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Habitat files of 28 Antarctic Toothfish (Dissostichus mawsoni) prey species and the Antarctic Toothfish itself, regridded to the FESOM-REcoM model grid, in NetCDF format. For use in article '21st-century environmental change decreases habitat overlap of Antarctic toothfish (Dissostichus mawsoni) and its prey'. This dataset is a collection of the following: 1) 2-dimensional distribution data for 3 squid species, derived from Raymond et al. (2015) 2) 2-dimensional distribution data for 25 prey species and the Antarctic Toothfish, derived from AquaMaps distribution data 3) the model grid file. A species is present in a certain gridcell when its value is 1, elsewhere the species is considered absent. 1) Original files for the 3 squid species galiteuthis glacialis, kondakovia longimana and mesonychoteuthis hamiltoni are taken from previously published data of Raymond et al. (2015) and regridded to the FESOM-REcoM model grid. These squid species are considered to have their habitat anywhere where habitat suitability in the original Raymond et al. (2015) dataset exceeds the habitat suitability thresholds of 0.228 for Galiteuthis glacialis, 0.281 for Kondakovia longimana and 0.121 for Mesonychoteuthis hamiltoni (threshold values as in Xavier et al. (2016), personal communication with Ben Raymond 16.01.2023 to get exact values). These data have an original resolution of 0.1x0.1 degrees (regular longitude-latitude grid). 2) The 25 files taken from AquaMaps start with Default_* or Reviewed_* and are the native predicted range data as provided by Kaschner et al. (2019). They were shared by Kathleen Kesner-Reyes from AquaMaps in March and April 2023 after being reviewed by her for correctness and actuality. The files that start with Reviewed_* have had adjustments made to the original Default_* based on this review, which is described in more detail on the AquaMaps website. These data have an original resolution of 0.5x0.5 degrees (regular longitude-latitude grid). 3) The model grid file 'Mesh_ancillary_information_v20220919.nc' contains the longitude and latitude data needed for regridding to the FESOM-REcoM model grid. Regridding was done using CDO version 1.9.6 (http://mpimet.mpg.de/cdo; developed by U. Schulzweida) and CDO function 'remaplaf', which performs largest area fraction remapping. References Xavier, J.C., Raymond, B., Jones, D.C. et al. Biogeography of Cephalopods in the Southern Ocean Using Habitat Suitability Prediction Models. Ecosystems 19, 220–247 (2016). https://doi.org/10.1007/s10021-015-9926-1 Raymond, B., Xavier, J., Griffiths, H., Jones, D. (2015) Habitat suitability predictions for 15 species of cephalopods in the Southern Ocean, Ver. 1, Australian Antarctic Data Centre - doi:10.4225/15/563AC33450A28, Accessed: 2023-01-16 Kaschner, K., Kesner-Reyes, K., Garilao, C., Segschneider, J., Rius-Barile, J. Rees, T., & Froese, R. (2019, October). AquaMaps: Predicted range maps for aquatic species. Retrieved from https://www.aquamaps.org.

This dataset contains CTD measurements taken in 2015, 2016, 2017, 2018, 2019, 2022, and 2023 during expeditions to Herschel Island - Qikiqtaruk, Yukon, Canada. For the data recording a CTD CastAway was used. The sampling was conducted from a boat from which the CTD was lowered to the sea floor at the specific sampling sites. The sampling information encompasses the GPS position, water depth, pressure, temperature, electrical conductivity, specific conductance, and salinity.

Macroalgal surveys were performed at Hansneset, Blomstrand in Kongsfjorden, Svalbard, from the infralittoral fringe down to 15 m depth in June – August 2021. This dataset is part of a time series currently spanning over 25 years and complements the studies conducted in 1996/98 (Hop et al., 2012) and 2012-14 (Bartsch et al., 2016). The aim was to document changes in Arctic kelp forest dynamics in an Arctic fjord system influenced by glacial melt by repeatedly sampling the same site in a standardized manner. As ocean temperatures in the Arctic have risen substantially and underwater light climate continuously deteriorated over this time period, alterations were observed in the seaweed community, especially kelps as major coastal foundation species. This Zenodo upload contains all datasets supporting the findings of the manuscript Düsedau et al. (2023) "Arctic kelp forest decline - a consequence of melting glaciers?".

