The 2020-2021 catalyst project Refining Techniques for High-Frequency Monitoring of Chlorophyll in the NERRS brought together twelve biogeochemically diverse reserves to compare results from new YSI in situ sensor technology with ex
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Resources
A repository of data, publications, tools, and other products from project teams, Science Collaborative program, and partners.
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This resource includes links to five datasets generated by a collaborative research project that measured nitrogen removal from oyster aquaculture using complement biogeochemistry and genetic methods.
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These marsh sustainability and hydrology datasets were collected as part of a 2017 collaborative research project.
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This dataset contains processed Surface Elevation Table data from five reserves along with metadata, R scripts, reports, and figures, illustrating how SET can be processed, analyzed and visualized.
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These five related carbon storage, greenhouse gas flux and environmental variable datasets were generated by the Bringing Wetlands to Market research team and used to develop a coastal wetland greenhouse gas model for New England.
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Thin-layer placement (TLP) is an emergent climate adaptation strategy that mimics natural deposition processes in tidal marshes by adding a small amount of sediment on top of marsh in order to maintain elevation relative to sea level rise.
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This dataset compiles salt marsh monitoring from four New England NERRs from 2010 to 2018, as part of a catalyst project to sythesize and identify regional trends in salt marsh data in the reserve system.
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These high school STEM curriculum resources, created as part of the project Bringing Wetlands to Market Phase 2: Expanding Blue Carbon Implementation, examine the relationship between climate change impacts and carbon storage in New England salt marsh.
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This high school STEM curriculum module, created as part of the project Bringing Wetlands to Market Phase 1: Nitrogen and Coastal Blue Carbon, examine the relationship between climate change impacts and carbon storage in New England salt marsh.
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This code (R and MATLAB) can be used to analyze NERRS System-Wide Monitoring Program time series data.