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- Creators: Barrett, The Honors College
- Creators: Poulenc, Francis, 1899-1963
Description
Despite years of effort, the field of conservation biology still struggles to incorporate theories of animal behavior. I introduce in Chapter I the issues surrounding the disconnect between behavioral ecology and conservation biology, and propose the use of behavioral knowledge in population viability analysis. In Chapter II, I develop a framework that uses three strategies for incorporating behavior into demographic models, outline the costs of each strategy through decision analysis, and build on previous work in behavioral ecology and demography. First, relevant behavioral mechanisms should be included in demographic models used for conservation decision-making. Second, I propose rapid behavioral assessment as a useful tool to approximate demographic rates through regression of demographic phenomena on observations of related behaviors. This technique provides behaviorally estimated parameters that may be applied to population viability analysis for use in management. Finally, behavioral indices can be used as warning signs of population decline. The proposed framework combines each strategy through decision analysis to provide quantitative rules that determine when incorporating aspects of conservation behavior may be beneficial to management. Chapter III applies this technique to estimate birthrate in a colony of California sea lions in the Gulf of California, Mexico. This study includes a cost analysis of the behavioral and traditional parameter estimation techniques. I then provide in Chapter IV practical recommendations for applying this framework to management programs along with general guidelines for the development of rapid behavioral assessment.
ContributorsWildermuth, Robert (Author) / Gerber, Leah R. (Thesis advisor) / Collins, James (Committee member) / Smith, Andrew (Committee member) / Arizona State University (Publisher)
Created2012
Description
Parental care provides many benefits to offspring. One widely realized benefit is enhanced regulation of offspring's thermal environment. The developmental thermal environment during development can be optimized behaviorally through nest site selection and brooding, and it can be further enhanced by physiological heat production. In fact, enhancement of the developmental thermal environment has been proposed as the initial driving force for the evolution of endothermy in bird and mammals. I used pythons (Squamata: Pythonidae) to expand existing knowledge of behavioral and physiological parental tactics used to regulate offspring thermal environment. I first demonstrated that brooding behavior in the Children's python (Antaresia childreni) is largely driven by internal mechanisms, similar to solitary birds, suggesting that the early evolution of the parent-offspring association was probably hormonally driven. Two species of python are known to be facultatively thermogenic (i.e., are endothermic during reproduction). I expand current knowledge of thermogenesis in Burmese pythons (Python molurus) by demonstrating that females use their own body temperature to modulate thermogenesis. Although pythons are commonly cited as thermogenic, the actual extent of thermogenesis within the family Pythonidae is unknown. Thus, I assessed the thermogenic capability of five previously unstudied species of python to aid in understanding phylogenetic, morphological, and distributional influences on thermogenesis in pythons. Results suggest that facultative thermogenesis is likely rare among pythons. To understand why it is rare, I used an artificial model to demonstrate that energetic costs to the female likely outweigh thermal benefits to the clutch in species that do not inhabit cooler latitudes or lack large energy reserves. In combination with other studies, these results show that facultative thermogenesis during brooding in pythons likely requires particular ecological and physiological factors for its evolution.
