Bayesian spatio‐temporal CPUE standardization: Case study of European sardine (Sardina pilchardus) along the western coast of Portugal

Abstract

AbstractUnderstanding the key factors influencing population dynamics of fish stocks requires knowledge of their spatial distribution and seasonal habitat selection, but these spatio‐temporal dynamics are often not explicitly included in ecological studies and stock assessment models. This study standardized the data of sardine fishery‐dependent catch‐per‐unit‐ effort (CPUE) from the west coast of Portugal using Bayesian hierarchical spatio‐temporal models (BHSTM) with the integrated nested Laplace approximation (INLA). Sardine CPUE was best explained by length of the vessel, vessel ID, month, year, and location (latitude, longitude). In terms of spatio‐temporal distribution, sardine biomass prediction maps showed a constant pattern that changed every quarter of the year. In addition, sardine CPUE index showed a cyclical trend along the year with minimum values in July and maximum peak in November. This approach provided insights on variables and corresponding modelling effects that may be relevant in spatio‐temporal fishery‐dependent data standardization, and that could be applied to other fish species and areas.