This project overview describes a 2018 Catalyst project led by Grand Bay Reserve that developed standardized tools to quality-check, analyze, and visualize Surface Elevation Table data.
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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 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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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 project overview describes a 2018 Catalyst project that created the web-based toolkit Resilience Metrics to share lessons learned on successful climate adaptation planning within the National Estuarine Research Reserve System.
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This project overview describes a 2018 Catalyst project that created an Olympia oyster restoration network to enhance the success of West Coast restoration efforts.
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This project overview describes a 2017 science transfer project that developed a risk communication training for reserves to build risk communication capacity in four coastal communities.
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This project overview describes a project led by Elkorn Slough National Estuarine Research Reserve to communicate the results of a recent national synthesis of NERR Sentinel Site data on marsh resilience to sea level rise.
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This project overview describes a 2012 Collaborative Research project that developed a decision-making framework and tools to guide coastal wetland recovery and management in Southern California.
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This project overview describes a 2012 Collaborative Research project that worked to enhance resilience on Maryland's Deal Island by building a stakeholder network and integrating research to understand how different management practices will impact marsh and community resilience.
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This code (R and MATLAB) can be used to analyze NERRS System-Wide Monitoring Program time series data.