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Die visuelle Kommunikation ist eine effiziente Methode, um dynamische Phänomene zu beschreiben. Informationsobjekte präzise wahrzunehmen, einen schnellen Zugriff auf strukturierte und relevante Informationen zu ermöglichen, erfordert konsistente und nach dem formalen Minimalprinzip konzipierte Analyse- und Darstellungsmethoden. Dynamische Raumphänomene in Geoinformationssystemen können durch den Mangel an konzeptionellen Optimierungsanpassungen aufgrund ihrer statischen Systemstruktur nur bedingt die Informationen von Raum und Zeit modellieren. Die Forschung in dieser Arbeit ist daher auf drei interdisziplinäre Ansätze fokussiert. Der erste Ansatz stellt eine echtzeitnahe Datenerfassung dar, die in Geodatenbanken zeitorientiert verwaltet wird. Der zweite Ansatz betrachtet Analyse- und Simulationsmethoden, die das dynamische Verhalten analysieren und prognostizieren. Der dritte Ansatz konzipiert Visualisierungsmethoden, die insbesondere dynamische Prozesse abbilden. Die Symbolisierung der Prozesse passt sich bedarfsweise in Abhängigkeit des Prozessverlaufes und der Interaktion zwischen Datenbanken und Simulationsmodellen den verschiedenen Entwicklungsphasen an. Dynamische Aspekte können so mit Hilfe bewährter Funktionen aus der GI-Science zeitnah mit modularen Werkzeugen entwickelt und visualisiert werden. Die Analyse-, Verschneidungs- und Datenverwaltungsfunktionen sollen hierbei als Nutzungs- und Auswertungspotential alternativ zu Methoden statischer Karten dienen. Bedeutend für die zeitliche Komponente ist das Verknüpfen neuer Technologien, z. B. die Simulation und Animation, basierend auf einer strukturierten Zeitdatenbank in Verbindung mit statistischen Verfahren. Methodisch werden Modellansätze und Visualisierungstechniken entwickelt, die auf den Bereich Verkehr transferiert werden. Verkehrsdynamische Phänomene, die nicht zusammenhängend und umfassend darstellbar sind, werden modular in einer serviceorientierten Architektur separiert, um sie in verschiedenen Ebenen räumlich und zeitlich visuell zu präsentieren. Entwicklungen der Vergangenheit und Prognosen der Zukunft werden über verschiedene Berechnungsmethoden modelliert und visuell analysiert. Die Verknüpfung einer Mikrosimulation (Abbildung einzelner Fahrzeuge) mit einer netzgesteuerten Makrosimulation (Abbildung eines gesamten Straßennetzes) ermöglicht eine maßstabsunabhängige Simulation und Visualisierung des Mobilitätsverhaltens ohne zeitaufwendige Bewertungsmodellberechnungen. Zukünftig wird die visuelle Analyse raum-zeitlicher Veränderungen für planerische Entscheidungen ein effizientes Mittel sein, um Informationen übergreifend verfügbar, klar strukturiert und zweckorientiert zur Verfügung zu stellen. Der Mehrwert durch visuelle Geoanalysen, die modular in einem System integriert sind, ist das flexible Auswerten von Messdaten nach zeitlichen und räumlichen Merkmalen.
