A Physiological Trait-Based Approach To Predicting The Responses Of Species To Experimental Climatic Warming

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A Physiological Trait-Based Approach To Predicting The Responses Of Species To Experimental Climatic Warming

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Title: A Physiological Trait-Based Approach To Predicting The Responses Of Species To Experimental Climatic Warming
Author: Ellison, Aaron M.; Diamond, Sarah E.; Nichols, Lauren M.; McCoy, Neil; Hirsch, Christopher; Pelini, Shannon Lynn; Sanders, Nathan J.; Gotelli, Nicholas J.; Dunn, Robert R.

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Citation: Diamond, Sarah E., Lauren M. Nichols, Neil McCoy, Christopher Hirsch, Shannon L. Pelini, Nathan J. Sanders, Aaron M. Ellison, Nicholas J. Gotelli, and Robert R. Dunn. Forthcoming. A physiological trait-based approach to predicting the responses of species to experimental climatic warming. Ecology.
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Abstract: Physiological tolerance of environmental conditions can influence species-level responses to climatic change. Here, we used species-specific thermal tolerances to predict the community responses of ant species to experimental forest-floor warming at the northern and southern boundaries of temperate hardwood forests in eastern North America. We then compared the predictive ability of thermal tolerance versus correlative species distribution models (SDMs) which are popular forecasting tools for modeling the effects of climatic change. Thermal tolerances predicted the responses of 19 ant species to experimental climatic warming at the southern site, where environmental conditions are relatively close to the ants' upper thermal limits. In contrast, thermal tolerances did not predict the responses of the 6 species in the northern site, where environmental conditions are relatively far from the ants' upper thermal limits. Correlative SDMs were not predictive at either site. Our results suggest that, in environments close to a species' physiological limits, physiological trait-based measurements can successfully forecast the responses of species to future conditions. Although correlative SDMs may predict large-scale responses, such models may not be accurate for predicting site-level responses.
Published Version: doi:10.1890/11-2296.1
Terms of Use: This article is made available under the terms and conditions applicable to Other Posted Material, as set forth at http://nrs.harvard.edu/urn-3:HUL.InstRepos:dash.current.terms-of-use#LAA
Citable link to this page: http://nrs.harvard.edu/urn-3:HUL.InstRepos:9723074

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  • FAS Scholarly Articles [7374]
    Peer reviewed scholarly articles from the Faculty of Arts and Sciences of Harvard University
 
 

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