Uncertainty is pervasive in risk assessment. In ecotoxicological risk assessments, it arises from such sources as a lack of data, the simplification and abstraction of complex situations, and ambiguities in assessment endpoints (Burgman 2005; Suter 1993). When evaluating and managing risks, uncertainty needs to be explicitly considered in order to avoid erroneous decisions and to be able to make statements about the confidence that we can place in risk estimates. Although informative, previous approaches to dealing with uncertainty in ecotoxicological modelling have been found to be limited, inconsistent and often based on assumptions that may be false (Ferson & Ginzburg 1996; Suter 1998; Suter et al. 2002; van der Hoeven 2004; van Straalen 2002a; Verdonck et al. 2003a). In this thesis a Generalised Linear Modelling approach is proposed as an alternative, congruous framework for the analysis and prediction of a wide range of ecotoxicological effects. This approach was used to investigate the results of toxicity experiments on the effect of 2,4-Dichlorophenoxyacetic Acid (2,4-D) formulations and 4-Chlorophenol (4-CP, an associated breakdown product) on Daphnia carinata. Differences between frequentist Maximum Likelihood (ML) and Bayesian Markov-Chain Monte-Carlo (MCMC) approaches to statistical reasoning and model estimation were also investigated. These approaches are inferentially disparate and place different emphasis on aleatory and epistemic uncertainty (O'Hagan 2004). Bayesian MCMC and Probability Bounds Analysis methods for propagating uncertainty in risk models are also compared for the first time. For simple models, Bayesian and frequentist approaches to Generalised Linear Model (GLM) estimation were found to produce very similar results when non-informative prior distributions were used for the Bayesian models. Potency estimates and regression parameters were found to be similar for identical models, signifying that Bayesian MCMC techniques are at least a suitable and objective replacement for frequentist ML for the analysis of exposureresponse data. Applications of these techniques demonstrated that Amicide formulations of 2,4-D are more toxic to Daphnia than their unformulated, Technical Acid parent. Different results were obtained from Bayesian MCMC and ML methods when more complex models and data structures were considered. In the analysis of 4-CP toxicity, the treatment of 2 different factors as fixed or random in standard and Mixed-Effect models was found to affect variance estimates to the degree that different conclusions would be drawn from the same model, fit to the same data. Associated discrepancies in the treatment of overdispersion between ML and Bayesian MCMC analyses were also found to affect results. Bayesian MCMC techniques were found to be superior to the ML ones employed for the analysis of complex models because they enabled the correct formulation of hierarchical (nested) datastructures within a binomial logistic GLM. Application of these techniques to the analysis of results from 4-CP toxicity testing on two strains of Daphnia carinata found that between-experiment variability was greater than that within-experiments or between-strains. Perhaps surprisingly, this indicated that long-term laboratory culture had not significantly affected the sensitivity of one strain when compared to cultures of another strain that had recently been established from field populations. The results from this analysis highlighted the need for repetition of experiments, proper model formulation in complex analyses and careful consideration of the effects of pooling data on characterising variability and uncertainty. The GLM framework was used to develop three dimensional surface models of the effects of different length pulse exposures, and subsequent delayed toxicity, of 4-CP on Daphnia. These models described the relationship between exposure duration and intensity (concentration) on toxicity, and were constructed for both pulse and delayed effects. Statistical analysis of these models found that significant delayed effects occurred following the full range of pulse exposure durations, and that both exposure duration and intensity interacted significantly and concurrently with the delayed effect. These results indicated that failure to consider delayed toxicity could lead to significant underestimation of the effects of pulse exposure, and therefore increase uncertainty in risk assessments. A number of new approaches to modelling ecotoxicological risk and to propagating uncertainty were also developed and applied in this thesis. In the first of these, a method for describing and propagating uncertainty in conventional Species Sensitivity Distribution (SSD) models was described. This utilised Probability Bounds Analysis to construct a nonparametric 'probability box' on an SSD based on EC05 estimates and their confidence intervals. Predictions from this