This dataset contains geochemical variables measured in six depth profiles from ombrotrophic peatlands in North and Central Europe. Peat cores were taken during the spring and summer of 2022 from Amtsvenn (AV1), Germany; Drebbersches Moor (DM1), Germany; Fochteloër Veen (FV1), the Netherlands; Bagno Kusowo (KR1), Poland; Pichlmaier Moor (PI1), Austria and Pürgschachen Moor (PM1), Austria. The cores AV1, DM1 and KR1 were taken using a Wardenaar sampler (Royal Eijkelkamp, Giesbeek, the Netherlands) and had diameter of 10 cm. The cores FV1, PM1 and PI1 had an 8 cm diameter and were obtained using an Instorf sampler (Royal Eijkelkamp, Giesbeek, the Netherlands). The cores FV1, DM1 and KR1 were 100 cm, core AV1 was 95 cm, core PI1 was 85 cm and core PM1 was 200 cm. The cores were subsampeled in 1 cm (AV1, DM1, KR1, FV1) and 2 cm (PI1, PM1) sections. The subsamples were milled after freeze drying in a ballmill using tungen carbide accesoires. X-Ray Fluorescence (WD-XRF; ZSX Primus II, Rigaku, Tokyo, Japan) was used to determine Al (μg g-1), As (μg g-1), Ba (μg g-1), Br (μg g-1), Ca (g g-1), Cl (μg g-1), Cr (μg g-1), Cu (μg g-1), Fe (g g-1), K (g g-1), Mg (μg g-1), Mn (μg g-1), Na (μg g-1), P (μg g-1), Pb (μg g-1), Rb (μg g-1), S (μg g-1), Si (μg g-1), Sr (μg g-1), Ti (μg g-1) and Zn (μg g-1). These data were processed and calibrated using the iloekxrf package (Teickner & Knorr, 2024) in R. C, N and their stable isotopes were determined using an elemental analyser linked to an isotope ratio mass spectrometer (EA-3000, Eurovector, Pavia, Italy & Nu Horizon, Nu Instruments, Wrexham, UK). C and N were given in units g g-1 and stable isotopes were given as δ13C and δ15N for stable isotopes of C and N, respectively. Raw data C, N and stable isotope data were calibrated with certified standard and blank effects were corrected with the ilokeirms package (Teickner & Knorr, 2024). Using Fourier Transform Mid-Infrared Spectroscopy (FT-MIR) (Agilent Cary 670 FTIR spectromter, Agilent Technologies, Santa Clara, Ca, USA) humification indices (HI) were determined. Spectra were recorded from 600 cm-1 to 4000 cm-1 with a resolution of 2 cm-1 and baselines corrected with the ir package (Teickner, 2025) to estimate relative peack heights. The HI (no unit) for each sample was calculated by taking the ratio of intensities at 1630 cm-1 to the intensities at 1090 cm-1. Bulk densities (g cm-3) were estimated from FT-MIR data (Teickner et al., in preparation).
