Computationally efficient joint species distribution modeling of big spatial data

Gleb Tikhonov*, Li Duan, Nerea Abrego, Graeme Newell, Matt White, David Dunson, Otso Ovaskainen

*Tämän työn vastaava kirjoittaja

Tutkimustuotos: LehtiartikkeliArticleScientificvertaisarvioitu

3 Sitaatiot (Scopus)
30 Lataukset (Pure)

Abstrakti

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.

AlkuperäiskieliEnglanti
Artikkelie02929
JulkaisuECOLOGY
Vuosikerta101
Numero2
Varhainen verkossa julkaisun päivämäärä2019
DOI - pysyväislinkit
TilaJulkaistu - helmikuuta 2020
OKM-julkaisutyyppiA1 Julkaistu artikkeli, soviteltu

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  • Siteeraa tätä

    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