ASU Electronic Theses and Dissertations
This collection includes most of the ASU Theses and Dissertations from 2011 to present. ASU Theses and Dissertations are available in downloadable PDF format; however, a small percentage of items are under embargo. Information about the dissertations/theses includes degree information, committee members, an abstract, supporting data or media.
In addition to the electronic theses found in the ASU Digital Repository, ASU Theses and Dissertations can be found in the ASU Library Catalog.
Dissertations and Theses granted by Arizona State University are archived and made available through a joint effort of the ASU Graduate College and the ASU Libraries. For more information or questions about this collection contact or visit the Digital Repository ETD Library Guide or contact the ASU Graduate College at gradformat@asu.edu.
Displaying 1 - 10 of 11
Filtering by
- All Subjects: Biology
- Creators: Bateman, Heather L
- Creators: Kostelich, Eric
Description
Modified and artificial water sources can be used as a management tool for game and non-game wildlife species. State, federal, and private agencies allocate significant resources to install and maintain artificial water sources (AWS) annually. Capture mark recapture methods were used to sample small mammal communities in the vicinity of five AWS and five paired control sites (treatments) in the surrounding Sonoran desert from October 2011 to May 2012. I measured plant species richness, density, and percent cover in the spring of 2012. A Multi-response Permutation Procedure was used to identify differences in small mammal community abundance, biomass, and species richness by season and treatment. I used Principle Component Analysis to reduce 11 habitat characteristics to five habitat factors. I related rodent occurrence to habitat characteristics using multiple and logistic regression. A total of 370 individual mammals representing three genera and eight species of rodents were captured across 4800 trap nights. Desert pocket mouse (Chaetodipus penicillatus) was the most common species in both seasons and treatments. Whereas rodent community abundance, biomass, and richness were similar between seasons, community variables of AWS were greater than CS. Rodent diversity was similar between treatments. Desert pocket mouse abundance and biomass were twice as high at AWS when compared to controls. Biomass of white-throated woodrat (Neotoma albigula) was five times greater at AWS. Habitat characteristics were similar between treatments. Neither presence of water nor distance to water explained substantial habitat variation. Occurrence of rodent species was associated with habitat characteristics. Desert rodent communities are adapted for arid environments (i.e. Heteromyids) and are not dependent on "free water". Higher abundances of desert pocket mouse at AWS were most likely related to increased disturbance and debris and not the presence of water. The results of this study and previous studies suggest that more investigation is needed and that short term studies may not be able to detect interactions (if any) between AWS and desert small mammal communities.
ContributorsSwitalski, Aaron (Author) / Bateman, Heather L (Thesis advisor) / Miller, William (Committee member) / Alford, Eddie (Committee member) / Arizona State University (Publisher)
Created2013
Description
Biological diversity is threatened by increasing anthropogenic modification of natural environments and increasing demands on natural resources. Sonoran desert tortoises (Gopherus morafkai) currently have Candidate status under the Endangered Species Act (ESA) based on health and habitat threats. To ensure this animal persists in the midst of multiple threats requires an understanding of the life history and ecology of each population. I looked at one physiological and one behavioral aspect of a population of tortoises at the Sugarloaf Mountain (SL) study site in central Arizona, USA. I used 21 years of capture-recapture records to estimate growth parameters of the entire population. I investigated habitat selection of juvenile tortoises by selecting 117 locations of 11 tortoises that had been tracked by radio-telemetry one to three times weekly for two years, selecting locations from both summer active season and during winter hibernation. I compared 22 microhabitat variables of tortoise locations to random SL locations to determine habitat use and availability. Male tortoises at SL reach a greater asymptotic length than females, and males and females appear to grow at the same rate. Juvenile tortoises at the SL site use steep rocky hillsides with high proportions of sand and annual vegetation, few succulents, and enclosed shelters in summer. They use enclosed shelters on steep slopes for winter hibernation. An understanding of these features can allow managers to quantify Sonoran desert tortoise habitat needs and life history characteristics and to understand the impact of land use policies.
ContributorsBridges, Andrew (Author) / Bateman, Heather L (Thesis advisor) / Miller, William (Committee member) / Ulrich, Jon (Committee member) / Arizona State University (Publisher)
Created2012
Description
In 1968, phycologist M.R. Droop published his famous discovery on the functional relationship between growth rate and internal nutrient status of algae in chemostat culture. The simple notion that growth is directly dependent on intracellular nutrient concentration is useful for understanding the dynamics in many ecological systems. The cell quota in particular lends itself to ecological stoichiometry, which is a powerful framework for mathematical ecology. Three models are developed based on the cell quota principal in order to demonstrate its applications beyond chemostat culture.
