数据缺乏条件下的渔业资源评估方法研究进展

Research Progress in Fishery Stock Assessment Using Data-poor/limited Methods

全球范围内绝大多数渔业处于数据缺乏的状态,而常规的资源评估方法需要准确的生物学信息和完整的渔获数据,通常并不适合此类渔业资源的评估。数据缺乏方法(DLM)正是适用于此类情况的渔业资源评估方法,DLM的研究已经成为渔业资源研究的热点。作者对DLM的近期发展作了简要回顾,重点对主要的DLM方法和数据需求进行了分析比较,指出这些方法的应用需要注意的问题。历史渔获量是大多数DLM方法运用的必要数据(如DCAC、Catch-MSY模型),而关于资源丰度的相对指标和重要生活史参数(如种群内禀增长率、自然死亡系数),则是运用这些方法的必要补充。此外,对运用DLM所需的主要生物学参数的估算方法进行了简介。最后对DLM方法的使用及其在国内渔业中的运用提出了建议。

The majority of fisheries in the world are in poor/limited data situations.The formal stock assessment models are only applicable for stocks with reliable biological information and rich fishery data.Data-poor/limited methods(DLM)in fishery stock assessment are quantitative approaches for developing management advices for fishery stocks that was unable to be assessed by formal stock assessment models.Over the past ten years,DLM has been one of the top priorities in fisheries research.In this study,we reviewed the commonly used DLMs and analyzed the data requirements and key assumptions for their applications.The results showed that the historical catch is the essential data needed for conducting most of the DLMs.Abundance indices and key life history parameters(e.g.intrinsic growth rate and natural mortality)are important information for more reliably use of DLM.We briefly reviewed the methods for estimating key life history parameters.It is found that suitability and assumption are the most important considerations when DLMs are to be selected for use in real fisheries,and simulation testing is a recommended approach for justifying their applicability.