Computationally efficient joint species distribution modeling of big spatial data.
Ecology
; 101(2): e02929, 2020 02.
Article
em En
| MEDLINE
| ID: mdl-31725922
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.
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Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Algoritmos
/
Modelos Estatísticos
Tipo de estudo:
Prognostic_studies
/
Risk_factors_studies
Idioma:
En
Revista:
Ecology
Ano de publicação:
2020
Tipo de documento:
Article