White-tailed deer (Odocoileus virginianus) are important game mammals and potential reservoirs of diseases of domestic livestock, so diseases of deer are of great concern to wildlife managers. In many situations, models can be useful for integrating existing data, understanding disease transmission patterns, and predicting effects on host populations. Individual-based modeling (IBM) has become more commonplace in ecology as a tool to link individual behavior to population dynamics and community interactions, especially for gauging the effects of management actions. Spatially explicit IBMs are especially useful when ecological processes, such as disease transmission, are affected by the spatial composition of the environment. I developed a spatially explicit IBM, DeerLandscapeDisease (DLD), to simulate direct and indirect disease transmission in white-tailed deer. Using data from GPS-collared deer in southern Illinois, I developed methods to identify habitats and times of high contact probability. I parameterized movement models, for use in DLD, using field data from GPS-collared deer in both southern and east-central Illinois. I then used DLD to simulate deer movements and epizootiology in two different landscapes: a predominantly agricultural landscape with fragmented forest patches in east-central Illinois and a landscape dominated by forest in southern Illinois. Behavioral and demographic parameters that could not be estimated from the field data were estimated using published literature of deer ecology. I assumed that bioavailability of infectious pathogens deposited in the environment decreased exponentially. Transmission probabilities were estimated by fitting to published trends in infection prevalence, assuming that infection probability during an encounter was equal for all age classes, so infection prevalence varied with sex- and age-specific behavior. DLD simulations of chronic wasting disease epizootiology demonstrated significant effects of landscape structure, social behavior, and mode of transmission on prevalence, emphasizing the importance of spatial, temporal and behavioral heterogeneity in disease modeling. These results demonstrate the utility of IBMs in incorporating spatio-temporal variables as well as animal behavior when predicting and modeling disease spread.

Ecological systems are difficult to understand, let alone predict. The reason is their enormous complexity that arises from numerous organisms interacting with each other and their environment in a multitude of ways. However, this understanding is crucial to secure a plentitude of services that are provided by ecological systems. A substantial proportion of these services are carried out by microorganisms such as bacteria, fungi, and archaea. For example, microorganisms contribute to degradation of organic matter, water purification, and even regulation of the global climate. Therefore, a thorough understanding of the ecology of microorganisms is particularly relevant for our future well-being. While microorganisms are comparatively well-suited for experimental studies, owing also to recent technological advances in molecular biology, it is necessary to apply theory and modeling in order to fully benefit from the empirical data. A widely used theoretical method in microbial ecology is individual-based modeling, in which population or community dynamics emerge from the behavior and interplay of individual entities that are simulated according to a predefined set of rules. However, existing individual-based models of microbial communities are often specialized on particular research questions or require proficiency in specific programming languages or software. These limitations can be hampering for a broad and systematic application of individual-based modeling in microbial ecology. For this thesis, McComedy, a framework and software tool for the creation and running of individual-based models of microbial consumer-resource systems, was developed. It allows for fast and user-friendly model development by flexibly combining pre-implemented building blocks that represent physical, biological, and evolutionary processes. The ability of McComedy to capture the essential dynamics of microbial consumer-resource systems was demonstrated by reproducing and furthermore adding to the results of two distinct studies from the literature. McComedy was furthermore applied to study the evolution of metabolic interactions between bacteria. More specifically, it was assessed whether cooperative exchange of costly metabolites can evolve in bacterial multicellular aggregates. The results indicate that this is in principle possible, however, it depends on the mechanism by which the metabolites are exchanged. If metabolites are exchanged via diffusion through extracellular space, cooperation is not expected to evolve. On the other hand, if metabolites are transferred by contact-dependent means, for instance via intercellular nanotubes, cooperation is likely to evolve. Overall, contributions from this thesis comprise, first, a user-friendly modeling tool that can be used by microbial ecologists, second, insights into the evolution of metabolic interactions in bacterial systems, and, third, awareness of how the mechanistic consideration of a process can drastically affect the outcome of a modeling study.

