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不同模态下气候变化对智利竹筴鱼补充量的影响

张畅 李纲 陈新军

张畅,李纲,陈新军. 不同模态下气候变化对智利竹筴鱼补充量的影响[J]. 海洋学报,2021,43(9):48–58 doi: 10.12284/hyxb2021130
引用本文: 张畅,李纲,陈新军. 不同模态下气候变化对智利竹筴鱼补充量的影响[J]. 海洋学报,2021,43(9):48–58 doi: 10.12284/hyxb2021130
Zhang Chang,Li Gang,Chen Xinjun. Impact of climate change on recruitment of Trachurus murphyi based on different regimes[J]. Haiyang Xuebao,2021, 43(9):48–58 doi: 10.12284/hyxb2021130
Citation: Zhang Chang,Li Gang,Chen Xinjun. Impact of climate change on recruitment of Trachurus murphyi based on different regimes[J]. Haiyang Xuebao,2021, 43(9):48–58 doi: 10.12284/hyxb2021130

不同模态下气候变化对智利竹筴鱼补充量的影响

doi: 10.12284/hyxb2021130
基金项目: 国家重点研发计划(2019YFD0901404);上海市科技创新行动计划(19DZ1207500)
详细信息
    作者简介:

    张畅(1994-),女,浙江省金华市人,博士研究生,主要研究方向为渔业资源。E-mail:itschang@foxmail.com

    通讯作者:

    陈新军(1967-),男,教授,浙江省义乌市人,从事渔业资源与渔场学等研究。E-mail:xjchen@shou.edu.cn

  • 中图分类号: S931.4; S917

Impact of climate change on recruitment of Trachurus murphyi based on different regimes

  • 摘要: 智利竹筴鱼(Trachurus murphyi)是东南太平洋重要的经济鱼类之一,其资源量受补充量影响明显,了解补充量状况对智利竹筴鱼资源可持续利用和科学管理具有重要意义。本文基于模态分析将1971−2017年间智利竹筴鱼补充量划分不同模态,运用贝叶斯模型平均法,分析海表面温度、海表面盐度、海表面高度、厄尔尼诺和太平洋年代际振荡5个环境因子在不同模态中对补充量的解释能力,并探讨模态变动对补充量预测的影响。结果表明,第1模态(1971−1980年)更多的受捕捞因素的干扰;第2模态(1981−1990年)厄尔尼诺对补充量变动的解释概率最高;第3模态(1991−2001年)解释概率最高的环境因子为太平洋年代际振荡;第4模态(2002−2015年)厄尔尼诺为解释概率最高的环境因子。对比不考虑模态变动的分析结果,两者存在明显差异,基于不同模态的分析结果对智利竹筴鱼补充量变动的解释更为合理。研究认为,智利竹筴鱼补充量变动受到多个环境因子的影响,在不同模态时期起主导作用的环境因子也不同,推测年代际太平洋年代际振荡冷暖期交替与厄尔尼诺现象可能是诱发智利竹筴鱼补充量发生模态转变的重要因素。建议在未来智利竹筴鱼资源评估与管理中,应该考虑不同的模态变化及其影响因子。
  • 图  1  智利竹筴鱼产卵场

    Fig.  1  The spawning ground of Chilean jack mackerel

    图  2  1971−2017年智利竹筴鱼补充量模态检测

    Fig.  2  Regime shift detection in recruitment for different values of Chilean jack mackerel during 1971 to 2017

    图  3  1971−2017年智利竹筴鱼补充量跃变指数

    Fig.  3  Regime shift index values of recruitment for Chilean jack mackerel during 1971 to 2017

    图  4  1971−2015年间厄尔尼诺指数的年际变化

    图中数字为El Niño指数

    Fig.  4  Annual changes of El Niño index during 1971 to 2015

    The number in the figure is the El Niño index

    图  5  1971−2015年间PDO指数的年际变化

    图中数字为PDO指数

    Fig.  5  Annual changes of PDO index during 1971 to 2015

    The number in the figure is the PDO index

    图  6  基于BMA的ln(R/S)预测概率

    Fig.  6  Predictive probabilities for ln(R/S) based on BMA

    表  1  不同影响因子在不同模态阶段的PIP(%)

    Tab.  1  PIP (%) of different impact factors on different regimes

    影响因子不分模态阶段第1模态第2模态第3模态第4模态
    亲体量99348610098
    SSH7641262332
    SSS2046272422
    SST1940323125
    PDO1628344124
    El Niño1424492333
    下载: 导出CSV

    表  2  基于BMA的PMP最高的前5个模型的后验概率(%)

    Tab.  2  Posterior distribution (%) of top five models with highest PMP of all models from BMA for no shift model

    影响因子模型1模型2模型3模型4模型5
    亲体量
    SSS
    PDO
    SSH
    El Niño
    SST
    PMP(MCMC)36%9%9%9%7%
    PMP(Exact)36%9%9%9%7%
      注:√表示该模型中包含的因子。
    下载: 导出CSV

    表  3  不同方案中基于BMA的PMP最高的前5个模型的后验概率(%)

    Tab.  3  Posterior distribution (%) of top five models with highest PMP of all models from BMA for different program

    模型方案a方案b方案c
    模型1241922
    模型2121311
    模型3699
    模型4666
    模型5566
    累计525153
    下载: 导出CSV
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  • 收稿日期:  2021-01-28
  • 修回日期:  2021-04-29
  • 网络出版日期:  2021-06-10
  • 刊出日期:  2021-09-06

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