Opportunity Information: Apply for G17AS00064
This funding opportunity, titled "Cooperative Ecosystem Studies Unit, Great Plains CESU" (Funding Opportunity Number G17AS00064), was offered by the U.S. Geological Survey (USGS) within the Department of the Interior as a discretionary cooperative agreement under the Science and Technology and other Research and Development activity category (CFDA 15.808). The program was structured to make a single award, with an expected award count of 1 and an award ceiling of $14,000. It was posted on May 8, 2017, with an original application closing date of May 22, 2017. Eligibility was limited to a CESU partner (the notice lists eligible applicants as "Others," with clarification provided in the opportunity's eligibility text), meaning it was intended for organizations participating in the Cooperative Ecosystem Studies Unit network rather than the general public or any applicant type.
The core purpose of the project was to use advanced statistical modeling on existing fisheries monitoring data to better understand fish habitat needs at a community level. Specifically, USGS sought research that would apply multivariate parametric models to data from the Long Term Resource Monitoring element (LTRM). Rather than focusing on single-species habitat relationships in isolation, the project emphasized evaluating patterns across multiple species simultaneously, which is important when habitat restoration and river management actions tend to influence whole fish assemblages instead of one species at a time. The analysis focus was tied directly to environmental covariates that are commonly changed through restoration work, including variables such as water depth, current velocity, and temperature. By targeting these kinds of covariates, the resulting findings could be more directly useful for planning, prioritizing, and evaluating habitat restoration projects.
A key methodological feature highlighted in the opportunity is the use of latent guild models. In practical terms, these models aim to identify hidden or "latent" groupings of species that share similar responses to environmental gradients, even if those groupings are not obvious from taxonomy alone. The approach allows researchers to estimate how abundance and distribution shift along gradients (for example, how certain sets of species respond as velocity increases or as water temperature changes), and then translate those patterns into community-wide habitat requirements. This is meant to produce insights that are broader than traditional analyses, because it supports inference about how groups of species collectively use habitat and respond to environmental conditions.
Seasonality was also an explicit emphasis. The opportunity called for inferring guild structure by seasonal sampling period, then comparing model-derived seasonal habitat associations to existing hypotheses based on life history guild concepts. In other words, the work was expected to test whether the statistical groupings and habitat-response patterns that emerge from seasonal data match what biologists would predict based on species life history characteristics (such as spawning timing, growth strategies, or habitat use at different life stages). This seasonal framing matters because fish habitat needs and distribution can shift substantially across the year, and restoration outcomes can vary depending on when conditions are measured.
Another important objective was improving understanding of less-studied and potentially rare species. The notice explains that if the model-based habitat associations align with known life history guilds, then researchers can use the clustering to "borrow strength" from common, well-understood species to infer likely habitat associations or life history attributes for rarer or poorly understood species that are harder to analyze on their own due to limited observations. This is a common advantage of community modeling approaches: they can provide more stable inference for sparse data situations by leveraging shared structure across species rather than treating each species independently.
Finally, the opportunity aimed to extend the modeling across multiple study reaches. By developing latent guild models for more than one reach, the project would be able to evaluate how species-level responses vary from place to place and how fish communities differ in their collective responses across reaches. This reach-to-reach comparison is valuable for understanding how local geomorphology, hydrology, management history, or habitat restoration context may influence community structure and habitat relationships, and it can help determine whether findings from one reach generalize to others or whether responses are highly location-dependent.
Overall, the grant was a small, targeted cooperative agreement intended to support a CESU partner in applying modern multivariate and latent-group modeling to existing long-term fisheries data. The practical end goal was to generate actionable, restoration-relevant insight into community-wide fish habitat requirements, evaluate seasonal consistency with life-history expectations, improve inference for rare species via shared guild structure, and compare patterns across multiple river reaches to understand variability in fish community responses.Apply for G17AS00064
- The Department of the Interior, Geological Survey in the science and technology and other research and development sector is offering a public funding opportunity titled "Cooperative Ecosystem Studies Unit, Great Plains CESU" and is now available to receive applicants.
- Interested and eligible applicants and submit their applications by referencing the CFDA number(s): 15.808.
- This funding opportunity was created on May 08, 2017.
- Applicants must submit their applications by May 22, 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 $14,000.00 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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Frequently Asked Questions (FAQs)
What is the title of this funding opportunity?
The opportunity is titled "Cooperative Ecosystem Studies Unit, Great Plains CESU."
What is the Funding Opportunity Number?
The Funding Opportunity Number is G17AS00064.
Which agency offered this opportunity?
The opportunity was offered by the U.S. Geological Survey (USGS) within the Department of the Interior.
