Developing spatio-temporal models using multiple data types for evaluating population trends and habitat usage

Spatio-temporal models have become key tools for evaluating population trends and habitat usage. We developed a spatio-temporal modelling framework employing a combination of encounter/non-encounter, count, and biomass data collected by different monitoring programs (“combined data”). The three data types are predicted using a computationally efficient approximation to a compound Poisson-gamma process. We fitted spatio-temporal models to combined data for Gulf of Mexico (GOM) red snapper (Lutjanus campechanus) for 2006–2014. These spatio-temporal models provided insights into GOM red snapper spatial distribution patterns, which we corroborated by comparing to past predictions generated using only encounter/non-encounter data. However, relying on biomass and count data in addition to encounter/non-encounter data also allowed us to reconstruct biomass trends for GOM red snapper and to examine patterns of distribution shifts and range expansion/contraction for this population for the first time. Moreover, combining multiple data types improved the precision of reconstructed population trends and some variables quantifying habitat usage. Finally, scenarios and simulation experiments conditioned upon red snapper data showed that the improvement in fitting to combined data is greater when biomass data for the study population are lacking for an entire subregion and, to a lesser extent, for an entire time period (e.g. in early years).