Each reserve within the National Estuarine Research Reserve System maintains a specific site profile that synthesizes knowledge about its physical and biological characteristics to guide research and monitoring activities.

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

With funding from the NERRS Science Collaborative, scientists from 12 biogeographically diverse Reserves compared fluorescence measurements taken by the YSI EXO TAL sensor to extracted chlorophyll concentrations processed in the lab.

Standardized protocols for sensor-based chlorophyll monitoring are now available for use by staff around the system to implement high frequency chlorophyll monitoring at their reserves.

This tool provides an overview of acoustic monitoring in aquatic ecosystems, including sources of sound, metrics for measurement, data collection and analysis, and applications for habitat assessment, stewardship, and education.

This factsheet, developed by a 2020 catalyst project, provides a brief overview of CES frameworks and categories to complement the information contained in the factsheet “Expanding and Deepening the Application of Cultural Ecosystem Services in Estuary Stewardship and Management ”.

This factsheet, developed by a 2021 catalyst project, summarizes information to strengthen the conceptual foundation and meaningful application of cultural ecosystem services (CES) in the NERRS.

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

A 2018 catalyst project developed tools for working with SET data including a series of computer codes - R scripts - for processing, quality checking, analyzing and visualizing these complex datasets. The statistical codes re available through GitHub and are explained in a Guide to the SETr Workflow.