Construction of CPUE standardization model and its simulation testing for chub mackerel (Scomber japonicus) in the Northwest Pacific Ocean

Abstract

Standardized catch per unit effort (CPUE) data not only yield precise and biologically meaningful abundance indices but also provide crucial insights into the spatio-temporal distribution of fisheries resources, critical for their sustainable utilization and management. In this study, we integrated chub mackerel fishery statistics from the Northwest Pacific Ocean (NPO) and marine remote sensing environmental data, and constructed CPUE standardization models for chub mackerel based on the generalized linear mixed model (GLMM) and spatio-temporal GLMM (Vector Autoregressive Spatio-Temporal, VAST) and evaluated their performance. GLMM is a statistical technique extending GLM with random effects for complex datasets with non-independent observations and correlations or hierarchies. The VAST model is a statistical model for multivariate time series data with observations at different spatial locations, displaying both temporal autocorrelation and spatial dependence. Additionally, influence analysis and simulation testing were employed to quantify the effects of explanatory variables on the differences between nominal CPUE and standardized CPUE, and to assess the estimation accuracy of different models in CPUE standardization, respectively. Finally, the distribution pattern of chub mackerel in the NPO was analyzed by estimating centers of gravity (COGs). The results indicated that: 1) The GLMM model containing all explanatory variables was considered the best model with the smallest conditional Akaike Information Criterion (CAIC), while the VAST model, with only the covariate SST, performed the best. 2) Interactions of year and spatial effects in GLMM and the spatio-temporal variable in VAST, exerted a notable influence on annual standardized CPUE and should be accounted for in the model. 3) Based on simulation testing results, the VAST model exhibited lower model error (root mean square error, RMSE) and model bias, outperforming the GLMM model in CPUE standardization. 4) From 2014 to 2018, chub mackerel exhibited high biomass density widely distributed throughout almost the entire NPO, which shifted to the central-north region from 2019 to 2021. There was no significant northing and easting shift in COGs for the population (P > 0.05). The findings of this research enhance our understanding of the variations in resource abundance indices and spatio-temporal distribution patterns of chub mackerel in the NPO.