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Während die Auswirkungen von Klimawandel auf physiologische und ökologische Prozesse das Thema zahlreicher Untersuchungen waren, sind evolutionäre Prozesse im Zusammenhang mit Klimawandel weit weniger gut untersucht. Insbesondere mangelt es an Studien zu möglichen komplexen Wechselwirkungen zwischen ökologischen und evolutionären Prozessen in einer sich ändernden Umwelt. Artspezifische Unterschiede in Anpassungsraten könnten die Dynamik der gesamten Art-Gemeinschaft beeinflussen, umgekehrt könnten sich ökologische Prozesse wie Interaktionen zwischen Arten, Immigration und Emigration auf das Anpassungspotential von Arten auswirken. Die Tatsache, dass Klimawandel zu Veränderungen in mehreren Umweltfaktoren führt, macht Vorhersagen über mögliche Auswirkungen noch schwieriger, da sich Veränderungen in mehreren Stressoren interaktiv auf ökologische und evolutionäre Prozesse auswirken könnten. Die Ziele des vorgeschlagenen Projektes sind die Analyse von ökologischen und evolutionären Prozessen und deren Wechselwirkung (1) bei Veränderung von mehreren Stressoren, (2) bei Umweltveränderung in trophisch einfachen versus trophisch komplexen Gemeinschaften, und (3) bei Umweltveränderung in isolierten versus verbundenen Habitaten. Diese Fragestellungen sollen mit einer Kombination aus Modellierung, Mikrokosmen- und Mesokosmen-Experimenten untersucht werden. In einem Selektionsexperiment über hunderte von Generationen werden mehrere Algenarten bei konstanten bzw. steigenden CO2- und/oder Temperatur-Werten exponiert. Ebenso werden mehrere Ciliatenarten bei konstanter bzw. steigender Temperatur gehalten. Reziproke Transplantationsexperimente testen, ob eine mögliche Anpassung von Algen an steigende CO2-Werte durch gleichzeitige Erhöhung der Temperatur beeinflusst wird. Weiters wird getestet, ob sich Arten von verschiedenen trophischen Ebenen (Algen versus Ciliaten) in ihrer Anpassungsfähigkeit unterscheiden. Reziproke Transplantationsexperimente der gesamten Gemeinschaft werden testen, ob evolutionäre Prozesse die Dynamik der Gemeinschaft beeinflussen. Interaktive Effekte von Umweltveränderung und Habitatkonnektivität auf ökologische und evolutionäre Prozesse werden sowohl in einem Mikrokosmenexperiment als auch in einem Mesokosmenexperiment untersucht. Der Effekt von steigender Temperatur (Mikrokosmenexperiment) bzw. abnehmendem pH-Wert (Mesokosmenexperiment) wird in isolierten bzw. verbundenen Habitaten verglichen. In einem theoretischen Ansatz werden die drei Fragestellungen in einem Modell verknüpft. Zunächst werden Evolution und Umweltveränderung in ein Metagemeinschaftsmodell integriert. Entlang eines Konnektivitäts-Gradienten wird die relative Bedeutung von lokaler Anpassung im Vergleich zu Wanderungsprozessen untersucht. usw.
Data presented here were collected between 2019-09 and 2023-09 at station BEFmate_S4low within the research unit DynaCom (Spatial community ecology in highly dynamic landscapes: From island biogeography to metaecosystems, https://uol.de/dynacom/ ) involving the Universities of Oldenburg, Göttingen, and Münster, the iDiv Leipzig and the Nationalpark Niedersächsisches Wattenmeer. Experimental islands and saltmarsh enclosed plots were established in the back-barrier tidal flat and in the saltmarsh zone of the island of Spiekeroog (Germany). Salinity at different elevation zones was measured using conductivity loggers deployed in dip wells within experimental islands as well as in the saltmarsh enclosed plots. Measurements were obtained using HOBO U24 Conductivity Logger U24-002-C (Onset Computer Corporation, Bourne, MA/USA). All devices were pre-calibrated by the manufacturer. Logged data were retrieved in the field using a Hobo Underwater Shuttle (U-DTW-1) and were read out with the HOBOware Pro (V3.7.28) software. Salinity was derived in HOBOware Pro using temperature-dependent, nonlinear seawater conductivity compensation following the Practical Salinity Scale (PSS-78). Subsequent data processing was done using MATLAB (R2024b). Post-processing and quality control included (a) the removal of data covering maintenance activities, (b) the removal of implausible values using fixe thresholds (salinity > 40 psu and < 5 psu; temperature > 35 °C and < -5 °C), c) an outlier detection using the Hampel filter method, and (d) visual checks. Identified outliers were removed and synchronously removed across all associated parameters (temperature and salinity).
