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To investigate the reliability and stability of spherical harmonic models based on archeo/-paleomagnetic data, 2000 Geomagnetic models were calculated. All models are based on the same data set but with randomized uncertainties. Comparison of these models to the geomagnetic field model gufm1 showed that large scale magnetic field structures up to spherical harmonic degree 4 are stable throughout all models. Through a ranking of all models by comparing the dipole coefficients to gufm1 more realistic uncertainty estimates were derived than the authors of the data provide.
The derived uncertainty estimates were used in further modelling, which combines archeo/-paleomagnetic and historical data. The huge difference in data count, accuracy and coverage of these two very different data sources made it necessary to introduce a time dependent spatial damping, which was constructed to constrain the spatial complexity of the model. Finally 501 models were calculated by considering that each data point is a Gaussian random variable, whose mean is the original value and whose standard deviation is its uncertainty. The final model arhimag1k is calculated by taking the mean of the 501 sets of Gauss coefficients. arhimag1k fits different dependent and independent data sets well. It shows an early reverse flux patch at the core-mantle boundary between 1000 AD and 1200 AD at the location of the South Atlantic Anomaly today. Another interesting feature is a high latitude flux patch over Greenland between 1200 and 1400 AD. The dipole moment shows a constant behaviour between 1600 and 1840 AD.
In the second part of the thesis 4 new paleointensities from 4 different flows of the island Fogo, which is part of Cape Verde, are presented. The data is fitted well by arhimag1k with the exception of the value at 1663 of 28.3 microtesla, which is approximately 10 microtesla lower than the model suggest.
Unter atmosphärischen Zirkulationsregimen versteht man bevorzugte quasi-stationäre Zustände der atmosphärischen Zirkulation auf der planetaren Skala, die für eine bis mehrere Wochen persistieren können. Klimaänderungen, ob natürlich entstanden oder anthropogen verursacht, äußern sich in erster Linie durch Änderungen der Auftrittswahrscheinlichkeiten der natürlichen Regime. In der vorliegenden Arbeit wurden dynamische Mechanismen des Regimeverhaltens und der dekadischen Klimavariabilität der Atmosphäre bei Abwesenheit zeitlich veränderlicher externer Einflussfaktoren untersucht. Das Hauptwerkzeug dafür war ein quasi-geostrophisches Dreischichtenmodell der winterlichen atmosphärischen Zirkulation auf der Nordhemisphäre, das eine spektrale T21-Auflösung, einen orographischen und einen zeitlich konstanten thermischen Antrieb mit nicht-zonalen Anteilen besitzt. Ein solches Modell vermag großskalige atmosphärische Strömungsvorgänge außerhalb der Tropen mit einiger Genauigkeit zu simulieren. Nicht berücksichtigt werden Feuchteprozesse, die Wechselwirkung der Atmosphäre mit anderen Teilen des Klimasystems sowie anthropogene Einflüsse. Für das Dreischichtenmodell wurde ein automatisiertes, iteratives Verfahren zur Anpassung des thermischen Modellantriebs neu entwickelt. Jede Iteration des Verfahrens besteht aus einer Testintegration des Modells, ihrer Auswertung, dem Vergleich der Ergebnisse mit den NCEP-NCAR-Reanalysedaten aus den