Incorporating Biotic Information in Species Distribution Models: A Coregionalized Approach
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....
| Autores: | , , , , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión aceptada para publicación |
| Fecha de publicación: | 2021 |
| País: | España |
| Institución: | Consejo Superior de Investigaciones Científicas (CSIC) |
| Repositorio: | DIGITAL.CSIC. Repositorio Institucional del CSIC |
| OAI Identifier: | oai:digital.csic.es:10261/326441 |
| Acceso en línea: | http://hdl.handle.net/10261/326441 |
| Access Level: | acceso abierto |
| Palabra clave: | Pesquerías Bayesian hierarchical models Centro Oceanográfico de Murcia coregionalized models INLA species interaction fish mathematics distribution models species |
| Sumario: | 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. |
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