Spatial hidden Markov models and species distributions

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.

KEYWORDS:

• Atlas surveys
• autologistic model
• binary images
• exchange algorithm
• mutation operators

Acknowledgments

Comments from Mark Brewer and two anonymous referees improved the quality of the final paper.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

Alessandro Gimona and Luigi Spezia's research was funded by the Scottish Government's Rural and Environment Science and Analytical Services Division.