Cooperative Ecosystem Studies Unit, Rocky Mountain CESU | Apply Online

Frequently Asked Questions (FAQs)

What is this funding opportunity about?

This Rocky Mountain Cooperative Ecosystem Studies Unit (CESU) opportunity, issued by the U.S. Geological Survey (USGS) through the Northern Rocky Mountain Science Center (NOROCK), funded research to improve how bear sightings are classified and used to estimate bear abundance. The work focuses on reducing bias and improving how uncertainty is represented in population estimates.

What problem is the research trying to solve?

Bear monitoring programs often face multiple plausible "true" sighting histories for individual bears, especially when sightings come from mixed sources (for example, radio-collared bears, aerial observations, and ground surveys). Traditional approaches may effectively choose one interpretation of those histories and proceed as if it were correct, which can bias the abundance estimate (N) and make uncertainty around N appear unrealistically small.

What is the proposed solution supported by the grant?

The grant supports a probabilistic approach that treats the actual sighting history as an unobserved (latent) variable. Instead of picking one interpretation, the method explicitly models uncertainty about sighting histories and carries that uncertainty through to the final estimate of bear abundance. The goal is an estimate of N that is less biased and more transparent about uncertainty.

What kinds of data are intended to be used in the model?

The planned hierarchical model jointly incorporates (1) the locations and timing of sightings of radio-collared bears and (2) the locations and timing of sightings from observational flights and ground surveys, which can include sightings of both collared and uncollared bears.

How is this different from classifying sightings after the fact?

In this framework, predicting latent sighting histories is not a separate, after-the-fact classification step. It is an intermediate step embedded within the overall statistical inference about population size, so uncertainty about identification and sighting histories directly influences the final abundance estimate.

What statistical approach is described?

The work is aimed at developing and fitting a hierarchical statistical model using Bayesian fitting procedures. The expected output is a probability-based estimate of bear abundance (N) that naturally reflects uncertainty from imperfect detection, ambiguous identification, and incomplete observation processes.

What practical research tasks were included?

The opportunity describes several key tasks: extracting and organizing agency-provided data, designing the algebraic structure of the joint model (specifying the probability model linking movements, sightings, and cub counts to recorded observations), and writing computer code to fit the model.

Was any prior work already completed before this award?

Yes. The description notes that an initial model had already been started, but substantial additional work was still required to make the approach complete and usable.

What specific model extensions were still needed?

Two extensions were highlighted as still needed: (1) a component describing changes in the number of cubs during the survey period, and (2) a spatial model describing the distribution of females with cubs-of-the-year across the Greater Yellowstone area.

Why does the model need to describe changes in cub numbers during the survey period?

The opportunity notes this is important because cub survival and detectability can change within a season. Modeling changes in cub numbers during the survey window is part of making the inference more realistic and better aligned with how observations are actually generated.

Why is a spatial model for females with cubs-of-the-year important?

The opportunity emphasizes that space use and clustering across the Greater Yellowstone area affect sighting probabilities and how observations should be interpreted. A spatial component helps the model reflect how the distribution of females with cubs influences what observers record.

What is the role of MCMC in this project?

A later stage of the project includes developing a Markov chain Monte Carlo (MCMC) updater, which serves as the computational engine commonly used to fit Bayesian hierarchical models when closed-form solutions are not available.

What type of award was this?

This was a discretionary funding opportunity offered as a cooperative agreement, categorized under science and technology and other research and development.

How many awards were anticipated?

The opportunity anticipated a single award.

What was the estimated total funding amount?

The estimated total funding amount was $23,216.

Was cost sharing or matching required?

No cost sharing or matching requirement was stated for this opportunity.

Who was eligible to apply?

Eligibility was restricted to organizations that are participating partners in the Rocky Mountain CESU network, consistent with the CESU partnership model supporting research, technical assistance, and education for natural resource management.

Which agency issued the opportunity and through what center?

The opportunity was issued by the U.S. Geological Survey (USGS) through the Northern Rocky Mountain Science Center (NOROCK).

What is the CFDA number associated with this opportunity?

The CFDA number listed is 15.808 (U.S. Geological Survey Research and Data Collection).

When was the opportunity posted and when did it close?

It was posted on July 15, 2009, and the closing date was July 27, 2009.

When was the opportunity archived?

The opportunity was later archived on August 26, 2009.

Who was the point of contact listed for access issues?

The listed point of contact for access issues was Faith Graves, Contract Specialist, at 703-648-7356.