Taxonomy, species richness and biogeography of Finnish crane flies (Diptera, Tipuloidea).

Biodiversity is unequally spread throughout terrestrial ecosystems. The highest species richness of animals and plants is encountered around the Equator, and naturalists observe a decrease in the number of creatures with increasing latitude. Some animal groups, however, display an anomalous species richness pattern, but these are exceptions to the general rule. Crane flies (Diptera, Tipuloidea) are small to large sized, non-biting nematoceran insects, being mainly associated with moist environments. The species richness of crane flies is highest in the tropics, but these insects are species rich and abundant in all biogeographic realms, boreal and arctic biomes included. The phylogeny and systematics of crane flies are still at an early stage and somewhat controversial. New species are constantly discovered even from temperate Europe, faunistically the best known continent. Crane flies have been rather neglected group of insects in Finland. The history of Finnish crane fly taxonomy and faunistics started in 1907, the year when Carl Lundström published his two first articles on tipuloids. Within roughly 100 years there have been only a handful of entomologists studying the Finnish fauna, and the species richness and natural history of these flies have remained poorly understood and mapped. The aim of this thesis is to clarify the taxonomy of Finnish crane flies, present an updated and annotated list of species and seek patterns in regional species richness and assemblage composition. Tipula stackelbergi Alexander has been revised (I). This species was elevated to a species rank from a subspecific rank under T. pruinosa Wiedemann and T. stackelbergi was also deleted from the list of European crane flies. Two new synonyms were found: T. subpruinosa Mannheims is a junior synonym of T. freyana Lackschewitz and T. usuriensis Alexander is a junior synonym of T. pruinosa. A new species Tipula recondita Pilipenko & Salmela has been described (II). Both morphology and COI (mtDNA) sequences were used in the assessment of the status of the species. The new species is highly disjunct, known from Finland and Russian Far East. A list of Finnish crane flies was presented, including the presence of species in the Finnish biogeographical provinces (III). A total of twenty-four species were formally reported for the first time from Finland and twenty-two previously reported species were deleted from the list. A short historical review on the studies of Finnish crane flies has been provided. The current list of Finnish species consists of 338 crane flies (IV, Appendix I). Species richness of all species and saproxylic/fungivorous species is negatively correlated with latitude, but mire-dwelling species show a reversed species richness gradient (i.e. an increase in the number of species toward north). Provincial assemblages displayed a strong latitudinal gradient and faunistic distance increased with increasing geographical distance apart of the provinces. Nearly half (48 %) of the Finnish crane flies are Trans-Palaearctic, roughly one-third (34 %) are West Palaearctic and only 16 and 2 % are Holarctic and Fennoscandian, respectively. Due to the legacy of Pleistocene glaciations, endemic Fennoscandian species are problematic and it is thus concluded that there are probably no true endemic crane flies in this region. Finally, there are probably species living within Finnish borders that have hitherto remained unnoticed. Based on subjective assessment, the number of “true” (i.e. recorded + unknown species) species count of Finnish crane flies is at minimum 350.

Bag-of-Features Approach to Unsupervised Visual Object Categorisation

The large and growing number of digital images is making manual image search laborious. Only a fraction of the images contain metadata that can be used to search for a particular type of image. Thus, the main research question of this thesis is whether it is possible to learn visual object categories directly from images. Computers process images as long lists of pixels that do not have a clear connection to high-level semantics which could be used in the image search. There are various methods introduced in the literature to extract low-level image features and also approaches to connect these low-level features with high-level semantics. One of these approaches is called Bag-of-Features which is studied in the thesis. In the Bag-of-Features approach, the images are described using a visual codebook. The codebook is built from the descriptions of the image patches using clustering. The images are described by matching descriptions of image patches with the visual codebook and computing the number of matches for each code. In this thesis, unsupervised visual object categorisation using the Bag-of-Features approach is studied. The goal is to find groups of similar images, e.g., images that contain an object from the same category. The standard Bag-of-Features approach is improved by using spatial information and visual saliency. It was found that the performance of the visual object categorisation can be improved by using spatial information of local features to verify the matches. However, this process is computationally heavy, and thus, the number of images must be limited in the spatial matching, for example, by using the Bag-of-Features method as in this study. Different approaches for saliency detection are studied and a new method based on the Hessian-Affine local feature detector is proposed. The new method achieves comparable results with current state-of-the-art. The visual object categorisation performance was improved by using foreground segmentation based on saliency information, especially when the background could be considered as clutter.

Variational Ensemble Kalman Filtering in Hydrology

The current thesis manuscript studies the suitability of a recent data assimilation method, the Variational Ensemble Kalman Filter (VEnKF), to real-life fluid dynamic problems in hydrology. VEnKF combines a variational formulation of the data assimilation problem based on minimizing an energy functional with an Ensemble Kalman filter approximation to the Hessian matrix that also serves as an approximation to the inverse of the error covariance matrix. One of the significant features of VEnKF is the very frequent re-sampling of the ensemble: resampling is done at every observation step. This unusual feature is further exacerbated by observation interpolation that is seen beneficial for numerical stability. In this case the ensemble is resampled every time step of the numerical model. VEnKF is implemented in several configurations to data from a real laboratory-scale dam break problem modelled with the shallow water equations. It is also tried in a two-layer Quasi- Geostrophic atmospheric flow problem. In both cases VEnKF proves to be an efficient and accurate data assimilation method that renders the analysis more realistic than the numerical model alone. It also proves to be robust against filter instability by its adaptive nature.