First, a data-driven model is derived for neutral lipid synthesis in green microalgae with respect to nitrogen limitation. This model synthesizes several established frameworks in phycology and ecological stoichiometry. The model demonstrates how the cell quota is a useful abstraction for understanding the metabolic shift to neutral lipid production that is observed in certain oleaginous species.
Next a producer-grazer model is developed based on the cell quota model and nutrient recycling. The model incorporates a novel feedback loop to account for animal toxicity due to accumulation of nitrogen waste. The model exhibits rich, complex dynamics which leave several open mathematical questions.
Lastly, disease dynamics in vivo are in many ways analogous to those of an ecosystem, giving natural extensions of the cell quota concept to disease modeling. Prostate cancer can be modeled within this framework, with androgen the limiting nutrient and the prostate and cancer cells as competing species. Here the cell quota model provides a useful abstraction for the dependence of cellular proliferation and apoptosis on androgen and the androgen receptor. Androgen ablation therapy is often used for patients in biochemical recurrence or late-stage disease progression and is in general initially effective. However, for many patients the cancer eventually develops resistance months to years after treatment begins. Understanding how and predicting when hormone therapy facilitates evolution of resistant phenotypes has immediate implications for treatment. Cell quota models for prostate cancer can be useful tools for this purpose and motivate applications to other diseases.
First, a data-driven model is derived for neutral lipid synthesis in green microalgae with respect to nitrogen limitation. This model synthesizes several established frameworks in phycology and ecological stoichiometry. The model demonstrates how the cell quota is a useful abstraction for understanding the metabolic shift to neutral lipid production that is observed in certain oleaginous species.
Next a producer-grazer model is developed based on the cell quota model and nutrient recycling. The model incorporates a novel feedback loop to account for animal toxicity due to accumulation of nitrogen waste. The model exhibits rich, complex dynamics which leave several open mathematical questions.
Lastly, disease dynamics in vivo are in many ways analogous to those of an ecosystem, giving natural extensions of the cell quota concept to disease modeling. Prostate cancer can be modeled within this framework, with androgen the limiting nutrient and the prostate and cancer cells as competing species. Here the cell quota model provides a useful abstraction for the dependence of cellular proliferation and apoptosis on androgen and the androgen receptor. Androgen ablation therapy is often used for patients in biochemical recurrence or late-stage disease progression and is in general initially effective. However, for many patients the cancer eventually develops resistance months to years after treatment begins. Understanding how and predicting when hormone therapy facilitates evolution of resistant phenotypes has immediate implications for treatment. Cell quota models for prostate cancer can be useful tools for this purpose and motivate applications to other diseases.
ContributorsPacker, Aaron (Author) / Kuang, Yang (Thesis advisor) / Nagy, John (Committee member) / Smith, Hal (Committee member) / Kostelich, Eric (Committee member) / Kang, Yun (Committee member) / Arizona State University (Publisher)
Created2014
Description
Predicting resistant prostate cancer is critical for lowering medical costs and improving the quality of life of advanced prostate cancer patients. I formulate, compare, and analyze two mathematical models that aim to forecast future levels of prostate-specific antigen (PSA). I accomplish these tasks by employing clinical data of locally advanced prostate cancer patients undergoing androgen deprivation therapy (ADT). I demonstrate that the inverse problem of parameter estimation might be too complicated and simply relying on data fitting can give incorrect conclusions, since there is a large error in parameter values estimated and parameters might be unidentifiable. I provide confidence intervals to give estimate forecasts using data assimilation via an ensemble Kalman Filter. Using the ensemble Kalman Filter, I perform dual estimation of parameters and state variables to test the prediction accuracy of the models. Finally, I present a novel model with time delay and a delay-dependent parameter. I provide a geometric stability result to study the behavior of this model and show that the inclusion of time delay may improve the accuracy of predictions. Also, I demonstrate with clinical data that the inclusion of the delay-dependent parameter facilitates the identification and estimation of parameters.
