We propose to support the revision and implementation of the South Atlantic Landscape Conservation Cooperative’s Conservation Blueprint by integrating its Ecosystem Indicators into a structured decision support system that makes explicit how the Indicators are interrelated and how these will respond to management and policy interventions aimed at improving the conservation status of the South Atlantic region. Our specific objectives are to (1) develop ecological production functions that predict theecological impacts of selected conservation actions relative to current conditions, and to propagate these impacts through other affected systems or functions; (2) codify protocols for updating and curating geospatial datasets of the Indicators and datasets needed to estimate these over time in response to natural events and human activities; and (3) design a structured framework for updating the Blueprint to incorporate these changes and to facilitate formal exploration of conservation priorities implied by the Indicators. Our approach has the key features of a structured decision support system: path models, which are heuristic models that trace the effects of management actions (or other interventions) on ecosystems structure and function to generate changes in the Indicators; production functions that that capture these impacts empirically (e.g., via regression); and a weighting scheme that makes explicit the co-benefits or trade-offs implicit in management alternatives, and which thus provides a vehicle for using the Blueprint to set priorities for on-the-ground conservation actions. We will implement this approach for terrestrial systems and marine systems separately, integrating these in coastal/estuarine systems. Our project will apply to the entire South Atlantic LCC, excluding the marine systems of the Gulf of Mexico. Our approach will augment the scientific underpinnings of the Blueprint while also helping to ensure that it becomes a living document that guides actual conservation planning and is updated into the future.
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