Computationally efficient joint species distribution modeling of big spatial data.

Published

Journal Article

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 datasets due to 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 modelling by exploiting two spatial-statistics techniques that facilitate the analysis of large spatial datasets: Gaussian Predictive Process and Nearest Neighbor Gaussian Process. We devised an efficient Gibbs posterior sampling algorithm for Bayesian model fitting that allows to analyze community datasets consisting of hundreds of species sampled from up to hundreds of thousands of spatial units. The performance of these methods is demonstrated using an extensive plant dataset of 30,955 spatial units as a case study. We provide an implementation of the presented methods as an extension to the Hierarchical Modeling of Species Communities (HMSC) framework.

Full Text

Duke Authors

Cited Authors

  • Tikhonov, G; Duan, L; Abrego, N; Newell, G; White, M; Dunson, D; Ovaskainen, O

Published Date

  • November 14, 2019

Published In

Start / End Page

  • e02929 -

PubMed ID

  • 31725922

Pubmed Central ID

  • 31725922

Electronic International Standard Serial Number (EISSN)

  • 1939-9170

International Standard Serial Number (ISSN)

  • 0012-9658

Digital Object Identifier (DOI)

  • 10.1002/ecy.2929

Language

  • eng