基于随机森林模型的秘鲁外海茎柔鱼资源丰度与环境因子关系研究

彭伟图; 陈峰; 李建雄; 赵艺翔; 董钇江; 戴乾; 徐汉祥; 朱文斌

doi:10.12284/hyxb2024078

基于随机森林模型的秘鲁外海茎柔鱼资源丰度与环境因子关系研究

doi: 10.12284/hyxb2024078

彭伟图^{1, 2,},
陈峰²,
李建雄^{1, 2},
赵艺翔^{1, 2},
董钇江^{1, 2},
戴乾²,
徐汉祥^{1, 2},
朱文斌^2, ,

1.
浙江海洋大学海洋与渔业研究所，浙江舟山 316021
2.
浙江省水产研究所/农村农业部重点渔场渔业资源科学观测实验站/浙江省海洋渔业资源可持续利用技术研究重点实验室，浙江舟山 316021

基金项目: 国家重点研发计划项目（2019YFD0901505）；浙江省重点研发计划项目（2018C02026）；2022−2023浙江省远洋渔业资源常规检测（2023HZ024）

详细信息

作者简介:
彭伟图（1997—），男，湖南省娄底市人，主要研究领域为渔业资源。E-mail：1367101299@qq.com

通讯作者:
朱文斌（1982—），男，浙江省义乌市人，正高级工程师，主要从事渔业资源与管理研究。E-mail：foolse@126.com

中图分类号: P714⁺.5
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- 文章访问数: 175
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- 被引次数: 0
出版历程
- 收稿日期: 2023-12-05
- 修回日期: 2024-04-01
- 网络出版日期: 2024-06-17
- 刊出日期: 2024-07-01

Relationship between abundance of Dosidicus gigas and environment factors in the offshore waters of Peru based on random forest model

Peng Weitu^{1, 2
,},
Chen Feng²,
Li Jianxiong^{1, 2},
Zhao Yixiang^{1, 2},
Dong Yijiang^{1, 2},
Dai Qian²,
Xu Hanxiang^{1, 2},
Zhu Wenbin^{2
, ,}

1.
Marine and Fishery Institute of Zhejiang Ocean University, Zhoushan 316021, China
2.
Zhejiang Marine Fisheries Research Institute/Scientific Observing and Experimental Station of Fishery Resources for Key Fishing Grounds, Ministry of Agriculture and Rural Affairs/Key Laboratory of Sustainable Utilization of Technology Research for Fishery Resources of Zhejiang Province, Zhoushan 316021, China

摘要

摘要: 秘鲁外海茎柔鱼是大洋性经济鱼类，其资源丰度受环境因素变化的影响较大。研究基于2018−2021年9−12月秘鲁外海茎柔鱼的渔捞日志数据，并结合卫星遥感获取海表温度（SST）、海表盐度（SSS）、海表高度（SSH）和叶绿素a浓度（Chl a）4个环境数据，运用ArcGIS和随机森林模型分析秘鲁外海茎柔鱼资源丰度与环境因子之间的关系。研究结果表明，2018−2021年9−12月茎柔鱼渔场重心分布范围集中于13°～21°S，76°～87°W海域，9−12月渔场重心呈现西北向东南方向移动趋势。随机森林模型分析结果显示，各月环境因子对秘鲁外海茎柔鱼资源丰度和分布的影响存在一定的差异性，最适SST范围16.3～18.5℃，最适SSS范围35.1～35.4，最适SSH范围0.55～0.60 m，最适Chl a浓度范围0.18～0.46 mg/m³，通过随机森林模型的CPUE预测值与名义CPUE随时间变化趋势基本一致，表明随机森林模型适用于分析秘鲁外海茎柔鱼与环境因子之间的关系。本研究对了解该海域茎柔鱼资源变动规律、指导茎柔鱼生产具有重要意义。
- 茎柔鱼 /
- 随机森林模型 /
- 资源丰度 /
- 环境因子
Abstract: Dosidicus gigas is an oceanic economic fish in the offshore waters of Peru, and its abundance is greatly affected by environmental factors. This study obtained the abundance of D. gigas through fishing logs from September to December in 2018−2021, combined with environmental factors, including surface temperature (SST), sea surface salinity (SSS), sea surface height (SSH) and chlorophyll a concentration (Chl a) acquired from satellite remote sensing. Random forest model and ArcGIS were applied to analyze the correlations between the abundance of D. gigas and environmental factors. Results showed that the distribution of the center of gravity of fishing ground was concentrated in the range of 13°−21°S, 76°−87°W during 2018−2021, and the center of gravity shifted from northwest to the southeast from September to December. The results of random forest model analysis showed that, the impact of environmental factors on the abundance and distribution of D. gigas in the offshore waters of Peru varied among different months. The optimal SST ranged from 16.3℃ to 18.5℃, The optimal SSS ranged from 35.1 to 35.4, the optimal SSH ranged from 0.55 m to 0.60 m, the optimal Chla concentration ranged from 0.18 mg/m³ to 0.46 mg/m³. The predicted CPUE values derived from the random forest model were generally consistent with the nominal CPUE distribution, indicating the suitability of the random forest model for analyzing the relationship between D. gigas and environmental factors in the offshore waters of Peru. This study is of great significance for understanding the resource dynamics of D. gigas and guiding its production.
- Dosidicus gigas /
- random forest model /
- abundance /
- environmental factors

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图 1 2018−2021年9−12月茎柔鱼CPUE日平均分布

Fig. 1 The average daily distribution of CPUE of Dosidicus gigas from September to December in 2018−2021

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图 2 9−12月秘鲁外海环境因子月平均值空间分布

Fig. 2 The spatial distribution map of monthly average environmental factors in offshore waters of Peru from September to December

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图 3 各因子之间的相关系数

Fig. 3 Correlation coefficient between factors

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图 4 9−12月模型交叉验证结果泰勒图

Fig. 4 Taylor plot of model cross-validation results from September to December

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图 5 9−12月茎柔鱼资源核密度分布

Fig. 5 Kernel density distribution of Dosidicus gigas resources from September to December

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图 6 随机森林模型中环境因子贡献率

Fig. 6 Contribution rate of environmental factors in Random forest model

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图 7 基于随机森林模型分析环境因子对茎柔鱼资源量的影响

Fig. 7 Effects of environmental factors based on factor Random forest model on the biomass of Dosidicus gigas

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图 8 2018−2021年9−12月秘鲁外海茎柔鱼名义CPUE与CPUE预测值

Fig. 8 Nominal and predicted CPUE for Dosidicus gigas in offshore waters of Peru from September to December 2018 to 2021

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图 9 秘鲁外海洋流及茎柔鱼洄游路线

Fig. 9 Currents distribution of offshore waters of Peru and the migration routes of Dosidicus gigas

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