and compare the status estimates to the accepted International Council for the Exploration of the Sea (ICES) age-based assessments. Comparison statistics and receiver operator analysis showed that both methods often differed considerably from the ICES assessment, with CMSY showing a tendency to overestimate relative fishing mortality and underestimate relative stock biomass, whilst SPiCT showed the opposite. CMSY assessments were poor when the default depletion prior ranges differed from the ICES assessments, particularly towards the end of the time series, where some stocks showed signs of recovery. SPiCT assessments showed better correlation with the ICES assessment but often failed to correctly estimate the scale of either F/F-MSY of B/B-MSY, with the indices lacking the contrast to be informative about catchability and either the intrinsic growth rate or carrying capacity. Results highlight the importance of understanding model tendencies relative to data-rich approaches and warrant caution when adopting these models.
机译:所有鱼类股票应可持续管理,但对于大多数库存来说,数据通常是有限的,并且需要不同的股票评估方法。两种流行和广泛使用的方法是连续时间(幼虫)捕获 - MSY(CMSY)和剩余生产模型。我们将这些方法应用于17个数据丰富的股票,并将地位估计与已接受的国际委员会探索的海洋(ICE)基于年龄的评估进行了比较。比较统计和接收机操作员分析表明,两种方法通常与谢谢评估相当不同,CMSY显示出高估相对捕捞死亡率和低估相对股的生物量的趋势,同时捕捉表现相反。当违约的耗尽与ICES评估不同的耗尽区不同,特别是在时间序列结束时,CMSY评估差,其中一些股票显示出恢复迹象。幼虫评估表现出与谢谢评估的相关性更好,但往往未能正确估计B / B-MSY的F / F-MSY的比例,缺乏对比较的对比度有关可利用性和内在的生长速度或携带的信息容量。结果强调了了解模型趋势相对于具有数据丰富的方法的重要性,并在采用这些模型时小心谨慎。

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