Collaborative Research: MRA: Evaluating hypotheses of long-term woody carbon dynamics with empirical data

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Changes in the vegetation of the American Upper Midwest over a ten-thousand year period, between the end of the last ice age and the 19th century are recorded in fossil pollen buried in the sediments of lakes, swamps and bogs. It is necessary to use this fossil record because written vegetation surveys did not begin until the industrial period. Past investigation of these pollen assemblages has revealed shifts in vegetation with climate change (such as post-glacial warming) and natural disturbance (such as the wildfires that prevented trees from invading prairies). This project takes advantage of new statistical and computer modeling approaches to quantify post-glacial changes in Midwestern vegetation and climate in terms that are useful to society (changes in the numbers and biomass of trees on the landscape over centuries). Better quantification of pre-industrial Midwestern vegetation is important for two reasons related to this project. First, the pre-industrial landscape is a key benchmark for: land managers (a baseline for conservation, timber management, etc.); global change researchers (changes in pre-industrial vegetation biomass are among the most uncertain components of the global carbon cycle); and the public (pre-industrial vegetation helps define the character of the land). This project will engage with each of these communities through: conferences, publicly-available reconstructions of thousands of years of vegetation change, and free, online education modules that will be taught to a broad array of students, from high-schoolers to professional resource managers. Second, better quantification of long-term vegetation fills a key gap in our basic understanding of the ecology of vegetation change. Researchers know that changing climate and disturbances, like fire, can change vegetation suddenly and irreversibly over large areas, but the absence of well-quantified records of long-term vegetation change has been an obstacle to ecological understanding of how and when such changes actually occur. This project uses an integrated modeling framework to ensure that the new reconstructions of vegetation changes, which span centuries of tree-lifetimes, will complement the existing understanding of vegetation change, which is primarily based on data only collected over recent decades. In addition to the conferences, public outreach and online training, this project will also educate and train 4 graduate students in quantitative paleo-ecology.The project builds on and improves a recently-developed statistical model of changes in aboveground woody biomass pools (AGWB) across the Midwest over the last 10,000 years. These reconstructions of AGWB over space and time are the data that test a set of hypotheses about Midwestern vegetation history centered on the overall question of whether internal feedbacks (for example between vegetation and wildfire) played important roles in stabilizing or destabilizing the response of AGWB to changing climates over the Holocene, and whether modern land-use has altered these feedbacks significantly. The project will make joint probabilistic inference about changing biomass pools, demographic rates in forests, and climate drivers constrained by the AGWB reconstructions, an ensemble of modeled Holocene climate trajectories, and alternative models of the ecological process of forest change. Nonlinear threshold responses of vegetation to climate are a special focus of these retrospective “hindcasts”. The quantitative tools developed in the project underlie education and outreach programs to students, land managers, and other researchers through exercises, modules, and curricula that the researchers will test-drive in a diverse set of short courses, then made available to the public online. Training of four graduate students will also be interwoven with the research and outreach objectives.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.