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.
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