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Fig. 2 in The prevalence of Corynosoma parasite worms in the great cormorants and the Baltic herring in the northern Baltic Sea, Finland

Fig. 2. Mean annual salinity (PSU) and temperature (T, ◦C) of the winter months (January–April) in the Bothnian Sea at 0–50 m depth during 1980–2021. Data from ICES Oceanographic dataset, 2021. ICES, Copenhagen.

Fig. 8 in The prevalence of Corynosoma parasite worms in the great cormorants and the Baltic herring in the northern Baltic Sea, Finland

Fig. 8. Size differences of cormorants between sexes (a, b, and c) and adults and juveniles (d, e, and f). The black line represents the median length (a, b, d, and e) and median weight (c and f). The grey box represents the middle 50% of the data (n = 65).

Fig. 3 in The prevalence of Corynosoma parasite worms in the great cormorants and the Baltic herring in the northern Baltic Sea, Finland

Fig. 3. Acanthocephala parasites in the body cavity of a herring (left; the red circle indicates the position of worms) and on the inner surface of the intestine of a cormorant (right). The upper photo indicates the size of parasites compared to a match (photos: J. Sahlst´en). (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)

Fig. 1 in The prevalence of Corynosoma parasite worms in the great cormorants and the Baltic herring in the northern Baltic Sea, Finland

Fig. 1. Map showing the sampling sites in the northern Baltic Sea: A = Archipelago Sea, B = Uusikaupunki, C = Merikarvia. The sampling locations in region A: 1 = Taivassalo, 2 = Velkua trawl area, 3 = western Rym¨attyl¨a, 4 = northern Airisto Inlet, 5 = Peimari.

Fig. 5 in The prevalence of Corynosoma parasite worms in the great cormorants and the Baltic herring in the northern Baltic Sea, Finland

Fig. 5. Prevalence (%) of the corynosoma infection in the Baltic herring in the Bothnian Sea (n = 1528) and the Archipelago Sea in 2018 (n = 1167). The black solid line expresses a linear trend: y = 7.55x + 6.33, r = 1.00.

opencc-by-4.0Jun 2024View details →

Eutrophication indicators in the Baltic Sea 1970-2100 and nutrient loads to three coastal systems. BALTSEM model simulations and observations.

Dataset and model code accompanying manuscript:  Ehrnsten, E. Humborg C., Gustafsson, E. and Gustafsson B. G. 2024. Disaster avoided: current state of the Baltic Sea without human intervention to reduce nutrient loads. Resubmitted to Limnology & Oceanography Letters 2024-09-13.    This repository contains the following files:   1_Data_description.pdf Description of data sets and details on model forcing and data collection methods.   Eutrophication_indicators1970-2021_BALTSEM_and_observations.xlsx Eutrophication indicators in the Baltic Sea: BALTSEM model simulation output from real load and no reduction scenarios as well as observations 1970-2021.   BALTSEM_output_future_1970-2100.xlsx BALTSEM model simulation output 1970-2021 with observed nutrient loads (Real loads scenario) and statistics of 100 model runs 2022-2100 with present (2021) nutrient loads. The 100 runs represent statistical variations in forcing and boundary conditions to account for uncertainty in future weather and sea level conditions.   NPloads_BS_M_C.xlsx Nitrogen and phosphorus loads from the Baltic Sea, Mississippi and Changjiang catchments 1950-2021 collected from several published sources.   baltsem9.5_carbon.tar.gz Copressed folder with model code for BALTSEM 9.5 as well as forcing data used in the simulations. Information on folder contents and a user guide to run the model simuations can be found in the file BALTSEMGettingStartedCarbon.pdf

opencc-by-4.0Dec 2020View details →

Differences in carbon acquisition could explain adaptive responses in a Baltic Sea pico-phytoplankton

