Global Plant Extinction Risk Assessment Inform Novel Biodiversity Hotspots

Abstract Curbing biodiversity loss and its impact on ecosystem services, resilience and Nature’s Contributions to People is one of the main challenges of our generation (IPBES, 2019b, 2019a; Secretariat of the United Nations Convention on Biological Diversity, 2020). A global baseline assessment of the threat status of all of biodiversity is crucial to monitor the progress of conservation policies worldwide (Mace & al., 2000; Secretariat of the United Nations Convention on Biological Diversity, 2021) and target priority areas for conservation (Walker & al., 2021). However, the magnitude of the task seems insurmountable, as even listing the organisms already known to science is a challenge (Nic Lughadha & al., 2016; Borsch & al., 2020; Govaerts & al., 2021). A new approach is needed to overcome this stumbling block and scale-up the assessment of extinction risk. Here we show that analyses of natural history mega-datasets using artificial intelligence allows us to predict a baseline conservation status for all vascular plants and identify target areas for conservation corresponding to hotspots optimally capturing different aspects of biodiversity. We illustrate the strong potential of AI-based methods to reliably predict extinction risk on a global scale. Our approach not only retrieved recognized biodiversity hotspots but identified new areas that may guide future global conservation action (Myers & al., 2000; Brooks & al., 2006). To further work in this area and guide the targets of the post-2020 biodiversity framework (Díaz & al., 2020a; Secretariat of the United Nations Convention on Biological Diversity, 2020; Mair & al., 2021), it will be necessary to accelerate the acquisition of fundamental data and allow inclusion of social and economic factors (Possingham & Wilson, 2005).