The nature of the interaction between prehistoric humans and their environment, especially the vegetation, has long been of interest. The Qinghai Lake Basin in North China is well-suited to exploring the interactions between prehistoric humans and vegetation in the Tibetan Plateau, because of the comparatively dense distribution of archaeological sites and the ecologically fragile environment. Previous pollen studies of Qinghai Lake have enabled a detailed reconstruction of the regional vegetation, but they have provided relatively little information on vegetation change within the Qinghai Lake watershed. To address the issue we conducted a pollen-based vegetation reconstruction for an archaeological site (YWY), located on the southern shore of Qinghai Lake. We used high temporal-resolution pollen records from the YWY site and from Qinghai Lake, spanning the interval since the last deglaciation (15.3 kyr BP to the present) to quantitatively reconstruct changes in the local and regional vegetation using Landscape Reconstruction Algorithm models. The results show that, since the late glacial, spruce forest grew at high altitudes in the surrounding mountains, while the lakeshore environment was occupied mainly by shrub-steppe. From the lateglacial to the middle Holocene, coniferous woodland began to expand downslope and reached the YWY site at 7.1 kyr BP. The living environment of the local small groups of Paleolithic-Epipaleolithic humans (during 15.3-13.1 kyr BP and 9-6.4 kyr BP) changed from shrub-steppe to coniferous forest-steppe. The pollen record shows no evidence of pronounced changes in the vegetation community corresponding to human activity. However, based on a comparison of the local and regional vegetation reconstructions, low values of biodiversity and a significant increase in two indicators of vegetation degradation, Chenopodiaceae and Rosaceae, suggest that prehistoric hunters-gatherers likely disturbed the local vegetation during 9.0-6.4 kyr BP. Our findings are a preliminary attempt to study human-environment interactions at Paleolithic-Epipaleolithic sites in the region, and they contribute to ongoing environmental archaeology research in the Tibetan Plateau.
In this dissertation the lattice and the magnetic recovery dynamics of the two heavy rare-earth metals Dy and Gd after femtosecond photoexcitation are described. For the investigations, thin films of Dy and Gd were measured at low temperatures in the antiferromagnetic phase of Dy and close to room temperature in the ferromagnetic phase of Gd. Two different optical pump-x-ray probe techniques were employed: Ultrafast x-ray diffraction with hard x-rays (UXRD) yields the structural response of heavy rare-earth metals and resonant soft (elastic) x-ray diffraction (RSXD), which allows measuring directly changes in the helical antiferromagnetic order of Dy. The combination of both techniques enables to study the complex interaction between the magnetic and the phononic subsystems.
In this thesis, the two prototype catalysts Fe(CO)₅ and Cr(CO)₆ are investigated with time-resolved photoelectron spectroscopy at a high harmonic setup. In both of these metal carbonyls, a UV photon can induce the dissociation of one or more ligands of the complex. The mechanism of the dissociation has been debated over the last decades. The electronic dynamics of the first dissociation occur on the femtosecond timescale.
For the experiment, an existing high harmonic setup was moved to a new location, was extended, and characterized. The modified setup can induce dynamics in gas phase samples with photon energies of 1.55eV, 3.10eV, and 4.65eV. The valence electronic structure of the samples can be probed with photon energies between 20eV and 40eV. The temporal resolution is 111fs to 262fs, depending on the combination of the two photon energies.
The electronically excited intermediates of the two complexes, as well as of the reaction product Fe(CO)₄, could be observed with photoelectron spectroscopy in the gas phase for the first time. However, photoelectron spectroscopy gives access only to the final ionic states. Corresponding calculations to simulate these spectra are still in development. The peak energies and their evolution in time with respect to the initiation pump pulse have been determined, these peaks have been assigned based on literature data. The spectra of the two complexes show clear differences. The dynamics have been interpreted with the assumption that the motion of peaks in the spectra relates to the movement of the wave packet in the multidimensional energy landscape. The results largely confirm existing models for the reaction pathways. In both metal carbonyls, this pathway involves a direct excitation of the wave packet to a metal-to-ligand charge transfer state and the subsequent crossing to a dissociative ligand field state. The coupling of the electronic dynamics to the nuclear dynamics could explain the slower dissociation in Fe(CO)₅ as compared to Cr(CO)₆.
