The zeamine antibiotics affect the integrity of bacterial membranes

The zeamines (zeamine, zeamine I, and zeamine II) constitute an unusual class of cationic polyamine-polyketide-nonribosomal peptide antibiotics produced by Serratia plymuthica RVH1. They exhibit potent bactericidal activity, killing a broad range of Gram-negative and Gram-positive bacteria, including multidrug-resistant pathogens. Examination of their specific mode of action and molecular target revealed that the zeamines affect the integrity of cell membranes. The zeamines provoke rapid release of carboxyfluorescein from unilamellar vesicles with different phospholipid compositions, demonstrating that they can interact directly with the lipid bilayer in the absence of a specific target. DNA, RNA, fatty acid, and protein biosynthetic processes ceased simultaneously at subinhibitory levels of the antibiotics, presumably as a direct consequence of membrane disruption. The zeamine antibiotics also facilitated the uptake of small molecules, such as 1-N-phenylnaphtylamine, indicating their ability to permeabilize the Gram-negative outer membrane (OM). The valine-linked polyketide moiety present in zeamine and zeamine I was found to increase the efficiency of this process. In contrast, translocation of the large hydrophilic fluorescent peptidoglycan binding protein PBDKZ-GFP was not facilitated, suggesting that the zeamines cause subtle perturbation of theOMrather than drastic alterations or defined pore formation. At zeamine concentrations above those required for growth inhibition, membrane lysis occurred as indicated by time-lapse microscopy. Together, these findings show that the bactericidal activity of the zeamines derives from generalized membrane permeabilization, which likely is initiated by electrostatic interactions with negatively charged membrane components

Forest site productivity in temperate regions: empirical modelling in environmental and geographical space

