The prediction of species distribution in suitable regions is essential for planning conservation and management strategies. Unfortunately, quite often the only available information is the presence of the species at few locations while the associated environmental covariates can be observed over the complete area of interest. This kind of situation can be seen as a missing data problem with asymmetric and partial information, we say that data are presence-only data. In this paper we present a Bayesian approach to handle with presence-only data, we also consider the case when a spatial effect acts among the observations. MCMC computation has been implemented through a data augmentation algorithm allowing us to result consistent estimates for the regression parameters jointly with the unknown species prevalence.
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