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Computationally efficient joint species distribution modeling of big spatial data.

Publication ,  Journal Article
Tikhonov, G; Duan, L; Abrego, N; Newell, G; White, M; Dunson, D; Ovaskainen, O
Published in: Ecology
February 2020

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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Published In

Ecology

DOI

EISSN

1939-9170

ISSN

1939-9170

Publication Date

February 2020

Volume

101

Issue

2

Start / End Page

e02929

Related Subject Headings

  • Models, Statistical
  • Ecology
  • Biodiversity
  • Bayes Theorem
  • Algorithms
  • 4102 Ecological applications
  • 3109 Zoology
  • 3103 Ecology
  • 0603 Evolutionary Biology
  • 0602 Ecology
 

Citation

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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, 101(2), e02929. https://doi.org/10.1002/ecy.2929
Tikhonov, Gleb, Li Duan, Nerea Abrego, Graeme Newell, Matt White, David Dunson, and Otso Ovaskainen. “Computationally efficient joint species distribution modeling of big spatial data.Ecology 101, no. 2 (February 2020): e02929. https://doi.org/10.1002/ecy.2929.
Tikhonov G, Duan L, Abrego N, Newell G, White M, Dunson D, et al. Computationally efficient joint species distribution modeling of big spatial data. Ecology. 2020 Feb;101(2):e02929.
Tikhonov, Gleb, et al. “Computationally efficient joint species distribution modeling of big spatial data.Ecology, vol. 101, no. 2, Feb. 2020, p. e02929. Epmc, doi:10.1002/ecy.2929.
Tikhonov G, Duan L, Abrego N, Newell G, White M, Dunson D, Ovaskainen O. Computationally efficient joint species distribution modeling of big spatial data. Ecology. 2020 Feb;101(2):e02929.
Journal cover image

Published In

Ecology

DOI

EISSN

1939-9170

ISSN

1939-9170

Publication Date

February 2020

Volume

101

Issue

2

Start / End Page

e02929

Related Subject Headings

  • Models, Statistical
  • Ecology
  • Biodiversity
  • Bayes Theorem
  • Algorithms
  • 4102 Ecological applications
  • 3109 Zoology
  • 3103 Ecology
  • 0603 Evolutionary Biology
  • 0602 Ecology