This resource contains the presenter slides, Q&A responses, recording, and presenter bios from the two-part February and March 2024 webinar series, "A Collaborative Approach to Advancing Blue Carbon Research and Data Applications."
This resource is a collection of media materials developed for education and outreach for the NY-NJ Eel Partnership that emerged from a two-year science transfer project focused on community eel monitoring.
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Keywords: communication, community science, eels, education (place-based)
This resource contains the presenter slides, Q&A responses, recording, and presenter bios from the October 2023 webinar "Building Capacity for Reserves to be Motus Wildlife Tracking Leaders."
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Keywords: shorebird habitat, climate change, estuarine habitat, wildlife, motus
Reserves: ACE Basin, SC, Grand Bay, MS, Hudson River, NY, San Francisco Bay, CA
Cultural ecosystem services (CES), one of four main categories of ecosystem services, are often described as the non-material benefits that humans receive from their interactions with the environment.
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Reserves: He‘eia, HI, Kachemak Bay, AK, Tijuana River, CA, Wells, ME
This report summarizes five cultural ecosystem service assessment methods piloted by the 2020 catalyst project, Cultural Ecosystem Services in Estuary Stewardship and Management.
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Keywords: ecosystem services, cultural ecosystem services, assessment, methods, reserve exchange
Educators from the Chesapeake Bay National Estuarine Research Reserve in Virginia (CBNERRVA) and the Virginia Institute of Marine Science's (VIMS) Marine Advisory Program cre
This resource contains the presenter slides, Q&A responses, recording, and presenter bios from the September 2022 webinar "Cultural Ecosystem Services in Estuary Stewardship and Management."
This 2022 paper which appeared in Nature discusses a modeling approach to examine the marsh ’s buffering capacity in a changing climate (from 2020 to 2100), considering a potential marsh restoration plan (from 2020 to 2025) and potential marsh loss due to sea-level rise.
This 2021 paper from the University of South Florida discusses how machine learning was used to map aquifers throughout the Kenai Lowlands to locate groundwater discharge, providing a framework to extend this method of modeling groundwater to other reserves.