The data uploaded are the basis of study on the carbon update in a Baltic Sea Pico-plankton species. Our study addresses a challenging question concerning the adaptive responses to warming and carbon uptake related strategies in a pico-phytoplankton species, which are, unlike bloom-forming, larger phytoplankton, contributing to the carbon cycle and the base of food-webs all year round. In addition, we address this question also in non-exponential growth conditions. This is important considering that most phytoplankton species are likely not at exponential phase all the time. We show that space for time substitution experiments provide a valid method for investigating questions about the adaptive potential in pico-phytoplankton that the measurements of potential growth on organic carbon sources can potentially highlight differences in carbon acquisition strategies.

opencc-by-4.0Jan 2023View details →

The computation results of coupled hydrological and hydrodynamic modelling application for the Nemunas River watershed – Curonian Lagoon – South-Eastern Baltic Sea continuum

The datasets provided here were used to analyse the cumulative impacts of climate change in a Nemunas River watershed – Curonian Lagoon – South‑Eastern Baltic Sea continuum by applying a state-of-the-art coupled modelling system, which consists of hydrological and hydrodynamic models. Meteorological data used for running the models were acquired from CORDEX (Coordinated Regional Downscaling Experiment) scenarios for Europe from the Rossby Centre high-resolution regional atmospheric climate model (RCA4), which consisted of four sets of simulations (downscaling) driven by four global climate models: <table> <tbody> <tr> <th>Abbreviation in datasets</th> <th>Model</th> <th>Institution</th> </tr> </tbody> <tbody> <tr> <td>ICHEC</td> <td>EC-Earth</td> <td>Irish Centre for High-End Computing</td> </tr> <tr> <td>IPSL</td> <td>IPSL-CM 5A-MR</td> <td>The Institut Pierre-Simon Laplace</td> </tr> <tr> <td>MOHC</td> <td>HadGEM2-ES</td> <td>Met Office Hadley Centre</td> </tr> <tr> <td>MPI</td> <td>MPI-ESM-LR</td> <td>Max Planck Institute for Meteorology</td> </tr> </tbody> </table>   Climate change scenarios and periods: <ul> <li>Historical/reference (1970-2005);</li> <li>RCP4.5 (2005-2100);</li> <li>RCP8.5 (2005-2100).</li> </ul> The datasets consist of time series for the parameters of: <ul> <li>Ice thickness - average ice thickness in the Curonian Lagoon;</li> <li>Meteorological data - bias-corrected temperature and precipitation data for the marine and terrestrial areas;</li> <li>Nemunas River discharge - simulated average daily values for the discharge and water temperature;</li> <li>Salinity - selected points in the south-eastern Baltic Sea and one point next to Juodkrantė (in the Curonian Lagoon);</li> <li>Water fluxes - through four predefined cross-sections in the Curonian Lagoon;</li> <li>Water level - in 10 preselected points in the Curonian Lagoon and South-eastern Baltic Sea;</li> <li>Water residence time - in the total Curonian Lagoon area, as well as its northern and southern parts;</li> <li>Water temperature - in 10 preselected points in the Curonian Lagoon and South-eastern Baltic Sea.</li> </ul> Some of the datasets (zip files) have additional information (coordinates, data column explanations, units, etc.) in READ_ME.txt files.

opencc-by-4.0May 2023View details →

Coastal flood maps and extreme sea levels for the German Baltic Sea coast

The provided data was produced as part of the Ecas-Baltic project (2020 - 2023). The project is funded by the Federal Ministry of Education and Research in Germany (BMBF, funding code 03F0860H). The dataset contains information supporting the conclusions presented in the following publication (the final, revised version of the article will also be accessible via the preprint given below): Kiesel, J., Lorenz, M., König, M., Gräwe, U., and Vafeidis, A. T.: A new modelling framework for regional assessment of extreme sea levels and associated coastal flooding along the German Baltic Sea coast, Nat. Hazards Earth Syst. Sci. Discuss. [preprint], https://doi.org/10.5194/nhess-2022-275, in review, 2023. The dataset contains: - the location and names of flood boundary stations - the boundary conditions provided by the coastal ocean model at each of the flood boundary stations for all storm surge events simulated in the study cited above - the flood maps containing both the maximum flood extent and maximum inundation depth at every grid cell of the coastal inundation model - the spatially explicit results of the extreme value analysis for every grid cell in the coastal ocean model - the modelled monthly peak water levels between 1961 and 2018 for every grid cell of the coastal ocean model - the modelled timeseries of water levels during the storm surge from January 2nd 2019 and the entire hindcast period (1961-2018) for all tide gauges along the German Baltic Sea coast For further information, we refer the reader to the readme file in this dataset or the publication itself.

