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.

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.

These datasets and statistical analysis codes model surge barrier effects on the Hudson River estuary, developed as part of the 2018 catalyst project Assessing the Physical Effects of Storm Surge Barriers on the Harbor and Hudson River Estuary.

These datasets contain sediment core samples from dam impoundments on tributaries to the Hudson River and tidal wetland complexes in the Hudson River estuary, collected as part of the 2016-2020 collaborative research project Dams and Sediment on the Hudson (DaSH).

This case study discusses an example of an Ecosystem Services Conceptual Model for mangrove habitat restoration at Rookery Bay National Estuarine Research Reserve in Florida.

These case studies, part of a Resilience Metrics toolkit, show how particular communities have defined and tracked their progress on climate adaptation goals.

The collaborative research project, Re-engineering Living Shorelines for High-Energy Coastal Environments, produced four datasets as part of their assessment of living shoreline installations at GTM Reserve in Florida.

Three related datasets were generated by the 2015 - 2019 collaborative research project Evaluating Living Shorelines to Inform Regulatory Decision-Making in South Carolina.

This dataset includes a suite of measures of ecological and physical functions of built sustainable shoreline structures at a set of demonstration sites along the Hudson River.

This code (R and MATLAB) can be used to analyze NERRS System-Wide Monitoring Program time series data.