Wintermonaten Dezember, Januar und Februar sowie einer auf diesem Vergleich basierenden Antriebskorrektur. Nach Konvergenz des Verfahrens stimmt das Modell sowohl bezüglich des zonal gemittelten Klimazustandes als auch bezüglich der zeitgemittelten nicht-zonalen außertropischen diabatischen Erwärmung nahezu perfekt mit den wintergemittelten Reanalysedaten überein. In einer 1000-jährigen Simulation wurden die beobachtete mittlere Zirkulation im Winter sowie ihre Variabilität realitätsnah reproduziert, insbesondere die Arktische Oszillation (AO) und ihre vertikale Ausdehnung. Der AO-Index des Modells weist deutliche dekadische Schwankungen auf, die allein durch die interne Modelldynamik bedingt sind. Darüber hinaus zeigt das Modell ein Regimeverhalten, das gut mit den Beobachtungsdaten übereintimmt. Es besitzt ein Regime, das in etwa der negativen Phase der Nordatlantischen Oszillation (NAO) entspricht und eines, das der positiven Phase der AO ähnelt. Eine weit verbreitete Hypothese ist die näherungsweise Übereinstimmung zwischen Regimen und stationären Lösungen der Bewegungsgleichungen. In der vorliegenden Arbeit wurde diese Hypothese für das Dreischichtenmodell überprüft, mit negativem Resultat. Es wurden mittels eines Funktionalminimierungsverfahrens sechs verschiedene stationäre Zustände gefunden. Diese sind allesamt durch eine äußerst unrealistische Zirkulation gekennzeichnet und sind daher weit vom Modellattraktor entfernt. Fünf der sechs Zustände zeichnen sich durch einen extrem starken subtropischen Jet in der mittleren und obereren Modellschicht aus. Da die Ursache des Regimeverhaltens des Dreischichtenmodells nach wie vor unklar war, wurde auf ein einfacheres Modell, nämlich ein barotropes Modell mit T21-Auflösung zurückgegriffen. Für die Anpassung des Oberflächenantriebs wurde eine modifizierte Form der iterativen Prozedur verwendet. Die zeitgemittelte Zirkulation des barotropen Modells stimmt sehr gut mit der zeitlich und vertikal gemittelten Zirkulation des Dreischichtenmodells überein. Das dominierende räumliche Muster der Variabilität besitzt eine AO-ähnliche Struktur. Zudem besitzt das barotrope Modell zwei Regime, die näherungsweise der positiven und negativen Phase der AO entsprechen und somit auch den Regimen des Dreischichtenmodells ähneln. Im Verlauf der Justierung des Oberflächenantriebs konnte beobachtet werden, dass die zwei Regime des barotropen Modells durch die Vereinigung zweier koexistierender Attraktoren entstanden. Der wahrscheinliche Mechanismus der Attraktorvereinigung ist eine Randkrise eines der beiden Attraktoren, gefolgt von einer explosiven Bifurkation des anderen Attraktors. Es wird die Hypothese aufgestellt, dass der beim barotropen Modell vorgefundene Mechanismus der Regimeentstehung für atmosphärische Zirkulationsmodelle mit realitätsnahem Regimeverhalten Allgemeingültigkeit besitzt. Gestützt wird die Hypothese durch vier Experimente mit dem Dreischichtenmodell, bei denen jeweils der Parameter der Bodenreibung verringert und die Antriebsanpassung wiederholt wurde. Bei diesen Experimenten erhöhte sich die Persistenz und die Separiertheit der Regime bei abnehmender Reibung drastisch und damit auch der Anteil dekadischer Zeitskalen an der Variabilität. Die Zunahme der Persistenz der Regime ist charakteristisch für die Annäherung an eine inverse innere Krise, deren Existenz aber nicht nachgewiesen werden konnte.
