Computationally efficient joint species distribution modeling of big spatial data.

ABSTRACT: The ongoing global change and the increased interest in macroecological processes call for the analysis of spatially extensive data on species communities to understand and forecast distributional changes of biodiversity. Recently developed joint species distribution models can deal with numerous species efficiently, while explicitly accounting for spatial structure in the data. However, their applicability is generally limited to relatively small spatial data sets because of their severe computational scaling as the number of spatial locations increases. In this work, we propose a practical alleviation of this scalability constraint for joint species modeling by exploiting two spatial-statistics techniques that facilitate the analysis of large spatial data sets: Gaussian predictive proces

SUBMITTER: Tikhonov G 

PROVIDER: S-EPMC7027487 | biostudies-literature | 2020 Feb

REPOSITORIES: biostudies-literature