Inferring the extinction risk of Data Deficient and Not Evaluated Australian squamates

Abstract

AbstractThe world is facing a biodiversity crisis, and species are in danger of slipping towards extinction before having their conservation status formally determined. Australian squamates (snakes and lizards) form a highly diverse (over 1000 species) fauna, with 12% being either Data Deficient or Not Evaluated. We examined attributes of Australian squamates categorized as Data Deficient or Not Evaluated and compared key traits that are linked with threatened categories via univariate and multivariate models. We further used the machine learning model of Caetano et al. (2022, PloS Biology, 20, e3001544) to predict the putative extinction risk categories for Data Deficient and Not Evaluated Australian squamate species based on an analysis of reptiles globally. We found that Data Deficient Australian squamates are often lacking information on their drivers of threat and distribution, but not intrinsic traits or uncertain taxonomy. Data Deficient, Not Evaluated and threatened species often possess similar traits, including having small range sizes, being insular endemics and recently described, indicating that they may require some similar conservation management. Meanwhile, Not Evaluated species exhibit certain unique traits relative to evaluated species. We predicted 21% of Data Deficient and Not Evaluated species are threatened which is three times greater than currently assessed species (7%). This may indicate that a larger proportion of poorly known squamate species are more likely to be threatened than previously thought. Overall, our findings provide an important resource for the conservation management of Australian squamates by highlighting key traits and missing data, as well as providing a list of Data Deficient and Not Evaluated species that should be prioritized for research.