Mathematical modeling of biological phenomena has experienced increasing interest since new high-throughput technologies give access to growing amounts of molecular data. These modeling approaches are especially able to test hypotheses which are not yet experimentally accessible or guide an experimental setup. One particular attempt investigates the evolutionary dynamics responsible for today's composition of organisms. Computer simulations either propose an evolutionary mechanism and thus reproduce a recent finding or rebuild an evolutionary process in order to learn about its mechanism. The quest for evolutionary fingerprints in metabolic and gene-coexpression networks is the central topic of this cumulative thesis based on four published articles. An understanding of the actual origin of life will probably remain an insoluble problem. However, one can argue that after a first simple metabolism has evolved, the further evolution of metabolism occurred in parallel with the evolution of the sequences of the catalyzing enzymes. Indications of such a coevolution can be found when correlating the change in sequence between two enzymes with their distance on the metabolic network which is obtained from the KEGG database. We observe that there exists a small but significant correlation primarily on nearest neighbors. This indicates that enzymes catalyzing subsequent reactions tend to be descended from the same precursor. Since this correlation is relatively small one can at least assume that, if new enzymes are no "genetic children" of the previous enzymes, they certainly be descended from any of the already existing ones. Following this hypothesis, we introduce a model of enzyme-pathway coevolution. By iteratively adding enzymes, this model explores the metabolic network in a manner similar to diffusion. With implementation of an Gillespie-like algorithm we are able to introduce a tunable parameter that controls the weight of sequence similarity when choosing a new enzyme. Furthermore, this method also defines a time difference between successive evolutionary innovations in terms of a new enzyme. Overall, these simulations generate putative time-courses of the evolutionary walk on the metabolic network. By a time-series analysis, we find that the acquisition of new enzymes appears in bursts which are pronounced when the influence of the sequence similarity is higher. This behavior strongly resembles punctuated equilibrium which denotes the observation that new species tend to appear in bursts as well rather than in a gradual manner. Thus, our model helps to establish a better understanding of punctuated equilibrium giving a potential description at molecular level. From the time-courses we also extract a tentative order of new enzymes, metabolites, and even organisms. The consistence of this order with previous findings provides evidence for the validity of our approach. While the sequence of a gene is actually subject to mutations, its expression profile might also indirectly change through the evolutionary events in the cellular interplay. Gene coexpression data is simply accessible by microarray experiments and commonly illustrated using coexpression networks where genes are nodes and get linked once they show a significant coexpression. Since the large number of genes makes an illustration of the entire coexpression network difficult, clustering helps to show the network on a metalevel. Various clustering techniques already exist. However, we introduce a novel one which maintains control of the cluster sizes and thus assures proper visual inspection. An application of the method on Arabidopsis thaliana reveals that genes causing a severe phenotype often show a functional uniqueness in their network vicinity. This leads to 20 genes of so far unknown phenotype which are however suggested to be essential for plant growth. Of these, six indeed provoke such a severe phenotype, shown by mutant analysis. By an inspection of the degree distribution of the A.thaliana coexpression network, we identified two characteristics. The distribution deviates from the frequently observed power-law by a sharp truncation which follows after an over-representation of highly connected nodes. For a better understanding, we developed an evolutionary model which mimics the growth of a coexpression network by gene duplication which underlies a strong selection criterion, and slight mutational changes in the expression profile. Despite the simplicity of our assumption, we can reproduce the observed properties in A.thaliana as well as in E.coli and S.cerevisiae. The over-representation of high-degree nodes could be identified with mutually well connected genes of similar functional families: zinc fingers (PF00096), flagella, and ribosomes respectively. In conclusion, these four manuscripts demonstrate the usefulness of mathematical models and statistical tools as a source of new biological insight. While the clustering approach of gene coexpression data leads to the phenotypic characterization of so far unknown genes and thus supports genome annotation, our model approaches offer explanations for observed properties of the coexpression network and furthermore substantiate punctuated equilibrium as an evolutionary process by a deeper understanding of an underlying molecular mechanism.
This study presents the development of 1D and 2D Surface Evolution Codes (SECs) and their coupling to any lithospheric-scale (thermo-)mechanical code with a quadrilateral structured surface mesh.
Both SECs involve diffusion as approach for hillslope processes and the stream power law to reflect riverbed incision. The 1D SEC settles sediment that was produced by fluvial incision in the appropriate minimum, while the supply-limited 2D SEC DANSER uses a fast filling algorithm to model sedimantation. It is based on a cellular automaton. A slope-dependent factor in the sediment flux extends the diffusion equation to nonlinear diffusion. The discharge accumulation is achieved with the D8-algorithm and an improved drainage accumulation routine. Lateral incision enhances the incision's modelling. Following empirical laws, it incises channels of several cells width.