Worldwide forests play an essential role in sustaining prosperity and well-being of human society thanks to the services they deliver like climate regulation, soil and water protection, provision of wood and non-wood forest products, recreational and spiritual significance and by hosting large part of the terrestrial biodiversity. In the last century, forests are experiencing an abiotic environment that changes much faster than during the past hundreds of years. Moreover, forests are susceptible to large pressures of different kinds, including biodiversity loss, pests and diseases, soil degradation, etc. In order to guarantee future provision of all currently provided ecosystem services forest management should continue to evolve into more sustainable and multifunctional directions. Accurate forest site productivity estimation, e.g. by means of site index, is one of the crucial elements of good forest resource management, since site productivity is a key indicator of forest ecosystem services like wood production and carbon sequestration. It is therefore crucial in estimating the future wood stocks, selecting appropriate locations for planting specific tree species or choosing the most appropriate management at a given location.In homogeneous even-aged forest stands, site index of the standing species can be directly inferred from measurements of tree height and age, using appropriate species-specific dominant height growth curves. But in mixed or uneven-aged stands, or in the case of stand conversion to other tree species, or afforestation of unforested land, or under site conditions which change with time, direct estimation is not possible. For such cases site index needs to be estimated indirectly from environmental factors like climate, topography and soil characteristics, using appropriate models. The overall aim of this research was to contribute to the development of a generic approach for multifactor modelling and spatial prediction of forest site productivity, and to identify the most important influential site quality variables for three important tree species of European temperate lowlands: pedunculate oak (Quercus robur L.), common beech (Fagus sylvatica L.) and Scots pine (Pinus sylvestris L.). We established in homogeneous, even-aged stands of each of these species an amount of research plots which were surveyed in detail for dendrometrical, topographical, litterfall, vegetation, humus and soil characteristics. Based on these data, a stepwise approach starting from the evaluation of the performance of different non-spatial modelling techniques to predict site productivity, revealed boosted regression trees (BRTs) and generalized additive models (GAMs) as the most appropriate empirical modelling techniques to predict forest site productivity in environmental space. Both their accurate predictive performance and good ecological interpretability make these techniques preferred in a wide range of situations. GAM predictions reached a higher predictive accuracy, whereas BRT models hadsome additional advantages with respect to interpretability of ecological processes. Besides, scale proved to be an important issue in successful forest site productivity modelling. Empirical site index models proved to be very scale-dependent and their applicability was limited to the scale of development.In a final step, empirical modelling was expanded from environmental space to geographical space, incorporating the geographical location of observations explicitly, in combination or not with environmental attributes, to end up with predictive maps estimating the site productivity for the entire study area of Flanders. Although hybrid regionalisation techniques, as regression-kriging or co-kriging, accounting for both spatial dependence and environmental contrasts would be expected to result in the best regionalisation approaches for forest site productivity predictions, this was not the case in all situations. Depending on the availability and the nature of the geodata, different spatial empirical modelling approaches were recommended for predictive mapping. Since no approach outperformed the others under all circumstances a decision tree was developed providing guidance in selecting the most appropriate technique considering the availability and nature of the geodata.Searching for the most influential site productivity variables, BRTs revealed that, although with different effects and in interaction with other co-variables, soil granulometric fractions and litterfall nitrogen concentrations were the most effective predictors of all three important tree species of a temperate lowland region: pedunculate oak, common beech, and Scots pine. Since Flanders is characterised by a pronounced north-south gradient of decreasing sand and increasing silt fractions, and since soil granulometry plays an important role in an optimal water holding capacity and a better nutrient retention, it is not surprising that for all species improved site productivity was recorded at sites with increasing silt fractions. More surprising was the negative effect of litterfall nitrogen concentrations for all species. Although many studies revealed a fertilising effect of increased nitrogen deposition, nitrogen saturation seemed to reduce species productivity in this region characterised by high nitrogen deposition. Tree ring analysis revealed moreover for common beech a long-term growth trend over the last century. The trend was characterized by an initial growth increase, reaching its maximum around the 1960 s, and followed by a recent growth decrease lasting until present. With an observed growth increase of maximal 19 to 24% and an overall growth increase over the 20th century of 12 to 18%, the observed long-term changes were very similar to the changes described in comparable studies of common beech in the temperate lowlands as well as those observed at beech s southern and elevation range edges. This consistency with observations elsewhere in Europe suggests an overall recent decreased vitality of common beech in Europe.Dankwoord Table of Contents List of Tables List of Figures Abbreviations and Symbols Abstract Samenvatting Introduction Chapter 1 Comparison and ranking of different modelling techniques for predicting site index in Mediterranean mountain forests Chapter 2 Evaluation of modelling techniques for forest site productivity prediction in contrasting ecoregions using Stochastic Multicriteria Acceptability Analysis (SMAA) Chapter 3 Predicting forest site productivity in temperate lowland from forest floor, soil and litterfall characteristics using boosted regression trees Chapter 4 Long-term growth changes of common beech in a temperate lowland region during the last century: a tree ring analysis comparing linear and non-linear mixed modelling approaches Chapter 5 Effects of scale and scaling in predictive modelling of forest site productivity Chapter 6 Comparison of location-based, attribute-based and hybrid regionalisation techniques for mapping forest site productivity Conclusions and perspectives References Appendix 1: supporting figures to Chapter 3 Appendix 2: Belgian drainage classification system as a function of the soil texture List of publicationsnrpages: 185status: publishe

Pollination and seed set of an obligatory outcrossing plant in an urban–peri-urban gradient

Urban land use strongly affects pollinators and plant pollination due to fragmentation and isolation of natural plant populations. On the other hand, urban land use can potentially be beneficial to pollinators through the presence of urban gardens and parks which are usually designed to ensure abundant flowering. However, little is known about the effects of urban land use on bees and the pollination services they provide. In an urban–peri-urban gradient around Leuven, Belgium we studied flower visitation rates to, and seed set of Trifolium repens (white clover) in public lawns. T. repens is an obligatory outcrossing plant and is therefore sensitive to reductions in pollinator services. We related our results to both local and regional variables using Boosted Regression Trees. The two variables that best explained the variability in visitation rates and seed set were the amount of green areas (gardens, parks, grasslands) in the surroundings and the abundance of T. repens in the lawns. Surprisingly, an increasing amount of green areas in the surroundings had a negative effect on both flower visitation rates to, and seed set of T. repens. Flower visitation rates by bumblebees responded positively to urban land use resulting in higher visitation rates and increased seed set in the more urban sites. This may point either to increased abundance of bumblebees in more urban sites or to a concentration effect of bumblebees in our urban study sites due to a lack of alternative forage resources. Responses will likely differ for other bee and plant species, but this shows that at least for T. repens, pollination is not compromised by urban land use.publisher: Elsevier articletitle: Pollination and seed set of an obligatory outcrossing plant in an urban–peri-urban gradient journaltitle: Perspectives in Plant Ecology, Evolution and Systematics articlelink: http://dx.doi.org/10.1016/j.ppees.2014.03.002 content_type: article copyright: Copyright © 2014 Geobotanisches Institut ETH, Stiftung Ruebel. Published by Elsevier GmbH All rights reserved.status: publishe

