The Use of Observational Datasets to Strengthen Distribution Mapping of Red List Assessments

As anthropogenic pressures on global biodiversity continue to rise, it is imperative that action is taken to protect species threatened with extinction. The International Union for the Conservation of Nature (IUCN)’s Red List is a tool that can be used to prioritize conservation action, but only 8% of all species have been assessed for extinction risk. More species must be added to the Red List; however, the lack of published studies on many species can make it difficult to perform an initial assessment. Here, we discuss the use of open observational datasets to improve initial Red List assessments by making more accurate distribution maps. We also provide a list of databases spanning a range of taxonomic groups and geographic locations that can be used by Red List assessors, as well as a comparison of their strengths and weaknesses. Some advantages of using this data include improved distribution mapping and the identification of areas that may require more systematic sampling. Some limitations of this data include the potential for sampling bias to affect spatial metrics and the presence of taxonomic and spatiotemporal errors.