uncertain SSD and the confidence interval extrapolation methods described by Aldenberg and colleagues (2000; 2002a) were compared. It was found that the extrapolation techniques underestimated the width of uncertainty (confidence) intervals by 63% and the upper bound by 65%, when compared to the Probability Bounds (P3 Bounds) approach, which was based on actual confidence estimates derived from the original data. An alternative approach to formulating ecotoxicological risk modelling was also proposed and was based on a Binomial GLM. In this formulation, the model is first fit to the available data in order to derive mean and uncertainty estimates for the parameters. This 'uncertain' GLM model is then used to predict the risk of effect from possible or observed exposure distributions. This risk is described as a whole distribution, with a central tendency and uncertainty bounds derived from the original data and the exposure distribution (if this is also 'uncertain'). Bayesian and P-Bounds approaches to propagating uncertainty in this model were compared using an example of the risk of exposure to a hypothetical (uncertain) distribution of 4-CP for the two Daphnia strains studied. This comparison found that the Bayesian and P-Bounds approaches produced very similar mean and uncertainty estimates, with the P-bounds intervals always being wider than the Bayesian ones. This difference is due to the different methods for dealing with dependencies between model parameters by the two approaches, and is confirmation that the P-bounds approach is better suited to situations where data and knowledge are scarce. The advantages of the Bayesian risk assessment and uncertainty propagation method developed are that it allows calculation of the likelihood of any effect occurring, not just the (probability)bounds, and that the same software (WinBugs) and model construction may be used to fit regression models and predict risks simultaneously. The GLM risk modelling approaches developed here are able to explain a wide range of response shapes (including hormesis) and underlying (non-normal) distributions, and do not involve expression of the exposure-response as a probability distribution, hence solving a number of problems found with previous formulations of ecotoxicological risk. The approaches developed can also be easily extended to describe communities, include modifying factors, mixed-effects, population growth, carrying capacity and a range of other variables of interest in ecotoxicological risk assessments. While the lack of data on the toxicological effects of chemicals is the most significant source of uncertainty in ecotoxicological risk assessments today, methods such as those described here can assist by quantifying that uncertainty so that it can be communicated to stakeholders and decision makers. As new information becomes available, these techniques can be used to develop more complex models that will help to bridge the gap between the bioassay and the ecosystem.

Die vorliegende Studie untersucht die nächtliche Habitatnutzung von Feldhasen in drei unterschiedlichen Habitaten. Das Untersuchungsgebiet Opferbaum ist stark ackerbaulich geprägt und das Untersuchungsgebiet Güntersleben sehr strukturreich durch das Vorkommen von Gehölzen und Waldrändern. Das Untersuchungsgebiet Fritzlar besitzt einen waldrandgeprägten sowie einen ackerbaulich intensiv genutzten Landschaftsteil. Die nächtlichen Aufenthaltsorte von Feldhasen wurden mittels Wärmebildkamera zwischen September 2004 bis April 2005 und September 2005 bis April 2006 kartiert. In jedem der Untersuchungsgebiete wurden einmal monatlich sogenannte Festpunkte angefahren, die umliegenden Landschaftsbereiche abgesucht und beobachtete Feldhasen in Arbeitskarten eingezeichnet. Eine Kartierung der von den Festpunkten einsehbaren Landschaftsteile geschah vor jedem Erfassungstermin bei Tageslicht. Den kartierten Feldhasen (Präsenz-Punkte) wurde im GIS eine zufällige Punktverteilung im beobachteten Landschaftsraum gegenüber gestellt (Pseudo-Absenz-Punkte). Für jeden dieser Punkte wurden bis zu 20 Minimaldistanzen zu verschiedenen Strukturelementen der Landschaft berechnet. In Generalisierten Linearen Modellen (GLM) wurden die univariaten und multivariaten Zusammenhänge der erklärenden Variablen mit der binomialen Zielvariablen modelliert. Zeitliche Aspekte der Habitatnutzung im Verlauf des Winterhalbjahres wurden mit einer multitemporalen Modellierung für zusammengefasste Zwei-Monats-Zeiträume untersucht. Die Modellselektion geschah mit Hilfe des Akaike Information Criterion (AIC). Insgesamt wurden 4.494 Standorte von Feldhasen in Opferbaum, 2.418 in Güntersleben und 1.391 in Fritzlar kartiert. Die univariate Analyse zeigt eine Meidung von Verkehrs- und Siedlungsstrukturen. Waldränder, Gehölze, Buntbrachen und Grünland werden in den Untersuchungsgebieten Fritzlar und Opferbaum bevorzugt, in Güntersleben werden die zwei letzteren