Opportunity Information: Apply for F16AS00338
Apply for F16AS00338
- The Department of the Interior, Fish and Wildlife Service in the natural resources, science and technology and other research and development sector is offering a public funding opportunity titled "Finite block kriging with detection functions: Development of a new model for estimating the abundance of moose in Alaska by building on the Geospatial Population Estimator" and is now available to receive applicants.
- Interested and eligible applicants and submit their applications by referencing the CFDA number(s): 15.678.
- This funding opportunity was created on Jul 12, 2016.
- Applicants must submit their applications by Jul 30, 2016. (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 $41,000.00 in funding.
- The number of recipients for this funding is limited to 1 candidate(s).
- Eligible applicants include: Public and State controlled institutions of higher education.
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Opportunity Summary:
The opportunity describes a US Fish and Wildlife Service (USFWS), Region 7 plan to fund a targeted research project to improve how moose abundance is estimated in Alaska when survey conditions are not ideal. The award is structured as a cooperative agreement under the Cooperative Ecosystem Studies Unit (CESU) Network, and it is explicitly single-source: USFWS stated it intended to make the award to Oregon State University under existing authority (505 DM 2.14(B)). Because of that, the notice was informational rather than competitive, meaning it was not a request for proposals and the government did not plan to accept submissions from other applicants. The activity falls under CFDA 15.678 and is categorized as natural resources research and development, with one expected award and an award ceiling of $41,000.
The core technical goal is to develop an updated statistical model for moose population estimation that builds on the Geospatial Population Estimator (GSPE), also referred to as finite block kriging for plot-based sampling. GSPE has been widely used in Alaska because it can produce abundance estimates that are both relatively unbiased and precise when surveys are conducted under recommended field conditions. Historically, one of the key conditions supporting strong performance has been adequate snow cover, which improves visibility and consistency during aerial or plot-based counts. The problem motivating this work is that, over roughly the prior five years referenced in the notice, reliable snowfall across much of Alaska had become less consistent, undermining a major assumption behind the quality of traditional counts.
A central issue under poor snow conditions is imperfect and variable detectability. In practical terms, observers do not have the same chance of seeing moose in every sampled unit, and that chance shifts with environmental conditions like snow cover. If detection probability drops, raw counts can systematically understate the true number of animals present, introducing bias into abundance estimates. To address this, USFWS had been collecting additional data using radio-collared moose. Those marked animals provide a way to estimate, for each sampled unit and set of conditions, the probability that a moose present is actually detected by the survey. The notice indicates that USFWS was fitting this detection component using a logistic model, which is a common approach for modeling probabilities as a function of covariates (for example, snow depth, canopy cover, light conditions, or other survey-relevant factors).
What the grant is specifically funding is the methodological bridge between GSPE and that detection modeling. It is not enough to simply adjust counts using a detection probability; managers also need correct measures of uncertainty around the final abundance estimate. The opportunity emphasizes that to properly quantify precision, a new model must be derived that correctly estimates the variance of GSPE when a detection function is incorporated. In other words, the project is aimed at producing a statistically sound framework that combines spatial prediction (finite block kriging) with detection correction, while also propagating uncertainty from both the spatial estimation and the detection process into a defensible variance (and therefore confidence intervals) for the abundance estimate.
The expected outcome is a model that can be used operationally to predict moose abundance across a range of real-world survey conditions in western Alaska, including conditions known to affect observed counts. By incorporating detection functions informed by radio-collar data and integrating them into the GSPE framework with an appropriate variance estimator, the resulting approach is intended to reduce bias when snowfall is poor or otherwise inconsistent, while still providing managers with transparent, statistically valid measures of precision for decision-making and population monitoring.
Frequently Asked Questions (FAQs)
What is this funding opportunity about?
This opportunity describes a US Fish and Wildlife Service (USFWS), Region 7 effort to fund a targeted research project focused on improving how moose abundance is estimated in Alaska when survey conditions are not ideal, especially when snow cover is inconsistent.
Is this a competitive grant where multiple applicants can apply?
No. The notice is informational rather than competitive. It is explicitly single-source, and USFWS stated it intended to make the award to Oregon State University under existing authority (505 DM 2.14(B)). The government did not plan to accept submissions from other applicants.
What type of award is being used?
The award is structured as a cooperative agreement under the Cooperative Ecosystem Studies Unit (CESU) Network.
