Adaptive Integrated Framework (AIF): a new methodology for

1. Adaptive Integrated Framework (AIF): a new methodology for managing impacts of multiple stressors in coastal ecosystems A bit more on AIF, project components and timelines

2. Anthropogenic stressors, especially “new” stressors such as invasive species, contaminants, land-use and global climate change, can cause unforeseen, devastating consequences even in ecosystems with well-defined management strategies to alleviate stressor impacts. Project Justification The complexity* and uncertainty of multiple interacting stressors makes planning and management very difficult for those tasked with balancing multiple and often competing stakeholder concerns in coastal ecosystems. *Complexity: Nonlinearities. Synergies. Alternative stable states … managers are usually faced with limited resources to sample the ecosystem for maximum benefit to management decision making. Solutions for adequately dealing with the impacts of multiple stressors will require novel approaches for understanding the dynamics of the ecosystem as well as appropriately assessing the costs and benefits of alternative management practices.

3. Project Goal Adaptive Integrative Framework (AIF) approach.

4. AIF incorporates a series of feedback loops between experimental scientists, modelers, agency managers, and the public for developing management tools and programs as well as monitoring impacts. • Multidirectional exchange of information between agency managers, modelers, experimental scientists, and interested stakeholders will improve and optimize management strategies, model development, and program monitoring. • Continuously evaluates the development and implementation of ecosystem models, experimental results, and management actions. • Includes individual models, experimental protocols, and human dimension research AIF Highlights Adaptive Integrative Framework (AIF) approach.

5. Adaptive Integrative Framework (AIF): Merging of Adaptive Management (AM) and Integrated Assessment (IA). • Adaptive Management • Summary: • AM is based on the tenet that management actions in ecosystems may be treated as ecosystem-scale experiments capable of generating new information for updating and improving management decisions • AM uses an iterative approach with continuous feedback between management actions and scientific understanding of observed changes. • Gaps: • AM typically lacks a rigorous short-term framework for facilitating and interpreting feedback, thereby confounding the true integration of scientific investigation and ecosystem management. • integration is often complicated by divergent expectations and goals of multiple researchers and managers. • AIF: • Managers and researchers are both engaged in a mutually beneficial process that allows for short-term assessment of specific environmental issues and long-term management of ecosystems.

6. Adaptive Integrative Framework (AIF): Merging of Adaptive Management (AM) and Integrated Assessment (IA). • Integrated Assessment • Summary: • IA involves a multi-step process for assessing the status of key ecosystem properties and characteristics; making and testing quantitative predictions (including an explicit assessment of uncertainty) of how ecosystems will respond to specific stressors; and developing technical guidance based upon such predictions • IA is a linear approach which facilitates the integration and analyses of diverse ecosystem data and subsequent communication with key stakeholders. • Gaps: • IA alone fails to provide a rigorous framework for data synthesis and analysis efforts to guide future data acquisition and adaptive management actions. • In most cases development and parameterization of ecosystem models are based on ad hoc collections of data (i.e., data collected for some other purpose) which results in modeling syntheses that are linear processes--first, data collection and then modeling synthesis. • AIF: • AIF will incorporate adaptive process on IA so explicit uncertainty and sensitivity of model outputs can provide the basis for future monitoring and experimental efforts.

7. Human health Fish production Predictions Regional economics Project Components

8. Climate change Multiple stressors Land use Invasive species Project Components

9. Retrospective Statistical Analysis 4 Modeling approaches Bayesian Probability Neural Networks Process-Based SAGEM & SB-IBM Project Components

10. Model Feedback Model Evaluation Model Comparison Model Uncertainty Project Components

11. Watershed nutrient loading Hydrodynamic model Ecosystem characterization Historical data collection Project Components

12. Field monitoring Data reclamation Field data collection Experiments Project Components

13. Management- Research Workshops Economic Assessment Socio-Economic Factors Public support assessment Project Components

14. Management- Research Workshops Economic Assessment Retrospective Statistical Analysis Socio-Economic Factors 4 Modeling approaches Public support assessment Bayesian Probability Watershed nutrient loading Hydrodynamic model Neural Networks Model Feedback Model Evaluation Ecosystem characterization Model Comparison Historical data collection Model Uncertainty Process-Based SAGEM & SB-IBM Climate change Field monitoring Data reclamation Human health Multiple stressors Fish production Field data collection Predictions Land use Invasive species Experiments Regional economics Project Components

16. FLAVOR • We don’t know what we are examining in the field a priori  adaptive framework. • The problems we examine will be guided by management/stakeholder input, and the factors we measure will be guided by modeling yet to be done.

17. Stakeholder workshops Data Collection* Field survey Field Experiments Modeling Spring Workshop 2008 Summer Low level No Nov: Present needs Fall Spring Workshop 2009 Summer Intense Yes Fall Spring Workshop 2010 Summer Intermediate No Nov: Present needs Fall Spring Workshop 2011 Summer Intense Yes Fall Spring 2012 Summer * Data collection: Hydrodynamics, historical data, hydrology, land use, human dimensions