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When is a species really extinct? Testing extinction inference from a sighting record to inform conservation assessment

Collen, B; Purvis, A; Mace, GM; (2010) When is a species really extinct? Testing extinction inference from a sighting record to inform conservation assessment. Diversity and Distributions , 16 (5) 755 - 764. 10.1111/j.1472-4642.2010.00689.x.

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Abstract

Aim: The global extinction of a species typically represents the end point in a series of population extinctions, during which unique evolutionary history is lost at every stage. Insight into the process of extinction can provide the means to identify species at high risk, but the number of extinctions being identified languishes far behind true totals. More proactive ways of inferring extinction from limited data are required. Location: Historic sightings, collections and specimen data from Australia and Asia. Method: We used a technique called optimal linear estimation to analyse the sightings record of mammal and bird species of varying ecology, life history and population demography. The mammal species chosen were all considered regionally extinct in the literature, while the bird species chosen had all been highlighted as candidates for the new IUCN Red List category flag: Critically Endangered (Possibly Extinct). Results: Nine of the ten mammal species were predicted to be probably extinct, but only two with 95% certainty. Seven of the ten bird species were predicted to be probably extinct, four with 95% certainty. Main conclusions: Superficially, determining whether a species is extinct might seem a simple task, whereby we either find a species extant, or it is extinct. In reality, however, the task is much more complex. Techniques such as optimal linear estimation, in combination with other data sources, and knowledge of recording effort, may prove useful in inferring extinction across a variety of taxa but should not be used in isolation. © 2010 Blackwell Publishing Ltd.

Type:Article
Title:When is a species really extinct? Testing extinction inference from a sighting record to inform conservation assessment
DOI:10.1111/j.1472-4642.2010.00689.x
UCL classification:UCL > School of Life and Medical Sciences > Faculty of Life Sciences > Biosciences (Division of) > Genetics, Evolution and Environment

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