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Ecological–economic optimization of biodiversity conservation under climate change
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Journal Article
Ecological–economic optimization of biodiversity conservation under climate change
2011
Overview
Little information exists to guide conservation planning under climate change uncertainty. Now a study combines ecological predictions with an economic decision framework to investigate strategies to minimize extinction risk in one of the world’s most threatened ecosystems—the South African fynbos. The research finds that the best conservation options vary nonlinearly with available budget.
Substantial investment in climate change research has led to dire predictions of the impacts and risks to biodiversity. The Intergovernmental Panel on Climate Change fourth assessment report
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cites 28,586 studies demonstrating significant biological changes in terrestrial systems
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. Already high extinction rates, driven primarily by habitat loss, are predicted to increase under climate change
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,
4
,
5
,
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. Yet there is little specific advice or precedent in the literature to guide climate adaptation investment for conserving biodiversity within realistic economic constraints
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. Here we present a systematic ecological and economic analysis of a climate adaptation problem in one of the world’s most species-rich and threatened ecosystems: the South African fynbos. We discover a counterintuitive optimal investment strategy that switches twice between options as the available adaptation budget increases. We demonstrate that optimal investment is nonlinearly dependent on available resources, making the choice of how much to invest as important as determining where to invest and what actions to take. Our study emphasizes the importance of a sound analytical framework for prioritizing adaptation investments
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. Integrating ecological predictions in an economic decision framework will help support complex choices between adaptation options under severe uncertainty. Our prioritization method can be applied at any scale to minimize species loss and to evaluate the robustness of decisions to uncertainty about key assumptions.
Publisher
Nature Publishing Group UK,Nature Publishing Group
