This dataset comprises the data collected and produced as part of the 2016 research project Investigating the Interconnectedness of Climate Change, Nuisance Mosquitoes, and Resilience of Coastal Salt Marsh Systems.
Resources
Resources
A repository of data, publications, tools, and other products from project teams, Science Collaborative program, and partners.
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This project overview describes a 2018 Catalyst project that created the web-based toolkit Resilience Metrics to share lessons learned on successful climate adaptation planning within the National Estuarine Research Reserve System.
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This project overview describes a 2017 science transfer project that developed a risk communication training for reserves to build risk communication capacity in four coastal communities.
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This project overview describes a 2018 Catalyst project where researchers from Duke University and the North Carolina and Rookery Bay reserves partnered to develop ecosystem services models for coastal habitats.
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This project overview describes a 2018 Catalyst project led by Lisa Auermuller in which Jacques Cousteau National Estuarine Research Reserve and Rutgers University collaborate to streamline coastal resilience tools, data, and techniques in New Jersey.
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This project overview describes a 2018 Catalyst project where Rookery Bay Reserve staff and University of South Florida researchers are partnered to update and create maps of mangrove habitat to measure changes over time.
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Mangroves and other habitats in Rookery Bay National Estuarine Research Reserve have been changing as a result of chronic stressors and severe hurricanes in recent years.
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This project overview describes a 2011 Collaborative Research project that developed a science-based framework for stakeholders to use in making decisions about water resource management in the Rookery Bay Estuary.
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This project overview describes a 2013 project that created an online portal for scientists and fisheries managers to share and use data on larval fish recruitment and environmental variables.
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This code (R and MATLAB) can be used to analyze NERRS System-Wide Monitoring Program time series data.