This dataset contains geochemical variables measured in six depth profiles from ombrotrophic peatlands in North and Central Europe. Peat cores were taken during the spring and summer of 2022 from Amtsvenn (AV1), Germany; Drebbersches Moor (DM1), Germany; Fochteloër Veen (FV1), the Netherlands; Bagno Kusowo (KR1), Poland; Pichlmaier Moor (PI1), Austria and Pürgschachen Moor (PM1), Austria. The cores AV1, DM1 and KR1 were taken using a Wardenaar sampler (Royal Eijkelkamp, Giesbeek, the Netherlands) and had diameter of 10 cm. The cores FV1, PM1 and PI1 had an 8 cm diameter and were obtained using an Instorf sampler (Royal Eijkelkamp, Giesbeek, the Netherlands). The cores FV1, DM1 and KR1 were 100 cm, core AV1 was 95 cm, core PI1 was 85 cm and core PM1 was 200 cm. The cores were subsampeled in 1 cm (AV1, DM1, KR1, FV1) and 2 cm (PI1, PM1) sections. The subsamples were milled after freeze drying in a ballmill using tungen carbide accesoires. X-Ray Fluorescence (WD-XRF; ZSX Primus II, Rigaku, Tokyo, Japan) was used to determine Al (μg g-1), As (μg g-1), Ba (μg g-1), Br (μg g-1), Ca (g g-1), Cl (μg g-1), Cr (μg g-1), Cu (μg g-1), Fe (g g-1), K (g g-1), Mg (μg g-1), Mn (μg g-1), Na (μg g-1), P (μg g-1), Pb (μg g-1), Rb (μg g-1), S (μg g-1), Si (μg g-1), Sr (μg g-1), Ti (μg g-1) and Zn (μg g-1). These data were processed and calibrated using the iloekxrf package (Teickner & Knorr, 2024) in R. C, N and their stable isotopes were determined using an elemental analyser linked to an isotope ratio mass spectrometer (EA-3000, Eurovector, Pavia, Italy & Nu Horizon, Nu Instruments, Wrexham, UK). C and N were given in units g g-1 and stable isotopes were given as δ13C and δ15N for stable isotopes of C and N, respectively. Raw data C, N and stable isotope data were calibrated with certified standard and blank effects were corrected with the ilokeirms package (Teickner & Knorr, 2024). Using Fourier Transform Mid-Infrared Spectroscopy (FT-MIR) (Agilent Cary 670 FTIR spectromter, Agilent Technologies, Santa Clara, Ca, USA) humification indices (HI) were determined. Spectra were recorded from 600 cm-1 to 4000 cm-1 with a resolution of 2 cm-1 and baselines corrected with the ir package (Teickner, 2025) to estimate relative peack heights. The HI (no unit) for each sample was calculated by taking the ratio of intensities at 1630 cm-1 to the intensities at 1090 cm-1. Bulk densities (g cm-3) were estimated from FT-MIR data (Teickner et al., in preparation).
This dataset contains geochemical variables measured in six depth profiles from ombrotrophic peatlands in North and Central Europe. Peat cores were taken during the spring and summer of 2022 from Amtsvenn (AV1), Germany; Drebbersches Moor (DM1), Germany; Fochteloër Veen (FV1), the Netherlands; Bagno Kusowo (KR1), Poland; Pichlmaier Moor (PI1), Austria and Pürgschachen Moor (PM1), Austria. The cores AV1, DM1 and KR1 were taken using a Wardenaar sampler (Royal Eijkelkamp, Giesbeek, the Netherlands) and had diameter of 10 cm. The cores FV1, PM1 and PI1 had an 8 cm diameter and were obtained using an Instorf sampler (Royal Eijkelkamp, Giesbeek, the Netherlands). The cores FV1, DM1 and KR1 were 100 cm, core AV1 was 95 cm, core PI1 was 85 cm and core PM1 was 200 cm. The cores were subsampeled in 1 cm (AV1, DM1, KR1, FV1) and 2 cm (PI1, PM1) sections. The subsamples were milled after freeze drying in a ballmill using tungen carbide accesoires. X-Ray Fluorescence (WD-XRF; ZSX Primus II, Rigaku, Tokyo, Japan) was used to determine Al (μg g-1), As (μg g-1), Ba (μg g-1), Br (μg g-1), Ca (g g-1), Cl (μg g-1), Cr (μg g-1), Cu (μg g-1), Fe (g g-1), K (g g-1), Mg (μg g-1), Mn (μg g-1), Na (μg g-1), P (μg g-1), Pb (μg g-1), Rb (μg g-1), S (μg g-1), Si (μg g-1), Sr (μg g-1), Ti (μg g-1) and Zn (μg g-1). These data were processed and calibrated using the iloekxrf package (Teickner & Knorr, 2024) in R. C, N and their stable isotopes were determined using an elemental analyser linked to an isotope ratio mass spectrometer (EA-3000, Eurovector, Pavia, Italy & Nu Horizon, Nu Instruments, Wrexham, UK). C and N were given in units g g-1 and stable isotopes were given as δ13C and δ15N for stable isotopes of C and N, respectively. Raw data C, N and stable isotope data were calibrated with certified standard and blank effects were corrected with the ilokeirms package (Teickner & Knorr, 2024). Using Fourier Transform Mid-Infrared Spectroscopy (FT-MIR) (Agilent Cary 670 FTIR spectromter, Agilent Technologies, Santa Clara, Ca, USA) humification indices (HI) were determined. Spectra were recorded from 600 cm-1 to 4000 cm-1 with a resolution of 2 cm-1 and baselines corrected with the ir package (Teickner, 2025) to estimate relative peack heights. The HI (no unit) for each sample was calculated by taking the ratio of intensities at 1630 cm-1 to the intensities at 1090 cm-1. Bulk densities (g cm-3) were estimated from FT-MIR data (Teickner et al., in preparation).
