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Marine prey and predators will respond to future climate through physiological and behavioral adjustments. However, our understanding of how such direct effects may shift the outcome of predator–prey interactions is still limited. Here, we investigate the effects of ocean warming and acidification on foraging behavior and biomass of a common prey (shrimps, Palaemon spp.) tested in large mesocosms harboring natural resources and habitats. Acidification did not alter foraging behavior in prey. Under warming, however, prey showed riskier behavior by foraging more actively and for longer time periods, even in the presence of a live predator. No effects of longer-term exposure to climate stressors were detected on prey biomass. Our findings suggest that ocean warming may increase the availability of some prey to predators via a behavioral pathway (i.e., increased risk-taking by prey), likely by elevating metabolic demand of prey species. In order to allow full comparability with other ocean acidification data sets, the R package seacarb (Gattuso et al, 2020) was used to compute a complete and consistent set of carbonate system variables, as described by Nisumaa et al. (2010). In this dataset the original values were archived in addition with the recalculated parameters (see related PI). The date of carbonate chemistry calculation by seacarb is 2020-12-08.

Nets are towed obliquely at approx. 1 knot, from the surface to approx. 175 m. Towing time is approx. 20 minutes. Zooplankton (weak swimmers >200µm) are collected using oblique tows of a 1 m**2 net (3m length) with 202µm mesh Nitex netting.

Preliminary ages were assigned to sample datasets by assigning seasonal trace element ratios minima and maxima in datasets to warmest and coolest months in satellite SST records and then linearly interpolating in-between

In order to allow full comparability with other ocean acidification data sets, the R package seacarb (Lavigne and Gattuso, 2011) was used to compute a complete and consistent set of carbonate system variables, as described by Nisumaa et al. (2010). In this dataset the original values were archived in addition with the recalculated parameters (see related PI). The date of carbonate chemistry calculation by seacarb is 2013-10-14.

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

The samples were concentrated down to 50 cm**3 by slow decantation after storage for 20 days in a cool and dark place. The species identification was done under light microscope OLIMPUS–BS41 connected to a video-interactive image analysis system at magnification of the ocular 10X and objective – 40X. A Sedgwick-Rafter camera (1ml) was used for counting. 400 specimen were counted for each sample, while rare and large species were checked in the whole sample (Manual of phytoplankton, 2005). Species identification was mainly after Carmelo T. (1997) and Fukuyo, Y. (2000). Total phytoplankton abundance was calculated as sum of taxon-specific abundances. Total phytoplankton biomass was calculated as sum of taxon-specific biomasses.The cell biovolume was determined based on morpho-metric measurement of phytoplankton units and the corresponding geometric shapes as described in detail in (Edier, 1979).

The data set has been processed and contains quality flags for different kinds for erroneous data. Flag values are the sum of individual error codes. The value of 0 refers to no error. Quality flag, sun: If the suns position is close to the horizon, the radiometers measure a very noisy signal. Radiometer measurements and variables which are computed from them are flagged +1 if the sun elevation is below 10 degrees; +2 if the broad band albedo exceeds the threshold 1.05 .This buoy had no own GPS source. It was located at the Central Observertory (CO1) of MOSAiC. The drift track of CO1 is published here:Nicolaus, Marcel; Riemann-Campe, Kathrin; Bliss, Angela; Hutchings, Jennifer K; Granskog, Mats A; Haas, Christian; Hoppmann, Mario; Kanzow, Torsten; Krishfield, Richard A; Lei, Ruibo; Rex, Markus; Li, Tao; Rabe, Benjamin (2021): Drift trajectory of the Central Observatory 1 (CO1) of the Distributed Network of MOSAiC 2019/2020. Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research, Bremerhaven, PANGAEA, https://doi.org/10.1594/PANGAEA.937184 Solar radiation over and under sea ice was measured by radiation station 2020R13, an autonomous platform, installed on drifting First-Year-Ice (FYI) in the Arctic Ocean during MOSAiC (Leg 3) 2019/20. The resulting time series describes radiation measurements as a function of place and time between 06 May 2020 and 15 May 2020 in sample intervals of 10 minutes. The radiation measurements have been performed with spectral radiometers. All data are given in full spectral resolution interpolated to 1.0 nm, and integrated over the entire wavelength range (broadband, total: 320 to 950 nm). Two sensors, solar irradiance and upward reflected solar irradiance, were mounted on a on a platform about 1 m above the sea ice surface. The third sensor was mounted 0.5 m underneath the sea ice measuring the downward transmitted irradiance.

Data were provided through and converted from Schlüter, M & Jerosch, K (2009) Digital Atlas of the North Sea, hdl:10013/epic.34893.d001 (pdf 3.7 MB).