Refine
Year of publication
Document Type
- Article (90)
- Monograph/Edited Volume (11)
- Part of a Book (2)
- Postprint (2)
- Conference Proceeding (1)
- Other (1)
Keywords
- Nitrogen (4)
- Biodiversity exploratories (3)
- Phosphorus (3)
- fertilization (3)
- mowing (3)
- phosphorus (3)
- plant functional traits (3)
- Fertilization (2)
- land-use intensity (2)
- nutrient availability (2)
Institute
- Institut für Informatik und Computational Science (29)
- Institut für Biochemie und Biologie (25)
- Wirtschaftswissenschaften (13)
- Institut für Umweltwissenschaften und Geographie (9)
- Institut für Chemie (7)
- Institut für Mathematik (6)
- Institut für Physik und Astronomie (5)
- Extern (4)
- Institut für Ernährungswissenschaft (2)
- Institut für Geowissenschaften (2)
Land use is increasingly recognized as a major driver of biodiversity and ecosystem functioning in many current research projects. In grasslands, land use is often classified by categorical descriptors such as pastures versus meadows or fertilized versus unfertilized sites. However, to account for the quantitative variation of multiple land-use types in heterogeneous landscapes, a quantitative, continuous index of land-use intensity (LUI) is desirable. Here we define such a compound, additive LUI index for managed grasslands including meadows and pastures. The LUI index summarizes the standardized intensity of three components of land use, namely fertilization, mowing, and livestock grazing at each site. We examined the performance of the LUI index to predict selected response variables on up to 150 grassland sites in the Biodiversity Exploratories in three regions in Germany(Alb, Hainich, Schorlheide). We tested the average Ellenberg nitrogen indicator values of the plant community, nitrogen and phosphorus concentration in the aboveground plant biomass, plant-available phosphorus concentration in the top soil, and soil C/N ratio, and the first principle component of these five response variables.
The LUI index significantly predicted the principal component of all five response variables, as well as some of the individual responses. Moreover, vascular plant diversity decreased significantly with LUI in two regions (Alb and Hainich).
Inter-annual changes in management practice were pronounced from 2006 to 2008, particularly due to variation in grazing intensity. This rendered the selection of the appropriate reference year(s) an important decision for analyses of land-use effects, whereas details in the standardization of the index were of minor importance. We also tested several alternative calculations of a LUI index, but all are strongly linearly correlated to the proposed index.
The proposed LUI index reduces the complexity of agricultural practices to a single dimension and may serve as a baseline to test how different groups of organisms and processes respond to land use. In combination with more detailed analyses, this index may help to unravel whether and how land-use intensities, associated disturbance levels or other local or regional influences drive ecological processes.
Although temporal heterogeneity is a well-accepted driver of biodiversity, effects of interannual variation in land-use intensity (LUI) have not been addressed yet. Additionally, responses to land use can differ greatly among different organisms; therefore, overall effects of land-use on total local biodiversity are hardly known. To test for effects of LUI (quantified as the combined intensity of fertilization, grazing, and mowing) and interannual variation in LUI (SD in LUI across time), we introduce a unique measure of whole-ecosystem biodiversity, multidiversity. This synthesizes individual diversity measures across up to 49 taxonomic groups of plants, animals, fungi, and bacteria from 150 grasslands. Multidiversity declined with increasing LUI among grasslands, particularly for rarer species and aboveground organisms, whereas common species and belowground groups were less sensitive. However, a high level of interannual variation in LUI increased overall multidiversity at low LUI and was even more beneficial for rarer species because it slowed the rate at which the multidiversity of rare species declined with increasing LUI. In more intensively managed grasslands, the diversity of rarer species was, on average, 18% of the maximum diversity across all grasslands when LUI was static over time but increased to 31% of the maximum when LUI changed maximally over time. In addition to decreasing overall LUI, we suggest varying LUI across years as a complementary strategy to promote biodiversity conservation.
Myasthenia gravis is an autoimmune disease affecting neuromuscular transmission and causing skeletal muscle weakness. Additionally, systemic inflammation, cognitive deficits and autonomic dysfunction have been described.
