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 the 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. Many climatic and land use explanatory variables are also available: they are included in the SHMM and a subset of them is selected stochastically. The distribution of the species is explained under the Bayesian paradigm and Markov chain Monte Carlo algorithms are developed.
Year
2017
Category
Refereed journal
Output Tags
WP3.4 - Resilience of Scotland's biodiversity to change