Arctic tundra landscapes are composed of a complex mosaic of patterned ground features, varying in soil moisture, vegetation composition, and surface hydrology over small spatial scales (10-100 m). The importance of microtopography and associated geomorphic landforms in influencing ecosystem structure and function is well founded, however, spatial data products describing local to regional scale distribution of patterned ground or polygonal tundra geomorphology are largely unavailable. Thus, our understanding of local impacts on regional scale processes (e.g., carbon dynamics) may be limited. We produced two key spatiotemporal datasets spanning the Arctic Coastal Plain of northern Alaska (similar to 60,000 km(2)) to evaluate climate-geomorphological controls on arctic tundra productivity change, using (1) a novel 30m classification of polygonal tundra geomorphology and (2) decadal-trends in surface greenness using the Landsat archive (1999-2014). These datasets can be easily integrated and adapted in an array of local to regional applications such as (1) upscaling plot-level measurements (e.g., carbon/energy fluxes), (2) mapping of soils, vegetation, or permafrost, and/or (3) initializing ecosystem biogeochemistry, hydrology, and/or habitat modeling.
Peatlands represent large terrestrial carbon banks. Given that most peat accumulates in boreal regions, where low temperatures and water saturation preserve organic matter, the existence of peat in (sub)tropical regions remains enigmatic. Here we examined peat and plant chemistry across a latitudinal transect from the Arctic to the tropics. Near-surface low-latitude peat has lower carbohydrate and greater aromatic content than near-surface high-latitude peat, creating a reduced oxidation state and resulting recalcitrance. This recalcitrance allows peat to persist in the (sub)tropics despite warm temperatures. Because we observed similar declines in carbohydrate content with depth in high-latitude peat, our data explain recent field-scale deep peat warming experiments in which catotelm (deeper) peat remained stable despite temperature increases up to 9 degrees C. We suggest that high-latitude deep peat reservoirs may be stabilized in the face of climate change by their ultimately lower carbohydrate and higher aromatic composition, similar to tropical peats.
This work presents mathematical and computational approaches to cover various aspects of metabolic network modelling, especially regarding the limited availability of detailed kinetic knowledge on reaction rates. It is shown that precise mathematical formulations of problems are needed i) to find appropriate and, if possible, efficient algorithms to solve them, and ii) to determine the quality of the found approximate solutions. Furthermore, some means are introduced to gain insights on dynamic properties of metabolic networks either directly from the network structure or by additionally incorporating steady-state information. Finally, an approach to identify key reactions in a metabolic networks is introduced, which helps to develop simple yet useful kinetic models. The rise of novel techniques renders genome sequencing increasingly fast and cheap. In the near future, this will allow to analyze biological networks not only for species but also for individuals. Hence, automatic reconstruction of metabolic networks provides itself as a means for evaluating this huge amount of experimental data. A mathematical formulation as an optimization problem is presented, taking into account existing knowledge and experimental data as well as the probabilistic predictions of various bioinformatical methods. The reconstructed networks are optimized for having large connected components of high accuracy, hence avoiding fragmentation into small isolated subnetworks. The usefulness of this formalism is exemplified on the reconstruction of the sucrose biosynthesis pathway in Chlamydomonas reinhardtii. The problem is shown to be computationally demanding and therefore necessitates efficient approximation algorithms. The problem of minimal nutrient requirements for genome-scale metabolic networks is analyzed. Given a metabolic network and a set of target metabolites, the inverse scope problem has as it objective determining a minimal set of metabolites that have to be provided in order to produce the target metabolites. These target metabolites might stem from experimental measurements and therefore are known to be produced by the metabolic network under study, or are given as the desired end-products of a biotechological application. The inverse scope problem is shown to be computationally hard to solve. However, I assume that the complexity strongly depends on the number of directed cycles within the metabolic network. This might guide the development of efficient approximation algorithms. Assuming mass-action kinetics, chemical reaction network theory (CRNT) allows for eliciting conclusions about multistability directly from the structure of metabolic networks. Although CRNT is based on mass-action kinetics originally, it is shown how to incorporate further reaction schemes by emulating molecular enzyme mechanisms. CRNT is used to compare several models of the Calvin cycle, which differ in size and level of abstraction. Definite results are