<p>As human activity expands across the globe, disturbance of wildlife by anthropogenic activities such as fragmentation of habitat, and wildlife-human conflicts escalate. The Pueblo of Santa Ana is receiving pressure from road expansion and urban development and is concerned with the impacts of those activities upon wildlife populations. Specifically, mule deer is a species of concern for their Department of Natural Resources (DNR). Mule deer are important economically, culturally, and for recreational purposes. The DNR understands the need for better understanding mule deer ecology to manage for potential conflicts in their interactions with expanding human infrastructure. My objectives were first to model mule deer habitat use in and around the Pueblo of Santa Ana during the summer and winter at different times of the day. My second objective was to understand the relative impacts of different scenarios for road development in the Pueblo of Santa Ana upon the disturbance of mule deer using an Individual Based Modeling (IBM) framework.</p> <p> Using Geospatial Positioning System telemetry collar data collected on mule deer I used proximity based habitat predictors in a general linear mixed model to create resource selection functions. Generally I found that the season had a greater impact on mule deer habitat use than the time of day. Female and male mule deer select for similar habitat but sexually segregate in their summer distributions. My findings are consistent with results from other locations where mule deer studies have been conducted. In chapter two, I used the Simulation of Disturbance Activities (SODA) modeling framework to investigate the impact of vehicles on mule deer disturbance response behaviors, alert and fleeing. Using this framework I compared a baseline scenario to road expansion scenarios (DamRoad, ByPass, DeerCrossing) estimating the frequency of disturbance behavior of mule deer for each such scenario. My results show that mule deer were disturbed most in the baseline model. There were no significant differences in the frequency of disturbance for female mule deer across scenarios. Male mule deer did have some significant differences in alert and fleeing behavior across scenarios. My results may be a function of assumptions made in my modeling. Specifically, I assumed that mule deer would shift their areas of activity to new portions of the Pueblo of Santa Ana in response to altered habitat quality caused by new roads. If mule deer did not shift their areas of activity accordingly, my models may provide inaccurate assessments of disturbance patterns. </p> In conclusion my findings are similar to results from other locations. Specifically, the inferences that roads and road development are important to consider for mule deer management transcends variation associated with the unique characteristics of the Pueblo of Santa Ana mule deer population. Finally, my results suggest that the use of an IBM modeling framework has the potential to provide insights into the disturbance of mule deer by vehicular traffic even if my conclusions were constrained by study design.

Sexually transmitted diseases affect millions of lives every year. In order to most effectively use prevention resources epidemiologists deploy models to understand how the disease spreads through the population and which intervention methods will be most effective at reducing disease perpetuation. Increasingly agent-based models are being used to simulate population heterogeneity and fine-grain sociological effects that are difficult to capture with traditional compartmental and statistical models. A key challenge is using a sufficiently large number of agents to produce robust and reliable results while also running in a reasonable amount of time. In this thesis we show the effectiveness of agent-based modeling in planning coordinated responses to a sexually transmitted disease epidemic and present efficient algorithms for running these models in parallel and in a distributed setting. The model is able to account for population heterogeneity like age preference, concurrent partnership, and coital dilution, and the implementation scales well to large population sizes to produce robust results in a reasonable amount of time. The work helps epidemiologists and public health officials plan a targeted and well-informed response to a variety of epidemic scenarios.

