When the only available information is the true presence of a species at few locations of a study area we refer to the data as presence-only data. Presence-only data problem can be seen as a missing data problem with asymmetric and partial information on a presence-absence process. This problem often characterizes ecological studies requiring the prediction of potential spatial extent of a species in suitable areas. Here we propose a Bayesian logistic spatial model adapted to presence-only data with environmental covariates available over the entire area. The spatial dependence among the observations is modelled indirectly as a latent Gaussian Markov field over the landscape, through a data augmentation MCMC algorithm we are able to estimate regression parameters jointly with the prevalence.

(2011). Spatial Bayesian modeling of presence-only data [conference presentation - intervento a convegno]. Retrieved from http://hdl.handle.net/10446/25257

Spatial Bayesian modeling of presence-only data

2011-01-01

Abstract

When the only available information is the true presence of a species at few locations of a study area we refer to the data as presence-only data. Presence-only data problem can be seen as a missing data problem with asymmetric and partial information on a presence-absence process. This problem often characterizes ecological studies requiring the prediction of potential spatial extent of a species in suitable areas. Here we propose a Bayesian logistic spatial model adapted to presence-only data with environmental covariates available over the entire area. The spatial dependence among the observations is modelled indirectly as a latent Gaussian Markov field over the landscape, through a data augmentation MCMC algorithm we are able to estimate regression parameters jointly with the prevalence.
2011
Divino, Fabio; Golini, Natalia; Lasinio, Giovanna Jona; Penttinen, Antti
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10446/25257
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