The coupling method enables different temporal and spatial resolutions of the SEC and the thermo-mechanical code. It transfers vertical as well as horizontal displacements to the surface model. A weighted smoothing of the 3D surface displacements is implemented. The smoothed displacement vectors transmit the deformation by bilinear interpolation to the surface model. These interpolation methods ensure mass conservation in both directions and prevent the two surfaces from drifting apart.
The presented applications refer to the evolution of the Pamir orogen. A calibration of DANSER's parameters with geomorphological data and a DEM as initial topography highlights the advantage of lateral incision. Preserving the channel width and reflecting incision peaks in narrow channels, this closes the huge gap between current orogen-scale incision models and observed topographies.
River capturing models in a system of fault-bounded block rotations reaffirm the importance of the lateral incision routine for capturing events with channel initiation. The models show a low probability of river capturings with large deflection angles. While the probability of river capturing is directly depending on the uplift rate, the erodibility inside of a dip-slip fault speeds up headward erosion along the fault: The model's capturing speed increases within a fault.
Coupling DANSER with the thermo-mechanical code SLIM 3D emphasizes the versatility of the SEC. While DANSER has minor influence on the lithospheric evolution of an indenter model, the brittle surface deformation is strongly affected by its sedimentation, widening a basin in between two forming orogens and also the southern part of the southern orogen to south, east and west.
The protection of species is one major focus in conservation biology. The basis for any management concept is the knowledge of the species autecology. In my thesis, I studied the life-history traits and population dynamics of the endangered Lesser Spotted Woodpecker (Picoides minor) in Central Europe. Here, I combine a range of approaches, from empirical investigations of a Lesser Spotted Woodpecker population in the Taunus low mountain range in Germany, the analysis of empirical data and the development of an individual-based stochastic model simulating the population dynamics. In the field studies I collected basic demographic data of reproductive success and mortality. Moreover, breeding biology and behaviour were investigated in detail. My results showed a significant decrease of the reproductive success with later timing of breeding, caused by deterioration in food supply. Moreover, mate fidelity was of benefit, since pairs composed of individuals that bred together the previous year started earlier with egg laying and obtained a higher reproductive success. Both sexes were involved in parental care, but the care was only shared equally during incubation and the early nestling stage. In the late nestling stage, parental care strategies differed between sexes: Females considerably decreased feeding rate with number of nestlings and even completely deserted small broods. Males fed their nestlings irrespective of brood size and compensated for the females absence. The organisation of parental care in the Lesser Spotted Woodpecker is discussed to provide the possibility for females to mate with two males with separate nests and indeed, polyandry was confirmed. To investigate the influence of the observed flexibility in the social mating system on the population persistence, a stochastic individual-based model simulating the population dynamics of the Lesser Spotted Woodpecker was developed, based on empirical results. However, pre-breeding survival rates could not be obtained empirically and I present in this thesis a pattern-oriented modelling approach to estimate pre-breeding survival rates by comparing simulation results with empirical pattern of population structure and reproductive success on population level. Here, I estimated the pre-breeding survival for two Lesser Spotted Woodpecker populations on different latitudes to test the reliability of the results. Finally, I used the same simulation model to investigate the effect of flexibility in the mating system on the persistence of the population. With increasing rate of polyandry in the population, the persistence increased and even low rates of polyandry had a strong influence. Even when presuming only a low polyandry rate and costs of polyandry in terms of higher mortality and lower reproductive success for the secondary male, the positive effect of polyandry on the persistence of the population was still strong. This thesis greatly helped to increase the knowledge of the autecology of an endangered woodpecker species. Beyond the relevance for the species, I could demonstrate here that in general flexibility in mating systems are buffer mechanisms and reduce the impact of environmental and demographic noise.
