Pflanzenmanagement- und Agrarsysteme erlangen international eine steigende Bedeutung. In der vorliegenden Studie werden Pappeln und Weiden mit einheimischen Pflanzenspezies kombiniert, um Agrarsysteme weiter zu verbessern. Zwei in landwirtschaftlichen Systemen relevante Schadstoffe (Cadmium und Stickstoff) wurden ausgewählt, um die Pflanzen bezüglich Phytoremediation und Effizienz von Schadstoffanreicherung in Pflanzenteilen zu untersuchen. Pflanzen-Mikroben-Interaktionen spielen eine Hauptrolle in Agrarsystemen, weshalb mikrobielle Veränderungen in der Rhizosphäre durch Schadstoffeintrag in Böden einen wichtigen Schwerpunkt darstellen. Um solche Veränderungen in einer pflanzenspezifischen, mikrobiellen Gemeinschaft zu detektieren werden Phospholipidfettsäuren (PLFA) im Boden bestimmt, da diese in allen lebenden Zellen vorkommen und nach Zelltod rasch abgebaut werden. Die erzielten Ergebnisse werden mit DNA-basierten Methoden zur Bestimmung mikrobieller Gemeinschaften verglichen. Weiterhin soll die Analytik von Terpenen, Flavonoiden und Fettsäuren im Pflanzenmaterial Auskunft über pysiologische Veränderungen von Pflanzen geben, welche durch die verschiedenen Schadstoffe ausgelöst werden. Ein 13CO2 Puls, welcher vor der Ernte appliziert wird, ermöglicht eine genaue Untersuchung, wie Pflanzenstoffwechsel und Kohlenstofftranslokation in die Rhizosphäre durch Schadstoffe verändert werden. In diesem Zusammenhang wird die Stabilisotopenanalytik von PLFA und DNA verglichen, sowie weitere 13C-Analysen des Pflanzenmaterials durchgeführt. Um den Schwerpunkt von Pflanzenmanagement Systemen zu vertiefen werden weitere Analysen von Pflanzenteilen (Wurzeln, Stamm, Blätter, Früchte, Samen) bezüglich Cadmium und Stickstoff durchgeführt. Massiv kontaminiertes Pflanzenmaterial kann für die Biogasproduktion verbrannt und anschließend zum Recycling kompostiert werden. Pflanzenteile mit hohem Stickstoffgehalt und fehlender Akkumulation von Cadmium kann als Tierfutter in Wintermonaten verwendet werden; eine Verwendung für kommerzielle Produkte ist ebenfalls denkbar und soll im Rahmen des Forschungsantrags untersucht werden.
Die Verweilzeit von Grundwasser in ausgedehnten Grundwasserleitern liegt oft im Bereich von Dekaden, so dass auch langsame mikrobielle Stoffumsätze (z.B. von Nitrat, Atrazin und dessen Abbauprodukten) die Stofffracht in solchen Systemen erheblich beeinflussen können. In diesem Projekt werden mittels geologischer und geochemischer Analysen die reaktiven Zonen und die zugehörigen Verweil- und Kontaktzeiten des Wassers eines Kluftgrundwasserleiters bestimmt. Omics und molekularbiologische Methoden werden genutzt, um Abbaupotential und Aktivität der mikrobiellen Gemeinschaften zu untersuchen. In begleitenden Laborexperimenten werden effektive Diffusions-konstanten und metabolische Raten, deren limitierende Faktoren und die beteiligten Mikroorganismen quantifiziert.
Dieses Projekt erforscht die Bedeutung von Chemolithoautotrophie und Oberflächeneintrag als Quellen von reduziertem Kohlenstoff für die mikrobielle Gemeinschaft in den Hainich-Aquiferen mittels Mikrokosmen-Experimenten. Basierend auf Raman-Mikrospektroskopie in Kombination mit Isotopenmarkierungs-Experimenten wird eine neue Methode zur Hochdurchsatzsortierung von Zellen etabliert um metabolisch aktive mikrobielle Subpopulationen zu isolieren. Mittels Metagenomanalyse kann dann gezielt deren Rolle in den biogeochemischen Kreisläufen im Grundwasser untersucht werden.
Orale Dosen von Natriumfluorid (a. Einzeldosis von 100 mg/kg, b. tgl. Dosen von 30 mg/kg ueber eine Woche) werden an Ratten verabreicht. Wirkungen auf den Sauerstoffverbrauch und verschiedene Parameter des Fettstoffwechsels werden untersucht. Bisherige Hauptergebnisse: Die oben angegebenen Dosen hemmen die Lipolyse, steigern die Fettsaeuresynthese in der Leber und setzen den Grundumsatz signifikant herab.
