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基于产卵场和索饵场适宜性的西北太平洋柔鱼丰度预测

魏广恩 陈新军

魏广恩,陈新军. 基于产卵场和索饵场适宜性的西北太平洋柔鱼丰度预测[J]. 海洋学报,2020,42(12):14–25 doi: 10.3969/j.issn.0253-4193.2020.12.002
引用本文: 魏广恩,陈新军. 基于产卵场和索饵场适宜性的西北太平洋柔鱼丰度预测[J]. 海洋学报,2020,42(12):14–25 doi: 10.3969/j.issn.0253-4193.2020.12.002
Wei Guang’en,Chen Xinjun. Forecasting Northwest Pacific Ocean neon flying squid abundance based on suitability of spawning and feeding grounds[J]. Haiyang Xuebao,2020, 42(12):14–25 doi: 10.3969/j.issn.0253-4193.2020.12.002
Citation: Wei Guang’en,Chen Xinjun. Forecasting Northwest Pacific Ocean neon flying squid abundance based on suitability of spawning and feeding grounds[J]. Haiyang Xuebao,2020, 42(12):14–25 doi: 10.3969/j.issn.0253-4193.2020.12.002

基于产卵场和索饵场适宜性的西北太平洋柔鱼丰度预测

doi: 10.3969/j.issn.0253-4193.2020.12.002
基金项目: 国家重点研发计划(2019YFD0901404);国家自然科学基金面上项目(NSFC41876141);上海市科技创新行动计划(10DZ1207500)。
详细信息
    作者简介:

    魏广恩(1991-),男,安徽省淮南市人,研究方向为渔业资源。E-mail:wge1991@163.com

    通讯作者:

    陈新军(1967-),男,教授。E-mail:xjchen@shou.edu.cn

  • 中图分类号: S931.1

Forecasting Northwest Pacific Ocean neon flying squid abundance based on suitability of spawning and feeding grounds

  • 摘要: 柔鱼(Ommastrephes bartramii)是北太平洋重要的经济头足类。短生命周期的特征使其资源丰度主要取决于补充量,早期生活史阶段的海洋环境将直接影响补充量大小。利用2004−2015年我国鱿钓船队在西北太平洋的生产统计数据,以及产卵场和索饵场海表水温,将产卵场和索饵场等分成不同数量的海区,运用相关性分析和随机森林模型,筛选出产卵期各月产卵场最适海表水温范围占总面积的比值(Ps)和索饵期各月索饵场最适海表水温范围占总面积的比值(Pf)与单位捕捞努力量渔获量(CPUE)显著相关的海区,将Ps值或Pf值作为神经网络模型的输入变量,分别构建基于产卵场、索饵场的资源丰度预报模型,分析模型的优劣与预报准确度。结果表明:产卵场划分方案为5°×5°,索饵场为2.5° (经)×4° (纬)较为合适。随机森林筛选出的海区与相关性分析筛选出的适宜海区范围大致吻合,且随机森林能够识别与CPUE相关的潜在海域。模型的预报结果表明,其预报准确度均达到90%以上,随机森林筛选出的海区的最适海表水温范围占该海区的比值作为神经网络模型输入因子构建的模型优于相关性分析,预报准确度更高。基于产卵场海域构建的模型相对于索饵场,模型精确度更高、更稳定。
  • 图  1  2004−2015年西北太平洋柔鱼产量及CPUE

    Fig.  1  The annual total catch and catch per unit effort (CPUE) of O. bartramii from 2004 to 2015 in the Northwest Pacific Ocean

    图  2  西北太平洋柔鱼产卵场和索饵场分布

    Fig.  2  Distribution of spawning and feeding grounds of O. bartramii in the Northwest Pacific Ocean

    图  3  西北太平洋海域柔鱼产卵场的划分方案

    Fig.  3  The division of spawning grounds of O. bartramii in the Northwest Pacific Ocean

    图  4  西北太平洋海域柔鱼索饵场的划分方案

    Fig.  4  The division of feeding grounds of O. bartramii in the Northwest Pacific Ocean

    图  5  随机森林工作原理

    Fig.  5  The working principle of random forest

    图  6  不同决策树节点分支变量数模型的误判率均值

    Fig.  6  The mean of misjudgment rate of models with different numbers of branch variable on the decision trees node

    图  7  决策树数量和模型误差关系

    Fig.  7  The relation between model error and numbers of decision trees

    图  8  产卵期各海区重要性分布

    Fig.  8  The distribution of each sea area’s importance during spawning period

    图  9  索饵期各海区重要性分布

    Fig.  9  The distribution of each sea area’s importance during the feeding period

    图  10  神经网络模型的模拟结果和预报准确度

    Fig.  10  The simulation results and forecast accuracy of neural network

    图  11  2015年产卵场1−4月最适海表水温分布情况

    Fig.  11  The distribution of the optimal sea surface temperature in the spawning grounds from January to April of 2015

    图  12  精度平均减少值为10产卵期各月份重要性海区筛选结果

    Fig.  12  The results of important sea areas in period of spawning each month under the condition that mean decrease accuracy was 10

    图  13  精度平均减少值为10索饵期各月份重要性海区筛选结果

    Fig.  13  The results of important sea areas in period of feeding each month under the condition that mean decrease accuracy was 10

    表  1  2004−2015年各年份作业船数

    Tab.  1  The numbers of fishing vessels from 2004 to 2015

    年份2004年2005年2006年2007年2008年2009年2010年2011年2012年2013年2014年2015年
    作业船数/艘212227327255258273262191225237186116
    下载: 导出CSV

    表  2  西北太平洋海域柔鱼产卵场Ps与CPUE时间序列的相关性和显著性

    Tab.  2  The correlation and significance of Ps in the spawning grounds with CPUE time series of O. bartramii in the Northwest Pacific Ocean

    方案1月2月3月4月
    rprprprp
    A
    B3−0.660<0.05
    C50.600<0.05
    C60.710<0.05
    D3−0.670<0.05
    D90.650<0.05
    D110.780<0.01
    D13−0.600<0.05
      注:−表示该月份研究海域产卵场Ps与CPUE时间序列值相关性分析结果不显著。
    下载: 导出CSV

    表  3  西北太平洋海域柔鱼索饵场Pf与CPUE时间序列的相关性和显著性

    Tab.  3  The correlation and significance of Pf in the feeding grounds with CPUE time series of O. bartramii in the Northwest Pacific Ocean

    方案8月9月10月11月
    rprprprp
    A1−0.580<0.05
    B1−0.610<0.050.590<0.05
    C1−0.600<0.05
    C40.840<0.01
    D1−0.610<0.05
    D60.750<0.01
    D7−0.570<0.050.740<0.01
    D80.710<0.05
    D100.590<0.05
      注:−表示该月份研究海域索饵场Pf与CPUE时间序列值相关性分析结果不显著。
    下载: 导出CSV

    表  4  神经网络预报模型构建方案

    Tab.  4  Construct the scenarios of neural network

    海域编号输入因子网络结构
    相关性产卵场海域D9-1、D3-2、D11-2、D13-44∶5∶1
    索饵场海域D1-10、D6-11、D7-8、D8-11、D8-11、D10-116∶7∶1
    随机森林产卵场海域D9-1、D10-1、D11-2、D13-4、D16-45∶6∶1
    索饵场海域D5-8、D7-8、D6-9、D1-10、D1-11、D6-11、D7-11、D8-118∶9∶1
    下载: 导出CSV
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出版历程
  • 收稿日期:  2019-07-09
  • 修回日期:  2019-10-30
  • 网络出版日期:  2020-12-23
  • 刊出日期:  2020-12-25

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