Maintaining plant species richness by cattle grazing - mesic semi-natural grasslands as focal habitats

Semi-natural grasslands are the most important agricultural areas for biodiversity. The present study investigates the effects of traditional livestock grazing and mowing on plant species richness, the main emphasis being on cattle grazing in mesic semi-natural grasslands. The two reviews provide a thorough assessment of the multifaceted impacts and importance of grazing and mowing management to plant species richness. It is emphasized that livestock grazing and mowing have partially compensated the suppression of major natural disturbances by humans and mitigated the negative effects of eutrophication. This hypothesis has important consequences for nature conservation: A large proportion of European species originally adapted to natural disturbances may be at present dependent on livestock grazing and / or mowing. Furthermore, grazing and mowing are key management methods to mitigate effects of nutrient-enrichment. The species composition and richness in old (continuously grazed), new (grazing restarting 3-8 years ago) and abandoned (over 10 years) pastures differed consistently across a range of spatial scales, and was intermediate in new pastures compared to old and abandoned pastures. In mesic grasslands most plant species were shown to benefit from cattle grazing. Indicator species of biologically valuable grasslands and rare species were more abundant in grazed than in abandoned grasslands. Steep S-SW-facing slopes are the most suitable sites for many grassland plants and should be prioritized in grassland restoration. The proportion of species trait groups benefiting from grazing was higher in mesic semi-natural grasslands than in dry and wet grasslands. Consequently, species trait responses to grazing and the effectiveness of the natural factors limiting plant growth may be intimately linked High plant species richness of traditionally mowed and grazed areas is explained by numerous factors which operate on different spatial scales. Particularly important for maintaining large scale plant species richness are evolutionary and mitigation factors. Grazing and mowing cause a shift towards the conditions that have occurred during the evolutionary history of European plant species by modifying key ecological factors (nutrients, pH and light). The results of this Dissertation suggest that restoration of semi-natural grasslands by private farmers is potentially a useful method to manage biodiversity in the agricultural landscape. However, the quality of management is commonly improper, particularly due to financial constraints. For enhanced success of restoration, management regulations in the agri-environment scheme need to be defined more explicitly and the scheme should be revised to encourage management of biodiversity.

Search for a Light Higgs Boson in Central Exclusive Diffraction: Method and Detectors

By detecting leading protons produced in the Central Exclusive Diffractive process, p+p → p+X+p, one can measure the missing mass, and scan for possible new particle states such as the Higgs boson. This process augments - in a model independent way - the standard methods for new particle searches at the Large Hadron Collider (LHC) and will allow detailed analyses of the produced central system, such as the spin-parity properties of the Higgs boson. The exclusive central diffractive process makes possible precision studies of gluons at the LHC and complements the physics scenarios foreseen at the next e+e− linear collider. This thesis first presents the conclusions of the first systematic analysis of the expected precision measurement of the leading proton momentum and the accuracy of the reconstructed missing mass. In this initial analysis, the scattered protons are tracked along the LHC beam line and the uncertainties expected in beam transport and detection of the scattered leading protons are accounted for. The main focus of the thesis is in developing the necessary radiation hard precision detector technology for coping with the extremely demanding experimental environment of the LHC. This will be achieved by using a 3D silicon detector design, which in addition to the radiation hardness of up to 5×10^15 neutrons/cm2, offers properties such as a high signal-to- noise ratio, fast signal response to radiation and sensitivity close to the very edge of the detector. This work reports on the development of a novel semi-3D detector design that simplifies the 3D fabrication process, but conserves the necessary properties of the 3D detector design required in the LHC and in other imaging applications.

Semi-supervised learning of WLAN radio maps

In this thesis a manifold learning method is applied to the problem of WLAN positioning and automatic radio map creation. Due to the nature of WLAN signal strength measurements, a signal map created from raw measurements results in non-linear distance relations between measurement points. These signal strength vectors reside in a high-dimensioned coordinate system. With the help of the so called Isomap-algorithm the dimensionality of this map can be reduced, and thus more easily processed. By embedding position-labeled strategic key points, we can automatically adjust the mapping to match the surveyed environment. The environment is thus learned in a semi-supervised way; gathering training points and embedding them in a two-dimensional manifold gives us a rough mapping of the measured environment. After a calibration phase, where the labeled key points in the training data are used to associate coordinates in the manifold representation with geographical locations, we can perform positioning using the adjusted map. This can be achieved through a traditional supervised learning process, which in our case is a simple nearest neighbors matching of a sampled signal strength vector. We deployed this system in two locations in the Kumpula campus in Helsinki, Finland. Results indicate that positioning based on the learned radio map can achieve good accuracy, especially in hallways or other areas in the environment where the WLAN signal is constrained by obstacles such as walls.