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SPATIAL AND BEHAVIORAL ECOLOGY OF WHITE-TAILED DEER: IMPLICATIONS FOR WILDLIFE DISEASE TRANSMISSION
No full textAnimal behavior has important impacts on animal populations and the ecosystem at large, but the impact of such behavior on many ecological phenomena is understudied. For example, behavior drives transmission between wildlife disease hosts. Space use and resource selection determines where hosts will make contact, movement determines how pathogens may spread over the landscape, and other fine-scale behaviors determine the rate of contact and transmission. Spatial and movement data from GPS telemetry are useful for studying the causes and consequences of many behavioral processes. One particular focus of such spatial analyses is the behavioral responses of prey to predation risk. While many studies have highlighted the broad impacts of these antipredator behaviors, few studies have emphasized how predation risk may impact the behavioral drivers of disease transmission. White-tailed deer (Odocoileus virginianus) are an excellent system to study these questions for three reasons. First, deer exhibit a fission-fusion social structure, so contacts are dependent on numerous interacting factors. Second, deer face varying predation risks and respond to these risks with varying strategies including spatial avoidance, foraging, and grouping behavior. Third, deer are host to many important diseases with differing transmission mechanisms. In this dissertation, I had three main objectives; 1) to evaluate the factors that produced variation in deer-to-deer contact, 2) to evaluate multiple behavioral responses of deer to predation risk and, 3) to use these behavioral patterns to make predictions of the relative risk of deer-to-deer contact.In chapter one, I evaluated population variation in contact and tested the impact of variation in contact-related behavior on inferences from social network analysis. I used camera trap recordings of visits and behaviors by deer to scrapes throughout DeSoto National Wildlife Refuge, Nebraska from 2005 and 2006. Based on 2,013 interactions by 169 unique identifiable males and 75 females, I produced social networks based on indirect contact among deer at scrapes, with edges weighted based on the frequency, duration, and types of behaviors. Social networks based on scrape-related behavior were highly connected and dependent upon the frequency, duration, and type of behavior exhibited at scrapes (e.g., scraping, interacting with a scrape or overhanging branch, rub-urinating, grazing) as well as the age of the deer. Including behavior when defining edges did not preserve the network properties of simpler measures (i.e., unweighted networks) confirming that heterogeneity in behaviors that affect transmission probability are important for inferring transmission networks from contact networks. In chapters two through five, I evaluated the behavior of deer using movement data from GPS collars. I captured and collared white-tailed deer (Odocoileus virginianus) at two sites: Shelbyville, IL, and Carbondale, IL from January 2020 to March 2022. I collared a total of 156 deer across both sites, 71 in Shelbyville and 85 in Carbondale. Of these deer, 45 in the Shelbyville sample were female and 26 male, and in Carbondale, 54 deer were female and 31 male. Deer were tracked with remotely-sensed GPS telemetry collars for periods of roughly one year on average, resulting in a total of 1,933,465 GPS locations. In chapter two, I used this GPS data to develop a method to relate resources to the relative probability of encounter based on a scale-integrated habitat selection framework. This framework integrates habitat selection estimates at multiple scales to obtain an appropriate estimate of availability for encounters. Using this approach, I related encounter probabilities to landscape resources and predicted the relative probability of encounter. Additionally, I further tested the usefulness of this approach by applying this framework to two other systems representing social contact and predator-prey contact respectively. This predicted distribution of encounters was more accurate when predicting novel encounters than a naïve approach or any individual scale alone. In chapter three, I improved estimates of the drivers of movement by developing novel methods for step selection analysis (SSA). To determine the impact of long-term behavior on local selection from SSA, I simulated movement trajectories including bias toward locations simulating different types of long-term behavior. Based on these simulated trajectories, I evaluated the impact of long-term behavior by identifying frequently reused locations based on a three-dimensional kernel density estimate including latitude, longitude, and time of day. Following this, I developed two approaches to account for spatial and temporal patterns of long-term behavior. I then compared estimates of known values of selection from models using these correction methods to previously established methods based on factors such as spatial memory. In chapter four, I