Cooperative Ecosystem Studies Unit, Rocky Mountain CESU | Apply Online

Frequently Asked Questions (FAQs)

What is the Rocky Mountain CESU funding opportunity (G14AS00105)?

This was a U.S. Geological Survey (USGS) funding opportunity offered through the Cooperative Ecosystem Studies Unit (CESU) network, specifically the Rocky Mountain CESU. The Funding Opportunity Number was G14AS00105, and it was issued by USGS through the National Wildlife Health Center (NWHC).

Which federal agency offered this opportunity?

The opportunity was offered by the U.S. Geological Survey (USGS). The work was specifically associated with USGS's National Wildlife Health Center (NWHC).

What was the main goal of the project?

The central goal was to improve how scientists and practitioners predict wildlife disease patterns across space and time. The project emphasis was on evaluating, improving, and potentially developing better spatial risk factor analysis methods used in wildlife disease ecology.

What is meant by "spatial risk factor analysis" in this opportunity?

In this context, spatial risk factor analysis refers to statistical and modeling approaches used to identify which environmental, biological, and landscape factors are associated with disease occurrence. These relationships are then mapped to estimate disease risk in areas that have not been sampled.

What types of activities was USGS looking to fund under this award?

USGS was seeking a CESU partner to critically assess the tools and methods currently used for spatial and spatiotemporal disease risk modeling, test how well those methods perform, improve them where needed, and possibly develop new methods or a unifying theoretical framework explaining when and why certain approaches perform better.

What was the intended practical outcome of the work?

The intended outcome was practical, usable guidance: clear analysis recommendations that field and public health practitioners could rely on for effective predictive modeling in disease ecology.

What funding mechanism was used for this opportunity?

The opportunity was structured as a discretionary cooperative agreement. This typically indicates a more collaborative relationship between the awardee and the federal agency than a standard grant.

What does a cooperative agreement imply about USGS involvement?

A cooperative agreement generally implies USGS expected to be actively involved in aspects of the project, such as providing input on study design, data sources, interpretation of results, and/or dissemination.

What was the funding activity category?

The funding activity category was listed as Science and Technology and other Research and Development, reflecting a focus on advancing analytical capabilities rather than routine monitoring or operational work.

What CFDA program was associated with this opportunity?

The opportunity was tied to CFDA 15.808, titled U.S. Geological Survey Research and Data Collection.

How much funding was available?

The estimated total funding availability was $200,000.

How many awards were expected?

The announcement indicated an expectation of a single award.

Was there an award minimum or maximum listed?

No. The posting listed both the award floor and award ceiling as 0, which typically means the agency did not commit to a specific minimum or maximum in the notice itself, even though it provided an overall total funding estimate.

Was cost sharing or matching required?

No. The opportunity stated there was no cost sharing or matching requirement, meaning applicants were not required to contribute non-federal funds as a condition of eligibility.

Who was eligible to apply?

Eligibility was limited to active participating partners within the Rocky Mountains Cooperative Ecosystem Studies Unit (CESU) Program.

Were there additional eligibility requirements beyond Rocky Mountain CESU membership?

Yes. The partner institution was required to include a statistical department (or equivalent unit) with demonstrated expertise in spatial and spatiotemporal statistical modeling.

Why did the opportunity require expertise in spatial and spatiotemporal statistical modeling?

The description indicates USGS wanted a team capable of rigorous method evaluation and development, such as assessing model accuracy, bias, uncertainty, sensitivity to sampling design, and performance under common real-world limitations found in wildlife disease data.

What real-world data challenges was the project expected to consider?

The opportunity highlighted common challenges in wildlife disease systems, including uneven reporting effort, imperfect detection, spatial autocorrelation, and changing conditions over time.

Was this opportunity open at the time of this posting?

No. The opportunity is described as time-limited and archived. It was originally posted in 2014 and is no longer open for applications.

What were the key dates for the opportunity?

The announcement was posted and created on June 26, 2014. The original (and current) closing date was July 18, 2014. The opportunity was archived on September 26, 2014.

Where could applicants access the full announcement details?

The listing pointed applicants to Grants.gov for full announcement details at the time.

Who was listed as the contact for assistance?

The listing provided a point of contact for assistance: Faith Graves, reachable by phone at 703-648-7356 or by email at fgraves@usgs.gov.

What kind of organization is a CESU?

CESUs are formal research partnerships designed to connect federal agencies with universities, nonprofits, and other partners to deliver research, technical assistance, and education. This opportunity specifically fell under the Rocky Mountains CESU.

What kind of research orientation did this opportunity emphasize?

It emphasized benchmarking and strengthening analytical methods for spatial risk modeling, with the goal of making wildlife disease risk predictions more defensible and useful for decision-making.

What types of questions was the funded work trying to help practitioners answer?

Based on the description, the work aimed to help practitioners predict where disease risk is highest, how that risk may shift over space and time, and what environmental, biological, and landscape factors appear to be driving observed patterns.