It appears to be unanimous that alien species in island environments tend to cause considerably more negative than positive impacts. To assess the potential level of threat aliens may pose to the native environment, understanding a species’ population structure and dynamics is of ultimate importance. Assessing both impacts and consequences of management interventions to alien species is likewise only possible through the comprehension of its population structure and dynamics. This can be achieved by estimating the number of individuals in the study site, as well as other population parameters through time, applying population models such as capture-recapture to the collected datasets. Nonetheless, alien species that have low capture rates, such as small mammals, might present a considerable obstacle for conservation, as available capturerecapture models need a relatively large dataset to precisely and accurately estimate population parameters. To improve accuracy and precision of estimates that use sparse datasets, the present study developed an integrated concurrent marking-observation capture-recapture model (C-MOM). The model proposed here, contrary to the commonly available mark-recapture and mark-resight models, allows for two different datasets (i.e. a capture-recapture and a population count) to be integrated, as well as for marking and observation (recapture) data to be collected simultaneously. While few models can integrate different datasets, no model is known to allow for concomitant capture-markobservation activities. To assess the performance of the C-MOM when estimating population parameters for sparse datasets, a virtual ecology study was carried out. The population dynamics of a small rodent, the rock cavy (Kerodon rupestris), as well as capture-recapture and population count datasets, were simulated under different scenarios. The sampled datasets were then analyzed by the C-MOM, and by two other established statistical models: a classical mark recapture (CMR) (based on the Jolly-Seber model), and a zero-truncated Poisson log-normal mixed effects (ZPNE), the only integrated mark-resight model that allows for recapture sampling with replacement. Estimates of population parameters provided by the three models were then compared in terms of bias, precision and accuracy. C-MOM and ZPNE models were afterwards applied to real data collected on a rock cavy colony in the island of Fernando de Noronha. The estimated parameters were used to extrapolate the number of individuals in the rock cavy colony to the whole population in the island. Subsequently, these results were used to develop a risk assessment for the species by modelling historical and management scenarios, simulating both the establishment of the species in the island, and the consequences of different management interventions applied to it. The virtual ecology study showed that, in comparison to the CMR and the ZPNE, the C-MOM presented improved accuracy without overestimating the precision of population parameter’s estimates. The last also presented reduced amplitude of the calculated credible interval at 95% when applied to real data in comparison to the ZPNE. While the extrapolation of C-MOM estimates suggests that the rock cavy population in Fernando de Noronha is 6,652 ± 1,587, ZPNE estimates are of 5,854 ± 3,269 individuals. In the risk assessment, historical simulation models demonstrated that even though different combinations of uncertainty in reproductive parameters of the rock cavy might be possible for the species, these did not interfere significantly in either establishment or spread of the rock cavy population in the island. Moreover, historical yearly mortality has most likely been under 30%. Regarding the species’ management simulations, the most effective management interventions to achieve population extinction were spaying and neutering of both sexes, although harvest effort presented the highest influence on this populations’ extirpation. Nonetheless, the relative influence of female and both sexes’ based interventions did not differ significantly regarding the frequency of extinction of stochastic replicates’. Moreover, none of the management interventions guaranteed the population extinction within the time span and harvest effort proposed for the management program. Neutering of both sexes was most inversely influential on time to extinction of this population, followed by removal of both sexes. Briefly, the C-MOM has proven to be a resourceful and precise model to estimate population parameters when low capture rates result in sparse datasets. Moreover, the rock cavy is well established in the island and likely at carrying capacity. In general, the risk assessment showed that the management interventions in the time span and harvest effort simulated in the present study were ineffective to extinguish the rock cavy population in Fernando de Noronha. Considering this, as well as the importance of investigating other vital factors to decide in favour of or contrary to the management of this species, it is recommended that both an impact assessment of the rock cavy and a cost-effectiveness analysis of the management interventions should be performed to complement the current study.:Acknowledgement III Abstract IV Zusammenfassung VI Resumen IX Table of Contents XII List of Tables and Figures XIV List of Abbreviations XIX 1. Introduction 1 1.1. Invasive alien species and their consequences 1 1.2. Population dynamics analysis 2 Capture-recapture models 3 Observation models 4 Integrated population models 5 Software 7 Model analysis 8 1.3. Fernando de Noronha and the rock cavy 10 1.4. Objectives 12 Overall Objectives 12 Specific Objectives 13 2. Study Framework 15 3. Methods 19 3.1. Study area 19 3.2. Study case species 21 3.3. Research Steps 24 RESEARCH STEP I: Comparing the C-MOM to established models – does this concurrent marking-observation model produces accurate estimates of population parameters for sparse datasets? 