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Publication Details
AFRICAN RESEARCH NEXUS
SHINING A SPOTLIGHT ON AFRICAN RESEARCH
agricultural and biological sciences
Predicting extinction risks under climate change: Coupling stochastic population models with dynamic bioclimatic habitat models
Biology Letters, Volume 4, No. 5, Year 2008
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Description
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. © 2008 The Royal Society.
Available Materials
https://efashare.b-cdn.net/share/pmc/articles/PMC2610061/bin/rsbl20080049s13.doc
Authors & Co-Authors
Keith, David A.
Unknown Affiliation
Akçakaya, H. Reşit
Unknown Affiliation
Thuiller, Wilfried
Unknown Affiliation
Midgley, Guy F.
Unknown Affiliation
Pearson, Richard G.
Unknown Affiliation
Phillips, Steven J.
Unknown Affiliation
Regan, Helen M.
Unknown Affiliation
Araújo, Miguel B.
Unknown Affiliation
Rebelo, Tony G.
Unknown Affiliation
Statistics
Citations: 639
Authors: 9
Affiliations: 10
Identifiers
Doi:
10.1098/rsbl.2008.0049
ISSN:
17449561
e-ISSN:
1744957X
Research Areas
Environmental
Study Design
Cross Sectional Study