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
EMOTIONAL LABOR AT WORK AND RECOVERY AFTER WORK: A MULTILEVEL DAILY STUDY OF THE DIFFERENTIAL EFFECTS OF SURFACE AND DEEP ACTING ON RECOVERY EXPERIENCES
(Colorado State University. Libraries, 2025) Colley, Kelsie Lee, author; Prasad, Joshua, advisor; Prince, Mark, committee member; Riggs, Nathan, committee member; Gardner, Danielle, committee member
The purpose of this study was to explore how daily experiences of self-regulation at work spilled over into after-work experiences. Specifically, this study examined whether the relationship between daily emotional labor at work and after-work experiences (recovery experiences) was mediated by perceived gratitude and/or motivation to detach from work. To investigate my hypotheses, I conducted an experience sampling study with Amazon’s Mechanical Turk (Mturk) with participants in the service-providing industry to better understand the process of emotional labor. This study heeds the call to understand better daily surface-acting and deep-acting relationships with variables outside of work and to explore the differential effects of different forms of emotional labor on recovery through more novel mediators. Contrary to expectations, many hypothesized relationships were not supported, suggesting that predicting recovery outcomes through emotional labor processes may be more complex than initially theorized. Nonetheless, a subset of findings indicates that surface acting and deep acting produce differential effects; specifically, surface acting appeared to more negatively impact recovery, whereas deep acting sometimes helped cultivate more recovery experiences—though these effects were inconsistent. The study further highlights that perceived customer gratitude and motivation to detach from work operate in nuanced ways, underscoring the complexity of pinpointing exact pathways to successful recovery. Taken together, the results challenge simplistic views of emotional labor as purely detrimental or beneficial and encourage more distinct theoretical and applied perspectives. These findings may prompt practitioners and organizational leaders to rethink emotional demands and how at-work experiences impact after-work experiences.
EXPLORING RADAR AS A TOOL FOR STUDYING MIGRATORY BIRDS AND THEIR RELATIONSHIPS WITH DYNAMIC LANDSCAPES
(Colorado State University. Libraries, 2025) Jimenez, Miguel, author; Horton, Kyle G., advisor; Koons, David N., committee member; Ruegg, Kristen C., committee member; Yovovich, Veronica, committee member
As a crisis-based discipline, conservation biology necessitates that we make timely management decisions to protect species based on the best available information. In effect, as new scientific tools become available, we must contend with simultaneously applying them in ways that provide novel insights and evaluating their limitations to assess the validity of those insights. The integration of radar with machine learning is one such tool that has revolutionized aeroecology, or the study of airborne organisms. Burgeoning methodologies leverage this integration, offering unique opportunities to understand how migratory birds are responding to large-scale environmental changes, such as urbanization and shifting light regimes. In this dissertation, my goal was to elucidate the promises and shortcomings of radar and machine learning as tools for informing migratory bird conservation management amid rapid global change. As a first step, I focused on validating observations from a local weather radar station. Weather radar systems have become a central tool in the study of nocturnal bird migration. Yet, while studies have sought to validate weather radar data through comparison to other sampling techniques, few have explicitly examined the impact of range and topographical blockage on sampling detection—critical dimensions that can bias broader inferences. In my first chapter, I assess these biases with relation to the Cheyenne, WY Next Generation Weather Radar (NEXRAD) site, one of the large-scale radars in a network of 160 weather surveillance stations based in the United States. I compared local density measures collected using a mobile, vertically looking radar with reflectivity from the NEXRAD station in the corresponding area. Both mean nightly migration activity and within night migration activity between NEXRAD and the mobile radar were strongly correlated (r = 0.85 and 0.70, respectively), but this relationship degraded with both increasing distance and beam blockage. Range-corrected NEXRAD reflectivity was a stronger predictor of observed mobile radar densities than uncorrected reflectivity at the mean nightly scale, suggesting that current range correction methods are somewhat effective at correcting for this bias. At the within night temporal scale, corrected and uncorrected reflectivity models performed similarly up to 65 km, but beyond this distance, uncorrected reflectivity became a stronger predictor than range-corrected reflectivity, suggesting range limitations to these corrections. Together, these findings further validate weather radar as an ornithological tool, but also highlight and quantify potential sampling biases. In my second chapter, I focused on using NEXRAD to study habitat transitions by migratory birds. During migration, birds regularly transition between terrestrial and aerial habitats. Yet, much of our understanding of migratory behavior is centered around either terrestrial habitat quality or atmospheric conditions separately, at relatively coarse temporal scales. I employed NEXRAD to study the dynamic drivers and relative