Evidence map›Paper›PMID 31999806›Full record

ArticlePloS one2020

A geospatially resolved wetland vulnerability index: Synthesis of physical drivers.

Zafer Defne, Alfredo L Aretxabaleta, Neil K Ganju, Tarandeep S Kalra, Daniel K Jones, Kathryn E L Smith

Abstract read
In one paragraph

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.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

6 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
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  5. A Conterminous USA-Scale Map of Relative Tidal Marsh Elevation.Estuaries and coasts : journal of the Estuarine Research Federation · 2022
    Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Zafer DefneWoods Hole Coastal and Marine Science Center, U.S. Geological Survey, Woods Hole, MA, United States of America.ORCID 0000-0003-4544-4310
Alfredo L AretxabaletaWoods Hole Coastal and Marine Science Center, U.S. Geological Survey, Woods Hole, MA, United States of America.ORCID 0000-0002-9914-8018
Neil K GanjuWoods Hole Coastal and Marine Science Center, U.S. Geological Survey, Woods Hole, MA, United States of America.
Tarandeep S KalraIntegrated Statistics, U.S. Geological Survey, Woods Hole, MA, United States of America.
Daniel K JonesUtah Water Science Center, U.S. Geological Survey, Salt Lake City, UT, United States of America.ORCID 0000-0003-0724-8001
Kathryn E L SmithSt. Petersburg Coastal and Marine Science Center, U.S. Geological Survey, St. Petersburg, FL, United States of America.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

WetlandsClimate ChangeEnvironmental MonitoringHydrodynamicsPrincipal Component AnalysisRemote Sensing Technology

Identifiers

PMID31999806
PMCPMC6992177

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.