Chemische Diversität sowie metabolische Aktivitäten innerhalb der Critical Zone (CZ) sind Gegenstand dieses Projekts. Mit einer multi-Methoden massenspektrometrischen Plattform werden wir spezifische Markerverbindungen identifizieren, diese strukturell charakterisieren und funktionelle Untersuchungen durchführen. Metabolische Muster zur Aufklärung von biogeochemischen Prozessen in der CZ werden erstellt. Mit den identifizierten Markern werden Inkubationsexperimente mit mikrobiellen Gemeinschaften durchgeführt um Schlüsselorganismen zu identifizieren, die für die charakteristischen metabolischen Umwandlungen in den jeweiligen Bereichen verantwortlich sind. Letztendlich sollen in diesem Projekt Informationen aus strukturchemischen Untersuchungen genutzt werden um die Prozesse zu identifizieren, die sie in verschieden Tiefen und Standorten der CZ hervorrufen.
Warming and acidification of the oceans as a consequence of increasing CO2-concentrations occur at large scales. Numerous studies have shown the impact of single stressors on individual species. However, studies on the combined effect of multiple stressors on a multi-species assemblage, which is ecologically much more realistic and relevant, are still scarce. Therefore, we orthogonally crossed the two factors warming and acidification in mesocosm experiments and studied their single and combined impact on the brown alga Fucus vesiculosus associated with its natural community (epiphytes and mesograzers) in the Baltic Sea in all seasons (from April 2013 to April 2014). We superimposed our treatment factors onto the natural fluctuations of all environmental variables present in the Benthocosms in so-called delta-treatments. Thereby we compared the physiological responses of F. vesiculosus (growth and metabolites) to the single and combined effects of natural Kiel Fjord temperatures and pCO2 conditions with a 5 °C temperature increase and/or pCO2 increase treatment (1100 ppm in the headspace above the mesocosms). Responses were also related to the factor photoperiod which changes over the course of the year. Our results demonstrate complex seasonal pattern. Elevated pCO2 positively affected growth of F. vesiculosus alone and/or interactively with warming. The response direction (additive, synergistic or antagonistic), however, depended on season and daylength. The effects were most obvious when plants were actively growing during spring and early summer. Our study revealed for the first time that it is crucial to always consider the impact of variable environmental conditions throughout all seasons. In summary, our study indicates that in future F. vesiculosus will be more affected by detrimental summer heat-waves than by ocean acidification although the latter consequently enhances growth throughout the year. The mainly negative influence of rising temperatures on the physiology of this keystone macroalga may alter and/or hamper its ecological functions in the shallow coastal ecosystem of the Baltic Sea.
Increased maintenance costs at cellular, and consequently organism level, are thought to be involved in shaping the sensitivity of marine calcifiers to ocean acidification (OA). Yet, knowledge of the capacity of marine calcifiers to undergo metabolic adaptation is sparse. In Kiel Fjord, blue mussels thrive despite periodically high seawater PCO2, making this population interesting for studying metabolic adaptation under OA. Consequently, we conducted a multi-generation experiment and compared physiological responses of F1 mussels from 'tolerant' and 'sensitive' families exposed to OA for 1 year. Family classifications were based on larval survival; tolerant families settled at all PCO2 levels (700, 1120, 2400 µatm) while sensitive families did not settle at the highest PCO2 (>=99.8% mortality). We found similar filtration rates between family types at the control and intermediate PCO2 level. However, at 2400 µatm, filtration and metabolic scope of gill tissue decreased in tolerant families, indicating functional limitations at the tissue level. Routine metabolic rates (RMR) and summed tissue respiration (gill and outer mantle tissue) of tolerant families were increased at intermediate PCO2, indicating elevated cellular homeostatic costs in various tissues. By contrast, OA did not affect tissue and routine metabolism of sensitive families. However, tolerant mussels were characterised by lower RMR at control PCO2 than sensitive families, which had variable RMR. This might provide the energetic scope to cover increased energetic demands under OA, highlighting the importance of analysing intra-population variability. The mechanisms shaping such difference in RMR and scope, and thus species' adaptation potential, remain to be identified.
The "Germany Mosaic" is a time series of Landsat satellite images and vectorized segments covering the entirety of Germany from 1984 to 2023. The image data are divided into TK100 sheet sections (see further details: Blattschnitt der Topographischen Karte 1:100 000). The dataset provides optimized 6-band imagery for each year, representing summer (May to July) and autumn (August to October) seasons, along with vegetation indices such as NDVI (Normalized Difference Vegetation Index) and NirV (Near-Infrared Reflectance of Vegetation) for the same periods. Additionally, vectorized "zones" of approximately homogeneous pixels are available for each year. The spectral properties of the image data and the morphological characteristics of these zones are included as vector attributes (see Documentation: "Mosaic (1984–2023) - Data Description"). An overview of the coverage and quality of all sheet sections is provided as a vector layer titled D-Mosaik_Sheet-Sections within this document. The Germany Mosaic can also be considered a spatial-temporal Data Cube, enabling advanced analysis and integration into workflows requiring multi-dimensional data. This structure allows users to perform operations such as querying data across specific time periods, analyzing trends over decades, or aggregating spatial information to generate tailored insights for a wide range of research applications. In mid-latitudes, seasonal variations in vegetation—and consequently in the image data—are typically more pronounced than changes occurring over several years. The temporal segmentation of the dataset has been designed to encompass the entire vegetation period (May to October), with the division into summer and autumn periods capturing seasonal metabolic shifts in natural biotopes. This segmentation also records most agricultural changes, including sowing and harvesting activities. Depending on weather conditions, the individual image data represent either the median, mean value, or the best available image for the specified time period (see Documentation: "Mosaic (1984–2023) - Data Description). Remote sensing has become an indispensable tool for environmental research, particularly in landscape analysis. Beyond conventional applications, the Germany Mosaic supports the development of digital twins in environmental system research. By providing detailed spatial and temporal data, this dataset enables the modeling of virtual ecosystems, facilitating simulations, scenario testing, and predictive analyses for sustainable management. Moreover, the spatial and temporal trends captured by remotely sensed parameters complement traditional approaches in biological, ecological, geographical, and epidemiological research.
