Friday, August 8, 2008: 8:00 AM-11:30 AM
102 C, Midwest Airlines Center
SYMP 23 - Toward Ecological Forecasting: Applications of Model-Data Fusion Techniques
The field of ecology has been quickly transformed into a data-rich scientific enterprise due to (1) development and implementation of environmental sensors and (2) continuous measurements of ecological processes by research networks such as LTER and AmeriFlux. The implementation of NEON (National Ecological Observatory Network) will dramatically increase the amount of ecological time-series data collection. There is a rapidly growing demand to process massive data into ecologically meaningful information products to support decision making for resource management and climate change mitigation. Forecasting generally means a capability to project future states of the system by modeling the evolution of the system as a function of its state at an initial time. To forecast dynamics of ecological systems, we need models to represent ecological systems. Data are required to accurately define model parameters, characterize the initial state of the system, and observe its evolution over time. The latter is particularly important in systems with complex or chaotic dynamics, where accurate parameters and initial conditions are insufficient to model time evolution. The goals of this symposium are (1) to prepare ESA for a data-rich, NEON-type era and (2) to catalyze transformation of ecological research to data processing, data-model assimilation, and ecological forecasting. Traditional ecological research is usually focused on data collection. A graduate student or a researcher typically collects a dataset and analyzes it for publication. In the data-rich, NEON era, national networks of sensors generate millions of data points every day. Ecologists have not been prepared for processing of such massive datasets. It is critical to build up a capacity to process the massive data sets and to generate data products that will enable the development, rigorous testing and application of models, lead to fast advancement of the science, and provide support of decision making for resource management and climate change mitigation.
Organizer:	Yiqi Luo, University of Oklahoma
Co-organizer:	David S. Schimel, National Ecological Observatory Network
Moderator:	Yiqi Luo, University of Oklahoma
8:00 AM	SYMP 23-1		A conceptual framework of ecological forecasting using data assimilation David S. Schimel, National Ecological Observatory Network
8:25 AM	SYMP 23-2		Techniques of data assimilation and forecasting S. Lakshmivarahan, University of Oklahoma
8:50 AM	SYMP 23-3		Data assimilation in modeling species distribution dynamics Andrew M. Latimer, University of Connecticut, John A. Silander Jr., University of Connecticut
9:10 AM	SYMP 23-4		Uncertainty analysis of carbon turnover time and sequestration potential in terrestrial ecosystems of the conterminous USA Xuhui Zhou, University of Oklahoma
9:35 AM			Break
9:55 AM	SYMP 23-5		Forecasting infectious disease: Fusing process models with data Thompson Hobbs, Colorado State University, Shannon L. LaDeau, Cary Insitute of Ecosystem Studies
10:20 AM	SYMP 23-6		Data-model integration for partitioning belowground ecosystem processes Kiona Ogle, University of Wyoming, Jessica M. Cable, University of Wyoming
10:45 AM	SYMP 23-7		A network platform for understanding biodiversity change: Data and models to maximize learning James S. Clark, Duke University, Michael C. Dietze, Harvard University, Richard W. Lucas, University of Wyoming, Andrew M. Latimer, University of Connecticut, Sean McMahon, Duke University, Jessica Metcalf, Duke University
11:10 AM			Discussion

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See more of The 93rd ESA Annual Meeting (August 3 -- August 8, 2008)