gemieden. Die multivariaten Modelle zeigen eine Präferenz der Nahrungshabitate Wintergetreide und Raps, in Fritzlar und Opferbaum wird auch Grünland bevorzugt. Nach dem Nahrungshabitat wird von Feldhasen die Nähe zu potentiellen Deckungshabitaten präferiert, dabei werden nur Buntbrachen in allen Untersuchungsgebieten bevorzugt. Besonders Verkehrswege und Siedlungen werden gemiedenen, Ausnahme ist die Bevorzugung von Siedlungsbereichen in Güntersleben. Teilweise gegensätzliche Ergebnisse zeigt die Modellierung der Zwei-Monats-Zeiträume zwischen den Untersuchungsgebieten. Sie zeigen aber nur geringe Veränderungen der Habitatnutzung von Feldhasen im Verlauf des Winterhalbjahres. Allen selektierten Modellen gemein ist die geringe Erklärungsgüte von weniger als 5 % der Datenvarianz. Die Eignung der entwickelten Aufnahmemethodik und die Ergebnisse werden anhand der umfangreichen Literatur diskutiert. Die Art des Habitats ist von großer Bedeutung für die Habitatnutzung der Feldhasen. Durch die landwirtschaftliche Fruchtfolge bedingte strukturelle Veränderungen verändern ebenso die kleinräumige Habitatnutzung wie die Veränderungen der landwirtschaftlichen Schläge im Verlauf des Herbstes und Winters. Das opportunistische Habitatverhalten von Feldhasen erschwert dabei die Beobachtung von speziellem Habitatverhalten. Die zum Teil gegensätzlichen Ergebnisse werden auch vor dem Hintergrund potentieller Fehlerquellen der Methodik und einem möglichen Einfluss vernachlässigter Variablen diskutiert. Dabei stellt sich die Frage nach grundsätzlichen Konsequenzen für zukünftige Untersuchungen. Die unterschiedliche Habitatnutzung des Feldhasen in unterschiedlichen Habitaten muss sowohl bei der Wahl der Methodik als auch bei der Wahl der Gebietskulisse berücksichtigt werden. / The study presented in this thesis examined the nocturnal habitat use by hares in three different habitats. The study area Opferbaum is strongly influenced by agriculture whereas the landscape of the study area Güntersleben has very diverse structures such as groves and forest edges. The study area Fritzlar has a forest dominated landscape on the one hand and a landscape of intensive agricultural activities on the other hand. Hare locations were mapped using thermography between September 2004 to April 2005 and September 2005 to April 2006. In each of the study sites the surrounding landscape of selected viewpoints was observed once a month and hare distribution was plotted in topographical maps. Mapping of the visible landscape of the viewpoints took place during daytime. Up to 20 minimum distances to different structural elements of the landscape were calculated for each hare location (presence-points) and randomly distributed points (pseudo-absence points) in the observed landscape. Generalized linear models (GLM) were applied to model the univariate and multivariate relationships of explanatory variables with the binomial response variables (hare 1; pseudo-absence 0). Temporal aspects of habitat use during the winter were analyzed by multi-temporal modeling for combined two-month periods. The model selection was done using the Akaike Information Criterion (AIC). A total of 4,494 locations by hares were mapped in Opferbaum, 2,418 in Güntersleben and 1,391 in Fritzlar. The univariate analysis shows an avoidance of traffic and urban areas. Forest edges and groves are preferred in all study areas. Pasture and wildlife-friendly set-asides are preferred in Fritzlar and Opferbaum, but avoided in Güntersleben. The multivariate models show a preference of feeding habitats such as winter cereals and oilseed rape, hares also prefer pasture in Fritzlar and Opferbaum. After the feeding habitat, hares show a preference to be in proximity to shelter providing habitats. Wildlife-friendly set-asides were preferred in all study sites. Traffic and urban areas are avoided in Opferbaum and Fritzlar but urban areas preferred in Güntersleben. Modeling the two-month periods shows different results between the study areas but only small changes in habitat use by brown hares during the winter months. All selected models explain less than 5 % of the variance of data. The consideration of comparable studies shows that besides methodology and surveying time, the results of habitat use of brown hares are primarily influenced by the kind of the examined landscapes. The small-scale habitat use of brown hare is also influenced by structural changes in the agricultural crop rotation as well as a changing vegetation in autumn and winter. The opportunistic behaviour of brown hares make the observation of special habitat use difficult. The results are discussed in connection with error in methodology and unconsidered variables but also to fundamental consequences for future investigations. The differences in habitat use of brown hares in different habitats have to be considered in both, the choice of methodology and when choosing the study sites.

