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ClosedOpportunity G23AS00223

Cooperative Agreement for CESU-affiliated Partner with Great Lakes Northern Forests Cooperative Ecosystem Studies Unit

Geological Survey · Department of the Interior

At a glance

Cooperative Agreement for CESU-affiliated Partner with Great Lakes Northern Forests Cooperative Ecosystem Studies Unit is a closed federal cooperative agreement opportunity from the Geological Survey (Department of the Interior). The application deadline was March 2, 2023. Individual awards are listed as up to $53K. Applicant types listed as eligible include others (see notice). Cost sharing is not required. It is funded under Assistance Listing 15.808 (U.S. Geological Survey Research and Data Collection).

Summary assembled from the official notice's fields. The official notice governs.

Key facts

Posted
Feb 2, 2023
Close date
Mar 2, 2023
Award floor
Not specified
Award ceiling
$52,960
Est. total funding
Not specified
Expected awards
Not specified
Cost sharing
Not required
Funding instrument
Cooperative Agreement
Opportunity category
Discretionary

Who can apply

  • Others (see notice)

Additional information on eligibility (from the notice)

This financial assistance opportunity is being issued under a Cooperative Ecosystem Studies Unit (CESU) Program. CESU’s are partnerships that provide research, technical assistance, and education. Eligible recipients must be a participating partner of the Great Lakes Northern Forest CESU Program.

Based on the published notice. Review the official notice for complete eligibility requirements.

What it funds

Official synopsis as published by the agency, formatted for readability.

The USGS is offering a funding opportunity to a CESU partner for research to provide Computer modeling to support Lake Erie nutrient adaptive management. This CESU project will provide support for up to one year. The goal of this work is to provide modeling analysis as part of and in support of the Lake Erie Data and Modeling Working Group of Adaptive Management Task Team, under the GLWQA Nutrient Annex. This CESU will complement ongoing work being conducted by USGS to understand the interaction of nutrient, invasive species, and other stressors in the Great Lakes and provide scientific leadership to the Adaptive Management Task Team. CESU and USGS partners will work closely through the Lake Erie Data and Modeling Working Group to select an appropriate set of ecosystem models; identify data requirements; acquire, harmonize, and synthesize data needed for models; identify modeling scenarios; and compare modelled results to one another and to in-lake outcomes. Additionally, the CESU partner will provide model calibration, validation, and test runs of agreed upon scenarios using their in-house ecosystem model(s).  Results from these efforts will extend abilities to predict Lake Erie response to nutrient management and concurrent ecosystem stressors and support adaptive management of this system.

Assistance Listing

What is an ALN?

Reported obligations under ALN 15.808

Historical data · not a prediction
FY24$125M
FY25$54.7M
FY26$60M est.
Source: SAM.gov Assistance Listing 15.808. Program-wide totals, not awards from this specific opportunity.

Agency contact (as published)

Geological Survey
fgraves@usgs.gov
703-648-7376

Quick answers

Who is eligible to apply for this opportunity?

The official notice lists these applicant types as eligible: Others (see notice). Additional restrictions may apply; review the full notice.

How much funding is available through this opportunity?

Per the notice, individual awards are listed as up to $53k.

How do I apply for this opportunity?

Applications are submitted through the official source, not through GrantsJunction. Open the official notice on Grants.gov (opportunity G23AS00223), and review the full announcement and application package. Organizations applying through Grants.gov generally need an active SAM.gov registration and a Grants.gov account, which can take several weeks to set up.

Matched on shared Assistance Listings, agency, categories and eligible applicant types.