The paper presents a simulation and parameter-estimation approach for evaluating stochastic patterns of population growth and spread of an annual forest herb, Melampyrum pratense (Orobanchaceae). The survival of a species during large-scale changes in land use and climate will depend, to a considerable extent, on its dispersal and colonisation abilities. Predictions on species migration need a combination of field studies and modelling efforts. Our study on the ability of M. pratense to disperse into so far unoccupied areas was based on experiments in secondary woodland in NE Germany. Experiments started in 1997 at three sites where the species was not yet present, with 300 seeds sown within one square meter. Population development was then recorded until 2001 by mapping of individuals with a resolution of 5 cm. Additional observations considered density dependence of seed production. We designed a spatially explicit individual-based computer simulation model to explain the spatial patterns of population development and to predict future population spread. Besides primary drop of seeds (barochory) it assumed secondary seed transport by ants (myrmecochory) with an exponentially decreasing dispersal tail. An important feature of populationpattern explanation was the simultaneous estimation of both population-growth and dispersal parameters from consistent spatio-temporal data sets. As the simulation model produced stochastic time series and random spatially discrete distributions of individuals we estimated parameters by minimising the expectation of weighted sums of squares. These sums-ofsquares criteria considered population sizes, radial population distributions around the area of origin and distributions of individuals within squares of 25*25 cm, the range of density action. Optimal parameter values, together with the precision of the estimates, were obtained from calculating sums of squares in regular grids of parameter values. Our modelling results showed that transport of fractions of seeds by ants over distances of 1…2 m was indispensable for explaining the observed population spread that led to distances of at most 8 m from population origin within 3 years. Projections of population development over 4 additional years gave a diffusion-like increase of population area without any “outposts”. This prediction generated by the simulation model gave a hypothesis which should be revised by additional field observations. Some structural deviations between observations and model output already indicated that for full understanding of population spread the set of dispersal mechanisms assumed in the model may have to be extended by additional features of plant-animal mutualism.

The complex demographic changes that underlie the expansion of anatomically modern humans out of Africa have important consequences on the dynamics of natural selection and our ability to detect it. In this thesis, I aimed to refine our knowledge on human population history using ancient genomes, and then used a climate-informed, spatially explicit framework to explore the interplay between complex demographies and selection. I first analysed a high-coverage genome from Upper Palaeolithic Romania from ~37.8 kya, and demonstrated an early diversification of multiple lineages shortly after the out-of-Africa expansion (Chapter 2). I then investigated Late Upper Palaeolithic (~13.3ky old) and Mesolithic (~9.7 ky old) samples from the Caucasus and a Late Upper Palaeolithic (~13.7ky old) sample from Western Europe, and found that these two groups belong to distinct lineages that also diverged shortly after the out of Africa, ~45-60 ky ago (Chapter 3). Finally, I used East Asian samples from ~7.7ky ago to show that there has been a greater degree of genetic continuity in this region compared to Europe (Chapter 4). In the second part of my thesis, I used a climate-informed, spatially explicit demographic model that captures the out-of-Africa expansion to explore natural selection. I first investigated whether the model can represent the confounding effect of demography on selection statistics, when applied to neutral part of the genome (Chapter 5). Whilst the overlap between different selection statistics was somewhat underestimated by the model, the relationship between signals from different populations is generally well-captured. I then modelled natural selection in the same framework and investigated the spatial distribution of two genetic variants associated with a protective effect against malaria, sickle-cell anaemia and β⁰ thalassemia (Chapter 6). I found that although this model can reproduce the disjoint ranges of different variants typical of the former, it is incompatible with overlapping distributions characteristic of the latter. Furthermore, our model is compatible with the inferred single origin of sickle-cell disease in most regions, but it can not reproduce the presence of this disorder in India without long-distance migrations.

