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Thermal Plasticity of Photosynthesis: the Role of Acclimation in Forest Responses to a Warming Climate...

by Carla A Gunderson, Keiran H O'hara, Christina M Campion, Ashley V Walker, Nelson T Edwards
Publication Type
Journal
Journal Name
Global Change Biology
Publication Date
Page Numbers
2272 to 2286
Volume
16
Issue
8

The increasing air temperatures central to climate change predictions have the potential to alter forest ecosystem function and structure by exceeding temperatures optimal for carbon gain. Such changes are projected to threaten survival of sensitive species, leading to local extinctions, range migrations, and altered forest composition. This study investigated photosynthetic sensitivity to temperature and the potential for acclimation in relation to the climatic provenance of five species of deciduous trees, Liquidambar styraciflua, Quercus rubra, Quercus falcata, Betula alleghaniensis, and Populus grandidentata. Open-top chambers supplied three levels of warming (+0, +2, and +4 °C above ambient) over 3 years, tracking natural temperature variability. Optimal temperature for CO2 assimilation was strongly correlated with daytime temperature in all treatments, but assimilation rates at those optima were comparable. Adjustment of thermal optima was confirmed in all species, whether temperatures varied with season or treatment, and regardless of climate in the species' range or provenance of the plant material. Temperature optima from 17° to 34° were observed. Across species, acclimation potentials varied from 0.55 °C to 1.07 °C per degree change in daytime temperature. Responses to the temperature manipulation were not different from the seasonal acclimation observed in mature indigenous trees, suggesting that photosynthetic responses should not be modeled using static temperature functions, but should incorporate an adjustment to account for acclimation. The high degree of homeostasis observed indicates that direct impacts of climatic warming on forest productivity, species survival, and range limits may be less than predicted by existing models.