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.

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

This thesis represents the first study to examine a full individual energy budget for the triploid Eastern oyster, Crassostrea virginica, with implications for shellfish aquaculture in the southeastern United States.

This is a Senior Honors Thesis written by Kinsey Fischer, an advisee of Rachel Noble. This study was conducted as part of a 2016 - 2020 collaborative research project about stormwater impacts in Beaufort, North Carolina.

This 2019 report describes the findings of a University of Michigan SEAS Master's project which provides insights for KBNERR regarding current ecosystem services valued in Kachemak Bay using a socio-cultural, place-based, ecosystem services framework.

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 geodatabase contains GIS layers that illustrate the distribution of existing wetlands and identify locations where restoration is likely to have the greatest positive environmental impact in Douglas County, WI.

The Communities, Lands & Waterways Data Source is an encyclopedic compilation of all available data describing the socioeconomic and environmental conditions in the Coos Bay area.

These case studies summarize findings from a 2012 Collaborative Research project studying climate change adaptation and risks in four New England communities.

These case studies highlight towns in coastal New Hampshire that used low impact development and green infrastructure strategies to reduce stormwater runoff and adapt to climate change.