importance of terrestrial, aerial, and sampling predictors as birds transition between the terrestrial and airspace boundary. I found that atmospheric conditions were consistently strong predictors of migration activity throughout the night, and across spring and fall seasons. Key sampling predictors, such as the time after local sunset, fluctuated throughout the night, with high importance shortly after sunset and diminishing importance in the middle of the night. Yet, terrestrial variables were not a strong predictor of nightly variation in migration activity. My results demonstrate that the importance of predictors of activity varies temporally, both within a single night and across seasons. These findings illuminate bird migration as a dynamic process, highlighting limitations and opportunities for employing weather surveillance radar to study transitions between terrestrial and aerial habitats. In my third chapter, I used NEXRAD to study migratory stopover in urban areas. Despite global expansion, the role of cities in macroecological processes remains understudied. Using radar estimates of migratory bird stopover across the U.S., I assessed urban landscapes' contributions to stopover and links to social demographics for 2,130 parks across 88 cities. Stopover hotspots disproportionately occurred on urban landscapes relative to land area, with nearly 50% of spring migration hotspots falling within Metropolitan Statistical Areas. The relationship between urbanization and stopover varied regionally, correlating negatively in eastern flyways and positively in western flyways. Finally, stopover was positively correlated with income but varied considerably, with many cities showing no effect or an effect in the opposite direction. This study highlights the significance of cities in a hemispheric-scale ecological process and demonstrate radar as tool for studying urban social-ecological interactions. Finally, in my fourth chapter, I investigated the effects of different light spectrums on birds in flight for the purpose of informing novel conservation management approaches. Artificial light at night (ALAN) has been shown to influence the behavior of migratory birds, yet how different light spectra modulate these effects is somewhat unclear. I conducted a field experiment across 31 nights at a remote site in northern Colorado using LED floodlights with white, red, amber, and blue lighting treatments during fall migration in 2023 and 2024. Using a vertically looking radar system, I quantified avian in-flight responses in migration traffic rate, flight height, and flight direction. I found that short wavelength, white light significantly reduced flight height, and this response was stronger than red, amber, or blue lights. Beyond providing insight into avian biology, my results could have implications for the conservation management of ALAN. Further, the ability to detect behavior changes from a small point source in a low-density migration system supports the notion that ALAN may be more pervasive than is often recognized. At its core, my dissertation is indicative of a broader shift in ecology and conservation science. Advances in remote sensing offer an opportunity to vastly expand the way we characterize social-ecological systems thereby diversifying the options we have for managing them. However, this “big data” approach must be validated and informed by local inference. My work emphasizes this point. As the integration of large datasets and machine learning become increasingly prominent in conservation biology, I urge the conservation community to explore their potential with creativity while remaining vigilant of the potential biases they may introduce.
FAMILY TIES: EXAMINING FAMILY FUNCTIONING AND ALCOHOL USE AMONG AMERICAN INDIAN YOUTH
(Colorado State University. Libraries, 2024) Douglass, Morgan A., author; Prince, Mark A., advisor; Davalos, Deana, committee member; Riggs, Nathaniel, committee member; Emery, Noah, committee member
Objective: American Indian (AI) adolescents report earlier initiation and higher frequencies of alcohol use than their non-AI peers. Early initiation and higher frequency alcohol use are associated with worse health outcomes. Researchers have been called to identify factors which protect AI youth from harmful alcohol use behaviors and other risk factors such as peer use. Method: This study is a secondary data analysis of an ongoing epidemiological research survey with AI youth. Data was collected in the Fall of 2021 and Spring of 2022. Participants were 4,373 AI adolescents from grades 6-12 across seven regions of the contiguous United States. Structural Equation Modeling (SEM) was used to test a second-order latent variable of family functioning built from measures of family cohesion, family norms against adolescent alcohol use (FN), and parental monitoring. Structural paths and interaction terms between peer use and family functioning were added to the SEM to explore direct effects and moderations Results: Family cohesion, FN, and parental monitoring were best represented by a second-order latent variable of family functioning. Family functioning was related a later initiation of alcohol use and lower alcohol use frequency. Family functioning moderated the relationship between peer use and alcohol outcomes. Conclusions: The latent variable of family functioning and its component measures are appropriate for use in AI samples. Additionally, family functioning, which is an inherent resilience factor in AI communities, was shown to be protective against harmful alcohol use behaviors. Results have implications for prevention/intervention research.
