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.
Die Erforschung von Artbildungs- und Anpassungsprozessen ist zentral, um zu verstehen, wie Biodiversität entsteht und auf wechselnde Umweltbedingungen reagiert.. Ein idealer Ort für solche Studien ist das Südpolarmeer: Es beherbergt eine reiche und hochgradig endemische Fauna. Neuere Studien zeigen, dass viele benthische Arten aus Gruppen von genetisch distinkten Kladen bestehen, die als früher übersehene Arten pleistozänen Ursprungs interpretiert werden. Diese kryptischen Arten können durch molekulare Methoden (z. B. DNA-Barcoding) und z.T. auch durch morphologische Analysen unterschieden werden. Es wird angenommen, dass die Artbildung per Zufall erfolgte, als ehemals große Populationen während glazialer Maxima in kleinen allopatrischen Refugien isoliert wurden, wo sie starker genetischer Drift ausgesetzt waren. Alternative Artbildungsmodelle wurden bislang wegen fehlender molekularer Methoden kaum erforscht. Studien aus anderen Ökosystemen zeigen, dass ökologische Artbildung, d.h. Aufspaltungsereignisse durch unterschiedliche Selektion, ein naheliegendes alternatives Artbildungsmodell ist. In dem hier vorgestellten Projekt sollen erstmals hochauflösende genomische Methoden zusammen mit morphologischen Analysen benutzt werden, um konkurrierende Artbildungsmodelle für das Südpolarmeer zu testen. Als Fallstudie sollen hierfür Muster genetischer Drift und Selektion in einer besonders erfolgreichen Gruppe benthischer Arten des Südpolarmeeres untersucht werden, den Asselspinnen (Pycnogonida). Aufbauend auf vorangehenden Studien sollen genomische Muster neutraler und nicht neutraler Marker bei zwei Artkomplexen untersucht werden: Colossendeis megalonyx und Pallenopsis patagonica. Diese beiden Artkomplexe von Asselspinnen sind aufgrund mehrerer Merkmale hervorragende Modelle für die Themen dieses Antrages: 1) Es existieren zahlreiche genetisch divergente kryptische Arten, 2) erste morphologische Unterschiede wurden gefunden, 3) die weite Verbreitung der Vertreter sowohl auf dem antarktischen Kontinentalschelf als auch in weniger von den Vereisungen betroffenen subantarktischen Regionen, 4) ihre geringe Mobilität. Sollte eine durch genetische Drift bedingte allopatrische Artbildung in glazialen Refugialpopulationen der Hauptantrieb der Evolution sein, ist zu erwarten, dass Zufallsfixierung neutraler Allele und Signaturen von Populations-Bottlenecks in stark vereisten Gebieten am höchsten sind. Wenn andererseits natürliche Selektion der Hauptantrieb der Artbildung war, so sind starke Signaturen von Selektion auf Geno- und Phänotyp zu erwarten. Diese sollte am stärksten bei sympatrischen Arten sein (Kontrastverstärkung). Die Variation entlang von Genomen soll untersucht werden, um das Ausmaß zufälliger bzw. nicht zufälliger Variation einzuschätzen. Das vorgeschlagene Projekt wird ein wichtiger erster Schritt einer systematischen Erforschung der relativen Bedeutung von genetischer Drift und Selektion für die Evolution im Südpolarmeer sein.
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.
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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