基于GAM和GWR模型分析环境因子对鱼类分布的影响

简盈; 张云雷; 宋业晖; 张崇良; 纪毓鹏; 任一平

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

简盈^{1, 3,},
张云雷^{1, 3},
宋业晖^{1, 3},
张崇良^{1, 3},
纪毓鹏^{1, 3},
任一平^{1, 2, 3, ,}

1.
中国海洋大学水产学院，山东青岛 266003
2.
青岛海洋科学与技术试点国家实验室海洋渔业科学与食物产出过程功能实验室，山东青岛 266237
3.
海州湾渔业生态系统教育部野外科学观测研究站，山东青岛 266003

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
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- 文章访问数: 398
- HTML全文浏览量: 95
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- 被引次数: 0
出版历程
- 收稿日期: 2021-12-21
- 修回日期: 2022-03-23
- 网络出版日期: 2022-07-01
- 刊出日期: 2022-07-01

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

Jian Ying^{1, 3
,},
Zhang Yunlei^{1, 3},
Song Yehui^{1, 3},
Zhang Chongliang^{1, 3},
Ji Yupeng^{1, 3},
Ren Yiping^{1, 2, 3
, ,}

1.
Fisheries College, Ocean University of China, Qingdao 266003, China
2.
Laboratory for Marine Fisheries Science and Food Production Processes, Pilot National Laboratory for Marine Science and Technology (Qingdao), Qingdao 266237, China
3.
Field Observation and Research Station of Haizhou Bay Fishery Ecosystem, Ministry of Education, Qingdao 266003, China

摘要

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，水深的偏差解释率最大，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。
- 多鳞鱚 /
- 地理加权回归模型 /
- 广义可加模型 /
- 空间非平稳性 /
- 空间分布
Abstract: Sillago sihama is an important fishery species in China and plays an important role in the marine ecosystem of the Yellow Sea. Species distribution models can be used to predict its distribution by establishing the relationships between its abundance and environmental factors. However, due to high mobility of the marine animals, the relationship between their distribution and environmental factors is often nonlinear and variable with spatial locations. Based on data collected from bottom trawl survey in the Shandong coastal waters in autumn of 2016, both generalized additive model (GAM) and geographically weighted regression (GWR) model were used to analyze nonlinear and spatial nonstationary relationships between distribution of the species and environmental factors, and results from the two models were compared. Results from the GAM indicated that the main environmental factors were depth, sea bottom temperature and salinity, and depth had the largest deviance explained (23.50%). GWR model results showed that there were spatial non-stationary relationships between distribution of the species and depth and sea bottom temperature. GWR model results indicated a negative correlation between depth and biomass of the species, and a positive correlation between sea bottom temperature and biomass of species. Regarding performance of the models, GWR model showed advantages over GAM in identifying influencing factors and predicting distribution, and GWR model was recommended for use in similar applications.
- Sillago sihama /
- geographically weighted regression model /
- generalized additive model /
- spatial nonstationarity /
- spatial distribution

HTML全文

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

下载: 全尺寸图片幻灯片

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

下载: 全尺寸图片幻灯片

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

下载: 全尺寸图片幻灯片

图 4 最优GWR模型决定系数（R²）的空间分布

Fig. 4 Spatial distribution of R²values in the optimal GWR model

下载: 全尺寸图片幻灯片

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 sea bottom temperature (b) in the GWR model

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

下载: 全尺寸图片幻灯片

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值	偏差解释率/%	R²
Depth	177.058	23.50	0.222
SBS	201.342	11.00	0.096
SBT	199.252	13.10	0.113
Depth+SBS	170.596	28.70	0.264
Depth+SBT	169.368	29.80	0.272
Depth+SBT+SBS	168.113	31.87	0.285

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	AIC值	R²	带宽
Depth	126.478	0.446	0.099
SBS	139.011	0.387	0.154
SBT	135.911	0.405	0.104
Depth+SBS	130.291	0.426	0.167
Depth+SBT	126.296	0.449	0.113
SBS+ SBT	137.733	0.398	0.163
Depth+SBS+SBT	129.613	0.436	0.167

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	−3.901	1.265	1.814	7.805	<0.01	32.8	67.2
底层海水温度	−0.349	−0.066	−0.039	0.249	0.755	<0.01	68.8	31.2
水深	−0.028	−0.015	−0.009	−0.005	0.029	<0.01	23.4	76.6

下载: 导出CSV

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