Presentation on theme: "Modeling Complex Interactions of Overlapping River and Road Networks in a Changing Landscape Programmatic overview Hypothesis Preliminary findings."— Presentation transcript:
Modeling Complex Interactions of Overlapping River and Road Networks in a Changing Landscape Programmatic overview Hypothesis Preliminary findings
NSF BIOCOMPLEXITY IN THE ENVIRONMENT FY 2003 SPECIAL COMPETITION DYNAMICS OF COUPLED NATURAL AND HUMAN SYSTEMS LARGE RESEARCH PROPOSALS 249 Biocomplexity proposals 74 Coupled Natural & Human systems –6 “High priority” funding (8%)
NSF Emphasis Integrate different disciplines Apply modern technology –Data acquisition Remote sensing, DNA…. –Information management Public access to data, monitoring 5 year rule Public relevance –Pure vs applied science
Solution RFP’s for Integrated Research Proposals Multidisciplinary research teams focused on broad proposals Environmental Molecular Science Institutes –Chemists & environmental scientists & industry Biocomplexity –Complex biological interactions over range of spatial and temporal scales –Beyond biodiversity; interactions
Successful proposals must Address the inherent complexity and highly coupled nature of relevant natural and human systems as well as their interactions Describe plans for the work of interdisciplinary teams from the natural, social, mathematical sciences, engineering, and education –Whose coordinated work will enhance theoretical understanding
Projects must include Quantitative approaches or advanced conceptual models Specific plans for education –Graduate students –Road seminar –K-12 education program
Evaluation Criteria Strength of the collaborations planned and degree of interdisciplinary Effectiveness of the group organization and management plan Value to education in these topical areas Strength of the dissemination plans Extent, effectiveness, and long-term potential of collaborations with industries, national laboratories, and comparable research centers abroad, when appropriate.
Our main overarching hypothesis is that an integrated individual-based model will more accurately predict environmental effects than any single physical, biotic or social model by reducing unexplained variation.