The Andes are a ~7000 km long N-S trending mountain range developed along the South American western continental margin. Driven by the subduction of the oceanic Nazca plate beneath the continental South American plate, the formation of the northern and central parts of the orogen is a type case for a non-collisional orogeny. In the southern Central Andes (SCA, 29°S-39°S), the oceanic plate changes the subduction angle between 33°S and 35°S from almost horizontal (< 5° dip) in the north to a steeper angle (~30° dip) in the south. This sector of the Andes also displays remarkable along- and across- strike variations of the tectonic deformation patterns. These include a systematic decrease of topographic elevation, of crustal shortening and foreland and orogenic width, as well as an alternation of the foreland deformation style between thick-skinned and thin-skinned recorded along- and across the strike of the subduction zone. Moreover, the SCA are a very seismically active region. The continental plate is characterized by a relatively shallow seismicity (< 30 km depth) which is mainly focussed at the transition from the orogen to the lowland areas of the foreland and the forearc; in contrast, deeper seismicity occurs below the interiors of the northern foreland. Additionally, frequent seismicity is also recorded in the shallow parts of the oceanic plate and in a sector of the flat slab segment between 31°S and 33°S. The observed spatial heterogeneity in tectonic and seismic deformation in the SCA has been attributed to multiple causes, including variations in sediment thickness, the presence of inherited structures and changes in the subduction angle of the oceanic slab. However, there is no study that inquired the relationship between the long-term rheological configuration of the SCA and the spatial deformation patterns. Moreover, the effects of the density and thickness configuration of the continental plate and of variations in the slab dip angle in the rheological state of the lithosphere have been not thoroughly investigated yet. Since rheology depends on composition, pressure and temperature, a detailed characterization of the compositional, structural and thermal fields of the lithosphere is needed. Therefore, by using multiple geophysical approaches and data sources, I constructed the following 3D models of the SCA lithosphere: (i) a seismically-constrained structural and density model that was tested against the gravity field; (ii) a thermal model integrating the conversion of mantle shear-wave velocities to temperature with steady-state conductive calculations in the uppermost lithosphere (< 50 km depth), validated by temperature and heat-flow measurements; and (iii) a rheological model of the long-term lithospheric strength using as input the previously-generated models.
The results of this dissertation indicate that the present-day thermal and rheological fields of the SCA are controlled by different mechanisms at different depths. At shallow depths (< 50 km), the thermomechanical field is modulated by the heterogeneous composition of the continental lithosphere. The overprint of the oceanic slab is detectable where the oceanic plate is shallow (< 85 km depth) and the radiogenic crust is thin, resulting in overall lower temperatures and higher strength compared to regions where the slab is steep and the radiogenic crust is thick. At depths > 50 km, largest temperatures variations occur where the descending slab is detected, which implies that the deep thermal field is mainly affected by the slab dip geometry.
The outcomes of this thesis suggests that long-term thermomechanical state of the lithosphere influences the spatial distribution of seismic deformation. Most of the seismicity within the continental plate occurs above the modelled transition from brittle to ductile conditions. Additionally, there is a spatial correlation between the location of these events and the transition from the mechanically strong domains of the forearc and foreland to the weak domain of the orogen. In contrast, seismicity within the oceanic plate is also detected where long-term ductile conditions are expected. I therefore analysed the possible influence of additional mechanisms triggering these earthquakes, including the compaction of sediments in the subduction interface and dehydration reactions in the slab. To that aim, I carried out a qualitative analysis of the state of hydration in the mantle using the ratio between compressional- and shear-wave velocity (vp/vs ratio) from a previous seismic tomography. The results from this analysis indicate that the majority of the seismicity spatially correlates with hydrated areas of the slab and overlying continental mantle, with the exception of the cluster within the flat slab segment. In this region, earthquakes are likely triggered by flexural processes where the slab changes from a flat to a steep subduction angle.
