nerc.ac.uk

Uncovering hidden spatial structure in species communities with spatially explicit joint species distribution models

Ovaskainen, Otso; Roy, David B.; Fox, Richard; Anderson, Barbara J.. 2016 Uncovering hidden spatial structure in species communities with spatially explicit joint species distribution models. Methods in Ecology and Evolution, 7 (4). 428-436. 10.1111/2041-210X.12502

Before downloading, please read NORA policies.
[img]
Preview
Text
N514587PP.pdf - Accepted Version

Download (852kB) | Preview

Abstract/Summary

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'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 10 km × 10 km grid in Great Britain consisting of 2609 grid cells.

Item Type: Publication - Article
Digital Object Identifier (DOI): 10.1111/2041-210X.12502
CEH Sections: Pywell
ISSN: 2041-210X
Additional Keywords: community models, joint species distribution models, latent factors, spatial models
NORA Subject Terms: Ecology and Environment
Date made live: 26 Sep 2016 11:24 +0 (UTC)
URI: http://nora.nerc.ac.uk/id/eprint/514587

Actions (login required)

View Item View Item

Document Downloads

Downloads for past 30 days

Downloads per month over past year

More statistics for this item...