ContributorsBrashears, Jake (Author) / DeNardo, Dale (Thesis advisor) / Harrison, Jon (Committee member) / Deviche, Pierre (Committee member) / McGraw, Kevin (Committee member) / Smith, Andrew (Committee member) / Arizona State University (Publisher)
Created2012
ContributorsPoulenc, Francis, 1899-1963 (Composer)
ContributorsPoulenc, Francis, 1899-1963 (Composer)
ContributorsPoulenc, Francis, 1899-1963 (Composer)
ContributorsPoulenc, Francis, 1899-1963 (Composer)
Description
Geology and its tangential studies, collectively known and referred to in this thesis as geosciences, have been paramount to the transformation and advancement of society, fundamentally changing the way we view, interact and live with the surrounding natural and built environment. It is important to recognize the value and importance of this interdisciplinary scientific field while reconciling its ties to imperial and colonizing extractive systems which have led to harmful and invasive endeavors. This intersection among geosciences, (environmental) justice studies, and decolonization is intended to promote inclusive pedagogical models through just and equitable methodologies and frameworks as to prevent further injustices and promote recognition and healing of old wounds. By utilizing decolonial frameworks and highlighting the voices of peoples from colonized and exploited landscapes, this annotated syllabus tackles the issues previously described while proposing solutions involving place-based education and the recentering of land within geoscience pedagogical models. (abstract)
ContributorsReed, Cameron E (Author) / Richter, Jennifer (Thesis director) / Semken, Steven (Committee member) / School of Earth and Space Exploration (Contributor, Contributor) / School of Sustainability (Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
Description
The ASU COVID-19 testing lab process was developed to operate as the primary testing site for all ASU staff, students, and specified external individuals. Tests are collected at various collection sites, including a walk-in site at the SDFC and various drive-up sites on campus; analysis is conducted on ASU campus and results are distributed virtually to all patients via the Health Services patient portal. The following is a literature review on past implementations of various process improvement techniques and how they can be applied to the ABCTL testing process to achieve laboratory goals. (abstract)
ContributorsKrell, Abby Elizabeth (Co-author) / Bruner, Ashley (Co-author) / Ramesh, Frankincense (Co-author) / Lewis, Gabriel (Co-author) / Barwey, Ishna (Co-author) / Myers, Jack (Co-author) / Hymer, William (Co-author) / Reagan, Sage (Co-author) / Compton, Carolyn (Thesis director) / McCarville, Daniel R. (Committee member) / Industrial, Systems & Operations Engineering Prgm (Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
Description
Brain Computer Interfaces are becoming the next generation controllers not only in the medical devices for disabled individuals but also in the gaming and entertainment industries. In order to build an effective Brain Computer Interface, which accurately translates the user thoughts into machine commands, it is important to have robust and fail proof signal processing and machine learning modules which operate on the raw EEG signals and estimate the current thought of the user.
In this thesis, several techniques used to perform EEG signal pre-processing, feature extraction and signal classification have been discussed, implemented, validated and verified; efficient supervised machine learning models, for the EEG motor imagery signal classification are identified. To further improve the performance of system unsupervised feature learning techniques have been investigated by pre-training the Deep Learning models. Use of pre-training stacked autoencoders have been proposed to solve the problems caused by random initialization of weights in neural networks.
Motor Imagery (imaginary hand and leg movements) signals are acquire using the Emotiv EEG headset. Different kinds of features like mean signal, band powers, RMS of the signal have been extracted and supplied to the machine learning (ML) stage, wherein, several ML techniques like LDA, KNN, SVM, Logistic regression and Neural Networks are applied and validated. During the validation phase the performances of various techniques are compared and some important observations are reported. Further, deep Learning techniques like autoencoding have been used to perform unsupervised feature learning. The reliability of the features is analyzed by performing classification by using the ML techniques mentioned earlier. The performance of the neural networks has been further improved by pre-training the network in an unsupervised fashion using stacked autoencoders and supplying the stacked autoencoders’ network parameters as initial parameters to the neural network. All the findings in this research, during each phase (pre-processing, feature extraction, classification) are directly relevant and can be used by the BCI research community for building motor imagery based BCI applications.
Additionally, this thesis attempts to develop, test, and compare the performance of an alternative method for classifying human driving behavior. This thesis proposes the use of driver affective states to know the driving behavior. The purpose of this part of the thesis was to classify the EEG data collected from several subjects while driving simulated vehicle and compare the classification results with those obtained by classifying the driving behavior using vehicle parameters collected simultaneously from all the subjects. The objective here is to see if the drivers’ mental state is reflected in his driving behavior.
In this thesis, several techniques used to perform EEG signal pre-processing, feature extraction and signal classification have been discussed, implemented, validated and verified; efficient supervised machine learning models, for the EEG motor imagery signal classification are identified. To further improve the performance of system unsupervised feature learning techniques have been investigated by pre-training the Deep Learning models. Use of pre-training stacked autoencoders have been proposed to solve the problems caused by random initialization of weights in neural networks.
