西北太平洋秋刀鱼CPUE标准化研究

秋刀鱼(Cololabis saira)是西北太平洋海域重要的渔业种类之一，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，本研究利用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、海表温度梯度、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。结果显示，根据BIC准则，在GLM模型结果中，年份、月份、经度、纬度、海表面温度、海表面高度、海表温度梯度及年份与月份对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，交互项月份与经度和月份与纬度也对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。

Study on CPUE Standardization of Chinese Pacific Saury (Cololabis saira) Fishery in the Northwest Pacific Ocean

Pacific saury (Cololabis saira) is an important high-seas fishery resource in the Northwest Pacific Ocean for the Chinese mainland. The species is widely distributed in the international waters of the Northwestern Pacific Ocean ranging from the subarctic to subtropical regions. With a long-distance and large-scale migratory route, sauries experience extremely complicated oceanographic and climatic conditions throughout their entire lifecycle. They are 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 the countries of Japan, Russia, South Korea, Taiwan Province, and mainland China. China began Pacific saury fishing in the high seas in 2003, and it has since become one of the most important fisheries for China. Owing to increasingly commercial, cultural, bioeconomic, and ecological values, saury has been listed among the priority species by North Pacific Fisheries Commission (NPFC). Catch per unit effort (CPUE) is an important relative index and dataset of fish abundance commonly used in fisheries stock assessment. Reliable and accurate CPUE plays a significant role 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 fisheries in the Northwest Pacific Ocean from 2003 to 2017 (excluding data from Taipei of China) 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 five-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 five-fold cross-validation results showed that the best GAM performed better than the best GLM. Therefore, we recommend a 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 for standardizing Pacific saury CPUE data and improve the quality of future stock assessment for Pacific saury.