Model selection between traditional and popular methods for standardizing catch rates of target species: A case study of Japanese Spanish mackerel in the gillnet fishery

Abstract

Improving existing catch per unit effort (CPUE) models for construction of a fishery abundance index is important to fish stock assessment and management. CPUE standardization research is a rapidly developing field, and many statistical models have been used, including generalized linear models (GLMs), generalized additive models (GAMs), regression trees (RTs) and artificial neural networks (ANNs). However, the popular and influential methods, random forests (RFs) and support vector machines (SVMs) have not been used in this field. We evaluate the performance of six candidate methods (GLMs, GAMs, RTs, RFs, ANNs and SVMs) using gillnet data for Japanese Spanish mackerel (Scomberomorus niphonius) collected by a fishery-dependent survey (National Basic Research Program of China, NBRPC) in the south of the Yellow Sea from 2006 to 2012. Predictive performance metrics and Regression Error Characteristic (REC) curves computed by 10-fold cross-validation results showed that the SVM provided the best performance among the six candidate models and slightly improved the prediction accuracies compared to RF. However, the traditional methods GLM and GAM were inferior to the other four nonlinear statistical models (RTs, ANNs, RFs and SVMs). In general, RFs and SVMs should be considered as potential statistical methods for CPUE standardization. Model performance was affected by several factors, including data structure and model construction. Therefore, further research should focus these factors to improve model functionality.