Refine
Has Fulltext
- no (181)
Year of publication
- 2013 (181) (remove)
Document Type
- Article (181) (remove)
Language
- English (181) (remove)
Is part of the Bibliography
- yes (181)
Keywords
- Arabidopsis thaliana (4)
- Individual-based model (3)
- Nitrogen (3)
- climate change (3)
- Aldehyde oxidoreductase (2)
- Biodiversity Exploratories (2)
- Calcium (2)
- Climate change (2)
- Daphnia (2)
- Development (2)
Institute
- Institut für Biochemie und Biologie (181) (remove)
Questions Can forest site characteristics be used to predict Ellenberg indicator values for soil moisture? Which is the best averaged mean value for modelling? Does the distribution of soil moisture depend on spatial information? Location Bavarian Alps, Germany. Methods We used topographic, climatic and edaphic variables to model the mean soil moisture value as found on 1505 forest plots from the database WINALPecobase. All predictor variables were taken from area-wide geodata layers so that the model can be applied to some 250 000 ha of forest in the target region. We adopted methods developed in species distribution modelling to regionalize Ellenberg indicator values. Therefore, we use the additive georegression framework for spatial prediction of Ellenberg values with the R-library mboost, which is a feasible way to consider environmental effects, spatial autocorrelation, predictor interactions and non-stationarity simultaneously in our data. The framework is much more flexible than established statistical and machine-learning models in species distribution modelling. We estimated five different mboost models reflecting different model structures on 50 bootstrap samples in each case. Results Median R2 values calculated on independent test samples ranged from 0.28 to 0.45. Our results show a significant influence of interactions and non-stationarity in addition to environmental covariates. Unweighted mean indicator values can be modelled better than abundance-weighted values, and the consideration of bryophytes did not improve model performance. Partial response curves indicate meaningful dependencies between moisture indicator values and environmental covariates. However, mean indicator values <4.5 and >6.0 could not be modelled correctly, since they were poorly represented in our calibration sample. The final map represents high-resolution information of site hydrological conditions. Conclusions Indicator values offer an effect-oriented alternative to physically-based hydrological models to predict water-related site conditions, even at landscape scale. The presented approach is applicable to all kinds of Ellenberg indicator values. Therefore, it is a significant step towards a new generation of models of forest site types and potential natural vegetation.
A Biosensor for aromatic aldehydes comprising the mediator dependent PaoABC-Aldehyde oxidoreductase
(2013)
A novel aldehyde oxidoreductase (PaoABC) from Escherichia coli was utilized for the development of an oxygen insensitive biosensor for benzaldehyde. The enzyme was immobilized in polyvinyl alcohol and currents were measured for aldehyde oxidation with different one and two electron mediators with the highest sensitivity for benzaldehyde in the presence of hexacyanoferrate(III). The benzaldehyde biosensor was optimized with respect to mediator concentration, enzyme loading and pH using potassium hexacyanoferrate(III). The linear measuring range is between 0.5200 mu M benzaldehyde. In correspondence with the substrate selectivity of the enzyme in solution the biosensor revealed a preference for aromatic aldehydes and less effective conversion of aliphatic aldehydes. The biosensor is oxygen independent, which is a particularly attractive feature for application. The biosensor can be applied to detect contaminations with benzaldehyde in solvents such as benzyl alcohol, where traces of benzaldehyde in benzyl alcohol down to 0.0042?% can be detected.
The human face shows individual features and features that are characteristic for sex and age (the loss of childlike characteristics during maturation). The analysis of facial dimensions is essential for identifying individual features also for forensic issues.
The analysis of facial proportions was performed on photogrammetric data from front views of 125 children. The data were pooled from 2 different studies. The children's data were obtained from a longitudinal study and reduced by random generator to ensure the data of adults from a separate cross-sectional study.
We applied principal component analysis on photogrammetric facial proportions of 169 individuals: 125 children (63 boys and 62 girls) aged 2-7 years and 44 adults (18 men and 26 women) aged 18-65 years.
Facial proportions depend on age and sex. Three components described age: (1) proportions of facial height to head height, (2) proportions that involve endocanthal breadth, and (3) bigonial to bizygonial proportions. Proportions that associate with sex are connected with nasal distances and nasal to bizygonial distances.
Twenty-three percent of the variance, particularly variance that are connected with proportions of lower and middle face heights to head height, do neither depend on sex nor on age and thus appear useful for screening purposes, eg, for dysmorphic genetic syndromes.
