Cooperative Ecosystem Studies Unit, Rocky Mountain CESU | Apply Online

Opportunity Summary:

This funding opportunity (Funding Opportunity Number G14AS00100), titled "Cooperative Ecosystem Studies Unit, Rocky Mountain CESU," was issued by the U.S. Geological Survey (USGS), specifically through the Northern Rocky Mountain Science Center (NOROCK). It is a discretionary science and technology research award made as a cooperative agreement, meaning the USGS is likely to have substantial involvement during the project (for example, coordination on study design, data use, or interpretation). The core purpose is to strengthen long-term ecological monitoring on federal lands so land managers and scientists can better measure current resource condition (often described as "status") and detect whether conditions are improving or declining over time, particularly in relation to management actions and climate change.

The focus of the work is statistical and methodological rather than field implementation alone. USGS is looking for rigorous guidance on how to design monitoring programs (sampling design development) and how to analyze the resulting data to estimate status and trends in ways that are defensible and statistically sound. In practical terms, this can include recommending how to choose sampling locations, how frequently to revisit sites, how to handle incomplete or uneven sampling across years, and how to quantify uncertainty so that reported trends are credible for decision-making. Because long-term monitoring programs often operate under real-world constraints (limited budgets, changing priorities, inconsistent access, and varying methods over time), the opportunity emphasizes approaches that remain robust even when ideal assumptions do not hold.

A major element mentioned in the announcement is the possible use of simulation studies. Simulations are a way to test how well different sampling and analysis choices perform under controlled, hypothetical scenarios that mimic real ecological monitoring challenges. The announcement highlights several specific issues that simulations might explore. One is the effect of sampling design misspecification on ratio estimators. Ratio estimators are commonly used when an outcome is expressed relative to some measure of exposure or size (for example, prevalence, density relative to area, or condition relative to a baseline). If the sampling design assumed by the analysis does not match the design actually used in the field, estimates can become biased or have misleading confidence intervals. Another issue is detection bias and how it affects ratio estimators, with an example tied to estimating blister rust prevalence in whitebark pine. Detection bias occurs when the probability of observing a condition (like disease symptoms) varies by observer, site conditions, season, tree characteristics, or survey method. If not addressed, this can make disease prevalence appear to change over time even when the true underlying prevalence is stable, or it can mask real changes. The opportunity also points to sample size calculations under different sampling designs for ecological indicators, which essentially means determining how many plots, trees, reaches, or other sampling units are needed to reliably detect meaningful changes, and how those requirements shift depending on design choices (for example, simple random sampling versus stratified designs, rotating panels, clustered designs, or spatially balanced sampling).

Beyond design and estimator performance, the announcement notes interest in trend modeling for complex survey data, especially the kind of data produced when programs collaborate across geographic boundaries. That is an important point because many federal and interagency monitoring efforts try to integrate datasets collected by different offices or partners, sometimes using different protocols, different spatial footprints, and different sampling intensities. Modeling trends in that context can require methods that account for survey weights, stratification, clustering, spatial correlation, changing sampling frames over time, and differences in detectability or measurement processes. The underlying goal is to support integration and comparability so that broader-scale assessments (across parks, forests, regions, or multi-jurisdiction landscapes) can still produce coherent trend estimates and uncertainty measures.

Administratively, the opportunity anticipated a single award, with an estimated total funding amount of $20,501. The listing shows no cost sharing or matching requirement, which generally reduces barriers to participation for eligible partners. The award ceiling and floor are both listed as $0, a common artifact in some postings that typically means the precise award amount is determined by the available funds and the negotiated scope rather than a fixed range published in the announcement.

Eligibility is limited to partners in the Rocky Mountain Cooperative Ecosystem Studies Unit (CESU) network. CESUs are formal partnerships that connect federal agencies with universities, tribes, non-profits, and other organizations to deliver research, technical assistance, and education. For this specific opportunity, applicants must be participating members of the Rocky Mountain CESU Program, so an organization outside the CESU would generally need to partner with an eligible CESU member rather than applying directly.

Key dates in the record indicate the opportunity was posted June 26, 2014, created in the system July 21, 2014, and ultimately had a current closing date of August 1, 2014, with an archive date of September 26, 2014. The associated CFDA number is 15.808 (U.S. Geological Survey Research and Data Collection), which aligns with the research-focused nature of the work. For applicants or interested parties who needed help accessing the full announcement through Grants.gov, the contact listed was Faith Graves at USGS (phone 703-648-7356, email fgraves@usgs.gov).

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