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.
6 Varianten organische Duengung, 1 Variante ohne organische Duengung in Kombination mit 4 Varianten Mineralduengung = 28 Varianten. Organische Varianten: I = 250 dt/ha Tiefstallmist, II = 300 dt/ha Stapelmist, III = 300 dt/Ha Frischmist, IV = 60 dt/ha Gerstenstroh, V = 250 dt/ha Kompostmist, VI = 17-stuendiger Schafpferch, VII = ohne organische Duengung. Die organische Duengung erfolgt jedes dritte Jahr zur Hackfrucht. In der Fruchtfolge Zuckerrueben - Weizen - Hafer werden folgende Parameter erfasst: Ertraege, Naehrstoffumsatz, Naehrstoffbilanz, Naehrstoffuntersuchungen im Boden, bodenbiologische Untersuchungen, Klimafaktoren.
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.
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 biotrophic fungus Ustilago maydis infects corn and induces the formation of tumors. In order for the fungus to proliferate in the infected tissue, U. maydis has to redirect the metabolism of the host to the site of infection. We wish to elucidate how this is accomplished. To this end we will perform transcript profiling during the time course of infection for both, the fungus and the maize plant. This will be complemented by metabolome analysis of different tissues during infection as well as by apoplastic fluid analysis. The goals will be to identify the carbon sources taken up by the fungus during biotrophic growth, to identify the transporters required for uptake, determine their specificity and elucidate how these carbon sources are provided by the plant. Fungal mutants affected in discrete stages of pathogenic development will be included in these studies. Likely candidate genes for carbon uptake/supply as well as for redirecting host metabolism will be functionally characterized by generating knockouts in the fungus and by isolating plants carrying mutations in respective genes or by generating transgenic plants expressing RNAi constructs.
Nitratreduktion von Wurzeln hat positive Auswirkungen auf die Überflutungstoleranz, doch sind die Mechanismen nur unzureichend verstanden. Nitratreduktase(NR)-haltige Wurzeln eines Tabakwildtyps produzierten unter Anoxia viel weniger Ethanol und Lactat als Wurzeln einer Tabaktransformante (LNR-H), die keine lösliche NR in der Wurzel (aber normale NR-Aktivität in den Blättern) besitzt. Auch der cytosolische pH und der Energiezustand der Wurzeln waren in WT unter Anoxia besser und die Pflanzen zeigten im Gegensatz zur LNR-H keine Welkeerscheinungen. Wir wollen nun überprüfen, inwieweit Nitrat- und Nitritreduktion, Atmung und oxidativer Pentosephosphatzyklus um Metabolite konkurrieren, und weshalb unter Anoxia in WT-Wurzeln die NR-Expression gesteigert und/oder die Proteolyse gehemmt ist. nitratreduzierende Pflanzen ermittieren unter Anoxia auch vermehrt Stickstoffmonoxid (NO). Wir wollen die NO-Emission von Wurzeln unter Normoxia/Anoxia/Post-Anoxia quantifizieren und beteiligte Reaktionen identifizieren. Eine mögliche Korrelation zwischen NO- und Ethylenemission sowie eine vermutete Akkumulation von NO-Verbindungen (Nitrosothiole und Nitrotyrosin) soll untersucht werden. Alle Experimente werden mit dem WT, der LNR-H-Transformante sowie an der Nitritreduktaseantisensetransformante von Tabak durchgeführt.
Ruminants play an important role as assets and sources of high quality food and income for rural populations in the developing world. Ruminant productivity is usually low due to inadequate nutrition (i.e. protein deficiency). Promising forage species, mainly legumes, have been identified to overcome these limitations. Many of these legume species contain tannins that could be either advantageous or disadvantageous in terms of feed efficiency and metabolizable protein supply to the animal. The overall aim of this project is to develop efficient feeding systems based on tanniniferous shrub and tree legumes in order to improve livestock productivity and to alleviate poverty of smallholders in the tropics. This will by achieved through (i) studying the effect of plant nutritional status on the accumulation of condensed tannins in legumes, and the influence of these tannins in ruminant nutrition and the nitrogen fertilizer value of animal excreta for plants, and (ii) designing optimal feeding strategies based on the use of tropical forage legumes with different tannin content to overcome the limitations of ruminant diets in protein supply. Finally, the results will be assessed by an ex-ante economic analysis at farm level and at ecoregional levels. The outcomes of this work will provide the necessary background information for better feeding practices based on tanniniferous legumes. The project is part of the Research Priority Area on 'Livestock Systems Research in Support of Poor People of the Swiss Centre for International Agriculture.
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