ArticlePloS one2020
A geospatially resolved wetland vulnerability index: Synthesis of physical drivers.
Article in PloS one, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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6 citing papers in PubMed.
- Metrics for assessing stability of marsh sill living shorelines: Identifying main drivers of marsh boundary degradation.PloS one · 2025Article
- Cultural adaptation and validation of the mental illness associated stigma scale for Arabic-speaking population in Saudi Arabia.Frontiers in psychiatry · 2023Article
- Marsh migration and beyond: A scalable framework to assess tidal wetland resilience and support strategic management.PloS one · 2023Article
- Combining stakeholder perception and ecological approaches for assessing vulnerability of floodplain wetlands in changing climate: a regional study.International journal of biometeorology · 2022Article
- A Conterminous USA-Scale Map of Relative Tidal Marsh Elevation.Estuaries and coasts : journal of the Estuarine Research Federation · 2022Article
- Machine learning algorithm-based risk assessment of riparian wetlands in Padma River Basin of Northwest Bangladesh.Environmental science and pollution research international · 2021Article
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Authors and funding
6 authors.
Funding
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Abstract
Assessing wetland vulnerability to chronic and episodic physical drivers is fundamental for establishing restoration priorities. We synthesized multiple data sets from E.B. Forsythe National Wildlife Refuge, New Jersey, to establish a wetland vulnerability metric that integrates a range of physical processes, anthropogenic impact and physical/biophysical features. The geospatial data are based on aerial imagery, remote sensing, regulatory information, and hydrodynamic modeling; and include elevation, tidal range, unvegetated to vegetated marsh ratio (UVVR), shoreline erosion, potential exposure to contaminants, residence time, marsh condition change, change in salinity, salinity exposure and sediment concentration. First, we delineated the wetland complex into individual marsh units based on surface contours, and then defined a wetland vulnerability index that combined contributions from all parameters. We applied principal component and cluster analyses to explore the interrelations between the data layers, and separate regions that exhibited common characteristics. Our analysis shows that the spatial variation of vulnerability in this domain cannot be explained satisfactorily by a smaller subset of the variables. The most influential factor on the vulnerability index was the combined effect of elevation, tide range, residence time, and UVVR. Tide range and residence time had the highest correlation, and similar bay-wide spatial variation. Some variables (e.g., shoreline erosion) had no significant correlation with the rest of the variables. The aggregated index based on the complete dataset allows us to assess the overall state of a given marsh unit and quickly locate the most vulnerable units in a larger marsh complex. The application of geospatially complete datasets and consideration of chronic and episodic physical drivers represents an advance over traditional point-based methods for wetland assessment.
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