This dataset contains geochemical variables measured in six depth profiles from ombrotrophic peatlands in North and Central Europe. Peat cores were taken during the spring and summer of 2022 from Amtsvenn (AV1), Germany; Drebbersches Moor (DM1), Germany; Fochteloër Veen (FV1), the Netherlands; Bagno Kusowo (KR1), Poland; Pichlmaier Moor (PI1), Austria and Pürgschachen Moor (PM1), Austria. The cores AV1, DM1 and KR1 were taken using a Wardenaar sampler (Royal Eijkelkamp, Giesbeek, the Netherlands) and had diameter of 10 cm. The cores FV1, PM1 and PI1 had an 8 cm diameter and were obtained using an Instorf sampler (Royal Eijkelkamp, Giesbeek, the Netherlands). The cores FV1, DM1 and KR1 were 100 cm, core AV1 was 95 cm, core PI1 was 85 cm and core PM1 was 200 cm. The cores were subsampeled in 1 cm (AV1, DM1, KR1, FV1) and 2 cm (PI1, PM1) sections. The subsamples were milled after freeze drying in a ballmill using tungen carbide accesoires. X-Ray Fluorescence (WD-XRF; ZSX Primus II, Rigaku, Tokyo, Japan) was used to determine Al (μg g-1), As (μg g-1), Ba (μg g-1), Br (μg g-1), Ca (g g-1), Cl (μg g-1), Cr (μg g-1), Cu (μg g-1), Fe (g g-1), K (g g-1), Mg (μg g-1), Mn (μg g-1), Na (μg g-1), P (μg g-1), Pb (μg g-1), Rb (μg g-1), S (μg g-1), Si (μg g-1), Sr (μg g-1), Ti (μg g-1) and Zn (μg g-1). These data were processed and calibrated using the iloekxrf package (Teickner & Knorr, 2024) in R. C, N and their stable isotopes were determined using an elemental analyser linked to an isotope ratio mass spectrometer (EA-3000, Eurovector, Pavia, Italy & Nu Horizon, Nu Instruments, Wrexham, UK). C and N were given in units g g-1 and stable isotopes were given as δ13C and δ15N for stable isotopes of C and N, respectively. Raw data C, N and stable isotope data were calibrated with certified standard and blank effects were corrected with the ilokeirms package (Teickner & Knorr, 2024). Using Fourier Transform Mid-Infrared Spectroscopy (FT-MIR) (Agilent Cary 670 FTIR spectromter, Agilent Technologies, Santa Clara, Ca, USA) humification indices (HI) were determined. Spectra were recorded from 600 cm-1 to 4000 cm-1 with a resolution of 2 cm-1 and baselines corrected with the ir package (Teickner, 2025) to estimate relative peack heights. The HI (no unit) for each sample was calculated by taking the ratio of intensities at 1630 cm-1 to the intensities at 1090 cm-1. Bulk densities (g cm-3) were estimated from FT-MIR data (Teickner et al., in preparation).
