Refine
Has Fulltext
- no (74) (remove)
Year of publication
Document Type
- Article (69)
- Part of a Book (4)
- Doctoral Thesis (1)
Language
- English (74) (remove)
Keywords
- Germany (74) (remove)
Institute
- Department Psychologie (10)
- Institut für Biochemie und Biologie (10)
- Institut für Geowissenschaften (9)
- Institut für Umweltwissenschaften und Geographie (8)
- Fachgruppe Politik- & Verwaltungswissenschaft (6)
- Sozialwissenschaften (6)
- Department für Inklusionspädagogik (5)
- Department Sport- und Gesundheitswissenschaften (4)
- Fachgruppe Soziologie (4)
- Institut für Anglistik und Amerikanistik (4)
Rabbit associated genotype 3 hepatitis E virus (HEV) strains were detected in feral, pet and farm rabbits in different parts of the world since 2009 and recently also in human patients. Here, we report a serological and molecular survey on 72 feral rabbits, collected along a rural-urban transect in and next to Frankfurt am Main, Central Germany. ELISA investigations revealed in 25 of 72 (34.7%) animals HEV-specific antibodies. HEV derived RNA was detected in 18 of 72 (25%) animals by reverse transcription-polymerase chain reaction assay. The complete genomes from two rabbitHEV-strains, one from a rural site and the other from an inner-city area, were generated by a combination of high-throughput sequencing, a primer walking approach and 5′- and 3′- rapid amplification of cDNA ends. Phylogenetic analysis of open reading frame (ORF)1-derived partial and complete ORF1/ORF2 concatenated coding sequences indicated their similarity to rabbit-associated HEV strains. The partial sequences revealed one cluster of closely-related rabbitHEV sequences from the urban trapping sites that is well separated from several clusters representing rabbitHEV sequences from rural trapping sites. The complete genome sequences of the two novel strains indicated similarities of 75.6–86.4% to the other 17 rabbitHEV sequences; the amino acid sequence identity of the concatenated ORF1/ORF2-encoded proteins reached 89.0–93.1%. The detection of rabbitHEV in an inner-city area with a high human population density suggests a high risk of potential human infection with the zoonotic rabbitHEV, either by direct or indirect contact with infected animals. Therefore, future investigations on the occurrence and frequency of human infections with rabbitHEV are warranted in populations with different contact to rabbits.
Models for the predictions of monetary losses from floods mainly blend data deemed to represent a single flood type and region. Moreover, these approaches largely ignore indicators of preparedness and how predictors may vary between regions and events, challenging the transferability of flood loss models. We use a flood loss database of 1812 German flood-affected households to explore how Bayesian multilevel models can estimate normalised flood damage stratified by event, region, or flood process type. Multilevel models acknowledge natural groups in the data and allow each group to learn from others. We obtain posterior estimates that differ between flood types, with credibly varying influences of water depth, contamination, duration, implementation of property-level precautionary measures, insurance, and previous flood experience; these influences overlap across most events or regions, however. We infer that the underlying damaging processes of distinct flood types deserve further attention. Each reported flood loss and affected region involved mixed flood types, likely explaining the uncertainty in the coefficients. Our results emphasise the need to consider flood types as an important step towards applying flood loss models elsewhere. We argue that failing to do so may unduly generalise the model and systematically bias loss estimations from empirical data.
Feminist Solidarities after Modulation produces an intersectional analysis of transnational feminist movements and their contemporary digital frameworks of identity and solidarity. Engaging media theory, critical race theory, and Black feminist theory, as well as contemporary feminist movements, this book argues that digital feminist interventions map themselves onto and make use of the multiplicity and ambiguity of digital spaces to question presentist and fixed notions of the internet as a white space and technologies in general as objective or universal. Understanding these frameworks as colonial constructions of the human, identity is traced to a socio-material condition that emerges with the modernity/colonialism binary. In the colonial moment, race and gender become the reasons for, as well as the effects of, technologies of identification, and thus need to be understood as and through technologies. What Deleuze has called modulation is not a present modality of control, but is placed into a longer genealogy of imperial division, which stands in opposition to feminist, queer, and anti-racist activism that insists on non-modular solidarities across seeming difference. At its heart, Feminist Solidarities after Modulation provides an analysis of contemporary digital feminist solidarities, which not only work at revealing the material histories and affective ""leakages"" of modular governance, but also challenges them to concentrate on forms of political togetherness that exceed a reductive or essentialist understanding of identity, solidarity, and difference.
