>Magister Scientiae - MSc / Human-wildlife conflict is an ongoing issue worldwide. Within South Africa, human-carnivore conflict (HCC) as a result of carnivore depredation on small-livestock causes large-scale losses, and promotes the use of predator management tools by farmers. Despite being one of the major offenders involved in HCC, caracals, and their ecology in particular, are understudied. This is mainly due to high levels of persecution, coupled with their elusive nature. Within the Karoo region of South Africa, pastoralists make use of large-scale lethal predator controls in an attempt to remove the offenders, or non-lethal predator controls to protect livestock and deter predators. However, the effects of these various predator control techniques on caracal ecology have not been widely tested. Therefore, the aims of this thesis were, firstly, to assess caracal diet on lethal and non-lethal treatment farms in the Karoo; secondly, to investigate the drivers of caracal habitat selection on a non-lethal farm in the Karoo; and finally, to evaluate caracal activity patterns on the non-lethal farm. To do this, I made use of two non-invasive techniques (i.e. scat analysis and camera trapping). Caracals appeared to favour natural prey across all sites, with livestock remains only found in scats collected at the lethal treatment farm. Caracals had the broadest dietary breadth on the non-lethal treatment site, and the narrowest on the lethal treatment site. The main components of caracal diet included small mammals, lagomorphs, rock hyraxes (Procavia capensis), wild ungulates and arthropods. Caracal habitat selection was explained by resource dispersion and environmental features Interestingly, the presence of livestock was not a major consideration in caracal habitat selection. Caracals were mainly nocturnal but became increasingly crepuscular during winter. In addition, caracal activity overlapped significantly with black-backed jackals (Canis mesomelas), lagomorphs and common duikers (Sylvicapra grimmia). Caracals showed plasticity in their activity patterns based on seasonal temperature fluctuations, and partially human avoidance. This study contributes to understanding how caracal ecology differs on differently managed livestock farms. Throughout this study, the importance of both a natural prey base, as well as natural lands to caracal survival have been revealed. These findings can contribute to caracal conservation in HCC areas.

The four activities exploring, gathering, building and fighting are common across many genres of video games. Although these activities can come in many different forms such as fighting alone or in groups, against humans, beasts or fictional monsters, gathering sticks and stones to build a shelter or gathering cards to build the perfect deck, most games include at least one of these activities and some genres such as survival games include most of them. A test video game is used to allow participants to experience each activity and report discrete emotions felt during the activity. The purpose of this experiment is to find a link between each activity and a particular discrete emotion. Against our prediction, results indicate that rather than each activity being connected to a single discrete emotion, each activity leans towards either multiple positive emotions or multiple negative emotions. / De fyra aktiviteterna utforska, samla, bygga samt strida förekommer i många genrer av spel. Även om dessa aktiviteter kan visa sig i många olika former, såsom t.ex. att strida ensam eller i grupper, emot människor, djur eller fiktionella monster, samla på sig pinnar och sten för att bygga ett skydd, eller samla på sig kort för att bygga den perfekta kortleken, innehåller de flesta spel åtminstone en av dessa aktiviteter. Vissa genrer såsom överlevnadsspel innehåller de flesta av dem. Ett test spel används för att tillåta deltagare att uppleva varje aktivitet och sedan rapportera diskreta känslor de känt under aktiviteten. Syftet med detta experiment är att hitta en länk mellan varje aktivitet och en viss diskret känsla. Förvånansvärt nog indikerar inte resultaten att inte att varje aktivitet är kopplad till en viss diskret känsla, men snarare att varje aktivitet lutar mot antingen ett flertal positiva känslor eller ett flertal negativa känslor.

The coexistence of species is probably one of the most interesting and complex phenomenon in nature. We constructed an agent based model to study the coexistence dynamics of prey - predator populations by varying productivity levels of producers in fragmented and connected habitats along with different levels of quality of predators. Our results indicated that productivity levels of producers in fragmented and connected habitats along with levels of predator quality are significantly responsible for overall predator - prey population size and survivorship. In the absence of predation, competition between identical prey populations is more probable in connected habitats than in unfragmented or fragmented habitats. Implementing low quality predators in the habitats positively influences the overall coexistence dynamics whereas implementing high quality predators tend to decrease the prey populations. Fragmented habitats provide for greater prey population survival time in highly productive environments but low prey population survival time in less productive environments.

