| Hua Chuanxiang,Zhu Qingcheng,Shi Yongchuang,Liu Yu. 2019. Comparative analysis of CPUE standardization of Chinese Pacific saury (Cololabis saira) fishery based on GLM and GAM. Acta Oceanologica Sinica, 38(10):100－110 |
| Comparative analysis of CPUE standardization of Chinese Pacific saury (Cololabis saira) fishery based on GLM and GAM |
| 基于GAM和GLM模型的西北太平洋秋刀鱼CPUE标准化比较研究 |
| Received:November 02, 2018 |
| DOI：10.1007/s13131-019-1486-3 |
| Key words:Cololabis saira|CPUE standardization|generalized linear model|generalized additive model |
| 中文关键词: 秋刀鱼|CPUE标准化|GLM模型|GAM模型 |
| 基金项目:The National Sci-Tech Support Plan "Fishing Technology and New Resources in Oceanic Fisheries" under contract No. 2013BAD13B05. |
| Author Name | Affiliation | E-mail | | Hua Chuanxiang | College of Marine Sciences, Shanghai Ocean University, Shanghai 201306, China
National Engineering Research Center for Pelagic Fishery, Shanghai 201306, China | | | Zhu Qingcheng | College of Marine Sciences, Shanghai Ocean University, Shanghai 201306, China
National Engineering Research Center for Pelagic Fishery, Shanghai 201306, China | qczhu@shou.edu.cn | | Shi Yongchuang | College of Marine Sciences, Shanghai Ocean University, Shanghai 201306, China | | | Liu Yu | College of Marine Sciences, Shanghai Ocean University, Shanghai 201306, China
National Engineering Research Center for Pelagic Fishery, Shanghai 201306, China | |
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| Abstract: |
| Pacific saury is an important high-seas fishery resource in the Northwest Pacific Ocean for the Chinese Mainland. 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 21.57% of the total model-explained deviance, while GAMs accounted for 38.95%. 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. |
| 中文摘要: |
| 秋刀鱼（Cololabis saira）是中国在西北太平洋海域的重要的捕捞对象之一，单位捕捞努力量渔获量（CPUE） 标准化是开展其资源评估研究的重要内容，许多统计模型被运用到CPUE标准化研究中。本文根据2003-2017年中国大陆在西北太平洋海域的秋刀鱼生产统计资料，结合卫星遥感获得的海洋环境数据如：海表面温度、海表面高度以及海温梯度等，基于广义线性模型（general linear model，GLM） 和广义加性模型（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，并对两种模型的结果进行了对比分析研究。通过贝叶斯信息准则选择最佳GLM和GAM模型，使用解释偏差和5-fold交差验证来对比两个模型结果。GLM模型的最佳模型对CPUE偏差的解释率为21.57%，GAM的最佳模型对CPUE偏差的解释率为38.95%。通过5-fold交差验证分析发现，GAM模型标准化结果较优于GLM模型，因此，认为GAM模型更适合于西北太平洋秋刀鱼渔业CPUE标准化。 |
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