A geospatially resolved wetland vulnerability index: synthesis of physical drivers

dc.contributor.author Defne, Zafer
dc.contributor.author Aretxabaleta, Alfredo L.
dc.contributor.author Ganju, Neil K.
dc.contributor.author Kalra, Tarandeep S.
dc.contributor.author Jones, Daniel K.
dc.contributor.author Smith, Kathryn E. L.
dc.date.accessioned 2020-06-16T18:33:09Z
dc.date.available 2020-06-16T18:33:09Z
dc.date.issued 2020-01-30
dc.description © The Author(s), 2020. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Defne, Z., Aretxabaleta, A. L., Ganju, N. K., Kalra, T. S., Jones, D. K., & Smith, K. E. L. A geospatially resolved wetland vulnerability index: synthesis of physical drivers. Plos One, 15(1), (2020): e0228504, doi:10.1371/journal.pone.0228504. en_US
dc.description.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. en_US
dc.description.sponsorship This study was part of the Estuarine Physical Response to Storms project (GS2-2D awarded to NKG), supported by the Department of the Interior Hurricane Sandy Recovery program. Support was also provided by the U.S. Geological Survey, Coastal and Marine Hazards/Resources Program. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. en_US
dc.identifier.citation Defne, Z., Aretxabaleta, A. L., Ganju, N. K., Kalra, T. S., Jones, D. K., & Smith, K. E. L. (2020). A geospatially resolved wetland vulnerability index: synthesis of physical drivers. Plos One, 15(1), e0228504. en_US
dc.identifier.doi 10.1371/journal.pone.0228504
dc.identifier.uri https://hdl.handle.net/1912/25872
dc.publisher Public Library of Science en_US
dc.relation.uri https://doi.org/10.1371/journal.pone.0228504
dc.rights CC0 1.0 Universal *
dc.rights.uri http://creativecommons.org/publicdomain/zero/1.0/ *
dc.title A geospatially resolved wetland vulnerability index: synthesis of physical drivers en_US
dc.type Article en_US
dspace.entity.type Publication
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