1. Programmatic overview Hypothesis Preliminary findings
Modeling Complex Interactions of Overlapping River and Road Networks in a Changing Landscape
Programmatic overview
Hypothesis
Preliminary findings

2. 249 Biocomplexity proposals 74 Coupled Natural & Human systems
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%)

3. 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

6. 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

7. 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

8. Projects must include
Quantitative approaches or advanced conceptual models
Specific plans for education
Graduate students
Road seminar
K-12 education program

9. 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.

10. 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.