This thesis analyses the highly structured and densely populated farmland surrounding Kakamega Forest (western Kenya) in a spatially-explicit manner. The interdisciplinary approach combines methodologies and technologies from different scientific disciplines: remote sensing with OBIA, GIS and spatially explicit modelling (geomatics and geographic science) with socio-economic as well as agro-economic considerations (human and social sciences) as well as cartographic science. Furthermore, the research is related to conservation biology (biological sciences). Based on an in-situ ground truthing and visual image interpretation, very high spatial resolution QuickBird satellite imagery covering 466 km² of farmland was analysed using the concept of object-based image analysis (OBIA). In an integrative workflow, statistical analysis and expert knowledge were combined to develop a sophisticated rule set. The classification result distinguishing 15 LULC classes was used alongside with temporally extrapolated and spatially re-distributed population data as well as socio-/agro-economic factors in order to create a spatially-explicit typology of the farmland and to model scenarios of rural livelihoods. The farmland typology distinguishes ten types of farmland: 3 sugarcane types (covering 48% of the area), 3 tea types (30%), 2 transitional types (15%), 1 steep terrain type (2%), and 1 central type (5%). The scenarios consider different developments of possible future yields and prices for the main agricultural products sugarcane, tea, and maize. Out of all farmland types, the ‘marginal sugarcane type’ is best prepared to cope with future problems. Besides a comparably low population density, a high share of land under cultivation of food crops coupled with a moderate cultivation of cash crops is characteristic for this type. As part of the research conducted, several novel methodologies were introduced. These include a new conceptual framework for categorizing parameter optimization studies, the area fitness rate (AFR) as a novel discrepancy measure, the technique of ‘classification-based nearest neighbour classification’ for classes which are difficult to separate from others, and a novel approach for accessing the accuracy of OBIA classifications. Finally, this thesis makes a number of recommendations and elaborates promising starting points for further scientific research. / Die vorliegende Arbeit untersucht räumlich-expliziten das stark strukturierte und dicht besiedelte Agrarland um den Kakamega Wald (Westkenia). Dabei kombiniert der interdisziplinäre Ansatz Methoden und Technologien verschiedener Wissenschaftsbereiche: die Fernerkundung mit der objekt-basierten Bildanalyse (OBIA), GIS und die räumlich-explizite Modellierung (Geoinformatik und Geographie) mit sozio- und agro-ökonomische Aspekten (Human- und Sozialwissenschaft) sowie der Kartographie. Zudem steht die Arbeit in Bezug zum Schutz der biologischen Vielfalt (Biologie). Ausgehend von einer Referenzdatenerfassung vor Ort und einer visuellen Bildinterpretation wurden räumlich sehr hochauflösende QuickBird-Satellitenbilddaten, die 466 km² des Agrarlandes abdecken, mit Hilfe von OBIA ausgewertet. In einem integrativen Ansatz wurden dabei statistische Verfahren und Expertenwissen kombiniert, um einen ausgefeilten Regelsatz zur Klassifizierung zu erzeugen. Das Klassifizierungsergebnis unterscheidet 15 Klassen der Landnutzung bzw. -bedeckung; zusammen mit zeitlich extrapolierten und räumlich neu verteilten Bevölkerungsdaten sowie sozio- und agro-ökonomischen Faktoren ermöglichte es, eine räumlich-explizite Typologie des Agrarlandes zu erstellen und Szenarien zum ländlichen Auskommen zu modellieren. Die Agrarlandtypologie unterscheidet zehn Landtypen: 3 Zuckerrohr-dominierte Typen (48% des Gebietes), 3 Tee-dominierte Typen (30%), 2 Übergangstypen (15%), 1 Typ steilen Geländes (2%) und 1 zentralen Typ (5%). Die Szenarien betrachten mögliche zukünftige Entwicklungen der Erträge und Preise der Hauptanbauarten Zuckerrohr, Tee und Mais. Von allen Agrarlandtypen ist der „marginal Zuckerrohr-dominierte Typ“ am besten gerüstet, um zukünftigen Problemen zu begegnen. Bezeichnend für diesen Typ sind – neben einer vergleichsweise geringen Bevölkerungsdichte – ein hoher Anteil an Nahrungsmittelanbau zusammen mit einem gemäßigten Anbau von exportorientierten Agrarprodukten. Als Teil der Forschungsarbeit werden verschiedene neuartige Methoden vorgestellt, u.a. ein neuer konzeptioneller Rahmen für das Kategorisieren von Studien zur Parameteroptimierung, die „area fitness rate“ (AFR) als neue Messgröße für Flächendiskrepanzen, die klassifikations-basierte Nächster-Nachbar Klassifizierung sowie ein Ansatz zum Bestimmen der Güte von OBIA-Klassifizierungen. Schließlich gibt die Arbeit eine Reihe von Empfehlungen und bietet vielversprechende Ausgangspunkte für weiterführende wissenschaftliche Forschungen.