Who is the intended recipient of the award?
USFWS indicated it intended to make the award to Oregon State University.
How many awards are expected?
One award is expected.
What is the maximum funding amount?
The award ceiling is $41,000.
What is the CFDA number and program area for this work?
The activity falls under CFDA 15.678 and is categorized as natural resources research and development.
What problem is this project trying to solve?
The project is motivated by worsening survey conditions for moose counts in Alaska, particularly less consistent and less reliable snowfall across much of Alaska over roughly the prior five years referenced in the notice. Adequate snow cover has historically been a key condition for strong performance of traditional aerial or plot-based counts, because it improves visibility and consistency. When snow is poor, counts can become less reliable.
Why does inconsistent snow cover affect moose abundance estimates?
Inconsistent snow cover can reduce and destabilize detectability, meaning observers do not have the same chance of seeing moose in every sampled unit. When detection probability drops, raw counts can systematically understate the true number of animals present, introducing bias into abundance estimates.
What is detectability in the context of moose surveys?
Detectability refers to the probability that a moose that is present in a sampled unit is actually seen and counted by observers. Under poor conditions (such as low or inconsistent snow cover), detectability can be imperfect and variable across space and time.
What approach has been used historically to estimate moose abundance in Alaska?
The notice describes the Geospatial Population Estimator (GSPE), also referred to as finite block kriging for plot-based sampling, as a widely used approach in Alaska. It can provide abundance estimates that are relatively unbiased and precise when surveys are conducted under recommended field conditions, including adequate snow cover.
What is GSPE (finite block kriging) intended to do in these surveys?
GSPE is a spatial estimation approach used for plot-based sampling that supports predicting abundance across an area using information from sampled units and spatial structure. The notice highlights its strong performance under recommended conditions.
What new technical work is being funded by this opportunity?
The funding supports development of an updated statistical model for moose population estimation that builds on GSPE but explicitly accounts for imperfect and variable detectability when conditions are not ideal.
How is USFWS estimating detectability under poor conditions?
USFWS has been collecting additional data using radio-collared moose. These marked animals allow estimation of the probability that a moose present in a sampled unit is detected under specific conditions. The notice indicates USFWS was fitting this detection component using a logistic model.
What role do radio-collared moose play in the approach described?
Radio-collared moose provide a reference for whether animals present in a sampled unit were detected by observers. This supports estimating detection probability as conditions change, which is central to correcting bias in counts when detectability is less than perfect.
What kind of statistical model is mentioned for detection probability?
The notice mentions a logistic model to fit the detection component. Logistic models are commonly used to model probabilities as a function of covariates.
What kinds of factors could be used as covariates in the detection model?
The notice provides examples of survey-relevant covariates such as snow depth, canopy cover, and light conditions, along with other environmental or survey conditions that may influence whether moose are seen.
Why is it not enough to just adjust counts by a detection probability?
The opportunity emphasizes that managers also need correct measures of uncertainty around the final abundance estimate. Simply adjusting counts without correctly estimating uncertainty can lead to misleading precision. The project focuses on deriving a model that correctly estimates the variance of GSPE when a detection function is incorporated.
What does the project mean by "variance" and "precision" in abundance estimates?
Variance is the statistical measure that underlies uncertainty in the abundance estimate. Precision refers to how tightly the estimate is determined (often reflected in confidence intervals). The project aims to produce a defensible variance estimator so resulting abundance estimates include transparent, statistically valid measures of precision.
What is the key methodological "bridge" this project is trying to build?
The project is intended to combine spatial prediction (GSPE / finite block kriging) with detection correction informed by radio-collar data, and to propagate uncertainty from both components into a correct overall variance estimate for the final abundance prediction.
What is the intended operational outcome of the project?
The expected outcome is a model that can be used operationally to predict moose abundance across a range of real-world survey conditions in western Alaska, including conditions known to affect observed counts. The model is intended to reduce bias when snowfall is poor or inconsistent while still providing statistically valid measures of precision for decision-making and monitoring.
What geographic focus is mentioned for applying the resulting model?
The notice specifically references operational prediction of moose abundance in western Alaska.
What is the main benefit to managers and monitoring programs?
The benefit described is improved decision-making and population monitoring through abundance estimates that are less biased under poor snow conditions and that include defensible uncertainty (variance and confidence intervals) derived from a statistically sound combined framework.
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