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By the work presented in this thesis, the CH4 emissions of the River Saar were quantified in space and time continuously and all relevant processes leading to the observed pattern were identified. The direct comparison between reservoir zones and free-flowing intermediate reaches revealed, that the reservoir zones are CH4 emission hot spots and emitted over 90% of the total CH4. On average, the reservoir zones emitted over 80 times more CH4 per square meter than the intermediate reaches between dams (0.23 vs. 19.7 mol CH4 m-2 d-1). The high emission rates measured in the reservoir zones fall into the range of emissions observed in tropical reservoirs. The main reason for this is the accumulation of thick organic rich sediments and we showed that the net sedimentation rate is an excellent proxy for estimating ebullitive emissions. Within the hot spot zones, the ebullitive flux enhanced also the diffusive surface emissions as well as the degassing emissions at dams.
To resolve the high temporal variability, we developed an autonomous instrument for continuous measurements of the ebullition rate over long periods (> 4 weeks). With this instrument we could quantify the variability and identify the relevant trigger mechanisms. At the Saar, ship-lock induces surges and ship waves were responsible for over 85% of all large ebullition events. This dataset was also used to determine the error associated with short sampling periods and we found that with sampling periods of 24 hours as used in other studies, the ebullition rates were systematically underestimated by ~50%. Measuring the temporal variability enabled us to build up a conceptual framework for estimating the temporal pattern of ebullition in other aquatic systems. With respect to the contribution of freshwater systems to the global CH4 emissions, hot spot emission sites in impounded rivers have the potential to increase the current global estimate by up to 7%.
Larvae of Cx.pipiens coocurred with Cladocera, but the latter established delayed in time. Biotope structure influenced time of species occurrence with ponds at reed-covered wetlands favouring crustacean development, while ponds at grassland biotopes favoured colonization by mosquito larvae. The mechanisms driving the negative effect of crustaceans on mosquito larvae were investigated within an experiment under artificial conditions. Crustacean communities were found to reduce both oviposition and larval development of Cx.pipiens. Crustacean communities of high taxa diversity, including both predatory and competing crustaceans, were more effective compared with crustacean communities dominated by single taxa. Presence of crustacean communities characterised by high taxa diversity increased the sensitivity of Cx.pipiens larvae towards Bti and prolonged the time of recolonization. In a final step the combined approach, using Bti and crustaceans, was evaluated under field conditions. The joint application of Bti and crustaceans was found to reduce mosquito larval populations over the whole observation period, while single application of Bti caused only short-term reduction of mosquito larvae. Single application of crustaceans had no significant effect, because high abundances of prior established mosquito larvae impeded propagation of crustaceans. At combined treatment, mosquito larvae were reduced by Bti application and hence crustaceans were able to proliferate without disturbance by interspecific competition. In conclusion, natural competitors were found to have a strong negative impact on mosquito larval populations. However, a time span of about 2 weeks has to be bridged, before crustacean communities reached a level sufficient for mosquito control. Results of a combined approach, complementing the short-term effect of the biological insecticide Bti with the long-term effect of crustaceans, were promising. Using natural competitors within an integrated control strategy could be an important tool for an effective, environmentally friendly and sustainable mosquito management.
Diffusion imaging captures the movement of water molecules in tissue by applying varying gradient fields in a magnetic resonance imaging (MRI)-based setting. It poses a crucial contribution to in vivo examinations of neuronal connections: The local diffusion profile enables inference of the position and orientation of fiber pathways. Diffusion imaging is a significant technique for fundamental neuroscience, in which pathways connecting cortical activation zones are examined, and for neurosurgical planning, where fiber reconstructions are considered as intervention related risk structures.
Diffusion tensor imaging (DTI) is currently applied in clinical environments in order to model the MRI signal due to its fast acquisition and reconstruction time. However, the inability of DTI to model complex intra-voxel diffusion distributions gave rise to an advanced reconstruction scheme which is known as high angular resolution diffusion imaging (HARDI). HARDI received increasing interest in neuroscience due to its potential to provide a more accurate view of pathway configurations in the human brain.
In order to fully exploit the advantages of HARDI over DTI, advanced fiber reconstructions and visualizations are required. This work presents novel approaches contributing to current research in the field of diffusion image processing and visualization. Diffusion classification, tractography, and visualizations approaches were designed to enable a meaningful exploration of neuronal connections as well as their constitution. Furthermore, an interactive neurosurgical planning tool with consideration of neuronal pathways was developed.
The research results in this work provide an enhanced and task-related insight into neuronal connections for neuroscientists as well as neurosurgeons and contribute to the implementation of HARDI in clinical environments.
