The larval stage of the European fire salamander (Salamandra salamandra) inhabits both lentic and lotic habitats. In the latter, they are constantly exposed to unidirectional water flow, which has been shown to cause downstream drift in a variety of taxa. In this study, a closed artificial creek, which allowed us to keep the water flow constant over time and, at the same time, to simulates with predefined water quantities and durations, was used to examine the individual movement patterns of marked larval fire salamanders exposed to unidirectional flow. Movements were tracked by marking the larvae with VIAlpha tags individually and by using downstream and upstream traps. Most individuals showed stationarity, while downstream drift dominated the overall movement pattern. Upstream movements were rare and occurred only on small distances of about 30 cm; downstream drift distances exceeded 10 m (until next downstream trap). The simulated flood events increased drift rates significantly, even several days after the flood simulation experiments. Drift probability increased with decreasing body size and decreasing nutritional status. Our results support the production hypothesis as an explanation for the movements of European fire salamander larvae within creeks.

Soil organic matter (SOM) is an indispensable component of terrestrial ecosystems. Soil organic carbon (SOC) dynamics are influenced by a number of well-known abiotic factors such as clay content, soil pH, or pedogenic oxides. These parameters interact with each other and vary in their influence on SOC depending on local conditions. To investigate the latter, the dependence of SOC accumulation on parameters and parameter combinations was statistically assessed that vary on a local scale depending on parent material, soil texture class, and land use. To this end, topsoils were sampled from arable and grassland sites in south-western Germany in four regions with different soil parent material. Principal component analysis (PCA) revealed a distinct clustering of data according to parent material and soil texture that varied largely between the local sampling regions, while land use explained PCA results only to a small extent. The PCA clusters were differentiated into total clusters that contain the entire dataset or major proportions of it and local clusters representing only a smaller part of the dataset. All clusters were analysed for the relationships between SOC concentrations (SOC %) and mineral-phase parameters in order to assess specific parameter combinations explaining SOC and its labile fractions hot water-extractable C (HWEC) and microbial biomass C (MBC). Analyses were focused on soil parameters that are known as possible predictors for the occurrence and stabilization of SOC (e.g. fine silt plus clay and pedogenic oxides). Regarding the total clusters, we found significant relationships, by bivariate models, between SOC, its labile fractions HWEC and MBC, and the applied predictors. However, partly low explained variances indicated the limited suitability of bivariate models. Hence, mixed-effect models were used to identify specific parameter combinations that significantly explain SOC and its labile fractions of the different clusters. Comparing measured and mixed-effect-model-predicted SOC values revealed acceptable to very good regression coefficients (R2=0.41–0.91) and low to acceptable root mean square error (RMSE = 0.20 %–0.42 %). Thereby, the predictors and predictor combinations clearly differed between models obtained for the whole dataset and the different cluster groups. At a local scale, site-specific combinations of parameters explained the variability of organic carbon notably better, while the application of total models to local clusters resulted in less explained variance and a higher RMSE. Independently of that, the explained variance by marginal fixed effects decreased in the order SOC > HWEC > MBC, showing that labile fractions depend less on soil properties but presumably more on processes such as organic carbon input and turnover in soil.

The process of land degradation needs to be understood at various spatial and temporal scales in order to protect ecosystem services and communities directly dependent on it. This is especially true for regions in sub-Saharan Africa, where socio economic and political factors exacerbate ecological degradation. This study identifies spatially explicit land change dynamics in the Copperbelt province of Zambia in a local context using satellite vegetation index time series derived from the MODIS sensor. Three sets of parameters, namely, monthly series, annual peaking magnitude, and annual mean growing season were developed for the period 2000 to 2019. Trend was estimated by applying harmonic regression on monthly series and linear least square regression on annually aggregated series. Estimated spatial trends were further used as a basis to map endemic land change processes. Our observations were as follows: (a) 15% of the study area dominant in the east showed positive trends, (b) 3% of the study area dominant in the west showed negative trends, (c) natural regeneration in mosaic landscapes (post shifting cultivation) and land management in forest reserves were chiefly responsible for positive trends, and (d) degradation over intact miombo woodland and cultivation areas contributed to negative trends. Additionally, lower productivity over areas with semi-permanent agriculture and shift of new encroachment into woodlands from east to west of Copperbelt was observed. Pivot agriculture was not a main driver in land change. Although overall greening trends prevailed across the study site, the risk of intact woodlands being exposed to various disturbances remains high. The outcome of this study can provide insights about natural and assisted landscape restoration specifically addressing the miombo ecoregion.

