Department of Statistics, University of Wyoming
in collaboration with Kiona Ogle, Departments of Statistics and Botany, University of Wyoming
and Darren Gemoets, Department of Statistics, University of Wyoming
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.