24 RESEARCH STEP II: C-MOM application to a real case study 40 RESEARCH STEP III: The rock cavy population in Fernando de Noronha 45 RESEARCH STEP IV: The colonization and eradication of the rock cavy in Fernando de Noronha 47 4. Results 63 4.1. RESEARCH STEP I: Comparing the C-MOM to established models – does this concurrent marking-observation model produces accurate estimates of population parameters for sparse datasets? 63 4.2. RESEARCH STEP II: C-MOM application to a real case study 72 4.3. RESEARCH STEP III: The rock cavy population in Fernando de Noronha 73 4.4. RESEARCH STEP IV: The colonization and eradication of the rock cavy in Fernando de Noronha 74 Sensitivity analysis 74 Simulation experiments 80 5. Discussion 83 5.1. Bias, precision and accuracy of population dynamic models for sparse datasets 85 Simulated data 85 Study case 90 5.2. Advantages and disadvantages of the C-MOM approach 93 5.3. Development and applications of the integrated models and the C-MOM 96 5.4. The reversed use of the PVA software Vortex to simulate AS and IAS populations’ extinction 97 5.5. Status of the rock cavy population in the island of Fernando de Noronha 100 The colonization of the rock cavy in Fernando de Noronha 101 Management of the rock cavy in Fernando de Noronha 104 Study case limitations and future researches 112 6. Conclusion 116 References 118 Appendices 124 APPENDIX I – Assessment of biological invasions 124 APPENDIX II – Population dynamics simulation and dataset sampling 125 APPENDIX III – CMR and C-MOM model codes in R 134 APPENDIX IV – ZPNE model code in R 138 APPENDIX V – C-MOM model used for real datasets 143 APPENDIX VI – Rock cavy colony sizes and number of individuals in Fernando de Noronha 145 APPENDIX VII – Parameter’s ranking of C-MOM, CMR and ZPNE models 148 APPENDIX VIII – Bias, precision and accuracy table 149

Die effektive sowie effiziente Planung von Energieeffizienzmaßnahmen hat große Bedeutung für Produktionsunternehmen. Die um energetische Betrachtungen erweiterte Materialflusssimulation wird in diesem Kontext zunehmend als Werkzeug zur Untersuchung komplexer Wechselwirkungen von Material- und Energieflüssen in Fabriken eingesetzt. Die vorliegende Arbeit hat die Konzeptionierung und Systematisierung einer Methodik zur Ausgestaltung derartiger Simulationsstudien speziell in Umplanungsprojekten zur Energieeffizienzsteigerung zum Inhalt. Unter besonderer Beachtung entscheidungstheoretischer Ansätze soll es diese ermöglichen, unabhängig von der jeweils eingesetzten Simulationslösung projektspezifische Ausprägungen für die relevanten Gestaltungsaspekte einer Simulationsstudie in diesem Problemfeld zu bestimmen (z. B. zur Art und Weise der Modellierung des Energieverbrauchs). Das entwickelte Lösungskonzept besteht aus einem 13 Schritte umfassenden Vorgehensmodell sowie neun Lösungsmodulen zur Unterstützung von Entscheidungen hinsichtlich der Ausprägung ausgewählter Gestaltungsaspekte. Auf diesem Wege wird der gesamte Prozess von der Entwicklung einer Effizienzmaßnahme über die eigentliche Simulationsanwendung (in Anlehnung an die VDI-Richtlinie 3633 Blatt 1:2014) bis hin zur finalen Investitionsentscheidung unterstützt. Exemplarisch wurde die im Ergebnis der Arbeit stehende Methodik an drei Fallstudien erprobt. Dabei konnten alle definierten Anforderungen erfolgreich geprüft werden. / Effective and efficient planning of energy efficiency measures is of great importance to manufacturing companies. Material flow simulation that has been extended to also include energy is increasingly used in this context as a tool for the analysis of complex interactions between material flows and energy flows in factories. This thesis deals with the conception and the systemization of a methodology for designing such simulation studies specifically in re-planning projects that aim for energy efficiency improvements. Taking basic approaches from decision theory into particular consideration, it is intended to provide guidance in deciding on the project-specific manifestation for relevant characteristics of a simulation study in this problem area (e.g. the manner to model energy consumption), regardless of the utilised simulation solution. The developed solution comprises a 13 steps spanning process model as well as nine solution modules to support decisions concerning the choice of manifestation for selected characteristics. In this way, the entire process from the development of an energy efficiency measure through the actual application of simulation (following VDI guideline 3633 Part 1:2014) to the eventual investment decision is assisted. The results of this thesis were exemplarily tested in three case studies. All initially defined requirements could thereby be positively verified.

