Automated conservation assessment of the orchid family with deep learning

Alexander Zizka*, Daniele Silvestro, Pati Vitt, Tiffany M. Knight

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

47 Scopus citations


International Union for Conservation of Nature (IUCN) Red List assessments are essential for prioritizing conservation needs but are resource intensive and therefore available only 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. We conducted 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), for which most species (13,049) were previously unassessed by IUCN. We used a novel method based on a deep neural network (IUC-NN). We identified 4,342 orchid species (31.2% of the evaluated species) as possibly threatened with extinction (equivalent to IUCN categories critically endangered [CR], endangered [EN], or vulnerable [VU]) and Madagascar, East Africa, Southeast Asia, and several oceanic islands as priority areas for orchid conservation. Orchidaceae provided a model with which to test the sensitivity of automated assessment methods to problems with data availability, data quality, and geographic sampling bias. The IUC-NN identified possibly threatened species with an accuracy of 84.3%, with significantly lower geographic evaluation bias relative to the IUCN Red List and was robust even when data availability was low and there were geographic errors in the input data. Overall, our results demonstrate that automated assessments have an important role to play in identifying species at the greatest risk of extinction.

Original languageEnglish (US)
Pages (from-to)897-908
Number of pages12
JournalConservation Biology
Issue number3
StatePublished - Jun 2021


  • IUC-NN
  • IUCN Red List
  • Lista Roja UICN
  • Orchidaceae
  • aprendizaje mecánico
  • biodiversidad
  • biodiversity
  • calidad de datos
  • data quality
  • machine learning
  • sampling bias
  • sesgo de muestreo

ASJC Scopus subject areas

  • Ecology, Evolution, Behavior and Systematics
  • Nature and Landscape Conservation
  • Ecology


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