Computationally efficient joint species distribution modeling of big spatial data

Show full item record



Permalink

http://hdl.handle.net/10138/311124

Citation

Tikhonov , G , Duan , L , Abrego , N , Newell , G , White , M , Dunson , D & Ovaskainen , O 2020 , ' Computationally efficient joint species distribution modeling of big spatial data ' , Ecology , vol. 101 , no. 2 , 02929 . https://doi.org/10.1002/ecy.2929

Title: Computationally efficient joint species distribution modeling of big spatial data
Author: Tikhonov, Gleb; Duan, Li; Abrego, Nerea; Newell, Graeme; White, Matt; Dunson, David; Ovaskainen, Otso
Contributor organization: Research Centre for Ecological Change
Organismal and Evolutionary Biology Research Programme
Spatial Foodweb Ecology Group
Faculty of Biological and Environmental Sciences
Otso Ovaskainen / Principal Investigator
Date: 2020-02
Language: eng
Number of pages: 8
Belongs to series: Ecology
ISSN: 0012-9658
DOI: https://doi.org/10.1002/ecy.2929
URI: http://hdl.handle.net/10138/311124
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 process and nearest-neighbor Gaussian process. We devised an efficient Gibbs posterior sampling algorithm for Bayesian model fitting that allows us to analyze community data sets 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 data set 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 framework.
Subject: 1181 Ecology, evolutionary biology
community modeling
ecological communities
Gaussian process
joint species distribution model
latent factors
spatial statistics
HMSC
hierarchical modeling of species communities
Peer reviewed: Yes
Rights: unspecified
Usage restriction: openAccess
Self-archived version: acceptedVersion


Files in this item

Total number of downloads: Loading...

Files Size Format View
Tikhonov_et_al_2019_Ecology.pdf 8.410Mb PDF View/Open
Tikhonov_et_al_2020_Ecology.pdf 2.513Mb PDF View/Open

This item appears in the following Collection(s)

Show full item record