Evapotranspiration is often estimated by numerical simulation. However, to produce accurate simulations, these models usually require on-site measurements for parameterization or calibration. We have to make sure that the model realistically reproduces both, the temporal patterns of soil moisture and evapotranspiration. In this study, we combine three sources of information: (i) measurements of sap velocities; (ii) soil moisture; and (iii) expert knowledge on local runoff generation and water balance to define constraints for a “behavioral” forest stand water balance model. Aiming for a behavioral model, we adjusted soil moisture at saturation, bulk resistance parameters and the parameters of the water retention curve (WRC). We found that the shape of the WRC influences substantially the behavior of the simulation model. Here, only one model realization could be referred to as “behavioral”. All other realizations failed for a least one of our evaluation criteria: Not only transpiration and soil moisture are simulated consistently with our observations, but also total water balance and runoff generation processes. The introduction of a multi-criteria evaluation scheme for the detection of unrealistic outputs made it possible to identify a well performing parameter set. Our findings indicate that measurement of different fluxes and state variables instead of just one and expert knowledge concerning runoff generation facilitate the parameterization of a hydrological model.

The dissertation includes three published articles on which the development of a theoretical model of motivational and self-regulatory determinants of the intention to comprehensively search for health information is based. The first article focuses on building a solid theoretical foundation as to the nature of a comprehensive search for health information and enabling its integration into a broader conceptual framework. Based on subjective source perceptions, a taxonomy of health information sources was developed. The aim of this taxonomy was to identify most fundamental source characteristics to provide a point of reference when it comes to relating to the target objects of a comprehensive search. Three basic source characteristics were identified: expertise, interaction and accessibility. The second article reports on the development and evaluation of an instrument measuring the goals individuals have when seeking health information: the ‘Goals Associated with Health Information Seeking’ (GAINS) questionnaire. Two goal categories (coping focus and regulatory focus) were theoretically derived, based on which four goals (understanding, action planning, hope and reassurance) were classified. The final version of the questionnaire comprised four scales representing the goals, with four items per scale (sixteen items in total). The psychometric properties of the GAINS were analyzed in three independent samples, and the questionnaire was found to be reliable and sufficiently valid as well as suitable for a patient sample. It was concluded that the GAINS makes it possible to evaluate goals of health information seeking (HIS) which are likely to inform the intention building on how to organize the search for health information. The third article describes the final development and a first empirical evaluation of a model of motivational and self-regulatory determinants of an intentionally comprehensive search for health information. Based on the insights and implications of the previous two articles and an additional rigorous theoretical investigation, the model included approach and avoidance motivation, emotion regulation, HIS self-efficacy, problem and emotion focused coping goals and the intention to seek comprehensively (as outcome variable). The model was analyzed via structural equation modeling in a sample of university students. Model fit was good and hypotheses with regard to specific direct and indirect effects were confirmed. Last, the findings of all three articles are synthesized, the final model is presented and discussed with regard to its strengths and weaknesses, and implications for further research are determined.

This dissertation develops a rationale of how to use fossil data in solving biogeographical and ecological problems. It is argued that large amounts of fossil data of high quality can be used to document the evolutionary processes (the origin, development, formation and dynamics) of Arealsystems, which can be divided into six stages in North America: the Refugium Stage (before 15,000 years ago: > 15 ka), the Dispersal Stage (from 8,000 to 15,000 years ago: 8.0 - 15 ka), the Developing Stage (from 3,000 to 8,000 years ago: 3.0 - 8.0 ka), the Transitional Stage (from 1,000 to 3,000 years ago: 1 - 3 ka), the Primitive Stage (from 5,00 to 1,000 years ago: 0.5 - 1 ka) and the Human Disturbing Stage (during the last 500 years: < 0.5 ka). The division into these six stages is based on geostatistical analysis of the FAUNMAP database that contains 43,851 fossil records collected from 1860 to 1994 in North America. Fossil data are one of the best materials to test the glacial refugia theory. Glacial refugia represent areas where flora and fauna were preserved during the glacial period, characterized by richness in species and endemic species at present. This means that these (endemic) species should have distributed purely or primarily in these areas during the glacial period. The refugia can therefore be identified by fossil records of that period. If it is not the case, the richness in (endemic) species may not be the result of the glacial refugia. By exploring where mammals lived during the Refugium Stage (> 15 ka), seven refugia in North America can be identified: the California Refugium, the Mexico Refugium, the Florida Refugium, the Appalachia Refugium, the Great Basin Refugium, the Rocky Mountain Refugium and the Great Lake Refugium. The first five refugia coincide well with De Lattin- dispersal centers recognized by biogeographical methods using data on modern distributions. The individuals of a species are not evenly distributed over its Arealsystem. Brown- Hot Spots Model shows that in most cases there is an enormous variation in abundance within an areal of a species: In a census, zero or only a very few individuals occur at most sample locations, but tens or hundreds are found at a few sample sites. Locations where only a few individuals can be sampled in a survey are called "cool spots", and sites where tens or hundreds of individuals can be observed in a survey are called "hot spots". Many areas within the areal are uninhabited, which are called "holes". This model has direct implications for analyzing fossil data: Hot spots have a much higher local population density than cool spots. The chances to discover fossil individuals of a species are much higher in sediments located in a "hot spot" area than in a "cool spot" area. Therefore much higher MNIs (Minimum Number of Individuals) of the species should be found in fossil localities located in the hot spot than in the cool spot area. There are only a few hot spots but many cool spots within an areal of a single hypothetical species, consequently only a few fossil sites can provide with much high MNIs, whereas most other sites can only provide with very low MNIs. This prediction has been proved to be true by analysis of 70 species in FAUMAP containing over 100 fossil records. The temporal and spatial variation in abundance can be reconstructed from the temporospatial distribution of the MNIs of a species over its Arealsystem. Areas with no fossil records from the last thousands of years may be holes, and sites with much higher MNIs may be hot spots, while locations with low MNIs may be cool spots. Although the hot spots of many species can remain unchanged in an area over thousands of years, our study shows that a large shift of hot spots occurred mainly around 1,500-1,000 years ago. There are three directions of movement: from the west side to the east side of the Rockies, from the East of the USA to the east side of the Rockies and from the west side of the Rockies to the Southwest of the USA. The first two directions of shift are called Lewis and Clark- pattern, which can be verified with the observations mad by Lewis and Clark during their expedition in 1805-1806. The historical process of this pattern may well explain the 200-year-old puzzle why big game then abundant on the east side were rare on the west side of the Rocky Mountains noted by modern ecologists and biogeographers. The third direction of shift is called Bayham- pattern. This pattern can be tested by the model of Late Holocene resource intensification first described by Frank E. Bayham. The historical process creating the Bayham pattern will challenge the classic explanation of the Late Holocene resource intensification. An environmental change model has been proposed to account for the shift of hot spots. Implications of glacial refugia and hot spots areas for wildlife management and effective conservation are discussed. Suggestions for paleontologists and zooarchaeologists regarding how to provide more valuable information in their future excavation and research for other disciplines are given.

