AI-based Mapping of the Conservation Status of Orchid Assemblages at
  Global Scale
Joaquim Estopinan Maximilien Servajean Pierre Bonnet Alexis Joly François Munoz
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
1M occurrences of 14K 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.