Each National Estuarine Research Reserve develops a site profile synthesizing knowledge about its physical, historical, social and biological characteristics to guide research activities. This digital site profile helps users orient to the Lake Superior Reserve and understand its context.
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)
As climate change and development threaten the natural and cultural resources of the Guana Peninsula, this 2021 collaborative research project used a combination of archaeological investigations and applied anthropological methods to increase understanding of how people past and present have used the resources to inform their future management.
As climate change and development threaten the natural and cultural resources of the Guana Peninsula, this 2021 collaborative research project used a combination of archaeological investigations and applied anthropological methods to increase understanding of how people past and present have used the resources to inform their future management.
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Keywords: cultural ecosystem services, sea level rise, archaeological sites
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
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 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.