Environmental problems related to natural resource management span a wide variety of spatial, temporal and biotic hierarchy levels. Resource managers are charged with local, short-term decisions regarding controlling access and types of use for sub-areas within larger natural areas, as well as much longer-term planning for entire areas as large as many thousands of hectares. Similarly, regional-scale planning for water flows, hunting regulations, preserve allocation, and forest harvesting requires stakeholders to provide input to the process, and such planning has led to numerous contentious public debates throughout the US. The main objective of this project is the development of computational tools, utilizing grid-computing, to assist natural resource managers in assessing the impacts of alternative management plans. The central theme of this proposal is that evaluating the impacts of human actions to manage natural systems requires the use of computational models utilizing the best available science to analyze potential effects. To be effective, such models must be defensible scientifically, readily available to various stakeholders and extensible to allow new users to evaluate alternative plans, assumptions and choices of criteria to prioritize management actions. This requires the use of computational ecology to develop models, visualize these models, consider alternative human controls within these models and analyze from an optimization perspective the various controls, based upon social choice criteria that may vary from stakeholder to stakeholder.
Due to the variety of scales involved in natural resource management, the use of a multimodeling approach is appropriate. In this, different models with potentially different mathematical and computational forms are chosen for different components of the system, based upon the necessary levels of detail and available data for different components. These models are linked into a multimodel to evaluate the interactions of biotic and abiotic components. Grid computing provides a natural framework to develop toolsets for ecological multimodels due to the dispersed nature of datasets, software and computational resources, in addition to the general lack of appropriate hardware at many natural resource agencies. The challenges go well beyond the current state-of-the-art in geographic information systems, requiring spatially-explicit dynamic models linked to spatio/temporal optimization schemes. Courses for managers and the engagement of underrepresented groups are planned.
