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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.

Total mesozooplankton biomass was measured as dry weight on a fresh sample. Samples were sieved on 200 ?m 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.

During PS93.2 (in 2015) bacteria density, meiofauna density, macrofauna density and macrofauna biomass was determined. For the bacterial density determination, sediment subsamples were taken with modified syringes (1.17 cm² cross-sectional area) from MUC recovered sediment cores and from benthic chambers. The first centimetre of each sample was stored in a 2 % filtered formalin solution at 4 °C. The acridine orange direct count (AODC) method (Hobbie et al., 1977) was used to stain bacteria in the subsamples and subsequently bacteria were counted with a microscope (Axioskop 50, Zeiss) under UV-light (CQ-HXP-120, LEj, Germany). For the determination of the meiofauna density and identification of meiofauna taxa, sediment subsamples were taken with modified syringes (3.14 cm² cross-sectional area) from MUC recovered sediment cores. The first centimetre of each sample was stored in borax buffered 4 % formaldehyde solution at 4 °C. The samples were sieved over a 1000 µm and 32 µm mesh. Both fractions were centrifuged three times in a colloidal silica solution (Ludox TM-50) with a density of 1.18 g/cm³ and stained with Rose 20 Bengal (Heip et al., 1985). Afterwards, the taxa were identified and counted. Foraminifera are not considered, as the extraction efficiency of Ludox for different groups of foraminifera is insufficient for a quantitative assessment of the group. Therefore, only metazoan meiofauna is recorded. After taking subsamples for bacteria and meiofauna densities, the remaining sediment from MUC recovered sediment cores and from the benthic chambers was used for macrofauna taxonomical identification, and density and biomass determination. For these macrofauna analyses only the 0-5 cm horizon from MUC sediment cores and the entire remaining sediment from the benthic chambers was used, sieved over a 500 µm mesh and stored in borax buffered 4 % formaldehyde and stained with Rose Bengal (Heip et al., 1985). Afterwards, macrofauna taxa were identified to the highest taxonomic level, counted and weighted (blotted wet weight).

Mouth diameter of the net was 113 cm, mesh size 200 µm. Sample aliquots were immediately immediately utilized for biomass measurements. Volumes of filtered seawater were estimated by multiplying the area of the net mouth by heights of sampled layers from winch readings. Total mesozooplankton biomass was measured as dry weight on a fresh aliquot of an original sample. The samples were filtered through pre-weighed GF/C filters, briefly rinsed with distilled water to remove salt, dried in an oven at 60 °C for some hours onboard and preserved frozen. Later in the lab, the samples were dried again in the oven for 24 hours and then weighted on a microbalance.

In the future, the Baltic Sea ecosystem will be impacted both by climate change and by riverine and atmospheric nutrient inputs. Multi-model ensemble simulations comprising one IPCC scenario (A1B), two global climate models, two regional climate models, and three Baltic Sea ecosystem models were performed to elucidate the combined effect of climate change and changes in nutrient inputs. This study focuses on the occurrence of extreme events in the projected future climate. Results suggest that the number of days favoring cyanobacteria blooms could increase, anoxic events may become more frequent and last longer, and salinity may tend to decrease. Nutrient load reductions following the Baltic Sea Action Plan can reduce the deterioration of oxygen conditions.