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Resources

Resources

A repository of data, publications, tools, and other products from project teams, Science Collaborative program, and partners.

Displaying 1 - 9 of 9
Data |
About the project

The 2020-2022 catalyst project  Bridging the gap between quadrats and satellites: assessing utility of drone-based imagery to enhance emergent vegetation biomonitoring conducted a regionally coordinated effort, working in salt marshes an

Data |
About this project

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

Factsheet |
About the project

A multi-Reserve study explored the feasibility of including high frequency, in situ chlorophyll a monitoring in the National Estuarine Research Reserve System-wide Monitoring Program (NERR SWMP).

Factsheet |

This factsheet, written as a resource for a three-year Collaborative Research project, describes measures and proposed management plans for marsh resilience to create a long-term monitoring programs and national-level synthesis efforts.

Data |

This dataset comprises the data collected and produced as part of the 2016 research project Investigating the Interconnectedness of Climate Change, Nuisance Mosquitoes, and Resilience of Coastal Salt Marsh Systems.

Data |

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.

Data |
About this Project

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.

Factsheet |

This summary brochure describes thin-layer placement (TLP) as a strategy for marsh resilience, and National Estuarine Research Reserve System research and recommendations for TLP use.

Data |

This code (R and MATLAB) can be used to analyze NERRS System-Wide Monitoring Program time series data.