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自然死亡和亲体补充关系对黄鳍金枪鱼资源评估的影响

崔明远 麻秋云 田思泉 林龙山 李渊

崔明远,麻秋云,田思泉,等. 自然死亡和亲体补充关系对黄鳍金枪鱼资源评估的影响[J]. 海洋学报,2023,45(3):40–51 doi: 10.12284/hyxb2023044
引用本文: 崔明远,麻秋云,田思泉,等. 自然死亡和亲体补充关系对黄鳍金枪鱼资源评估的影响[J]. 海洋学报,2023,45(3):40–51 doi: 10.12284/hyxb2023044
Cui Mingyuan,Ma Qiuyun,Tian Siquan, et al. Influence of natural mortality and stock-recruitment relationship on yellowfin tuna (Thunnus albacares) stock assessment[J]. Haiyang Xuebao,2023, 45(3):40–51 doi: 10.12284/hyxb2023044
Citation: Cui Mingyuan,Ma Qiuyun,Tian Siquan, et al. Influence of natural mortality and stock-recruitment relationship on yellowfin tuna (Thunnus albacares) stock assessment[J]. Haiyang Xuebao,2023, 45(3):40–51 doi: 10.12284/hyxb2023044

自然死亡和亲体补充关系对黄鳍金枪鱼资源评估的影响

doi: 10.12284/hyxb2023044
基金项目: 国家自然科学基金(32202934);全球变化与海气相互作用(二期)专项(GASI-01-EIND-YD01/02spr/aut);国家重点研发计划(2019YFD0901404)。
详细信息
    作者简介:

    崔明远(1994-),男,山东省潍坊市人,博士研究生,主要从事渔业资源评估研究。E-mail:470235020@qq.com

    通讯作者:

    麻秋云(1987-),女,讲师,主要从事种群动力学和渔业资源评估研究。E-mail:qyma@shou.edu.cn

  • 中图分类号: S932.4

Influence of natural mortality and stock-recruitment relationship on yellowfin tuna (Thunnus albacares) stock assessment

  • 摘要: 黄鳍金枪鱼(Thunnus albacares)是全球经济和生态价值最重要的鱼类之一,其资源养护和管理受到各方的高度关注。本文依据年龄结构产量模型研究了印度洋黄鳍金枪鱼的资源状态,着重探讨了其生活史特征的不确定性对资源评估结果的影响。研究结果显示,1960−1985年间印度洋黄鳍金枪鱼资源量保持相对稳定,之后开始逐渐下降,相应的捕捞死亡系数也在2010年之后迅速增加,目前其种群可能存在过度捕捞(F2020/FMSY>1,SSB2020/SSBMSY<1)。印度洋黄鳍金枪鱼的资源评估结果对自然死亡系数(M)和亲体−补充量关系陡度参数(h)的改变较为敏感。当h增大时,SSBMSY和初始SSB(即SSB0)的变化较大,分别减少了约25.53万t和34.04万t;F2020/FMSY减小了1.15。当M增大时,F2020/FMSYSSBMSYSSB0均减小。综上所述,今后应重视印度洋黄鳍金枪鱼资源的开发程度,重视其资源养护管理,同时充分了解黄鳍金枪鱼的生活史特征,提高自然死亡系数和陡度参数估算的准确性,以期为印度洋黄鳍金枪鱼的资源评估和渔业管理提供更准确的信息,实现该渔业的长期可持续发展。
  • 图  1  印度洋黄鳍金枪鱼渔获量及年龄组成

    Fig.  1  Catch and age composition of yellowfin tuna in the Indian Ocean

    图  2  印度洋黄鳍金枪鱼丰度指数观测值与预测值(基准模型)

    Fig.  2  Observed and predicted indices of yellowfin tuna in the Indian Ocean (S-base case)

    图  3  印度洋黄鳍金枪鱼年渔获量的观测值和预测值(基准模型)

    Fig.  3  Observed and predicted catch of yellowfin tuna in the Indian Ocean (S-base case)