ContributorsBaez, Javier (Author) / Kuang, Yang (Thesis advisor) / Kostelich, Eric (Committee member) / Crook, Sharon (Committee member) / Gardner, Carl (Committee member) / Nagy, John (Committee member) / Arizona State University (Publisher)
Created2017
Description
The built environment increases radiant heat exchange in urban areas by several degrees hotter compared to non-urban areas. Research has investigated how urbanization and heat affect human health; but there is scant literature on the effects of urban heat on wildlife. Animal body condition can be used to assess overall health. This parameter estimates the storage of energy-rich fat, which is important for growth, survival, and reproduction. The purpose of my research was to examine the Urban Heat Island effect on wild rodents across urban field sites spanning three strata of land surface temperature. Site level surface temperatures were measured using temperature data loggers and I captured 116 adult pocket mice (Chaetodipus spp. and Perognathus spp.) and Merriam’s kangaroo rats (Dipodomys merriami) to measure their body condition using accurate and noninvasive quantitative magnetic resonance. I used baited Sherman live traps from mid-May to early September during 2019 and 2020 in mountainous urban parks and open spaces over two summers. Rodents were captured at seven sites near the Phoenix metropolitan area; an ideal area for examining the effect of extreme heat experienced by urban wildlife. Results supported the prediction that rodent body condition was greatest in the cooler temperature stratas compared to the hottest temperature strata. I related rodent body condition to environmental predictors to dispute to environmental predictors to dispute alternative hypotheses; such as vegetation cover and degree of urbanization. Results based on measures of body fat and environmental predictors show pocket mice have more fat where vegetation is higher, nighttime temperatures are lower, surface temperatures are lower, and urbanization is greater. Kangaroo rats have more fat where surface temperature is lower. My results contribute to understanding the negative effects of extreme heat on body condition and generalized health experienced by urban wildlife because of the built environment. This research shows a need to investigate further impacts of urban heat on wildlife. Management suggestions for urban parks and open spaces include increasing vegetation cover, reducing impervious surface, and building with materials that reduce radiant heat.
ContributorsAllen, Brittany D'Ann (Author) / Bateman, Heather L (Thesis advisor) / Moore, Marianne S (Committee member) / Hondula, David M (Committee member) / Arizona State University (Publisher)
Created2021
Description
The Arizona toad (Anaxyrus microscaphus) is unique among bufonids because they primarily breed in streams of Arizona, New Mexico, Utah, and Nevada. Arizona toad is a species of conservation concern throughout their range. The non-native northern crayfish (Orconectes virilis) are opportunistic omnivores implicated in the declines of other native aquatic species. I wanted to determine occupancy, habitat use, and species interactions of the Arizona toad throughout its range Visual encounter surveys (VES) were completed by ASU and natural resource agency partners in the summers of 2021 and 2022 (n = 232) throughout Arizona toad range in Arizona. I used VES data and crayfish occurrence records, to determine interactions between the two species. I used broadscale environmental variables (1 km resolution) from WorldClim and EarthEnv to evaluate a relationship with Arizona toad occupancy across transects. These broadscale variables included bioclimatic variables, measures of habitat heterogeneity, measures of solar radiation, and topographic variables. In 2022 I collected fine-scale habitat data evaluating available vegetation cover and substrate composition within paired habitat plots. Fine-scale variables included canopy cover, substrate type, vegetation cover, and water depth. I applied multiple occupancy modeling approaches. Single-species model results found low toad occupancy, but high detection, as this is a rare species. Multi-species results showed no positive or negative relationship between Arizona toad and northern crayfish for both seasons. Two principal component analyses (PCA) were run on broadscale environmental variables and fine-scale habitat variables for 2021 and 2022, respectively, creating new synthetic variables for use in analysis. In 2021, the broadscale components were added to the single-species occupancy models and the top model included bioclimatic variables related to annual temperature range and precipitation. Arizona toad occupancy is lower with extreme hot temperatures and less precipitation. A logistic regression was run with the fine-scale habitat variables and the top model included PC1 and PC3. PC1 described elements related to riparian complexity, while PC3 described elements related to algae presence, including attached to cobble substrate. Arizona Toad select for certain habitats including canopy cover, shallow water, algae cover, and pebble cover. It is important to maintain riparian area habitat complexity and conserve habitat for the Arizona toad, a riparian stream specialist.
ContributorsMontgomery, Brett Joseph (Author) / Bateman, Heather L (Thesis advisor) / Albuquerque, Fabio S (Committee member) / Bogan, Michael T (Committee member) / Arizona State University (Publisher)
Created2023
Description
Efforts to treat prostate cancer have seen an uptick, as the world’s most commoncancer in men continues to have increasing global incidence. Clinically, metastatic
prostate cancer is most commonly treated with hormonal therapy. The idea behind
hormonal therapy is to reduce androgen production, which prostate cancer cells
require for growth. Recently, the exploration of the synergistic effects of the drugs
used in hormonal therapy has begun. The aim was to build off of these recent
advancements and further refine the synergistic drug model. The advancements I
implement come by addressing biological shortcomings and improving the model’s
internal mechanistic structure. The drug families being modeled, anti-androgens,
and gonadotropin-releasing hormone analogs, interact with androgen production in a
way that is not completely understood in the scientific community. Thus the models
representing the drugs show progress through their ability to capture their effect
on serum androgen. Prostate-specific antigen is the primary biomarker for prostate
cancer and is generally how population models on the subject are validated. Fitting
the model to clinical data and comparing it to other clinical models through the
ability to fit and forecast prostate-specific antigen and serum androgen is how this
improved model achieves validation. The improved model results further suggest that
the drugs’ dynamics should be considered in adaptive therapy for prostate cancer.