Le risque attribuable (RA) mesure la proportion de cas de maladie qui peuvent être attribués à une exposition au niveau de la population. Plusieurs définitions et méthodes d'estimation du RA ont été proposées pour des données de survie. En utilisant des simulations, nous comparons quatre méthodes d'estimation du RA dans le contexte de l'analyse de survie : deux méthodes non paramétriques basées sur l'estimateur de Kaplan-Meier, une méthode semi-paramétrique basée sur le modèle de Cox à risques proportionnels et une méthode paramétrique basée sur un modèle à risques proportionnels avec un risque de base constant par morceaux. Nos travaux suggèrent d'utiliser les approches semi-paramétrique et paramétrique pour l'estimation du RA lorsque l'hypothèse des risques proportionnels est vérifiée. Nous appliquons nos méthodes aux données de la cohorte E3N pour estimer la proportion de cas de cancer du sein invasif attribuables à l'utilisation de traitements hormonaux de la ménopause (THM). Nous estimons qu'environ 9 % des cas de cancer du sein sont attribuables à l'utilisation des THM à l'inclusion. Dans le cas d'une exposition protectrice, une alternative au RA est la fraction préventive (FP) qui mesure la proportion de cas de maladie évités. Cette mesure n'a pas été considérée dans le contexte de l'analyse de survie. Nous proposons une définition de la FP dans ce contexte et des méthodes d'estimation en utilisant des approches semi-paramétrique et paramétrique avec une extension permettant de prendre en compte les risques concurrents. L'application aux données de la cohorte des Trois Cités (3C) estime qu'environ 9 % de cas d'accident vasculaire cérébral peuvent être évités chez les personnes âgées par l'utilisation des hypolipémiants. Notre étude montre que la FP peut être utilisée pour évaluer l'impact des médicaments bénéfiques dans les études de cohorte tout en tenant compte des facteurs de confusion potentiels et des risques concurrents. / The attributable risk (AR) measures the proportion of disease cases that can be attributed to an exposure in the population. Several definitions and estimation methods have been proposed for survival data. Using simulations, we compared four methods for estimating AR defined in terms of survival functions: two nonparametric methods based on Kaplan-Meier's estimator, one semiparametric based on Cox's model, and one parametric based on the piecewise constant hazards model. Our results suggest to use the semiparametric or parametric approaches to estimate AR if the proportional hazards assumption appears appropriate. These methods were applied to the E3N women cohort data to estimate the AR of breast cancer due to menopausal hormone therapy (MHT). We showed that about 9% of cases of breast cancer were attributable to MHT use at baseline. In case of a protective exposure, an alternative to the AR is the prevented fraction (PF) which measures the proportion of disease cases that could be avoided in the presence of a protective exposure in the population. The definition and estimation of PF have never been considered for cohort studies in the survival analysis context. We defined the PF in cohort studies with survival data and proposed two estimation methods: a semiparametric method based on Cox’s proportional hazards model and a parametric method based on a piecewise constant hazards model with an extension to competing risks. Using data of the Three-City (3C) cohort study, we found that approximately 9% of cases of stroke could be avoided using lipid-lowering drugs (statins or fibrates) in the elderly population. Our study shows that the PF can be estimated to evaluate the impact of beneficial drugs in observational cohort studies while taking potential confounding factors and competing risks into account.