applied this method to estimate local-scale step selection of deer in response to sources of risk. Additionally, I evaluated the impact of risk variables on behavioral states using hidden Markov models (HMMs) and determined state-specific estimates of selection. I found that deer avoided human modification but were more likely to change behavioral state in response to mesopredators. Since different sources of risk induce different behavioral responses, it is likely necessary to account for all of these behavioral responses when estimating the impacts of predation risk and its potential consequences. In chapter five, I used inferences from the preceding three chapters to build a mechanistic model of home range selection and movement that can be used to infer contact distributions. This approach could include varying levels of complexity including local-scale step selection, behavioral state transitions, and antipredator response. I ran models with varying levels of complexity and compared the performance of those models to the approach in chapter 2 for predicting contacts. I found that this method could predict contacts accurately even with limited data, but still had difficulty when transferring predictions to new locations
REINFORCEMENT LEARNING-BASED POST DISASTER RESOURCE ALLOCATION FOR ENHANCED INFRASTRUCTURE RESILIENCE
No full textNatural calamities such as floods, earthquakes, fire are increasing, and infrastructure networks are exposed to such risks during their lifetime. Modern infrastructures are interconnected with each other and depend on one another to fulfill their intended purpose, making the analysis of the network harder. This necessitates the enhancement of the resilience of the infrastructure during recovery. The recovery strategy adopted without the interdependencies is not optimal. In this study, the framework to allocate the resources (human resources and capital) effectively to improve the overall infrastructure system resilience considering the interdependencies among and within the infrastructure facilities is proposed. Typically, the resource allocation is treated as a high-dimensional, multi-objective optimization problem. In this study, this task was formulated as sequential decision-making problem. By using Agent-Based modelling to simulate the effects of infrastructure interdependencies, and deep Reinforcement Learning to solve the sequential decision-making problem, the proposed framework can find the resource allocation strategies enhancing the overall infrastructure systems resilience. Particularly, the aim of this study is to allocate the limited number of available repair crews to the damaged facilities within budget constraint. The results illustrate the efficacy of the proposed framework in simulating interdependency and finding the allocation strategies for post-disaster scenarios
EVALUATING FAMILIARITY AND EMOTIONS IN SHAPING RURAL RESIDENTS’ ATTITUDES TOWARD CRIMINAL JUSTICE INVOLVED PERSONS WITH A MENTAL ILLNESS
No full textIntro: Justice-involved persons with a mental illness are dually stigmatized, possessing two heavily stigmatized characteristics (i.e., mental illness and criminal history). Consequently, they are impacted by several barriers to re-entry, which are exacerbated in rural communities due to the lack of existing infrastructural supports. Thus, rural residents bear the responsibility to supply the conditions, resources, and opportunities necessary to increase re-entry success (e.g., employment, social support). As a result, it is critical to explore factors that contribute to and/or could reduce stigmatization among rural residents. Prior research suggests that different dimensions of familiarity and emotions evoked during contact with criminal justice involved persons with a mental illness may act as the operating mechanism through which familiarity impacts stigma. Aims: Thus, the present study employs an inductive approach to qualitatively examine the intersectionality of gradients of familiarity (e.g., intimacy and quality of contact), emotions (e.g., fear, disgust, sympathy), desire to social distance, government support, and perceptions regarding risk to reoffend for justice-involved persons with a mental illness. Methods: 47 rural residents participated in a semi-structured qualitative interview. Results: A thematic analysis revealed that negative quality interactions with mental illness and negative emotionality (e.g., fear, anger) were associated with increased stigmatizing beliefs and increased desire for social distance from persons with a mental illness. However, level of intimacy was not consistently associated with stigmatizing attitudes and beliefs. Further, many residents endorsed perceptions supporting re-entry (e.g., willingness to hire, government support). Implications: The findings provide insight into re-framing re-entry in rural communities and capitalizing on existing perceptions that are supportive of re-entry efforts
EVALUATING DROUGHT USING MACHINE LEARNING AND HYDROLOGICAL MODEL BY INCORPORATING SATELLITE-BASED PRECIPITATION DATA IN THE UNGAUGED BASIN
