Monitoring plays a central role in detecting climate and anthropogenic stressors and associated changes in wetlands. There is a need for wetland monitoring programs to bridge the gap between ground-based surveys, which can miss important spatial heterogeneity and cause wetland disturbance, a
Resources
Resources
A repository of data, publications, tools, and other products from project teams, Science Collaborative program, and partners.
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Recommendations for the NERRS SWMP, summarizing outputs in an archivable format deemed useful by end users (NERRS research staff).
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Research staff from 12 reserves assessed sensor performance by comparing field and laboratory sensor measurements to concentrations of chlorophyll extracted from water samples.
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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.
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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.
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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.
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This curriculum was developed as part of a 2018 Science Transfer project to share knowledge and lessons learned about managing conflict in collaborative science.
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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.
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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.