What type of funding instrument is being used?
This opportunity was offered as a discretionary cooperative agreement.
What is the activity category for this opportunity?
The activity category is Science and Technology and other Research and Development.
What is the CFDA number associated with this program?
The CFDA number listed is 15.808.
How many awards were expected?
The program was structured to make a single award, with an expected award count of 1.
What was the maximum funding amount (award ceiling)?
The award ceiling was $14,000.
When was the opportunity posted?
The opportunity was posted on May 8, 2017.
What was the application closing date?
The original application closing date was May 22, 2017.
Who was eligible to apply?
Eligibility was limited to a CESU partner. The notice lists eligible applicants as "Others," with clarification in the eligibility text indicating it was intended for organizations participating in the Cooperative Ecosystem Studies Unit (CESU) network rather than the general public or broad applicant categories.
Was this opportunity open to the general public or any applicant type?
No. Based on the eligibility description provided, it was intended for CESU partners and not for the general public or unrestricted applicant types.
What was the main purpose of the project?
The core purpose was to use advanced statistical modeling on existing fisheries monitoring data to better understand fish habitat needs at the community level.
What data source was the project expected to use?
The project focus was on applying models to data from the Long Term Resource Monitoring element (LTRM), using existing fisheries monitoring data.
What kind of statistical approach was emphasized?
The opportunity emphasized multivariate parametric models that evaluate patterns across multiple species simultaneously, rather than analyzing single-species habitat relationships in isolation.
Why did the opportunity emphasize multi-species (community-level) analysis?
The rationale provided is that habitat restoration and river management actions tend to influence whole fish assemblages, so understanding community-wide responses can be more relevant for planning and evaluating restoration than single-species analyses alone.
What environmental variables (covariates) were specifically mentioned?
The opportunity highlighted environmental covariates commonly altered through restoration, including water depth, current velocity, and temperature.
Why were those covariates important for the project?
Because those variables are often changed by restoration actions, modeling fish community relationships to them was intended to produce findings that are directly useful for planning, prioritizing, and evaluating habitat restoration projects.
What are latent guild models in the context of this opportunity?
As described in the notice, latent guild models are intended to identify hidden (latent) groupings of species that share similar responses to environmental gradients, even when those groupings are not obvious from taxonomy alone.
What outputs or insights were latent guild models expected to provide?
The models were expected to estimate how abundance and distribution change along environmental gradients (for example, how sets of species respond as velocity increases or temperature changes) and translate those patterns into community-wide habitat requirements.
How did seasonality factor into the requested work?
Seasonality was an explicit emphasis. The opportunity called for inferring guild structure by seasonal sampling period and then comparing model-derived seasonal habitat associations to existing hypotheses based on life history guild concepts.
What does it mean to compare model-derived guilds to life history guild concepts?
The work was expected to test whether statistical groupings and habitat-response patterns that emerge from seasonal data align with what biologists would hypothesize based on species life history characteristics such as spawning timing, growth strategies, or habitat use at different life stages.
Why was seasonal analysis considered important?
The notice emphasized that fish habitat needs and distributions can shift across the year, and restoration outcomes can vary depending on when conditions are measured, making a seasonal framing important for interpretation.
Did the opportunity include a focus on rare or less-studied species?
Yes. An objective was improving understanding of less-studied and potentially rare species by using community modeling to make more stable inferences when individual species have limited observations.
How were researchers expected to improve inference for rare species?
The notice described a "borrow strength" concept: if model-based habitat associations align with known life history guilds, the clustering can use information from common, well-understood species to infer likely habitat associations or life history attributes for rarer or poorly understood species.
Was the modeling expected to be limited to a single location?
No. The opportunity aimed to extend modeling across multiple study reaches.
What was the purpose of analyzing multiple reaches?
Developing models for more than one reach would allow evaluation of how species-level responses vary from place to place and how fish communities differ in their collective responses across reaches, helping assess whether findings generalize or are location-dependent.
What practical use was intended for the results?
The practical end goal was to generate actionable, restoration-relevant insight into community-wide fish habitat requirements, support planning and prioritization of restoration projects, and help evaluate restoration outcomes using covariates commonly influenced by restoration.
Was the project expected to rely on new field sampling?
The opportunity description emphasized applying advanced modeling to existing fisheries monitoring data (LTRM). No requirement for new field sampling was stated in the provided information.
In one sentence, how would you summarize what this grant supported?
A small, targeted cooperative agreement for a CESU partner to apply modern multivariate and latent-group modeling to long-term fisheries monitoring data to produce restoration-relevant, community-level habitat insights across seasons and multiple reaches, including improved inference for rare species.
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