First-order variations in the observed tectonic patterns also seem to be influenced by the thermomechanical configuration of the lithosphere. The mechanically strong domains of the forearc and foreland, due to their resistance to deformation, display smaller amounts of shortening than the relatively weak orogenic domain. In addition, the structural and thermomechanical characteristics modelled in this dissertation confirm previous analyses from geodynamic models pointing to the control of the observed heterogeneities in the orogen and foreland deformation style. These characteristics include the lithospheric and crustal thickness, the presence of weak sediments and the variations in gravitational potential energy.
Specific conditions occur in the cold and strong northern foreland, which is characterized by active seismicity and thick-skinned structures, although the modelled crustal strength exceeds the typical values of externally-applied tectonic stresses. The additional mechanisms that could explain the strain localization in a region that should resist deformation are: (i) increased tectonic forces coming from the steepening of the slab and (ii) enhanced weakening along inherited structures from pre-Andean deformation events. Finally, the thermomechanical conditions of this sector of the foreland could be a key factor influencing the preservation of the flat subduction angle at these latitudes of the SCA.
Complete protection against flood risks by structural measures is impossible. Therefore flood prediction is important for flood risk management. Good explanatory power of flood models requires a meaningful representation of bio-physical processes. Therefore great interest exists to improve the process representation. Progress in hydrological process understanding is achieved through a learning cycle including critical assessment of an existing model for a given catchment as a first step. The assessment will highlight deficiencies of the model, from which useful additional data requirements are derived, giving a guideline for new measurements. These new measurements may in turn lead to improved process concepts. The improved process concepts are finally summarized in an updated hydrological model. In this thesis I demonstrate such a learning cycle, focusing on the advancement of model evaluation methods and more cost effective measurements. For a successful model evaluation, I propose that three questions should be answered: 1) when is a model reproducing observations in a satisfactory way? 2) If model results deviate, of what nature is the difference? And 3) what are most likely the relevant model components affecting these differences? To answer the first two questions, I developed a new method to assess the temporal dynamics of model performance (or TIGER - TIme series of Grouped Errors). This method is powerful in highlighting recurrent patterns of insufficient model behaviour for long simulation periods. I answered the third question with the analysis of the temporal dynamics of parameter sensitivity (TEDPAS). For calculating TEDPAS, an efficient method for sensitivity analysis is necessary. I used such an efficient method called Fourier Amplitude Sensitivity Test, which has a smart sampling scheme. Combining the two methods TIGER and TEDPAS provided a powerful tool for model assessment. With WaSiM-ETH applied to the Weisseritz catchment as a case study, I found insufficient process descriptions for the snow dynamics and for the recession during dry periods in late summer and fall. Focusing on snow dynamics, reasons for poor model performance can either be a poor representation of snow processes in the model, or poor data on snow cover, or both. To obtain an improved data set on snow cover, time series of snow height and temperatures were collected with a cost efficient method based on temperature measurements on multiple levels at each location. An algorithm was developed to simultaneously estimate snow height and cold content from these measurements. Both, snow height and cold content are relevant quantities for spring flood forecasting. Spatial variability was observed at the local and the catchment scale with an adjusted sampling design. At the local scale, samples were collected on two perpendicular transects of 60 m length and analysed with geostatistical methods. The range determined from fitted theoretical variograms was within the range of the sampling design for 80% of the plots. No patterns were found, that would explain the random variability and spatial correlation at the local scale. At the watershed scale, locations of the extensive field campaign were selected according to a stratified sample design to capture the combined effects of elevation, aspect and land use. The snow height is mainly affected by the plot elevation. The expected influence of aspect and land use was not observed. To better understand the deficiencies of the snow module in WaSiM-ETH, the same approach, a simple degree day model was checked for its capability to reproduce the data. The degree day model was capable to explain the temporal variability for plots with a continuous snow pack over the entire snow season, if parameters were estimated for single plots. However, processes described in the simple model are not sufficient to represent multiple accumulation-melt-cycles, as observed for the lower catchment. Thus, the combined spatio-temporal variability at the watershed scale is not captured by the model. Further tests on improved concepts for the representation of snow dynamics at the Weißeritz are required. From the data I suggest to include at least rain on snow and redistribution by wind as additional processes to better describe spatio-temporal variability. Alternatively an energy balance snow model could be tested. Overall, the proposed learning cycle is a useful framework for targeted model improvement. The advanced model diagnostics is valuable to identify model deficiencies and to guide field measurements. The additional data collected throughout this work helps to get a deepened understanding of the processes in the Weisseritz catchment.
