Wood-inhabiting Basidiomycetes in the Caucasus Region : Systematics and Biogeography

The Caucasus region is a hotspot of biodiversity and is one of the few areas in the Northern Hemisphere which harbor Pleistocene glacial refugia. The region encompasses Armenia, Azerbaijan, Georgia, the southernmost European Russia, NE Turkey, and northern Iran. The study on fungal composition of the Caucasus region and its connection and possible contribution to the present mycota of Europe has largely escaped empirical scrutiny. Using taxonomic surveys, phylogenetic reconstruction methods, haplotype analysis, and similarity tests, this study has aimed to, 1) summarize the knowledge on the occurrence of corticioids and polypores in the Caucasus region, 2) resolve the phylogenetic relationships of selected, resupinate wood-inhabiting basidiomycetes for which the Caucasus region is currently the mere, or one of the noteworthy areas of distribution, and, 3) assess the similarity of Caucasian corticioid fungi to those of Europe and important areas in the Northern Hemisphere, and to examine the significance of the Caucasus region as a glacial refugium for these fungi. This study provides the first catalogue of corticioids and polypores (635 species) occurring in the Caucasus region. The phylogeny and systematics of the Caucasian resupinate taxa in focus has been resolved and the usefulness of some morphological characters has been re-evaluated. In this context, four new genera and two new species were described and five new combinations were proposed, two of which were supplemented with modern descriptions. The species composition of corticioids in the Caucasus region is found to be distinctly more similar to Europe and North America than to East Asia and India. The highest molecular diversity and within population pairwise distance for Peniophorella praetermissa has been detected in the Caucasus and East Asia, with the isolates of the latter area being highly divergent from the European ones. This, and the assignment of root haplotype to the Caucasian isolates in a haplotype network for Phlebia tuberucalta and P. livida, call attention to the role of the Caucasus region in shaping the current mycota of Europe.

Phylogenetics of Myrmica ants and their social parasites

Understanding the overwhelming diversity of life calls for complex organisational schemes. The field of systematics may thus be seen as the cornerstone of evolutionary biology. In the last few decades, systematics has been rejuvenated through the introduction of molecular methods such as DNA barcoding and multi-gene phylogenetic approaches. These methods may shed new light on established taxonomic ideas and problems. For example, the classification of ants has aroused much debate due to reinterpretation of morphological characters or contradictions between molecular data and morphology. Only in the last few years a consensus was reached regarding the phylogeny of ant subfamilies. However, the situation remains deplorable for lower taxonomic ranks such as subfamilies, tribes and genera. This thesis describes the systematics and evolution of the Holarctic ant genus Myrmica and the tribe to which it belongs, Myrmicini. Using barcoding, molecular-phylogenetic data and divergence time estimations, it addresses questions regarding the taxonomy, morphology and biogeography of this group. Furthermore, the interrelationships between socially parasitic Myrmica species and their hosts (other species in the genus) were inferred. The phylogeny suggests that social parasitism evolved several times in Myrmica. Finally, this thesis investigated whether coevolution shaped the phylogeny of socially parasitic Maculinea butterflies that live inside Myrmica colonies. No evidence was found for coevolution.

Markov random fields and spatial smoothing in improving of forest inventory estimates

Markov random fields (MRF) are popular in image processing applications to describe spatial dependencies between image units. Here, we take a look at the theory and the models of MRFs with an application to improve forest inventory estimates. Typically, autocorrelation between study units is a nuisance in statistical inference, but we take an advantage of the dependencies to smooth noisy measurements by borrowing information from the neighbouring units. We build a stochastic spatial model, which we estimate with a Markov chain Monte Carlo simulation method. The smooth values are validated against another data set increasing our confidence that the estimates are more accurate than the originals.