文章摘要
基于GLM和GAM模型的西北太平洋秋刀鱼CPUE标准化
Comparative analysis of CPUE standardization of Chinese Pacific saury (Cololabis saira) fishery based on GLM and GAM
投稿时间:2019-04-07  修订日期:2019-06-05
DOI:
中文关键词: 秋刀鱼  广义可加模型  广义线性模型  CPUE标准化  西北太平洋
英文关键词: Cololabis saira  generalized linear model  generalized additive model  CPUE standardization  northwest Pacific Ocean
基金项目:
作者单位E-mail
石永闯 上海海洋大学海洋科学学院 syc13052326091@163.com 
朱清澄 上海海洋大学海洋科学学院 qczhu@shou.edu.cn 
黄硕琳 上海海洋大学海洋科学学院  
花传祥 上海海洋大学海洋科学学院  
冯慧丽 上海海洋大学海洋科学学院  
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中文摘要:
      秋刀鱼(Cololabis saira)是西北太平洋海域重要的渔业之一,其资源评估工作已成为热点问题,单位捕捞努力量渔获量( CPUE) 标准化可以为开展有效的资源评估研究提供科学依据。为此,本文利用2003-2017年中国大陆西北太平洋秋刀鱼渔业生产统计资料,结合卫星遥感获得的海洋环境数据如:海表面温度、海表温梯度、海表面高度等,基于广义线性模型( general linear model,GLM ) 和广义可加模型( generalized additive model,GAM) 对中国大陆西北太平洋秋刀鱼渔业进行CPUE 标准化。结果表明:根据BIC准则在GLM模型结果中,年、月、经度、纬度、海表面温度、海表面高度、海温梯度以及年与月对CPUE具有显著影响并组成了GLM模型的最佳模型,对CPUE偏差的解释率为52.47%;在GAM模型结果中,除上述8个影响变量外,交互项月与经度和月与纬度也对CPUE影响较大,GAM的最佳模型对CPUE偏差的解释率为61.9%。通过5-fold交叉验证分析发现,GAM模型标准化结果较优于GLM模型,更适合于西北太平洋秋刀鱼渔业CPUE标准化。
英文摘要:
      Pacific saury is an important high-seas fishery resource in the Northwest Pacific Ocean for the Chinese Mainland. The specie is widely distributed in the international waters of the Northwestern Pacific Ocean range from subarctic to subtropical region. With long-distance and large-scale migratory route, saury experiences extremely complicated oceanographic and climatic conditions throughout the whole life that is known to pass northwards through the Kuroshio-Oyashio Transition Zone (TZ), and then return southwards to the coastal waters of Japan in winter. The species is harvested primarily by Japan, Russia, South Korea, Taiwan province, and mainland China. China began Pacific saury fishing in the high seas in 2003 and has become one of the most important fisheries for China since then. Owing to the increasingly commercial, cultural, bioeconomic and ecological values, saury has been listed among the priority species by NPFC. CPUE is commonly used as an important relative index of fish abundance and is one of the most important dataset used in fisheries stock assessment. Reliable and accurate catch per unit effort (CPUE) plays a significant rule in Pacific saury stock assessment. Many statistical models have been used in the previous CPUE standardization research. Here, we compare the performance of Generalized Linear Models (GLMs) and Generalized Additive Models (GAMs) using CPUE data collected from Chinese saury fishery in the Northwest Pacific Ocean from 2003 to 2017 (excluding data from Chinese Taipei), and evaluate the influence of spatial, temporal, environmental variables and vessel length on CPUE. Optimal GLM/GAM models were selected using the Bayesian information criterion (BIC). Explained Deviance and 5-fold bootstrap cross-validation results were used to compare the performance of the two model types. Fitted GLMs accounted for 52.47% of the total model-explained Deviance, while GAMs accounted for 61.9%. Predictive performance metrics and 5-fold cross-validation results showed that the best GAM performed better than the best GLM. Therefore, we recommend GAM as the preferred model for standardizing CPUE of Pacific saury in the Northwest Pacific Ocean. The goal of this study was to identify the best method to the standardization of Pacific saury CPUE data and improve the quality of future stock assessment for Pacific saury.
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