Amphibians over the edge: silent extinction risk of Data Deficient species

AIM: To apply mathematical models to the task of predicting extinction risk for species currently listed as ‘Data Deficient’ (DD) by the International Union for the Conservation of Nature (IUCN). We demonstrate this approach by applying it globally to amphibians, the vertebrate group recognized as b...

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Bibliographic Details
Published in:Diversity & distributions 2014-07, Vol.20 (7), p.837-846
Main Authors: Howard, Sam D, Bickford, David P, Ferrier, Simon
Format: Article
Language:English
Subjects:
Amphibia. Reptilia
amphibians
Animal and plant ecology
Animal, plant and microbial ecology
Anura
Applied ecology
artificial intelligence
Biodiversity
BIODIVERSITY RESEARCH
Biological and medical sciences
Caudata
Conservation biology
Endangered & extinct species
environmental factors
extinction
Fundamental and applied biological sciences. Psychology
General aspects
geographic information systems
Gymnophiona
habitat destruction
IUCN Redlist
life history
machine learning
Mathematical models
prediction
predictive model
prioritization
Reptiles & amphibians
risk
Synecology
Vertebrates: general zoology, morphology, phylogeny, systematics, cytogenetics, geographical distribution
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Summary:AIM: To apply mathematical models to the task of predicting extinction risk for species currently listed as ‘Data Deficient’ (DD) by the International Union for the Conservation of Nature (IUCN). We demonstrate this approach by applying it globally to amphibians, the vertebrate group recognized as being most extinction threatened and having the largest proportion of DD species. We combine model predictions with current extinction risk knowledge to highlight regions of greatest disparity between known and predicted risk, where potential species extinctions may be overlooked. LOCATION: Global. METHODS: Using global amphibian distribution data obtained from the IUCN and species trait data, we apply machine learning randomForest models to predict extinction risk of DD species from life history traits, environmental variables and habitat loss. These models are trained using data for species that have been assigned to an extinction risk category (other than DD) by the IUCN. We then combine predictions for DD species with IUCN assessment data in a GIS framework to highlight anomalies between current knowledge of amphibian extinction risk and our model predictions. RESULTS: We show that DD amphibian species are likely to be more threatened with extinction than their fully assessed counterparts. Regions in South America, central Africa and North Asia are particularly at risk due to lack of species knowledge and higher extinction risk than currently recognized. MAIN CONCLUSIONS: Application of predictive models ranking regions and species most in need of primary research allows prioritization of limited resources in an informed context, minimizing risk of unnoticed species' extinction.
ISSN:1366-9516
1472-4642
DOI:10.1111/ddi.12218