The Connecticut River watershed is experiencing a rapid invasion of aggressive non-native plant species, which threaten watershed function and structure. Volunteer-based monitoring programs such as the University of Massachusetts’ OutSmart Invasives Species Project, Early Detection Distribution Mapping System (EDDMapS) and the Invasive Plant Atlas of New England (IPANE) have gathered valuable invasive plant data. These programs provide a unique opportunity for researchers to model invasive plant species utilizing citizen-sourced data. This study took advantage of these large data sources to model invasive plant distribution and to determine environmental and biophysical predictors that are most influential in dispersion, and to identify a suitable presence-only model for use by conservation biologists and land managers at varying spatial scales. This research focused on the invasive plant species of high interest - Japanese stiltgrass (Mircostegium vimineum). This was identified as a threat by U.S. Fish and Wildlife Service refuge biologists and refuge managers, but for which no mutli-scale practical and systematic approach for detection, has yet been developed. Environmental and biophysical variables include factors directly affecting species physiology and locality such as annual temperatures, growing degree days, soil pH, available water supply, elevation, closeness to hydrology and roads, and NDVI. Spatial scales selected for this study include New England (regional), the Connecticut River watershed (watershed), and the U.S. Fish and Wildlife, Silvio O. Conte National Fish and Wildlife Refuge, Salmon River Division (local). At each spatial scale, three software programs were implemented: maximum entropy habitat model by means of the MaxEnt software, ecological niche factor analysis (ENFA) using Openmodeller software, and a generalized linear model (GLM) employed in the statistical software R. Results suggest that each modeling algorithm performance varies among spatial scales. The best fit modeling software designated for each scale will be useful for refuge biologists and managers in determining where to allocate resources and what areas are prone to invasion. Utilizing the regional scale results, managers will understand what areas on a broad-scale are at risk of M. vimineum invasion under current climatic variables. The watershed-scale results will be practical for protecting areas designated as most critical for ensuring the persistence of rare and endangered species and their habitats. Furthermore, the local-scale, or fine-scale, analysis will be directly useful for on-the-ground conservation efforts. Managers and biologists can use results to direct resources to areas where M. vimineum is most likely to occur to effectively improve early detection rapid response (EDRR).

The aim of this thesis is to create an exploratory tool for functional magnetic resonance imaging data, which allows quickly and easily making a selection of persons and areas suitable for group analysis of connectivity. In the first chapters of this work is mentioned history of brain research and comparison of methods used in functional imaging. Next they are discussed the theoretical basis of fMRI methods, such as the formation of BOLD signal, acquisition parameters of MRI images and methods for designing experiments. The following chapter describes in detail the analysis of recorded data from the pre-processing to the interpretation of results. The last chapter of the first part describes problems of group analysis in SPM8 software. The second half of this work is dedicated to the description of developed program from data input to saving the results, including detailed descriptions of key features. In conclusion, there is a chapter characterizing the application of developed program on real data from clinical studies, including the results and evaluation of the usability of program. The program will mainly be used in neuroscience research.

