Evaluation of the influence of spatial treatments on catch-per-unit-effort standardization: A fishery application and simulation study of Pacific saury in the Northwestern Pacific Ocean

Fishery-dependent catch-per-unit-effort (CPUE) data often exhibit spatial heterogeneity over space and time, which means that the spatial treatment in statistical models used to standardize CPUE is critically important. We evaluated several spatial treatments to standardize CPUE data using Generalized Linear Mixed Models (GLMMs). Results include a real-world application and a simulation based on the Taiwanese stick-held dip net fishery for Pacific saury in the Northwestern Pacific Ocean. We compared the performance of three spatially stratified approaches in GLMMs, (i) Ad hoc; (ii) Binary (binary recursive area partitioning based on model selection criteria); and (iii) Spatial clustering (partitioning of grids into discrete strata based on the spatial proximity and average CPUE in each grid), to a spatio-temporal GLMM (VAST). An influence analysis was constructed to quantify discrepancies between unstandardized and standardized indices that assisted in identifying the annual influence of explanatory variables in GLMMs. We developed a simulation to corroborate the results from the case study and evaluated the four spatial treatments using data generated from two contrasted, random and prefer-ential, sampling scenarios. Results from the real-world application indicated that VAST was statistically superior to the other approaches, based on conditional deviance explained, conditional Akaike Information Criterion, and five-fold cross-validations. The influence analysis indicated that the interaction of year and spatial effect or spatio-temporal variable had a major influence on the standardized CPUE. Both simulation scenarios showed that VAST performed the best, with the lowest model error (measured by root mean square error) and bias, for estimating relative abundance indices. Although the spatial clustering approach created a flexible shape for the area strata, the simulation results under preferential samplings showed that clustering with a stronger emphasis placed on average CPUE could lead to bias in estimated abundance indices. However, spatial clustering that balanced average CPUE with spatial proximity could be a reasonable alternative if it is not possible to apply a spatio-temporal approach. The importance of conducting influence analysis and the greater performance of a spatio-temporal approach are highlighted.