Die Begrenzung der Länge von Nahrungsketten ist eine der klassischen, aber immer noch unbeantworteten Fragen der Ökologie von Lebensgemeinschaften. In diesem Projekt und in einem parallelen Projekt limnologischer Ausrichtung (Antragsteller: Dr. H. Stibor, LMU München) soll versucht werden, zwei zentrale Hypothesen zu überprüfen: Die Omnivorie-Hypothese und die Hypothese der energetischen Begrenzung. Omnivore sind Organismen, die ihre Nahrung mindestens zwei tropischen Ebenen entnehmen. Dadurch werden sie gleichzeitig zu Nahrungskonkurrenten ihrer Beuteorganismen auf der unmittelbar unter den Omnivoren angesiedelten tropischen Ebene. Diese können dem doppelten Druck (Konkurrenz, Fraß) nicht widerstehen und werden aus dem System verdrängt, wodurch es zu einer Verkürzung der Nahrungskette kommt. Hypothese der energetischen Begrenzung. Wegen der Energieverluste, die bei jedem Transferschritt in der Nahrungskette auftreten, begrenzt die Höhe der Primärproduktion die Länge von Nahrungsketten, da bei zu langen Ketten die Energiezufuhr zu niedrig wäre, um die tropische Ebene der terminalen Räuber zu unterhalten. Überprüfung der Omnivorie-Hypothese. In künstlich zusammengestellte Modell-Nahrungsnetze im Labor (Mikrokosmen) werden an der Basis (mixotrophe Algen) und in der Mitte (omnivore Zooplankter) Omnivore eingefügt und die Struktur der Nahrungsnetze mit Kontroll-Nahrungsnetzen ohne Omnivore verglichen. Überprüfung der Hypothese der energetischen Begrenzung. Die Entwicklung der omnivorenhaltigen und omnivorenfreien Modell-Nahrungsnetze wird bei unterschiedlicher Trophie und damit Primärproduktion verfolgt.
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.
The publication examines how nitrogen pollution affects ecosystems and biodiversity across the UNECE region, using harmonized Critical Load maps, national data, and scenario modelling. It asks which ecosystems remain at risk under future emission pathways and whether the 2040 biodiversity-reduction target is achievable. The publication is aimed at scientists, policy makers, and informed readers involved in CLRTAP and Gothenburg Protocol revision.
The publication examines how nitrogen pollution affects ecosystems and biodiversity across the UNECE region, using harmonized Critical Load maps, national data, and scenario modelling. It evaluates which ecosystems remain at risk under future emission pathways and whether the 2040 biodiversity risk-reduction target is achievable. The publication is aimed at scientists, policy makers, and informed readers involved in CLRTAP and Gothenburg Protocol revision.
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.
This pre-study pilot project will be carried out in Kenya and Tanzania and is part of a more extensive remote sensing project (initiated by the European Space Agency, ESA) aiming to develop a monitoring system for the assessment of land cover change of farmlands, rangelands and forest standings (logging, fires, uncontrolled deforestation, new settlements, etc.) at a national regional level. An integrated approach of remote sensing techniques (both through the use of satellite and ground data), physical vegetation models and ground measurements will be adopted. Operatively, the execution will consist of a 6-month period (pre-study) consisting in a ground campaign along a north-south transect, which is almost unknown to the current vegetation cartography. Based on the field results of the pre-study and within an on-going 30 month period (extended study, see Annexed 3), new classification methods and algorithms will be developed for assessment of land use and cover change using ENVISAT-data. An outcoming of this research will be a system capable to monitor and plan the available agricultural food resources for those developing regions.
Der Klimawandel während der mittelalterlichen Klimaanomalie (MCA) und der kleinen Eiszeit (LIA) führte zur Ausdehnung bzw. Verringerung der hypoxischen Bodenbedeckung in der Ostsee. Hier schlagen wir eine Modellierungsstudie vor, um Mechanismen, durch die der Klimawandel zu den beobachteten Trends geführt hat, systematisch zu analysieren und Modellergebnisse anhand von geochemischen Sedimentkerndaten zu validieren. Das Zusammenspiel zwischen physikalischen und biogeochemischen Prozessen führt zu einer komplexen Dynamik, die den Sauerstoffgehalt in der Ostsee steuert. Die Sedimente spielen eine wichtige Rolle, indem sie sowohl als Quelle als auch als Senke für Phosphat fungieren, das den wichtigsten biolimitierenden Nährstoff bildet. Es ist jedoch kaum bekannt, wie der Klimawandel während der MCA zur Ausbreitung von Hypoxie führte. Es wurden bereits verschiedene Auslöser vorgeschlagen, um die Ausbreitung der Hypoxie während der MCA zu erklären, wie z.B. eine erhöhte Produktion von Cyanobakterien unter wärmeren Bedingungen, eine erhöhte / verringerte Stratifikation aufgrund sich ändernder Niederschlagsmuster und eine sedimentäre Freisetzung von Phosphaten. Im ersten Teil des Projekts (Arbeitspaket AP1) werden wir ein modernes Ökosystemmodell verwenden, um Szenarien zu identifizieren, die den Zusammenhang zwischen Klimawandel und Hypoxie im Mittelalter erklären können. Das Modell wird durch die Implementierung eines frühen diagenetischen Moduls verbessert, das chemische Profile im Sediment vertikal auflösen kann (AP2). Für biogeochemische Reaktionen werden temperaturabhängige Ratenausdrücke implementiert. Das Sedimentmodul wird zunächst auf den aktuellen Zustand der Sedimente kalibriert (AP3). Szenarien aus AP1, die die Sauerstofftrends erfolgreich erklären können, werden anschließend in Modellläufen vom Mittelalter bis zur Gegenwart getestet (AP4). Die Simulation des Mittelalters kann durch verschiedene Sedimentproxies validiert werden, die Trends in den Redoxbedingungen des Tiefenwassers, in der Zufuhr von Metallen aus Schelfe in tiefere Becken, welche die Sequestrierung von Phosphat beeinflusst, und in der Menge an in Sedimenten erhaltenem Phosphor und organischer Substanz rekonstruieren können. Die erwarteten Ergebnisse des Projekts sind die Zuordnung der Ausbreitung von Hypoxie während der MCA zu einem Mechanismus und ein verbessertes Verständnis der Rolle der benthischen Dynamik, die die Eutrophierung als Reaktion auf den Klimawandel beeinflusst.
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