Object recognition is a well-investigated area in image-based computer vision and several methods have been developed. Approaches based on Implicit Shape Models have recently become popular for recognizing objects in 2D images, which separate objects into fundamental visual object parts and spatial relationships between the individual parts. This knowledge is then used to identify unknown object instances. However, since the emergence of aσordable depth cameras like Microsoft Kinect, recognizing unknown objects in 3D point clouds has become an increasingly important task. In the context of indoor robot vision, an algorithm is developed that extends existing methods based on Implicit Shape Model approaches to the task of 3D object recognition.
The adoption of the EU Water Framework Directive (WFD) in 2000 marked the beginning of a new era of European water policy. However, more than a decade later, the majority of European rivers are still failing to meet one of the main objectives of the WFD: the good ecological status. Pesticides are a major stressor for stream ecosystems. This PhD thesis emphasises the need for WFD managers to consider all main agricultural pesticide sources and influencing landscape parameters when setting up River Basin Management Plans and Programmes of Measures. The findings and recommendations of this thesis can help to successfully tackle the risk of pesticide contamination to achieve the WFD objectives.
A total of 663 sites that were situated in the German Federal States of Saxony, Saxony-Anhalt, Thuringia and Hesse were studied (Chapter 3 and 4). In addition to an analysis of the macroinvertebrate data of the governmental WFD monitoring network, a detailed GIS analysis of the main agricultural pesticide sources (arable land and garden allotments as well as wastewater treatment plants (WWTPs)) and landscape elements (riparian buffer strips and forested upstream reaches) was conducted. Based on the results, a screening approach was developed that allows an initial rapid and cost-effective identification of those sites that are potentially affected by pesticide contamination. By using the trait-based bioindicator SPEARpesticides, the insecticidal long-term effects of the WWTP effluents on the structure of the macroinvertebrate community were identified up to at least 1.5 km downstream (in some cases even 3 km) of the WWTPs. The results of the German Saprobic Index revealed that the WWTPs can still be important sources of oxygen-depleting substances. Furthermore, the results indicate that forested upstream reaches and riparian buffer strips at least 5 m in width can be appropriate measures in mitigating the effects and exposure of pesticides.
There are concerns that the future expansion of energy crop cultivation will lead to an increased pesticide contamination of ecosystems in agricultural landscapes. Therefore, the potential of energy crops for pesticide contamination was examined based on an analysis of the development of energy crop cultivation in Germany and a literature search on perennial energy crops (Chapter 5). The results indicate that the future large-scale expansion of energy crop cultivation will not necessarily cause an increase or decrease in the amounts of pesticides that are released into the environment. The potential effects will depend on the future design of the agricultural systems. Instead of creating energy monocultures, annual energy crops should be integrated into the existing food production systems. Financial incentives and further education are needed to encourage the use of sustainable crop rotations, innovative cropping systems and perennial energy crops, which may contribute to crop diversity and generate lower pesticide demands than do intensive farming systems.
Through the increasing availability of access to the web, more and more interactions between people take place in online social networks, such as Twitter or Facebook, or sites where opinions can be exchanged. At the same time, knowledge is made openly available for many people, such as by the biggest collaborative encyclopedia Wikipedia and diverse information in Internet forums and on websites. These two kinds of networks - social networks and knowledge networks - are highly dynamic in the sense that the links that contain the important information about the relationships between people or the relations between knowledge items are frequently updated or changed. These changes follow particular structural patterns and characteristics that are far less random than expected.
The goal of this thesis is to predict three characteristic link patterns for the two network types of interest: the addition of new links, the removal of existing links and the presence of latent negative links. First, we show that the prediction of link removal is indeed a new and challenging problem. Even if the sociological literature suggests that reasons for the formation and resolution of ties are often complementary, we show that the two respective prediction problems are not. In particular, we show that the dynamics of new links and unlinks lead to the four link states of growth, decay, stability and instability. For knowledge networks we show that the prediction of link changes greatly benefits from the usage of temporal information; the timestamp of link creation and deletion events improves the prediction of future link changes. For that, we present and evaluate four temporal models that resemble different exploitation strategies. Focusing on directed social networks, we conceptualize and evaluate sociological constructs that explain the formation and dissolution of relationships between users. Measures based on information about past relationships are extremely valuable for predicting the dissolution of social ties. Hence, consistent for knowledge networks and social networks, temporal information in a network greatly improves the prediction quality. Turning again to social networks, we show that negative relationship information such as distrust or enmity can be predicted from positive known relationships in the network. This is particularly interesting in networks where users cannot label their relationships to other users as negative. For this scenario we show how latent negative relationships can be predicted.
Non-Consumptive Effects of Spiders and Ants: Does Fear Matter in Terrestrial Interaction Webs?