Listening has proved to be a difficult skill to teach in the language classroom. Research has shown that pre-listening activities, or those activities done with students prior to listening, can have an effect on listening comprehension outcomes. This research addressed the effectiveness of two types of pre-listening activities: top-down and bottom-up. Volunteers from intermediate level courses taught at Brigham Young University were divided into two treatment groups and a control group. The treatment groups followed a mixed models design by each going through a top-down and bottom-up pre-listening activity, followed by listening to a passage in Mandarin Chinese and taking a multiple-choice test. The bottom-up activity chosen for this research was a vocabulary preview activity, with an advance organizer being chosen for the top-down activity. Results showed both treatment groups significantly outperformed the control group for both the top-down and bottom-up activities (p=0.0123 and p=0.0181 respectively). No significant difference existed in scores between top-down and bottom-up activities (p=0.9456). It was determined that both the vocabulary activity and the advance organizer helped to increase the listening comprehension of intermediate level students of Mandarin Chinese.

Describing a comprehensive neurofunctional model of sentence comprehension has always been a complex challenge. On one hand, disentangling the subprocesses that are necessary for computing the meaning of a sentence and their neural underpinnings is insidious. Each subprocess is closely interconnected with the others, and isolating only one as if it were separable can undermine the investigation of the overall process above. On the other hand, available data on the neural basis of sentence processing are not straightforward. This thesis explores relevant contributions and attempts to highlight open questions regarding the neural basis of two key processes in sentence comprehension, namely morphosyntactic processing and thematic role assignment. It presents and discusses original data resulting from an experiment that, to our knowledge, represents the first investigation of the neural basis of these two processes in the same sentential context. Results demonstrate that morphosyntactic and thematic processing rely on functionally distinct neural correlates in the left hemisphere. Morphosyntactic aspects are mostly processed in a left prefrontal network including the left inferior frontal gyrus (IFG) and the middle frontal gyrus (MFG), whereas thematic role assignment correlates with a left parietal node including the left intraparietal sulcus (IPS). Moreover, it is argued that results support the view that these regions play a language-related rather than domain-general role in human cognition. Finally, two statistical approaches to the analysis of the same TMS language data (ANOVA and Linear Mixed Models – LMMs) are compared. Their outcomes are discussed and an attempt is made at accounting for similarities and differences. Results suggest that the two models should not be considered on a sort of quality hierarchy according to which one has greater or lesser explanatory power than the other. Rather, they both represent legitimate and reliable approaches to account for data variability.

Dentre as possíveis propostas encontradas na literatura estatística para analisar dados oriundos de estudos com observações correlacionadas, estão os modelos condicionais e os modelos marginais. Diversas técnicas têm sido propostas para a análise de diagnóstico nesses modelos. O objetivo deste trabalho é apresentar algumas das técnicas de diagnóstico disponíveis para os dois tipos de modelos e avaliá-las por meio de estudos de simulação. As técnicas apresentadas também foram aplicadas em um conjunto de dados reais. / Conditional and marginal models are among the possibilities in statistical literature to analyze data from studies with correlated observations. Several techniques have been proposed for diagnostic analysis in these models. The objective of this work is to present some of the diagnostic techniques available for both modeling approaches and to evaluate them by simulation studies. The presented techniques were also applied in a real dataset.