Spatially explicit knowledge on grassland extent and management is critical to understand and monitor the impact of grassland use intensity on ecosystem services and biodiversity. While regional studies allow detailed insights into land use and ecosystem service interactions, information on a national scale can aid biodiversity assessments. However, for most European countries this information is not yet widely available. We used an analysis-ready-data cube that contains dense time series of co-registered Sentinel-2 and Landsat 8 data, covering the extent of Germany. We propose an algorithm that detects mowing events in the time series based on residuals from an assumed undisturbed phenology, as an indicator of grassland use intensity. A self-adaptive ruleset enabled to account for regional variations in land surface phenology and non-stationary time series on a pixelbasis. We mapped mowing events for the years from 2017 to 2020 for permanent grassland areas in Germany. The results were validated on a pixel level in four of the main natural regions in Germany based on reported mowing events for a total of 92 (2018) and 78 (2019) grassland parcels. Results for 2020 were evaluated with combined time series of Landsat, Sentinel-2 and PlanetScope data. The mean absolute percentage error between detected and reported mowing events was on average 40% (2018), 36% (2019) and 35% (2020). Mowing events were on average detected 11 days (2018), 7 days (2019) and 6 days (2020) after the reported mowing. Performance measures varied between the different regions of Germany, and lower accuracies were found in areas that are revisited less frequently by Sentinel-2. Thus, we assessed the influence of data availability and found that the detection of mowing events was less influenced by data availability when at least 16 cloud-free observations were available in the grassland season. Still, the distribution of available observations throughout the season appeared to be critical. On a national scale our results revealed overall higher shares of less intensively mown grasslands and smaller shares of highly intensively managed grasslands. Hotspots of the latter were identified in the alpine foreland in Southern Germany as well as in the lowlands in the Northwest of Germany. While these patterns were stable throughout the years, the results revealed a tendency to lower management intensity in the extremely dry year 2018. Our results emphasize the ability of the approach to map the intensity of grassland management throughout large areas despite variations in data availability and environmental conditions.
Firms engage in forecasting and foresight activities to predict the future or explore possible future states of the business environment in order to pre-empt and shape it (corporate foresight). Similarly, the dynamic capabilities approach addresses relevant firm capabilities to adapt to fast change in an environment that threatens a firm’s competitiveness and survival. However, despite these conceptual similarities, their relationship remains opaque. To close this gap, we conduct qualitative interviews with foresight experts as an exploratory study. Our results show that foresight and dynamic capabilities aim at an organizational renewal to meet future challenges. Foresight can be regarded as a specific activity that corresponds with the sensing process of dynamic capabilities. The experts disagree about the relationship between foresight and sensing and see no direct links with transformation. However, foresight can better inform post-sensing activities and, therefore, indirectly contribute to the adequate reconfiguration of the resource base, an increased innovativeness, and firm performance.
Understanding and quantifying total economic impacts of flood events is essential for flood risk management and adaptation planning. Yet, detailed estimations of joint direct and indirect flood-induced economic impacts are rare. In this study an innovative modeling procedure for the joint assessment of short-term direct and indirect economic flood impacts is introduced. The procedure is applied to 19 economic sectors in eight federal states of Germany after the flood events in 2013. The assessment of the direct economic impacts is object-based and considers uncertainties associated with the hazard, the exposed objects and their vulnerability. The direct economic impacts are then coupled to a supply-side Input-Output-Model to estimate the indirect economic impacts. The procedure provides distributions of direct and indirect economic impacts which capture the associated uncertainties. The distributions of the direct economic impacts in the federal states are plausible when compared to reported values. The ratio between indirect and direct economic impacts shows that the sectors Manufacturing, Financial and Insurance activities suffered the most from indirect economic impacts. These ratios also indicate that indirect economic impacts can be almost as high as direct economic impacts. They differ strongly between the economic sectors indicating that the application of a single factor as a proxy for the indirect impacts of all economic sectors is not appropriate.
