A Theoretical and Computational Framework for Linking Tree form and Function to Forest Diversity and Productivity

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The University of Wyoming is awarded a grant to develop a scaling framework for understanding forest diversity and productivity. This study addresses three questions that are paramount to developing this framework. (1) How do traits related to tree form (e.g., allometries, morphology) and function (e.g., physiology, growth, allocation, survival) vary between species, and how do evolutionary versus environmental drivers affect trait variability?(2) Is a species-specific representation of form and function necessary to accurately describe community and ecosystem properties (e.g., diversity, succession, productivity, carbon cycling)? (3) How do we develop a general scaling framework for predicting large-scale forest dynamics that includes species-specific trait variability and key physiological mechanisms? Towards addressing these questions, this study develops and applies data-model integration methodologies, including: (i) dynamic process models that link tree form and function, incorporate key plant functional traits, and are applicable to broad spatial and temporal scales; (ii) new meta-analysis methods for analyzing vast amounts of literature information on species-specific traits that incorporate phylogenetic relationships and overcome limitations common to ³classical² meta-analytic approaches; and(iii) rigorous statistical and computational methods for informing the process model with large and disparate data sources (i.e., literature, forest inventory, and tree-ring width databases). This highly integrative approach will provide a major step towards building and testing a general scaling framework. The broader impacts of this work include multiple training opportunities in data-model integration methods for undergraduates through post-graduate scientists. Methods developed in this study will be partly disseminated through an annual, daylong workshop on Bayesian analysis in ecology for the Ecological Society of America annual meetings. Training in data-model integration, and specifically Bayesian methods, is lacking in many university curriculums and two new graduate-level courses in Bayesian data analysis and advanced/computational Bayesian will be further developed and integrated, providing a modern curriculum in applied statistical modeling and computing at the University of Wyoming (UW). Training in modern statistical modeling will offer a unique educational opportunity for those PhD students in UW¹s new and vibrant graduate Program in Ecology. This study will also create independent research opportunities for UW undergraduates, and it will provide one post-doctoral and two PhD students with unique interdisciplinary teaching, mentoring, and research training in ecology, statistics, mathematics, and computational science.