A new statistical framework for the quantification of covariate associations with species distributions

Colin M. Beale*, Mark J. Brewer, Jack J. Lennon

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

27 Citations (Scopus)

Abstract

Identifying processes that shape species geographical ranges is a prerequisite for understanding environmental change. Currently, species distribution modelling methods do not offer credible statistical tests of the relative influence of climate factors and typically ignore other processes (e.g. biotic interactions and dispersal limitation). We use a hierarchical model fitted with Markov Chain Monte Carlo to combine ecologically plausible niche structures using regression splines to describe unimodal but potentially skewed response terms. We apply spatially explicit error terms that account for (and may help identify) missing variables. Using three example distributions of European bird species, we map model results to show sensitivity to change in each covariate. We show that the overall strength of climatic association differs between species and that each species has considerable spatial variation in both the strength of the climatic association and the sensitivity to climate change. Our methods are widely applicable to many species distribution modelling problems and enable accurate assessment of the statistical importance of biotic and abiotic influences on distributions.

Original languageEnglish
Pages (from-to)421-432
Number of pages12
JournalMethods in Ecology and Evolution
Volume5
Issue number5
Early online date19 May 2014
DOIs
Publication statusPublished - May 2014

Keywords

  • species distribution models
  • spatial autocorrelation
  • bioclimate envelope
  • generalized additive models
  • climate change
  • Melodious Warbler Hippolais polyglotta
  • Grey Heron Ardea cinerea
  • Temminck's Stint Calidris temminckii
  • BIOCLIMATE ENVELOPE MODELS
  • CLIMATE-CHANGE
  • SPATIAL AUTOCORRELATION
  • ECOLOGICAL THEORY
  • NICHE
  • IMPACTS
  • BIRDS
  • BIODIVERSITY
  • UNCERTAINTY
  • VARIABILITY

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