Mountain Scholar
Mountain Scholar is an open access repository service that collects, preserves, and provides access to digitized library collections and other scholarly and creative works from Colorado State University and the University Press of Colorado. It also serves as a dark archive for the Open Textbook Library.
Communities in Mountain Scholar
Select a community to browse its collections.
- Explore the Colorado State University community’s scholarly output as well as items from the University at large and the CSU Libraries.
- A limited number of titles are available here. To see all OTL titles, please visit the Open Textbook Library at https://open.umn.edu/opentextbooks. Only Open Textbook Library staff have access to all OTL Archive titles held in Mountain Scholar.
- Access is limited to University Press of Colorado members. Non-members: to purchase books, please visit https://upcolorado.com/.
Recent Submissions
Questionnaire for project titled "Preweaned calf transportation practices in the United States: a cross-sectional survey of dairies, haulers, and calf raisers"
(Colorado State University. Libraries, 2025-06-23) Machuca, E., author; Pempek, J. A., author; Edwards-Callaway, L., author; Román-Muñiz, I. N., author; Cramer, M. C., author
17th annual Rocky Mountain reproductive sciences symposium: scientific proceedings
(Colorado State University. Libraries, 2025-05-09) Twiggs, Jasmin, author; Thomas, Hansen, author; Winger, Quint, author
The Rocky Mountain Reproductive Science Symposium (RMRSS) is a one-day public conference held annually since 2008. Developed and hosted by the Animal Reproduction and Biotechnology Laboratory at Colorado State University, the symposium was established to foster regional collaboration and serve as a forum for the exchange of ideas between the animal agricultural (USDA-NIFA) and human biomedical (NIH) research communities. RMRSS provides a platform for students, trainees, faculty, and professionals to share expertise, generate new scientific ideas, and expand access to shared resources. Its mission is to promote student training, enhance communication, and support collaborative research across diverse reproductive science models. While the event primarily draws regional participants, it also welcomes attendees from across the country each spring. The 17th annual Rocky Mountain Reproductive Sciences Symposium, held on May 9, 2025, received a record number of 62 abstract submissions and welcomed over 160 participants. Centered on the theme Ovarian Aging and Pathophysiology, the program featured two keynote presentations, eight trainee platform talks, five elevator pitch talks, and two poster sessions showcasing diverse research across reproductive science disciplines. The clinical science keynote address was delivered by Dr. Nanette Santoro (University of Colorado Anschutz School of Medicine), titled "Understanding the Physiology of Ovarian Aging." The basic science keynote was presented by Dr. John S. Davis (University of Nebraska Medical Center), titled "Ovarian Hippo/YAP1 Signaling Across the Reproductive Health Span." The 2025 RMRSS was made possible through generous support from the U.S. Department of Agriculture National Institute of Food and Agriculture (NIFA) via the Agriculture and Food Research Initiative (AFRI) and Animal Reproduction Program (award number 2024-67015-43034), the Society for the Study of Reproduction, the Animal Reproduction and Biotechnology Laboratory, and the Department of Biomedical Sciences.
A method to quantify and depict uncertainty in wildlife habitat suitability models using Bayesian inference and expert opinion
(Colorado State University. Libraries, 2005) O'Brien, Lee E., author; Wiens, John, advisor; Theobald, Dave, advisor; Flather, Curtis, committee member
Knowing the distribution of wildlife habitats across the landscape is an important component in biological conservation planning. Many conservation planning projects use wildlife habitat suitability models as the basis for predicting the distribution of habitat for terrestrial species. The predictions are typically binary GIS maps depicting the distribution of suitable versus unsuitable habitat, without indication of how strong the evidence is for these predictions across the area. There are many sources of uncertainty in these models as each data layer, with its own level of uncertainty, is incorporated into the models. Habitat suitability models are often knowledge-based and do not quantify their inherent uncertainty. Or, if the models are empirically-based, there are usually insufficient data to derive habitat distribution predictions and to test the predictions to determine the level of uncertainty associated with them. To make evident the uncertainty inherent in knowledge-based habitat suitability models, Bayesian inference procedures were used to combine expert opinions about the strength of wildlife habitat relationships with prior model parameters to create probability maps that depict the state of knowledge about the distribution of suitable habitat for terrestrial wildlife species. The Bayesian method has several advantages. One is that probability in a Bayesian framework is a direct representation of uncertainty. Thus models produced using this method are easy to understand and interpret. This method can be used on any species, regardless of the amount of empirical data available. Modeling species with deficient habitat relationship data produces appropriate results showing high levels of uncertainty. Bayesian methods allow the combination of empirical and knowledge-based evidence, so that all sources of information about species habitat may be incorporated. Bayesian models may also be updated, so that models can be improved as new information arises. The models can also incorporate landscape context and depict the associated uncertainty. With binary models, a priori decisions are made to include or reject specific habitat conditions. This tends to either over or under predict suitable habitat by including or rejecting borderline conditions. The portrayal of the results (habitat is suitable: yes or no) also implies a certainty that is unwarranted. With the Bayesian method, all possible habitat conditions are retained in the models, revealing areas of potentially suitable habitat that may have been omitted by binary models, and the certainty of the predictions is forthrightly depicted. The models derived by this method produce simple, honest, spatial depictions of what is known about the distribution of suitable wildlife habitat that can be used to support more informed decisions in species conservation planning and management.
Master of tuplets (Bach's revenge): concert orchestra
(Colorado State University. Libraries, 2025-05-06) Colorado State University. Concert Orchestra, producers (managers)
Graduate string trio
(Colorado State University. Libraries, 2025-05-08) Colorado State University. School of Music, Theatre and Dance, producers (managers)