Thursday, August 5, 2010: 8:00 AM-11:30 AM
401-402, David L Lawrence Convention Center
OOS 43 - Combining Experiments, Process Studies, and Models to Forecast the Future of Ecosystems, Communities, and Populations
Changing climate, land use change, and invasive species will cause significant impacts to ecosystem structure and function. In order to prepare for these changes, society needs ecological forecasts. Ecological forecasts are quantitative predictions that are critical for documenting and advancing scientific understanding and useful in societal application of knowledge (Katz and Murphy, 2005). Forecasting is necessary for advancing theory because it regularly confronts theory with observations via predictions. Ecological forecasting includes two closely related activities. The first is similar to a weather forecast, that is, an attempt to discern the most likely future state of an ecological system. The second activity adds an additional factor to study the most likely future state of a system, given a decision today (Clark et al., 2001). The first activity is often relevant for short-term forecasts where the system’s own dynamics most strongly govern its change over time (for example, forecasting the likely rate of spread of an invasive species). The second comes into play when alternate management actions or scenarios are being considered (for example, forecasting the likely impacts of alternate forest fire risk mitigation practices on biodiversity). While ecological forecasting typically requires deterministic knowledge of the process being modeled, forecasts are usually probabilistic and provide an estimate of the probability of the future state, not just a point estimate of its value. This session examines ecological forecasts across ecosystems, communities, and populations that are informed by observational data, process studies, and experiments. The session will introduce concepts of forecasting and data assimilation. Speakers will discuss ecosystem forecasts of the carbon cycle in both unmanaged ecosystems and highly managed agricultural settings. The session will also consider forecasts of phenology, community dynamics and populations under changing climate conditions.
Organizer:Michael Keller, NEON Inc.
Co-organizer:Yiqi Luo, University of Oklahoma
Moderator:Michael Keller, NEON Inc.
8:00 AMOOS 43-1Data assimilation and ecological forecasting in a data-rich era
Yiqi Luo, University of Oklahoma, Kiona Ogle, University of Wyoming, Colin Tucker, University of Wyoming, Shenfeng Fei, University of Oklahoma, Shannon L. LaDeau, Cary Insitute of Ecosystem Studies, James S. Clark, Duke University, David S. Schimel, National Ecological Observatory Network
8:20 AMOOS 43-2The carbon cycle of Arctic Fennoscandia: Assimilating multi-scale observations into ecological models
Mathew Williams, University of Edinburgh, Paul C. Stoy, University of Edinburgh, Robert Baxter, Durham University, Gareth Phoenix, University of Sheffield, T. Hill, University of Edinburgh, J. Moncrieff, University of Edinburgh, V. Sloan, University of Sheffield, J. Evans, Centre for Ecology and Hydrology, Richard Harding, Centre for Ecology and Hydrology, B. Fletcher, University of Sheffield, R. Poyatos, Durham University, I. Hartley, University of Stirling, L. Street, University of Edinburgh, T. Wade, University of Edinburgh, J. Subke, University of York, Mathias Disney, UCL, Ana Prieto-Blanco, UCL, Maurizio Mencuccini, University of Edinburgh, Phil Ineson, University of York, Brian Huntley, Durham University, Andreas Heinemeyer, University of York, Philip Wookey, University of Stirling
8:40 AMOOS 43-3Real time forecast of forest carbon dynamics using ensemble Kalman filtering method
Shenfeng Fei, University of Oklahoma, Zhongda Zhang, University of Oklahoma, Yiqi Luo, University of Oklahoma
9:00 AMOOS 43-4PECAn, a workflow management tool for real-time data assimilation and forecasting: Evaluation of a switchgrass (Panicum virgatum) cropping system
David LeBauer, University of Illinois Urbana-Champaign, Dan Wang, University of Illinois, Xiaohui Feng, University of Illinois, Michael Dietze, University of Illinois
9:20 AMOOS 43-5Forecasts of phenological responses to climate change
Andrew D. Richardson, Harvard University
9:40 AM Break
9:50 AMOOS 43-6Merging evidence to quantify climate impacts on large competing trees
James S. Clark, Duke University
10:10 AMOOS 43-7Population forecasting: Assimilating models and data to understand dynamics of brucellosis in the Yellowstone bison population
N. Thompson Hobbs, Colorado State University, Chris Geremia, National Park Service, P.J. White, National Park Service, John Treanor, National Park Service, Rick Wallen, National Park Service, Jennifer A. Hoeting, Colorado State University
10:30 AMOOS 43-8Data assimilation and simulation in the NEON observatory
David S. Schimel, National Ecological Observatory Network, Paul Duffy, Neptune and Company, Inc

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