[pt] Esta dissertação descreve a aplicação de Modelos Lineares Generalizados (MLGs) à análise de um experimento visando identificar a combinação dos níveis das variáveis independentes: concentração de hidróxido de sódio (A), volume de hipoclorito de sódio (B) e sua interação (AB), que minimiza a variável resposta: proporção de itens com defeitos, em um processo de beneficiamento numa indústria têxtil de pequeno porte. A variável resposta encontra-se na forma de proporção, violando os pressupostos básicos do Modelo Linear Clássico e com isso as estimativas dos coeficientes pelo método de Mínimos Quadrados Ordinários (MQO) é menos confiável. O planejamento utilizado foi o fatorial completo 22 com ponto central e replicado. Após o planejamento, a modelagem pelo MLG é aplicada, só então é possível identificar uma subdispersão dos dados, verificar que o modelo empregado está correto e que o volume de hipoclorito de sódio (B) é o único fator significativo, no processo de alvejamento industrial da empresa. Portanto, como a finalidade é minimizar a resposta, utiliza-se o nível inferior (-1) desta variável. Consequentemente, como o intuito é reduzir os custos com insumos químicos pode-se utilizar o nível mínimo da concentração de hidróxido de sódio (A) e o nível máximo da interação entre os fatores (AB), já que eles não são significativos ao modelo. / [en] This dissertation describes the application of Generalized Linear Models (GLMs) to the analysis of an experiment with the purpose identify the levels combination of independent variables: concentration of sodium hydroxide (A) volume of sodium hypochlorite (B) and their interaction (AB), that minimizes the response variable: proportion of defective items, in a process in a small plant of the textile industry. The response variable takes the form of a proportion, that violates the basic assumptions of the Classic Linear Model and, as a result, the estimates of the coefficients by Ordinary Least Squares method is less reliable. The design employed was a replicated complete 22 factorial design with central point. After doing the planning, the modeling by MLG is applied, and then it is possible to identify a underdispersion data; to verify that the model used is correct and that the volume of sodium hypochlorite (B) is the only significant factor in the industrial process of bleaching the company. Therefore, as the purpose is to minimize the response, it is used the lower level (-1) of this variable. Consequently, as the aim is to reduce costs of chemical inputs can use the minimum level of concentration of hydroxide sodium (A) and the maximum level of interaction between factors (AB), since they are not significant to the model.

Die vorliegende Studie untersucht die nächtliche Habitatnutzung von Feldhasen in drei unterschiedlichen Habitaten. Das Untersuchungsgebiet Opferbaum ist stark ackerbaulich geprägt und das Untersuchungsgebiet Güntersleben sehr strukturreich durch das Vorkommen von Gehölzen und Waldrändern. Das Untersuchungsgebiet Fritzlar besitzt einen waldrandgeprägten sowie einen ackerbaulich intensiv genutzten Landschaftsteil. Die nächtlichen Aufenthaltsorte von Feldhasen wurden mittels Wärmebildkamera zwischen September 2004 bis April 2005 und September 2005 bis April 2006 kartiert. In jedem der Untersuchungsgebiete wurden einmal monatlich sogenannte Festpunkte angefahren, die umliegenden Landschaftsbereiche abgesucht und beobachtete Feldhasen in Arbeitskarten eingezeichnet. Eine Kartierung der von den Festpunkten einsehbaren Landschaftsteile geschah vor jedem Erfassungstermin bei Tageslicht. Den kartierten Feldhasen (Präsenz-Punkte) wurde im GIS eine zufällige Punktverteilung im beobachteten Landschaftsraum gegenüber gestellt (Pseudo-Absenz-Punkte). Für jeden dieser Punkte wurden bis zu 20 Minimaldistanzen zu verschiedenen Strukturelementen der Landschaft berechnet. In Generalisierten Linearen Modellen (GLM) wurden die univariaten und multivariaten Zusammenhänge der erklärenden Variablen mit der binomialen Zielvariablen modelliert. Zeitliche Aspekte der Habitatnutzung im Verlauf des Winterhalbjahres wurden mit einer multitemporalen Modellierung für zusammengefasste Zwei-Monats-Zeiträume untersucht. Die Modellselektion geschah mit Hilfe des Akaike Information Criterion (AIC). Insgesamt wurden 4.494 Standorte von Feldhasen in Opferbaum, 2.418 in Güntersleben und 1.391 in Fritzlar kartiert. Die univariate Analyse zeigt eine Meidung von Verkehrs- und Siedlungsstrukturen. Waldränder, Gehölze, Buntbrachen und Grünland werden in den Untersuchungsgebieten Fritzlar und Opferbaum