In the UN Report Stemming the Decline of the Coastal Environment: Rethinking Environmental Management, Sale et al., 2008, appeal for “more critical evaluation of [coastal] management tools in order to either reduce uncertainty, or help decision-makers realize the logical options in the face of high uncertainty”. Boesch (2006) calls for better integration of modeling, observations, and empirical research to facilitate adaptive management in coastal ecosystems.
Ecological forecasts can predict the impacts of stressors and the associated chemical, biological, and physical changes on ecosystems, ecosystem components, and human communities. Ecological Forecasting has been an area of activity within NOAA National Ocean Service (http://oceanservice.noaa.gov/topics/coasts/ecoforecasting/welcome.html) and NOAAA Oceanic and Atmospheric Research (http://www.research.noaa.gov/oceans/t_ecologicalobserving.html) for the last decade, and one could argue that every stock assessment done by NOAA National Marine Fisheries Service (http://www.st.nmfs.noaa.gov/StockAssessment/StockAssessment.html) is an ecological forecast. However, these activities have not been integrated across NOAA. This paper outlines strategies to develop a NOAA-wide Ecological Forecasting Systems (EFS) that builds on existing physical and ecological modeling within NOAA and develops the next generation of ecosystem models and forecasts applicable to many NOAA managerial responsibilities (Valette-Silver and Scavia, 2003).
Ecological forecasts are generally focused on specific species or processes (e.g., a particular invasive species, or nutrient load in an estuary), as opposed to ecosystem forecasts, which predict ecosystem-scale parameters and emergent properties (e.g., total secondary production or ecosystem resilience). Some ecological forecasts rely on physical modeling, such as modeling trajectories and impacts of pollutants from an estuary into the coastal ocean. Other ecological forecasts can be based on empirical or statistical relationships, without the need for heavily parameterized physical models. For the purposes of this paper, we constrain our discussion to ecological forecasts with direct linkages to physical and weather phenomena, with a focus toward a management endpoint.