Computationally efficient joint species distribution modeling of big spatial data
Loading...
Access rights
openAccess
Journal Title
Journal ISSN
Volume Title
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
This publication is imported from Aalto University research portal.
View publication in the Research portal
View/Open full text file from the Research portal
Other link related to publication
View publication in the Research portal
View/Open full text file from the Research portal
Other link related to publication
Date
2020-02-01
Department
Major/Subject
Mcode
Degree programme
Language
en
Pages
Series
ECOLOGY, Volume 101, issue 2
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.Description
Keywords
community modeling, ecological communities, Gaussian process, hierarchical modeling of species communities, joint species distribution model, latent factors, spatial statistics
Other note
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