bevorzugt, in Güntersleben werden die zwei letzteren gemieden. Die multivariaten Modelle zeigen eine Präferenz der Nahrungshabitate Wintergetreide und Raps, in Fritzlar und Opferbaum wird auch Grünland bevorzugt. Nach dem Nahrungshabitat wird von Feldhasen die Nähe zu potentiellen Deckungshabitaten präferiert, dabei werden nur Buntbrachen in allen Untersuchungsgebieten bevorzugt. Besonders Verkehrswege und Siedlungen werden gemiedenen, Ausnahme ist die Bevorzugung von Siedlungsbereichen in Güntersleben. Teilweise gegensätzliche Ergebnisse zeigt die Modellierung der Zwei-Monats-Zeiträume zwischen den Untersuchungsgebieten. Sie zeigen aber nur geringe Veränderungen der Habitatnutzung von Feldhasen im Verlauf des Winterhalbjahres. Allen selektierten Modellen gemein ist die geringe Erklärungsgüte von weniger als 5 % der Datenvarianz. Die Eignung der entwickelten Aufnahmemethodik und die Ergebnisse werden anhand der umfangreichen Literatur diskutiert. Die Art des Habitats ist von großer Bedeutung für die Habitatnutzung der Feldhasen. Durch die landwirtschaftliche Fruchtfolge bedingte strukturelle Veränderungen verändern ebenso die kleinräumige Habitatnutzung wie die Veränderungen der landwirtschaftlichen Schläge im Verlauf des Herbstes und Winters. Das opportunistische Habitatverhalten von Feldhasen erschwert dabei die Beobachtung von speziellem Habitatverhalten. Die zum Teil gegensätzlichen Ergebnisse werden auch vor dem Hintergrund potentieller Fehlerquellen der Methodik und einem möglichen Einfluss vernachlässigter Variablen diskutiert. Dabei stellt sich die Frage nach grundsätzlichen Konsequenzen für zukünftige Untersuchungen. Die unterschiedliche Habitatnutzung des Feldhasen in unterschiedlichen Habitaten muss sowohl bei der Wahl der Methodik als auch bei der Wahl der Gebietskulisse berücksichtigt werden.:Inhalt 1 Einleitung 1 1.1 Motivation 1 1.2 Methodenüberblick 2 1.3 Stand des Wissens 4 1.4 Ziele 13 2 Material und Methoden 14 2.1 Untersuchungsgebiete 14 2.1.1 Fritzlar 14 2.1.2 Güntersleben 18 2.1.3 Opferbaum 21 2.2 Feldökologische Methoden 24 2.2.1 Methodenentwicklung 24 2.2.2 Feldhasenerfassung 27 2.2.3 GIS-Anwendung 31 2.2.4 Flächennutzungs- & Habitatkartierung 32 2.3 Statistik 34 2.3.1 Bestimmung der Variablen 34 2.3.2 Modellbildung 39 2.3.2.1 Präsenz- und Pseudo-Absenz-Verteilung 39 2.3.2.2 Logistische Regression 40 2.3.2.3 Modellselektion 42 3 Ergebnisse 48 3.1 Anzahl und Dichte beobachteter Feldhasen 48 3.2 Struktur der untersuchten Landschaften 49 3.3 Generalisierte Lineare Modelle zur nächtlichen Habitatnutzung von Feldhasen 53 3.3.1 Univariate Analyse der potentiellen erklärenden Variablen 53 3.3.2 Multivariate Analyse der potentiellen erklärenden Variablen 55 3.3.2.1 Multivariate Modelle für das Untersuchungsgebiet Fritzlar 55 3.3.2.2 Multivariate Modelle für das Untersuchungsgebiet Güntersleben 57 3.3.2.3 Multivariate Modelle für das Untersuchungsgebiet Opferbaum 59 3.3.2.4 Multitemporale Modelle der Zwei-Monats-Zeiträume 62 3.3.2.5 Multivariate Modelle für alle Untersuchungsgebiete 68 4 Diskussion 72 4.1 Methodenkritik 72 4.1.1 Einfluss der maximalen Erfassungsdistanz 72 4.1.2 Eignung der entwickelten Methodik 73 4.2 Habitatnutzung von Feldhasen 75 4.2.1 Nutzung einzelner Strukturelemente 75 4.2.2 Habitatnutzung im Untersuchungsgebiet Fritzlar 85 4.2.3 Habitatnutzung im Untersuchungsgebiet Güntersleben 87 4.2.4 Habitatnutzung im Untersuchungsgebiet Opferbaum 93 4.2.5 Habitatnutzung im zeitlichen Verlauf 96 4.2.6 Multivariates Gesamtmodell 98 4.3 Betrachtung unberücksichtigter Variablen 99 4.4 Schlussbetrachtung und Ausblick 102 5 Zusammenfassung 105 6 Literatur 109 7 Anhang 121 / The study presented in this thesis examined the nocturnal habitat use by hares in three different habitats. The study area Opferbaum is strongly influenced by agriculture whereas the landscape of the study area Güntersleben has very diverse structures such as groves and forest edges. The study area Fritzlar has a forest dominated landscape on the one hand and a landscape of intensive agricultural activities on the other hand. Hare locations were mapped using thermography between September 2004 to April 2005 and September 2005 to April 2006. In each of the study sites the surrounding landscape of selected viewpoints was observed once a month and hare distribution was plotted in topographical maps. Mapping of the visible landscape of the viewpoints took place during daytime. Up to 20 minimum distances to different structural elements of the landscape were calculated for each hare location (presence-points) and randomly distributed