Application of finite mixture models to catch rate standardization better represents data distribution and fleet behavior

Abstract

Catch-per-unit-effort (CPUE) data are routinely standardized to account for factors that influence catch rates that are not related to resource abundance. Despite improvement in the methods applied to CPUE standardization, for many datasets model diagnostics can still indicate poor conformity to modeling assumptions, imprecision and unexplained fishing behaviors. In this study we examine catch rate data of an Irish mid-water pair trawl fleet targeting albacore tuna (Thunnus alalunga) in the North East Atlantic. A fleet strategy of searching and congregating on fish aggregations combined with negative skew in model residuals suggest that multiple components exist within the dataset. Assuming up to five components, finite mixture models are applied and compared using the Bayesian information criterion. The two component model most consistently explained observed distributions in fishing behaviors and catch rates. Finite mixture modeling markedly improved conformity to modeling assumptions, resulting in substantial improvement in the precision of specific components used in CPUE standardization and reduced inter-annual variability of the catch rate trend. These methods may facilitate investigations of technological creep but also raise questions on how best to use the results in assessment.