<p>Environmental changes, such as climate change, can have differential effects on species, with important consequences for community structure and ultimately, for ecosystem functioning. In the Arctic, where ecosystems are experiencing warming at twice the rate as elsewhere, these effects are expected to be particularly strong. A proper characterization of the link between warming and biotic interactions in these particular communities is of global importance because the tundra's permafrost stores a vast amount of carbon that could be released through decomposition as greenhouse gases and alter the global rate of climate change. In this dissertation, I examine how arthropod communities are responding to warming in the Arctic and how these responses might be affecting ecosystem functioning. </p><p>I first address the question of whether and how long-term changes in climate are affecting individual groups and overall community structure in a high-arctic arthropod food web. I find that increasingly warm springs and summers between 1996-2011 differentially affected some arthropod groups and that this led to major changes in the relative abundances of different trophic groups within the arthropod community. Specifically, spring and summer warming are associated with relatively more herbivores and parasitoids and fewer detritivores within the community. These changes are particularly pronounced in heath sites, suggesting that arthropod communities in dry habitats are more responsive to climate change than those in wet habitats. I also show that herbivores and parasitoids are sensitive to conditions at subzero temperatures, even during periods of diapause, and that all trophic groups benefit from a longer transition period between summer and winter. These results suggest that the projected winter and springtime warming in Greenland may have unexpected consequences for northern arthropod communities. Moreover, the relative increase in herbivores and loss of detritivores may be changing the influence of the arthropod community over key ecosystem processes such as decomposition, nutrient cycling, and primary productivity in the tundra. </p><p>Predator-induced trophic cascades have been shown to impact both community structure and ecosystem processes, yet it is unclear how climate change may exacerbate or dampen predator effects on ecosystems. In the second chapter of my dissertation, I investigate the role of one of the dominant tundra predators within the arctic ecosystem, wolf spiders, and how their impact might be changing with warming. Using results from a two-year-long field experiment, I test the influence of wolf spider density over the structure of soil microarthropod communities and decomposition rates under both ambient and artificially warmed temperatures. I find that predator effects on soil microarthropods change in response to warming and that these changes translate into context-specific indirect effects of predators on decomposition. Specifically, while high densities of wolf spiders lead to faster decomposition rates at ambient temperatures, they are associated with slower decomposition rates in experimentally warmed plots. My results suggest that if warming causes an increase in arctic wolf spider densities, these spiders may buffer the rate at which the massive pool of stored carbon is lost from the tundra. </p><p>Wolf spiders in the Arctic are expected to become larger with warming, but it is unclear how this change in body size will affect spider populations or the role of wolf spiders within arctic food webs. In the third chapter of my dissertation, I explore wolf spider population structure and juvenile recruitment at three sites of the Alaskan Arctic that naturally differ in mean spider body size. I find that there are fewer juveniles in sites where female body sizes are larger and that this pattern is likely driven by a size-related increase in the rate of intraspecific cannibalism. These findings suggest that across the tundra landscape, there is substantial variation in the population structure and trophic position of wolf spiders, which is driven by differences in female spider body sizes. </p><p>Overall, this dissertation demonstrates that arctic arthropod communities are changing as a result of warming. In the long-term, warming is causing a shift in arthropod community structure that is likely altering the functional role of these animals within the ecosystem. However even in the short-term, warming can alter species interactions and community structure, with important consequences for ecosystem function. Arthropods are not typically considered to be major players in arctic ecosystems, but I provide evidence that this assumption should be questioned. Considering that they are the largest source of animal biomass across much of the tundra, it is likely that their activities have important consequences for regional and global carbon dynamics.</p> / Dissertation

In this thesis we consider two research problems, namely, (i) language constructs for modelling stochastic programming (SP) problems and (ii) solution methods for processing instances of different classes of SP problems. We first describe a new design of an SP modelling system which provides greater extensibility and reuse. We implement this enhanced system and develop solver connections. We also investigate in detail the following important classes of SP problems: singlestage SP with risk constraints, two-stage linear and stochastic integer programming problems. We report improvements to solution methods for single-stage problems with second-order stochastic dominance constraints and two-stage SP problems. In both cases we use the level method as a regularisation mechanism. We also develop novel heuristic methods for stochastic integer programming based on variable neighbourhood search. We describe an algorithmic framework for implementing decomposition methods such as the L-shaped method within our SP solver system. Based on this framework we implement a number of established solution algorithms as well as a new regularisation method for stochastic linear programming. We compare the performance of these methods and their scale-up properties on an extensive set of benchmark problems. We also implement several solution methods for stochastic integer programming and report a computational study comparing their performance. The three solution methods, (a) processing of a single-stage problem with second-order stochastic dominance constraints, (b) regularisation by the level method for two-stage SP and (c) method for solving integer SP problems, are novel approaches and each of these makes a contribution to knowledge.

No description available.

