Mapping global orchid assemblages with deep learning provides novel conservation insights - INRAE - Institut national de recherche pour l’agriculture, l’alimentation et l’environnement Access content directly
Journal Articles Ecological Informatics Year : 2024

Mapping global orchid assemblages with deep learning provides novel conservation insights

Abstract

Although increasing threats on biodiversity are now widely recognised, there are no accurate global maps showing whether and where species assemblages are at risk. We hereby assess and map at kilometre resolution the conservation status of the iconic orchid family, and discuss the insights conveyed at multiple scales. We introduce a new Deep Species Distribution Model trained on 1 M occurrences of 14 K orchid species to predict their assemblages at global scale and at kilometre resolution. We propose two main indicators of the conservation status of the assemblages: (i) the proportion of threatened species, and (ii) the status of the most threatened species in the assemblage. We show and analyze the variation of these indicators at World scale and in relation to currently protected areas in Sumatra island. Global and interactive maps available online show the indicators of conservation status of orchid assemblages, with sharp spatial variations at all scales. The highest level of threat is found at Madagascar and the neighbouring islands. In Sumatra, we found good correspondence of protected areas with our indicators, but supplementing current IUCN assessments with status predictions results in alarming levels of species threat across the island. Recent advances in deep learning enable reliable mapping of the conservation status of species assemblages on a global scale. As an umbrella taxon, orchid family provides a reference for identifying vulnerable ecosystems worldwide, and prioritising conservation actions both at international and local levels.
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hal-04581266 , version 1 (21-05-2024)

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Joaquim Estopinan, Maximilien Servajean, Pierre Bonnet, Alexis Joly, François Munoz. Mapping global orchid assemblages with deep learning provides novel conservation insights. Ecological Informatics, 2024, 81, pp.102627. ⟨10.1016/j.ecoinf.2024.102627⟩. ⟨hal-04581266⟩
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