Fire prone Mediterranean-type vegetation systems like those in the Mediterranean Basin and South-Western Australia are global hot spots for plant species diversity. To ensure management programs act to maintain these highly diverse plant communities, it is necessary to get a profound understanding of the crucial mechanisms of coexistence. In the current literature several mechanisms are discussed. The objective of my thesis is to systematically explore the importance of potential mechanisms for maintaining multi-species, fire prone vegetation by modelling. The model I developed is spatially-explicit, stochastic, rule- and individual-based. It is parameterised on data of population dynamics collected over 18 years in the Mediterranean-type shrublands of Eneabba, Western Australia. From 156 woody species of the area seven plant traits have been identified to be relevant for this study: regeneration mode, annual maximum seed production, seed size, maximum crown diameter, drought tolerance, dispersal mode and seed bank type. Trait sets are used for the definition of plant functional types (PFTs). The PFT dynamics are simulated annual by iterating life history processes. In the first part of my thesis I investigate the importance of trade-offs for the maintenance of high diversity in multi-species systems with 288 virtual PFTs. Simulation results show that the trade-off concept can be helpful to identify non-viable combinations of plant traits. However, the Shannon Diversity Index of modelled communities can be high despite of the presence of ‘supertypes’. I conclude, that trade-offs between two traits are less important to explain multi-species coexistence and high diversity than it is predicted by more conceptual models. Several studies show, that seed immigration from the regional seed pool is essential for maintaining local species diversity. However, systematical studies on the seed rain composition to multi-species communities are missing. The results of the simulation experiments, as presented in part two of this thesis, show clearly, that without seed immigration the local species community found in Eneabba drifts towards a state with few coexisting PFTs. With increasing immigration rates the number of simulated coexisting PFTs and Shannon diversity quickly approaches values as also observed in the field. Including the regional seed input in the model is suited to explain more aggregated measures of the local plant community structure such as species richness and diversity. Hence, the seed rain composition should be implemented in future studies. In the third part of my thesis I test the sensitivity of Eneabba PFTs to four different climate change scenarios, considering their impact on both local and regional processes. The results show that climate change clearly has the potential to alter the number of dispersed seeds for most of the Eneabba PFTs and therefore the source of the ‘immigrants’ at the community level. A classification tree analysis shows that, in general, the response to climate change was PFT-specific. In the Eneabba sand plains sensitivity of a PFT to climate change depends on its specific trait combination and on the scenario of environmental change i.e. development of the amount of rainfall and the fire frequency. This result emphasizes that PFT-specific responses and regional process seed immigration should not be ignored in studies dealing with the impact of climate change on future species distribution. The results of the three chapters are finally analysed in a general discussion. The model is discussed and improvements and suggestions are made for future research. My work leads to the following conclusions: i) It is necessary to support modelling with empirical work to explain coexistence in species-rich plant communities. ii) The chosen modelling approach allows considering the complexity of coexistence and improves the understanding of coexistence mechanisms. iii) Field research based assumptions in terms of environmental conditions and plant life histories can relativise the importance of more hypothetic coexistence theories in species-rich systems. In consequence, trade-offs can play a lower role than predicted by conceptual models. iv) Seed immigration is a key process for local coexistence. Its alteration because of climate change should be considered for prognosis of coexistence. Field studies should be carried out to get data on seed rain composition. / Feuer geprägte, mediterrane Vegetationstypen, wie sie im Mittelmeerraum und Süd-West Australien zu finden sind, gelten als globale „hotspots“ für Pflanzendiversität. Um sicher zu stellen, dass Managementprogramme zum Erhalt dieser hoch diversen Pflanzengesellschaften zielgerichtet beitragen, ist ein profundes Verständnis der wesentlichen Koexistenzmechanismen notwendig. In der aktuellen Literatur werden verschiedene Mechanismen diskutiert. Das Ziel meiner Doktorarbeit ist es, die Bedeutung der Mechanismen für den Erhalt der artenreichen, feuergeprägten Vegetation anhand eines Modells systematisch zu untersuchen. Das von mir dafür entwickelte Modell ist räumlich-explizit, stochastisch und regel- und individuenbasiert. Es ist unter Zuhilfenahme von Daten zu Populationsdynamiken parametrisiert, die über 18 Jahre im Mediterranen Buschland von Eneabba Westaustraliens gesammelt wurden. Anhand von 156 Arten sind sieben für meine Studie relevante Pflanzeneigenschaften identifiziert wurden: Regenerationsart, jährlich maximale Samenproduktion, Samengröße, maximaler Durchmesser, Trockentoleranz, Ausbreitungsart und Samenbanktyp. Kombinationen der Eigenschaften bilden funktionelle Pflanzentypen (PFTs), deren jährliche