Environmental Limitations to Forest Growth and Productivity in North America Public Deposited

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  • March 21, 2019
  • Dannenberg, Matthew
    • Affiliation: College of Arts and Sciences, Department of Geography
  • Terrestrial primary production—the carbohydrates produced by plants via photosynthesis—is the entry point of energy and carbon into ecosystems, forming the base of the food chain and a sink for anthropogenic CO2. Primary production can be limited by unfavorable environmental conditions, including non-optimal temperatures, water deficits, or inadequate nutrient supply. At present, our ability to model how environmental factors reduce primary production remains limited. This leads to uncertainty both in the remotely sensed models used to monitor primary production and in climate models that depend on accurate representation of the land surface and biosphere. Given the importance of vegetation to humanity and the Earth system, in this dissertation I use tree rings and remote sensing to examine the environmental drivers of forest growth and productivity in North America. In particular, this research examines how forests are influenced by climate, atmospheric circulation, and land surface characteristics like topography and soil quality. I first examine how the seasonality of temperature and precipitation affect growth of ponderosa pine in the U.S. Pacific Northwest. I then develop a new tree-ring “environmental stress” index, which I use to model the climatic, topographic, and edaphic drivers of forest growth across the conterminous U.S. Finally, I examine how variability of the Pacific storm track acts as a synoptic-scale driver of hydroclimate and vegetation activity in western North America. In this research, I show that forest primary productivity is significantly influenced by moisture supply across multiple seasons, particularly in western North America. Westerly Pacific storm tracks are largely responsible for delivery of moisture to this region, and I show that northerly shifts of these storm tracks reduce both water supply and primary production in the northwestern U.S. Using a set of machine learning model experiments, I also demonstrate that models of forest growth that incorporate topographic and soil characteristics outperform those based solely on climate. Taken together, these findings provide a framework for improving the models used to reconstruct past climate from tree-ring data and to monitor primary production with remote sensing, while also providing insight into potential influences of a warming climate on the biosphere.
Date of publication
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Rights statement
  • In Copyright
  • Song, Conghe
  • Riveros-Iregui, Diego
  • Wise, Erika
  • Pavelsky, Tamlin
  • Moody, Aaron
  • Doctor of Philosophy
Degree granting institution
  • University of North Carolina at Chapel Hill Graduate School
Graduation year
  • 2017

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