CHEMICALLY RECYCLABLE POLYMERS VIA ACCEPTORLESS DEHYDROGENATIVE POLYMERIZATION: SYNTHESIS AND CHARACTERIZATION OF FUNCTIONAL POLYESTERS AND POLYAMIDES
(Colorado State University. Libraries, 2025) Harry, Katherine Leigh, author; Miyake, Garret M., advisor; Chen, Eugene Y.-X., committee member; Kennan, Alan, committee member; Peers, Graham, committee member
This dissertation presents advancements in the development of acceptorless dehydrogenative polymerization (ADP) and its application to the synthesis of polyesters, polyamides, and their copolymers. ADP is an emerging catalytic strategy that overcomes many limitations of traditional polymerization methods, offering key advantages such as improved atom economy, enhanced sustainability, and a broader monomer scope. These features position ADP as a powerful platform for the synthesis of functional, structurally diverse polymers. The motivation for this work stems from the escalating plastic waste crisis. While plastics have undeniably advanced modern society through their performance and versatility, the linear nature of their life cycle continues to drive global pollution. Polyolefins, in particular, combine excellent material properties with extreme resistance to degradation, allowing them to persist in the environment for decades. The central challenge is to create materials that not only rival polyolefins in performance but also offer improved pathways for depolymerization and recycling. In this context, both ruthenium- and manganese-catalyzed ADP are explored as strategies to synthesize a range of polymers with tunable properties and built-in degradability via ester linkages. These polymers can be selectively deconstructed, offering a pathway to closed-loop recycling. The dissertation highlights recent progress in ADP, its mechanistic underpinnings, and its potential to support a circular polymer economy.
Statistical inference on reproducibility in high-throughput experiments
(Colorado State University. Libraries, 2025) Ellingworth, Austin, author; Guan, Yawen, advisor; Zhou, Wen, advisor; Keller, Kayleigh, committee member; Kokoszka, Piotr, committee member; Mykles, Donald, committee member
Results in high-throughput genomics are known to have large variability across independent replicate studies. For this reason, the formal assessment of the agreement of results for many hypotheses across replicate studies has been a burgeoning area of research in statistical genomics. Hypotheses with consistent results are called reproducible, while those without consistency are called irreproducible. The presence of reproducibility in experimental research is critical, as it ensures the validity of findings. In this dissertation, we devise three methods for assessing the reproducibility of results from high-throughput genomic studies, each with advantages under certain settings. First, we notice that many of the existing approaches to assessing the reproducibility of results from two replicate high-throughput genomics studies either depend on strict parametric assumptions on available summary statistics or fail to properly consider the consistency of reproducible signal across experiments in addition to its strength. Motivated by \cite{philtron2018maximum}, we introduce a function based on the rankings of summary statistics from each experiment to define a notion for reproducibility and identify reproducible hypotheses. The proposed nonparametric statistic takes into account both the signal strength and consistency of results. By examining the geometry of the space of ranks of summary statistics and utilizing the negative association dependence structure of ranks, a novel procedure is introduced for recognizing reproducible findings while controlling the false discovery rate (FDR). This method controls FDR under relatively mild assumptions. The theoretical FDR findings are validated through simulations that also reveal the method to be more powerful than existing procedures. Finally, the procedure is applied to two large-scale TWAS datasets, uncovering reproducible features. Second, we notice that existing methods for assessing the reproducibility of high-throughput studies ignore the known group structures of genetic features, such as transcripts belonging to the same gene or genes belonging to the same pathway. Motivated by \cite{li2011measuring} and \cite{Liu2016ANATMTOGH}, we present an empirical Bayesian framework for reproducibility that incorporates this group structure. Additionally, we introduce algorithms for testing reproducibility at the hypothesis and group levels that maintain control of posterior FDR. Next, a data-driven estimation procedure based on the EM algorithm is proposed to enable the application of these algorithms when the parameters it relies on are unknown. In simulation, we show that the inclusion of the group structure in the hypothesis-level procedure leads to superior performance in terms of power and FDR control compared to more naive methods, and that the group-level procedure outperforms methods that rely on aggregation prior to analysis. The proposed procedures enable researchers to integrate known group structure information into the reproducibility problem, yielding higher-quality results. Finally, while there is a dearth of existing literature for analyzing reproducibility across two replicate studies, there are strikingly few methods that consider cases with more than two studies, and those that exist generally assume the distributions of irreproducible summary statistics are known. Leveraging Kendall's coefficient of concordance, we introduce a rank-based statistic that quantifies the agreement of results for a particular hypothesis without enforcing such strict assumptions. Noticing that in real high-throughput genomic settings, we have many ``housekeeping'' genes that are unrelated to the disease of interest and thus can be considered as a control set, we utilize conformal inferential and bootstrapping techniques to devise three procedures for calculating approximate $p$-values from a set of the proposed statistics that can be used to discover reproducible hypotheses at a nominal level of FDR. Simulation studies reveal that the three methods show preferable performance to existing methods in terms of power and FDR control. Applying the methods to single-cell expression data from five COVID-19 studies, we show that the proposed statistic and its procedures can identify genes and gene pathways associated with COVID-19.