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.
Resources
Resources
A repository of data, publications, tools, and other products from project teams, Science Collaborative program, and partners.
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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.
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This web resources includes a compilation of lesson plans for grades K - 12 about coastal and estuarine ecology that are intended to complement programs that involve schools in local wetland restoration projects.
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This 2021 article which appeared in Estuaries and Coasts provides a synthesis of native oyster restoration projects conducted from California, USA, to British Columbia, Canada.
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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.
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This resource contains the presenter slides, Q&A responses, recording, and presenter bios from the November 2020 webinar Measuring Climate Adaptation Success and Progress: Introduction to the Resilience Metrics Toolkit
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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.
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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.
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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.