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基于GAM和GWR模型分析环境因子对鱼类分布的影响—以山东近海多鳞鱚为例

简盈 张云雷 宋业晖 张崇良 纪毓鹏 任一平

简盈,张云雷,宋业晖,等. 基于GAM和GWR模型分析环境因子对鱼类分布的影响—以山东近海多鳞鱚为例[J]. 海洋学报,2022,44(7):1–9 doi: 10.12284/hyxb2022146
引用本文: 简盈,张云雷,宋业晖,等. 基于GAM和GWR模型分析环境因子对鱼类分布的影响—以山东近海多鳞鱚为例[J]. 海洋学报,2022,44(7):1–9 doi: 10.12284/hyxb2022146
Jian Ying,Zhang Yunlei,Song Yehui, et al. Effect of environmental factors on fish distribution based on GAM and GWR model : A case study of Sillago sihama in the Shandong coastal waters[J]. Haiyang Xuebao,2022, 44(7):1–9 doi: 10.12284/hyxb2022146
Citation: Jian Ying,Zhang Yunlei,Song Yehui, et al. Effect of environmental factors on fish distribution based on GAM and GWR model : A case study of Sillago sihama in the Shandong coastal waters[J]. Haiyang Xuebao,2022, 44(7):1–9 doi: 10.12284/hyxb2022146

基于GAM和GWR模型分析环境因子对鱼类分布的影响—以山东近海多鳞鱚为例

doi: 10.12284/hyxb2022146
基金项目: 国家重点研发计划(2019YFD0901204)
详细信息
    作者简介:

    简盈(1996—),女,福建省漳州市人,主要从事渔业生态学研究。E-mail: jianying2536@163.com

    通讯作者:

    任一平,教授,博士生导师,主要从事渔业资源生态学研究。E-mail: renyip@ouc.edu.cn

  • 中图分类号: S932.4

Effect of environmental factors on fish distribution based on GAM and GWR model : A case study of Sillago sihama in the Shandong coastal waters

  • 摘要: 多鳞鱚(Sillago sihama)是山东近海重要的渔业种类之一。本研究根据2016年秋季(10月)在山东近海开展渔业资源底拖网调查取得的数据,分析该海域多鳞鱚的空间分布特征,并运用广义可加模型(GAM)和地理加权回归(GWR)模型探究影响其分布的因素及其与环境因子的非线性和空间非平稳性关系。GAM拟合结果显示,影响秋季多鳞鱚分布的环境因子主要有水深、底层水温和底层盐度,其偏差解释率水深最大,为23.50%。GWR模型拟合结果显示,多鳞鱚分布与水深和底层水温之间存在空间非平稳性关系。水深与多鳞鱚相对资源量呈负相关关系,底层水温与多鳞鱚相对资源量呈正相关关系。赤池信息准则和决定系数R2指标对比结果显示,GWR模型的表现优于GAM,在渔业生态数据分析中表现出较好的发展潜力。本研究为今后开展渔业生物空间分布提供了一种新的方法。
  • 图  1  山东近海渔业资源底拖网调查区域

    Fig.  1  Bottom trawl survey areas for fishery resources in the Shandong coastal waters

    图  2  山东近海多鳞鱚的空间分布

    Fig.  2  Spatial distribution of Sillago sihama in the Shandong coastal waters

    图  3  GAM中不同环境因子对山东近海多鳞鱚相对资源量的影响

    Fig.  3  Effects of different environmental variables on relative abundance of Sillago sihama in the Shandong coastal waters in GAM

    图  4  最优GWR模型决定系数R2的空间分布

    Fig.  4  Spatial distribution of R2 values in the optimal GWR model

    图  5  GWR模型中水深(a)、底层海水温度(b)局部回归系数值的空间分布

    空心圆表示未捕获到多鳞鱚站位;蓝色实心圆表示与环境变量呈负相关关系;红色实心圆表示与环境变量呈正相关关系

    Fig.  5  Spatial distribution of regression coefficient values for depth (a) and SBT (b) in the GWR model

    Small open circles indicate absence of Sillago sihama; blue filled circles indicate a negative correlation with the environmental variable; red filled circles indicate a positive correlation with the environmental variables

    表  1  GAM变量筛选及影响因子的参数分析

    Tab.  1  The variable screening process for GAM and parameters analysis

    模型因子AIC偏差解释率/%R2
    Depth177.05823.500.222
    SBS201.34211.000.096
    SBT199.25213.100.113
    Depth+SBS170.59628.700.264
    Depth+SBT169.36829.800.272
    Depth+SBT+SBS168.11331.870.285
    下载: 导出CSV

    表  2  多鳞鱚空间分布的环境影响因子的GWR模型筛选过程

    Tab.  2  Forward-selection procedure of GWR model for environmental influencing factors on spatial distribution of Sillago sihama

    模型AICR2带宽
    Depth126.4780.4460.099
    SBS139.0110.3870.154
    SBT135.9110.4050.104
    Depth+SBS130.2910.4260.167
    Depth+SBT126.2960.4490.113
    SBS+ SBT137.7330.3980.163
    Depth+SBS+SBT129.6130.4360.167
    下载: 导出CSV

    表  3  最优GWR模型局部参数估计汇总统计

    Tab.  3  Summary statistics of the local parameter estimates for the optimal GWR model

    变量最小值1/4分位数中位数3/4分位数最大值p正相关/%负相关/%
    截距−13.722−3.9011.2651.8147.805<0.0132.867.2
    底层海水温度−0.349−0.066−0.0390.2490.755<0.0168.831.2
    水深−0.028−0.015−0.009−0.0050.029<0.0123.476.6
    下载: 导出CSV
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