Diese Arbeit untersucht vollständige Zeichenkettenfrequenzverteilungen natürlichsprachiger Texte auf ihren linguistischen und anwendungsbezogenen Gehalt. Im ersten Teil wird auf dieser Datengrundlage ein unüberwachtes Lernverfahren entwickelt, das Texte in Morpheme zerlegt. Die Zerlegung geht von der Satzebene aus und verwendet jegliche vorhandene Kontextinformation. Es ergibt sich ein sprachunabhängiger Algorithmus, der die gefundenen Morpheme teilweise zu Baumstrukturen zusammenordnet. Die Evaluation der Ergebnisse mit Hilfe statistischer Modelle ermöglicht die Identifizierung auch kleiner Performanzunterschiede. Diese sind einer linguistischen Interpretation zugänglich. Der zweite Teil der Arbeit besteht aus stilometrischen Untersuchungen anhand eines Textähnlichkeitsmaßes, das ebenfalls auf vollständigen Zeichenkettenfrequenzen beruht. Das Textähnlichkeitsmaß wird in verschiedenen Varianten definiert und anhand vielfältiger stilometrischer Fragestellungen und auf Grundlage unterschiedlicher Korpora ausgewertet. Dabei ist ein wiederholter Vergleich mit der Performanz bisheriger Forschungsansäzte möglich. Die Performanz moderner Maschinenlernverfahren kann mit dem hier vorgestellten konzeptuell einfacheren Verfahren reproduziert werden. Während die Segmentierung in Morpheme ein lokaler Vorgang ist, besteht Stilometrie im globalen Vergleich von Texten. Daher bietet die Untersuchung dieser zwei unverbunden scheinenden Fragestellungen sich gegenseitig ergänzende Perspektiven auf die untersuchten Häufigkeitsdaten. Darüber hinaus zeigt die Diskussion der rezipierten Literatur zu beiden Themen ihre Verbindungen durch verwandte Konzepte und Denkansätze auf. Aus der Gesamtheit der empirischen Untersuchungen zu beiden Fragestellungen kann abgeleitet werden, dass den längeren und damit selteneren Zeichenketten wesentlich mehr Informationsgehalt innewohnt, als in der bisherigen Forschung gemeinhin angenommen wird. / This thesis investigates the linguistic and application specific content of complete character substring frequency distributions of natural language texts. The first part develops on this basis an unsupervised learning algorithm for segmenting text into morphemes. The segmentation starts from the sentence level and uses all available context information. The result is a language independent algorithm which arranges the found morphemes partly into tree like structures. The evaluation of the output using advanced statistical modelling allows for identifying even very small performance differences. These are accessible to linguistic interpretation. The second part of the thesis consists of stylometric investigations by means of a text similarity measure also rooted in complete substring frequency statistics. The similarity measure is defined in different variants and evaluated for various stylometric tasks and on the basis of diverse corpora. In most of the case studies the presented method can be compared with publicly available performance figures of previous research. The high performance of modern machine learning methods is reproduced by the considerably simpler algorithm developed in this thesis. While the segmentation into morphemes is a local process, stylometry consists in the global comparison of texts. For this reason investigating of these two seemingly unconnected problems offers complementary perspectives on the explored frequency data. The discussion of the recieved litarature concerning both subjects additionally shows their connectedness by related concepts and approaches. It can be deduced from the totality of the empirical studies on text segmentation and stylometry conducted in this thesis that the long and rare character sequences contain considerably more information then assumed in previous research.

