Combining phylogeny and co‐occurrence to improve single species distribution models. Issue 6 (24th March 2017)

• Record Type: Journal Article
• Authors: Morales‐Castilla, Ignacio; Davies, T. Jonathan; Pearse, William D.; Peres‐Neto, Pedro
• Other Names: Swenson Nathan checker.
• Is Part Of: Global ecology & biogeography. Volume 26:Issue 6(2017)
• Page Start: 740
• Page End: 752
• Publication Date: 2017-03-24
• Subjects: co‐occurrence -- community data matrix -- latent trait -- phylogenetic distance -- phylogenetic predictor -- species distribution models; Ecology -- Periodicals; Biogeography -- Periodicals; Biodiversity -- Periodicals; Macroevolution -- Periodicals; 577
• DOI: 10.1111/geb.12580
• Languages: English
• ISSNs: 1466-822X

Abstract

Abstract: Aim: We present a novel quantitative framework that combines information on phylogeny and the spatial distributions of related species to enhance the single‐species distributional models commonly used in ecology. Innovation: While species distribution models (SDMs) are becoming increasingly sophisticated, they rarely take into consideration the shared evolutionary histories of species. Species are not independent entities, and phylogenies may capture how species have configured their spatial distributions as a response to their ecological similarities and interactions in space and through evolutionary time. Our framework provides a flexible approach to include phylogenies as a surrogate for missing trait data within SDMs, and may be particularly valuable for disentangling current and historical drivers of species distributions and modelling data‐poor species when their close relatives are better sampled. Main conclusions: Using both simulations and empirical examples, we demonstrate how the inclusion of phylogenetic information can significantly improve the fit of species distribution models by up to 30% for certain species. We show that the potential of phylogenies to improve model fit directly relates to the phylogenetic structure of species distributions and suggest that our framework has the potential to reconcile the apparent conflict between current and historical drivers of biodiversity patterns.