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The heterogeneity in species assemblages of epigeal spiders was studied in a natural forest and in a managed forest. Additionally the effects of small-scale microhabitat heterogeneity of managed and unmanaged forests were determined by analysing the spider assemblages of three different microhabitat structures (i. vegetation, ii. dead wood. iii. litter cover). The spider were collected in a block design by pitfall traps (n=72) in a 4-week interval. To reveal key environmental factors affecting the spider distribution abiotic and biotic habitat parameters (e.g. vegetation parameters, climate parameters, soil moisture) were assessed around each pitfall trap. A TWINSPAN analyses separated pitfall traps from the natural forest from traps of the managed forest. A subsequent discriminant analyses revealed that the temperature, the visible sky, the plant diversity and the mean diameter at breast height as key discriminant factors between the microhabitat groupings designated by the TWINSPAN analyses. Finally a Redundant analysis (RDA) was done revealing similar environmental factors responsible for the spider species distribution, as a good separation of the different forest types as well as the separation of the microhabitat groupings from the TWINSPAN. Overall the study revealed that the spider communities differed between the forest types as well as between the microhabitat structures and thus species distribution changed within a forest stand on a fine spatial scale. It was documented that the structure of managed forests affects the composition of spider assemblages compared to natural forests significantly and even small scale-heterogeneity seems to influence the spider species composition.
Today about 24 Million people worldwide suffer from dementia, Alzheimer’s Disease accounts for approximately 50-60% of all dementia cases. As the prevalence of dementia grows with increasing age Alzheimer’s Disease becomes more and more of an issue for society as the proportion of elderly people increases from year to year. It is well established, that the amino acid glutamate - quantitatively being the most important neurotransmitter in the central nervous system (CNS) - may reach toxic concentrations if not cleared from the synaptic cleft into which it is released during transmittance of action potentials. In Alzheimer’s Disease there is strong evidence for a generally impaired glutamate uptake system which in turn is thought to result in toxic levels of the amino acid with the potential to kill off neurons. The excitatory amino acid transporter 1 (EAAT1) belongs to the family of Na+-dependent glutamate transporter and accounts together with EAAT2 for most of the glutamate uptake in the CNS. In this project a new splice variant of EAAT1, skipping exon 3 was detected in human brain samples and subsequently called EAAT1Δ3, this being the second splice variant found after the recent detection of EAAT1Δ9. A method was developed to quantify the transcript of EAAT1 wt, EAAT1Δ3 and EAAT1Δ9 by means of real-time PCR. Samples were taken from different brain areas of a set of control and AD cases. The areas chosen for examination are affected differently in Alzheimer’s Disease, this was used an internal control for the experiments done in this project as to determine whether any effect observed is specific for AD, i.e. AD affected areas or is generally seen in all areas examined. The results of this project show that EAAT1Δ3 is transcribed in very low copy numbers making up a proportion of 0.15% of EAAT1 wt whereas EAAT1Δ9 is transcribed in a considerably large proportion of EAAT1 wt of 26.6%. It was moreover found that all EAAT1 variants are transcribed at significantly lower rates (P<0.0001) in AD cases, supporting the theory that EAAT1 protein expression is reduced to a point where glutamate uptake normally mediated by this transporter is impaired. This in turn is thought to result in toxic levels glutamate accounting for neuronal loss in the disease. No area-dependent effects were found, suggesting that the reduction of EAAT1 transcription is rather a result of an underlying general mechanism present in AD. Further research will have to be done to assess the degree of EAAT1 expression in AD and whether those future findings match with the result of this project.
Different habitat models were created for the White Stork (Ciconia ciconia) in the region of the former German province of East Prussia (equals app. the current Russian oblast Kaliningrad and the Polish voivodship Warmia-Masuria). Different historical data sets describing the occurrence of the White Stork in the 1930s, as well as selected variables for the description of landscape and habitat, were employed. The processing and modeling of the applied data sets was done with a geographical information system (ArcGIS) and a statistical modeling approach that comes from the disciplines of machine-learning and data mining (TreeNet by Salford Systems Ltd.). Applying historical habitat descriptors, as well as data on the occurrence of the White Stork, models on two different scales were created: (i) a point scale model applying a raster with a cell size of 1 km2 and (ii) an administrative district scale model based on the organization of the former province of East Prussia. The evaluation of the created models show that the occurrence of White Stork nesting grounds in the former East Prussia for most parts is defined by the variables ‘forest’, ‘settlement area’, ‘pasture land’ and ‘proximity to coastline’. From this set of variables it can be assumed that a good food supply and nesting opportunities are provided to the White Stork in pasture and meadows as well as in the proximity to human settlements. These could be seen as crucial factors for the choice of nesting White Stork in East Prussia. Dense forest areas appear to be unsuited as nesting grounds of White Storks. The high influence of the variable ‘coastline’ is most likely explained by the specific landscape composition of East Prussia parallel to the coastline and is to be seen as a proximal factor for explaining the distribution of breeding White Storks. In a second step, predictions for the period of 1981 to 1993 could be made applying both scales of the models created in this study. In doing so, a decline of potential nesting habitat was predicted on the point scale. In contrast, the predicted White Stork occurrence increases when applying the model of the administrative district scale. The difference between both predictions is to be seen in the application of different scales (density versus suitability as breeding ground) and partly dissimilar explanatory variables. More studies are needed to investigate this phenomenon. The model predictions for the period 1981 to 1993 could be compared to the available inventories of that period. It shows that the figures predicted here were higher than the figures established by the census. This means that the models created here show rather a capacity of the habitat (potential niche). Other factors affecting the population size e.g. breeding success or mortality have to be investigated further. A feasible approach on how to generate possible habitat models was shown employing the methods presented here and applying historical data as well as assessing the effects of changes in land use on the White Stork. The models present the first of their kind, and could be improved by means of further data regarding the structure of the habitat and more exact spatially explicit information on the location of the nesting sites of the White Stork. In a further step, a habitat model of the present times should be created. This would allow for a more precise comparison regarding the findings from the changes of land use and relevant conditions of the environment on the White Stork in the region of former East Prussia, e.g. in the light of coming landscape changes brought by the European Union (EU).