from 5 organizations
from 5 organizations
What is the NES-LTER?
The Northeast U.S. Shelf (NES) Long-Term Ecological Research (LTER) project integrates observations, experiments, and models to understand and predict how planktonic food webs are changing in the region, and how those changes may impact the productivity of higher trophic levels.
Data Jam Creativity Continues into 2022-23
In our fifth consecutive year of Data Jamming as part of education and outreach for the Northeast US Shelf Long Term Ecological Research (NES-LTER) project, we are pleased to announce the winners! From 30 full Data Jam projects (116 students) and 6 Mini Jam projects (23 students), 3 high schools, and 2 middle schools– we commend…READ MORE
ASM is where the LTER Network shines
Four years ago, when our team attended the LTER Network All Scientists’ Meeting (ASM), we were “newbies”, only a year into playing our role in this long term network of researchers. After an extra 1-year delay of the meeting, our team returned with more attendees, more connections, more posters, more workshops, and more appreciation. Our…READ MORE
Data Jam 2021-22 Jammin’ more than ever
120 students and 38 projects from grades 7 through 12 generated raps, a symphony, dancing scallops and wind and satellites, claymation, cupcake data points, board games, poems, comic strips, and puppet shows. Students used 13 different datasets from the provided Data Jam datasets and entertained 17 judges from the NES research team for days. While…READ MORE
Scallop Balance- Modeling for Management
In the recent publication of Fisheries Oceanography, Zhengchen Zang et al. share their sea scallop scope for growth (SFG) model. Scallop energy dynamics depend on the spatial and seasonal variability on the Northeast US Shelf. The scallop SFG model is therefore driven by high-resolution hydrodynamic and biological models and provides key information about scallop growth…READ MORE
L&O Letters- When It’s Spring in the Gulf of Maine
In a recent publication to Limnology and Oceanography Letters, Zhengchen Zang et al. share work on the role of silicate in the Gulf of Maine. In this study, they employed an artificial neural network method to identify the spring blooms from satellite images and reconstructed the spring bloom magnitude with strong interannual variability. This study…READ MORE
The NES-LTER produces observational data, derived data products, and model data. Observational data are obtained in real-time from moored underwater instruments, underway and from sampling on research cruises, and post-cruise with laboratory analyses.