    图  4  基准模型下印度洋黄鳍金枪鱼捕捞死亡系数(FMSY)和产卵亲体生物量(SSB)估计值

    Fig.  4  Estimated fishing mortality (FMSY) and spawning stock biomass (SSB) of yellowfin tuna in the Indian Ocean in base case model

    图  5  印度洋黄鳍金枪鱼资源数量的年龄结构(基准模型)

    Fig.  5  Estimates of stock numbers by age of yellowfin tuna in the Indian Ocean (S-base case)

    图  6  不同陡度下印度洋黄鳍金枪鱼捕捞死亡系数(FMSY)和产卵亲体生物量(SSB)估计值

    横线与曲线的交点为FMSYSSBMSY

    Fig.  6  Estimated fishing mortality (FMSY) and spawning stock biomass (SSB) of yellowfin tuna in the Indian Ocean in different steepness

    The intersections of straight lines and curves are FMSY and SSBMSY

    图  7  不同自然死亡系数下印度洋黄鳍金枪鱼捕捞死亡系数(FMSY)和产卵亲体生物量(SSB)估计值

    横线与曲线的交点为FMSYSSBMSY

    Fig.  7  Estimated fishing mortality (FMAY) and spawning stock biomass (SSB) of yellowfin tuna in the Indian Ocean in different natural mortality

    The intersections of straight lines and curves are FMSY and SSBMSY

    图  8  不同模型下印度洋黄鳍金枪鱼各个年龄对应的资源数量

    Fig.  8  Estimates of stock numbers by age of yellowfin tuna in the Indian Ocean in different scenarios

    表  1  各个模型的目标函数值及其主要组成

    Tab.  1  The objective function of each model and its main components

    模型目标函
    数值
    渔获量
    成分
    丰度
    指数
    渔获年龄
    组成成分
    补充量
    偏差
    S14 048696812998273
    S-base case4 044696862993269
    S24 042696922988266
    S34 1297531072994275
    下载: 导出CSV

    表  2  各个模型的生物学参考点及相关参数

    Tab.  2  Biological reference points and related parameters from each assessment model

    生物参考点 模型
    S1S-base caseS2S3
    F20200.980.930.900.96
    SSB2020/t558 920.7575 049.9589 316.9602 132.8
    SSB0/t3 046 7672 849 7652 706 4072 469 225
    FMSY0.340.420.520.52
    F2020/FMSY2.882.211.731.85
    MSY/t327 559341 191354 961334 197
    C2020/MSY1.321.271.221.30
    SSBMSY/t991 582854 369736 282745 318
    SSB2020/SSBMSY0.560.670.800.81
    注:F2020为2020年捕捞死亡系数,SSB2020为2020年产卵亲体生物量,SSB0为未开发时的产卵亲体生物量,FMSY为最大可持续产量对应的捕捞死亡系数,MSY为最大可持续产量,SSBMSY为最大可持续产量对应的产卵亲体生物量,C2020为2020年渔获量。
    下载: 导出CSV

    表  3  印度洋黄鳍金枪鱼ASAP模型中的性成熟度、不同模型中设置的自然死亡系数和陡度参数

    Tab.  3  Maturity, natural mortality and steepness parameter set in the ASAP model of yellowfin tuna in the Indian Ocean

    年龄S1S-base caseS2S3性成熟度[30]
    M1h1M1h2M1h3M2h2
    10.9630.70.9630.80.9630.91.0680.80
    20.6630.70.6630.80.6630.90.7350.80.3
    30.5480.70.5480.80.5480.90.6080.81
    40.4930.70.4930.80.4930.90.5470.81
    50.4630.70.4630.80.4630.90.5140.81
    6+0.4460.70.4460.80.4460.90.4950.81
    下载: 导出CSV

    表  4  各个模型均方根误差

    Tab.  4  Root mean square error for each model

    模型 渔获量成分丰度指数成分补充量偏差成分
    S10.621.371.58
    S-base case0.631.391.58
    S20.631.411.57
    S30.901.471.54
    下载: 导出CSV
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  • 收稿日期:  2022-08-17
  • 修回日期:  2022-10-11
  • 网络出版日期:  2022-10-21
  • 刊出日期:  2023-02-01

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