Evaluating Bayesian spatial methods for modelling species distributions with clumped and restricted occurrence data.

ABSTRACT: Statistical approaches for inferring the spatial distribution of taxa (Species Distribution Models, SDMs) commonly rely on available occurrence data, which is often clumped and geographically restricted. Although available SDM methods address some of these factors, they could be more directly and accurately modelled using a spatially-explicit approach. Software to fit models with spatial autocorrelation parameters in SDMs are now widely available, but whether such approaches for inferring SDMs aid predictions compared to other methodologies is unknown. Here, within a simulated environment using 1000 generated species' ranges, we compared the performance of two commonly used non-spatial SDM methods (Maximum Entropy Modelling, MAXENT and boosted regression trees, BRT), to a spatial Bayesian

SUBMITTER: Redding DW 

PROVIDER: S-EPMC5708625 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature