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We simulated an experimental summer storm in large-volume (~1200 m3, ~16m depth) enclosures in Lake Stechlin (https://www.lake-lab.de) by mixing deeper water masses from the meta- and hypolimnion into the mixed layer (epilimnion). The mixing included the disturbance of a deep chlorophyll maximum (DCM) which was present at the same time of the experiment in Lake Stechlin and situated in the metalimnion of each enclosure during filling. Size-fractionated Bacterial Protein Production (BPP) of particle associated (PA, >3.0 µm) and free-living bacteria (FL, 0.2-3.0 µm) (14C-Leu incorporation) as well as abundances of PA (microscopy of DAPI stained cells on 3.0 µm polycarbonate filters) and FL heterotrophic prokaryotes and picocyanobacteria (flow cytometry of SYBR green I stained cells) were monitored for 42 days after the experimental disturbance event. Mixing increased bacterial abundance and production about 3 weeks after mixing, which was associated to a mixing-induced stimulation of phytoplankton growth in the mixed enclosures compared to the controls. Simultaneously, decreased abundances of picocyanobacteria could be observed in mixed enclosures. Empty cells = NAFurther Project information: Core Facility grant; Award: GE 1775/2-1

We simulated an experimental summer storm in large-volume (~1200 m3, ~16m depth) enclosures in Lake Stechlin by mixing deeper water masses from the meta- and hypolimnion into the mixed layer (epilimnion). The mixing included the disturbance of a deep chlorophyll maximum (DCM) which was present at the same time of the experiment in Lake Stechlin and situated in the metalimnion of each enclosure during filling. Copepod and Cladocera biomass was monitored for 42 days after the experimental disturbance event (Utermöhl counting at 60x magnification and biomass calculation from length-dry mass relationships). Sampling was performed using a 90 µm mesh size Apstein-cone. Method: 90 µm mesh size Apstein-cone, Utermöhl counting at 60x, TSO image analysis program (KIO VIDMESS-2011, Version 2.60.50, including a TSO-KST1003832 camera, TSO - Thalheim-Spezial-Optik-Gerätebau, Pulsnitz, Germany), biomass calculations from length-dry mass relationships (Bottrell et al. 1976; Kasprzak 1983).Further Project information:Core Facility grant; Award: GE 1775/2-1

At each station, seawater was collected for trace metals sampling at 20 m depth using a Teflon pump (Furon®) with a 40 m Teflon tube connected with a AcroPakTM 1500 capsule w/Supor Memb 0.8/0.2 μm filter. The pump was left to pump to rinse the inner hose and 1 L sample was collected in LDPE bottles (NALGENE) and stored at -20oC until the land-based laboratory analysis. The material was previously cleaned following the trace metals GEOTRACES protocol and the experiments were done in Class 100 clean laboratory or trace metal analysis (QUIMA- IOCAG TM lab).

The data describes plant gas exchange dynamics (CO2, H2O) together with online proton transfer reaction mass spectrometry measurements of biogenic volatile organic compound emissions of Pinus halepensis seedlings exposed to two similar heatwaves together with drought and a recovery period. Measured in a scientific glasshouse facility at KIT IMK-IFU Garmisch-Partenkirchen, Germany, via an automated chamber setup. #0 means "Inf"

The data contains vulnerability estimates (climate niche factor analysis) for 49 native fish species in the upper Danube River basin. The upper Danube River basin is mainly located in Germany and Austria. The time frame covered is 300 years from 1800 to 2100 including two Representative Concentration Pathways, RCP 4.5 and RCP 8.5. Vulnerability estimates are calculated for three time frames (1800-1830; 1900-1930and 2070-2100 (including two RCPs)) with the time frame 1970-2000 as the baseline. In all files the zone column gives the basin ID for the master basins layer. The mean column gives the mean vulnerability estimate for a sub-basin for a certain species. For the future scenarios the different predictions based on the different GCM-RCM combinations have to be combined using the median and the zone as unique identifier.

Zooplankton treatment: HZ: zooplankton added, LZ: Zooplankton removedCarbon treatment: 0C: No glucose addition, 0.5: 0.5 x Redfield. 1: 1xRedfield, 2: 2xRedfield, 3: 3XRedfieldpH Treatment:NpH: Normal pH, LpH: Low pH

The “Hydroblack91” dataset is based on samples collected in the summer of 1991 and covers part of North-Western in front of Romanian coast and Western Black Sea (Bulgarian coasts) (between 43°30' - 42°10' N latitude and 28°40'- 31°45' E longitude). Mesozooplankton sampling was undertaken at 20 stations. The whole dataset is composed of 72 samples with data of zooplankton species composition, abundance and biomass. Samples were collected in discrete layers 0-10, 0-20, 0-50, 10-25, 25-50, 50-100 and from bottom up to the surface at depths depending on water column stratification and the thermocline depth. Zooplankton samples were collected with vertical closing Juday net,diameter - 36cm, mesh size 150 µm. Tows were performed from surface down to bottom meters depths in discrete layers. Samples were preserved by a 4% formaldehyde sea water buffered solution. Sampling volume was estimated by multiplying the mouth area with the wire length.Mesozooplankton abundance: The collected materia was analysed using the method of Domov (1959). Samples were brought to volume of 25-30 ml depending upon zooplankton density and mixed intensively until all organisms were distributed randomly in the sample volume. After that 5 ml of sample was taken and poured in the counting chamber which is a rectangle form for taxomomic identification and count. Large (> 1 mm body length) and not abundant species were calculated in whole sample. Counting and measuring of organisms were made in the Dimov chamber under the stereomicroscope to the lowest taxon possible. Taxonomic identification was done at the Institute of Oceanology by Asen Konsulov using the relevant taxonomic literature (Mordukhay-Boltovskoy, F.D. (Ed.). 1968, 1969,1972). The biomass was estimated as wet weight by Petipa, 1959 (based on species specific wet weight). Wet weight values were transformed to dry weight using the equation DW=0.16*WW as suggested by Vinogradov & Shushkina, 1987.Taxon-specific abundance: The collected material was analysed using the method of Domov (1959). Samples were brought to volume of 25-30 ml depending upon zooplankton density and mixed intensively until all organisms were distributed randomly in the sample volume. After that 5 ml of sample was taken and poured in the counting chamber which is a rectangle form for taxomomic identification and count. Copepods and Cladoceras were identified and enumerated; the other mesozooplankters were identified and enumerated at higher taxonomic level (commonly named as mesozooplankton groups). Large (> 1 mm body length) and not abundant species were calculated in whole sample. Counting and measuring of organisms were made in the Dimov chamber under the stereomicroscope to the lowest taxon possible. Taxonomic identification was done at the Institute of Oceanology by Asen Konsulov using the relevant taxonomic literature (Mordukhay-Boltovskoy, F.D. (Ed.). 1968, 1969,1972). The biomass was estimated as wet weight by Petipa, 1959 ussing standard average weight of each species in mg/m3. WW were converted to DW by equation DW=0.16*WW (Vinogradov ME, Sushkina EA, 1987).

Total mesozooplankton biomass was measured as dry weight on a fresh sample. Samples were sieved on 200 µm nitex, briefly rinsed with distilled water to remove salt, transferred on pre-weighted alluminium foil and placed in an oven at 60°C. The samples were weighted on a microbalance after 24 hours and again 2-3 times within the following seven days, until the weight was constant. This latter value was considered as dry weight.