Data presented here were collected between September 2022 to July 2023 within the research unit DynaCom (Spatial community ecology in highly dynamic landscapes: From island biogeography to metaecosystems, https://uol.de/dynacom/ ) of the Universities of Oldenburg, Göttingen, and Münster, the iDiv Leipzig and the Nationalpark Niedersächsisches Wattenmeer. Experimental islands and saltmarsh enclosed plots were created in the back barrier tidal flat and in the saltmarsh zone of the island of Spiekeroog. Local tide and wave conditions were recorded with a RBRduo TDǀwave sensor (RBR Ltd., Ontario/Canada). The sensor was bottom mounted in a shallow tidal creek (0.77 m NHN) through a steel girder (buried 0.3m deep in the sediment) and was positioned 10 cm above sediment surface, as was determined by using a portable differential GPS. This resulted in the sensor falling dry during low tide. For accurate depth calculations, raw pressure data were manually corrected for atmospheric pressure derived from a locally installed weather station. The sensor was pre-calibrated by the manufacturer and the sampling rate was 3 Hz with 1024 samples per burst at a sample interval of 10 min. Recorded data were internally logged until the readout with the Ruskin (V1.13.13) software. Date and time is given in UTC. Data handling was performed according to Zielinski et al. (2018): Post-processing of collected data was done using MATLAB (R2018a). Quality control was performed by (a) erasing data covering maintenance activities, (b) removing outliers, and (c) visually checks. Low-tide data is not removed, but were easily identified through the manually calculated water depth data, where all depths < 0.05m represented low tide data.
Data presented here were collected between January 2025 to December 2025 within the research unit DynaCom (Spatial community ecology in highly dynamic landscapes: From island biogeography to metaecosystems, https://uol.de/dynacom/ ) of the Universities of Oldenburg, Göttingen, and Münster, the iDiv Leipzig and the Nationalpark Niedersächsisches Wattenmeer. Experimental islands and saltmarsh enclosed plots were created in the back barrier tidal flat and in the saltmarsh zone of the island of Spiekeroog. Meteorological data were collected near the experimental setup, with a locally installed weather station located approximately 500m north of the southern shoreline. The weather station system used here was a ClimaSensor US 4.920x.00.00x that was pre-calibrated by the manufacturer (Adolf Thies GmbH & Co. KG, D-Göttingen). Data were recorded and saved within the Processcontrol Weather (c) -4H- JENA engineering GmbH (v20.1.0.1 2020) software in a sampling interval of 1 min, with an averaging time of 10 s. Date and time were given in UTC and the position was derived from the internal GPS system. Data handling was performed according to Zielinski et al. (2018): Post-processing of collected data was done using MATLAB (R2024b). Quality control was performed by (a) erasing data covering maintenance activities, (b) removing outliers, defined as data exhibiting changes of more than two standard deviations within one time step, and (c) visually checks.
Data presented here were collected between 2019-09 and 2021-07 at station BEFmate_S3upp within the research unit DynaCom (Spatial community ecology in highly dynamic landscapes: From island biogeography to metaecosystems, https://uol.de/dynacom/ ) involving the Universities of Oldenburg, Göttingen, and Münster, the iDiv Leipzig and the Nationalpark Niedersächsisches Wattenmeer. Experimental islands and saltmarsh enclosed plots were established in the back-barrier tidal flat and in the saltmarsh zone of the island of Spiekeroog (Germany). Salinity at different elevation zones was measured using conductivity loggers deployed in dip wells within experimental islands as well as in the saltmarsh enclosed plots. Measurements were obtained using HOBO U24 Conductivity Logger U24-002-C (Onset Computer Corporation, Bourne, MA/USA). All devices were pre-calibrated by the manufacturer. Logged data were retrieved in the field using a Hobo Underwater Shuttle (U-DTW-1) and were read out with the HOBOware Pro (V3.7.28) software. Salinity was derived in HOBOware Pro using temperature-dependent, nonlinear seawater conductivity compensation following the Practical Salinity Scale (PSS-78). Subsequent data processing was done using MATLAB (R2024b). Post-processing and quality control included (a) the removal of data covering maintenance activities, (b) the removal of implausible values using fixe thresholds (salinity > 40 psu and < 5 psu; temperature > 35 °C and < -5 °C), c) an outlier detection using the Hampel filter method, and (d) visual checks. Identified outliers were removed and synchronously removed across all associated parameters (temperature and salinity).