Die Praxishefte Demokratische Schulkultur erscheinen halbjährlich und bieten Schulleitungen und Schulpersonal theoretische Grundlagen und praxisorientierte Anleitungen zur demokratiepädagogischen Schulentwicklung. Jedes Themenheft ist jeweils einer demokratiepädagogischen Bauform oder strategischen Frage der Schulentwicklung gewidmet. Die Praxishefte werden allen Luxemburger Schulen als Printausgabe zur Verfügung gestellt und online mit zusätzlichen Materialien und in französischer Fassung vorgehalten.

Agricultural monitoring is necessary. Since the beginning of the Holocene, human agricultural practices have been shaping the face of the earth, and today around one third of the ice-free land mass consists of cropland and pastures. While agriculture is necessary for our survival, the intensity has caused many negative externalities, such as enormous freshwater consumption, the loss of forests and biodiversity, greenhouse gas emissions as well as soil erosion and degradation. Some of these externalities can potentially be ameliorated by careful allocation of crops and cropping practices, while at the same time the state of these crops has to be monitored in order to assess food security. Modern day satellite-based earth observation can be an adequate tool to quantify abundance of crop types, i.e., produce spatially explicit crop type maps. The resources to do so, in terms of input data, reference data and classification algorithms have been constantly improving over the past 60 years, and we live now in a time where fully operational satellites produce freely available imagery with often less than monthly revisit times at high spatial resolution. At the same time, classification models have been constantly evolving from distribution based statistical algorithms, over machine learning to the now ubiquitous deep learning. In this environment, we used an explorative approach to advance the state of the art of crop classification. We conducted regional case studies, focused on the study region of the Eifelkreis Bitburg-Prüm, aiming to develop validated crop classification toolchains. Because of their unique role in the regional agricultural system and because of their specific phenologic characteristics we focused solely on maize fields. In the first case study, we generated reference data for the years 2009 and 2016 in the study region by drawing polygons based on high resolution aerial imagery, and used these in conjunction with RapidEye imagery to produce high resolution maize maps with a random forest classifier and a gaussian blur filter. We were able to highlight the importance of careful residual analysis, especially in terms of autocorrelation. As an end result, we were able to prove that, in spite of the severe limitations introduced by the restricted acquisition windows due to cloud coverage, high quality maps could be produced for two years, and the regional development of maize cultivation could be quantified. In the second case study, we used these spatially explicit datasets to link the expansion of biogas producing units with the extended maize cultivation in the area. In a next step, we overlayed the maize maps with soil and slope rasters in order to assess spatially explicit risks of soil compaction and erosion. Thus, we were able to highlight the potential role of remote sensing-based crop type classification in environmental protection, by producing maps of potential soil hazards, which can be used by local stakeholders to reallocate certain crop types to locations with less associated risk. In our third case study, we used Sentinel-1 data as input imagery, and official statistical records as maize reference data, and were able to produce consistent modeling input data for four consecutive years. Using these datasets, we could train and validate different models in spatially iv and temporally independent random subsets, with the goal of assessing model transferability. We were able to show that state-of-the-art deep learning models such as UNET performed significantly superior to conventional models like random forests, if the model was validated in a different year or a different regional subset. We highlighted and discussed the implications on modeling robustness, and the potential usefulness of deep learning models in building fully operational global crop classification models. We were able to conclude that the first major barrier for global classification models is the reference data. Since most research in this area is still conducted with local field surveys, and only few countries have access to official agricultural records, more global cooperation is necessary to build harmonized and regionally stratified datasets. The second major barrier is the classification algorithm. While a lot of progress has been made in this area, the current trend of many appearing new types of deep learning models shows great promise, but has not yet consolidated. There is still a lot of research necessary, to determine which models perform the best and most robust, and are at the same time transparent and usable by non-experts such that they can be applied and used effortlessly by local and global stakeholders.