The biological status of many commercially-exploited fishes remains unknown, mostly due to a lack of data necessary for their assessment. Investigating the spatio-temporal dynamics of such species can lead to new insights into population processes, and foster a path towards improved spatial management decisions. Here, we focused on striped red mullet (Mullus surmuletus), a widespread, yet data-limited species of high commercial importance. Aiming to quantify range dynamics in this data-poor scenario, we combined fishery-dependent and -independent datasets through a series of Bayesian mixed-effects models designed to capture monthly and seasonal occurrence patterns near the species’ northern range limit across 20 years. Combining multiple datasets allowed us to cover the entire distribution of the northern population of Mullus surmuletus, exploring dynamics at different spatio-temporal scales, and identifying key environmental drivers (i.e. sea surface temperature, salinity) that shape occurrence patterns. Our results demonstrate that even when process and/or observation uncertainty is high, or when data is sparse, by combining multiple datasets within a hierarchical modelling framework accurate and useful spatial predictions can still be made.