Historical drivers of extinction risk: using past evidence to direct future monitoring

Di Marco, Moreno, Collen, Ben, Rondinini, Carlo and Mace, Georgina M. (2015) Historical drivers of extinction risk: using past evidence to direct future monitoring. Royal Society of London. Proceedings B. Biological Sciences, 282 1813: 1-9. doi:10.1098/rspb.2015.0928

Author Di Marco, Moreno
Collen, Ben
Rondinini, Carlo
Mace, Georgina M.
Title Historical drivers of extinction risk: using past evidence to direct future monitoring
Journal name Royal Society of London. Proceedings B. Biological Sciences   Check publisher's open access policy
ISSN 1471-2954
Publication date 2015-08-05
Sub-type Article (original research)
DOI 10.1098/rspb.2015.0928
Open Access Status Not Open Access
Volume 282
Issue 1813
Start page 1
End page 9
Total pages 9
Place of publication London, United Kingdom
Publisher The Royal Society Publishing
Collection year 2016
Language eng
Abstract Global commitments to halt biodiversity decline mean that it is essential to monitor species' extinction risk. However, the work required to assess extinction risk is intensive. We demonstrate an alternative approach to monitoring extinction risk, based on the response of species to external conditions. Using retrospective International Union for Conservation of Nature Red List assessments, we classify transitions in the extinction risk of 497 mammalian carnivores and ungulates between 1975 and 2013. Species that moved to lower Red List categories, or remained Least Concern, were classified as ‘lower risk'; species that stayed in a threatened category, or moved to a higher category of risk, were classified as ‘higher risk'. Twenty-four predictor variables were used to predict transitions, including intrinsic traits (species biology) and external conditions (human pressure, distribution state and conservation interventions). The model correctly classified up to 90% of all transitions and revealed complex interactions between variables, such as protected areas (PAs) versus human impact. The most important predictors were: past extinction risk, PA extent, geographical range size, body size, taxonomic family and human impact. Our results suggest that monitoring a targeted set of metrics would efficiently identify species facing a higher risk, and could guide the allocation of resources between monitoring species' extinction risk and monitoring external conditions.
Keyword Biodiversity
Human threats
Random forest model
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: School of Geography, Planning and Environmental Management Publications
Official 2016 Collection
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Citation counts: TR Web of Science Citation Count  Cited 1 times in Thomson Reuters Web of Science Article | Citations
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