points (pseudo-absence points) in the observed landscape. Generalized linear models (GLM) were applied to model the univariate and multivariate relationships of explanatory variables with the binomial response variables (hare 1; pseudo-absence 0). Temporal aspects of habitat use during the winter were analyzed by multi-temporal modeling for combined two-month periods. The model selection was done using the Akaike Information Criterion (AIC). A total of 4,494 locations by hares were mapped in Opferbaum, 2,418 in Güntersleben and 1,391 in Fritzlar. The univariate analysis shows an avoidance of traffic and urban areas. Forest edges and groves are preferred in all study areas. Pasture and wildlife-friendly set-asides are preferred in Fritzlar and Opferbaum, but avoided in Güntersleben. The multivariate models show a preference of feeding habitats such as winter cereals and oilseed rape, hares also prefer pasture in Fritzlar and Opferbaum. After the feeding habitat, hares show a preference to be in proximity to shelter providing habitats. Wildlife-friendly set-asides were preferred in all study sites. Traffic and urban areas are avoided in Opferbaum and Fritzlar but urban areas preferred in Güntersleben. Modeling the two-month periods shows different results between the study areas but only small changes in habitat use by brown hares during the winter months. All selected models explain less than 5 % of the variance of data. The consideration of comparable studies shows that besides methodology and surveying time, the results of habitat use of brown hares are primarily influenced by the kind of the examined landscapes. The small-scale habitat use of brown hare is also influenced by structural changes in the agricultural crop rotation as well as a changing vegetation in autumn and winter. The opportunistic behaviour of brown hares make the observation of special habitat use difficult. The results are discussed in connection with error in methodology and unconsidered variables but also to fundamental consequences for future investigations. The differences in habitat use of brown hares in different habitats have to be considered in both, the choice of methodology and when choosing the study sites.:Inhalt 1 Einleitung 1 1.1 Motivation 1 1.2 Methodenüberblick 2 1.3 Stand des Wissens 4 1.4 Ziele 13 2 Material und Methoden 14 2.1 Untersuchungsgebiete 14 2.1.1 Fritzlar 14 2.1.2 Güntersleben 18 2.1.3 Opferbaum 21 2.2 Feldökologische Methoden 24 2.2.1 Methodenentwicklung 24 2.2.2 Feldhasenerfassung 27 2.2.3 GIS-Anwendung 31 2.2.4 Flächennutzungs- & Habitatkartierung 32 2.3 Statistik 34 2.3.1 Bestimmung der Variablen 34 2.3.2 Modellbildung 39 2.3.2.1 Präsenz- und Pseudo-Absenz-Verteilung 39 2.3.2.2 Logistische Regression 40 2.3.2.3 Modellselektion 42 3 Ergebnisse 48 3.1 Anzahl und Dichte beobachteter Feldhasen 48 3.2 Struktur der untersuchten Landschaften 49 3.3 Generalisierte Lineare Modelle zur nächtlichen Habitatnutzung von Feldhasen 53 3.3.1 Univariate Analyse der potentiellen erklärenden Variablen 53 3.3.2 Multivariate Analyse der potentiellen erklärenden Variablen 55 3.3.2.1 Multivariate Modelle für das Untersuchungsgebiet Fritzlar 55 3.3.2.2 Multivariate Modelle für das Untersuchungsgebiet Güntersleben 57 3.3.2.3 Multivariate Modelle für das Untersuchungsgebiet Opferbaum 59 3.3.2.4 Multitemporale Modelle der Zwei-Monats-Zeiträume 62 3.3.2.5 Multivariate Modelle für alle Untersuchungsgebiete 68 4 Diskussion 72 4.1 Methodenkritik 72 4.1.1 Einfluss der maximalen Erfassungsdistanz 72 4.1.2 Eignung der entwickelten Methodik 73 4.2 Habitatnutzung von Feldhasen 75 4.2.1 Nutzung einzelner Strukturelemente 75 4.2.2 Habitatnutzung im Untersuchungsgebiet Fritzlar 85 4.2.3 Habitatnutzung im Untersuchungsgebiet Güntersleben 87 4.2.4 Habitatnutzung im Untersuchungsgebiet Opferbaum 93 4.2.5 Habitatnutzung im zeitlichen Verlauf 96 4.2.6 Multivariates Gesamtmodell 98 4.3 Betrachtung unberücksichtigter Variablen 99 4.4 Schlussbetrachtung und Ausblick 102 5 Zusammenfassung 105 6 Literatur 109 7 Anhang 121