Bbil-TX, a PLA(2), was purified from Bothriopsis bilineata snake venom after only one chromatographic step using RP-HPLC on mu-Bondapak C-18 column. A molecular mass of 14243.8 Da was confirmed by -Tof ltima API ESI/ MS (TOF MS mode) mass spectrometry. The partial protein sequence obtained was then submitted to BLASTp, with the search restricted to PLA(2) from snakes and shows high identity values when compared to other PLA(2)s. PLA(2) activity was presented in the presence of a synthetic substrate and showed a minimum sigmoidal behavior, reaching its maximal activity at pH 8.0 and 25-37 degrees C. Maximum PLA(2) activity required Ca2+ and in the presence of Cd2+, Zn2+, Mn2+, and Mg2+ it was reduced in the presence or absence of Ca2+. Crotapotin from Crotalus durissus cascavella rattlesnake venom and antihemorrhagic factor DA2-II from Didelphis albiventris opossum sera under optimal conditions significantly inhibit the enzymatic activity. Bbil-TX induces myonecrosis in mice. The fraction does not show a significant cytotoxic activity in myotubes and myoblasts (C2C12). The infiammatory events induced in the serum of mice by Bbil-TX isolated from Bothriopsis bilineata snake venom were investigated. An increase in vascular permeability and in the levels of TNF-a, IL-6, and IL-1 was was induced. Since Bbil-TX exerts a stronger proinfiammatory effect, the phospholipid hydrolysis may be relevant for these phenomena.
The generation of antibodies with designated specificity requires cost-intensive and time-consuming screening procedures. Here we present a new method by which hybridoma cells can be selected based on the specificity of the produced antibody by the use of antigen-toxin-conjugates thus eliminating the need of a screening procedure. Initial experiments were done with methotrexate as low molecular weight toxin and fluorescein as model antigen. Methotrexate and a methotrexate-fluorescein conjugate were characterized regarding their toxicity. Afterwards the effect of the fluorescein-specific antibody B13-DE1 on the toxicity of the methotrexate-fluorescein conjugate was determined. Finally, first results showed that hybridoma cells that produce fluorescein specific antibodies are able to grow in the presence of fluorescein-toxin-conjugates.
Semi-natural grasslands, biodiversity hotspots in Central-Europe, suffer from the cessation of traditional land-use. Amount and intensity of these changes challenge current monitoring frameworks typically based on classic indicators such as selected target species or diversity indices. Indicators based on plant functional traits provide an interesting extension since they reflect ecological strategies at individual and ecological processes at community levels. They typically show convergent responses to gradients of land-use intensity over scales and regions, are more directly related to environmental drivers than diversity components themselves and enable detecting directional changes in whole community dynamics. However, probably due to their labor- and cost intensive assessment in the field, they have been rarely applied as indicators so far.
Here we suggest overcoming these limitations by calculating indicators with plant traits derived from online accessible databases. Aiming to provide a minimal trait set to monitor effects of land-use intensification on plant diversity we investigated relationships between 12 community mean traits, 2 diversity indices and 6 predictors of land-use intensity within grassland communities of 3 different regions in Germany (part of the German 'Biodiversity Exploratory' research network). By standardization of traits and diversity measures, use of null models and linear mixed models we confirmed (i) strong links between functional community composition and plant diversity, (ii) that traits are closely related to land-use intensity, and (iii) that functional indicators are equally, or even more sensitive to land-use intensity than traditional diversity indices. The deduced trait set consisted of 5 traits, i.e., specific leaf area (SLA), leaf dry matter content (LDMC), seed release height, leaf distribution, and onset of flowering. These database derived traits enable the early detection of changes in community structure indicative for future diversity loss. As an addition to current monitoring measures they allow to better link environmental drivers to processes controlling community dynamics.
