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基于最大熵模型模拟西印度洋剑鱼栖息地的时空分布

唐未 王学昉 吴峰 李渊

唐未,王学昉,吴峰,等. 基于最大熵模型模拟西印度洋剑鱼栖息地的时空分布[J]. 海洋学报,2022,44(10):100–108 doi: 10.12284/hyxb2022180
引用本文: 唐未,王学昉,吴峰,等. 基于最大熵模型模拟西印度洋剑鱼栖息地的时空分布[J]. 海洋学报,2022,44(10):100–108 doi: 10.12284/hyxb2022180
Tang Wei,Wang Xuefang,Wu Feng, et al. Simulation of spatio-temporal distribution of swordfish habitat in the western Indian Ocean based on maximum entropy model[J]. Haiyang Xuebao,2022, 44(10):100–108 doi: 10.12284/hyxb2022180
Citation: Tang Wei,Wang Xuefang,Wu Feng, et al. Simulation of spatio-temporal distribution of swordfish habitat in the western Indian Ocean based on maximum entropy model[J]. Haiyang Xuebao,2022, 44(10):100–108 doi: 10.12284/hyxb2022180

基于最大熵模型模拟西印度洋剑鱼栖息地的时空分布

doi: 10.12284/hyxb2022180
基金项目: 国家重点研发计划(2019YFD0901404);全球变化与海气相互作用专项(GASI-01-EIND-YD01aut/02aut);农业农村部远洋渔业国家观察员项目(17220255)。
详细信息
    作者简介:

    唐未(1998-),男,四川省巴中市人,研究方向为鱼类栖息地评估。E-mail: m200210753@st.shou.edu.cn

    通讯作者:

    王学昉(1983-),男,副教授,硕导,研究方向为海洋渔业资源监测与栖息地评估。E-mail: xfwang@shou.edu.cn

  • 中图分类号: S931.41;P724

Simulation of spatio-temporal distribution of swordfish habitat in the western Indian Ocean based on maximum entropy model

  • 摘要: 剑鱼(Xiphias gladius)是一种高度洄游性鱼类,其迁徙和栖息地利用受海洋环境影响明显,理解其空间分布格局形成的机制对于资源的养护和管理具有重要意义。本研究利用2017−2019年中国印度洋延绳钓渔业观察员数据中剑鱼的渔获物信息作为物种出现数据,结合西印度洋海域的海表温度、海面高度、叶绿素a浓度、混合层深度、海表盐度等环境数据,采用最大熵模型对剑鱼的栖息地适宜性分布进行了模拟。结果表明:(1)模型对印度洋西部剑鱼栖息地适宜性分布的模拟精度非常高,各个季节受试者工作特征曲线的曲线下面积都大于0.9,可用于模拟剑鱼潜在的栖息地适宜性分布;(2)研究区域内剑鱼适宜栖息地分布变化与实际作业位置变动基本一致,干季和湿季剑鱼栖息地高适宜性的区域分布都较为集中,但湿季分布范围要大于干季;(3)海表温度、海表盐度和混合层深度是影响西印度洋剑鱼栖息地适宜性分布的重要环境因子,在干季和湿季的最适范围分别为25.8~31.6℃、34.4~35.9、0.1~24.9 m和25.6~30.5℃、34.8~36.4、13.1~54.1 m。研究结果可为西印度洋海域剑鱼种群的可持续利用和科学管理提供必要参考信息。
  • 图  1  2017−2019年渔业观察员记录的西印度洋剑鱼渔获站点(黑点)的分布状况

    Fig.  1  Distribution of swordfish fishing stations (black dots) in the western Indian Ocean recorded by fishery observers during 2017 to 2019

    图  2  2017−2019年西印度洋剑鱼实际出现点与潜在栖息地的分布

    a. 2017年干季;b. 2018年干季;c. 2019年干季;d. 2017年湿季; e. 2018年湿季; f. 2019年湿季

    Fig.  2  Distribution of actual occurrence points and potential habitat of swordfish in the western Indian Ocean from 2017 to 2019

    a. Dry season in 2017; b. dry season in 2018; c. dry season in 2019; d. rainy season in 2017; e. rainy season in 2018; f. rainy season in 2019

    图  3  2017−2019年西印度洋剑鱼栖息地适宜性指数分布

    a. 2017年干季;b. 2018年干季;c. 2019年干季;d. 2017年湿季; e. 2018年湿季; f. 2019年湿季

    Fig.  3  Distribution of swordfish habitat suitability index in the western Indian Ocean from 2017 to 2019

    a. Dry season in 2017; b. dry season in 2018; c. dry season in 2019; d. rainy season in 2017; e. rainy season in 2018; f. rainy season in 2019

    图  4  2017−2019年干季和湿季西印度洋剑鱼栖息地适宜性指数比较

    Fig.  4  Comparison of habitat suitability index for swordfish in the western Indian Ocean during the dry and rainy seasons from 2017 to 2019

    图  5  主要环境因子对剑鱼栖息地适宜性指数的响应曲线

    Fig.  5  Response curves of main environmental factors to swordfish habitat suitability index

    表  1  模型预测评价及主要验证参数

    Tab.  1  Main parameters for model evaluation and test

    2017年干季2017年湿季2018年干季2018年湿季2019年干季2019年湿季
    训练数据(AUC值)0.965 70.93430.987 20.933 90.976 90.969 4
    测试数据 (AUC值)0.965 40.92470.981 80.925 30.965 60.965 2
    AUC值标准差0.005 40.01360.009 80.014 40.011 00.009 4
    ESS值0.251 20.35090.349 70.323 10.285 10.154 0
    遗漏率/%9.814.25.615.05.69.4
    下载: 导出CSV

    表  2  2017−2019年各季最大熵模型中环境因子的贡献率 (%)

    Tab.  2  The contribution rate (%) of environmental factors in the seasonal maximum entropy model from 2017 to 2019

    环境因子2017年
    干季
    2017年
    湿季
    2018年
    干季
    2018年
    湿季
    2019年
    干季
    2019年
    湿季
    SST41.3925.8732.3446.7654.9346.85
    SSS17.7533.6715.1831.6720.7313.69
    SSH18.7717.846.403.553.183.23
    Chl a浓度1.991.7128.449.0614.7828.07
    MLD20.1020.9117.648.966.388.16
    下载: 导出CSV

    表  3  2017−2019年各季Jackknife检验结果得分

    Tab.  3  The seasonal result score of Jackknife test from 2017 to 2019

    2017年
    干季
    2017年
    湿季
    2018年
    干季
    2018年
    湿季
    2019年
    干季
    2019年
    湿季
    不包含MLD2.2381.4712.2211.5342.4332.485
    不包含Chl a浓度2.2591.5872.8521.4102.0642.394
    不包含SSH2.2441.4782.6791.5372.3792.491
    不包含SSS2.1151.2022.4360.9702.3122.054
    不包含SST2.1601.5812.5641.2052.2472.285
    只包含MLD1.4160.7841.4620.6790.9850.959
    只包含Chl a浓度0.6900.2061.1890.4071.0630.161
    只包含SSH1.0790.8770.3670.5660.6361.015
    只包含SSS1.0440.8051.5120.5470.9980.874
    只包含SST1.1450.4200.9240.5741.1281.338
    下载: 导出CSV
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  • 收稿日期:  2022-04-14
  • 修回日期:  2022-06-07
  • 网络出版日期:  2022-07-01
  • 刊出日期:  2022-10-01

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