Site selection decisions remains a complex yet crucial process for strong business performance. Despite the extensive number of publications in this field, the emergence of new data collection technique, improved location analytics, and changes in consumers’ preferences call for testing of new models and hypothesis. This study compares traditional site selection indicators (e.g. property size, proximities, competition, and demographic profiles) with novel site-selection indicators (e.g. environmental sustainability performance and socio-demographic characteristics from Tapestry data). By investigating a case study of Starbucks coffee stores in Los Angeles, we argue that environmental sustainability performance and socio-demographic Tapestry segments correlate with business performance indicators of small retail shops in two ways. First, higher sustainability scores result in increased foot traffic, and by extension increased business performance. Second, Tapestry segmentation stands as significant indicator of business performance in site selection modeling – specifically, by demonstrating the significant correlation between socio-demographic consumers’ segments and the number of visitors per location. The output of this study offers an alternative location-driven site selection method, important for businesses and key industry-players in sharpening location-allocation decision-making processes.

Pricing of repair and maintenance (R&M) contracts is one among the most important processes carried out at Scania. Predictions of repair costs at Scania are carried out using experience-based prediction methods which do not involve statistical methods for the computation of average repair costs for contracts terminated in the recent past. This method is difficult to apply for a reference population of rigid Scania trucks. Hence, the purpose of this study is to perform suitable statistical modelling to predict repair costs of four variants of rigid Scania trucks. The study gathers repair data from multiple sources and performs feature selection using the Akaike Information Criterion (AIC) to extract the most significant features that influence repair costs corresponding to each truck variant. The study proved to show that the inclusion of operational features as a factor could further influence the pricing of contracts. The hurdle Gamma model, which is widely used to handle zero inflations in Generalized Linear Models (GLMs), is used to train the data which consists of numerous zero and non-zero values. Due to the inherent hierarchical structure within the data expressed by individual chassis, a hierarchical hurdle Gamma model is also implemented. These two statistical models are found to perform much better than the experience-based prediction method. This evaluation is done using the mean absolute error (MAE) and root mean square error (RMSE) statistics. A final model comparison is conducted using the AIC to draw conclusions based on the goodness of fit and predictive performance of the two statistical models. On assessing the models using these statistics, the hierarchical hurdle Gamma model was found to perform predictions the best

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This study investigated the relationship between capital structure, performance and replacement of chief executive officer in firms listed on the Nairobi Securities Exchange (NSE). Data was collected from a sample of 37 firms listed on the NSE over a period of 23 years, from 1990 to 2012. The analysis was conducted at three stages. The canonical correlation technique was employed to investigate the bi-directional relationship between capital structure and performance and to select competing indicators of performance and capital structure. Second, the general linear model (GLM) procedure was used to test the effect of performance and ownership structure and to test the effect of capital structure and ownership structure. Lastly, the generalised estimating equation (GEE) was used to assess effects of performance, capital structure and ownership structure on change in CEO. The results revealed that a bidirectional relationship exists between capital structure and debt capital. The indicators found to be useful in examining the relationship between performance and capital structure are asset turnover ratio and total debt to the total asset ratio. The findings support the efficiency hypothesis but not the franchise hypothesis. The results also indicated that firms with a low asset turnover are with a low asset turnover are 3.045 times likely to change CEO compared to firms with a high asset turnover. The results also indicated that firms with high leverage (debt) are he results also indicated that firms with high leverage (debt) are 3.430 times likely to change CEO compared to firms in low leverage, while the firms with medium leverage are are are are 6.491 times likely to change CEO. Therefore managers should not be passive when it comes to choosing between equity and debt capital played a disciplinary role on firms listed on the NSE. / Business Management / DCom (Business Management)