Causal models have been used in different areas of knowledge in order to comprehend the causal associations between variables. Over the past decades, the amount of studies using these models have been growing a lot, especially those related to biological systems where studying and learning causal relationships among traits are essential for predicting the consequences of interventions in such system. Graph analysis (GA) and structural equation modeling (SEM) are tools used to explore such associations. While GA allows searching causal structures that express qualitatively how variables are causally connected, fitting SEM with a known causal structure allows to infer the magnitude of causal effects. Also SEM can be viewed as multiple regression models in which response variables can be explanatory variables for others. In quantitative genetics studies, SEM aimed to study the direct and indirect genetic effects associated to individuals through information related to them, beyond the observed characteristics, such as the kinship relations. In those studies typically the assumptions of linear relationships among traits are made. However, in some scenarios, nonlinear relationships can be observed, which make unsuitable the mentioned assumptions. To overcome this limitation, this paper proposes to use a mixed effects polynomial structural equation model, second or superior degree, to model those nonlinear relationships. Two studies were developed, a simulation and an application to real data. The first study involved simulation of 50 data sets, with a fully recursive causal structure involving three characteristics in which linear and nonlinear causal relations between them were allowed. The second study involved the analysis of traits related to dairy cows of the Holstein breed. Phenotypic relationships between traits were calving difficulty, gestation length and also the proportion of perionatal death. We compare the model of multiple traits and polynomials structural equations models, under different polynomials degrees in order to assess the benefits of the SEM polynomial of second or higher degree. For some situations the inappropriate assumption of linearity results in poor predictions of the direct, indirect and total of the genetic variances and covariance, either overestimating, underestimating, or even assign opposite signs to covariances. Therefore, we conclude that the inclusion of a polynomial degree increases the SEM expressive power. / Modelos causais têm sido muitos utilizados em estudos em diferentes áreas de conhecimento, a fim de compreender as associações ou relações causais entre variáveis. Durante as últimas décadas, o uso desses modelos têm crescido muito, especialmente estudos relacionados à sistemas biológicos, uma vez que compreender as relações entre características são essenciais para prever quais são as consequências de intervenções em tais sistemas. Análise do grafo (AG) e os modelos de equações estruturais (MEE) são utilizados como ferramentas para explorar essas relações. Enquanto AG nos permite buscar por estruturas causais, que representam qualitativamente como as variáveis são causalmente conectadas, ajustando o MEE com uma estrutura causal conhecida nos permite inferir a magnitude dos efeitos causais. Os MEE também podem ser vistos como modelos de regressão múltipla em que uma variável resposta pode ser vista como explanatória para uma outra característica. Estudos utilizando MEE em genética quantitativa visam estudar os efeitos genéticos diretos e indiretos associados aos indivíduos por meio de informações realcionadas aos indivíduas, além das característcas observadas, como por exemplo o parentesco entre eles. Neste contexto, é tipicamente adotada a suposição que as características observadas são relacionadas linearmente. No entanto, para alguns cenários, relações não lineares são observadas, o que torna as suposições mencionadas inadequadas. Para superar essa limitação, este trabalho propõe o uso de modelos de equações estruturais de efeitos polinomiais mistos, de segundo grau ou seperior, para modelar relações não lineares. Neste trabalho foram desenvolvidos dois estudos, um de simulação e uma aplicação a dados reais. O primeiro estudo envolveu a simulação de 50 conjuntos de dados, com uma estrutura causal completamente recursiva, envolvendo 3 características, em que foram permitidas relações causais lineares e não lineares entre as mesmas. O segundo estudo envolveu a análise de características relacionadas ao gado leiteiro da raça Holandesa, foram utilizadas relações entre os seguintes fenótipos: dificuldade de parto, duração da gestação e a proporção de morte perionatal. Nós comparamos o modelo misto de múltiplas características com os modelos de equações estruturais polinomiais, com diferentes graus polinomiais, a fim de verificar os benefícios do MEE polinomial de segundo grau ou superior. Para algumas situações a suposição inapropriada de linearidade resulta em previsões pobres das variâncias e covariâncias genéticas diretas, indiretas e totais, seja por superestimar, subestimar, ou mesmo atribuir sinais opostos as covariâncias. Portanto, verificamos que a inclusão de um grau de polinômio aumenta o poder de expressão do MEE.

