Abstract
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 language | English (US) |
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Pages (from-to) | 897-908 |
Number of pages | 12 |
Journal | Conservation Biology |
Volume | 35 |
Issue number | 3 |
DOIs | |
State | Published - Jun 2021 |
Funding
We thank WCSP for provision of the species list and information on life form and geographic distribution and all data contributors and publishers contributing to GBIF's efforts to collect, digitize, store, and publish orchid records. We thank A. T. Clark for discussion and help on the permutation test and 3 anonymous reviewers for helpful comments on previous versions of the manuscript. The scientific results were in part computed at the High-Performance Computing (HPC) Cluster EVE, a joint effort of both the Helmholtz Centre for Environmental Research – UFZ (http://www.ufz.de/) and the German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig (http://www.idiv-biodiversity.de/). A.Z. and P.V. acknowledge funding by sDiv, the Synthesis Center for Biodiversity Sciences – a unit of the German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig – funded by the German Research Foundation (FZT 118). T.M.K. acknowledges funding from the Alexander von Humboldt Foundation in the framework of the Alexander von Humboldt Professorship. D.S. received funding from the Swiss National Science Foundation (PCEFP3_187012; FN-1749) and the Swedish Research Council (VR: 2019–04739). Open access funding enabled and organized by Projekt DEAL. We thank WCSP for provision of the species list and information on life form and geographic distribution and all data contributors and publishers contributing to GBIF's efforts to collect, digitize, store, and publish orchid records. We thank A. T. Clark for discussion and help on the permutation test and 3 anonymous reviewers for helpful comments on previous versions of the manuscript. The scientific results were in part computed at the High‐Performance Computing (HPC) Cluster EVE, a joint effort of both the Helmholtz Centre for Environmental Research – UFZ ( http://www.ufz.de/ ) and the German Centre for Integrative Biodiversity Research (iDiv) Halle‐Jena‐Leipzig ( http://www.idiv-biodiversity.de/ ). A.Z. and P.V. acknowledge funding by sDiv, the Synthesis Center for Biodiversity Sciences – a unit of the German Centre for Integrative Biodiversity Research (iDiv) Halle‐Jena‐Leipzig – funded by the German Research Foundation (FZT 118). T.M.K. acknowledges funding from the Alexander von Humboldt Foundation in the framework of the Alexander von Humboldt Professorship. D.S. received funding from the Swiss National Science Foundation (PCEFP3_187012; FN‐1749) and the Swedish Research Council (VR: 2019–04739).
Keywords
- 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