Die Gemeinde Geeste liegt im Landkreis Emsland und umfasst die Ortsteile Bramhar, Dalum, Geeste, Groß Hesepe, Klein Hesepe, Osterbrock und Varloh.
Globally, agriculture covers 40% of the earth’s surface and food systems are responsible for one-third of humanity’s contribution to global climate change. Yet, smallholder and subsistence farmers are among the most vulnerable to climate change, with extreme weather events and related food price volatility affecting livelihoods, biodiversity, and food security at multiple scales. This project builds on transdisciplinary research on agroecological transitions in vulnerable farming communities in Canada, Germany, India and Brazil. We will examine the influence of agroecological networks (farming organizations, institutional actors, and consumer groups) in promoting the perennialization of agriculture to support climate adaptation (improving resilience in livelihoods and food security) and mitigation (increasing carbon sequestration). Perennialization of agriculture integrates annual and perennial crops and trees into the same farming system. Compared to annual cropping systems which currently dominate global agriculture and markets, perennial crops show promise for climate adaptation and mitigation because of their contributions to carbon sequestration in tree biomass and soil organic carbon, and their buffering effects against soil degradation, drought, and other forms of extreme weather and climate variability. From a social wellbeing perspective, agroforestry and other diversified perennial systems offer opportunities to adapt to climate change and escape poverty traps, including higher and more stable farm incomes, balanced agricultural labour across growing seasons, improved working conditions compared to more input-intensive forms of agriculture and improved nutrition and health. Using a participatory action research approach, this project will use a novel methodology to test the relationships between personal, political, and practical leverage points driving the adoption of agroforestry and other practices supporting agricultural perennialization. We will sample farms and organizations in each case study across a diversification gradient from low-diversity farming systems to perennial and agroforestry-based management systems. We will then use qualitative and quantitative methods to assess climate resilience outcomes and estimate the potential of scaling adoption of perennial and agroforestry practices. A cross-case synthesis will take local institutional, environmental, and relational contexts into account to inform decision-making.
Water is an intrinsic component of ecosystems acting as a key agent of lateral transport for particulate and dissolved nutrients, forcing energy transfers, triggering erosion, and driving biodiversity patterns. Given the drastic impact of land use and climate change on any of these components and the vulnerability of Ecuadorian ecosystems with regard to this global change, indicators are required that not merely describe the structural condition of ecosystems, but rather capture the functional relations and processes. This project aims at investigating a set of such functional indicators from the fields of hydrology and biogeochemistry. In particular we will investigate (1) flow regime and timing, (2) nutrient cycling and flux rates, and (3) sediment fluxes as likely indicators. For assessing flow regime and timing we will concentrate on studying stable water isotopes to estimate mean transit time distributions that are likely to be impacted by changes in rainfall patterns and land use. Hysteresis loops of nitrate concentrations and calculated flux rates will be used as functional indicators for nutrient fluxes, most likely to be altered by changes in temperature as well as by land use and management. Finally, sediment fluxes will be measured to indicate surface runoff contribution to total discharge, mainly influenced by intensity of rainfall as well as land use. Monitoring of (1) will be based on intensive sampling campaigns of stable water isotopes in stream water and precipitation, while for (2) and (3) we plan to install automatic, high temporal-resolution field analytical instruments. Based on the data obtained by this intensive, bust cost effective monitoring, we will develop the functional indicators. This also provides a solid database for process-based model development. Models that are able to simulate these indicators are needed to enable projections into the future and to investigate the resilience of Ecuadorian landscape to global change. For the intended model set up we will couple the Catchment Modeling Framework, the biogeochemical LandscapeDNDC model and semi-empirical models for aquatic diversity. Global change scenarios will then be analyzed to capture the likely reaction of functional indicators. Finally, we will contribute to the written guidelines for developing a comprehensive monitoring program for biodiversity and ecosystem functions. Right from the beginning we will cooperate with four SENESCYT companion projects and three local non-university partners to ensure that the developed monitoring program will be appreciated by locals and stakeholders. Monitoring and modelling will focus on all three research areas in the Páramo (Cajas National Park), the dry forest (Reserva Laipuna) and the tropical montane cloud forest (Reserva Biologica San Francisco).