Collinearity a review of methods to deal with it and a simulation study evaluating their performance
(2013)
Collinearity refers to the non independence of predictor variables, usually in a regression-type analysis. It is a common feature of any descriptive ecological data set and can be a problem for parameter estimation because it inflates the variance of regression parameters and hence potentially leads to the wrong identification of relevant predictors in a statistical model. Collinearity is a severe problem when a model is trained on data from one region or time, and predicted to another with a different or unknown structure of collinearity. To demonstrate the reach of the problem of collinearity in ecology, we show how relationships among predictors differ between biomes, change over spatial scales and through time. Across disciplines, different approaches to addressing collinearity problems have been developed, ranging from clustering of predictors, threshold-based pre-selection, through latent variable methods, to shrinkage and regularisation. Using simulated data with five predictor-response relationships of increasing complexity and eight levels of collinearity we compared ways to address collinearity with standard multiple regression and machine-learning approaches. We assessed the performance of each approach by testing its impact on prediction to new data. In the extreme, we tested whether the methods were able to identify the true underlying relationship in a training dataset with strong collinearity by evaluating its performance on a test dataset without any collinearity. We found that methods specifically designed for collinearity, such as latent variable methods and tree based models, did not outperform the traditional GLM and threshold-based pre-selection. Our results highlight the value of GLM in combination with penalised methods (particularly ridge) and threshold-based pre-selection when omitted variables are considered in the final interpretation. However, all approaches tested yielded degraded predictions under change in collinearity structure and the folk lore'-thresholds of correlation coefficients between predictor variables of |r| >0.7 was an appropriate indicator for when collinearity begins to severely distort model estimation and subsequent prediction. The use of ecological understanding of the system in pre-analysis variable selection and the choice of the least sensitive statistical approaches reduce the problems of collinearity, but cannot ultimately solve them.
The ability of some chemical compounds to cause oxidative stress offers a fast and convenient way to study the responses of plants to reactive oxygen species (ROS). In order to unveil potential novel genetic players of the ROS-regulatory network, a population of similar to 2,000 randomly selected Arabidopsis thaliana T-DNA insertion mutants was screened for ROS sensitivity/resistance by growing seedlings on agar medium supplemented with stress-inducing concentrations of the superoxide-eliciting herbicide methyl viologen or the catalase inhibitor 3-amino-triazole. A semi-robotic setup was used to capture and analyze images of the chemically treated seedlings which helped interpret the screening results by providing quantitative information on seedling area and healthy-to-chlorotic tissue ratios for data verification. A ROS-related phenotype was confirmed in three of the initially selected 33 mutant candidates, which carry T-DNA insertions in genes encoding a Ring/Ubox superfamily protein, ABI5 binding protein 1 (AFP1), previously reported to be involved in ABA signaling, and a protein of unknown function, respectively. In addition, we identified six mutants, most of which have not been described yet, that are related to growth or chloroplast development and show defects in a ROS-independent manner. Thus, semi-automated image capturing and phenotyping applied on publically available T-DNA insertion collections adds a simple means for discovering novel mutants in complex physiological processes and identifying the genes involved.
Effects of intraspecific and community density on the lifetime fecundity of long-lived shrubs
(2013)
Intra- and interspecific density dependence has profound consequences for plant population and community dynamics. In long-lived plants, however, lifetime patterns and mechanisms of density dependence are difficult to study. Here, we examine effects of intraspecific and community density on the lifetime fecundity of two long-lived shrub species from South African Fynbos: Protea repens (animal-pollinated, hermaphroditic) and Leucadendron rubrum (wind-pollinated, dioecious). Both species are serotinous, retaining seeds in cones until fire kills the mother plant. We measured lifetime fecundity as the product of cone number, proportion of cones that are not damaged by predation and seed set (fertile seeds per intact cone). Intraspecific and community densities were quantified by counting individuals of target species and all Proteaceae in small- and large-scale neighbourhoods (10 m and 50 m radius) around each focal individual. Additionally, we determined the age and size of focal individuals. We found that lifetime fecundity of the wind-pollinated L rubrum is density independent. In contrast, the lifetime fecundity of the animal-pollinated P. repens increases with large-scale intraspecific density and shows a hump-shaped relationship to large-scale community density. Community density has a hump-shaped effect on seed set (probably through partial absence of generalized pollinators at low and competition for pollinators at high densities) and negatively affects cone number per individual. For both species, plant age decreases seed set while increasing lifetime fecundity. The qualitative differences in the density dependence of lifetime fecundity may arise from differences between animal and wind pollination. In particular, interactions with generalized animal pollinators may cause community-level Allee effects with profound consequences for the future dynamics of long-lived plant populations and communities.
A novel procedure has been developed to conduct cell structure measurements on increment core samples of conifers. The procedure combines readily available hardware and software equipment. The essential part of the procedure is the application of a confocal laser scanning microscope (CLSM) which captures images directly from increment cores surfaced with the advanced WSL core-microtome. Cell wall and lumen are displayed with a strong contrast due to the monochrome black and green nature of the images. Consecutive images are merged into long images representing entire increment cores which are then analysed for cell structures in suitable software.