opencc-by-4.0Jun 2023View details →

Boldness and physiological variation in round goby populations along their Baltic Sea invasion front

Data/code for the paper: Galli, A., Behrens, J. W., Gesto, M., & Moran, N. P. (2023). Boldness and physiological variation in round goby populations along their Baltic Sea invasion front. Physiology & Behavior, 114261. <a href="https://doi.org/10.1016/j.physbeh.2023.114261">https://doi.org/10.1016/j.physbeh.2023.114261</a>

dryad40/100

Chesapeake Bay and Baltic Sea phytoplankton sample metadata

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publicFeb 2022View details →

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Niche partitioning between planktivorous fish in the pelagic Baltic Sea assessed by DNA metabarcoding, qPCR and microscopy: Data and Analyses

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publicJul 2022View details →

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Common guillemots in the Baltic Sea studied with video surveillance and object detection: raw data, annotations, model, and model outputs

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publicDec 2022View details →

dryad36/100

Adaptation potential of the copepod Eurytemora affinis to a future warmer Baltic Sea

To predict effects of global change on zooplankton populations, it is important to understand how present species adapt to temperature and how they respond to stressors interacting with temperature. Here we ask if the calanoid copepod Eurytemora affinis from the Baltic Sea can adapt to future climate warming. Populations were sampled at sites with different temperatures. Full sibling families were reared in the lab and used in two common garden experiments (1) populations crossed over 3 temperature treatments 12, 17 and 22.5°C and (2) populations crossed over temperature in interaction with salinity and algae of different food quality. Genetic correlations of the full siblings' development time were not different from zero between 12°C and the two higher temperatures 17 °C and 22.5°C, but positively correlated between 17 °C and 22.5°C. Hence, a population at 12 °C is unlikely to adapt to warmer temperature, while a population at ≥ 17 °C can adapt to an even higher temperature, i.e. 22.5 °C. In agreement with the genetic correlations, the population from the warmest site of origin had comparably shorter development time at high temperature than the populations from colder sites, that is, a co-gradient variation. The population with the shortest development time at 22.5°C had in comparison lower survival on low quality food, illustrating a cost of short development time. Our results suggest that populations from warmer environments can at present indirectly adapt to a future warmer Baltic Sea, whereas populations from colder areas show reduced adaptation potential to high temperatures, simply because they experience an environment that is too cold.

opencc-zeroMar 2021View details →

opencc-by-4.0Jul 2017View details →

Raw hyperspectral imaging data of Baltic Sea algae cultures

This file archive contains the raw data from hyperspectral imaging of Baltic sea algae cultures performed on 16th of August, 2018 at the hyperspectral imaging laboratory of the Faculty of Information Technology, University of Jyväskylä, Finland. The dataset contains images of cultures of the following algal species in various dilutions and mixes: <ul> <li> Diatoma tenuis DTTV-1401 </li> <li> Melosira arctica MATV-1402 </li> <li> Scrippsiella hangoei (aka Apocalathium malmogiense) SHTV-1 </li> <li> Kryptopendinium foliaceum KFF-1001 </li> <li> Monoraphidinium sp. TV70 </li> <li> Chlorella pyrenoidosa TV216 </li> </ul> In addition, the dataset includes images of pure water samples, empty petri dishes and millimeter paper useful for transmittance calculations and size measurement. The imaging setup consisted of living samples pipeted on glass Petri dishes, with a halogen light source illuminating the dish from the bottom towards the camera on top. The signal in each image contains slight fluctuation in the spectral dimension due to the AC current light source used.