Motor Imagery (imaginary hand and leg movements) signals are acquire using the Emotiv EEG headset. Different kinds of features like mean signal, band powers, RMS of the signal have been extracted and supplied to the machine learning (ML) stage, wherein, several ML techniques like LDA, KNN, SVM, Logistic regression and Neural Networks are applied and validated. During the validation phase the performances of various techniques are compared and some important observations are reported. Further, deep Learning techniques like autoencoding have been used to perform unsupervised feature learning. The reliability of the features is analyzed by performing classification by using the ML techniques mentioned earlier. The performance of the neural networks has been further improved by pre-training the network in an unsupervised fashion using stacked autoencoders and supplying the stacked autoencoders’ network parameters as initial parameters to the neural network. All the findings in this research, during each phase (pre-processing, feature extraction, classification) are directly relevant and can be used by the BCI research community for building motor imagery based BCI applications.
Additionally, this thesis attempts to develop, test, and compare the performance of an alternative method for classifying human driving behavior. This thesis proposes the use of driver affective states to know the driving behavior. The purpose of this part of the thesis was to classify the EEG data collected from several subjects while driving simulated vehicle and compare the classification results with those obtained by classifying the driving behavior using vehicle parameters collected simultaneously from all the subjects. The objective here is to see if the drivers’ mental state is reflected in his driving behavior.
ContributorsManchala, Vamsi Krishna (Author) / Redkar, Sangram (Thesis advisor) / Rogers, Bradley (Committee member) / Sugar, Thomas (Committee member) / Arizona State University (Publisher)
Created2015
Description
Small-scale fisheries are globally ubiquitous, employing more than 99% of the world’s fishers and providing over half of the world’s seafood. However, small-scale fisheries face many management challenges including declining catches, inadequate resources and infrastructure, and overcapacity. Baja California Sur, Mexico (BCS) is a region with diverse small-scale fisheries; these fisheries are intense, poorly regulated, and overlap with foraging hot spots of endangered sea turtles. In partnership with researchers, fishers, managers, and practitioners from Mexico and the United States, I documented bycatch rates of loggerhead turtles at BCS that represent the highest known megafauna bycatch rates worldwide. Concurrently, I conducted a literature review that determined gear modifications were generally more successful than other commonly used fisheries management strategies for mitigating bycatch of vulnerable megafauna including seabirds, marine mammals, and sea turtles. I then applied these results by partnering with researchers, local fishers, and Mexico’s federal fisheries science agency to develop and test two gear modifications (i.e. buoyless and illuminated nets) in operating net fisheries at BCS as potential solutions to reduce bycatch of endangered sea turtles, improve fisheries sustainability, and maintain fisher livelihoods. I found that buoyless nets significantly reduced mean turtle bycatch rates by 68% while maintaining target catch rates and composition. By contrast, illuminated nets did not significantly reduce turtle bycatch rates across day-night periods, although they reduced mean turtle bycatch rates by 50% at night. Illuminated nets, however, significantly reduced mean rates of total bycatch biomass by 34% across day-night periods while maintaining target fish catch and market value. I conclude with a policy analysis of the unilateral identification of Mexico by the U.S. State Department under section 610 of the Magnusson-Stevens Fishery Conservation and Management Act for failure to manage bycatch of loggerhead turtles at BCS. Taken together, the gear modifications developed and tested here represent promising bycatch mitigation solutions with strong potential for commercial adoption, but fleet-wide conversion to more selective and turtle-friendly gear (e.g. hook and line and/or traps) at BCS, coupled with coordinated international conservation action, is ultimately needed to eliminate sea turtle bycatch and further improve fisheries sustainability.
ContributorsSenko, Jesse (Author) / Smith, Andrew (Thesis advisor) / Boggess, May (Committee member) / Chhetri, Nalini (Committee member) / Jenkins, Lekelia (Committee member) / Minteer, Ben (Committee member) / Arizona State University (Publisher)
Created2015