Cooperative Agreement for affiliated Partner with Great Basin Cooperative Ecosystem Studies Unit
Geological Survey · Other Federal · G27AS00044
Source: Grants.gov · View original posting ↗
- AwardWhat a single award can be worth — the funder's published per-award amount or floor–ceiling range.
- $1 – $250K
- DeadlineFinal application due date.
- Nov 30, 2026
- Letter of intentDue date for the letter of intent (a short pre-application some funders require or request before the full proposal).
- —
- MechanismNIH activity code — the grant type (R01 research project, R21 exploratory, K series career development, F series fellowship, …).
- —
- DurationMaximum project period for a single award.
- —
- Expected awardsHow many awards the funder anticipates making under this opportunity.
- 1
- Funding cycleHow often the program accepts applications (annual, multiple cycles per year, rolling, or one-time).
- Unknown
- Open dateWhen applications open (or opened).
- Oct 30, 2026
- Total fundingThe overall pool the funder expects to commit across ALL awards under this opportunity — not what one project receives.
- $250K total
- Clinical trialWhether proposed projects must, may, or must not include a clinical trial.
- Unspecified
Description
The U.S. Geological Survey (USGS) is offering a funding opportunity to a CESU partner for research in species barcoding. Environmental DNA (eDNA) is potentially a powerful new tool to monitor and assess the status of aquatic ecosystems and the societal values they support such as recreational fishing and species protection. Assessments based on metabarcoding can detect a wide array of species from microbes to fish, providing more comprehensive and cost-effect assessments than existing techniques. However, one pressing need in developing metabarcoding assessment tools is to enhance existing libraries of aquatic species barcodes. The USGS Water Resources Mission Area is leading a pilot project in the Rocky Mountains to evaluate metagenomics as a means of monitoring and assessing aquatic species. Metagenomics data from field samples will be combined with large geospatial datasets to develop artificial intelligence and machine-learning models that predict community composition. The project is more likely to succeed if current gaps in species barcode libraries are filled.