title: Towards a general framework for predicting threat status of data-deficient species from phylogenetic, spatial and environmental information.
abstract: In taxon-wide assessments of threat status many species remain not included owing to lack of data. Here, we present a novel spatial-phylogenetic statistical framework that uses a small set of readily available or derivable characteristics, including phylogenetically imputed body mass and remotely sensed human encroachment, to provide initial baseline predictions of threat status for data-deficient species. Applied to assessed mammal species worldwide, the approach effectively identifies threatened species and predicts the geographical variation in threat. For the 483 data-deficient species, the models predict highly elevated threat, with 69% 'at-risk' species in this set, compared with 22% among assessed species. This results in 331 additional potentially threatened mammals, with elevated conservation importance in rodents, bats and shrews, and countries like Colombia, Sulawesi and the Philippines. These findings demonstrate the future potential for combining phylogenies and remotely sensed data with species distributions to identify species and regions of conservation concern.
authors.affiliation: Department of Ecology and Evolutionary Biology, Yale University, PO Box 208106, New Haven, CT 06520-8106, USA Imperial College London, Silwood Park Campus, Buckhurst Road, Ascot, Berkshire SL5 7PY, UK walter.jetz@yale.edu.
authors.forename: Walter
authors.initials: W
authors.lastname: Jetz
authors.affiliation: Department of Animal and Plant Sciences, University of Sheffield, Sheffield S10 2TN, UK.
authors.forename: Robert P
authors.initials: RP
authors.lastname: Freckleton
mesh_terms: D000818:Animals; D049628:Body Size / Q000502:physiology; D003247:Conservation of Natural Resources / Q000379:methods*; D003710:Demography; D056727:Endangered Species; D004777:Environment; D005544:Forecasting / Q000379:methods*; D008322:Mammals; D008954:Models, Biological; D010802:Phylogeny
keywords: extinction risk; geographical range; imputation; phylogeny; remote sensing; threat status