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2019
Abstract
Addressing Uncertainties in Machine Learning Predictions of Conservation Status.
Extinction risk assessments are increasingly important to many stakeholders (Bennun et al. 2017) but there remain large gaps in our knowledge about the status of many species. The IUCN Red List of Threatened Species (IUCN 2019, hereafter Red List) is the most comprehensive assessment of extinction risk. However, it includes...Walker, Barnaby ; Leão, Tarciso ; Bachman, Steven ; Lucas, Eve ; Nic Lughadha, Eimear
Conservation assessment, Machine learning, Natural history collections, Uncertainty, and IUCN Red List
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2021
Journal article
Evidence-based guidelines for developing automated conservation assessment methods [PREPRINT].
Assessing species’ extinction risk is vital to setting conservation priorities. However, assessment endeavours like the IUCN Red List of Threatened Species have significant gaps in coverage of some taxonomic groups. Automated assessment (AA) methods are gaining popularity to fill these gaps, leveraging improvements in computing and digitally-available information. Choices made...Walker, Barnaby E. ; Leão, Tarciso C.C. ; Bachman, Steven P. ; Lucas, Eve ; Nic Lughadha, Eimear
Biodiversity conservation, Automation, Machine learning, IUCN Red List, and Conservation assessments