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.
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Recent Submissions
Changes In Functional Structure of Aquatic Insect Communities Across Environmental Gradients in Mountain Streams
(Colorado State University. Libraries, 2024) Gutierrez, Carolina, author; Poff, N. LeRoy, advisor; Ghalambor, Cameron, advisor; Neuwald, Jennifer, committee member; Webb, Colleen, committee member
This study investigates the functional diversity of aquatic insect communities across environmental gradients within Rocky Mountain headwater streams, aiming to better understand how elevation, water temperature, and canopy cover shape the structure and dynamics of these communities. Functional diversity (FD) is defined here as the range, distribution, and relative abundance of organismal traits, which together provide deeper insight into ecosystem functionality than species diversity alone. FD was quantified through three primary metrics: functional richness (FRic), functional evenness (FEve), and functional divergence (FDiv), each capturing distinct aspects of how species contribute to ecosystem functioning. This multidimensional approach enables a nuanced examination of how aquatic insect communities respond to various environmental stressors and spatial constraints, particularly as altitudinal changes present unique challenges in terms of temperature variability and resource availability.Field data were collected from twenty-four stream sites distributed across elevation bands ranging from 1,500 to 3,500 meters. Sites were replicated in three different drainage systems to account for regional variation, with insect specimens collected and assessed for twenty functional traits. These traits included parameters such as voltinism (number of life cycles per year), adult lifespan, emergence synchronization, and dispersal ability, all of which are critical in determining an insect’s role in the ecosystem. Canopy cover and water temperature were also measured to evaluate how localized microclimates and light availability influenced community composition. Results revealed a significant decline in functional richness with increasing elevation, with the steepest reductions observed in streams with sparse canopy cover. Functional richness was highest in areas where canopy cover ranged between 65-78%, and water temperature was between 8°C and 15°C, suggesting that moderate canopy cover and specific thermal conditions support more functionally diverse communities. Functional evenness and divergence, while showing less pronounced patterns, indicated that the most extreme trait values are critical for resilience in these systems, particularly under fluctuating environmental conditions. Trophic interactions further illustrate the importance of specific functional groups, such as predators, grazers, and filterers, in shaping community structure. The analysis of beta diversity demonstrated substantial turnover in functional traits across elevation gradients, emphasizing the heterogeneity of insect communities within low-order, high-altitude streams and reinforcing the role of environmental filtering in community assembly. These findings highlight the vulnerability of headwater stream ecosystems to environmental changes and underscore the importance of functional diversity metrics in ecological monitoring and conservation efforts. Overall, this study contributes to our understanding of how functional environmental gradients structure diversity and provides a foundation for comparative studies on functional diversity in tropical versus temperate mountain stream ecosystems, particularly in the context of global biodiversity conservation.
Cooking Up a Better AR Experience: Notification Design and the Liabilities of Imperfect Cues in Augmented Reality
(Colorado State University. Libraries, 2024) Raikwar, Aditya Ravindra, author; Ortega, Francisco R., advisor; Ray, Indrakshi, committee member; Moraes, Marcia, committee member; Soto, Hortensia, committee member
This dissertation investigates optimizing user experience in Augmented Reality (AR). A virtual cooking environment (ARtisan Bistro) serves as a testbed to explore factors influencing user interaction with AR interfaces. The research starts with notification design, examining strategically placed visual and audio notifications in ARtisan Bistro (Chapter 4). Building on this, Chapter 5 explores optimizing these designs for user awareness and delivering critical information, especially when audio is impractical. This involved exploring visual-only notifications, revealing consistent user performance and attention capture comparable to combined visual-audio notifications (no significant difference found). The research demonstrates that well-designed notifications can significantly improve user experience, but it also raises a crucial question: can users always trust the information presented in AR environments? The possibility of imperfect information delivery underscores the importance of reliable information delivery. Chapter 6 explores the impact of imperfect cues generated by machine learning (ML) on user performance in AR visual search tasks. This research highlights the potential for automation bias when users rely heavily on unreliable cues. By investigating both notification design and the limitations of ML systems for reliable information delivery, this dissertation emphasizes the importance of creating a well-rounded user experience in AR environments. The findings underscore the need for further research on optimizing visual notifications, mitigating automation bias, and ensuring reliable information delivery in AR applications.