Data presented here were collected between January 2023 to August 2023 within the research unit DynaCom (Spatial community ecology in highly dynamic landscapes: From island biogeography to metaecosystems) of the Universities of Oldenburg, Göttingen, and Münster, the iDiv Leipzig and the Nationalpark Niedersächsisches Wattenmeer. Experimental islands and saltmarsh enclosed plots were created in the back barrier tidal flat and in the saltmarsh zone of the island of Spiekeroog. Sediment samples for the determination of pH, water content and loss on ignition were taken bi-/monthly in surface sediments (0-3 cm depth) from the experimental plots. Samples were taken between 3 hours before and 3 hours after low tide. Samples were stored dark and cool (8 °C) until measurement. Samples were measured in the laboratory within two months after sampling. Water content (w, [-]) was determined by first weighing the fresh sample (mf; ~ 3-7 g) in pre-weighed aluminium trays and than placed in the drying chamber at 105 °C for 12 hours. After placing samples in the exsiccator for 60 min., samples were re-weight to determine dry weight (md). Water content was calculated using w = (mf - md) / md . Afterwards, samples were placed in the muffle furnace for 2 hours at 430 °C within their aluminium trays, and placed again in the exsiccator for 60 min. Samples were re-weighed to determine the new dry weight (mgl) to calculate loss on ignition (LOI, [%]) using LOI = ((md – mgl) / md ) * 100 . Values of pH were measured according to DIN ISO 10390. Therefore, soil samples were weighed in pre-weighed Falcon™ 50 mL conical centrifuge tubes. Sediment samples were homogenized using a pestle. Ultrapure water was used to measure pH directly within the tubes using a HQ40D digital two channel multi meter and an Intellical PHC101 field low maintenance gel filled pH electrode (Hach Lange GmbH, Germany). The pH electrode was calibrated before measurement using singlet pH buffer sets (pH 4.01, 7.00, 10.01) for single use (Hach Lange GmbH, Germany). Post-processing of measured values were done using MATLAB (R2024b). Quality control was performed by (a) visually checks, and hence (b) the classification into quality control flags using quality check algorithms.
Data presented here were collected between September 2018 to September 2023 within the research unit DynaCom (Spatial community ecology in highly dynamic landscapes: From island biogeography to metaecosystems) involving the Universities of Oldenburg, Göttingen, and Münster, the iDiv Leipzig and the Nationalpark Niedersächsisches Wattenmeer. Experimental islands and saltmarsh enclosed plots were established in the back-barrier tidal flat and in the saltmarsh zone of the island of Spiekeroog (Germany). To measure local turbidity, a turbidity recorder equipped with a Seapoint® turbidity meter (RBRsolo Tu, RBR Ltd., Ontario/Canada) was installed in the back-barrier tidal flat near the experimental islands in a shallow tidal creek (0.9 m NHN). Another one was installed at the saltmarsh edge (1.2 m NHN). Both loggers were bottom mounted through a steel girder (buried 0.3 m deep in the sediment) and were positioned 15 cm above sediment surface, as was determined by using a portable differential GPS. This resulted in the sensor falling dry during low tide. The turbidity recorders were pre-calibrated by the manufacturer (Seapoint Sensors, Inc., NH/USA). Recorded data were internally logged and exported using Ruskin software V2.24.3.x (RBR Ltd., Ontario/Canada). Subsequent data processing was done using MATLAB (R2024b). Post-processing and quality control included the removal of (a) low tide data (sensors exposed to air), (b) data covering maintenance activities, (c) data affected by biofouling, and (d) implausible values, i.e. negative values and values exceeding the linear response range of the sensor (1250 NTU). According to manufacturer specifications, the linear measurement range extends up to 1250 NTU, while 750 NTU represent a more conservative estimate of linearity. Therefore, 1250 NTU was adopted as the upper threshold for valid measurements in this dataset.
