Browsing by Author "Webb, Colleen, committee member"
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Item Open Access Characterization of big brown bat (Eptesicus fuscus) rabies virus in a mouse model(Colorado State University. Libraries, 2011) Ndaluka, Christina, author; Bowen, Richard A., advisor; Wilusz, Carol, advisor; Webb, Colleen, committee member; Mason, Gary, committee memberA majority of human rabies cases in the United States are either imported from countries where dog rabies is endemic or classified as cryptic human cases, where a route of exposure is not known. Notably, essentially all rabies virus (RV) variants associated with cryptic cases of human rabies are maintained in bats. Understanding how RV is maintained in populations of bats and characterizing the diversity of bat RV is thus a high priority problem for public health. Among the knowledge gaps related to bat rabies are understanding the variation in virulence within the population of a single variant and explaining the observation that a substantial number of healthy wild bats have neutralizing antibodies to RV, but no apparent clinical illness. The work described here was designed to address both of those issues. Nine RV isolates were isolated from big brown bats in Colorado and low-passage stocks of each were prepared. These isolates were evaluated for virulence, immunogenicity and salivary gland dissemination to investigate whether there were major differences in these characteristics within this virus population. Inoculated mice were maintained for 12 weeks after virus inoculation to assess mortality and were bled regularly to evaluate their humoral immune responses. Salivary glands from mice that developed clinical rabies were evaluated for virus replication as an indication for potential for further transmission. The dose of RV inoculated had a greater influence on the incubation period and mortality than the individual RV isolate. There was no difference in the humoral immune response in mice between those that were protected and those that succumbed to infection. The only salivary glands that were positive for RV replication were observed from mice in the high dose inoculation groups. Collectively, the results of this experiment indicated that there was low diversity in biologic behavior within the sample of Epitesicus fuscus viruses tested. The humoral immune response of mice to a big brown bat RV variant was explored to address the hypothesis that dose, route or frequency of inoculation may explain the prevalence of neutralizing rabies antibody seen in wild bat populations. Mice were inoculated via intramuscular, intradermal and intranasal routes, with two different low doses of virus and two inoculation schedules. The highest frequency of seroconversion was seen in mice inoculated intramuscularly with the higher of the two doses of RV. Mice that were inoculated intranasally experienced the highest mortality. Mice were rechallenged 3 months following the initial challenge with a high dose of virus intramuscularly to determine if the neutralizing rabies antibodies were protective and if priming of the immune system to RV had occurred in those that failed to seroconvert. The results of this experiment indicate that inoculation of low doses of virus by any of several routes can elicit a detectable humoral immune response without development of disease, which supports the hypothesis that exposure of wild bats to low doses of RV results in seroconversion without clinical disease.Item Open Access Constraints in the compensatory response of a tallgrass prairie plant community to the loss of a dominant species(Colorado State University. Libraries, 2020) Chaves RodrÃguez, Francis Andrea, author; Smith, Melinda, advisor; Knapp, Alan, committee member; Ocheltree, Troy, committee member; Sala, Osvaldo, committee member; Webb, Colleen, committee memberBiodiversity loss is one of the major consequences of global change driven by human activities. The loss of a dominant species is expected to have profound consequences on ecosystem processes (e.g. aboveground productivity) given their highest relative abundance and proportionally large contribution to community biomass production. However, through competitive release, the newly available resources following its lost, are expected to be utilized by the remaining species in the community to increase in abundance and compensate for the function lost. Complete functional compensation does not occur in every ecological community following the loss of dominant species or entire functional groups, and 1) limited resource availability, 2) absence of functionally redundant species, and 3) lack of functional traits that promote compensation have been proposed as possible constraints on compensation. In this dissertation, I evaluate the effect of removing these constraints on the biomass compensation response of a tallgrass prairie plant community following the loss of the dominant species, the C4 tallgrass, Andropogon gerardii Vitman. I experimentally removed the dominant species from a native intact tallgrass prairie plant community at Konza Prairie Biological Station, Kansas, where I selected two contrasting sites, one with functionally redundant species Panicum virgatum L. and Sorghastrum nutans (L.) Nash in low abundances, and a second site where those functionally redundant were codominants with A. gerardii. The first site was irrigated