prostate cancer is most commonly treated with hormonal therapy. The idea behind
hormonal therapy is to reduce androgen production, which prostate cancer cells
require for growth. Recently, the exploration of the synergistic effects of the drugs
used in hormonal therapy has begun. The aim was to build off of these recent
advancements and further refine the synergistic drug model. The advancements I
implement come by addressing biological shortcomings and improving the model’s
internal mechanistic structure. The drug families being modeled, anti-androgens,
and gonadotropin-releasing hormone analogs, interact with androgen production in a
way that is not completely understood in the scientific community. Thus the models
representing the drugs show progress through their ability to capture their effect
on serum androgen. Prostate-specific antigen is the primary biomarker for prostate
cancer and is generally how population models on the subject are validated. Fitting
the model to clinical data and comparing it to other clinical models through the
ability to fit and forecast prostate-specific antigen and serum androgen is how this
improved model achieves validation. The improved model results further suggest that
the drugs’ dynamics should be considered in adaptive therapy for prostate cancer.
ContributorsReckell, Trevor (Author) / Kostelich, Eric (Thesis advisor) / Kuang, Yang (Committee member) / Mahalov, Alex (Committee member) / Arizona State University (Publisher)
Created2020
Description
Human land use and land cover change alter key features of the landscape that may favor habitat selection by some species. Lizards are especially sensitive to these alterations because they rely on their external environment for regulating their body temperature. However, because of their diverse life-history traits and strategies, some are able to respond well to disturbance by using their habitat in various ways. To understand how they use their habitat and how human modifications may impact their ability to do this, biologists must identify where they occur and the habitat characteristics on which they depend. Therefore, I used species occupancy modeling to determine (1) whether disturbance predicts the presence of two sympatric congeneric (species of the same genus) lizard species Sceloporus grammicus and S. torquatus, and (2) which habitat characteristics are essential for predicting their occupancy and detection. I focused my study in central Mexico, a region of prevalent land use and land cover change. Here, I conducted visual encounter and habitat surveys at 100 1-hectare sites during the spring of 2019. I measured vegetation and ground cover, average tree diameter, and abundance of refuges. I recorded air temperature, relative humidity, and elevation. I summarized sites as either undisturbed or disturbed, based on the presence of human development. I also summarized sites by ecosystem type, desert or forest, based on vegetation composition (i.e., desert-adapted vs. non-desert-adapted plants), evidence of remnant forest, air temperature, and relative humidity. I found that S. torquatus was more likely to be present in disturbed habitat, whereas S. grammicus was more likely to be present in areas with leaf litter, tree cover, and woody debris. S. torquatus was twice as likely to be detected in forests than deserts, and S. grammicus was more likely to be detected at sites with high elevation and high relative humidity, low temperature, and herbaceous and grass cover. These results emphasize the utility of species occupancy modeling for estimating detection and occupancy in dynamic landscapes.
ContributorsFlores, Jennifer (Author) / Martins, Emília P. (Thesis advisor) / Bateman, Heather L (Thesis advisor) / Zuniga-Vega, J. Jaime (Committee member) / Arizona State University (Publisher)
Created2020
Description
Cancer is a disease involving abnormal growth of cells. Its growth dynamics is perplexing. Mathematical modeling is a way to shed light on this progress and its medical treatments. This dissertation is to study cancer invasion in time and space using a mathematical approach. Chapter 1 presents a detailed review of literature on cancer modeling.
Chapter 2 focuses sorely on time where the escape of a generic cancer out of immune control is described by stochastic delayed differential equations (SDDEs). Without time delay and noise, this system demonstrates bistability. The effects of response time of the immune system and stochasticity in the tumor proliferation rate are studied by including delay and noise in the model. Stability, persistence and extinction of the tumor are analyzed. The result shows that both time delay and noise can induce the transition from low tumor burden equilibrium to high tumor equilibrium. The aforementioned work has been published (Han et al., 2019b).
In Chapter 3, Glioblastoma multiforme (GBM) is studied using a partial differential equation (PDE) model. GBM is an aggressive brain cancer with a grim prognosis. A mathematical model of GBM growth with explicit motility, birth, and death processes is proposed. A novel method is developed to approximate key characteristics of the wave profile, which can be compared with MRI data. Several test cases of MRI data of GBM patients are used to yield personalized parameterizations of the model. The aforementioned work has been published (Han et al., 2019a).