Data presented here were collected between November 2019 to September 2023 within the research unit DynaCom (Spatial community ecology in highly dynamic landscapes: From island biogeography to metaecosystems, https://uol.de/dynacom/ ) involving the Universities of Oldenburg, Göttingen, and Münster, the iDiv Leipzig and the Nationalpark Niedersächsisches Wattenmeer. Experimental islands and saltmarsh enclosed plots were established in the back-barrier tidal flat and in the saltmarsh zone of the island of Spiekeroog (Germany). A recording current meter (RCM; SEAGUARD® Recording Current Meter, Aanderaa Data Instruments AS, Bergen/Norway) was installed in the back-barrier tidal flat near the experimental islands. The sensor was bottom-mounted in a shallow tidal creek (0.59 m NHN) using a steel girder buried in the sediment, which caused the sensor to be exposed during low tide. All low-tide data have been removed from the dataset. The system was equipped with a ZPulse Doppler Current Sensor (DCS), a conductivity sensor, an oxygen optode, and two analogue sensors for chlorophyll-a and turbidity (16445). All sensors were pre-calibrated by the manufacturer. Recorded data were internally logged until readout with the SeaGuard Studio software (V1.5.23). Salinity was derived in the SeaGuard Studio software using temperature-dependent, nonlinear seawater conductivity compensation following the Practical Salinity Scale (PSS-78). Subsequent data processing was done using MATLAB (R2024b). Turbidity and chlorophyll-a data were excluded from the final dataset, as the recorded signals show implausible values and did not pass quality-control criteria. Post-processing and quality control included (a) the removal of low tide data, data covering maintenance activities, and data affected by biofouling, (b) the removal of implausible values, c) an outlier detection using the Hampel filter method, and (d) visual checks. Identified outlier were removed and synchronously removed across all associated parameters of the respective sensor.
Data presented here were collected between 2020-01 and 2023-04 at station BEFmate_I4low within the research unit DynaCom (Spatial community ecology in highly dynamic landscapes: From island biogeography to metaecosystems, https://uol.de/dynacom/ ) involving the Universities of Oldenburg, Göttingen, and Münster, the iDiv Leipzig and the Nationalpark Niedersächsisches Wattenmeer. Experimental islands and saltmarsh enclosed plots were established in the back-barrier tidal flat and in the saltmarsh zone of the island of Spiekeroog (Germany). Groundwater levels at different elevation zones were measured using pressure loggers deployed in dip wells within the experimental islands as well as in the saltmarsh enclosed plots. Measurements were obtained using a DEFI-D Miniature Pressure Recorder (JFE Advantech Co., Ltd., Tokyo; DEFI-D). All devices were pre-calibrated by the manufacturer. Logged data were retrieved in the field using a Hobo Underwater Shuttle (U-DTW-1) and were read out with the DEFI Series software (V1.02), depending on the instrument. Subsequent data processing was done using MATLAB (R2024b). Atmospheric pressure correction for water-level calculations was applied using data from a nearby weather station. Post-processing and quality control included (a) the removal of data covering maintenance activities, (b) an outlier detection, and (c) visual checks. Outliers in water level and temperature time series were detected using a moving-median filter and a 3-sigma criterion, with additional cross-checking against a reference sensor. Identified outliers were removed, and height-corrected water level series were produced to ensure consistency across sensors and years.
Data presented here were collected between 2020-01 and 2022-05 at station BEFmate_I3low within the research unit DynaCom (Spatial community ecology in highly dynamic landscapes: From island biogeography to metaecosystems, https://uol.de/dynacom/ ) involving the Universities of Oldenburg, Göttingen, and Münster, the iDiv Leipzig and the Nationalpark Niedersächsisches Wattenmeer. Experimental islands and saltmarsh enclosed plots were established in the back-barrier tidal flat and in the saltmarsh zone of the island of Spiekeroog (Germany). Groundwater levels at different elevation zones were measured using pressure loggers deployed in dip wells within the experimental islands as well as in the saltmarsh enclosed plots. Measurements were obtained using a Hobo U20L Water Level Logger (Onset Computer Corporation, Bourne, MA/USA) that was pre-calibrated by the manufacturer. Logged data were retrieved in the field using a Hobo Underwater Shuttle (U-DTW-1) and were read out with the HOBOware Pro (V3.7.28) software. Subsequent data processing was done using MATLAB (R2024b). Atmospheric pressure correction for water-level calculations was applied using data from a nearby weather station. Post-processing and quality control included (a) the removal of data covering maintenance activities, (b) an outlier detection, and (c) visual checks. Outliers in water level and temperature time series were detected using a moving-median filter and a 3-sigma criterion, with additional cross-checking against a reference sensor. Identified outliers were removed, and height-corrected water level series were produced to ensure consistency across sensors and years.
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