to alleviate water limitation during four growing seasons and fertilized with nitrogen during the final season of the experiment. The second site did not exhibit water limitation and was fertilized during the second growing season of the two-year experiment. My results show that in the short-term removing resource limitation promoted aboveground primary productivity but not enough to produce full biomass compensation. The presence of functionally redundant species, also C4 tall grasses with similar functional effect traits as A. gerardii, did increase aboveground biomass production, but did not promote full biomass compensation, not even when they were present in high abundance. I hypothesize that additional to the constraints proposed, compensation is limited by response traits in the remaining species that limit their demographic response to the increased available space, light, water and soil resources following the loss of the dominant species. Overall, my results show the compensation approach is important to evaluate not only the effect of species loss on ecosystem processes, but also the response of the remaining species and their ability to compensate for the function lost. They also suggest the existence of additional mechanisms in play that need to be identified and tested in order to improve the understanding of how communities recover in the face of biodiversity loss.Item Open Access Detection and transmission of Renibacterium salmoninarum in Colorado inland trout(Colorado State University. Libraries, 2022) Riepe, Tawni Brooks, author; Winkelman, Dana, advisor; Fetherman, Eric, committee member; Huyvaert, Kate, committee member; Webb, Colleen, committee memberTo view the abstract, please see the full text of the document.Item Embargo Determining the impact of harvest and climate change on the demography of black brant (Branta bernicla nigricans)(Colorado State University. Libraries, 2024) Blommel, Caroline, author; Koons, David, advisor; Aubry, Lise, committee member; Webb, Colleen, committee memberAs a coastal long-distance migrant, black brant (Branta bernicla nigricans) are vulnerable to climate and land-use change while also facing harvest pressure from sport hunting along the Pacific Coast. To examine impacts of harvest pressure and environmental change on brant survival and reproductive components of fitness, I combined band-recovery data with live recapture and resighting data from 1990 to 2023 for the Tutakoke River breeding colony of brant on the Yukon-Kuskokwim Delta in western Alaska. I fit multiple Bayesian multistate mark-recapture models to these data to estimate temporal changes in cause-specific mortality and breeding probabilities over the last 33 years. Harvest rate increased over time and is significantly impacted by bag limit across sex and age groups. Adult non-harvest mortality has remained stable over the study period but is higher in years of extreme El Niño and Aleutian Low Beaufort Sea Anticyclone events. Breeding probability for previous breeders increased over time while breeding probability for previous non-breeders decreased, driven largely by differing responses to spring timing. My work describing brant demographic response to environmental change and harvest provides valuable information on how to manage brant most effectively across their migratory range.Item Open Access Ground beef pathogen dynamics and the current scope of the impact of foreign materials on meat and pet food products(Colorado State University. Libraries, 2023) Carlson, Chloé M., author; Martin, Jennifer, advisor; Engle, Terry, committee member; Delmore, Lynn, committee member; Webb, Colleen, committee memberThis thesis provides an overview of ground beef consumption and the state of microbial testing of ground beef. The study focuses on understanding dynamic growth relationships between pathogenic and non-pathogenic bacteria in ground beef and how this information might be used to predict the presence of pathogens or the onset of microbial spoilage. Additionally, this thesis examines current United States Department of Agriculture Food Safety Inspection Service (USDA-FSIS) and the Food and Drug Administration (FDA) regulations around foreign material adulterated meat and pet food. The review looks at the challenges between the two regulatory bodies and provides guidance for improvement.Item Open Access Gunnison sage-grouse demography and conservation(Colorado State University. Libraries, 2012) Davis, Amy Jane, author; Doherty, Paul F., advisor; Phillips, Michael L., committee member; Kendall, William, committee member; Pejchar, Liba, committee member; Webb, Colleen, committee memberTo view the abstract, please see the full text of the document.Item Open Access Hierarchical Bayesian models for population ecology(Colorado State University. Libraries, 2017) Ketz, Alison C., author; Hobbs, N. Thompson, advisor; Hooten, Mevin, committee member; Wittemyer, George, committee member; Webb, Colleen, committee memberModels, by their definition, are abstractions of the systems they describe and require a delicate balance of inclusion of information with reduction. Hierarchical Bayesian models are well suited for ecological problems, because they facilitate the decomposition of highly complex ecological systems into lower dimensional elements. We can partition variability that arises from the ecological processes separately from variability that arises