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.

翻译：尽管生物多样性面临的威胁日益增长已获广泛共识，但尚无精确的全球地图展示物种群落是否处于风险之中及具体位置。本研究对标志性兰花科植物群落进行公里级尺度的保护状况评估与制图，并探讨多尺度洞察。我们引入新型深度物种分布模型，该模型基于14000种兰花的100万条分布记录进行训练，以实现全球公里级尺度的群落预测。提出两个群落保护状况核心指标：（i）受威胁物种比例，（ii）群落中受威胁程度最高物种的保护状况。我们展示并分析了这些指标在全球尺度的空间变化，以及其与苏门答腊岛现有保护区的关联。在线提供的全球交互式地图揭示了兰花群落保护状况指标在各空间尺度上的显著差异，其中马达加斯加及邻近岛屿的威胁等级最高。在苏门答腊岛，我们发现保护区与指标存在良好对应关系，但将现状预测补充至现有IUCN评估后，该岛屿物种受到的威胁程度达到警戒水平。深度学习的最新进展使全球尺度物种群落保护状况的可靠制图成为可能。作为伞护类群，兰花科为识别全球脆弱生态系统、优先制定国际及地方级保护行动提供了重要参照。