4.7 Article

A physiological trait-based approach to predicting the responses of species to experimental climate warming

Journal

ECOLOGY
Volume 93, Issue 11, Pages 2305-2312

Publisher

WILEY
DOI: 10.1890/11-2296.1

Keywords

critical thermal maximum; Duke Forest; North Carolina; USA; ectotherm responses to global warming; Formicidae; global change; Harvard Forest; Massachusetts; USA; maximum entropy; physiology; species distribution model; temperate hardwood forests; eastern North America; thermal tolerance

Categories

Funding

  1. U.S. DOE PER award [DEFG02-08ER64510]
  2. NASA Biodiversity Grant [ROSES-NNX09AK22G]
  3. NSF Career grant [NSF 0953390]
  4. Division Of Environmental Biology
  5. Direct For Biological Sciences [0953390] Funding Source: National Science Foundation

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Physiological tolerance of environmental conditions can influence species-level responses to climate 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 vs. correlative species distribution models (SDMs) which are popular forecasting tools for modeling the effects of climate change. Thermal tolerances predicted the responses of 19 ant species to experimental climate 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 six 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 sitelevel responses.

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