In an exploratory approach to data analysis, it is often useful to consider the observations as generated from a set of latent generators or 'sources' via a generally unknown mapping. Reconstructing sources from their mixtures is an extremely ill-posed problem in general. However, solutions to such inverse problems can, in many cases, be achieved by incorporating prior knowledge about the problem, captured in the form of constraints. This setting is a natural candidate for the application of the Bayesian method- ology, allowing us to incorporate "soft" constraints in a natural manner. This Thesis proposes the use of sparse statistical decomposition methods for ex- ploratory analysis of datasets. We make use of the fact that many natural signals have a sparse representation in appropriate signal dictionaries. The work described in this Thesis is mainly driven by problems in the analysis of large datasets, such as those from functional magnetic resonance imaging of the brain for the neuro-scientific goal of extracting relevant 'maps' from the data. We first propose Bayesian Iterative Thresholding, a general method for solv- ing blind linear inverse problems under sparsity constraints, and we apply it to the problem of blind source separation. The algorithm is derived by maximiz- ing a variational lower-bound on the likelihood. The algorithm generalizes the recently proposed method of Iterative Thresholding. The probabilistic view en- ables us to automatically estimate various hyperparameters, such as those that control the shape of the prior and the threshold, in a principled manner. We then derive an efficient fully Bayesian sparse matrix factorization model for exploratory analysis and modelling of spatio-temporal data such as fMRI. We view sparse representation as a problem in Bayesian inference, following a ma- chine learning approach, and construct a structured generative latent-variable model employing adaptive sparsity-inducing priors. The construction allows for automatic complexity control and regularization as well as denoising. The performance and utility of the proposed algorithms is demonstrated on a variety of experiments using both simulated and real datasets. Experimental results with benchmark datasets show that the proposed algorithms outper- form state-of-the-art tools for model-free decompositions such as independent component analysis.

Currently the degree sequence problem for 3-hypergraphs is still unsolved efficiently. This paper researches the 3-hypergraphic problem in terms of edge switching and exchanges in the sequence to implement Dewdney’s reduction. It proposes the idea of an irreducible decomposition and makes use of it to find some sufficient conditions for a 3-hypergraphic sequence. In addition, this paper explores a related problem: intersection preserving mappings. / May 2016

Three Essays in Energy and Environmental Economics Author: Mgr. Lukáš Rečka Supervisor: Mgr. Milan Ščasný, Ph.D. Academic Year: 2018/2019 Abstract This thesis consists of three articles that share the main theme - energy and environment. The dissertation aims mainly at the Czech energy system and analyses it development after the Velvet Revolution and its possible future development. The first article applies Logarithmic Mean Divisia Index decomposition to analyses the main driving forces of significant reduction in air quality pollutants during the transition of the Czech economy towards market economy in the 1990s. It continues then to investigate how the driving forces affected the emissions volumes during succeeding the post-transition period up to 2016. The second article reacts on the 2015 governmental decision to lift brown coal mining limits in the North Bohemia coal basin. The paper analyses the impacts of maintaining the ban on mining coal reserves and compares them with three alternative options that would each weaken the environmental protections of the ban. The impacts of each of these alternative governmental propostions are analysed on the Czech energy system, the fuel- and the technology-mix, the costs of generating energy, related emissions and external costs associated with the emissions....

This thesis considers a novel problem of obtaining global channel state information (CSI) at every node in an ad-hoc wireless network. A class of protocols for dissemination and estimation are developed which attempt to minimize the staleness of the estimates throughout the network. This thesis also provides an optimal protocol for CSI dissemination in networks with complete graph topology and a near optimal protocol in networks having incomplete graph topology. In networks with complete graph topology, the protocol for CSI dissemination is shown to have a resemblance to finding Eulerian tours in complete graphs. For networks having incomplete graph topology, a lower bound on maximum staleness is given and a near optimal algorithm based on finding minimum connected dominating sets and proper scheduling is described in this thesis.

This thesis considers a novel problem of obtaining global channel state information (CSI) at every node in an ad-hoc wireless network. A class of protocols for dissemination and estimation are developed which attempt to minimize the staleness of the estimates throughout the network. This thesis also provides an optimal protocol for CSI dissemination in networks with complete graph topology and a near optimal protocol in networks having incomplete graph topology. In networks with complete graph topology, the protocol for CSI dissemination is shown to have a resemblance to finding Eulerian tours in complete graphs. For networks having incomplete graph topology, a lower bound on maximum staleness is given and a near optimal algorithm based on finding minimum connected dominating sets and proper scheduling is described in this thesis.

In forest ecosystems, substantial part of carbon enters soil in the form of plant litter. The decomposition of litter and soil organic matter represents an important process affecting nutrient cycling and carbon balance in soils. Fungi are considered the primary decomposers in terrestrial ecosystems due to the production of wide range of extracellular enzymes that allow them to attack the lignocellulose matrix in litter. Even if fungi represent key players in organic matter decomposition, the information about the structure and diversity of their communities is still limited and the roles of individual fungal taxa in forest soils remain unclear. This Ph.D. thesis focused on the characterization of fungal communities in forest soils and their potential to decompose plant litter. The method for in-depth analysis of complex microbial communities from environmental samples was established and used. In addition, single eukaryotic functional gene was analysed in soil for the first time at a depth that allowed reliable estimation of diversity. It was demonstrated that microbial community composition differs among horizons of forest soil profile. Despite similar diversity, significant differences in microbial community composition were observed between the DNA and RNA. Several microbial groups highly...

Dead plant biomass is a key pool of carbon in terrestrial ecosystems. Its decomposition in soil environments is thus an essential process of the carbon cycle. Fungi are considered to be the primary decomposers in soil ecosystems because of their physiological adaptations and enzymatic apparatus composed from highly effective oxidative and hydrolytic enzymes. Many recent works show that in addition to fungi, bacteria may also play a significant role in lignocellulose decomposition and among bacteria, the members of the phylum Actinobacteria are often regarded to significantly contribute to cellulose and lignocellulose decomposition. This thesis is focused on the evaluation of the role that fungi and Actinobacteria play in dead plant biomass degradation. First, it explored mechanisms involved in degradation, in particular the enzymatic breakdown of major lignocellulose components as cellulose, hemicelluloses and lignin. Enzymatic apparatus of the saprotrophic fungus Fomes fomentarius was explored both in vitro as well as in vivo. Several Actinobacteria were isolated from soil and comparative experiments, investigating production of hydrolytic enzymes, were carried out to track the transformation of polysaccharides and lignin by these strains. To explain the roles of lignocellulose decomposers in...