@article{708,
  author = {Ovaskainen O. and Roy D. B. and Fox Richard and Anderson Barbara J.},
  title = {Uncovering hidden spatial structure in species communities with spatially explicit joint species distribution models},
  abstract = {1. Modern species distribution models account for spatial autocorrelation in order to obtain unbiased statistical inference on the effects of covariates, to improve the model{\textquoteright}s predictive ability through spatial interpolation and to gain insight in the spatial processes shaping the data. Somewhat analogously, hierarchical approaches to community-level data have been developed to gain insights into community-level processes and to improve species-level inference by borrowing information from other species that are either ecologically or phylogenetically related to the focal species. 2. We unify spatial and community-level structures by developing spatially explicit joint species distribution models. The models utilize spatially structured latent factors to model missing covariates as well as species-to-species associations in a statistically and computationally effective manner. 3.We illustrate that the inclusion of the spatial latent factors greatly increases the predictive performance of the modelling approach with a case study of 55 species of butterfly recorded on a 10kmx10km grid in Great Britain consisting of 2609 grid cells.},
  year = {2016},
  journal = {Methods in Ecology and Evolution},
  volume = {7},
  number = {4},
  pages = {428-436},
  note = {Ovaskainen, Otso Roy, David B. Fox, Richard Anderson, Barbara J.},
  language = {eng},
}