Rationale Systemic environmental problems cannot always be understood using traditional modeling frameworks or small-scale experiments (Pfirman, 2003). Systems thinking and multi-scale analysis are key to understanding complex interactions, feedbacks, thresholds, and patterns of self-organization that emerge in ecosystems.   In the U.S. alone, millions of dollars are spent annually in piecemeal efforts to restore stream reaches and improve water quality in rivers, lakes, and estuaries (Bernhardt et al. 2005). However, without an adequate understanding of the underlying ecosystem dynamics in these systems, most of these efforts provide short-term relief at best. "For sustainable management of natural resources, we need to develop our understanding of the environment based on multiple control factors, long-term studies, multiple spatial scales, many species interacting in complex ways, and interdisciplinary interactions" (Gosz, 1999).   As the result of long term ecological research and environmental monitoring, the quantity and quality of environmental data collected continue to increase. There are now large repositories of disparate data for many environmental systems, including several for the Lake Champlain watershed region . Unfortunately, these data have been collected by independent teams of academics, environmental scientists, and state and federal agencies, at a variety of different temporal and spatial scales. Consequently, we need to develop the cyber-infrastructure necessary to integrate and make sense of all these rapidly growing stores of data (Pfirman, 2003).