Chapter 4 presents an innovative way of forecasting spatial cancer invasion. Most mathematical models, including the ones described in previous chapters, are formulated based on strong assumptions, which are hard, if not impossible, to verify due to complexity of biological processes and lack of quality data. Instead, a nonparametric forecasting method using Gaussian processes is proposed. By exploiting the local nature of the spatio-temporal process, sparse (in terms of time) data is sufficient for forecasting. Desirable properties of Gaussian processes facilitate selection of the size of the local neighborhood and computationally efficient propagation of uncertainty. The method is tested on synthetic data and demonstrates promising results.
Chapter 2 focuses sorely on time where the escape of a generic cancer out of immune control is described by stochastic delayed differential equations (SDDEs). Without time delay and noise, this system demonstrates bistability. The effects of response time of the immune system and stochasticity in the tumor proliferation rate are studied by including delay and noise in the model. Stability, persistence and extinction of the tumor are analyzed. The result shows that both time delay and noise can induce the transition from low tumor burden equilibrium to high tumor equilibrium. The aforementioned work has been published (Han et al., 2019b).
In Chapter 3, Glioblastoma multiforme (GBM) is studied using a partial differential equation (PDE) model. GBM is an aggressive brain cancer with a grim prognosis. A mathematical model of GBM growth with explicit motility, birth, and death processes is proposed. A novel method is developed to approximate key characteristics of the wave profile, which can be compared with MRI data. Several test cases of MRI data of GBM patients are used to yield personalized parameterizations of the model. The aforementioned work has been published (Han et al., 2019a).
Chapter 4 presents an innovative way of forecasting spatial cancer invasion. Most mathematical models, including the ones described in previous chapters, are formulated based on strong assumptions, which are hard, if not impossible, to verify due to complexity of biological processes and lack of quality data. Instead, a nonparametric forecasting method using Gaussian processes is proposed. By exploiting the local nature of the spatio-temporal process, sparse (in terms of time) data is sufficient for forecasting. Desirable properties of Gaussian processes facilitate selection of the size of the local neighborhood and computationally efficient propagation of uncertainty. The method is tested on synthetic data and demonstrates promising results.
ContributorsHan, Lifeng (Author) / Kuang, Yang (Thesis advisor) / Fricks, John (Thesis advisor) / Kostelich, Eric (Committee member) / Baer, Steve (Committee member) / Gumel, Abba (Committee member) / Arizona State University (Publisher)
Created2020
Description
The analysis focuses on a two-population, three-dimensional model that attempts to accurately model the growth and diffusion of glioblastoma multiforme (GBM), a highly invasive brain cancer, throughout the brain. Analysis into the sensitivity of the model to
changes in the diffusion, growth, and death parameters was performed, in order to find a set of parameter values that accurately model observed tumor growth for a given patient. Additional changes were made to the diffusion parameters to account for the arrangement of nerve tracts in the brain, resulting in varying rates of diffusion. In general, small changes in the growth rates had a large impact on the outcome of the simulations, and for each patient there exists a set of parameters that allow the model to simulate a tumor that matches observed tumor growth in the patient over a period of two or three months. Furthermore, these results are more accurate with anisotropic diffusion, rather than isotropic diffusion. However, these parameters lead to inaccurate results for patients with tumors that undergo no observable growth over the given time interval. While it is possible to simulate long-term tumor growth, the simulation requires multiple comparisons to available MRI scans in order to find a set of parameters that provide an accurate prognosis.
changes in the diffusion, growth, and death parameters was performed, in order to find a set of parameter values that accurately model observed tumor growth for a given patient. Additional changes were made to the diffusion parameters to account for the arrangement of nerve tracts in the brain, resulting in varying rates of diffusion. In general, small changes in the growth rates had a large impact on the outcome of the simulations, and for each patient there exists a set of parameters that allow the model to simulate a tumor that matches observed tumor growth in the patient over a period of two or three months. Furthermore, these results are more accurate with anisotropic diffusion, rather than isotropic diffusion. However, these parameters lead to inaccurate results for patients with tumors that undergo no observable growth over the given time interval. While it is possible to simulate long-term tumor growth, the simulation requires multiple comparisons to available MRI scans in order to find a set of parameters that provide an accurate prognosis.
ContributorsTrent, Austin Lee (Author) / Kostelich, Eric (Thesis advisor) / Gumel, Abba (Committee member) / Kuang, Yang (Committee member) / Arizona State University (Publisher)
Created2020