Incorporating Biotic Information in Species Distribution Models: A Coregionalized Approach

Xavier Barber, David Conesa, Antonio López-Quílez, Joaquín Martínez-Minaya, Iosu Paradinas and Maria Grazia Pennino
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Xavier Barber: Center of Operations Research (CIO), Universidad Miguel Hernández, 03202 Elche, Spain
David Conesa: Department of Statistics and Operations Research, University of Valencia, 46100 Valencia, Spain
Antonio López-Quílez: Department of Statistics and Operations Research, University of Valencia, 46100 Valencia, Spain
Joaquín Martínez-Minaya: Data Science Area, Basque Center for Applied Mathematics (BCAM), 14 E48009 Bilbao, Spain
Iosu Paradinas: Scottish Ocean’s Institute, University of St Andrews, St Andrews KY16 9AJ, UK
Maria Grazia Pennino: Centro Oceanográfico de Vigo, Instituto Español de Oceanografía, Subida a Radio Faro, 50-52, 36390 Vigo, Spain

Mathematics, 2021, vol. 9, issue 4, 1-12

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.

Keywords: Bayesian hierarchical models; coregionalized models; fisheries; INLA; predation; SPDE; species interaction (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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