Speaker
Description
The study of the spatial distribution of marine species has traditionally relied on single-species distribution models (SDMs), which implicitly assume independence among taxa. However, marine species evolve within structured communities whose dynamics are governed by complex biotic interactions. To account for this complexity, we use joint species distribution models (JSDMs) to model the distribution of endangered species bycaught by tropical tuna purse seine fisheries in the Atlantic Ocean. Unlike classical SDMs, JSDMs allow for the simultaneous modelling of multiple binary variables (species presence-absences) by isolating shared environmental effects from residual biotic interactions. By capturing the residual covariance not explained by abiotic variables alone, this methodology allows us to identify the actual interdependencies between species. This approach is particularly interesting in the context of bycatch of pelagic species, which has many rare, but sensitive, species and relatively low predictive power of classic SDMs. The objective of this presentation is to demonstrate how statistical inference on species dependencies improves the accuracy of spatial predictions and provides a robust tool for managing critical biodiversity areas, where traditional approaches fail to capture the ecological reality of marine communities.
