Unser Wissen zur Ökologie und Bedeutung von Mikroorganismen in Böden ist umfassend. Dies gilt im Gegensatz dazu nicht für die Ökologie der Viren. Erkenntnisse dazu hinken dem Kenntnisstand aus aquatischen Lebensräumen weit hinterher. Böden beherbergen eine große Anzahl an Viren und das Viren - Wirt Verhältnis liegt meist deutlich über jenem in aquatischen Systemen. Unterschiede in den Virenpopulationen können teilweise auf unterschiedliche Bodencharakteristika (pH, Wassergehalt, Anteil an organischem Material) erklärt werden. Dies lässt den Schluss zu, dass Unterschiede in der Landnutzung entsprechend die Virenabundanz als auch Viren - Wirt Interaktionen beeinflussen. In Böden tragen bis zu 68% aller Bakterien induzierbare Prophagen, ein Hinweis darauf, dass die Heterogenität im Boden und die ungleiche Verteilung der Mikroorganismen eine lysogene Vermehrung von Viren selektiert. Dies hat zur Folge, dass der Austausch von genetischer Information zwischen Virus und Wirt vorwiegend durch Transduktion stattfindet. Bis dato analysierte Virenmetagenome aus dem Boden bestanden bis zu 50% aus transduzierten Genen prokaryotischen Ursprungs. Obwohl davon ausgegangen werden kann, dass Viren im Boden, wie für aquatische Lebensräume gezeigt, einen signifikanten Einfluss auf die räumliche und zeitliche Dynamik ihrer Wirte (Killing the Winner Hypothese) und deren kontinuierliche Anpassung (Red Queen Hypothese), wichtige Ökosystemfunktionen und biogeochemische Prozesse haben, kennen wir die Art und Häufigkeit der Interaktionen nicht und empirische Daten fehlen. Wir postulieren, dass Transduktion eine wichtige Rolle für die Resilienz von Böden unter intensiver Landnutzung spielt, da in diesen Böden i) die mikrobielle Diversität vergleichsweise niedrig ist, was zu einer erhöhten Sensitivität gegenüber Veränderungen in den Umweltbedingungen führt. Andererseits, ii) hat die durch Düngung erhöhte spezifische Aktivität von Mikroorganismen eine erhöhte Transduktionsrate zur Folge, da Viren für ihre Vervielfältigung auf metabolisch aktive Wirte angewiesen sind. Um unsere Hypothese zu überprüfen, werden wir an 150 Standorten der Biodiversitäts-Exploratorien und im Detail an einer Auswahl an Grünlandstandorten mit unterschiedlicher Intensität der Bewirtschaftung Untersuchungen durchführen. Analysiert wird die Beziehung zwischen Virenabundanzen und VBRs mit der Bewirtschaftung, der Vegetationsperiode und den vorherrschenden Umweltbedingungen. Zusätzlich untersuchen wir mit Hilfe moderner molekularer Methoden die Zusammensetzung der Virengemeinschaften und ihre Diversität, sowie viren-assoziierte Funktionen prokaryotischen Ursprungs. Experimente zu Virus-Wirt Interaktionen und die Analyse von CRISPR like structures in den prokaryotischen Wirten werden Erkenntnisse zu der Ökologie bakterieller Gemeinschaften liefern. Nicht zuletzt werden wir Viren von abundanten Bodenbakterien (z.B. Pseudomonaden) für vergleichende Genomanalysen und Kreuzinfektionsversuche isolieren.
Monochloressigsaeure (MCAA) bzw. das Natriumsalz der Monochloressigsaeure (SMCA) wird in der chemischen Industrie vielfach als Zwischenprodukt fuer chemische Synthesen verwendet. In der Umwelt findet es sich in geringen Konzentrationen durch die Photooxidation chlorierter Kohlenwasserstoffe. Durch das Forschungsvorhaben soll geklaert werden: 1. Organspezifische Toxizitaet (Niere, Leber, Haut). 2. Wirkmechanismus nach Aufnahme (Zellorganellen, Metabolismus). 3. Antidots bei akuten Intoxikationen.
Boron (B) is an essential microelement for plants. Despite the use of modern fertilization methods, B deficiency still causes losses in agricultural plant production. Even though many positive effects of B on plant growth and physiology have been reported, a large majority of B functions and the regulatory mechanisms controlling the B nutritional status remain unknown. The main objective of this project is to elucidate how the greatly B deficiency-sensitive Brassica crop plants process and regulate their B status during vegetative and reproductive growth. In this context, the project aims at identifying the mode of action of B in mechanisms regulating the B status itself and uncovering those mechanisms contributing to B efficiency in different genotypes. Plant species subjected to investigation will be the agronomically important oilseed and vegetable plant Brassica napus (rapeseed) and its close relative the genetic and molecular model plant Arabidopsis thaliana. Questions addressed within the scope of this project should lead to a detailed understanding of mechanisms controlling B uptake and allocation from the level of the whole plant down to the cellular level. B transport routes and rates will be determined in sink- and source tissues and in developmental periods with a particularly high B demand. A special focus will be on the identification of B transport bottlenecks and the analysis of B deficiency-sensitive transport processes to and within the highly B-demanding reproductive organs. Recent studies in Arabidopsis suggest that Nodulin26-like Intrinsic Proteins (NIPs), which belong to the aquaporin channel protein family, are essential for plant B uptake and distribution. The systematic focus on the molecular and physiological characterization of B. napus NIPs will clarify their role in B transport and will identify novel NIP-associated mechanisms playing key roles in the B response network.To further resolve the mostly unknown impact of the B nutritional status on gene regulation and metabolism, a transcript and metabolite profile of B-sufficient and B-deficient rapeseed plants will be generated. Additionally, an Arabidopsis transcription factor knockout collection (greater 300 lines) will be screened for abnormalities in responses to the B nutritional status. This will identify yet unknown B-responsive genes (transcription factors and their targets) and gene products (enzymes or metabolite variations) playing key roles in signalling pathways and mechanisms regulating the B homeostasis. Boron (in form of boric acid) and arsenite (As) share in all likelihood the same NIP-mediated transport pathways. To assess the consequences of this dual transport pathway the so far unstudied impact of the plants B nutritional status on the accumulation and distribution of As will be investigated in B. napus. Moreover, the current dimension of the As contamination of Brassica-based food products, to which consumers are exposed to, will be analyzed. usw.
Das Ziel dieses Projektes ist die Entwicklung eines quantitativen Verständnisses der dynamischen biogeochemischen Prozesse in Auesedimenten, um zu einer prozessbasierten Abschätzung der Umsetzungsprozesse redox-sensitiver Spezies wie Stickstoff und Schwefel und von Herbiziden wie Glyphosat und MCPA zu gelangen. Untersuchungskampagnen (Bohrungen) unter unterschiedlichen Randbedingungen (Hochwasser, Trockenheit, Wiederbefeuchtung) in Kombination mit zielgerichteten Laborexperimenten sollen aufzeigen, wie hydrologische Faktoren, die im Hinblick auf die Umsetzung von Schadstoffen relevanten biogeochemischen Prozesse steuern.
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.
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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