However, little is known about myasthenia gravis-related reorganization of the brain. In this study, we thus investigated the structural and functional brain changes in myasthenia gravis patients.
Eleven myasthenia gravis patients (age: 70.64 +/- 9.27; 11 males) were compared to age-, sex- and education-matched healthy controls (age: 70.18 +/- 8.98; 11 males). Most of the patients (n = 10, 0.91%) received cholinesterase inhibitors.
Structural brain changes were determined by applying voxel-based morphometry using high-resolution T-1-weighted sequences. Functional brain changes were assessed with a neuropsychological test battery (including attention, memory and executive functions), a spatial orientation task and brain-derived neurotrophic factor blood levels.
Myasthenia gravis patients showed significant grey matter volume reductions in the cingulate gyrus, in the inferior parietal lobe and in the fusiform gyrus. Furthermore, myasthenia gravis patients showed significantly lower performance in executive functions, working memory (Spatial Span, P = 0.034, d = 1.466), verbal episodic memory (P = 0.003, d = 1.468) and somatosensory-related spatial orientation (Triangle Completion Test, P = 0.003, d = 1.200).
Additionally, serum brain-derived neurotrophic factor levels were significantly higher in myasthenia gravis patients (P = 0.001, d = 2.040). Our results indicate that myasthenia gravis is associated with structural and functional brain alterations. Especially the grey matter volume changes in the cingulate gyrus and the inferior parietal lobe could be associated with cognitive deficits in memory and executive functions.
Furthermore, deficits in somatosensory-related spatial orientation could be associated with the lower volumes in the inferior parietal lobe. Future research is needed to replicate these findings independently in a larger sample and to investigate the underlying mechanisms in more detail.
Klaus et al. compared myasthenia gravis patients to matched healthy control subjects and identified functional alterations in memory functions as well as structural alterations in the cingulate gyrus, in the inferior parietal lobe and in the fusiform gyrus.
Incremental Support Vector Machines (SVM) are instrumental in practical applications of online learning. This work focuses on the design and analysis of efficient incremental SVM learning, with the aim of providing a fast, numerically stable and robust implementation. A detailed analysis of convergence and of algorithmic complexity of incremental SVM learning is carried out. Based on this analysis, a new design of storage and numerical operations is proposed, which speeds up the training of an incremental SVM by a factor of 5 to 20. The performance of the new algorithm is demonstrated in two scenarios: learning with limited resources and active learning. Various applications of the algorithm, such as in drug discovery, online monitoring of industrial devices and and surveillance of network traffic, can be foreseen.
Birth weight variation is influenced by fetal and maternal genetic and non-genetic factors, and has been reproducibly associated with future cardio-metabolic health outcomes. In expanded genome-wide association analyses of own birth weight (n = 321,223) and offspring birth weight (n = 230,069 mothers), we identified 190 independent association signals (129 of which are novel). We used structural equation modeling to decompose the contributions of direct fetal and indirect maternal genetic effects, then applied Mendelian randomization to illuminate causal pathways. For example, both indirect maternal and direct fetal genetic effects drive the observational relationship between lower birth weight and higher later blood pressure: maternal blood pressure-raising alleles reduce offspring birth weight, but only direct fetal effects of these alleles, once inherited, increase later offspring blood pressure. Using maternal birth weight-lowering genotypes to proxy for an adverse intrauterine environment provided no evidence that it causally raises offspring blood pressure, indicating that the inverse birth weight-blood pressure association is attributable to genetic effects, and not to intrauterine programming.