No full textDrought is a complex environmental hazard to ecosystems and society. Decision-making on drought management options requires evaluating and predicting the extremity of future drought events. In this regard, quantifiable indices such as standardized precipitation index (SPI) and standardized precipitation evapotranspiration index (SPEI), and standardized streamflow index (SSI) have been commonly used to characterize meteorological and hydrological drought. In general, the estimation and prediction of the indices require an extensive range of precipitation (SPI and SPEI) and discharge (SSI) datasets in space and time domains. However, there is a challenge for long-term and spatially extensive data availability, leading to the insufficiency of data in estimating drought indices. In this regard, this study uses satellite precipitation data to estimate and predict the drought indices. The precipitation data to calculate the SPI is obtained from the Centre for Hydrometeorology and Remote Sensing (CHRS) data portal for a study water basin. This study employs a Hydrological model for calculating discharge and drought in the overall basin and uses Random Forest (RF) and Support Vector Regression (SVR) as a machine learning model for SSI prediction for a time scale of 1- and 3-month period, which is widely used for establishing interactions between predictors and predictands that are both linear and non-linear. This study aims to evaluate drought severity variation in the overall basin using the hydrological model and compare this result with the result obtained from the Machine Learning Models. The result from the prediction model, hydrological model, and the station data shows a better correlation. Moreover, the result revealed more precise predictions of machine learning models in the longer duration as compared to the shorter one. The results and discussion in this research will aid planners and decision-makers in managing hydrological drought in basins
Republican Klansmen: The Ku Klux Klan and the Grand Old Party in Prohibition Era Indiana
Get PDFSince the proliferation of Trumpism in American politics there has been newfound interest in Ku Klux Klan related studies. Scholars have turned to the rise of the “Invisible Empire” in the 1920s, and the massive political influence it yielded nationwide, to better understand and contextualize the MAGA Movement of Republicans currently dominating their own party, and American politics more broadly. This dissertation focuses on the rise of the Ku Klux Klan as a dominant force in Indiana politics throughout the 1920s. Indiana boasted one of the largest Klan groups at the time, and it was seen in popular culture as a model for what personified “real” America, and who personified “real” Americans. Unlike its southern chapters who dominated their state’s Democratic Party, the Indiana Klan engulfed the G.O.P., and created a massive Republican political machine, fueled in large part by stoking the reactionary sentiments white Protestant Americans felt towards immigrants, Catholics, African Americans, and the various forces of change at the center of the nation’s newfound modernization. This dissertation examines the rise and fall of the Ku Klux Klan as the most dominant, and most corrupt political power in Indiana from 1920-1929. It illustrates the profound grift and graft taking place as Klansmen and their allies gained control of the Indiana Republican Party, then the state government and several municipalities. Notorious Indiana Grand Dragon of the Ku Klux Klan, D. C. Stephenson was a central figure in the Klan’s success and studies have correctly situated him as a king-like figure in the making of the Ku Klux Klan in Indiana and beyond. This has led to a depiction of the Invisible Empire as being intrinsically linked to Stephenson. When the Klan leader was convicted of raping and murdering a young woman at the height of both his, and the organization’s reign, scholars have tended to depict it as the end of the Klan as well. That as went Stephenson, so did the Ku Klux Klan. The reality is that the Klan had already engulfed the state’s bureaucracy, and other opportunistic Klansmen had quickly filled the void created by Stephenson’s demise. As demonstrated in the following chapters, it was principled Indiana Republicans who engaged in the political and legal campaigns that ultimately removed the Klan from Indiana politics and government. Anti-Klan forces largely denounced the hooded order on the grounds that it was a corrupt super-government, and its members were involved in widespread graft at all levels. This dissertation also illustrates the connections between Ku Klux Klan and the state’s powerful radical Prohibition forces in the Indiana Anti-Saloon League. Their membership often overlapped, and many of those who preached Prohibition during the day adorned white hoods throughout the night. Just as much as D. C. Stephenson, the rise and fall of Prohibition was linked to Indiana Ku Klux Klan’s reign in mainstream politics and government
All Is Fair in Law and Warfare in the Ukrainian Crisis: A Look at the Growing Increase of Economic Sanctions as a Weapon of War and the Effects on the International Community
