Fishery data is one of the most accessible sources of information currently used for ecological studies and stock assessments. Unlike scientific surveys that are usually restricted to a given time of the year, fisheries dependent data is almost continuously available in time. Moreover, the information collected from the fisheries is less expensive and time consuming. However, for use as a relative abundance index, fishery-dependent data requires standardization as catch-per-unit-effort (CPUE) in order to remove the impact of vessel-specific differences and fishing behavior. Understanding the key factors that influence the population dynamics of fish species implies assessment of their spatial distribution and seasonal habitat selection but, spatio-temporal dependence issues are often not explicitly included in the modeling process. This study standardizes sardine fishery-dependent data obtained from the west coast of Portugal as CPUE by means of a Bayesian hierarchical spatio-temporal model using integrated nested Laplace approximation (INLA). This is one of the first studies of the region to provide maps of the relative abundance of this species for all months of the year. The best model included length of the vessel, vessel ID, month, year and location (latitude, longitude), while none of the five environmental covariates (Chl-a, SST, bathymetry, current velocity and direction) were relevant. In terms of spatial distribution, sardines were more abundant in the northern area, especially during the last quarter of the year. The applied methodology has contributed to improve our knowledge of European sardine distribution throughout the year, providing accurate predictive maps and insights into the standardization process of fishery-dependent data that could also be applied to other fish species and areas
