Predicting plant conservation priorities on a global scale

Significance The International Union for Conservation of Nature (IUCN) Red List of Threatened Species is a key tool for the conservation of biological diversity. The evaluation and addition of species to this list is a time-consuming and costly task, and as such, a large number of species are not listed. For example, only 5% of plant species housed in the Global Biodiversity Information Facility are currently listed on the IUCN Red List. The simple and integrated protocol presented here enables conservation researchers and managers to identify unassessed species most likely at risk and, thus, assists in the direction of resource allocation for conservation. Our results suggest that efforts have been highly skewed geographically, and identify conservation hotspots in need of further evaluation. The conservation status of most plant species is currently unknown, despite the fundamental role of plants in ecosystem health. To facilitate the costly process of conservation assessment, we developed a predictive protocol using a machine-learning approach to predict conservation status of over 150,000 land plant species. Our study uses open-source geographic, environmental, and morphological trait data, making this the largest assessment of conservation risk to date and the only global assessment for plants. Our results indicate that a large number of unassessed species are likely at risk and identify several geographic regions with the highest need of conservation efforts, many of which are not currently recognized as regions of global concern. By providing conservation-relevant predictions at multiple spatial and taxonomic scales, predictive frameworks such as the one developed here fill a pressing need for biodiversity science.