This "edutainment" packet, developed by a 2016 Science Transfer team, is an outreach tool that describes threats to water quality along the Mississippi-Alabama coastline and helps end users understand how they can they can take actionable steps to improve local water quality.
Resources
Resources
A repository of data, publications, tools, and other products from project teams, Science Collaborative program, and partners.
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This website contains information and resources from a 2012 Collaborative Research project that sought to reduce the vulnerabilities of Maryland's Deal Island Peninsula area to the impacts of climate change by creating partnerships between communities, decision-makers, and scientists.
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This project overview describes a 2013 Collaborative Research project that developed a protocol to accurately measure suspended sediment concentrations in tidal marshes, enhancing understanding of marsh accretion and informing marsh conservation and restoration.
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This project overview describes a 2012 Collaborative Research project that worked to enhance resilience on Maryland's Deal Island by building a stakeholder network and integrating research to understand how different management practices will impact marsh and community resilience.
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This project overview describes a 2010 Collaborative Research project that studied the legacy effects of land use change on water quality in Grand Bay, Mississippi.
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This project overview describes a 2017 Science Transfer project that developed products and tools that help end users on the Mississippi-Alabama coast learn how to minimize their impact on water quality.
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This document summarizes a tool developed by the NERRS to evaluate and compare the ability of tidal marshes to thrive as sea level rises.
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This paper, published in Biological Conservation, describes an innovative approach developed by the NERRS to evaluate the ability of tidal marshes to thrive as sea levels rise.
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This tool is a novel approach to compare the resilience of different marshes to sea level rise.
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This code (R and MATLAB) can be used to analyze NERRS System-Wide Monitoring Program time series data.