Parallel Processing for Individual-Based Ecological Models

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9318160 Gross The vast majority of ecological models applied to problems in ecosystem analysis and management have relied on aggregative properties of populations and communities. This award will take advantage of high performance computing systems to analyze new models based upon interactions between many individual organisms which comprise these populations and communities. These individual-based models may be used to predict the dynamics of populations and communities explicitly on a landscape, and thus provide a method to analyze the effect of alternative spatially explicit management schemes on ecosystems. Massively parallel machines offer the potential to deal with these analysis and management problems, as well as taking account of the vast array of information about individual organism difference that is ignored in more aggregated modeling approaches. This award involved the development and comparison of alternative methods to implement individual-based modes on several high performance computing systems and the application of results obtained to several specific problems of ecosystem analysis and management, in particular community-level interactions between several species and explicit models of populations and communities. This award is being jointly supported by programs in Ecology, Ecosystems, Applied Mathematics, Computational Mathematics and Computational Biology. ***