We revisit the concept of Diversified Quality Production (DQP), which we introduced about 30 years ago. Our purpose is to examine the extent to which the concept can still be considered tenable for describing and explaining the development of the interaction between the political economy and concepts of production, notably in Germany. First, we show why and in which ways DQP was more heterogeneous than we had originally understood. Then, on the basis of evidence with respect to political, business, and economic changes in Germany, we show that DQP Mark I, a regime by and large characteristic of the 1980s, turned into DQP Mark II. In the process, major ‘complementarities’ disappeared between the late 1980s and now—mainly the complementarity between production modes on the one hand and industrial relations and economic regulation on the other. While the latter exhibit greater change, business strategies and production organization show more continuity, which helps explain how Germany maintained economic performance after the mid-2000s, more than other countries in Europe. Conceptually, our most important result is that the complementarities emphasized in political economy are historically relative and limited, so that they should not be postulated as stable configurations.
Based on large representative German household survey data, we compare incomes of the self-employed with those of paid employees. We find that the entrepreneurial income gap is largest for those holding a tertiary degree, but in two directions: positive for employers (self-employed with further employees) and negative for solo entrepreneurs. Entrepreneurs holding a tertiary degree also face the greatest income variation. However, some solo self-employed earn more than their employed counterparts, in particular those with a university entrance degree as the highest level of education.
Flood damage estimation is a core task in flood risk assessments and requires reliable flood loss models. Identifying the driving factors of flood loss at residential buildings and gaining insight into their relations is important to improve our understanding of flood damage processes. For that purpose, we learn probabilistic graphical models, which capture and illustrate (in-)dependencies between the considered variables. The models are learned based on postevent surveys with flood-affected residents after six flood events, which occurred in Germany between 2002 and 2013. Besides the sustained building damage, the survey data contain information about flooding parameters, early warning and emergency measures, property-level mitigation measures and preparedness, socioeconomic characteristics of the household, and building characteristics. The analysis considers the entire data set with a total of 4,468 cases as well as subsets of the data set partitioned into single flood events and flood types: river floods, levee breaches, surface water flooding, and groundwater floods, to reveal differences in the damaging processes. The learned networks suggest that the flood loss ratio of residential buildings is directly influenced by hydrological and hydraulic aspects as well as by building characteristics and property-level mitigation measures. The study demonstrates also that for different flood events and process types the building damage is influenced by varying factors. This suggests that flood damage models need to be capable of reproducing these differences for spatial and temporal model transfers.
Large-scale crop yield failures are increasingly associated with food price spikes and food insecurity and are a large source of income risk for farmers. While the evidence linking extreme weather to yield failures is clear, consensus on the broader set of weather drivers and conditions responsible for recent yield failures is lacking. We investigate this for the case of four major crops in Germany over the past 20 years using a combination of machine learning and process-based modelling. Our results confirm that years associated with widespread yield failures across crops were generally associated with severe drought, such as in 2018 and to a lesser extent 2003. However, for years with more localized yield failures and large differences in spatial patterns of yield failures between crops, no single driver or combination of drivers was identified. Relatively large residuals of unexplained variation likely indicate the importance of non-weather related factors, such as management (pest, weed and nutrient management and possible interactions with weather) explaining yield failures. Models to inform adaptation planning at farm, market or policy levels are here suggested to require consideration of cumulative resource capture and use, as well as effects of extreme events, the latter largely missing in process-based models. However, increasingly novel combinations of weather events under climate change may limit the extent to which data driven methods can replace process-based models in risk assessments.