Evaluating future coastal landscape change through a decision-support framework

Decision-makers assessing the future vulnerability of people, species, habitat, and natural resources along the coast require an understanding of where landscape change is most likely. Producing products that make this information available in a meaningful and useful form requires an in-depth understanding of decision-making needs, research gaps and capabilities, and a recognition of communication nuances among resource managers, research disciplines, and end users. In 2012, eight researchers, managers, and decision-makers from local, state, federal, and non-governmental organizations throughout the Northeastern U.S. participated in a structured decision-making (SDM) workshop focused on landscape adaptation to sea-level rise (SLR). The intent of the four-day workshop was to define a shared vision and common goals for research outcomes prior to the initiation of landscape-scale SLR research. An iterative SDM process was used to define a decision problem, objectives, and potential management actions to be undertaken with SLR information, and participants emerged with a mutual understanding of intended product applications--including integration with a parallel habitat modeling effort--as they related to decision needs. In 2016, U.S. Geological Survey researchers published a probabilistic framework, or Bayesian network, directly informed by the SDM outcomes that predicts the likelihood of dynamic coastal response across Northeastern U.S. to future SLR. In addition to information needs, the results met technical requirements identified in the 2012 workshop, ensuring they were readily integrated into habitat models. The predicted outcomes are now stand-alone geospatial products, and also serve as foundational data for several publicly available tools including Nature’s Network, a U.S. Fish and Wildlife Service product which provides decision-makers with regional information to manage species through both ecosystem and habitat preservation and connectivity.