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Computationally efficient joint species distribution modeling of big spatial data
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 sp...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley and Sons Inc.
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7027487/ https://www.ncbi.nlm.nih.gov/pubmed/31725922 http://dx.doi.org/10.1002/ecy.2929 |
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author | Tikhonov, Gleb Duan, Li Abrego, Nerea Newell, Graeme White, Matt Dunson, David Ovaskainen, Otso |
author_facet | Tikhonov, Gleb Duan, Li Abrego, Nerea Newell, Graeme White, Matt Dunson, David Ovaskainen, Otso |
author_sort | Tikhonov, Gleb |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-7027487 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | John Wiley and Sons Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-70274872020-02-24 Computationally efficient joint species distribution modeling of big spatial data Tikhonov, Gleb Duan, Li Abrego, Nerea Newell, Graeme White, Matt Dunson, David Ovaskainen, Otso Ecology Statistical Reports 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. John Wiley and Sons Inc. 2019-12-20 2020-02 /pmc/articles/PMC7027487/ /pubmed/31725922 http://dx.doi.org/10.1002/ecy.2929 Text en © 2019 The Authors. Ecology published by Wiley Periodicals, Inc. on behalf of Ecological Society of America This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Statistical Reports Tikhonov, Gleb Duan, Li Abrego, Nerea Newell, Graeme White, Matt Dunson, David Ovaskainen, Otso Computationally efficient joint species distribution modeling of big spatial data |
title | Computationally efficient joint species distribution modeling of big spatial data |
title_full | Computationally efficient joint species distribution modeling of big spatial data |
title_fullStr | Computationally efficient joint species distribution modeling of big spatial data |
title_full_unstemmed | Computationally efficient joint species distribution modeling of big spatial data |
title_short | Computationally efficient joint species distribution modeling of big spatial data |
title_sort | computationally efficient joint species distribution modeling of big spatial data |
topic | Statistical Reports |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7027487/ https://www.ncbi.nlm.nih.gov/pubmed/31725922 http://dx.doi.org/10.1002/ecy.2929 |
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