| WANG Jintao,CHEN Xinjun,CHEN Yong.Projecting distributions of Argentine shortfin squid (Illex argentinus) in the Southwest Atlantic using a complex integrated model[J].Acta Oceanologica Sinica,2018,37(8):31-37 |
| 基于复合模型的西南大西洋阿根廷滑柔鱼（Illex argentinus）时空分布研究 |
| Projecting distributions of Argentine shortfin squid (Illex argentinus) in the Southwest Atlantic using a complex integrated model |
| 投稿时间：2018-03-07 修订日期：2018-04-24 |
| DOI：10.1007/s13131-018-1231-3 |
| 中文关键词: 阿根廷滑柔鱼 资源丰度 遥感数据 西南大西洋 |
| 英文关键词:Illex argentinus abundance index remote sensing environmental data Southwest Atlantic Ocean |
| 基金项目:The Public Science and Technology Research Funds Projects of Ocean under contract No. 20155014; the National Natural Science Fundation of China under contract No. NSFC31702343. |
| 作者 | 单位 | E-mail | | 汪金涛 | 上海海洋大学海洋科学学院, 上海 201306
海洋科学学院, 缅因大学, 缅因奥罗诺, 04469 | | | 陈新军 | 上海海洋大学海洋科学学院, 上海 201306
农业部大洋渔业开发重点实验室, 上海 201306
国家远洋渔业工程技术研究中心, 上海 201306
大洋渔业资源可持续开发教育部重点实验室, 上海 201306 | xjchen@shou.edu.cn | | 陈勇 | 农业部大洋渔业开发重点实验室, 上海 201306
海洋科学学院, 缅因大学, 缅因奥罗诺, 04469 | |
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| 中文摘要: |
| 本文利用2003-2011年西南大西洋阿根廷滑柔鱼渔业数据和海洋环境数据，包括海表温度（sea surface temperature， SST），海面高度（sea surface height， SSH）和叶绿素浓度（chlorophyll a， Chl a），开发基于广义加性模型（GAM）和神经网络模型（NNM）的复合模型研究滑柔鱼资源时空分布。GAM用于选择关键影响因子，并分析与单位捕捞努力量渔获量（catch per unit effort， CPUE）的关系，NNM用于建立关键影响因子与CPUE之间的预报模型。结果表明：GAM选择的影响因子的偏差解释率为53.8%，空间变量（经度和纬度），环境变量（SST、SSH、Chl a）均匀CPUE之间存在显著相关性。CPUE与SST和SSH之间为非线性关系，与Chl a之间为线性关系。NNM模型的MSE和ARV较低，其精度高且稳定。此复合模型也能够解释解释西南大西洋阿根廷滑柔鱼时空变化趋势和迁徙模式。 |
| 英文摘要: |
| We developed an approach that integrates generalized additive model (GAM) and neural network model (NNM) for projecting the distribution of Argentine shortfin squid (Illex argentinus). The data for this paper was based on commercial fishery data and relevant remote sensing environmental data including sea surface temperature (SST), sea surface height (SSH) and chlorophyll a (Chl a) from January to June during 2003 to 2011. The GAM was used to identify the significant oceanographic variables and establish their relationships with the fishery catch per unit effort (CPUE). The NNM with the GAM identified significant variables as input vectors was used for predicting spatial distribution of CPUE. The GAM was found to explain 53.8% variances for CPUE. The spatial variables (longitude and latitude) and environmental variables (SST, SSH and Chl a) were significant. The CPUE had nonlinear relationship with SST and SSH but a linear relationship with Chl a. The NNM was found to be effective and robust in the projection with low mean square errors (MSE) and average relative variances (ARV). The integrated approach can predict the spatial distribution and explain the migration pattern of Illex argentinus in the Southwest Atlantic Ocean. |
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