TY - T1 - Computationally efficient joint species distribution modeling of big spatial data SN - / UR - http://hdl.handle.net/10138/311124 T3 - A1 - Tikhonov, Gleb; Duan, Li; Abrego, Nerea; Newell, Graeme; White, Matt; Dunson, David; Ovaskainen, Otso A2 - PB - Y1 - 2020 LA - eng AB - 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 comput... VO - IS - SP - OP - KW - 1181 Ecology, evolutionary biology; community modeling; ecological communities; Gaussian process; joint species distribution model; latent factors; spatial statistics; HMSC; hierarchical modeling of species communities N1 - PP - ER -