Cooperative Agreement for affiliated Partner with the Great Rivers Cooperative Ecosystem Studies Unit
🏛 Geological Survey (DOI-USGS1)
✓ Free, no account · Source: Grants.gov · Last verified Nov 13, 2026
Can you apply?
This grant is for CESU-affiliated organizations conducting research on Bayesian statistical methods for bat acoustic monitoring. Applicants must be active partners in the Great Rivers Cooperative Ecosystem Studies Unit network. The funding supports novel research on species occurrence estimation, relative abundance modeling, and environmental predictor analysis. Projects should address spatial correlation and multiple observer data integration for acoustic monitoring datasets.
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Program description
The U.S. Geological Survey (USGS) invites proposals from Cooperative Ecosystem Studies Unit (CESU) Great Rivers network partners for novel research focusing on the development and application of Bayesian statistical methods. This funding opportunity seeks to advance methodologies for analyzing passive bat acoustic monitoring data, specifically for estimating species occurrence and relative abundance at un-surveyed locations and assessing their associations with meaningful environmental predictors. Proposed methods should consider accounting for spatial correlation and integrating of multiple observer information to address the misclassification process inherent in acoustic data.
Passive acoustic monitoring represents a cost-effective and technologically advanced approach for collecting extensive biological observations. The primary scientific challenge lies in translating these massive datasets into dynamic and interactive maps of species occurrence and relative abundance. Enhancing the ability to generate these maps directly supports land management and state agency conservation and restoration efforts, aligning with the Department of the Interior’s (DOI) strategic goals to restore lands and waters, protect species, and provide reliable scientific information through collaborative partnerships (SO 3356: Shooting, and Wildlife Conservation Opportunities and Coordination with States, Tribes, and Territories (September 15, 2017)). These efforts will directly support EO 14303 “restoring gold standard science” because fundamental to science is using statistical models that account for all sources of uncertainty when drawing conclusions from data.
This research will contribute significantly to monitoring the distributions and activity rates of the 47 bat species across North America. Given the estimated $3.7 to $53 billion/year in savings bats provide as agricultural pest eaters [Boyles et al. 2011], accurate monitoring is crucial. While current count detection models developed by the USGS account for species classification errors and estimate species occurrence and relative activity [Wright et al., 2020], they currently do not explicitly model spatial dependence among observations. Incorporating spatial processes in these models is known to improve predictive accuracy for species distributions [Wright et al., 2021] and will enable a more complete understanding of underlying ecological patterns such as animal movement and dispersal [Levin, 1992].
It is expected that the developed methods will lead to more robust and accurate predictions of bat species distributions, directly informing critical conservation and resource management decisions.
Who can apply
Eligible applicants
How to apply
Application links
Key dates & requirements
Required documents
- SF-424 form
- Project narrative/technical proposal
- Budget and budget justification
- Statement of CESU partnership/affiliation
- Curriculum vitae for key personnel
- Letters of support from collaborating institutions
Program contact
- 👤 Geological Survey
- 📧 rachel_miller@ios.doi.gov
- 📞 916-278-9331
Funding track record
Recent awards under CFDA 15.808 from the last 3 years — real organizations that won funding through this same program.
Top 10 Largest Recent Awards
-
$240,000,000
-
$11,148,115
-
$10,533,234
-
$10,055,533
-
$8,476,627
-
$8,454,102
-
$7,659,261
-
$6,894,612
-
$6,800,079
-
$6,644,228
Top States by Funding
- CO 9 awards $247.6M
- CA 24 awards $73.6M
- AK 17 awards $35.1M
- FL 7 awards $23.2M
- WA 4 awards $16.6M
Source: USAspending.gov — federal spending transparency. Data covers last 3 years.
Funding history
Annual funding for this program — Federal obligations (CFDA 15.808). How funding has trended year over year.
| 2021 | $35,000,000 | |
| 2022 | $157,201,151 | |
| 2023 | $263,107,440 | |
| 2024 | $125,066,719 | |
| 2025 est. | $110,000,000 | |
| 2026 est. | $60,000,000 |
FAQ
Who is eligible to apply for this grant?
Only active partners of the Great Rivers Cooperative Ecosystem Studies Unit can apply. You must verify your organization's current CESU partner status before submitting.
What types of research does this fund?
The grant supports Bayesian statistical method development for passive bat acoustic monitoring. Focus areas include species occurrence estimation, relative abundance prediction, and environmental correlation analysis.
Is there a cost-sharing requirement?
No. This grant does not require matching funds or cost-sharing from applicants.
What is the funding range?
Awards range from $1 to $350,000. The total available funding pool is $350,000 across all awards.
When is the deadline?
The fixed deadline is December 4, 2026. There is no rolling application period for this cycle.
💡 Tips for applicants
- Clearly articulate your team's expertise in Bayesian statistical methods and acoustic data analysis. Reviewers expect strong methodological foundation.
- Connect your research directly to conservation outcomes for bat species and land management agency needs. Show practical applications of your models.
- Address how your methods handle spatial correlation and misclassification inherent in acoustic data. These are evaluation priorities.
- Emphasize collaboration with CESU partner institutions and state/tribal conservation partners. The program values interagency partnerships.
- Include a timeline for model development, validation, and delivery of species occurrence maps or interactive visualization tools.
⚠️ Common mistakes
Failing to demonstrate CESU partnership eligibility before investing proposal effort. Submitting research outside passive acoustic monitoring and bat species focus area. Underestimating the importance of spatial statistical methods; generic species distribution models will score poorly.
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