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Disequilibrium and relaxation times for species responses to climate change

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  • Loehle, Craig

Abstract

Climate change is widely expected to pose a threat to many of Earth’s plant and animal species. Based on climate models, a multitude of studies project that certain species will not be able to migrate fast enough to keep up with changing environmental conditions, presenting a greater risk of their possible extinction. However, many of these studies are based on correlative climate niche models that represent the current living conditions of species and may not consider their ability to tolerate projected changes in future climate, including the stimulative effects of rising CO2 for plant growth and drought tolerance. SDMs also are not usually run with sufficiently detailed spatial data to account for refugia. These and other aspects of model-based niche studies can potentially combine to mischaracterize the risk to species due to climate change. Even a SDM that perfectly predicts future equilibrium geographic range following climate change, however, does not yield a direct estimate of extinction risk. The key question is “What is the transient response to a climate disequilibrium situation?” The concept of relaxation is introduced to evaluate time-scales for responses at the trailing edge of species’ ranges. Simulation of relaxation at a forest ecotone shows the relaxation response in some cases can be hundreds of years. A classification of relaxation responses based on tolerance and dispersal ability is proposed as a second stage analysis for SDM studies.

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  • Loehle, Craig, 2018. "Disequilibrium and relaxation times for species responses to climate change," Ecological Modelling, Elsevier, vol. 384(C), pages 23-29.
    • Handle: RePEc:eee:ecomod:v:384:y:2018:i:c:p:23-29
    DOI: 10.1016/j.ecolmodel.2018.06.004
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    References listed on IDEAS

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    3. Schratz, Patrick & Muenchow, Jannes & Iturritxa, Eugenia & Richter, Jakob & Brenning, Alexander, 2019. "Hyperparameter tuning and performance assessment of statistical and machine-learning algorithms using spatial data," Ecological Modelling, Elsevier, vol. 406(C), pages 109-120.

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