Many infectious diseases display strong seasonal dynamics. When both hosts and parasites are influenced by seasonal variables, it is unclear if the start of an epidemic is limited by host or parasite factors or both. The Daphnia-Pasteuria host-parasite system exhibits seasonal epidemics. We aimed to ascertain how temperature contributes to the timing of P. ramosa epidemics in early spring. To this aim, we experimentally disentangled this effect from the effects of temperature on host development and phenology and from that of host traits on parasite time to visible infection. We hypothesized that the parasite is additionally directly limited by low temperatures beyond its need for available hosts. We found that parasite time to visible infection decreased with increasing temperature at a faster rate than host time to hatching and maturity did, consistent with this hypothesis. We also found that hosts hatched from sexual resting stages are less likely to become infected than those produced clonally and that hosts resistant to many known parasite strains are slower to show signs of visible infection compared to those susceptible to many. Together, these results imply that climate change could lead to earlier seasonal epidemics for this host-parasite system, which may also impact longer-term population dynamics. Data were collected from experimental epidemics of the bacterial endoparasite Pasteuria ramosa in the waterflea Daphnia magna at different temperatures. Briefly, five different temperature treatments were applied to jars of Daphnia juveniles (of either a largely resistant phenotype or a largely susceptible phenotype) and jars of Daphnia ephippia. Each sampling day the number of juveniles, adults and infected individuals were counted. To calculate the main outcome variables (time to hatch for ephippia, time to maturation, time to infection), the number of newly hatched juveniles, number of new adults (with eggs) and number of new infections were calculated on each sampling day and multiplied by the day of the experiment. These were then divided by the total number hatched, matured or infected, respectively, to obtain an average time to hatch, maturation or infection for each experimental jar. For prevalence, the total number ever infected over the course of the experiment was divided by the maxiumum number of individuals ever present in the jar. Raw data file in CSV format can be opened in Excel, R, Google Sheets, etc. Code can be opened in R. README file can be opened in Notepad.

Particulate organic carbon (POC) concentration and POC vertical flux data were collected at site P3 in the South Atlantic during an austral spring bloom in 2017. Data were collected on the COMICS cruise (DY086) aboard the RRS Discovery. POC concentration and POC flux are averaged across each of the three site visits. POC concentration and fluxes are derived from three gliders (Wet Labs Eco Puck sensors on two Teledyne Webb Research Slocum G2 gliders (Unit-398 & Unit-404) and one Kongsberg Seaglider (SG-542)) that were deployed during the cruise and calibrated by ship-based deployments (CTD bottle collection, marine snow catchers and neutrally buoyant sediment traps). Fast, Slow and Total Fluxes were calculated by separating large spikes (Fast Flux) in the backscatter signal from small spikes (Slow Flux) and background noise, with total being a summation of Fast and Slow Fluxes. The glider mission was supported by a European Research Council Consolidator grant (GOCART, agreement number 724416) to S. A. Henson, as well as South Africa's Department of Science and Innovation (DST/CON0182/2017) and the National Research Foundation (SANAP: SNA170522231782) grants to S. J. Thomalla.

Second-year sea-ice thickness, draft, salinity, temperature, and density were measured during near-weekly surveys at the main second-year ice coring site (MCS-SYI) during the MOSAiC expedition (legs 1 to 3) and new second-year ice coring site leg 4, since the earlier site was not accessible any longer. The ice cores were extracted either with a 9-cm (Mark II) or 7.25-cm (Mark III) internal diameter ice corers (Kovacs Enterprise, US). This data set includes data from 18 coring site visits and were performed from 28 October 2019 to 20 July 2020 at coring locations within 50 m to each other in the MOSAiC Central Observatory. During each coring event, ice temperature was measured in situ from a separate temperature core, using Testo 720 thermometers in drill holes with a length of half-core-diameter at 5-cm vertical resolution. Ice bulk practical salinity was measured from melted core sections at 5-cm resolution using a YSI 30 conductivity meter. Ice density was measured using the hydrostatic weighing method (Pustogvar and Kulyakhtin, 2016) from a density core in the freezer laboratory onboard Polarstern at the temperature of –15°C. Relative volumes of brine and gas were estimated from ice salinity, temperature and density using Cox and Weeks (1983) for cold ice and Leppäranta and Manninen (1988) for ice warmer than –2°C. The data contains the event label (1), time (2), and global coordinates (3,4) of each coring measurement and sample IDs (13, 15). Each salinity core has its manually measured ice thickness (5), ice draft (6), core length (7), and mean snow height (22). Each core section has the total length of its top (8) and bottom (9) measured in situ, as well estimated depth of section top (10), bottom (11), and middle (12). The depth estimates assume that the total length of all core sections is equal to the measured ice thickness. Each core section has the value of its practical salinity (14), isotopic values (16, 17, 18) (Meyer et al., 2000), as well as sea ice temperature (19) and ice density (20) interpolated to the depth of salinity measurements. The global coordinates of coring sites were measured directly. When it was not possible, coordinates of the nearby temperature buoy 2019T62 (legs 1-3) or 2019T61 (leg 4) were used. Ice mass balance buoy 2019T62 installation is described in doi:10.1594/PANGAEA.940231, ice mass balance buoy 2020T61 installation is described in doi: 10.1594/PANGAEA.926580. Brine volume (21) fraction estimates are presented only for fraction values from 0 to 30%. Each core section also has comments (23) describing if the sample is from a new coring site or has any other special characteristics. Macronutrients from the salinity core will be published in a subsequent version of this data set.

