Closed · P19AS00286 · CFDA 15.945 · Discretionary
Forecasting in Support of Adaptive Management
Federal grant opportunity posted by National Park Service, cataloged on Grants.gov.
- up to $26K
- Award range
- Closed
- Status
- June 27, 2019
- Close date
- 1
- Expected awards
The verdict
Forecasting in Support of Adaptive Management is a closed discretionary listing from National Park Service that offered Up to $25,864 across 1 expected award. Future funding cycles may be published under the same CFDA number. PlainGrants indexes 8,506 other opportunities from National Park Service.
- up to $26K
- award range
- Closed
- application status
- 1
- expected award
- 15.945
- CFDA program
Archived opportunity record
Forecasting in Support of Adaptive Management is closed (last close June 27, 2019). Check the agency catalog for replacement NOFOs. National Park Service still indexes 8,506 other opportunities here.
According to Grants.gov (extract vintage 2026-08-10), this record reflects the officially published listing. See methodology.
Deadline tracker
Application window closed
Closed June 27, 2019
Est. Total Funding
$25,864
Expected Awards
1
Instrument
Cooperative Agreement
Description
he National Park Service manages the abundance of elk wintering in Rocky Mountain National Park (RMNP) to meet objectives specified in the Elk and Vegetation Management Plan (EVMP). The EVMP seeks to reestablish a healthy balance between the elk herd and the habitat they use, allowing for the restoration of over browsed aspen and willow communities, increases in biodiversity and maintaining and improving world class wildlife viewing opportunities. The EVMP specifies that the elk population will be maintained within a target range of 600 ¿ 800 animals. To achieve these objectives, the population is managed adaptively, based on the science produced, in part, by this partnership. Each year, the size of the population will be assessed relative to the target range and, based on this assessment, management actions will be chosen to assure that the trajectory of the population remains within that range. Essential to the success of this approach is a model of elk population dynamics that allows managers to forecast the effect of alternative management actions on the elk population. In earlier work, Hobbs and Hoeting (2009) and Ketz et al. (2016) developed forecasting models that predicts the future size of the RMNP elk population based on historic data and current census estimates. Because the model uses historic data to estimate uncertainties associated with these predictions, it is feasible to specify the probability that the next year¿s population will be within limits specified by park management. It is also feasible to estimate the probability distribution of the current population size in a way that includes historic as well as current data and that responds to all sources of uncertainty revealed by the full, historical time series of observations of the elk population. Counts are adjusted for animals that are present within park boundaries but not observed during ground counts using a prior distribution on detection probability obtained from the analysis of Ketz et al. (2018). This model provides a firm, statistically defensible basis for adaptive management of the park¿s elk herd. B. Project Objectives ¿ Adaptive management will be implemented as follows. Each year, the size of the park¿s population and its sex and age composition will be estimated using modern census methods developed as part of a previous project. Ground counts and classification will be conducted three times each month, October ¿ April. Using the forecasting model, the current year¿s data will be combined with the full time series of data in from previous years to estimate the probability distribution of the current and subsequent year¿s population size. These probability distributions form the basis for choice of management actions outlined under the EVMP, including culling of animals, aversive conditioning of animals and/or protecting vulnerable habitat with fencing projects. Moreover, the model¿s predictions from the previous year will be compared with the current, realized population estimate obtained from census. This comparison may motivate changes in the model to improve the accuracy of its predictions. If the park collects aerial census data on elk on the winter range, that information may also be used. This cycle will be repeated annually, allowing continuous improvement in the model and in management.
Campus applicant desk · Natural Resources
Grants.gov prints only higher-education applicant doors for Forecasting in Support of Adaptive Management (Public / state-controlled institutions of higher education). This reads as a campus competition desk, not a civic or commercial open call. The extract does not mark applicant cost-sharing as required. Instrument: Cooperative Agreement. Funding-activity code maps to Natural Resources (NR). 1,235 other National Park Service listings share this eligibility-code string (15% of 8,507).
- Code 06
- Public / state-controlled institutions of higher education
Codes are Grants.gov eligibility category values from the extract vintage 2026-08-10. Confirm the full eligibility statement on the official listing or the Grants.gov eligibility reference.
Official Listing on Grants.gov
View full details, application forms, and submission instructions.
Agency Contact
Grants Management Specialist Kelly Adams Kelly_Adams@nps.gov
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What to do next
Forecasting in Support of Adaptive Management is closed; use the parent program and agency catalog for the next funding cycle.
- Browse open federal opportunities in the same category when this listing is closed. Open opportunities
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Opportunity deadlines and eligibility change on Grants.gov; PlainGrants mirrors the extract and is not the submission system.
Disclaimer: This information is sourced from Grants.gov and SAM.gov and is for informational purposes only. Opportunity details, deadlines, and eligibility requirements change frequently. Always verify current information directly on Grants.gov before applying. PlainGrants is not affiliated with any federal agency.
Read our methodology - how this data is sourced, computed, and verified.
| Publisher | PlainGrants |
| Sources | the SAM.gov Assistance Listings and Grants.gov |