Dynamik über Lebenszyklusprozesse im Modell simuliert wird. Der erste Teil meiner Arbeit präsentiert die Studie zur Bedeutung von „trade-offs“ für den Erhalt der hohen Diversität in artenreichen Systemen. Die Simulationsergebnisse mit 288 virtuellen PFTs zeigen, dass das „trade-offs“-Konzept für die Identifizierung nicht-lebensfähiger Kombinationen von Pflanzeneigenschaften hilfreich sein kann. Allerdings kann der Shannon-Diversitäts-Index der modellierten Pflanzengesellschaft trotz der Anwesenheit von „Supertypen“ hoch sein. Ich schlussfolgere, dass „trade-off“ zwischen zwei Eigenschaften weniger wichtig für die Erklärung der Koexistenz von vielen Arten und hoher Diversität sind, als es durch konzeptionelle Modelle vorhergesagt wird. Viele Studien zeigen, dass Sameneintrag aus dem regionalen Samenpool essenziell für den Erhalt lokaler Artendiversität ist. Es gibt allerdings noch keine systematischen Studien zur Zusammensetzung des Samenregens artenreichen Systemen. Die Ergebnisse der Simulationsexperimente im zweiten Teil meiner Arbeit machen deutlich, dass ohne Sameneintrag die lokale Pflanzengesellschaft Eneabbas sich in eine Richtung entwickelt, in der nur wenige PFTs koexistieren. Mit steigender Samenimmigrationsrate erreicht die Anzahl an koexistierenden PFTs und die Shannon-Diversität schnell die Werte, die auch im Feld gefunden werden. Der regionale Sameneintrag kann also als Erklärung zur Struktur lokaler Pflanzengesellschaften dienen. Seine Zusammensetzung sollte jedoch in zukünftigen Studien berücksichtigt werden. Im dritten Teil meiner Doktorarbeit präsentiere ich Analysen zur Sensibilität der PFTs von Eneabba vorhergesagte Klimaszenarien und der Auswirkungen auf die Samenimmigration. Die Ergebnisse zeigen deutlich, dass Klimaänderungen das Potential haben, die Anzahl an ausgebreiteten Samen der meisten Eneabba PFTs zu verändern. Die Entscheidungsbaum-Analyse veranschaulicht, dass die Reaktion auf Klimaänderung PFT-spezifisch ist. In den Eneabba hängt die Sensitivität der PFTs gegenüber klimatischen Veränderungen von den PFT-spezifischen Eigenschaftskombinationen und vom Klimaszenarium ab, d.h. von der Entwicklung der Regenfallmenge und der Feuerfrequenz. Dieses Ergebnis betont, dass PFT-spezifische Reaktionen und die klimabedingten Änderungen in der Samenimmigration in Studien zum Einfluss von Klimaänderungen auf die zukünftige Artenverteilung berücksichtigt werden sollten. Die Ergebnisse aus den drei Kapiteln werden in der allgemeinen Diskussion zusammengeführt und analysiert. Das Modell wird diskutiert und Verbesserungen und Vorschläge für weitere Forschung aufgezeigt. Meine Arbeit führt zu folgenden Schlussfolgerungen: i) Es ist notwendig, empirische Arbeit und Modellierung zu kombinieren, um Koexistenz in artenreichen Systemen zu erklären. ii) Durch den gewählten Modellansatz kann die Komplexität von Koexistenz erfasst und das Verständnis vertieft werden. iii) Auf Felddaten basierende Annahmen bezüglich Umweltbedingungen und Lebenzyklus können zur Relativierung der Bedeutsamkeit von Mechanismen führen. So können Trade-offs eine geringere Rolle spielen, als konzeptionelle Modelle nahe legen. iv) Samenimmigration ist ein Schlüsselprozess für lokale Koexistenz. Deren Änderung aufgrund von Klimawandel sollte für Prognosen zu Artenvorkommen berücksichtigt werden. Feldstudien sollten durchgeführt werden, um die Datenlücken zur Samenregenzusammensetzung zu füllen.

Biofilms are communities of sessile microorganisms attached to a surface and imbeddedin a matrix of extracellular polysaccharide substances. These communities can be foundin diverse aquatic environments, such as in industrial pipes and in humans. By formingmicrocolony structures, which are highly resistant to adverse physical conditions as wellas antimicrobial agents, biofilms are very problematic when associated with e.g.persistent infections. In order to find new ways of controlling biofilm growth, theprocesses involved in biofilm development must be investigated further. The maininterest of this study is the occurrence of void formation inside biofilms. Thisphenomenon has been observed in several studies and has been correlated to cell deathinside the microcolonies. The occurrence of cell death has recently been associated withthe presence of nitric oxide in the biofilm. In this study, the implications of nitric oxideaccumulation on biofilm development were investigated using an individual-basedmodel. Specifically, the role of nitric oxide in void formation was considered. A largenumber of simulations were run using different parameter settings in order to determine ifnitric oxide could account for the occurrence of void formation observed experimentally.The general predictions made by the model system showed agreement to someexperimental data, but not to others. Sloughing, the detachment of chunks of cells fromthe biofilm, was observed in the majority of simulations. In some cases, the model alsopredicted the presence of live cells inside the voids, which has been observedexperimentally.

