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

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. 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 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. … managers are usually faced with limited resources to sample the ecosystem for maximum benefit to management decision making. Project Justification

Adaptive Integrative Framework (AIF) approach. Project Goal

Adaptive Integrative Framework (AIF) approach. 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): 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.

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.

Fish production Regional economics Human health Predictions Project Components

Land use Invasive species Climate change Multiple stressors Project Components

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

Model Uncertainty Model Comparison Model Feedback Model Evaluation Project Components

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

Field monitoring Experiments Data reclamation Field data collection

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

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

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.

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