Predator-prey interactions provide central links in food webs. These interaction are directly or indirectly impacted by a number of factors. These factors range from physiological characteristics of individual organisms, over specifics of their interaction to impacts of the environment. They may generate the potential for the application of different strategies by predators and prey. Within this thesis, I modelled predator-prey interactions and investigated a broad range of different factors driving the application of certain strategies, that affect the individuals or their populations. In doing so, I focused on phytoplankton-zooplankton systems as established model systems of predator-prey interactions.
At the level of predator physiology I proposed, and partly confirmed, adaptations to fluctuating availability of co-limiting nutrients as beneficial strategies. These may allow to store ingested nutrients or to regulate the effort put into nutrient assimilation. We found that these two strategies are beneficial at different fluctuation frequencies of the nutrients, but may positively interact at intermediate frequencies. The corresponding experiments supported our model results. We found that the temporal structure of nutrient fluctuations indeed has strong effects on the juvenile somatic growth rate of {\itshape Daphnia}.
Predator colimitation by energy and essential biochemical nutrients gave rise to another physiological strategy. High-quality prey species may render themselves indispensable in a scenario of predator-mediated coexistence by being the only source of essential biochemical nutrients, such as cholesterol. Thereby, the high-quality prey may even compensate for a lacking defense and ensure its persistence in competition with other more defended prey species.
We found a similar effect in a model where algae and bacteria compete for nutrients. Now, being the only source of a compound that is required by the competitor (bacteria) prevented the competitive exclusion of the algae. In this case, the essential compounds were the organic carbon provided by the algae. Here again, being indispensable served as a prey strategy that ensured its coexistence.
The latter scenario also gave rise to the application of the two metabolic strategies of autotrophy and heterotrophy by algae and bacteria, respectively. We found that their coexistence allowed the recycling of resources in a microbial loop that would otherwise be lost. Instead, these resources were made available to higher trophic levels, increasing the trophic transfer efficiency in food webs.
The predation process comprises the next higher level of factors shaping the predator-prey interaction, besides these factors that originated from the functioning or composition of individuals. Here, I focused on defensive mechanisms and investigated multiple scenarios of static or adaptive combinations of prey defense and predator offense. I confirmed and extended earlier reports on the coexistence-promoting effects of partially lower palatability of the prey community. When bacteria and algae are coexisting, a higher palatability of bacteria may increase the average predator biomass, with the side effect of making the population dynamics more regular. This may facilitate experimental investigations and interpretations. If defense and offense are adaptive, this allows organisms to maximize their growth rate. Besides this fitness-enhancing effect, I found that co-adaptation may provide the predator-prey system with the flexibility to buffer external perturbations.
On top of these rather internal factors, environmental drivers also affect predator-prey interactions. I showed that environmental nutrient fluctuations may create a spatio-temporal resource heterogeneity that selects for different predator strategies. I hypothesized that this might favour either storage or acclimation specialists, depending on the frequency of the environmental fluctuations.
We found that many of these factors promote the coexistence of different strategies and may therefore support and sustain biodiversity. Thus, they might be relevant for the maintenance of crucial ecosystem functions that also affect us humans. Besides this, the richness of factors that impact predator-prey interactions might explain why so many species, especially in the planktonic regime, are able to coexist.