<p>Biofilms are communities of sessile microorganisms attached to a surface and imbeddedin a matrix of extracellular polysaccharide substances. These communities can be foundin diverse aquatic environments, such as in industrial pipes and in humans. By formingmicrocolony structures, which are highly resistant to adverse physical conditions as wellas antimicrobial agents, biofilms are very problematic when associated with e.g.persistent infections. In order to find new ways of controlling biofilm growth, theprocesses involved in biofilm development must be investigated further. The maininterest of this study is the occurrence of void formation inside biofilms. Thisphenomenon has been observed in several studies and has been correlated to cell deathinside the microcolonies. The occurrence of cell death has recently been associated withthe presence of nitric oxide in the biofilm. In this study, the implications of nitric oxideaccumulation on biofilm development were investigated using an individual-basedmodel. Specifically, the role of nitric oxide in void formation was considered. A largenumber of simulations were run using different parameter settings in order to determine ifnitric oxide could account for the occurrence of void formation observed experimentally.The general predictions made by the model system showed agreement to someexperimental data, but not to others. Sloughing, the detachment of chunks of cells fromthe biofilm, was observed in the majority of simulations. In some cases, the model alsopredicted the presence of live cells inside the voids, which has been observedexperimentally.</p>

A renewable energy source, biogas, comprises of methane (80%) and carbon dioxide (15%), and is a great alternative to the conventional fossil-based fuels, such as coal, gas and oil. Biogas is created during anaerobic biological digestion of waste materials, such as landfill material, animal manure, wastewater, algal biomass, industrial organic waste etc. A biogas potential from organic waste in the United States is estimated at about 9 million tons per year and technology allows capture of greenhouse gases, such as methane and carbon dioxide, into a form of a fuel. In the light of global climate change and efforts to decrease carbon footprint of fuels in daily life, usage of biogas as an alternative fuel to fossil fuels looks especially promising. The goal of this research was to develop and test an approach for optimization of biogas production by engineering microorganisms digesting organic waste. Specifically, bacteria that can digest algal biomass, collected from the wastewater lagoons or open waterbodies. The research also expands on the previous efforts to analyze microbial interactions in wastewater treatment systems. A computational model is developed to aid with prognosis of microbial consortia ability to form complex aggregates in reactors with upflow mode of feeding substrate. Combining modeling predictions and laboratory experiments in organic matter digestion will lead to more stable engineered systems and higher yields of biogas.

Grasslands cover 40% of the earth’s land area and provide numerous valuable ecosystem services. However, climate change, global land use change and increasing intensive anthropogenic interventions make grasslands to one of the most endangered ecosystem types in the world. Effective protection in the future requires a fundamental understanding of the dynamics of grasslands and their major drivers. Field experiments have been conducted for impact analyses, for example, with different management intensities, plant community composition and altered climatic conditions. Complementary, ecological models allow to extend the analysis to long-term effects of changes as well as to a deeper understanding of the underlying ecological processes. In this thesis, an individual-based grassland model and network science were applied to understand the community structure and dynamics emerging from individual plant interactions – in relation to plant traits, ecological processes, environmental and anthropogenic impacts, and the small-scale spatial distribution of plants. In the first study, an individual-based process-oriented grassland model was parameterized to simulate field data of a local biodiversity experiment using the concept of plant functional types. The influence of various functional plant traits and ecological processes on grassland productivity and functional composition were analyzed. Different functional plant traits showed partly contrasting effects on plant growth. With regard to the modeled ecological processes, competition for space between plants affected grassland productivity more than shading of plants. In the second study, the parameterized grassland model was used to analyze the impact of functional diversity, mowing frequency and air temperature on ecological processes that lead to changes in grassland productivity. The model reproduced the increase of biomass yields with functional diversity as observed in the field experiment. Modeled plant competition for space showed to be the dominant process and was responsible for an increase in biomass yields in more frequently mown grasslands. In the third study, an approach to generate a regionally transferable parameterization of the grassland model is presented. The impact of management, environment and climate change on productivity and functional composition of grasslands was analyzed within a German-wide scenario analysis. Management intensity had more influence on grassland productivity than environmental factors and correlations of productivity with environmental factors become stronger in less managed grasslands. Climate change showed to have only a minor influence on simulated vegetation attributes. In the fourth study, network science was applied to forest megaplots to quantify the spatial neighborhood structure of species-rich ecosystems. Networks at the individual-tree and tree-species levels revealed similar structures at three investigated forest sites. Tropical tree species coexisted in small-scale networks and only up to 51% of all possible connections between species pairs were realized. A null community analysis showed that details on the tree position and tree size have no major influence on the network structures identified. In summary, this thesis presents the development of advanced methods and analysis tools as well as their application to vegetation ecosystems with high diversity. Thereby, complex structures and dynamics of ecological systems could be systematically explored by combining ecological models with extensive field measurements.