In marinen Lebensräumen können Seevögel als wertvolle Indikatoren für Nahrungsressourcen und die Produktivität des marinen Ökosystems dienen. Studien zeigen deutliche Veränderungen in marinen Ökosystemen, und eine Art, die auf solche Veränderungen empfindlich reagiert, ist der Südliche Felsenpinguin Eudyptes chrysocome (IUCN-Kategorie gefährdet). Analysen neuerer und historischer Daten deuten darauf hin, dass Felsenschreibepinguine in einem sich erwärmenden Ozean schlechter überleben und sich vermehren und dass der Klimawandel sie in mehreren Phasen der Brut- und Nicht-Brutsaison beeinflussen kann. Mehr als ein Drittel der Gesamtpopulation dieser Art brütet auf den Falklandinseln, wo die Populationen besonders stark zurückgehen, und unsere früheren Studien (2006-2011) hier haben auf reduzierte Überlebenswahrscheinlichkeiten unter zunehmend warmen Meerestemperaturen und leichtere Eier unter wärmeren Umweltbedingungen hingewiesen. Die zugrunde liegenden Ursachen für diese Veränderungen sind jedoch noch wenig bekannt. Das vorliegende Projekt knüpft an frühere Studien an, aber wir werden neu verfügbare Technologien anwenden, nämlich viel kleinere GPS-Beschleunigungs-Datenlogger, um die noch unbekannten Phasen der Brutzeit und die für die Futtersuche verwendete Energie zu untersuchen, und Analysemethoden aus dem Machine Learning („künstliche Intelligenz“) und der Energielandschaften-Modellierung. Komponentenspezifische stabile Isotopenanalysen und Metabarcodierung von Kotproben werden zudem eingesetzt, um die Ernährung während der verschiedenen Phasen des Brutzyklus zu untersuchen. Wir werden auch Zeitrafferkameras einsetzen und über "Penguin watch" - ein Toolkit zur Extraktion großflächiger Daten aus Kamerabildern und zur Einbeziehung der Öffentlichkeit - bürgernahe Wissenschaft betreiben. Insgesamt wollen wir verstehen, warum Südliche Felsenpinguine eine besonders empfindliche Art bei sich erwärmenden Meeresbedingungen sind.
In hydrology, the relationship between water storage and flow is still fundamental in characterizing and modeling hydrological systems. However, this simplification neglects important aspects of the variability of the hydrological system, such as stable or instable states, tipping points, connectivity, etc. and influences the predictability of hydrological systems, both for extreme events as well as long-term changes. We still lack appropriate data to develop theory linking internal pattern dynamics and integral responses and therefore to identify functionally similar hydrological areas and link this to structural features. We plan to investigate the similarities and differences of the dynamic patterns of state variables and the integral response in replicas of distinct landscape units. A strategic and systematic monitoring network is planned in this project, which contributes the essential dynamic datasets to the research group to characterize EFUs and DFUs and thus significantly improving the usual approach of subdividing the landscape into static entities such as the traditional HRUs. The planned monitoring network is unique and highly innovative in its linkage of surface and subsurface observations and its spatial and temporal resolution and the centerpiece of CAOS.
Littorina littorea was collected at the study site. The foot of Littorina littorea was used for stable isotope analysis (δ15N and δ13C). The stable isotope composition of possible food sources was also determined. Samples were taken in spring, summer and autumn. For the analysis a diet tissue discrimination factor (DTDF) of 2.4 for δ15N and 1.0 for δ13C was subtracted, respectively. The data in the sheet are the raw data without the DTDF.
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