A Theoretical and Statistical Modeling Framework for Linking Tree Form  and Function to Forest Diversity and Productivity

Forest ecosystems cover nearly 50% of the Earth's land area and  contain about 90% of the global vegetation carbon. Thus, climate  change impacts on forests will ultimately affect biodiversity,  productivity, and carbon cycling of the terrestrial biosphere.
This  talk gives an overview of our initial efforts and proposal to develop  and test a statistical scaling framework for understanding forest  diversity and productivity, which may be seen as a necessary step  toward future efforts to develop and apply this framework to forecast  impacts of climate change on forests.  We focus our talk on our recent  work on meta-analysis of literature data on tree growth traits and on  a relatively unique application of reversible jump MCMC for fitting  deterministic individual-based models (IBM) of tree growth and  mortality to forest inventory data. 
We discuss how this work relates  to the larger problem of estimating parameters in the IBM and  ultimately to developing our scaling framework.