Policy makers, individuals from government agencies, and natural resource managers face increasing demands to manage coastal areas in a way that meets economic, social, and ecological needs as sea levels rise. Scientific knowledge of how coastal processes drive beach and barrier island changes and how those changes affect habitat use can support decision makers as they balance sometimes conflicting human and ecological needs. However, uncertainties in the knowledge of the cumulative results of coastal processes make it challenging to forecast specific changes for a particular location and time. The U.S. Geological Survey is developing tools for identifying and forecasting barrier island characteristics as well as suitable coastal habitats for species of concern (such as piping plovers, Charadrius melodus) given ongoing sea-level rise. As part of this effort, we use three Bayesian networks to calculate probabilities of shoreline change rates, changes in barrier island biogeomorphic characteristics, and piping plover habitat availability, which together forecast the effects of different sea-level-rise rates and storm regimes. This report details the methodology used to derive geospatial biogeomorphic datasets that are used as inputs for two of these Bayesian networks, which forecast barrier island geomorphology and piping plover habitat availability at sites along the U.S. Atlantic coast (Maine to North Carolina). Further information about the project, including specific study sites, can be found at https://woodshole.er.usgs.gov/project-pages/beach-dependent-shorebirds/.
Citation Information
| Publication Year | 2019 |
|---|---|
| Title | Evaluating barrier island characteristics and piping plover (Charadrius melodus) habitat availability along the U.S. Atlantic Coast—Geospatial approaches and methodology |
| DOI | 10.3133/ofr20191071 |
| Authors | Sara L. Zeigler, Emily J. Sturdivant, Benjamin T. Gutierrez |
| Publication Type | Report |
| Publication Subtype | USGS Numbered Series |
| Series Title | Open-File Report |
| Series Number | 2019-1071 |
| Index ID | ofr20191071 |
| Record Source | USGS Publications Warehouse |
| USGS Organization | Woods Hole Coastal and Marine Science Center |
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