Forecasting in Support of Adaptive Management

The summary for the Forecasting in Support of Adaptive Management grant is detailed below. This summary states who is eligible for the grant, how much grant money will be awarded, current and past deadlines, Catalog of Federal Domestic Assistance (CFDA) numbers, and a sampling of similar government grants. Verify the accuracy of the data FederalGrants.com provides by visiting the webpage noted in the Link to Full Announcement section or by contacting the appropriate person listed as the Grant Announcement Contact. If any section is incomplete, please visit the website for the National Park Service, which is the U.S. government agency offering this grant.
Forecasting in Support of Adaptive Management: 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.
Federal Grant Title: Forecasting in Support of Adaptive Management
Federal Agency Name: National Park Service (DOI-NPS)
Grant Categories: Natural Resources
Type of Opportunity: Discretionary
Funding Opportunity Number: P19AS00286
Type of Funding: Cooperative Agreement
CFDA Numbers: 15.945
CFDA Descriptions: Information not provided
Current Application Deadline: June 27th, 2019
Original Application Deadline: June 27th, 2019
Posted Date: June 18th, 2019
Creation Date: June 18th, 2019
Archive Date: June 28th, 2019
Total Program Funding: $25,864
Maximum Federal Grant Award: $25,864
Minimum Federal Grant Award: $0
Expected Number of Awards: 1
Cost Sharing or Matching: No
Last Updated: June 18th, 2019
Applicants Eligible for this Grant
Public and State controlled institutions of higher education
Link to Full Grant Announcement
http://www.grants.gov
Grant Announcement Contact
Grants Management Specialist Kelly Adams
[email protected]
.
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