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Overcoming data deficiency in reptiles

Bland, LM; Bohm, M; (2016) Overcoming data deficiency in reptiles. Biological Conservation , 204 (Part A) pp. 16-22. 10.1016/j.biocon.2016.05.018. Green open access

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Abstract

We have no information on the risk of extinction of 21% of reptiles listed as Data Deficient on the Sampled Red List Index (SRLI), an indicator developed to track global change in species status. Data Deficient species are of high research priority, because they contribute to uncertainty in estimates of extinction risk and are neglected by conservation programmes. We review the causes of data deficiency in reptiles; the likely status of Data Deficient reptiles; and possible solutions for their re-assessment. We find that 52% of Data Deficient reptiles lack information on population status and trends, and that few species are only known from type specimens and old records. We build a random forest model for SRLI species of known extinction risk, based on life-history, environmental and threat information. The final model shows perfect classification accuracy (100%) in ten-fold cross validation. We use the model to predict that 56 of 292 Data Deficient reptiles (19%) are at risk of extinction, so the overall proportion of threatened reptiles in the SRLI (19%) remains unchanged. Regions predicted to contain large numbers of threatened Data Deficient reptiles overlap with known centres of threatened species richness. However, the model shows lower accuracy (79%) on 29 species recently re-assessed in the Global Reptile Assessment. Predictive models could be used to prioritize Data Deficient species and reptiles not included in the SRLI, and new reptile assessments could be used to improve model predictions through adaptive learning.

Type: Article
Title: Overcoming data deficiency in reptiles
Open access status: An open access version is available from UCL Discovery
DOI: 10.1016/j.biocon.2016.05.018
Publisher version: http://dx.doi.org/10.1016/j.biocon.2016.05.018
Language: English
Additional information: Copyright © 2016 Elsevier Ltd. All rights reserved. This manuscript version is made available under the CC-BY-NC-ND 4.0 license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Keywords: Data Deficient; Extinction risk; IUCN Red List; Random forests; Reptiles
UCL classification: UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences > Div of Biosciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences > Div of Biosciences > Genetics, Evolution and Environment
URI: http://discovery.ucl.ac.uk/id/eprint/1496817
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