

@mastersthesis {7096,
	title = {Quantifying plant trait relationships at local and global scales},
	year = {2011},
	note = {3480144},
	school = {Princeton University},
	type = {phdPh.{D}.},
	address = {Ann Arbor},
	abstract = {Plant traits are the physiological and morphological characteristics that comprise the carbon, nutrient and water economies of plants. Plant trait relationships are central to efforts to characterize the physiological strategies that plants employ to grow and coexist with one another in different environments and to describe the evolutionary processes by which these strategies have come to be. Characterizing inter- and intraspecific trends in the physiology and demography of plant species across environmental gradients is an important goal of plant ecology and is necessary for understanding the physiological basis of species distributions and creating predictive models of plant community dynamics in response to global climate change. This thesis is a substantial contribution toward both ensuring the accuracy of global leaf trait relationships and understanding the linkages between leaf and stem traits and the distributions and demography of woody plant species across soil moisture gradients in two distinct savannah and forest ecosystems. I define and resolve problems in the current prevailing methods of quantifying interspecific leaf trait relationships. I describe the most comprehensive examination to date of leaf size scaling of leaf physiological traits and its impact on the structure of trait relationships. I introduce statistical tools to properly analyze leaf trait data to resolve incongruities in previous analyses of leaf traits. I employ these techniques to re-analyze a large global leaf trait dataset and update the global leaf economics spectrum. I then apply these techniques to analyze my own data from two distinct field studies to link leaf and stem traits to species distributions and demographic patterns in sets of woody species growing across soil moisture (texture) gradients in a Kenyan acacia savannah and in forests in the northern Midwestern United States. These field data are an important contribution toward understanding the physiological basis of tree species distributions and creating a predictive model of forest dynamics. These techniques and analyses presented in this thesis will greatly improve the effectiveness and accuracy of the use of plant trait relationships in important applications ranging from predicting agricultural yields to the response of the biosphere to global change.},
	keywords = {0309:Plant biology, 0329:Ecology, Biological sciences, Ecology, Leaf traits, Plant biology, Plant traits, Specific leaf area, Wood},
	url = {http://search.proquest.com/docview/899263646?accountid=14270},
	author = {DeNoyer, Jeanne Lorraine}
}
