Global Plant Extinction Risk Assessment Inform Novel Biodiversity Hotspots
2023
Haevermans, Thomas | Tressou, Jessica | Kwon, Joon | Pellens, Roseli | Dubéarnès, Anne | Veron, Simon | Bel, Liliane | Dervaux, Stéphane | Dibie-Barthelemy, Juliette | Gaudeul, Myriam | Govaerts, Rafaël | Le Bras, Gwenaël | Muller, Serge | Rouhan, Germinal | Sarthou, Corinne | Soler, Lydie | Institut de Systématique, Evolution, Biodiversité (ISYEB) ; Muséum national d'Histoire naturelle (MNHN)-École Pratique des Hautes Études (EPHE) ; Université Paris Sciences et Lettres (PSL)-Université Paris Sciences et Lettres (PSL)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Université des Antilles (UA) | Mathématiques et Informatique Appliquées (MIA Paris-Saclay) ; AgroParisTech-Université Paris-Saclay-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE) | Royal Botanic Gardens [Kew] | Muséum national d'Histoire naturelle (MNHN)
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 megadatasets 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 (
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