Physiological and Phenological Responses to Experimental Throughfall Reduction and Parameterization of the 3-PG Model for Modeling Long-Term Responses to Climate Change in Longleaf Pine Forests
Type of DegreePhD Dissertation
Forestry and Wildlife Science
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Ongoing changes in the earth’s climate are having profound effects on forest ecosystems in many regions. Longleaf pine (Pinus palustris Mill.) is considered one of the most drought-resistant pines in the southeastern U.S. and could serve as a suitable long-term carbon sink and important species for adapting southern forests to climate change. However, questions remain about the sensitivity of longleaf pine to reduced water availability over prolonged periods. For this reason, understanding and forecasting how longleaf pine forests will respond to projected changes in precipitation is necessary for managing these forests in the face of climate change. Here, we studied how three years of reduced rainfall – imposed by a 40% experimental throughfall reduction – impacted leaf- and canopy-scale physiology, shoot and foliage development patterns, and canopy-scale leaf area and litterfall dynamics in established longleaf pine trees. Although among the most drought-resistant pine species in the southeastern U.S., we still expected that a 40% reduction in throughfall would result in significant changes in tree physiology and phenology, as well as canopy dynamics. However, we found that throughfall reduction resulted in rather small reductions in leaf- and canopy-scale function. We also found that reductions in throughfall had small effects on shoot and needle phenology and growth, as well as on leaf area index and litterfall dynamics. Our results show quite clearly that longleaf pine trees and established forests may be relatively resistant to reductions in total rainfall and reduced water availability. If reductions in water availability persist over the long term, more drought-resistant species such as longleaf could be favored over less drought-resistant species. To test this assumption, we also carried out the first parameterization of the 3-PG process-based model for planted longleaf pine stands. The 3-PG model uses a combination of climate, stand, and physiological parameters for performing predictions, and represents a practical tool for forecasting forest carbon sequestration potential, establishing better management strategies, and assessing the impact of projected climate changes on forests. We used a large and geographically extensive long-term dataset across the species' range to estimate important parameters for the model. The model was tested against data from stands of varying climate and soil characteristics that were distributed across the southeastern United States. Although some factors need further attention as new datasets become available, the parameters reported here allowed 3-PG to produce accurate estimates, with predictions showing good correspondence with observations of most stand growth and development variables. The use of the 3-PG model for longleaf pine stands can help improve the predictability of longleaf pine forest productivity and describe the growth and physiological dynamics of this species across a wide range of ages, stand, and climate characteristics.