dryad36/100

Bulk Carbon and Amino Acid nitrogen isotope data from Baltic cod (Gadus morhua) and European flounder (Platichthys flesus) muscle tissue samples from the western and central Baltic Sea

Eutrophication, increased temperatures and stratification can lead to massive, filamentous, N2-fixing cyanobacterial (FNC) blooms in coastal ecosystems with largely unresolved consequences for the mass and energy supply in pelagic and benthic food webs. Mesozooplankton adapt to not top-down controlled FNC blooms by switching diets from phytoplankton to microzooplankton, resulting in a directly quantifiable increase in its trophic position (TP) from 2.0 (herbivore) to as high as 3.0 (carnivore). If this process in mesozooplankton, we call trophic lengthening, was transferred up to higher trophic levels of a food web, a large loss of energy could result in massive declines of fish biomass. We used compound-specific nitrogen stable isotope data of amino acids (CSIA) to estimate and compare the nitrogen (N) sources and TPs of cod and flounder (mesopredators) from areas with influence of FNC blooms (central Baltic Sea) and without it (western Baltic Sea). We tested if FNC-caused trophic lengthening in mesozooplankton is carried over to fish. The TP of cod from the western Baltic, feeding mainly on decapods, was equal to the global mean value (4.1, secondary carnivore). Only cod from the central Baltic, mainly feeding on zooplanktivorous pelagics, had a higher TP (4.8, near-tertiary carnivore), indicating a strong carry-over effect of FNC-caused trophic lengthening from mesozooplankton. In contrast, the TP of molluscivorous flounder (3.2 ± 0.2 in both areas), associated with the benthic food web, was unaffected by trophic lengthening. This suggests that FNC blooms cause a large loss of energy in zooplanktivorous but not in molluscivorous mesopredators. If FNC blooms continue to detour energy at the base of the pelagic food web, the TP of cod will not return to global mean values and the fish stock not recover. Monitoring the TP of key species can identify fundamental changes in ecosystems and provide useful information for resource management.

opencc-zeroFeb 2024View details →

opencc-by-sa-4.0Nov 2024View details →

Supplementary material for "Tracing emerging contaminants from the Baltic Sea and North Sea in fjord waters in southern Norway with rare earth elements as far-field tracers"

Dataset presented and discussed in the manuscript of the research article “Tracing emerging contaminants from the Baltic Sea and North Sea in fjord waters in southern Norway with rare earth elements as far-field tracers” by Zocher et al. The manuscript will be submitted to Environmental Pollution and was prepared by the following authors:   Anna-Lena Zocher (1), Tomasz Maciej Ciesielski (2,3), Stefania Piarulli (4), Julia Farkas (4) and Michael Bau (1).    (1) School of Science, Constructor University, Bremen, Germany (2) Department of Biology, Norwegian University of Science and Technology, Trondheim, Norway (3) Department of Arctic Technology, The University Centre in Svalbard (UNIS), Longyearbyen, Norway (4) SINTEF Ocean, Trondheim, Norway   This work was conducted within the ELEMENTARY project, and we appreciate funding from the Norwegian Research Council (grant No. 301236).

opencc-by-sa-4.0Dec 2024View details →

Supplementary material for "Rare earth elements and yttrium in Polish rivers and the input of anthropogenic gadolinium into the Baltic Sea"

This dataset is presented and discussed in the research article “Rare earth elements and yttrium in Polish rivers and the input of anthropogenic gadolinium into the Baltic Sea” by Alemu et al. This manuscript will be submitted to Environmental Pollution and was prepared by the following authors: Addis Kokeb Alemu (1,2), Keran Zhang (1), David Ernst (1), and Michael Bau (1).  1Critical Metals for Enabling Technologies – CritMET, School of Science, Constructor University, Campus Ring 1, 28759 Bremen, Germany 2Department of Chemistry, College of Natural and Computational Sciences, University of Gondar, P.O. Box 196, Gondar, Ethiopia     Table A1 includes the general information and data for all sampling stations and reference materials used.  Figs. A1 and A2 show the concentrations of total Gd and anthropogenic Gd in samples from the Oder River (OD) and its major tributary, the Warta River (Wa), as well as the Vistula River (VS) and its major tributaries: San (Sn), Bug (BG), Brda (BR), and Narew (NR).

