Adaptive Science | F17AS00273

Opportunity Information: Apply for F17AS00273

The Adaptive Science grant opportunity (Funding Opportunity Number F17AS00273) is a U.S. Fish and Wildlife Service (USFWS) notice of intent to make a single-source cooperative agreement award to the University of Chicago. The core purpose is to strengthen the Services access to timely, defensible scientific information and to build collaboration around high-impact questions involving threats to fish and wildlife resources. The emphasis is on situations where management actions and/or mitigation are needed to keep species at healthy, sustainable, and otherwise desired population levels, especially in contexts where the Service must justify decisions to regulators, courts, land managers, and other stakeholders.

A key driver behind the award is the Services requirement to base regulatory decisions, biological opinions, and conservation recommendations on the best available science. In practice, that means supporting work that can improve the evidence base for decisions affecting species and habitats, reduce uncertainty, and clarify how different actions or environmental changes are likely to affect wildlife populations. The opportunity is framed around producing information that can stand up to scrutiny and meaningfully inform real-world management and policy choices.

The work is aligned with the Services Strategic Habitat Conservation (SHC) framework, which is described as a science-based, adaptive approach to setting and achieving broad-scale conservation objectives. SHC draws on adaptive management principles and relies on population and habitat data, ecological modeling, and targeted monitoring and assessment. The intent is to move beyond general conservation goals toward strategies that can be implemented, measured, and adjusted over time based on outcomes, with a focus on measurable fish and wildlife population results rather than activities alone.

Within this framework, the Service aims to use the best available scientific information to predict how fish and wildlife populations will respond to environmental change. That predictive capacity is important because it helps the Service prioritize where habitat conservation and other management actions will have the greatest impact. In other words, the funding supports science that improves decision-making efficiency: identifying which interventions are most likely to work, where to deploy them, and how to track whether they are actually producing the intended biological outcomes.

Administratively, this is a discretionary funding opportunity issued by the Department of the Interior, Fish and Wildlife Service, using a cooperative agreement mechanism, which typically implies substantial involvement by the federal agency during the project. The activity area is natural resources under CFDA 15.670. The opportunity lists eligibility as private institutions of higher education, but the announcement explicitly states it is a single-source intent to award to the University of Chicago, with one expected award and an award ceiling of $89,477. The opportunity was created on June 21, 2017, with an original closing date of June 26, 2017, reflecting a short window consistent with a targeted, non-competitive single-recipient intent.

• The Department of the Interior, Fish and Wildlife Service in the natural resources sector is offering a public funding opportunity titled "Adaptive Science" and is now available to receive applicants.
• Interested and eligible applicants and submit their applications by referencing the CFDA number(s): 15.670.
• This funding opportunity was created on Jun 21, 2017.
• Applicants must submit their applications by Jun 26, 2017. (Agency may still review applications by suitable applicants for the remaining/unused allocated funding in 2026.)
• Each selected applicant is eligible to receive up to $89,477.00 in funding.
• The number of recipients for this funding is limited to 1 candidate(s).
• Eligible applicants include: Private institutions of higher education.

Apply for F17AS00273

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