基于非连续调查数据的渔业资源评估模型构建及其适用性

Fisheries stock assessment based on best-available science is essential for sustainable management of fishery stocks. Improving the accuracy and precision of stock assessment mainly depends on the optimization and selection of assessment models to efficiently utilize available information on targeted fishery stocks. However, most fishery stocks worldwide lack continuous fisheries-dependent or -independent survey data in a long term and can not be assessed with formal stock assessment models. Existing data-limited fisheries stock assessment methods generally have no requirements of fisheries-independent survey data, impacting the accuracy and precision of assessment results. In this context, how to efficiently utilize non-continuous fisheries-independent survey data becomes a critical problem to be solved in the field of fisheries stock assessment. This project is based on long-term discrete fisheries-independent survey data in the Yellow Sea and the Bohai Sea. We are trying to evaluate various data-limited stock assessment methods related to stock reduction analysis and select one method with the potential to make additional use of non-continuous fisheries-independent survey data. We will start with optimizing certain selected method and aim at developing a novel stock assessment model with the capability to synthesize non-continuous fisheries-independent survey data, fishery catch statistics and available information on life history traits of fishery stocks. In addition, we are applying the novel model to more than 300 global fishery stocks which have been formally assessed and archived in the RAM Legacy Stock Assessment database. The validity and applicability of the novel model to these fishery stocks with different types of life history traits and exploitation status will be evaluated by comparing our assessment results with corresponding formal stock assessment estimates. Based on the applicability of the novel model, we will further apply this model to assess several major fishery stocks in the Yellow Sea and the Bohai Sea. If this proposal got funded, the predictable results would support our government in science and technology for better assessment and management of our coastal fishery stocks.

渔业资源科学评估是实现渔业资源可持续管理的基础，评估模型的优化选择和对渔业种群现有相关数据的有效利用是提高渔业资源评估精准性的关键。目前，大部分渔业种群的科学或生产调查数据缺乏连续性，使得常规性资源评估模型难以适用；现行基于有限数据的资源评估方法对科学调查数据需求低，但评估结果的精准性有待提高。因此，如何有效利用非连续调查数据成为渔业资源评估领域亟待解决的关键问题。本项目拟基于黄渤海长期不连续的渔业科学调查数据，结合渔业产量和渔业种群生活史特征信息，对现行一系列基于有限数据的渔业种群衰减分析评估方法进行筛选和优化，构建基于非连续调查数据的渔业资源评估新模型；基于全球300余种具有常规性评估的实际渔业种群数据，量化分析该模型对各个渔业种群资源评估的精准性，检验该模型对不同生活史特征、不同开发状态的渔业种群的适用性；并应用该模型对黄渤海重要渔业种群进行资源评估，以期为渔业资源管理提供科学依据。

渔业资源科学评估是实现渔业资源可持续管理的基础，评估模型的优化选择和对渔业种群现有相关数据的有效利用是提高渔业资源评估精准性的关键。大部分渔业种群的科学或生产调查数据缺乏连续性，使得常规性资源评估模型难以适用；基于有限数据的资源评估方法对科学调查数据需求低，但评估结果的精准性有待提高。如何有效利用非连续调查数据成为渔业资源评估领域亟待解决的关键问题。本项目拟基于黄渤海长期不连续的渔业科学调查数据，结合渔业产量和渔业种群生活史特征信息，在已有基于有限数据的种群衰减分析方法基础上，构建了一种基于非连续调查数据的种群衰减分析（NSB-SRA）新方法；从全球公共数据库RAM Legacy Stock Assessment database中筛选出资源量、捕捞量、生物学参考点（如BMSY）和捕捞限额等数据完备的92个渔业种群，按照生活史特征和当前开发状态进行分类，并应用NSB-SRA模型评估了各种群的相对资源量和可捕量；以各渔业种群的实际评估结果为参照，分析了NSB-SRA方法的评估精准性，并对比分析了NSB-SRA模型在不同类别渔业种群的应用效果。研究结果表明，相对于已有种群衰减分析方法，NSB-SRA方法能更准确地评估不同开发程度渔业种群的可捕量；对于适度开发的渔业种群，NSB-SRA方法通常低估其可捕限额；对于过度开发的渔业种群，若其生长速度较快、性成熟体长小，NSB-SRA方法对其可捕限额的评估准确性较高。我国近海渔业资源衰退严重，主要经济鱼类生长速度较快、性成熟体长小。因此，NSB-SRA方法适用于我国近海主要经济鱼类的资源状况评估。经评估，2020年黄渤海小黄鱼处于过度捕捞状态，可捕量约为6.5万吨；黄渤鳀处于充分开发利用状态，可捕量约为48.3万吨；渤海口虾蛄面临生长型过度捕捞，应适当降低捕捞压力，并设置最小捕捞规格。

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