Notice of Intent: Models and Data for Predicting the Abundance of Moose and Elk on the Summer Range in Rocky Mountain National Park | P17AS00440

Opportunity Information: Apply for P17AS00440

The Notice of Intent titled "Models and Data for Predicting the Abundance of Moose and Elk on the Summer Range in Rocky Mountain National Park" is a discretionary cooperative agreement opportunity from the Department of the Interior, National Park Service (NPS). Its main aim is to produce practical tools, datasets, and analytical methods that help Rocky Mountain National Park better understand and manage elk and moose populations during the summer range period, with particular attention to the riparian habitats these animals rely on. The project is framed as cooperative research, meaning the work is intended to be conducted in partnership with external researchers while directly supporting on-the-ground park management needs.

A central focus of the project is improving population estimation and prediction for elk on the park's summer range. Rather than starting from scratch, the work is designed to build on existing foundations, including prior elk population modeling efforts, census data, and information from collared animals. The intent is to strengthen the park's ability to estimate how many elk are present during summer and where they concentrate, which is important for understanding grazing and browsing pressure, habitat impacts, and potential conflicts with broader ecological objectives.

Another major component is the development of sampling procedures and data models to support aerial infrared surveys for elk and moose. Aerial infrared methods can be useful for detecting animals under certain field conditions, but they require carefully designed survey protocols and analytical approaches to convert detections into credible population estimates. This opportunity emphasizes not just collecting imagery, but creating repeatable procedures and modeling frameworks that can be applied over time to generate consistent, defensible estimates and trends.

For moose, the project emphasizes GPS collar data as a key source of ecological insight. By obtaining and analyzing location information from GPS-collared moose, the research is expected to clarify whether the park hosts a resident moose population, a transient population moving through the area, or some combination of both. The GPS data are also intended to identify periods of especially high use within the park, which can help managers understand seasonal patterns, habitat dependencies, and potential vulnerability windows tied to weather, forage availability, human activity, or habitat conditions.

Beyond movement and residency questions, the project calls for using GPS collar data to estimate moose survival probabilities. Survival estimation is a core demographic measure needed for population assessment, and incorporating telemetry data can improve the reliability of survival estimates compared to indirect approaches. These results can feed into broader population models, help interpret changes in abundance, and inform whether management attention should prioritize factors like habitat quality, disease, predation, human disturbance, or other stressors that can influence survival rates.

The final technical pillar described is the development of GIS-based estimates of habitat carrying capacity for moose. In practice, this means using spatial data and habitat variables to estimate how many moose the landscape can support under given conditions, and how that capacity might vary across the park or change over time. A GIS-based approach can link animal use patterns to habitat attributes and produce map-based outputs that are easier for managers to apply in planning, monitoring, and communication. Coupled with movement, survival, and survey-derived abundance estimates, carrying capacity analysis can help frame population objectives in a habitat context and support decisions related to riparian protection and restoration priorities.

The opportunity also highlights a public-benefit purpose beyond immediate park operations. By generating new natural resource knowledge through external researchers, the project is positioned to contribute to the broader scientific community and to strengthen shared environmental stewardship with university partners. The work is explicitly intended to inform park programs for the public and to be used in science communication materials, indicating that results should be translated into accessible products that help visitors and stakeholders understand elk and moose ecology, population dynamics, and the importance of riparian habitats.

From an administrative standpoint, the opportunity was posted under Funding Opportunity Number P17AS00440 and categorized under Natural Resources (CFDA 15.945). It was issued as a cooperative agreement with an expected single award, with an award ceiling of $1. The posting lists eligible applicants as "Others" with additional eligibility details referenced externally, and it notes that the modeling and data analysis component is intended to support knowledge and skill development among partners at Colorado State University. The notice was created on June 27, 2017, with an original closing date of July 7, 2017, suggesting it may have been targeted or time-sensitive, consistent with many cooperative research arrangements that are designed to formalize collaboration with a specific partner or small set of specialized collaborators.

• The Department of the Interior, National Park Service in the natural resources sector is offering a public funding opportunity titled "Notice of Intent: Models and Data for Predicting the Abundance of Moose and Elk on the Summer Range in Rocky Mountain National Park" and is now available to receive applicants.
• Interested and eligible applicants and submit their applications by referencing the CFDA number(s): 15.945.
• This funding opportunity was created on Jun 27, 2017.
• Applicants must submit their applications by Jul 07, 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 $1.00 in funding.
• The number of recipients for this funding is limited to 1 candidate(s).
• Eligible applicants include: Others (see text field entitled Additional Information on Eligibility for clarification).

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