The co-occurrence of polyploidy and clonal reproduction among plant species has long been recognized, but the evolutionary mechanisms underlying the association are unknown. Here, I investigate whether polyploidy increases the magnitude of clonality, either directly or indirectly, by comparing the extent and spatial structure of clones between diploid and tetraploid Chamerion angustifolium in a greenhouse environment and natural populations. In the greenhouse, tetraploid plants allocated 90.4% more dry mass to root buds, the primary mechanism of clonal reproduction, than diploids. Per unit root mass, tetraploids produced 44% fewer root buds and the average position of the root buds along the root was 47% closer to the stem than in diploids. In natural populations, the magnitude of clonality in tetraploid C. angustifolium was similar or less than in diploids. However, clones were spatially aggregated in all diploid populations but only in two of five tetraploid populations. Average clone patch diameter, however, was not significantly different between diploids (3.9 m) and tetraploids (2.5 m). These data do not support the hypothesis that clonality increases as a result of genome duplication. Rather, it is possible that clonality is linked to genome duplication because clonal diploids are predisposed for polyploid formation and establishment. / National Science and Engineering Research Council, Canada Research Chair Program, Canadian Foundation for Innovation

This thesis deals with content based automatic photo categorization. The aim of the work is to experiment with advanced techniques of image represenatation and to create a classifier which is able to process large image dataset with sufficient accuracy and computation speed. A traditional solution based on using visual codebooks is enhanced by computing color features, soft assignment of visual words to extracted feature vectors, usage of image segmentation in process of visual codebook creation and dividing picture into cells. These cells are processed separately. Linear SVM classifier with explicit data embeding is used for its efficiency. Finally, results of experiments with above mentioned techniques of the image categorization are discussed.