Xavier Barber (University Miguel Hernández of Elche), David Conesa, Antonio López-Quílez (Department of Statistics and Operations Research, University of Valencia), Joaquín Martínez-Minaya (Data Science Area, Basque Center for Applied Mathematics (BCAM)), Iosu Paradinas (Scottish Ocean’s Institute, University of St Andrews) and Maria Grazia Pennino (Centro Oceanográfico de Vigo, Instituto Español de Oceanografía)
Abstract: In this work, we discuss the use of a methodological approach for modelling spatial relationships among species by means of a Bayesian spatial coregionalized model. Inference and prediction is performed using the integrated nested Laplace approximation methodology to reduce the computational burden. We illustrate the performance of the coregionalized model in species interaction scenarios using both simulated and real data. The simulation demonstrates the better predictive performance of the coregionalized model with respect to the univariate models. The case study focus on the spatial distribution of a prey species, the European anchovy (Engraulis encrasicolus), and one of its predator species, the European hake (Merluccius merluccius), in the Mediterranean sea. The results indicate that European hake and anchovy are positively associated, resulting in improved model predictions using the coregionalized model.