Get PDFThe Russian parliament authorized the use of military force in Ukraine in March, 2014, ostensibly to protect “Russian interests,” and soon thereafter, Russia seized the entire Crimean region from Ukraine and annexed it into the Russian Federation. The United States and Europe have refused military intervention in the region, instead relying on legal measures, or “lawfare,” including economic sanctions against both Russian nationals and Russian businesses, including government entities. Economic sanctions, which have become the United States’ foreign policy tool of choice, often have unintended consequences. This comment acknowledges the advantages of economic sanctions, while explaining the frequent unintended consequences flowing from their use. The comment argues that economic sanctions should not be United States’ first option to implement foreign policy objections
HIGH-PERFORMANCE ALUMINUM COMPOSITES: STRUCTURAL, MECHANICAL, AND DAMPING BEHAVIOR OF ALUMINUM ENHANCED BY CARBON NANOPARTICLES
No full textAluminum matrix composites perform a major role in developing novelty materials with improved mechanical performance for applications in the automotive, electronics, construction, and aerospace industries. However, the most common materials utilized as reinforcement in these composites present difficulties of dispersion at high volume fractions, structural damage, and undesirable reactions with the aluminum matrix. In addition, aluminum composites can also exhibit a reduction in plastic deformation and an increase in density compared to the base matrix, which has limited their massive implementation. This has opened the search for alternative reinforcement materials. Carbon allotropes present a high potential to overcome the limitations of aluminum matrix composites owing to their structural, mechanical, and electrical properties as well as chemical and thermal stability. In this research, we aimed to evaluate the influence of small fractions of carbon allotropes (activated nanocarbon and graphene nanoplatelets) on the structures and properties of three different aluminum matrices (pure aluminum, 6061 alloy, and 2024 alloy). First, the characteristics, manufacturing methods, and state of the art of metal matrix composites and carbon allotropes are reviewed. Then, the experimental investigation for the aluminum composites reinforced with graphene nanoplatelets and activated nanocarbon obtained through powder metallurgy, induction casting, and heat treatment is presented. The microstructural study showed the degree of uniform distribution of the carbon nanoparticles in the metal matrix, which revealed the morphology of the particulate fillers, the changes in the matrices, and the characteristics at the interface of the composites during several stages of the manufacturing processes. The mechanical characterization presented enhancements of yield strength, ultimate strength, and hardness after the introduction of activated nanocarbon and graphene nanoplatelets as a function of the volume fractions. The materials followed different paths of strengthening mechanisms depending on the matrix and manufacturing techniques. Similarly, the materials showed variable plastic deformation before failure and damping behavior, which were highly influenced by the manufacturing method, aluminum matrix, heat treatment, and temperature. Therefore, this work demonstrates the potential of graphene nanoplatelets and activated nanocarbon to be considered ideal reinforcements for aluminum matrix composites compared to common ceramic materials. The carbonaceous materials exhibited excellent distribution and interface, leading to a general improvement of the properties of the composites for both solid and liquid manufacturing methods. It also provides a better understanding of the influence of a small volume fraction of carbon nanoparticle reinforcements, different aluminum matrices, and manufacturing techniques on the performance of aluminum matrix composites. The findings of this study can be tailored to obtain aluminum matrix composites for specific engineering applications that require higher specific strength and improved damping behavior
LIFE, ARISTOTLE, AND THE PURSUIT OF HAPPINESS: REIMAGINING VIRTUOUS ACTION AS A HIGHEST GOOD FOR THE INDIVIDUAL
No full textThe revival of Aristotelian virtue ethics has been fettered over the quibble that Aristotle’s ethics is unacceptably self-centered. Instead of arguing that personal happiness in itself is an appropriate basis for an ethical theory, the Nicomachean Ethics is typically defended against self-centeredness objections by showing how non-instrumental other-concern is included in it and how this kind of concern for others is an essential feature of virtue ethics in general. The virtues and corresponding actions are also commonly treated as means in contemporary virtue ethics. This is the case for some of the more popular works, including Alasdair MacIntyre’s After Virtue, Philippa Foot’s Natural Goodness, and Rosalind Hursthouse’s On Virtue Ethics. The character