The "Germany Mosaic" is a time series of Landsat satellite images and vectorized segments covering the entirety of Germany from 1984 to 2023. The image data are divided into TK100 sheet sections (see further details: Blattschnitt der Topographischen Karte 1:100 000). The dataset provides optimized 6-band imagery for each year, representing summer (May to July) and autumn (August to October) seasons, along with vegetation indices such as NDVI (Normalized Difference Vegetation Index) and NirV (Near-Infrared Reflectance of Vegetation) for the same periods. Additionally, vectorized "zones" of approximately homogeneous pixels are available for each year. The spectral properties of the image data and the morphological characteristics of these zones are included as vector attributes (see Documentation: "Mosaic (1984–2023) - Data Description"). An overview of the coverage and quality of all sheet sections is provided as a vector layer titled D-Mosaik_Sheet-Sections within this document. The Germany Mosaic can also be considered a spatial-temporal Data Cube, enabling advanced analysis and integration into workflows requiring multi-dimensional data. This structure allows users to perform operations such as querying data across specific time periods, analyzing trends over decades, or aggregating spatial information to generate tailored insights for a wide range of research applications. In mid-latitudes, seasonal variations in vegetation—and consequently in the image data—are typically more pronounced than changes occurring over several years. The temporal segmentation of the dataset has been designed to encompass the entire vegetation period (May to October), with the division into summer and autumn periods capturing seasonal metabolic shifts in natural biotopes. This segmentation also records most agricultural changes, including sowing and harvesting activities. Depending on weather conditions, the individual image data represent either the median, mean value, or the best available image for the specified time period (see Documentation: "Mosaic (1984–2023) - Data Description). Remote sensing has become an indispensable tool for environmental research, particularly in landscape analysis. Beyond conventional applications, the Germany Mosaic supports the development of digital twins in environmental system research. By providing detailed spatial and temporal data, this dataset enables the modeling of virtual ecosystems, facilitating simulations, scenario testing, and predictive analyses for sustainable management. Moreover, the spatial and temporal trends captured by remotely sensed parameters complement traditional approaches in biological, ecological, geographical, and epidemiological research.
The "Germany Mosaic" is a time series of Landsat satellite images and vectorized segments covering the entirety of Germany from 1984 to 2023. The image data are divided into TK100 sheet sections (see further details: Blattschnitt der Topographischen Karte 1:100 000). The dataset provides optimized 6-band imagery for each year, representing summer (May to July) and autumn (August to October) seasons, along with vegetation indices such as NDVI (Normalized Difference Vegetation Index) and NirV (Near-Infrared Reflectance of Vegetation) for the same periods. Additionally, vectorized "zones" of approximately homogeneous pixels are available for each year. The spectral properties of the image data and the morphological characteristics of these zones are included as vector attributes (see Documentation: "Mosaic (1984–2023) - Data Description"). An overview of the coverage and quality of all sheet sections is provided as a vector layer titled D-Mosaik_Sheet-Sections within this document. The Germany Mosaic can also be considered a spatial-temporal Data Cube, enabling advanced analysis and integration into workflows requiring multi-dimensional data. This structure allows users to perform operations such as querying data across specific time periods, analyzing trends over decades, or aggregating spatial information to generate tailored insights for a wide range of research applications. In mid-latitudes, seasonal variations in vegetation—and consequently in the image data—are typically more pronounced than changes occurring over several years. The temporal segmentation of the dataset has been designed to encompass the entire vegetation period (May to October), with the division into summer and autumn periods capturing seasonal metabolic shifts in natural biotopes. This segmentation also records most agricultural changes, including sowing and harvesting activities. Depending on weather conditions, the individual image data represent either the median, mean value, or the best available image for the specified time period (see Documentation: "Mosaic (1984–2023) - Data Description). Remote sensing has become an indispensable tool for environmental research, particularly in landscape analysis. Beyond conventional applications, the Germany Mosaic supports the development of digital twins in environmental system research. By providing detailed spatial and temporal data, this dataset enables the modeling of virtual ecosystems, facilitating simulations, scenario testing, and predictive analyses for sustainable management. Moreover, the spatial and temporal trends captured by remotely sensed parameters complement traditional approaches in biological, ecological, geographical, and epidemiological research.
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