| 利用后向预报方法分析印度洋黄鳍金枪鱼资源评估模型 |
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| 引用本文： | 官文江,吴佳文,曹友华.利用后向预报方法分析印度洋黄鳍金枪鱼资源评估模型[J].中国海洋大学学报(自然科学版),2020(2):52-59. |
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| 作者姓名： | 官文江 吴佳文 曹友华 |
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| 作者单位： | 上海海洋大学海洋科学学院;上海海洋大学大洋渔业资源可持续开发省部共建教育部重点实验室 |
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| 基金项目： | 国家自然科学基金-浙江两化融合联合基金项目(U1609202)资助~~ |
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| 摘 要： | 当前,对渔业资源评估模型的诊断与选择,主要依赖于模型对观察数据的拟合度,很少评价模型的预测能力、并将其作为评价渔业资源评估与管理质量的依据。为此,本文利用后向预报方法评价了印度洋黄鳍金枪鱼(Thunnus albacores)资源评估模型的预测能力,并在此基础上分析了印度洋黄鳍金枪鱼的资源评估与管理质量。研究表明,在利用贝叶斯剩余产量模型对印度洋黄鳍金枪鱼进行资源评估时,存在如下问题:(1)拟合较好的模型其预测能力较差;(2)利用不同时段数据拟合模型时,采用DIC(Deviance Information Criterion)选择的最佳模型缺少稳定性;(3)不同模型估计的TAC (Total Allowable Catch)存在较大差异。据此可以判断,利用贝叶斯剩余产量模型对印度洋黄鳍金枪鱼进行资源评估与管理效果较差。本研究结果表明:(1)利用后向预报方法可评价模型的预测能力、DIC选择模型的稳定性,从而能在一定程度上判断模型模拟的种群演化动态是否正确、资源评估结果是否存在问题;(2)利用后向预报方法可揭示评估结果的不确定性及其可能引起的渔业管理风险,从而有利于避免渔业管理风险、实现渔业管理目标。
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| 关 键 词： | 黄鳍金枪鱼 渔业管理 渔业资源评估 剩余产量模型 资源评估与管理 预报方法 模型估计 观察数据 |
Evaluating the Stock Assessment and Management Quality of Indian Ocean Yellowfin Tuna with Hindcasting Method |
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| GUAN Wen-Jiang,WU Jia-Wen,CAO You-Hua.Evaluating the Stock Assessment and Management Quality of Indian Ocean Yellowfin Tuna with Hindcasting Method[J].Periodical of Ocean University of China,2020(2):52-59. |
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| Authors: | GUAN Wen-Jiang WU Jia-Wen CAO You-Hua |
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| Institution: | (College of Marine Sciences,Shanghai Ocean University,Shanghai 201306,China;Key Laboratory of Sustainable Exploitation of Oceanic Fisheries Resources,Ministry of Education,Shanghai Ocean University,Shanghai 201306,China) |
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| Abstract: | At present, the diagnosis and choice of fisheries stock assessment model mainly depend on the goodness of fit of the models on observed data. Stock assessment scientists seldom assess the predictive ability of the fishery stock assessment model and use it as a basis for evaluating the quality of fishery stock assessment and management. In this paper, we evaluated the prediction skill of the stock assessment models of Indian Ocean yellowfin tuna(Thunnus albacores) using hindcasting and then analyzed the quality of the stock assessment and management. The results showed that when using Bayesian surplus production model to the assessment of the Indian Ocean yellowfin tuna, the following problems existed:(1) the prediction skill of the model with better goodness of fit was poor;(2) the best model chosen by DIC(Deviance Information Criterion) will be greatly different when the model was fitted by using different data sets from different time periods;(3) the TAC(Total Allowable Catch) estimated from different models were greatly different. Based on the results mentioned above, we speculated that the quality of stock assessment and management of Indian Ocean yellowfin tuna was poor when the Bayesian surplus production model was used. Therefore, the results of this paper showed that(1) the prediction skill and the stability of the stock assessment model chosen by using DIC can be assessed by using hindcasting and based on the results, and analyzer can know to a certain extent whether the model was correct in simulating population dynamics and whether there are problems in results of the stock assessment;(2) the uncertainties of the assessment and the possible risks of the corresponding management can be revealed by using the hindcasting, which is beneficial to avoiding the risks of fishery management and realizing the objectives of fishery management. |
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| Keywords: | hindcasting Thunnus albacores surplus production model deviance information criterion total allowable catch |
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