This resource contains the presenter slides, Q&A responses, recording, and presenter bios from the June 2022 webinar "Drone the SWMP: Assessing the Utility of Drones for Monitoring Coastal Wetlands."

This instructional and informational webinar features background information on the 2020 science transfer Storm Stories project, how end-user feedback was incorporated, the tools and products that have been developed through the project, and how reserves can access resources.

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.

This project overview describes a 2017 Collaborative Research project that is piloting and refining DNA-based monitoring protocols that can be applied to specific issues and species of interest in estuarine ecosystems.

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.

This factsheet describes the process of environmental DNA (eDNA) water sampling in estuarine systems, and provides specific methdology recommendations to facilitate detection of invasive species.

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 resource contains the presenter slides, Q&A responses, recording, and presenter bios from the July 2020 webinar Innovative Approaches to Integrating Research and K-12 Education to Advance Estuary Stewardship.

This national synthesis report analyzes SET data from 15 National Estuarine Research Reserves across the continental United States, summarizing wetland water level trends over a 19-year period.

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.