Causal models have been used in different areas of knowledge in order to comprehend the causal associations between variables. Over the past decades, the amount of studies using these models have been growing a lot, especially those related to biological systems where studying and learning causal relationships among traits are essential for predicting the consequences of interventions in such system. Graph analysis (GA) and structural equation modeling (SEM) are tools used to explore such associations. While GA allows searching causal structures that express qualitatively how variables are causally connected, fitting SEM with a known causal structure allows to infer the magnitude of causal effects. Also SEM can be viewed as multiple regression models in which response variables can be explanatory variables for others. In quantitative genetics studies, SEM aimed to study the direct and indirect genetic effects associated to individuals through information related to them, beyond the observed characteristics, such as the kinship relations. In those studies typically the assumptions of linear relationships among traits are made. However, in some scenarios, nonlinear relationships can be observed, which make unsuitable the mentioned assumptions. To overcome this limitation, this paper proposes to use a mixed effects polynomial structural equation model, second or superior degree, to model those nonlinear relationships. Two studies were developed, a simulation and an application to real data. The first study involved simulation of 50 data sets, with a fully recursive causal structure involving three characteristics in which linear and nonlinear causal relations between them were allowed. The second study involved the analysis of traits related to dairy cows of the Holstein breed. Phenotypic relationships between traits were calving difficulty, gestation length and also the proportion of perionatal death. We compare the model of multiple traits and polynomials structural equations models, under different polynomials degrees in order to assess the benefits of the SEM polynomial of second or higher degree. For some situations the inappropriate assumption of linearity results in poor predictions of the direct, indirect and total of the genetic variances and covariance, either overestimating, underestimating, or even assign opposite signs to covariances. Therefore, we conclude that the inclusion of a polynomial degree increases the SEM expressive power. / Modelos causais têm sido muitos utilizados em estudos em diferentes áreas de conhecimento, a fim de compreender as associações ou relações causais entre variáveis. Durante as últimas décadas, o uso desses modelos têm crescido muito, especialmente estudos relacionados à sistemas biológicos, uma vez que compreender as relações entre características são essenciais para prever quais são as consequências de intervenções em tais sistemas. Análise do grafo (AG) e os modelos de equações estruturais (MEE) são utilizados como ferramentas para explorar essas relações. Enquanto AG nos permite buscar por estruturas causais, que representam qualitativamente como as variáveis são causalmente conectadas, ajustando o MEE com uma estrutura causal conhecida nos permite inferir a magnitude dos efeitos causais. Os MEE também podem ser vistos como modelos de regressão múltipla em que uma variável resposta pode ser vista como explanatória para uma outra característica. Estudos utilizando MEE em genética quantitativa visam estudar os efeitos genéticos diretos e indiretos associados aos indivíduos por meio de informações realcionadas aos indivíduas, além das característcas observadas, como por exemplo o parentesco entre eles. Neste contexto, é tipicamente adotada a suposição que as características observadas são relacionadas linearmente. No entanto, para alguns cenários, relações não lineares são observadas, o que torna as suposições mencionadas inadequadas. Para superar essa limitação, este trabalho propõe o uso de modelos de equações estruturais de efeitos polinomiais mistos, de segundo grau ou seperior, para modelar relações não lineares. Neste trabalho foram desenvolvidos dois estudos, um de simulação e uma aplicação a dados reais. O primeiro estudo envolveu a simulação de 50 conjuntos de dados, com uma estrutura causal completamente recursiva, envolvendo 3 características, em que foram permitidas relações causais lineares e não lineares entre as mesmas. O segundo estudo envolveu a análise de características relacionadas ao gado leiteiro da raça Holandesa, foram utilizadas relações entre os seguintes fenótipos: dificuldade de parto, duração da gestação e a proporção de morte perionatal. Nós comparamos o modelo misto de múltiplas características com os modelos de equações estruturais polinomiais, com diferentes graus polinomiais, a fim de verificar os benefícios do MEE polinomial de segundo grau ou superior. Para algumas situações a suposição inapropriada de linearidade resulta em previsões pobres das variâncias e covariâncias genéticas diretas, indiretas e totais, seja por superestimar, subestimar, ou mesmo atribuir sinais opostos as covariâncias. Portanto, verificamos que a inclusão de um grau de polinômio aumenta o poder de expressão do MEE.

Dentre as possíveis propostas encontradas na literatura estatística para analisar dados oriundos de estudos com observações correlacionadas, estão os modelos condicionais e os modelos marginais. Diversas técnicas têm sido propostas para a análise de diagnóstico nesses modelos. O objetivo deste trabalho é apresentar algumas das técnicas de diagnóstico disponíveis para os dois tipos de modelos e avaliá-las por meio de estudos de simulação. As técnicas apresentadas também foram aplicadas em um conjunto de dados reais. / Conditional and marginal models are among the possibilities in statistical literature to analyze data from studies with correlated observations. Several techniques have been proposed for diagnostic analysis in these models. The objective of this work is to present some of the diagnostic techniques available for both modeling approaches and to evaluate them by simulation studies. The presented techniques were also applied in a real dataset.