Handling uncertainty through Bayesian inference for Species Distribution Modelling in the Amazon Basin region

Abstract

Species Distribution Modelling (SDM) is used for biodiversity preservation and wildlife management. In order to apply it, a large and reliable dataset about the species occurrence is required. However, in some cases, this can be difficult when there are only a few occurrence records. In this context, uncertainty handling techniques can be applied. Thus, Bayesian inference was used in this study to perform SDM in the Amazon Basin region near Manaus (AM) with data collected by the GoAmazon 2014/15 project. The results were compared with those obtained in statistical models and were similar.