Ricerc@Sapienza

The extinction risk of species is globally monitored by the IUCN Red List (RL). Despite the goal to update extinction risk assessments at least every 10 years, over 21,000 species (ca. 18% of those assessed) currently have outdated assessments. The resources needed to keep assessments up to date present a clear sustainability challenge. Furthermore, even a 10-year reassessment interval is too long to allow timely conservation interventions in many cases, and the application of RL criteria often suffer from inconsistencies across different groups. Several authors have proposed automated methods to conduct preliminary screenings to guide reassessment of species, considering species intrinsic vulnerability (inferred from their traits and other factors), historical trends, and satellite-derived imagery combined with macroecological modelling. So far, however, these approaches have remained largely academic exercises, and have rarely been incorporated in the official RL reassessment routines.
By bringing together RL practitioners and ecological modellers, we aim to develop an innovative, rapid and consistent framework for prioritizing RL assessments. We will build on recent approaches to derive input data for the RL assessment process, for example inferring population decline from satellite-borne estimates of habitat loss. We will automatize the identification of species likely to have changed their RL status. These species would be prioritised for re-assessment, to verify the validity of our predictions and update their extinction risk as appropriate. Prioritizing the reassessment of species will reduce assessors workload and deliver faster information for more rapid and effective conservation actions.