Predicting Extinction Risk for Data Deficient Bats

Conservation biology aims to identify species most at risk of extinction and to understand factors that forecast species vulnerability. The International Union for Conservation of Nature (IUCN) Red List is a leading source for extinction risk data of species globally, however, many potentially at risk species are not assessed by the IUCN owing to inadequate data. Of the approximately 1150 bat species (Chiroptera) recognized by the IUCN, 17 percent are categorized as Data Deficient. Here, we show that large trait databases in combination with a comprehensive phylogeny can identify which traits are important for assessing extinction risk in bats. Using phylogenetic logistic regressions, we show that geographic range and island endemism are the strongest correlates of binary extinction risk. We also show that simulations using two models that trade-off between data complexity and data coverage provide similar estimates of extinction risk for species that have received a Red List assessment. We then use our model parameters to provide quantitative predictions of extinction risk for 60 species that have not received risk assessments by the IUCN. Our model suggests that at least 20 bat species should be treated as threatened by extinction. In combination with expert knowledge, our results can be used as a quick, first-pass prioritization for conservation action.