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.
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- All Subjects: Climate Change
Description
By increasing the mean and variance of environmental temperatures, climate change has caused local extinctions and range shifts of numerous species. However, biologists disagree on which populations and species are most vulnerable to future warming. This debate arises because biologists do not know which physiological processes are most vulnerable to temperature or how to model these processes in complex environments. Using the South American locust (Schistocerca cancellata) as a model system, my dissertation addressed this debate and explained how climate limits the persistence of locust populations. Locusts of S. cancellata are serious agricultural pests with occasional outbreaks covering up to 4 million km2 over six countries. Because outbreaks are largely driven by climate, understanding how climate limits the persistence of locusts may help predict crop losses in future climates. To achieve this aim, I integrated observational, experimental, and computational approaches. First, I tested a physiological model of heat stress. By measuring the heat tolerance of locusts under different oxygen concentrations, I demonstrated that heat tolerance depends on oxygen supply during the hatchling stage only. Second, I modeled the geographic distribution of locusts using physiological traits. I started by measuring thermal effects on consumption and defecation of field-captured locusts, and I then used these data to model energy gain in current and future climates. My results indicated that incorporating physiological mechanisms can improve the accuracy of models and alter predicted impacts of climate change. Finally, I explored the causes and consequences of intraspecific variation in heat tolerance. After measuring heat tolerance of locusts in different hydration states and developmental stages, I modeled survival in historical microclimates. My models indicated that recent climate change has amplified the risk of overheating for locusts, and this risk depended strongly on shade availability, hydration state, and developmental stage. Therefore, the survival of locusts in future climates will likely depend on their access to shade and water. Overall, my dissertation argues that modeling physiological mechanisms can improve the ability of biologists to predict the impacts of climate change.
ContributorsYoungblood, Jacob (Author) / Angilletta, Michael (Thesis advisor) / Buckley, Lauren (Committee member) / Cease, Arianne (Committee member) / Smith, Brian (Committee member) / Vanden Brooks, John (Committee member) / Arizona State University (Publisher)
Created2022
Description
The subliminal impact of framing of social, political and environmental issues such as climate change has been studied for decades in political science and communications research. Media framing offers an “interpretative package" for average citizens on how to make sense of climate change and its consequences to their livelihoods, how to deal with its negative impacts, and which mitigation or adaptation policies to support. A line of related work has used bag of words and word-level features to detect frames automatically in text. Such works face limitations since standard keyword based features may not generalize well to accommodate surface variations in text when different keywords are used for similar concepts.
This thesis develops a unique type of textual features that generalize triplets extracted from text, by clustering them into high-level concepts. These concepts are utilized as features to detect frames in text. Compared to uni-gram and bi-gram based models, classification and clustering using generalized concepts yield better discriminating features and a higher classification accuracy with a 12% boost (i.e. from 74% to 83% F-measure) and 0.91 clustering purity for Frame/Non-Frame detection.
The automatic discovery of complex causal chains among interlinked events and their participating actors has not yet been thoroughly studied. Previous studies related to extracting causal relationships from text were based on laborious and incomplete hand-developed lists of explicit causal verbs, such as “causes" and “results in." Such approaches result in limited recall because standard causal verbs may not generalize well to accommodate surface variations in texts when different keywords and phrases are used to express similar causal effects. Therefore, I present a system that utilizes generalized concepts to extract causal relationships. The proposed algorithms overcome surface variations in written expressions of causal relationships and discover the domino effects between climate events and human security. This semi-supervised approach alleviates the need for labor intensive keyword list development and annotated datasets. Experimental evaluations by domain experts achieve an average precision of 82%. Qualitative assessments of causal chains show that results are consistent with the 2014 IPCC report illuminating causal mechanisms underlying the linkages between climatic stresses and social instability.
This thesis develops a unique type of textual features that generalize triplets extracted from text, by clustering them into high-level concepts. These concepts are utilized as features to detect frames in text. Compared to uni-gram and bi-gram based models, classification and clustering using generalized concepts yield better discriminating features and a higher classification accuracy with a 12% boost (i.e. from 74% to 83% F-measure) and 0.91 clustering purity for Frame/Non-Frame detection.
The automatic discovery of complex causal chains among interlinked events and their participating actors has not yet been thoroughly studied. Previous studies related to extracting causal relationships from text were based on laborious and incomplete hand-developed lists of explicit causal verbs, such as “causes" and “results in." Such approaches result in limited recall because standard causal verbs may not generalize well to accommodate surface variations in texts when different keywords and phrases are used to express similar causal effects. Therefore, I present a system that utilizes generalized concepts to extract causal relationships. The proposed algorithms overcome surface variations in written expressions of causal relationships and discover the domino effects between climate events and human security. This semi-supervised approach alleviates the need for labor intensive keyword list development and annotated datasets. Experimental evaluations by domain experts achieve an average precision of 82%. Qualitative assessments of causal chains show that results are consistent with the 2014 IPCC report illuminating causal mechanisms underlying the linkages between climatic stresses and social instability.
ContributorsAlashri, Saud (Author) / Davulcu, Hasan (Thesis advisor) / Desouza, Kevin C. (Committee member) / Maciejewski, Ross (Committee member) / Hsiao, Sharon (Committee member) / Arizona State University (Publisher)
Created2018