Life history and spatial traits predict extinction risk due to climate change

Journal name:
Nature Climate Change
Volume:
4,
Pages:
217–221
Year published:
DOI:
doi:10.1038/nclimate2113
Received
Accepted
Published online

There is an urgent need to develop effective vulnerability assessments for evaluating the conservation status of species in a changing climate1. Several new assessment approaches have been proposed for evaluating the vulnerability of species to climate change2, 3, 4, 5 based on the expectation that established assessments such as the IUCN Red List6 need revising or superseding in light of the threat that climate change brings. However, although previous studies have identified ecological and life history attributes that characterize declining species or those listed as threatened7, 8, 9, no study so far has undertaken a quantitative analysis of the attributes that cause species to be at high risk of extinction specifically due to climate change. We developed a simulation approach based on generic life history types to show here that extinction risk due to climate change can be predicted using a mixture of spatial and demographic variables that can be measured in the present day without the need for complex forecasting models. Most of the variables we found to be important for predicting extinction risk, including occupied area and population size, are already used in species conservation assessments, indicating that present systems may be better able to identify species vulnerable to climate change than previously thought. Therefore, although climate change brings many new conservation challenges, we find that it may not be fundamentally different from other threats in terms of assessing extinction risks.

At a glance

Figures

  1. Predictors of extinction risk due to climate change
by 2100.
    Figure 1: Predictors of extinction risk due to climate change by 2100.

    Results are for RF models under the Reference climate change scenario. a, Importance of each predictor variable, computed as the relative loss in predictive performance after shuffling (randomly reordering) the values of that predictor variable (Supplementary Methods). Demographic and spatial variables were estimated for the year 2000; recent trend variables were estimated from the simulated period 2000 to 2010. b, Univariate relationships between extinction risk due to climate change and the four most important predictor variables. Y axes are scaled so that 0.0 is the mean value of the response.

  2. Role of interactions between variables in predicting extinction risk due to climate change.
    Figure 2: Role of interactions between variables in predicting extinction risk due to climate change.

    a, Strength of two-way interactions between determinants of extinction risk due to climate change (Reference scenario). The six strongest two-way interactions are shown. b,c, Three-dimensional visualization of the first and second ranked interactions, respectively. Results are based on RF models.

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Author information

Affiliations

  1. Centre for Biodiversity and Environment Research, Department of Genetics, Evolution and Environment, University College London, Gower Street London WC1E 6BT, UK

    • Richard G. Pearson
  2. American Museum of Natural History, Central Park West at 79th Street New York 10024, USA

    • Richard G. Pearson,
    • Peter J. Ersts,
    • Ned Horning &
    • Christopher J. Raxworthy
  3. Department of Ecology and Evolution, Stony Brook University Stony Brook, New York 11794, USA

    • Jessica C. Stanton,
    • Kevin T. Shoemaker,
    • Matthew E. Aiello-Lammens,
    • Hae Yeong Ryu &
    • H. Reşit Akçakaya
  4. The Environment Institute and School of Earth and Environmental Sciences, University of Adelaide South Australia 5005, Australia

    • Damien A. Fordham
  5. NatureServe, 1101 Wilson Boulevard, 15th Floor Arlington, Virginia 22209, USA

    • Jason McNees

Contributions

R.G.P. and H.R.A. designed the study, analysed data and led writing the manuscript; R.G.P. ran ENMs; H.R.A. developed GLH models; J.C.S. selected variables for ENM and linked ENM and GLH models; J.C.S. and H.Y.R. collated demographic data and ran metapopulation simulations; K.T.S. led BRT and RF analyses; M.E.A.-L. sampled models from GLH and extracted simulation results; P.J.E. and N.H. developed ENM input data; D.A.F. led climate analyses; C.J.R. helped with species selection, variable selection and demographic data; J.M. collated species data; all authors discussed results and contributed to writing the manuscript.

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The authors declare no competing financial interests.

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