This project overview describes a 2018 Catalyst project that conducted a collaborative, scientific modeling investigation to improve oyster population sustainability and management on Florida's Atlantic coast.

This open access article describes an assessment of the storm buffering services provided by Piermont Marsh, New York.

This project overview describes a 2016 Collaborative Research project that designed and applied predictive models to better understand the buffering services provided by Piermont Marsh on New York's Hudson River.

This resource contains the presenter slides, Q&A responses, recording, and presenter bios from the March 2021 webinar Understanding the Role Coastal Marshes Play in Protecting Communities from Storm Surge and Flooding.

This outreach tool summarizes the key takeways for a project that assessed the value of a coastal marsh in protecting coastal communities from storm surge and flooding amid a changing climate.

This article, published in Scientific Reports in 2021, describes work done as part of a 2016-2020 collaborative research project conducted at Hudson River Reserve in New York. The article describes a regression model that can be used for wetland restoration planning to help reduce storm-related structural damage.

This journal article describes a new approach for statistically modeling boat wakes, which can help managers better understand how boat traffic impacts shoreline erosion and sediment transport.

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 project overview describes a 2017 Collaborative Research project that is piloting and refining DNA-based monitoring protocols that can be applied to specific issues and species of interest in estuarine ecosystems.

This project overview describes a 2018 Catalyst project led by Grand Bay Reserve that developed standardized tools to quality-check, analyze, and visualize Surface Elevation Table data.