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Incorporating Biotic Information in Species Distribution Models: A Coregionalized Approach

Author

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  • Xavier Barber

    (Center of Operations Research (CIO), Universidad Miguel Hernández, 03202 Elche, Spain
    Current address: Statistical Modeling Ecology Group (SMEG).
    Current address: Valencia Bayesian Research Group (VaBaR), C/Dr. Moliner 50, Burjassot, 46100 Valencia, Spain.)

  • David Conesa

    (Department of Statistics and Operations Research, University of Valencia, 46100 Valencia, Spain
    Current address: Statistical Modeling Ecology Group (SMEG).
    Current address: Valencia Bayesian Research Group (VaBaR), C/Dr. Moliner 50, Burjassot, 46100 Valencia, Spain.)

  • Antonio López-Quílez

    (Department of Statistics and Operations Research, University of Valencia, 46100 Valencia, Spain
    Current address: Statistical Modeling Ecology Group (SMEG).
    Current address: Valencia Bayesian Research Group (VaBaR), C/Dr. Moliner 50, Burjassot, 46100 Valencia, Spain.)

  • Joaquín Martínez-Minaya

    (Data Science Area, Basque Center for Applied Mathematics (BCAM), 14 E48009 Bilbao, Spain
    Current address: Statistical Modeling Ecology Group (SMEG).
    Current address: Valencia Bayesian Research Group (VaBaR), C/Dr. Moliner 50, Burjassot, 46100 Valencia, Spain.)

  • Iosu Paradinas

    (Scottish Ocean’s Institute, University of St Andrews, St Andrews KY16 9AJ, UK
    Current address: Statistical Modeling Ecology Group (SMEG).
    Current address: Asociación Ipar Perspective, Karabiondo kalea 14, 48600 Sopela, Spain.)

  • Maria Grazia Pennino

    (Centro Oceanográfico de Vigo, Instituto Español de Oceanografía, Subida a Radio Faro, 50-52, 36390 Vigo, Spain
    Current address: Statistical Modeling Ecology Group (SMEG).)

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.

Suggested Citation

  • Xavier Barber & David Conesa & Antonio López-Quílez & Joaquín Martínez-Minaya & Iosu Paradinas & Maria Grazia Pennino, 2021. "Incorporating Biotic Information in Species Distribution Models: A Coregionalized Approach," Mathematics, MDPI, vol. 9(4), pages 1-12, February.
  • Handle: RePEc:gam:jmathe:v:9:y:2021:i:4:p:417-:d:502732
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    References listed on IDEAS

    as
    1. Xavier Barber & David Conesa & Antonio López-Quílez & Javier Morales, 2019. "Multivariate Bioclimatic Indices Modelling: A Coregionalised Approach," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 24(2), pages 225-244, June.
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    6. Schlather, Martin & Malinowski, Alexander & Menck, Peter J. & Oesting, Marco & Strokorb, Kirstin, 2015. "Analysis, Simulation and Prediction of Multivariate Random Fields with Package RandomFields," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 63(i08).
    7. Fois, Mauro & Cuena-Lombraña, Alba & Fenu, Giuseppe & Bacchetta, Gianluigi, 2018. "Using species distribution models at local scale to guide the search of poorly known species: Review, methodological issues and future directions," Ecological Modelling, Elsevier, vol. 385(C), pages 124-132.
    8. Coll, M. & Pennino, M. Grazia & Steenbeek, J. & Sole, J. & Bellido, J.M., 2019. "Predicting marine species distributions: Complementarity of food-web and Bayesian hierarchical modelling approaches," Ecological Modelling, Elsevier, vol. 405(C), pages 86-101.
    9. Håvard Rue & Sara Martino & Nicolas Chopin, 2009. "Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 71(2), pages 319-392, April.
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    Cited by:

    1. Rodrigues, Lucas dos Santos & Daudt, Nicholas Winterle & Cardoso, Luis Gustavo & Kinas, Paul Gerhard & Conesa, David & Pennino, Maria Grazia, 2023. "Species distribution modelling in the Southwestern Atlantic Ocean: A systematic review and trends," Ecological Modelling, Elsevier, vol. 486(C).

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