from sampling error, thereby rigorously accounting for uncertainty. In this way, we can better answer questions pertaining to the ecology of populations and aid in better management of their ecosystems. Estimation of abundance is the central challenge in population ecology, and we begin this dissertation by addressing the problem of determining the population size of elk across multiple time and spatial scales during five winters. In Chapter 2, I build upon existing multi- state mark-recapture methods using a hierarchical Bayesian N-mixture model with multiple sources of commonly collected data on abundance, movement, and survival, to accurately estimate the abundance of a mobile population of elk on the winter range of Rocky Mountain National Park and Estes Park, CO. Classification data are used in ecology to examine population trends through model-based theoretical approaches. For ungulates such as elk, wildlife managers use sex-ratios and stable age or stage distributions to assess population growth or decline. However, physical ambiguities and observer skill can lead to biased results. In Chapter 3, I develop two hierarchical models to address the sample bias that results when data are missing-not-at-random, which occurs when individuals are observed but not classified. Forecasts are used to aid management to evaluate the probability that resource objectives will be met given different management actions. In Chapter 4, I develop a hierarchical model incorporating a discrete time, stage structured model assimilated with abundance and classification data, to provide forecasts under a variety of management actions to aid decision makers to meet objectives. I use Bayesian hierarchical models that incorporate multiple sources of information to address common estimation problems that arise in population ecology. We are frequently interested in constructs and latent processes that are not necessarily observable in ecological systems. I use theoretical models of the underlying processes to extract information pertaining to populations and management goals. Compounding the challenge is that we must rely upon survey samples rather than complete census. I illustrate the utility of hierarchical Bayesian models using data on the population of elk (Cervus elaphus nelsoni) on the winter range of Rocky Mountain National Park in Colorado, USA.Item Open Access Modeling riparian vegetation responses to flow alteration by dams and and climate change(Colorado State University. Libraries, 2013) Auerbach, Daniel Albert, author; Poff, N. LeRoy, advisor; Bledsoe, Brian, committee member; Boone, Randall, committee member; Merritt, David, committee member; Webb, Colleen, committee memberAs the interface between freshwater and terrestrial ecosystems, riparian vegetation is a critical influence on biodiversity maintenance and ecosystem service production along river corridors. Understanding how altered environmental drivers will affect this vegetation is therefore central to sound watershed management. A river's flow regime exerts a primary control on the type and abundance of riparian vegetation, as differing adaptations to changing discharge levels mediate plant recruitment and persistence. Models of the relationships between flow and vegetation, generalized across species in terms of flow response traits such as flood tolerance, provide a means to explore the consequences of hydrologic alteration resulting from dams and climate change. I addressed these issues through development of a stage-structured model of woody riparian vegetation driven by variation in annual high flows. Simulation experiments offered insight into the potential trajectories of competing vegetation trait types relative to scenarios of dam construction, re-operation and removal. Modifying the size and frequency of the floods responsible for both disturbance mortality and establishment opportunities altered the relative abundance of pioneer and upland cover. Yet, qualitative differences in simulated outcomes resulted from alternative assumptions regarding seed limitation and floodplain stabilization, illustrating the need to carefully consider how these factors may shape estimated and actual vegetation responses to river regulation. In addition, I linked this simulation approach with an integrated watershed-modeling framework to assess the relative risk of invasion by the introduced plant Tamarix under multiple climate change scenarios. Though warming may increase the potential for Tamarix range expansion by weakening thermal constraints, the results of this work supported the expectation that hydrogeomorphic variation will control how this potential is realized. With simulated invasion risk strongly dependent on shifts in both the magnitude and timing of high flows, model outcomes underscored the importance of accounting for multiple, interacting flow regime attributes when evaluating the spread of introduced species in river networks. This research suggested the utility of simplified but process-based simulations of riparian flow-ecology relationships, demonstrating that such models can establish a first approximation of the potential consequences of management decisions and can highlight key questions for additional research, particularly where data are scarce and uncertainty is high.Item Open Access Spatial probit models for multivariate ordinal data: computational efficiency and parameter identifiability(Colorado