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
We evaluated several spatial treatments to standardize CPUE data using Generalized Linear Mixed Models (GLMMs).
Fishery-dependent catch-per-unit-effort (CPUE) data often exhibit spatial diversity 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.
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.
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.
Hsu J, Change Y, Ducharme-Barth ND. 2022. 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. Fisheries Research, 255: 106440. https://doi.org/10.1016/j.fishres.2022.106440.