Plant conservation is often a lower priority compared to that of other taxa, a trend that has inadvertently become systemic across global conservation organizations. One such institution, the International Union for the Conservation of Nature (IUCN), plays an important role in setting conservation priorities; their Red List assessments draw substantial attention and funding to imperiled species. 70% of all described vertebrate species have been assessed by the IUCN. Only 9% of all described plant species have been assessed. Recent research demonstrates that two components of the IUCN’s existing assessment criteria—Extent of Occurrence (EOO) and Area of Occupancy (AOO)—can be used to accurately assign conservation status for various taxa. We tested this on a continental scale for the first time by procuring occurrence data from the Global Biodiversity Information Facility (GBIF) for those North American plant species previously assessed by the IUCN, calculating EOO and AOO metrics for each species, and assigning them Red List categories based on those metrics. We compared the results to their current IUCN classifications to determine the overlap and modeled what factors contributed to overlapping classifications.
Results/Conclusions
Preliminary results indicate this rapid method produces classifications with approximately 80% similarity to those of the full IUCN process. This approach links two of the largest and most comprehensive conservation datasets to leverage their combined power towards more rapid and reliable species conservation assessments. Big data is the future of ecology, and here we use globally aggregated data to produce the kind of less biased, more rapid conservation assessment process that is urgently necessary considering rates of contemporary biodiversity loss. As a supplement to the existing IUCN Red List assessment process, this innovative approach will enhance the ability of policymakers, managers, and scientists to identify threatened species and allocate resources for their protection.