Background: Psychoeducation positively influences the psychological components of chronic low back pain (CLBP) in conventional treatments. The digitalization of health care has led to the discussion of virtual reality (VR) interventions. However, CLBP treatments in VR have some limitations due to full immersion. In comparison, augmented reality (AR) supplements the real world with virtual elements involving one’s own body sensory perception and can combine conventional and VR approaches. Objective: The aim of this study was to review the state of research on the treatment of CLBP through psychoeducation, including immersive technologies, and to formulate suggestions for psychoeducation in AR for CLBP. Methods: A scoping review following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines was performed in August 2024 by using Livivo ZB MED, PubMed, Web of Science, American Psychological Association PsycINFO (PsycArticle), and PsyArXiv Preprints databases. A qualitative content analysis of the included studies was conducted based on 4 deductively extracted categories. Results: We included 12 studies published between 2019 and 2024 referring to conventional and VR-based psychoeducation for CLBP treatment, but no study referred to AR. In these studies, educational programs were combined with physiotherapy, encompassing content on pain biology, psychological education, coping strategies, and relaxation techniques. The key outcomes were pain intensity, kinesiophobia, pain catastrophizing, degree of disability, quality of life, well-being, self-efficacy, depression, attrition rate, and user experience. Passive, active, and gamified strategies were used to promote intrinsic motivation from a psychological point of view. Regarding user experience from a software development perspective, user friendliness, operational support, and application challenges were recommended.

The publication of statistical databases is subject to legal regulations, e.g. national statistical offices are only allowed to publish data if the data cannot be attributed to individuals. Achieving this privacy standard requires anonymizing the data prior to publication. However, data anonymization inevitably leads to a loss of information, which should be kept minimal. In this thesis, we analyze the anonymization method SAFE used in the German census in 2011 and we propose a novel integer programming-based anonymization method for nominal data. In the first part of this thesis, we prove that a fundamental variant of the underlying SAFE optimization problem is NP-hard. This justifies the use of heuristic approaches for large data sets. In the second part, we propose a new anonymization method belonging to microaggregation methods, specifically designed for nominal data. This microaggregation method replaces rows in a microdata set with representative values to achieve k-anonymity, ensuring each data row is identical to at least k − 1 other rows. In addition to the overall dissimilarities of the data rows, the method accounts for errors in resulting frequency tables, which are of high interest for nominal data in practice. The method employs a typical two-step structure: initially partitioning the data set into clusters and subsequently replacing all cluster elements with representative values to achieve k-anonymity. For the partitioning step, we propose a column generation scheme followed by a heuristic to obtain an integer solution, which is based on the dual information. For the aggregation step, we present a mixed-integer problem formulation to find cluster representatives. To this end, we take errors in a subset of frequency tables into account. Furthermore, we show a reformulation of the problem to a minimum edge-weighted maximal clique problem in a multipartite graph, which allows for a different perspective on the problem. Moreover, we formulate a mixed-integer program, which combines the partitioning and the aggregation step and aims to minimize the sum of chi-squared errors in frequency tables. Finally, an experimental study comparing the methods covered or developed in this work shows particularly strong results for the proposed method with respect to relative criteria, while SAFE shows its strength with respect to the maximum absolute error in frequency tables. We conclude that the inclusion of integer programming in the context of data anonymization is a promising direction to reduce the inevitable information loss inherent in anonymization, particularly for nominal data.