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Multivariate Bioclimatic Indices Modelling: A Coregionalised Approach

Author

Listed:
  • Xavier Barber

    (Universidad Miguel Hernández de Elche)

  • David Conesa

    (Universitat de València)

  • Antonio López-Quílez

    (Universitat de València)

  • Javier Morales

    (Universidad Miguel Hernández de Elche)

Abstract

A methodological approach for modelling the spatial multivariate distribution of multiple bioclimatic indices is presented. The value of the indices is modelled by means of a Bayesian conditional coregionalised linear model. Elicitation of prior distributions and approximation of posterior distributions of the parameters in the proposed model are also discussed. A posterior predictive distribution and a spatial bioclimatic probability distribution for each bioclimatic index are obtained. This allows researchers to obtain the probability of each location belonging to different bioclimates. The presented methodology is applied in a practical setting showing that the spatial bioclimatic probability distributions are more realistic than the ones obtained in the univariate setting, while providing an interesting tool in the context of climate change.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:jagbes:v:24:y:2019:i:2:d:10.1007_s13253-018-00345-z
    DOI: 10.1007/s13253-018-00345-z
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    References listed on IDEAS

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    1. Simona Canu & Leonardo Rosati & Michele Fiori & Andrea Motroni & Rossella Filigheddu & Emmanuele Farris, 2015. "Bioclimate map of Sardinia (Italy)," Journal of Maps, Taylor & Francis Journals, vol. 11(5), pages 711-718, October.
    2. Noel Cressie & Andrew Zammit-Mangion, 2016. "Multivariate spatial covariance models: a conditional approach," Biometrika, Biometrika Trust, vol. 103(4), pages 915-935.
    3. David J. Spiegelhalter & Nicola G. Best & Bradley P. Carlin & Angelika Van Der Linde, 2002. "Bayesian measures of model complexity and fit," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 64(4), pages 583-639, October.
    4. Andrea Catorci & Monica Foglia & Federico Maria Tardella & Alessandra Vitanzi & Daniele Sparvoli & Renata Gatti & Paola Galli & Luigi Paradisi, 2012. "Map of changes in landscape naturalness in the Fiastra and Salino catchment basins (central Italy)," Journal of Maps, Taylor & Francis Journals, vol. 8(1), pages 97-106.
    5. Finley, Andrew O. & Banerjee, Sudipto & Gelfand, Alan E., 2015. "spBayes for Large Univariate and Multivariate Point-Referenced Spatio-Temporal Data Models," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 63(i13).
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    Cited by:

    1. 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.

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