IUCN Red List assessments are essential for prioritizing conservation needs but are resource-intensive and therefore only available for a fraction of global species richness. Automated conservation assessments based on digitally available geographic occurrence records can be a rapid alternative, but it is unclear how reliable these assessments are. Here, we present automated conservation assessments for 13,910 species (47.3% of the known species in the family) of the diverse and globally distributed Orchid family (Orchidaceae), based on a novel method using a deep neural network (IUC-NN), most of which (13,049) were previously unassessed by the IUCN Red List. We identified 4,342 Orchid species (31.2 % of the evaluated species) as Possibly Threatened with extinction (equivalent to the IUCN categories CR, EN, or VU) and point to Madagascar, East Africa, south-east Asia, and several oceanic islands as priority areas for orchid conservation. Furthermore, the Orchid family provides a model, to test the sensitivity of automated assessment methods to issues with data availability, data quality and geographic sampling bias. IUC-NN identified threatened species with an accuracy of 84.3%, with significantly lower geographic evaluation bias compared to the IUCN Red List and was robust against low data availability and geographic errors in the input data. Overall, our results demonstrate that automated assessments have an important role to play in achieving goals of identifying the species that are at greatest risk of extinction. Article impact statement: An automated conservation assessment with deep learning reveals global centers of orchid extinction risk. This article is protected by copyright. All rights reserved.

中文翻译：

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使用深度学习对兰花科进行自动保护评估

IUCN 红色名录评估对于优先保护需求至关重要，但它是资源密集型的，因此仅适用于全球物种丰富度的一小部分。基于数字可用地理事件记录的自动保护评估可能是一种快速的替代方案，但尚不清楚这些评估的可靠性如何。在这里，我们基于使用深度神经网络 (IUC-NN) 的新方法，对全球分布广泛的兰花科 (Orchidaceae) 的 13,910 种物种（占该科已知物种的 47.3%）进行自动保护评估，大多数其中 (13,049) 之前未经 IUCN 红色名录评估。我们将 4,342 种兰花物种（占评估物种的 31.2%）确定为可能濒临灭绝（相当于 IUCN 类别 CR、EN 或 VU）并指向马达加斯加，东非、东南亚和几个海洋岛屿作为兰花保护的优先地区。此外，Orchid 系列提供了一个模型，用于测试自动评估方法对数据可用性、数据质量和地理抽样偏差问题的敏感性。IUC-NN 识别受威胁物种的准确率为 84.3%，与 IUCN 红色名录相比，地理评估偏差显着降低，并且对低数据可用性和输入数据中的地理错误具有稳健性。总体而言，我们的结果表明，自动化评估在实现识别面临最大灭绝风险的物种的目标方面可以发挥重要作用。文章影响声明：使用深度学习的自动保护评估揭示了兰花灭绝风险的全球中心。本文受版权保护。版权所有。