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This project focuses on the development of new computational methods, particularly using parallel and grid computing, to address environmental problems that require interactions between multiple types of computational models. These models include physical ones, hydrology and weather being examples, and biological ones, which may be for particular species populations or for communities of interacting populations. These models may be operating at different spatial and temporal resolutions, and may utilize quite different mathematical approaches, such as matrix models, differential equation models and individual-based models. An overall objective is to develop new techniques that allow for efficient solution of linked models for which parallel methods are appropriate. An additional objective is to provide a framework for potential use of these parallel methods by resource managers to compare and contrast the effects of alternative management of natural systems, and eventually lead to the capability to carry out optimal dynamic and spatial control of natural systems. This project has been supported through funding from the National Science Foundation to the University of Tennessee through Awards DEB-0219269 and IIS-0427471.

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