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Spatial hidden Markov models and species distributions

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  • Luigi Spezia
  • Nial Friel
  • Alessandro Gimona

Abstract

A spatial hidden Markov model (SHMM) is introduced to analyse the distribution of a species on an atlas, taking into account that false observations and false non-detections of the species can occur during the survey, blurring the true map of presence and absence of the species. The reconstruction of the true map is tackled as the restoration of a degraded pixel image, where the true map is an autologistic model, hidden behind the observed map, whose normalizing constant is efficiently computed by simulating an auxiliary map. The distribution of the species is explained under the Bayesian paradigm and Markov chain Monte Carlo (MCMC) algorithms are developed. We are interested in the spatial distribution of the bird species Greywing Francolin in the south of Africa. Many climatic and land-use explanatory variables are also available: they are included in the SHMM and a subset of them is selected by the mutation operators within the MCMC algorithm.

Suggested Citation

  • Luigi Spezia & Nial Friel & Alessandro Gimona, 2018. "Spatial hidden Markov models and species distributions," Journal of Applied Statistics, Taylor & Francis Journals, vol. 45(9), pages 1595-1615, July.
    • Handle: RePEc:taf:japsta:v:45:y:2018:i:9:p:1595-1615
    DOI: 10.1080/02664763.2017.1386771
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    Cited by:

    1. Francesco Bartolucci & Alessio Farcomeni, 2022. "A hidden Markov space–time model for mapping the dynamics of global access to food," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(1), pages 246-266, January.

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