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Current changes of biodiversity result almost exclusively from human activities. This anthropogenic conversion of natural ecosystems during the last decades has led to the so-called ‘biodiversity crisis’, which comprises the loss of species as well as changes in the global distribution patterns of organisms. Species richness is unevenly distributed worldwide. Altogether, 17 so-called ‘megadiverse’ nations cover less than 10% of the earth’s land surface but support nearly 70% of global species richness. Mexico, the study area of this thesis, is one of those countries. However, due to Mexico’s large extent and geographical complexity, it is impossible to conduct reliable and spatially explicit assessments of species distribution ranges based on these collection data and field work alone. In the last two decades, Species distribution models (SDMs) have been established as important tools for extrapolating such in situ observations. SDMs analyze empirical correlations between geo-referenced species occurrence data and environmental variables to obtain spatially explicit surfaces indicating the probability of species occurrence. Remote sensing can provide such variables which describe biophysical land surface characteristics with high effective spatial resolutions. Especially during the last three to five years, the number of studies making use of remote sensing data for modeling species distributions has therefore multiplied. Due to the novelty of this field of research, the published literature consists mostly of selective case studies. A systematic framework for modeling species distributions by means of remote sensing is still missing. This research gap was taken up by this thesis and specific studies were designed which addressed the combination of climate and remote sensing data in SDMs, the suitability of continuous remote sensing variables in comparison with categorical land cover classification data, the criteria for selecting appropriate remote sensing data depending on species characteristics, and the effects of inter-annual variability in remotely sensed time series on the performance of species distribution models. The corresponding novel analyses were conducted with the Maximum Entropy algorithm developed by Phillips et al. (2004). In this thesis, a more comprehensive set of remote sensing predictors than in the existing literature was utilized for species distribution modeling. The products were selected based on their ecological relevance for characterizing species distributions. Two 1 km Terra-MODIS Land 16-day composite standard products including the Enhanced Vegetation Index (EVI), Reflectance Data, and Land Surface Temperature (LST) were assembled into enhanced time series for the time period of 2001 to 2009. These high-dimensional time series data were then transformed into 18 phenological and 35 statistical metrics that were selected based on an extensive literature review. Spatial distributions of twelve tree species were modeled in a hierarchical framework which integrated climate (WorldClim) and MODIS remote sensing data. The species are representative of the major Mexican forest types and cover a variety of ecological traits, such as range size and biotope specificity. Trees were selected because they have a high probability of detection in the field and since mapping vegetation has a long tradition in remote sensing. The result of this thesis showed that the integration of remote sensing data into species distribution models has a significant potential for improving and both spatial detail and accuracy of the model predictions.
Nowadays, agriculturally used areas form a major part of the German landscape. The conversion from natural habitats to agriculturally used grasslands fundamentally influences the diversity of plants and animals. Intensive use of these areas increases indeed the productivity of crop or biomass on meadows as food source for cattle. How these influences affect biodiversity, ecosystems and trophic interactions over years is still not understood completely. To understand biodiversity functions in an agriculturally used area my study focused on the influence of land use (fertilization, grazing and mowing) on a herbivore-parasitoid system of Plantago lanceolata. The ribwort plantain is a generalist herb of cosmopolitan distribution. It can grow in a very broad range of ground conditions (both in wet and dry habitats), which makes P. lanceolata an ideal model system for investigating tritrophic interactions in a gradient of land use intensity. The weevils Mecinus labilis and M. pascuorum feed and oviposit on P. lanceolata. Mesopolobus incultus is a generalist parasitoid that parasitizes different insect orders. However its only hosts on P. lanceolata are the two weevil species mentioned before. The intention of my study was to investigate the influence of land use on a tritrophic system and its surrounding vegetation (structure, density and species richness) at different spatial scales like subplot, plot and landscape level in three different regions (north, middle and south of Germany). I studied the influence of land use intensity not only correlative but also experimentally. Additionally I aimed to reveal how vegetation composition changes host plant metabolites and whether these changes impact higher trophic levels in the field.