(2014)
Most animals suffer from predators. Besides killing prey, predators can affect prey physiology, morphology and behaviour. Spiders are among the most diverse and frequent predators in terrestrial ecosystems. Our behavioural arena experiments revealed that behavioural changes under spider predation risk are relatively scarce among arthropods. Wood crickets (Nemobius sylvestris), in particular, changed their behaviour in response to cues of various spider species. Thereby, more common and relatively larger spider species induced stronger antipredator behaviour in crickets.
Behavioural changes under predation risk are expected to enhance predator avoidance, but they come at a cost. Crickets previously confronted with cues of the nursery web spider (Pisaura mirabilis) were indeed more successful in avoiding predation. Surprisingly, crickets slightly increased food uptake and lost less weight under predation risk, indicating that crickets are able to compensate for short-term cost under predation risk. In a following plant choice experiment, crickets strongly avoided plants bearing spider cues, which in turn reduced the herbivory on the respective plants.
Similar to spiders, ants are ubiquitous predators and can have a strong impact on herbivores, but also on other predators. Juvenile spiders increased their propensity for long-distance dispersal if exposed to ant cues. Thus, spiders use this passive dispersal through the air (ballooning) to avoid ants and colonise new habitats.
In a field experiment, we compared arthropod colonisation between plants bearing cues of the nursery web spider and cue-free plants. We followed herbivory during the experimental period and sampled the arthropod community on the plants. In accordance with the plant choice experiment, herbivory was reduced on plants bearing spider cues. In addition, spider cues led to changes in the arthropod community: smaller spiders and black garden ants (Lasius niger) avoided plants bearing spider cues. In contrast, common red ants (Myrmica rubra) increased the recruitment of workers, possibly to protect their aphids.
Although behavioural changes were relatively rare on filter papers bearing spider cues, more natural experimental setups revealed strong and far-reaching effects of predation risk. We further suggest that risk effects influence the spatial distribution of herbivory, rather than reduce overall herbivory that is expected if predators kill herbivores. Consequently, the relative importance of predation and risk effects is crucial for the way predators affect lower trophic levels.
Engineered nanoparticles (ENP) are widely used in different industrial fields and products. In the last years, the risk potential for the release of ENP in the environment has increased as never before. ENP are expected to pass the wastewater-river-topsoil-groundwater pathway. In the terrestrial and aquatic environment ENP can undergo aging and transformation processes which can influence fate, transport and toxicological effects to different living organisms.
The scope of this workshop is to gather researchers, scientists, experts and specialists from nanoparticle and colloid science, soil and environmental chemistry, ecotoxicology or neighbouring disciplines to discuss the latest results and findings in the field of aging, fate, transport and toxicological effects of nanoparticles in the environment.
Web 2.0 provides technologies for online collaboration of users as well as the creation, publication and sharing of user-generated contents in an interactive way. Twitter, CNET, CiteSeerX, etc. are examples of Web 2.0 platforms which facilitate users in these activities and are viewed as rich sources of information. In the platforms mentioned as examples, users can participate in discussions, comment others, provide feedback on various issues, publish articles and write blogs, thereby producing a high volume of unstructured data which at the same time leads to an information overload. To satisfy various types of human information needs arising from the purpose and nature of the platforms requires methods for appropriate aggregation and automatic analysis of this unstructured data. In this thesis, we propose methods which attempt to overcome the problem of information overload and help in satisfying user information needs in three scenarios.
To this end, first we look at two of the main challenges of sparsity and content quality in Twitter and how these challenges can influence standard retrieval models. We analyze and identify Twitter content features that reflect high quality information. Based on this analysis we introduce the concept of "interestingness" as a static quality measure. We empirically show that our proposed measure helps in retrieving and filtering high quality information in Twitter. Our second contribution relates to the content diversification problem in a collaborative social environment, where the motive of the end user is to gain a comprehensive overview of the pros and cons of a discussion track which results from social collaboration of the people. For this purpose, we develop the FREuD approach which aims at solving the content diversification problem by combining latent semantic analysis with sentiment estimation approaches. Our evaluation results show that the FREuD approach provides a representative overview of sub-topics and aspects of discussions, characteristic user sentiments under different aspects, and reasons expressed by different opponents. Our third contribution presents a novel probabilistic Author-Topic-Time model, which aims at mining topical trends and user interests from social media. Our approach solves this problem by means of Bayesian modeling of relations between authors, latent topics and temporal information. We present results of application of the model to the scientific publication datasets from CiteSeerX showing improved semantically cohesive topic detection and capturing shifts in authors" interest in relation to topic evolution.
The way information is presented to users in online community platforms has an influence on the way the users create new information. This is the case, for instance, in question-answering fora, crowdsourcing platforms or other social computation settings. To better understand the effects of presentation policies on user activity, we introduce a generative model of user behaviour in this paper. Running simulations based on this user behaviour we demonstrate the ability of the model to evoke macro phenomena comparable to the ones observed on real world data.