State University. Libraries, 2013) Schliep, Erin M., author; Hoeting, Jennifer, advisor; Cooley, Daniel, committee member; Lee, Myung Hee, committee member; Webb, Colleen, committee memberThe Colorado Natural Heritage Program (CNHP) at Colorado State University evaluates Colorado's rare and at-risk species and habitats and promotes conservation of biological resources. One of the goals of the program is to determine the condition of wetlands across the state of Colorado. The data collected are measurements, or metrics, representing landscape condition, biotic condition, hydrologic condition, and physiochemical condition in river basins statewide. The metrics differ in variable type, including binary, ordinal, count, and continuous response data. It is common practice to uniformly discretize the metrics into ordinal values and combine them using a weighted-average to obtain a univariate measure of wetland condition. The weights assigned to each metric are based on best professional judgement. The motivation of this work was to improve on the user-defined weights by developing a statistical model to estimate the weights using observed data. The challenges of creating a model that fulfills this requirement are many. First, the observed data are multivariate and consist of different variable types which we wish to preserve. Second, the multivariate response data are not independent across river basin because wetlands at close proximity are correlated. Third, we want the model to provide a univariate measure of wetland condition that can be compared across the state. Lastly, it is of interest to the ecologists to predict the univariate measure of wetland condition at unobserved locations requiring covariate information to be incorporated into the model. We propose a multivariate multilevel latent variable model to address these challenges. Latent continuous response variables are used to model the different types of response variables. An additional latent variable, or common factor, is used as a univariate measure of wetland condition. The mean of the common factor contains observable covariate data in order to predict at unobserved locations. The variance of the common factor is defined by a spatial covariance function to account for the dependence between wetlands. The majority of the metrics reported by the CNHP are ordinal. Therefore, our primary focus is modeling multivariate ordinal response data where binary data is a special case. Probit linear models and probit linear mixed models are examples of models for ordinal response data. Probit models are attractive in that they can be defined in terms of latent variables. Computational efficiency is a major issue when fitting multivariate latent variable models in a Bayesian framework using Markov chain Monte Carlo (MCMC). There is also a high computation cost for running MCMC when fitting geostatistical spatial models. Data augmentation and parameter expansion are both modeling techniques that can lead to optimal iterative sampling algorithms for MCMC. Data augmentation allows for simpler and more feasible simulation from a posterior distribution. Parameter expansion is a method for accelerating convergence of iterative sample algorithms and can enhance data augmentation algorithms. We propose data augmentation and parameter-expanded data augmentation algorithms for fitting MCMC to spatial probit models for binary and ordinal response data. Parameter identifiability is another challenge when fitting multivariate latent variable models due to the multivariate response data, number of parameters, unobserved latent variables, and spatial random effects. We investigate parameter identifiability for the common factor model for multivariate ordinal response data. We extend the common factor model to include covariates and spatial correlation so we can predict wetland condition at unobserved locations. The partial sill and range parameter of a spatial covariance function are difficult to estimate because they are near-nonidentifiable. We propose a new parameterization for the covariance function of the spatial probit model that leads to better mixing and faster convergence of the MCMC. Whereas our spatial probit model for ordinal response data follows the common factor model approach, there are other forms of the spatial probit model. We give a comprehensive comparison of two types of spatial probit models, which we refer to as the first-stage and second-stage spatial probit model. We discuss the implications of fitting each model and compare them in terms of their impact on parameter estimation and prediction at unobserved locations. We propose a new approximation for predicting ordinal response data that is both accurate and efficient. We apply the multivariate multilevel latent variable model to data collected in the North Platte and Rio Grande River Basins to evaluate wetland condition. We obtain statistically derived weights for each of the response metrics with confidence limits. Lastly, we predict the univariate measure of wetland condition at unobserved locations.Item Open Access The spatial and behavioral ecology of human-elephant conflict(Colorado State University. Libraries, 2022) Hahn, Nathan Ravi, author; Wittemyer, George, advisor; Webb, Colleen, committee member; Reed, Sarah, committee member; Dinerstein, Eric, committee memberTo view the abstract, please see the full text of the document.