The physical properties of sea ice, including salinity, sea ice temperature, and sea ice texture as well as isotopic composition (oxygen and hydrogen) were analyzed from samples collected on the CAATEX 2019 expedition to the central Arctic Ocean (between North Pole and Svalbard) in late summer 2019. In addition, isotope samples were collected from precipitation, melt ponds, and under-ice seawater. Data was collected to understand the formation mechanisms of sea ice in the area. Ice cores were collected with an ice corer, and measurements (temperature) were directly carried out from ice cores after retrieval. From an additional ice core, samples (salinity and isotopes) were collected and melted onboard. Salinity of samples was measured onboard using a conductivity probe, while samples for isotopic composition were placed in sample bottles and measured at the FARLAB (University of Bergen) after the expedition . A complete ice core was brought back for texture analysis of the sea ice and processed at the freezer lab at the Norwegian Polar Institute. Texture was examined by first slicing ice samples very thin by use of a microtome. These thin ice slices were observed in polarized light that reveals the crystal structure of the ice. This data includes the photographs taken of the ice cores in natural light and in polarized light.

The physical properties of sea ice, including salinity, sea ice temperature, and sea ice texture as well as isotopic composition (oxygen and hydrogen) were analyzed from samples collected on the CAATEX 2019 expedition to the central Arctic Ocean (between North Pole and Svalbard) in late summer 2019. In addition, isotope samples were collected from precipitation, melt ponds, and under-ice seawater. Data was collected to understand the formation mechanisms of sea ice in the area. Ice cores were collected with an ice corer, and measurements (temperature) were directly carried out from ice cores after retrieval. From an additional ice core, samples (salinity and isotopes) were collected and melted onboard. Salinity of samples was measured onboard using a conductivity probe, while samples for isotopic composition were placed in sample bottles and measured at the FARLAB (University of Bergen) after the expedition . A complete ice core was brought back for texture analysis of the sea ice and processed at the freezer lab at the Norwegian Polar Institute. Texture was examined by first slicing ice samples very thin by use of a microtome. These thin ice slices were observed in polarized light that reveals the crystal structure of the ice. This data includes the photographs taken of the ice cores in natural light and in polarized light.

The physical properties of sea ice, including salinity, sea ice temperature, and sea ice texture as well as isotopic composition (oxygen and hydrogen) were analyzed from samples collected on the CAATEX 2019 expedition to the central Arctic Ocean (between North Pole and Svalbard) in late summer 2019. In addition, isotope samples were collected from precipitation, melt ponds, and under-ice seawater. Data was collected to understand the formation mechanisms of sea ice in the area. Ice cores were collected with an ice corer, and measurements (temperature) were directly carried out from ice cores after retrieval. From an additional ice core, samples (salinity and isotopes) were collected and melted onboard. Salinity of samples was measured onboard using a conductivity probe, while samples for isotopic composition were placed in sample bottles and measured at the FARLAB (University of Bergen) after the expedition . A complete ice core was brought back for texture analysis of the sea ice and processed at the freezer lab at the Norwegian Polar Institute. Texture was examined by first slicing ice samples very thin by use of a microtome. These thin ice slices were observed in polarized light that reveals the crystal structure of the ice. This data includes the photographs taken of the ice cores in natural light and in polarized light.