This thesis analyses the highly structured and densely populated farmland surrounding Kakamega Forest (western Kenya) in a spatially-explicit manner. The interdisciplinary approach combines methodologies and technologies from different scientific disciplines: remote sensing with OBIA, GIS and spatially explicit modelling (geomatics and geographic science) with socio-economic as well as agro-economic considerations (human and social sciences) as well as cartographic science. Furthermore, the research is related to conservation biology (biological sciences). Based on an in-situ ground truthing and visual image interpretation, very high spatial resolution QuickBird satellite imagery covering 466 km² of farmland was analysed using the concept of object-based image analysis (OBIA). In an integrative workflow, statistical analysis and expert knowledge were combined to develop a sophisticated rule set. The classification result distinguishing 15 LULC classes was used alongside with temporally extrapolated and spatially re-distributed population data as well as socio-/agro-economic factors in order to create a spatially-explicit typology of the farmland and to model scenarios of rural livelihoods. The farmland typology distinguishes ten types of farmland: 3 sugarcane types (covering 48% of the area), 3 tea types (30%), 2 transitional types (15%), 1 steep terrain type (2%), and 1 central type (5%). The scenarios consider different developments of possible future yields and prices for the main agricultural products sugarcane, tea, and maize. Out of all farmland types, the ‘marginal sugarcane type’ is best prepared to cope with future problems. Besides a comparably low population density, a high share of land under cultivation of food crops coupled with a moderate cultivation of cash crops is characteristic for this type. As part of the research conducted, several novel methodologies were introduced. These include a new conceptual framework for categorizing parameter optimization studies, the area fitness rate (AFR) as a novel discrepancy measure, the technique of ‘classification-based nearest neighbour classification’ for classes which are difficult to separate from others, and a novel approach for accessing the accuracy of OBIA classifications. Finally, this thesis makes a number of recommendations and elaborates promising starting points for further scientific research.:1. Introduction 2. Geodata and reference data 3. Object-based image analysis (OBIA) 4. Optimization of segmentation parameters 5. Feature selection and threshold determination 6. OBIA classification: rule set development and realisation 7. Classification results 8. Spatial farmland typology 9. Spatially explicit planning scenarios of rural livelihoods 10. Discussion / Die vorliegende Arbeit untersucht räumlich-expliziten das stark strukturierte und dicht besiedelte Agrarland um den Kakamega Wald (Westkenia). Dabei kombiniert der interdisziplinäre Ansatz Methoden und Technologien verschiedener Wissenschaftsbereiche: die Fernerkundung mit der objekt-basierten Bildanalyse (OBIA), GIS und die räumlich-explizite Modellierung (Geoinformatik und Geographie) mit sozio- und agro-ökonomische Aspekten (Human- und Sozialwissenschaft) sowie der Kartographie. Zudem steht die Arbeit in Bezug zum Schutz der biologischen Vielfalt (Biologie). Ausgehend von einer Referenzdatenerfassung vor Ort und einer visuellen Bildinterpretation wurden räumlich sehr hochauflösende QuickBird-Satellitenbilddaten, die 466 km² des Agrarlandes abdecken, mit Hilfe von OBIA ausgewertet. In einem integrativen Ansatz wurden dabei statistische Verfahren und Expertenwissen kombiniert, um einen ausgefeilten Regelsatz zur Klassifizierung zu erzeugen. Das Klassifizierungsergebnis unterscheidet 15 Klassen der Landnutzung bzw. -bedeckung; zusammen mit zeitlich extrapolierten und räumlich neu verteilten Bevölkerungsdaten sowie sozio- und agro-ökonomischen Faktoren ermöglichte es, eine räumlich-explizite Typologie des Agrarlandes zu erstellen und Szenarien zum ländlichen Auskommen zu modellieren. Die Agrarlandtypologie unterscheidet zehn Landtypen: 3 Zuckerrohr-dominierte Typen (48% des Gebietes), 3 Tee-dominierte Typen (30%), 2 Übergangstypen (15%), 1 Typ steilen Geländes (2%) und 1 zentralen Typ (5%). Die Szenarien betrachten mögliche zukünftige Entwicklungen der Erträge und Preise der Hauptanbauarten Zuckerrohr, Tee und Mais. Von allen Agrarlandtypen ist der „marginal Zuckerrohr-dominierte Typ“ am besten gerüstet, um zukünftigen Problemen zu begegnen. Bezeichnend für diesen Typ sind – neben einer vergleichsweise geringen Bevölkerungsdichte – ein hoher Anteil an Nahrungsmittelanbau zusammen mit einem gemäßigten Anbau von exportorientierten Agrarprodukten. Als Teil der Forschungsarbeit werden verschiedene neuartige Methoden vorgestellt, u.a. ein neuer konzeptioneller Rahmen für das Kategorisieren von Studien zur Parameteroptimierung, die „area fitness rate“ (AFR) als neue Messgröße für Flächendiskrepanzen, die klassifikations-basierte Nächster-Nachbar Klassifizierung sowie ein Ansatz zum Bestimmen der Güte von OBIA-Klassifizierungen. Schließlich gibt die Arbeit eine Reihe von Empfehlungen und bietet vielversprechende Ausgangspunkte für weiterführende wissenschaftliche Forschungen.:1. Introduction 2. Geodata and reference data 3. Object-based image analysis (OBIA) 4. Optimization of segmentation parameters 5. Feature selection and threshold determination 6. OBIA classification: rule set development and realisation 7. Classification results 8. Spatial farmland typology 9. Spatially explicit planning scenarios of rural livelihoods 10. Discussion