Predicting the behavior of groups and how it emerges from the behaviours of individuals is a difficult task. Not only are individuals and their behaviors affected by the group and vice versa, but the way individuals are affected by and react to various conditions is difficult to predict due to the complex nature of human beings. However, if one could build models that sufficiently capture the behavior of individuals, it would be possible to simulate groups and make a prediction for the emergent behavior that way. Public Goods Games (PGGs) are a type of economic game that explores how individuals engage in cooperation and where different types of collective behaviors emerge. In group-based settings such as PGGs, there is a high level behavior pattern belonging to the group as a whole. In this work, we study how the group behavior emerges from the collection of behaviors belonging to individuals in the group. To this end, we create a model that predicts the emergent collective behavior in a PGG given a set of individual behaviors present within the group. We devise a classification scheme that groups individuals into a small set of phenotypes based on the behavior they exhibit in a PGG. We then create a model that simulates the long term behavior of groups playing a PGG based on the relative distribution of these phenotypes. Our simulation uses cognitive modeling with ACT-R to individually simulate each participant in a game. We find that our model is able to simulate group behavior that resembles what is seen with human participants given only the relative distribution of phenotypes. However, the model is not able to generalize to a PGG where the rules of the game are slightly changed. In modifying the distribution of phenotypes present in simulations, we found that increasing the number of cooperative individuals resulted in a stronger upward trend in group average contribution, while increasing the number of non-cooperative individuals had the opposite effect. Increasing the number of conditional cooperative individuals resulted in slowing the movement of group average contribution trend. / Att förutspå gruppers beteenden och hur dessa uppstår från individernas beteenden är svårt av flera skäl. Dels påverkar individernas beteende gruppen och vice versa, och dels är det svårt att förutspå hur individer påverkas av och reagerar på olika situationer och förhållanden på grund av människans komplexa natur. Om man kunde bygga modeller som fångar individers beteenden tillräckligt väl skulle det vara möjligt att genom simulering kunna ge förutsägelser på gruppens beteende. Public Goods Games (PGGs) är en typ av ekonomiskt spel som utforskar hur individer väljer att sammarbeta och där kollektiva beteenden kan uppstå. Inom gruppbaserade miljöer, som till exempel PGGs, finns det beteenden som tillhör gruppen i sig. I detta arbete studerar vi hur det gruppbeteendet härstammar från samlingen av individuella beteenden inom gruppen. För det skapar vi en modell som ger förutsägelser om det framväxande kollektiva beteendet i en PGG, givet kunskap om fördelningen av olika typer av individuella beteenden som finns i gruppen. För att göra detta utvecklar vi ett klassificeringssystem som grupperar individer i olika fenotyper baserat på deras uppvisade beteende i ett PGG. Vi skapar sedan en modell som simulerar detta PGG med en given grupp av individer. Våran simulering använder kognitiv modellering med ACT-R för att simulera varje enskild deltagare i ett PGG. Vi finner att vår modell simulerar gruppbeteenden som liknar det som syns med mänskliga deltagare, givet att man vet fördelningen av fenotyper i grupper. Modellen kan dock inte generalisera till ett PGG där reglerna är ändrade. När vi ändrade distributionen av fenotyper i simuleringen fann vi att ett ökat nummer av sammarbetsvilliga individer gjorde så att trenden av gruppen genomsnittliga bidrag rörde sig uppåt, medans ett ökat nummer av ej sammarbetsvilliga individer hade motsatt effekt. Då vi ökade antalet vilkorligt sammarbetsvilliga individer fann vi att det saktade ner förändringar av gruppen genomsnittliga bidrag.