obtained for small models, but the available set of theorems and algorithms provided by CRNT can not be applied to larger models due to the computational limitations of the currently available implementations of the provided algorithms. Given the stoichiometry of a metabolic network together with steady-state fluxes and concentrations, structural kinetic modelling allows to analyze the dynamic behavior of the metabolic network, even if the explicit rate equations are not known. In particular, this sampling approach is used to study the stabilizing effects of allosteric regulation in a model of human erythrocytes. Furthermore, the reactions of that model can be ranked according to their impact on stability of the steady state. The most important reactions in that respect are identified as hexokinase, phosphofructokinase and pyruvate kinase, which are known to be highly regulated and almost irreversible. Kinetic modelling approaches using standard rate equations are compared and evaluated against reference models for erythrocytes and hepatocytes. The results from this simplified kinetic models can simulate acceptably the temporal behavior for small changes around a given steady state, but fail to capture important characteristics for larger changes. The aforementioned approach to rank reactions according to their influence on stability is used to identify a small number of key reactions. These reactions are modelled in detail, including knowledge about allosteric regulation, while all other reactions were still described by simplified reaction rates. These so-called hybrid models can capture the characteristics of the reference models significantly better than the simplified models alone. The resulting hybrid models might serve as a good starting point for kinetic modelling of genome-scale metabolic networks, as they provide reasonable results in the absence of experimental data, regarding, for instance, allosteric regulations, for a vast majority of enzymatic reactions.
We present a setup combining a liquid flatjet sample delivery and a MHz laser system for time-resolved soft X-ray absorption measurements of liquid samples at the high brilliance undulator beamline UE52-SGM at Bessy II yielding unprecedented statistics in this spectral range. We demonstrate that the efficient detection of transient absorption changes in transmission mode enables the identification of photoexcited species in dilute samples. With iron(II)-trisbipyridine in aqueous solution as a benchmark system, we present absorption measurements at various edges in the soft X-ray regime. In combination with the wavelength tunability of the laser system, the set-up opens up opportunities to study the photochemistry of many systems at low concentrations, relevant to materials sciences, chemistry, and biology.
Changes in species' distributions are classically projected based on their climate envelopes. For Siberian forests, which have a tremendous significance for vegetation-climate feedbacks, this implies future shifts of each of the forest-forming larch (Larix) species to the north-east. However, in addition to abiotic factors, reliable projections must assess the role of historical biogeography and biotic interactions. Here, we use sedimentary ancient DNA and individual-based modelling to investigate the distribution of larch species and mitochondrial haplotypes through space and time across the treeline ecotone on the southern Taymyr peninsula, which at the same time presents a boundary area of two larch species. We find spatial and temporal patterns, which suggest that forest density is the most influential driver determining the precise distribution of species and mitochondrial haplotypes. This suggests a strong influence of competition on the species' range shifts. These findings imply possible climate change outcomes that are directly opposed to projections based purely on climate envelopes. Investigations of such fine-scale processes of biodiversity change through time are possible using paleoenvironmental DNA, which is available much more readily than visible fossils and can provide information at a level of resolution that is not reached in classical palaeoecology.
Brownian motion and viscoelastic anomalous diffusion in homogeneous environments are intrinsically Gaussian processes. In a growing number of systems, however, non-Gaussian displacement distributions of these processes are being reported. The physical cause of the non-Gaussianity is typically seen in different forms of disorder. These include, for instance, imperfect "ensembles" of tracer particles, the presence of local variations of the tracer mobility in heteroegenous environments, or cases in which the speed or persistence of moving nematodes or cells are distributed. From a theoretical point of view stochastic descriptions based on distributed ("superstatistical") transport coefficients as well as time-dependent generalisations based on stochastic transport parameters with built-in finite correlation time are invoked. After a brief review of the history of Brownian motion and the famed Gaussian displacement distribution, we here provide a brief introduction to the phenomenon of non-Gaussianity and the stochastic modelling in terms of superstatistical and diffusing-diffusivity approaches.