These datasets are from an intensive field sampling in and adjacent to aquaculture operations in North Carolina, concentrating on wild shellfish resources and the physical and chemical environment, to assess ecosystem services and potential impacts of the oyster farms.

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.

This data resource includes eDNA sequences, fish species summary tables, and DNA extractions from Wells, Great Bay, Hudson, Apalachicola, South Slough, and Heʻeia National Estuarine Research Reserves.

This tool provides a framework for discussing a range of criteria when evaluating locations for shellfish aquaculture.

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.

These four case studies give examples of four best practices for conflict management in collaborative science. They were developed as part of the Resilience Dialogues project to share lessons learned about effective collaboration from within the National Estuarine Research Reserve System.

This curriculum was developed as part of a 2018 Science Transfer project to share knowledge and lessons learned about managing conflict in collaborative science.

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.

These guidance documents and videos provide field and lab protocols for preparing for, collecting and fitering water samples for use in eDNA analyses.

This advisory committee charter, developed for a National Estuarine Research Reserve project to evaluate a thin-layer placement as a strategy for marsh resilience, offers an example for engaging diverse end users in collaborative research.