Management intensity modifies soil properties, e.g., organic carbon (C-org) concentrations and soil pH with potential feedbacks on plant diversity. These changes might influence microbial P concentrations (P-mic) in soil representing an important component of the Pcycle. Our objectives were to elucidate whether abiotic and biotic variables controlling P-mic concentrations in soil are the same for forests and grasslands, and to assess the effect of region and management on P-mic concentrations in forest and grassland soils as mediated by the controlling variables. In three regions of Germany, Schwabische Alb, Hanich-Dun, and Schorfheide-Chorin, we studied forest and grassland plots (each n=150) differing in plant diversity and land-use intensity. In contrast to controls of microbial biomass carbon (C-mic), P-mic was strongly influenced by soil pH, which in turn affected phosphorus (P) availability and thus microbial Puptake in forest and grassland soils. Furthermore, P-mic concentrations in forest and grassland soils increased with increasing plant diversity. Using structural equation models, we could show that soil C-org is the profound driver of plant diversity effects on P-mic in grasslands. For both forest and grassland, we found regional differences in P-mic attributable to differing environmental conditions (pH, soil moisture). Forest management and tree species showed no effect on P-mic due to a lack of effects on controlling variables (e.g., C-org). We also did not find management effects in grassland soils which might be caused by either compensation of differently directed effects across sites or by legacy effects of former fertilization constraining the relevance of actual practices. We conclude that variables controlling P-mic or C-mic in soil differ in part and that regional differences in controlling variables are more important for P-mic in soil than those induced by management.
Indicators to assess sustainable land development often focus on either economic or ecologic aspects of landscape use. The concept of multifunctional land use helps merging those two focuses by emphasising on the rule that economic action is per se accompanied by ecological utility: commodity outputs (CO, e.g., yields) are paid for on the market, but non-commodity outputs (NCO, e.g., landscape aesthetics) so far are public goods with no markets. Agricultural production schemes often provided both outputs by joint production, but with technical progress under prevailing economic pressure, joint production increasingly vanishes by decoupling of commodity from non-commodity production. Simultaneously, by public and political awareness of these shortcomings, there appears a societal need or even demand for some non-commodity outputs of land use, which induces a market potential, and thus, shift towards the status of a commodity outputs. An approach is presented to merge both types of output by defining an indicator of social utility (SUMLU): production schemes are considered with respect to social utility of both commodity and non-commodity outputs. Social utility in this sense includes environmental and economic services as long as society expresses a demand for them. For each combination of parameters at specific frame conditions (e.g., soil and climate properties of a landscape) a production possibility curve can reflect trade-offs between commodity and non-commodity outputs. On each production possibility curve a welfare optimum can be identified expressing the highest achievable value of social utility as a trade-off between CO and NCO production. When applying more parameters, a cluster of welfare optimums is generated. Those clusters can be used for assessing production schemes with respect to sustainable land development. Examples of production possibility functions are given on easy applicable parameters (nitrogen leaching versus gross margin) and on more complex ones (biotic integrity). Social utility, thus allows to evaluate sustainability of land development in a cross-sectoral approach with respect to multifunctionality. (C) 2005 Elsevier Ltd. All rights reserved
Non-stationarities are ubiquitous in EEG signals. They are especially apparent in the use of EEG-based brain- computer interfaces (BCIs): (a) in the differences between the initial calibration measurement and the online operation of a BCI, or (b) caused by changes in the subject's brain processes during an experiment (e.g. due to fatigue, change of task involvement, etc). In this paper, we quantify for the first time such systematic evidence of statistical differences in data recorded during offline and online sessions. Furthermore, we propose novel techniques of investigating and visualizing data distributions, which are particularly useful for the analysis of (non-) stationarities. Our study shows that the brain signals used for control can change substantially from the offline calibration sessions to online control, and also within a single session. In addition to this general characterization of the signals, we propose several adaptive classification schemes and study their performance on data recorded during online experiments. An encouraging result of our study is that surprisingly simple adaptive methods in combination with an offline feature selection scheme can significantly increase BCI performance
Finding non-Gaussian components of high-dimensional data is an important preprocessing step for efficient information processing. This article proposes a new linear method to identify the '' non-Gaussian subspace '' within a very general semi-parametric framework. Our proposed method, called NGCA (non-Gaussian component analysis), is based on a linear operator which, to any arbitrary nonlinear (smooth) function, associates a vector belonging to the low dimensional non-Gaussian target subspace, up to an estimation error. By applying this operator to a family of different nonlinear functions, one obtains a family of different vectors lying in a vicinity of the target space. As a final step, the target space itself is estimated by applying PCA to this family of vectors. We show that this procedure is consistent in the sense that the estimaton error tends to zero at a parametric rate, uniformly over the family, Numerical examples demonstrate the usefulness of our method