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Predicting extinction risks under climate change: Coupling stochastic population models with dynamic bioclimatic habitat models

  • David A. Keith
  • , H. Resit Akçakaya
  • , Wilfried Thuiller
  • , Guy F. Midgley
  • , Richard G. Pearson
  • , Steven J. Phillips
  • , Helen M. Regan
  • , Miguel B. Araújo
  • , Tony G. Rebelo
  • NSW Department of Planning, Industry and Environment
  • CNRS
  • South African National Biodiversity Institute
  • American Museum of Natural History
  • AT&T
  • University of California at Riverside
  • National Museum of Natural Sciences

Research output: Contribution to journalArticlepeer-review

553 Scopus citations

Abstract

Species responses to climate change may be influenced by changes in available habitat, as well as population processes, species interactions and interactions between demographic and landscape dynamics. Current methods for assessing these responses fail to provide an integrated view of these influences because they deal with habitat change or population dynamics, but rarely both. In this study, we linked a time series of habitat suitability models with spatially explicit stochastic population models to explore factors that influence the viability of plant species populations under stable and changing climate scenarios in South African fynbos, a global biodiversity hot spot. Results indicate that complex interactions between life history, disturbance regime and distribution pattern mediate species extinction risks under climate change. Our novel mechanistic approach allows more complete and direct appraisal of future biotic responses than do static bioclimatic habitat modelling approaches, and will ultimately support development of more effective conservation strategies to mitigate biodiversity losses due to climate change.

Original languageEnglish
Pages (from-to)560-563
Number of pages4
JournalBiology Letters
Volume4
Issue number5
DOIs
StatePublished - Oct 23 2008

Keywords

  • Bioclimatic envelope
  • Fire
  • Fynbos
  • Niche model
  • Population viability analysis
  • Uncertainty

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