algorithm
; biodiversity
; community composition
; ecological modeling
; Gaussian method
; global change
; macroecology
; spatial data
; article
; species distribution
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)