This data resource includes marsh vegetation, water level data and modeling outputs from a project that examined how Piermont Marsh in New York buffers the impacts of storms.

This data resource includes eDNA sequences, fish species summary tables, and DNA extractions from Wells, Great Bay, Hudson, Apalachicola, South Slough, and Heʻeia National Estuarine Research Reserves.

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 climate adaptation planning toolkit compiles lessons learned by five National Estuarine Research Reserves. It is designed to help communities set goals and identify specific indicators to evaluate progress toward a climate resilient future.

This Master's thesis examines sediment accumulation in two disparate coastal environments, including the Hudson River Reserve, as part of a larger research project about marsh formation and resilience, sediment movement, and the potential impact of dam removals.

This website houses the Rapid Assessment Protocol for assessing the physical and ecological performance of nature-based engineered shoreline structures. You can also access additional resources associated with the Hudson River Sustainable Shorelines Project, including demonstration site case studies along the Hudson River.

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 dissertation was written by PhD student working at Hudson River Reserve on a project that assessed the buffering services of a coastal marsh in New York.

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