Hierarchial land classificiation and mapping of Aglasun Forest Ecosystems in the Mediterranean Region, Turkey

Hierarchical Land Classification of forest ecosystems is an attempt to classify the territory considering hierarchical distinctions of plant communities. Ecological land classification is especially crucial for semi natural or degraded forest ecosystems. In this study, a hierarchical land classification was generated for Aglasun forest ecosystems where urban and agricultural developments and non-stop human activity for fuel wood and timber have caused extensive degradation to native plant communities.Data obtained from 153 sample plots consisting of environmental characteristics and vascular plant speices were evaluated by using cluster analysis, stepwise discriminate analyses, and chi-qsquar test. Interspesific correlation analysis was applied to define the indicator species at each distinction level. Two sections, two subsections and four units were finally determined for the Aglasun forest district. the results of the stepwise discriminate analyses showed that the fundamental variables for classifying the district are altitude, exposition, latitude and longitudestatus: publishe

Evaluation of modelling techniques for forest site productivity prediction in contrasting ecoregions using Stochastic Multicriteria Acceptability Analysis (SMAA)

Accurate estimation of site productivity is crucial for sustainable forest resource management. In recent years, a variety of modelling approaches have been developed and applied to predict site index from a wide range of environmental variables, with varying success. The selection, application and comparison of suitable modelling techniques remains therefore a meticulous task, subject to ongoing research and debate. In this study, the performance of five modelling techniques was compared for the prediction of forest site index in two contrasting ecoregions: the temperate lowland of Flanders, Belgium, and the Mediterranean mountains in SW Turkey. The modelling techniques include statistical (multiple linear regression - MLR, classification and regression trees - CART, generalized additive models - GAM), as well as machine-learning (artificial neural networks - ANN) and hybrid techniques (boosted regression trees - BRT). Although the selected predictor variables differed largely, with mainly topographic predictor variables in the mountain area versus soil and humus variables in the lowland area, the techniques performed comparatively similar in both ecoregions. Stochastic Multicriteria Acceptability Analysis (SMAA) was found a well-suited multi-criteria evaluation method to evaluate the performance of the modelling techniques. It has been applied on the individual species models of Flanders, as well as a species-independent evaluation, combining all developed models from the two contrasting ecoregions. We came to the conclusion that non-parametric models are better suited for predicting site index than traditional MLR. GAM and BRT are the preferred alternatives for a wide range of weight preferences. CART is preferred when very high weight is given to user-friendliness, whereas ANN is recommended when most weight is given to pure predictive performance.status: publishe