Structuring process models
(2012)
One can fairly adopt the ideas of Donald E. Knuth to conclude that process modeling is both a science and an art. Process modeling does have an aesthetic sense. Similar to composing an opera or writing a novel, process modeling is carried out by humans who undergo creative practices when engineering a process model. Therefore, the very same process can be modeled in a myriad number of ways. Once modeled, processes can be analyzed by employing scientific methods. Usually, process models are formalized as directed graphs, with nodes representing tasks and decisions, and directed arcs describing temporal constraints between the nodes. Common process definition languages, such as Business Process Model and Notation (BPMN) and Event-driven Process Chain (EPC) allow process analysts to define models with arbitrary complex topologies. The absence of structural constraints supports creativity and productivity, as there is no need to force ideas into a limited amount of available structural patterns. Nevertheless, it is often preferable that models follow certain structural rules. A well-known structural property of process models is (well-)structuredness. A process model is (well-)structured if and only if every node with multiple outgoing arcs (a split) has a corresponding node with multiple incoming arcs (a join), and vice versa, such that the set of nodes between the split and the join induces a single-entry-single-exit (SESE) region; otherwise the process model is unstructured. The motivations for well-structured process models are manifold: (i) Well-structured process models are easier to layout for visual representation as their formalizations are planar graphs. (ii) Well-structured process models are easier to comprehend by humans. (iii) Well-structured process models tend to have fewer errors than unstructured ones and it is less probable to introduce new errors when modifying a well-structured process model. (iv) Well-structured process models are better suited for analysis with many existing formal techniques applicable only for well-structured process models. (v) Well-structured process models are better suited for efficient execution and optimization, e.g., when discovering independent regions of a process model that can be executed concurrently. Consequently, there are process modeling languages that encourage well-structured modeling, e.g., Business Process Execution Language (BPEL) and ADEPT. However, the well-structured process modeling implies some limitations: (i) There exist processes that cannot be formalized as well-structured process models. (ii) There exist processes that when formalized as well-structured process models require a considerable duplication of modeling constructs. Rather than expecting well-structured modeling from start, we advocate for the absence of structural constraints when modeling. Afterwards, automated methods can suggest, upon request and whenever possible, alternative formalizations that are "better" structured, preferably well-structured. In this thesis, we study the problem of automatically transforming process models into equivalent well-structured models. The developed transformations are performed under a strong notion of behavioral equivalence which preserves concurrency. The findings are implemented in a tool, which is publicly available.
Point processes are a common methodology to model sets of events. From earthquakes to social media posts, from the arrival times of neuronal spikes to the timing of crimes, from stock prices to disease spreading -- these phenomena can be reduced to the occurrences of events concentrated in points. Often, these events happen one after the other defining a time--series.
Models of point processes can be used to deepen our understanding of such events and for classification and prediction. Such models include an underlying random process that generates the events. This work uses Bayesian methodology to infer the underlying generative process from observed data. Our contribution is twofold -- we develop new models and new inference methods for these processes.
We propose a model that extends the family of point processes where the occurrence of an event depends on the previous events. This family is known as Hawkes processes. Whereas in most existing models of such processes, past events are assumed to have only an excitatory effect on future events, we focus on the newly developed nonlinear Hawkes process, where past events could have excitatory and inhibitory effects. After defining the model, we present its inference method and apply it to data from different fields, among others, to neuronal activity.
The second model described in the thesis concerns a specific instance of point processes --- the decision process underlying human gaze control. This process results in a series of fixated locations in an image. We developed a new model to describe this process, motivated by the known Exploration--Exploitation dilemma. Alongside the model, we present a Bayesian inference algorithm to infer the model parameters.
Remaining in the realm of human scene viewing, we identify the lack of best practices for Bayesian inference in this field. We survey four popular algorithms and compare their performances for parameter inference in two scan path models.
The novel models and inference algorithms presented in this dissertation enrich the understanding of point process data and allow us to uncover meaningful insights.