Runnels Reverse Mega‑pool Expansion and Improve Marsh Resiliency in the Great Marsh, Massachusetts (USA) (Dataset)
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https://figshare.com/articles/dataset/Pine_Island_Runnel_Great_Marsh_Massachusetts_2014_2021_Dataset/19666242
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Dataset Description:
Monitoring data set accompanying the publication, " Runnels Reverse Mega-pool Expansion and Improve Marsh Resiliency in the Great Marsh, Massachusetts (USA)" in the journal Wetlands (https://doi.org/10.1007/s13157-023-01683-6). Monitoring was conducted by the Coastal Habitat Restoration Team at Jackson Estuarine Laboratory, Unviersity of New Hampshire.
Dataset is broken down into 3 components:
(1) Compiled dataset of the monitoring data of the project including detailed metadata on monitoring and data analysiys. Metadata and explanaitions for input data to R code can be found in the dataset.
(2) Water Level Recorder Analysis R Code - R code used to process tidal water elevations from Hoboware CSV files
(3) Multivariate Analysis R Code - R code used to conduct non-metric dimensional ordination, PERMANOVA, and SIMPER analyses on the vegetation dataset
Abstract:
Coastal ecologists in New England have been implementing a restoration strategy of runnels, or shallow ditches, to enhance drainage of oversaturated and ponding interior marshes. In 2015, runnels were constructed to drain two large and expanding pools in the Great Marsh System of Massachusetts, USA. Vegetation, elevation, and hydrology were monitored using field sampling and remote sensing analysis conducted pre- and post-restoration over seven growing seasons to document the recovery of the vegetation community in the pool and salt marsh platform. Vegetation was monitored with 0.5 m2 plots with all species identified and percent cover estimated per species. Elevation was recorded with either laser level (2015) or RTK-GPS (2016, 2021) in the plots. Water level elevations were monitored with Odyssey capacitance loggers (2015, 2016) and Hobo pressure transducers (2018, 2021).
Contact Information:
Questions about the data set can be directed towards Grant McKown, james.mckown@unh.edu or jgrantmck@gmail.com
创建时间:
2022-04-27



