Incorporating Biotic Information in Species Distribution Models: A Coregionalized Approach

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.

Modelling the distribution of marine fishery resources: Where are we?

“…Biotic interactive variables (or bionomic variables, Soberón & Nakamura, 2009) appeared in only 13%. The use of this kind of variable is rare, but distribution estimates of species at broad scales may be more accurate if calibration of ENMs includes relevant biotic variables (Araújo et al, 2014; Barber et al, 2021; Gherghel et al, 2018; Stephenson et al, 2022). However, it is important to highlight that the inclusion of biotic variables in modelling must consider the complexity of the bidirectional effect of variables.…”