virtues do in fact serve well as means in their theories, but higher goods, especially those related to the fulfillment of human potential, are left unexamined. Foot and Hursthouse posit basic ends to maintain an egalitarian standard in their theories. MacIntyre includes higher ends through his concept of goods internal to practices, but he does not provide a detailed account of these goods and says nothing about their value. Contemporary virtue ethics is desperately lacking in research and debate that center around virtuous activity as an end. As a jumping-off point for reimagining virtuous action as a highest good in the life of the individual, Aristotle’s conception of eudaimonia or happiness as virtuous activity should be interpreted to allow for subjective differences in potential and interests. The value of virtuous action in itself, apart from external goods and practical benefits that may arise from it, can also be explored. Aristotle argues that virtuous action is the highest good in the life of the individual; as such, it should have tremendous value for those who engage in it
AN EXPLORATION OF FACTORS DRIVING PATTERNS OF HYBRIDIZATION IN TRIODANIS
Get PDFElucidating and preserving biodiversity is an essential component of biological research, yet many natural processes impede our ability to define basic patterns of biodiversity. Hybridization is a common process in flowering plants, with a range of outcomes that influence our understanding of species ecology and evolution. In many systems, factors that facilitate or prevent successful hybridization are poorly understood. In this study, I investigate various potential mechanisms driving patterns of hybridization between Triodanis biflora and T. perfoliata. Previous research in this system has documented extensive hybridization, but some work has also alluded to the potential role of the breeding system in limiting gene flow. These patterns are particularly interesting given conflicting evidence about species delimitation of this group, with some considering T. biflora a subspecies of T. perfoliata. Here I employ a large-scale field study as well as previously collected genetic data and synthesize our overall knowledge of factors influencing patterns of hybridization in this system. Specifically, I demonstrate the first potential genetic signature for hybridization in this system, and confirm morphological differences between T. biflora, T. perfoliata, and putative hybrids across multiple hybrid zones. Across multiple field sites, I found no evidence for microhabitat (i.e., soil texture, light availability) or pollinator visitation rates for consistently limiting gene flow. Congruent with previous work, variation in the breeding system between T. biflora and T. perfoliata appears to play a major role in apparent asymmetrical patterns of hybridization across multiple hybrid zones. These species exhibit dimorphic cleistogamy, with T. biflora producing relatively fewer open flowers, and thus, less potential to contribute to hybrid gene flow. Overall, this research, combined with multiple previous studies, emphasizes the importance of natural history studies for elucidating these patterns. Despite considerable potential for gene flow between T. biflora and T. perfoliata, variation in the breeding system appears to effectively drive the magnitude, as well as overall patterns of hybridization in this study system
Sustainable Manure Management in Intensified Corn Production Systems for Maintaining Crop Quality and Yield, Managing Soil Phosphorus, and Increasing Soil Health
Get PDFDairy farmers often surface apply phosphorus (P)-based liquid manure to corn (Zea mays L.) for silage, supplementing with N fertilizers for optimum corn nitrogen (N), optimizing crop production while decreasing P loss to the environment. However, injecting manure may further conserve losses and reduce synthetic N fertilizer need. An experiment was conducted on a dairy farm in Breese, IL from May 2019 to April 2022 with two main treatments including (i) surface application of manure at P-based rate supplemented with 123 kg ha-1 synthetic N and (ii) manure injection at P-based rate supplemented with 17 kg ha-1 of synthetic N fertilizer. Both treatments delivered 201 kg N ha-1 to meet corn N need. Our results indicated that yield and quality of silage corn and cereal rye were similar in both treatments. This suggests that injection can limit manure ammonium-N fraction losses and decrease the need for supplemental N fertilizer by 106 kg ha-1, which translates into up to $150 ha-1, while not affecting the quality and quantity of yields. Moreover, the effect of manure injection on soil test P (STP) was similar to that of surface application and did not increase STP over a three-year period. Elevated STP in high P-supplying soils can be an environmental concern, but our results show that neither treatment increased STP. Future research should focus on quantifying N loss through denitrification and leaching when manure is injected versus surface applied to provide a more holistic overview of the soil and environmental impact of each system