ILLUMINATING THE IMPACT OF REPRODUCTIVE EXTRACELLULAR VESICLES: MODELING MATERNAL SIGNALS DURING PREIMPLANTATION EMBRYO DEVELOPMENT
(Colorado State University. Libraries, 2024) Menjivar, Nico Graham , author; Tesfaye, Dawit, advisor; Krisher, Rebecca L., advisor; Chicco, Adam, committee member; Hollinshead, Fiona K., committee member
Pre-implantation embryo development is a complex process beginning around the time of gametic syngamy, the process of two gametes fusing to create a zygote (the first cell of a new organism). Passively transient through the oviduct, the presumptive zygote is then characterized by a series of timely cleavage divisions, activation of the embryonic genome, compaction (morula formation), cavitation (blastocyst formation), and summing in hatching from the encapsulated zona pellucida and implantation to the uterine wall. Unfortunately, the current IVF system that occurs ex vivo, completely bypasses the critical maternal-embryonic crosstalk that would inevitably persist during the primitive stages of pre-implantation development. It is thought that the low yield of developed embryos in vitro, is in part due to the failed ability to recapitulate a suitable system that mimics the maternal environment, shunting early cleavage stage embryos for failure. However, the reservations regarding maternal signals secreted to developing embryos, the reproductively inaccessible nature of the organs, and suboptimal in vitro systems to study replicate in vivo function has limited our complex understanding of these stages. In this dissertation, I aimed to interrogate multiple aspects of preimplantation embryo development, under the primary premise of modeling maternal signal during the pre-implantation period. Utilizing the intrinsic interest in the growing field of extracellular vesicle (EV) research and their significance in intercellular signaling, particularly their communicative role in selective biological information transfer, my first exertion was developing a source of EVs from in vitro cultured granulosa cells for use during IVM (necessitating maternal signals amid the follicle microenvironment). Through the analysis of this dataset (in combination with Gebremedhn et al. 2020) together with immunofluorescence and functional experiments, we characterized diverging miRNA profiles of EVs secreted by granulosa cells subjected to polarizing thermal conditions, that are abundantly up taken by COCs and modulate key developmental events that safeguard developing embryos exposed to conditions of stress. Next, I built upon this work by generating a functional 3D organoid model to study the cellular and extracellular response of the oviduct using a multi-omics approach. Using this atlas as a guide, I characterized the functional undertakings of the oviduct during applied levels of heat stress and found its crucial role in altering the metabolic activity of maternal tissues, which likely in part functionally augment developing embryos and assume failure. Given the functional applicability of reproductive EVs acting as maternal cues, I established this suitable model as a mechanism to generate physiologically relevant EVs (in vivo-like) to offset applied stress during the initial stages of development. These EVs secreted from 3D cultured oviductal organoids were then compared with those secreted from 2D OECs and from in vivo oviductal fluid (miRNAs), and used in an IVC setting, highlighting functional maternal—embryonic crosstalk. Altogether, this dissertation highlights key functional aspects of reproductive extracellular vesicles from both the follicular microenvironment and the oviduct, highlighting the novel and incredible power of suitable in vitro systems to propagate mechanisms to understand maternal signal absent in the current in vitro systems, beginning to illuminate the ‘black box’ of EVs in embryo development.
Multi-channel Factor Analysis: Properties, Extensions, and Applications
(Colorado State University. Libraries, 2024) Stanton, Gray Brewster, author; Wang, Haonan, advisor; Scharf, Louis, committee member; Kokoszka, Piotr, committee member; Wang, Tianying, committee member; Luo, Jie, committee member
Multi-channel Factor Analysis (MFA) extends factor analysis to the multi-channel or multi-view setting, where latent common factors influence all channels while distinct factors are specific to individual channels. The within- and across-channel covariance is determined by a low-rank matrix, a block-diagonal matrix with low-rank blocks, and a diagonal matrix, which provides a parsimonious model for both covariances. MFA and related multi-channel methods for data fusion are discussed in Chapter 1.Under conditions on the channel sizes and factor numbers, the results of Chapter 2 show that the generic global identifiability of the aforementioned covariance matrices can be guaranteed a priori, and the estimators obtained by maximizing a Gaussian likelihood are shown to be consistent and asymptotically normal even under misspecification. To handle temporal correlation in the latent factors, Chapter 3 introduces Multi-channel Factor Spectral Analysis (MFSA). Results for the identifiability and parameterization properties of the MFSA spectral density model are derived, and a Majorization-Minimization procedure to optimize the Whittle pseudo-likelihood is designed to estimate the MFSA parameters. A simulation study is conducted to explore how temporal correlations in the latent factors affect estimation, and it is demonstrated that MFSA significantly outperforms MFA when the factor series are highly autocorrelated. In Chapter \ref{chap:vehicles}, a locally stationary joint multivariate Gaussian process with MFA-type cross-sectional covariance is developed to model multi-vehicle trajectories in a highway environment. A dynamic model-based clustering procedure is designed to partition cohorts of nearby vehicles into pods based on the stability of the intra-pod relative vehicle configuration. The performance of this procedure is illustrated by its application to the Next GENeration SIMulation dataset of vehicle trajectories on U.S. Highway 101.
Evaluating the efficiency, equity, and effectiveness of wildfire suppression strategy using the microeconomic toolkit
(Colorado State University. Libraries, 2024) Bryan, Calvin Ross, author; Bayham, Jude, advisor; Manning, Dale T., committee member; Goemans, Chris, committee member; Wei, Yu, committee member
Most economic research related to wildfires focuses on their impact on people and populations. In my dissertation, I use economic tools to evaluate the efficiency and equity of wildfire suppression strategy. In the first chapter, I investigate whether socioeconomic factors of a community (income, race, age, etc.) are correlated with allocations of suppression effort. I use spatial data on retardant drops from large airtankers (LATs) and demographic information from the Census Bureau to find that communities threatened by wildfire with fewer minority residents, but more low-income residents, are more likely to receive LAT drops. I then find that socioeconomic factors aren't correlated with the decision to use LATs in suppression after conditioning on biophysical factors like fuels and burn probability. In my second chapter, I study whether the media's attention to wildfire influences suppression strategy. I instrument for the effect of media attention using the incidence of catastrophic events that would distract the media to find that media scrutiny of a wildfire has no tangible effect on the decision to use aviation on a fire. Finally, most economic research on wildfire suppression strategy has focused on the costs; little exists on its benefits. I use causal inference methods leveraging satellite data on wildfire growth and intensity, along with the spatial data on aerial suppression effort mentioned previously, to find that large airtankers are effective at limiting the physical extent of wildfire's spread, reducing the intensity of flames as it grows, and slows its spread.