渔获量不确定性对印度洋大眼金枪鱼资源评估的影响

Impact of Catch Uncertainty on the Stock Assessment of Bigeye Tuna (Thunnus obesus) in the Indian Ocean

中文关键词: 印度洋  大眼金枪鱼  资源评估  渔获量  误差

英文关键词: Indian Ocean  Thunnus obesus  Stock assessment  Catch  Uncertainty

中文摘要:

      大眼金枪鱼(Thunnus obesus)是最具经济价值的热带金枪鱼类，其资源状况一直是区域性金枪鱼渔业管理组织关注的重点。由于多种渔业作业、捕捞船队构成复杂，印度洋大眼金枪鱼的历史渔获量统计存在一定的偏差(Bias)，但国际上近些年开展资源评估时都忽略了这一偏差。本研究根据1979~2015年的年渔获量、年龄结构渔获量及相对丰度指数数据，运用年龄结构资源评估模型(ASAP)对印度洋大眼金枪鱼资源进行评估，重点考查渔获量的不确定性(观测误差和统计偏差)对资源评估结果的影响。结果显示，印度洋大眼金枪鱼当前资源总体没有过度捕捞，但2015年初显示轻微的过度捕捞，通过对比基础模型与8个灵敏度分析模型的评估结果发现，渔获量观测误差(CV)的预设对资源开发状态的判断有一定的影响。当渔获量统计偏差调整量为15%时(即历史渔获量被低估了)，评估结果与基础模型基本一致；统计偏差调整量为20%时，评估结果有过度捕捞的趋势。本研究结果表明，资源评估模型中渔获量观测误差的设定和历史渔获量统计偏差均会对评估结果产生影响，后者更为明显，因此，二者均不能忽略。

英文摘要:

      Bigeye tuna (BET), Thunnus obesus is a large epi- and mesopelagic species distributed in tropical and subtropical waters of the Indian Ocean. Its stock status has been the focus of regional tuna fisheries management organizations. Because of a variety of fishing gear and fishing fleet structures, there have some statistical biases in the historical nominal catches of the Indian Ocean BET. However, the impact of this bias on stock assessment has been neglected in recent years. This paper aimed to assess the impact of observation error and statistical bias of catch on the stock assessment of Indian Ocean BET, using Age-Structured Assessment Program (ASAP) based on fishery-specific catch, catch-at-age, and standardized catch-per-unit-effort data. The results showed that the current stock of BET in the Indian Ocean was not overfished. The results of base model and eight sensitivity analysis models showed that the observation error of catch had influences on the stock status evaluation. When the bias of nominal catch was assumed to be 15% (i.e., the historical catch was underestimated), the assessment result was consistent with the base model (i.e. not overfished). When the bias of nominal catch was assumed to be 20%, the stock might be overfished. Therefore, both the observation error and the statistical bias associated with catch data can have significant impacts on the assessment results, with the latter having a greater effect. This study highlights the importance of considering both the assumption of observation error and statistical bias in catch data in tuna fishery stock assessment, which has been neglected recently.