| 评估不同评估配置应对年龄空间结构的表现:基于印度洋长鳍金枪鱼的模拟研究 |
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| 引用本文： | 官文江,吴佳文,田思泉.评估不同评估配置应对年龄空间结构的表现:基于印度洋长鳍金枪鱼的模拟研究[J].海洋学报(英文版),2019,38(10):9-19. |
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| 作者姓名： | 官文江 吴佳文 田思泉 |
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| 作者单位： | 上海海洋大学海洋科学学院, 上海 201306, 中国;教育部海洋渔业资源可持续开发重点实验室, 上海 201306, 中国,上海海洋大学海洋科学学院, 上海 201306, 中国,上海海洋大学海洋科学学院, 上海 201306, 中国;教育部海洋渔业资源可持续开发重点实验室, 上海 201306, 中国 |
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| 基金项目： | The National Key Research and Development Program of China under contract No. 2016YFC1400903; the NSFC-Zhejiang Joint Fund for the Integration of Industrialization and Informatization under contract No. U1609202. |
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| 摘 要： | 不同种群结构或种群分布的空间异质性是导致模型错误的一个重要因素，并在渔业资源评估中对参数估计有重要影响。本文根据状态相关的洄游率与区域相关的捕捞死亡率，利用合成模型，模拟了印度洋长鳍金枪鱼年龄空间结构的异质性，并生成了资源评估数据。基于这些数据，本文研究了用于空间异质资源评估模型的不同空间配置、选择曲线及CPUE（Catch Per Unit Effort）使用场景的表现。本文结果表明：（1） 尽管同操作模型一致的空间动态配置能在所有模拟场景中对相对产卵生物量、相对死亡系数、最大可持续产量提供准确、无偏估计，但若由于知识与数据限制，使空间动态配置与操作模型不一致，则其表现可能相当差；（2） 对于空间配置，边界划分必须正确，但对于非空间配置，不管边界划分正确与否，只要划分的区域能合理反映现场数据的变化，并能通过增加空间参考参数从而能有效减少忽略空间结构的影响即为合理；（3） 尽管区域作为渔业的方法及灵活的时变选择曲线是一个较好备选方法，可用于解决空间结构问题，但这些方法并不能完全消除由空间结构而引起模型错误导致的影响，从而使模型的参数估计具有很大的不确定性、相同评估模型不同参数的估计质量不一致、相同评估配置的评估质量在不同模拟场景下存在很大的差异；（4） 尽管采用多个CPUE指数一般可以避免最差的参数估计，但没有更好的选择或生成CPUE指数的方法可以用于显著提高资源评估质量，因为忽略空间结构将导致所有资源评估模型预测的CPUE所包含的信息通常不同于观测的CPUE。对比不同建模者的模型配置的评估结果，除了与操作模型完全匹配的空间配置外，其他模型配置的表现均与具体案例有关。从这个意义上讲，本文研究结果不仅对当前印度洋长鳍金枪鱼资源评估有益，也将增加对空间结构配置效果的理解。
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| 关 键 词： | 空间结构|模拟|资源评估|印度洋|长鳍金枪鱼 |
| 收稿时间： | 2018/7/10 0:00:00 |
Evaluation of the performance of alternative assessment configurations to account for the spatial heterogeneity in age-structure: a simulation study based on Indian Ocean albacore tuna |
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| Guan Wenjiang,Wu Jiawen and Tian Siquan.Evaluation of the performance of alternative assessment configurations to account for the spatial heterogeneity in age-structure: a simulation study based on Indian Ocean albacore tuna[J].Acta Oceanologica Sinica,2019,38(10):9-19. |
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| Authors: | Guan Wenjiang Wu Jiawen and Tian Siquan |
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| Institution: | 1.College of Marine Sciences, Shanghai Ocean University, Shanghai 201306, China;Key Laboratory of Sustainable Exploitation of Oceanic Fisheries Resources, Ministry of Education, Shanghai 201306, China2.College of Marine Sciences, Shanghai Ocean University, Shanghai 201306, China |
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| Abstract: | Various population structures or spatial heterogeneities in population distribution have been an important source of model misspecification and have had an impact on estimation performance in fisheries stock assessment. In this study, we simulated the Indian Ocean albacore spatial heterogeneity in age-structure using Stock Synthesis according to the stage-dependent migration rate and region-dependent fishing mortality rate and generated the stock assessment data. Based on these data, we investigated the performances of different spatial configurations, selectivity curves and selections of CPUE(catch per unit effort) indices of the assessment models which were used to account for spatial heterogeneity. The results showed:(1) although the spatially explicit configurations, which exactly matched the operating model, provided unbiased and accurate estimates of relative spawning biomass, relative fishing mortality rate and maximum sustainable yield in all simulation scenarios, their performance may be very poor if there were mismatches between them and the operating model due to gaps in knowledge and data; (2) for spatially explicit assessment configuration, the correct boundary was required, but for non-spatially explicit assessment configuration, it seemed more important for analysts to partition the area to properly reflect the transition in field data and to effectively account for the impacts of ignoring the spatial structure by using the additional spatially referenced parameters; (3) although the areas-as-fleets methods and flexible time-varying selectivity curves could be used as better alternative approaches to account for spatial structure, these configurations could not completely eliminate the impacts of model misspecification and the quality of estimates of different quantities from the same assessment model may be inconsistent or the performance of the same assessment configuration may fluctuate significantly between simulation scenarios; (4) although the worst estimates could generally be avoided by using multiple CPUE indices, there were no best solutions to select or regenerate the CPUE indices to account for the impacts of the ignored spatial structure to obviously improve the quality of stock assessment. Compared with the results of assessment model configurations which are used to account for the spatial structure by different modelers, the performances of the configurations are always case-specific except for spatially explicit configurations which exactly match the operating model. In this sense, our study will not only provide some insights into the current Indian Ocean albacore stock assessment but also enrich existing knowledge regarding the performance of assessment configurations to account for spatial structure. |
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| Keywords: | spatial structure|simulation|stock assessment|Indian Ocean|Thunnus alalunga |
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