<p>Protein malfunction can cause human diseases, which makes the protein a target in the process of drug discovery. In-depth knowledge of how protein functions can widely contribute to the understanding of the mechanism of these diseases. Protein functions are determined by protein structures and their dynamic properties. Protein dynamics refers to the constant physical movement of atoms in a protein, which may result in the transition between different conformational states of the protein. These conformational transitions are critically important for the proteins to function. Understanding protein dynamics can help to understand and interfere with the conformational states and transitions, and thus with the function of the protein. If we can understand the mechanism of conformational transition of protein, we can design molecules to regulate this process and regulate the protein functions for new drug discovery. Protein Dynamics can be simulated by Molecular Dynamics (MD) Simulations.</p> <p>The MD simulation data generated are spatial-temporal and therefore very high dimensional. To analyze the data, distinguishing various atomic interactions within a protein by interpreting their 3D coordinate values plays a significant role. Since the data is humongous, the essential step is to find ways to interpret the data by generating more efficient algorithms to reduce the dimensionality and developing user-friendly visualization tools to find patterns and trends, which are not usually attainable by traditional methods of data process. The typical allosteric long-range nature of the interactions that lead to large conformational transition, pin-pointing the underlying forces and pathways responsible for the global conformational transition at atomic level is very challenging. To address the problems, Various analytical techniques are performed on the simulation data to better understand the mechanism of protein dynamics at atomic level by developing a new program called Probing Long-distance interactions by Tapping into Paired-Distances (PLITIP), which contains a set of new tools based on analysis of paired distances to remove the interference of the translation and rotation of the protein itself and therefore can capture the absolute changes within the protein.</p> <p>Firstly, we developed a tool called Decomposition of Paired Distances (DPD). This tool generates a distance matrix of all paired residues from our simulation data. This paired distance matrix therefore is not subjected to the interference of the translation or rotation of the protein and can capture the absolute changes within the protein. This matrix is then decomposed by DPD</p> <p>using Principal Component Analysis (PCA) to reduce dimensionality and to capture the largest structural variation. To showcase how DPD works, two protein systems, HIV-1 protease and 14-3-3 σ, that both have tremendous structural changes and conformational transitions as displayed by their MD simulation trajectories. The largest structural variation and conformational transition were captured by the first principal component in both cases. In addition, structural clustering and ranking of representative frames by their PC1 values revealed the long-distance nature of the conformational transition and locked the key candidate regions that might be responsible for the large conformational transitions.</p> <p>Secondly, to facilitate further analysis of identification of the long-distance path, a tool called Pearson Coefficient Spiral (PCP) that generates and visualizes Pearson Coefficient to measure the linear correlation between any two sets of residue pairs is developed. PCP allows users to fix one residue pair and examine the correlation of its change with other residue pairs.</p> <p>Thirdly, a set of visualization tools that generate paired atomic distances for the shortlisted candidate residue and captured significant interactions among them were developed. The first tool is the Residue Interaction Network Graph for Paired Atomic Distances (NG-PAD), which not only generates paired atomic distances for the shortlisted candidate residues, but also display significant interactions by a Network Graph for convenient visualization. Second, the Chord Diagram for Interaction Mapping (CD-IP) was developed to map the interactions to protein secondary structural elements and to further narrow down important interactions. Third, a Distance Plotting for Direct Comparison (DP-DC), which plots any two paired distances at user’s choice, either at residue or atomic level, to facilitate identification of similar or opposite pattern change of distances along the simulation time. All the above tools of PLITIP enabled us to identify critical residues contributing to the large conformational transitions in both HIV-1 protease and 14-3-3σ proteins.</p> <p>Beside the above major project, a side project of developing tools to study protein pseudo-symmetry is also reported. It has been proposed that symmetry provides protein stability, opportunities for allosteric regulation, and even functionality. This tool helps us to answer the questions of why there is a deviation from perfect symmetry in protein and how to quantify it.</p>