Compilation of 17 anthropogenic pressure gradients and 18 benthic indicators in the Baltic Sea, Atlantic Ocean and Mediterranean Sea

Compilation of 17 benthic datasets that sampled benthic ecosystems over gradients of commercial bottom trawling intensity (n=14), eutrophication (n=1), oxygen depletion (n=1) and pollution (n=1) (Table 1). Compilation of 18 benthic indicators that were calculated for each gradient dataset. The indicators estimated were community biomass, abundance, richness, relative Margalef diversity, Shannon index, Simpson index, Inverse Simpson,  AZTI’s Marine Biotic Index (AMBI), Multivariate AMBI (M-AMBI), BENTIX Biotic Index (BENTIX), Danish Quality Index (DKI), Trawling Disturbance Index (TDI), Modified TDI (mTDI), Modified vulnerability index (mT), Median longevity (Lm), Partial TDI (pTDI), Sentinels of Seabed (SoS), Long-lived fraction (Lf). The dataset has information on: 1) indicator outputs per sampling station; 2) combined data tables with gradient, station, and species information; 3) individual gradient information with replicate samples and the environmental variables reported in the original study. #---------------- NOTE: The dataset was updated in February 2025 due to incorrect description of units used for two gradients (Silver Pit and Thames) in the "Individual Gradient Studies" folder. #--------------- Table 1. Overview of anthropogenic gradient datasets <table> <tbody> <tr> <th>Location</th> <th>Sampling method</th> <th>Pressure gradient</th> </tr> </tbody> <tbody> <tr> <td>Adriatic Sea – Italian EEZ (sand)</td> <td>Rapido trawl</td> <td>Bottom trawling</td> </tr> <tr> <td>Adriatic Sea – Italian EEZ (mud)</td> <td>Rapido trawl</td> <td>Bottom trawling</td> </tr> <tr> <td>North Sea - Dutch EEZ</td> <td>Box core</td> <td>Bottom trawling</td> </tr> <tr> <td>North Sea - Dogger Bank</td> <td>Hamon grab</td> <td>Bottom trawling</td> </tr> <tr> <td>North Sea - Fladen Ground</td> <td>Day grab</td> <td>Bottom trawling</td> </tr> <tr> <td>North Sea - Long Forties</td> <td>Hamon grab</td> <td>Bottom trawling</td> </tr> <tr> <td>North Sea - Silver Pit</td> <td>Box core</td> <td>Bottom trawling</td> </tr> <tr> <td>North Sea – Thames</td> <td>Box core</td> <td>Bottom trawling</td> </tr> <tr> <td>Northern Iberian Coast (sand)</td> <td>Otter trawl</td> <td>Bottom trawling</td> </tr> <tr> <td>Northern Iberian Coast (mud)</td> <td>Otter trawl</td> <td>Bottom trawling</td> </tr> <tr> <td>Baltic Sea - Gotland</td> <td>van Veen grab</td> <td>Bottom trawling</td> </tr> <tr> <td>Baltic Sea – Polish EEZ</td> <td>Box core</td> <td>Bottom trawling</td> </tr> <tr> <td>NW Atlantic - Flemish Cap</td> <td>Otter trawl</td> <td>Bottom trawling</td> </tr> <tr> <td>Irish Sea - Sellafield</td> <td>Day grab</td> <td>Bottom trawling</td> </tr> <tr> <td>Gulf of Finland</td> <td>van Veen grab</td> <td>Oxygen depletion</td> </tr> <tr> <td>Saronikos Gulf</td> <td>Box core</td> <td>Eutrophication</td> </tr> <tr> <td>Vigo Estuary</td> <td>Box core</td> <td>Contaminants</td> </tr> </tbody> </table>