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Woods Hole Coastal and Marine Science Center > Sea Level Rise Hazards and Decision Support > Beach-dependent Shorebirds>Forecasting piping plover habitat under sea-level rise

Beach-dependent Shorebirds

Forecasting piping plover habitat under sea-level rise

Project researchers are conducting research needed to create tools for identifying suitable coastal habitats for species of concern or, conversely, areas of high hazard exposure for humans and infrastructure today and into the future. The most likely (a) shoreline change rate, (b) barrier island characteristics, and (c) piping plover habitat availability are forecasted under different SLR rates and storm regimes. This framework portrays results as probabilities, which can inform decision-making and highlight knowledge gaps and research avenues. This research is also generalizable to a variety of forecasting problems for coastal systems, including investigations of habitat availability for turtles, birds, and plants as well as the potential hazard exposure for human infrastructure along coastal environments throughout the United States. Piping plover habitat forecast maps are currently being created for 21 beaches and barrier islands, referred to as ‘Deep Dive Sites’, along the U.S. Atlantic coast.

model parameters for shoreline change, barrier islands and piping plover habitat availability

Parameters considered in models for shoreline change, barrier island characteristics, and piping plover habitat availability. Together, these three models allow for forecasts of most likely future barrier island characteristics and piping plover habitat availability given sea-level rise. Credit: Rob Thieler, USGS

Related Publications

Gutierrez, B.T., Plant, N.G., Pendleton, E.A., Thieler, E.R., 2014. Using a Bayesian network to predict shoreline-change vulnerability to sea-level rise for coasts of the United States. U.S. Geological Survey Open-File Report 2014-1083. https://doi.org/10.3133/ofr20141083

Gutierrez, B.T., Plant, N.G., Thieler, E.R., Turecek, Aaron, 2015. Using a Bayesian network to predict barrier island geomorphological characteristics. Journal of Geophysical Research: Earth. 120: 2451-2475. https://doi.org/10.1002/2015JF003671

 

 

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