期刊
METHODS IN ECOLOGY AND EVOLUTION
卷 7, 期 4, 页码 428-436出版社
WILEY
DOI: 10.1111/2041-210X.12502
关键词
community models; joint species distribution models; latent factors; spatial models
类别
资金
- consortium of government agencies
- Academy of Finland [250444]
- NERC [NE/FO18606/1]
- RSNZ Rutherford Discovery Fellowship
- Natural Environment Research Council [ceh020002] Funding Source: researchfish
- NERC [ceh020002] Funding Source: UKRI
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 10kmx10km grid in Great Britain consisting of 2609 grid cells.
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