globalchange  > 气候变化与战略
DOI: 10.1002/ecy.2929
论文题名:
Computationally efficient joint species distribution modeling of big spatial data
作者: Tikhonov G.; Duan L.; Abrego N.; Newell G.; White M.; Dunson D.; Ovaskainen O.
刊名: Ecology
ISSN: 129658
出版年: 2020
卷: 101, 期:2
语种: 英语
英文关键词: community modeling ; ecological communities ; Gaussian process ; hierarchical modeling of species communities ; joint species distribution model ; latent factors ; spatial statistics
Scopus关键词: algorithm ; biodiversity ; community composition ; ecological modeling ; Gaussian method ; global change ; macroecology ; spatial data ; article ; species distribution
英文摘要: 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. © 2019 The Authors. Ecology published by Wiley Periodicals, Inc. on behalf of Ecological Society of America
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资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/159504
Appears in Collections:气候变化与战略

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作者单位: Organismal and Evolutionary Biology Research Programme, University of Helsinki, P.O. Box 65, Helsinki, FI-00014, Finland; Computational Systems Biology Group, Department of Computer Science, Aalto University, P.O. Box 11000, Espoo, FI-00076, Finland; Department of Statistics, University of Florida, P.O. Box 118545, Gainesville, FL 32611, United States; Faculty of Biological and Environmental Sciences, University of Helsinki, P.O. Box 65, Helsinki, FI-00014, Finland; Biodiversity Division, Department of Environment, Land, Water & Planning, Arthur Rylah Institute for Environmental Research, 123 Brown Street, Heidelberg, VIC 3084, Australia; Department of Statistical Science, Duke University, P.O. Box 90251, Durham, NC, United States; Centre for Biodiversity Dynamics, Department of Biology, Norwegian University of Science and Technology, Trondheim, N-7491, Norway

Recommended Citation:
Tikhonov G.,Duan L.,Abrego N.,et al. Computationally efficient joint species distribution modeling of big spatial data[J]. Ecology,2020-01-01,101(2)
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