Effects of scale and scaling in predictive modelling of forest site productivity

Site productivity, commonly expressed by site index, is a key indicator of the potential of forested land to deliver ecosystem services like wood production and carbon sequestration. It is an important criterion for decision makers and managers of both production and multi-purpose forests. In many situations forest site index cannot be directly measured and must be estimated from site characteristics related to climate, topography and soil, using appropriate models. A major difficulty herewith is that the models must capture the spatial and temporal variability of the ecological processes, knowing that the magnitude and the variability of the driving forces and responses may show scale dependencies. Scale is therefore an important issue in successful forest site productivity modelling. In this study, empirical forest site productivity models are evaluated for their scale dependency whereby reference is made to the threefold concept of ’scale’ (extent, support, coverage) as proposed by Bierkens et al. (2000). We also addressed the applicability of models at other extents or other supports than the one they were developed at, i.e. the effect of scaling’. The results show that meaningful site index models for small extents require higher resolution support to catch the short-distance variability, whereas for larger extents a coarser support is sufficient to characterize the variability. Where it regards scaling, it is found that the validity of empirical site index models is restricted to the scale level for which they are calibrated. Also the application of site index models on an extent which is adjacent and not overlapping with the extent at which they were developed proved to result in inadequate predictions. Although the structure of site index models is scale-dependent and their applicability limited to the scale of development, it is beyond doubt that such models have the potential to provide good insight into the biophysical drivers of site productivity and can result in good predictions at unsampled locations whenever the scale of model establishment is adapted to the scale of the studied processes and predictions are restricted to the extent for which the model is calibrated.status: publishe

Predictive mapping for dryland forest ecosystem restoration

Predictive modeling is a very powerful toolbox of methods to map observed relationships between abiotic growth conditions and species, communities or species performance. It is an important tool for ecologically better founded and therefore more successful dryland forest restoration. Although empirical it can be linked to change models. Here we review how predictive modeling can assist solving site-related challenges of dryland forest restoration.status: publishe

Non-linear height-diameter models for oriental beech (Fagus orientalis Lipsky) in the Hyrcanian forests, Iran

The relationship between tree height and diameter is an important element in growth and yield models, in carbon budget and timber volume models, and in the description of stand dynamics. Six non-linear growth functions (i.e. Chapman-Richards, Schnute, Lundqvist/Korf, Weibull, Modified Logistic and Exponential) were fitted to tree height-diameter data of oriental beech in the Hyrcanian mixed hardwood forests of Iran. The predictive performance of these models was in the first place assessed by means of different model evaluation criteria such as adjusted R squared (adj R2), root mean square error (RMSE), Akaike information criterion (AIC), mean difference (MD), mean absolute difference (MAD) and mean square (MS) error criteria. Although each of the six models accounted for approximately 75% of total variation in height, a large difference in asymptotic estimates was observed. Apart from this, the predictive performance of the models was also evaluated by means of cross-validation and by splitting the data into 5-cm diameter classes. Plotting the MD in relation to these diameter at breast height (DBH) classes showed for all growth functions, except for the Modified Logistic function, similar mean prediction errors for small- and medium-sized trees. Large-sized trees, however, showed a higher mean prediction error. The Modified Logistic function showed the worst performance due to a large model bias. The Exponential and Lundqvist/Korf models were discarded due to their showing biologically illogical behavior and unreasonable estimates for the asymptotic coefficient, respectively. Considering all the above-mentioned criteria, the Chapman-Richards, Weibull, and Schnute functions provided the most satisfactory height predictions. However, we would recommend the Chapman-Richards function for further analysis because of its higher predictive performance.status: publishe

Wood biomass functions for Acacia abyssinica trees and shrubs and implications for provision of ecosystem services in a community managed exclosure in Tigray, Ethiopia

In the Ethiopian highlands, remarkable recovery of vegetation has been achieved using exclosures, protecting vegetation against livestock browsing and firewood harvesting. But these emerging forest resources require tools for sustainable use, implying knowledge on biomass stocks and growth. In this study we developed biomass functions estimating total, stem and branch biomass from diameter at stump height (DSH) and tree height (H) for an 11-year old exclosure in Tigray, Ethiopia. In a systematic grid of 55 plots, DSH and H of all trees and shrubs were recorded. 40 Acacia abyssinica trees were selected for destructive sampling. Allometric relationships using a natural logelog model were established between aboveground biomass, DSH and H. Models with only DSH were found best with R2 between 0.95 and 0.98. The functions were 10 fold cross-validated and R2 _cv ranged from 0.94 to 0.97, indicating good model performance. The models were found well in range with those of other seasonal forests in East Africa. Total aboveground biomass was estimated 25.4 ton ha1 with an annual production of 2.3 ton ha 1, allowing sustainable wood fuel use for 4 persons ha1. The presented predictive functions help to harmonize between ecological and societal objectives and are as such a first step towards an integrated planning tool for exclosures.status: publishe