Broad-scale habitat maps for the Baltic Sea have been produced in the EUSeaMap project in 2016. For German and Estonian marine areas, national (more accurate) datasets were used. German data included both substrate and light information (division into infralittoral/circalittoral). Estonian data included only substrate and the division into light regimes was obtained from the EuSeaMap data. Here, the habitat class “infralittoral mixed substrate” includes classes “mixed sediment” of the original data, in the infralittoral zone. The original polygon maps have been converted to 1 km x 1km grid. The scale of the substrate data used in broad-scale habitat maps varies from 1:250 000 to 1:1M (data from EMODnet Geology). Coarser resolution data has been used in areas, where 1: 250 000 substrate data has not been available. Due to different scales used, the habitat classes may show different sized patterns in different areas.

This dataset is built from following Human activities datasets: • http://metadata.helcom.fi/geonetwork/srv/eng/catalog.search#/metadata/81c917ea-492d-48e2-9f00-e1bb7fe3e4fc • http://metadata.helcom.fi/geonetwork/srv/eng/catalog.search#/metadata/4fcd51dd-b8be-4e83-8cad-37c566782e8f The game hunting of seabirds data (see separate metadata): The total number of hunted seabirds were averaged over 2011-2015 (number of hunted seabirds / year). The area of the reporting unit was used to calculate the number of hunted seabirds / km2 and the data was converted to 1km x 1km grid. The predator control of seabirds data (see separate metadata): The total number of hunted cormorants were averaged over 2011-2015 (number of hunted cormorants / year). The area of the reporting unit was used to calculate the number of hunted cormorants / km2 and the data was converted to 1km x 1km grid. The two datasets were first separately log transformed and then summed, to get the total value for each grid cell. Zero values were given to all grid cells with no reported seabird hunting activity. The layer was normalized.

The data represents the seabed slope of the Baltic Sea and has been derived from a bathymetry dataset. Both datasets have been produced by the BSR INTERREG IIIB project BALANCE. For more information see also the metadata file on bathymetry. Seabed slope (BALANCE) layer is expressed as percent slope (%).

Submarine structures made by leaking gases (according to Habitats Directive Annex I) are also known as “bubbling reefs”. These formations support a zonation of diverse benthic communities consisting of algae and/or invertebrate specialists of hard marine substrates different to that of the surrounding habitat. The distribution map is based on data submission by HELCOM contracting parties. Only Sweden and Denmark reported occurrences of submarine structures made by leaking gases.

Summary This dataset shows model results for the average bottom temperature in the Baltic region in the plant growth season from April to September. Description This dataset shows model results for the average bottom temperature in the Baltic region in the plant growth season from April to September.

Boreal Baltic islets and small islands (according to Habitats Directive Annex I) are groups of skerries, islets or single small islands, mainly in the outer archipelago or offshore areas. They are important nesting sites for birds and resting sites for seals. The surrounding sublittoral vegetation is also included. The distribution map is based on data submission by HELCOM contracting parties. Only Sweden and Finland reported occurrences of boreal Baltic islets and small islands.

Essential fish habitat (EFH) map on Potential spawning areas for sprat was prepared in PanBalticScope project (co-founded by the European Maritime and Fisheries Fund of the European Union) http://www.panbalticscope.eu/ Sprat (Sprattus sprattus) occurs in the entire Baltic Sea, and mainly in open sea areas. It is assessed as a single stock in the Baltic Sea within fisheries management. Sprat eggs are pelagic, and sprat spawning is well known from the deep basins in the central Baltic, where it typically occurs from February to August. Further north, spawning starts later in the year, and is less certain. Recent fisheries surveys indicate that sprat spawning does no longer occur in the Gulf of Finland. Sprat spawning areas were delineated using environmental variables due to lack of coherent field data across the Baltic Sea countries. “Potential sprat spawning areas” were delineated as areas with salinity > 6 and water depth > 30 m, but for the Arcona basin depth > 20 m was used (Grauman, 1980, Bauman et al. 2006, Voss et al. 2012). “High probability spawning areas” were delineated for areas deeper than 70 m. Stock: Sprat in subdivisions 22-32 (ICES) EFH type: Potential spawning areas Approach: Environmental envelope, corrected for areas 20-40 m south of Bornholm. Variables and thresholds: Potential spawning area: Depth > 30 m, Salinity > 6 (annual average) High probability spawning area: Depth >70 m, Salinity > 6 (annual average) Quality: The map is based on literature and environmental variables, not actual data on sprat spawning. The map might overestimate the spawning area west and north of Gotland. The data layers on environmental variables are based on modelling. Attribute information: Raster value representing no spawning (0), potential spawning area (0.5) and high probability spawning area (1). References: - Baumann, H, H Hinrichsen, C Mollmann, F Koster, A Malzahn, and A Temming (2006) Recruitment variability in Baltic Sea sprat (Sprattus sprattus) in tightly coupled to temperature and transport patterns affecting the larval and early juvenile stages. Canadian Journal of Fisheries and Aquatic Science 63:2191-2201 - Grauman GB (1980) Long term changes in the abundance data of eggs and larvae of sprat in the Baltic Sea. Fisheries research in the Baltic Sea, Riga. 15:138-150 (in Russian) - HELCOM (2018) Outcome of the regional expert workshop on essential fish habitats, organized by Pan Baltic Scope project and HELCOM (HELCOM Pan Baltic Scope EFH WS 1-2018) - Voss R, MA Peck, HH Hinrichsen, C Clemmesen, H Baumann, D Stepputis, M Bernreuther, JO Schmidt, A Temming, and FW Köster (2012) Recruitment processes in Baltic sprat - A re-evaluation of GLOBEC Germany hypotheses. Progress in Oceanography 107:61-79

Potential effect of continuous noise to mobile species assesses the cumulative potential effect of continuous noise on mobile species and their presence in the HELCOM area. The evaluation is based on the pressure layer on input of continuous noise, combined with information on the distribution of 15 mobile species and their habitats. The thematic analyses on the potential effect of continuous noise to mobile species is calculated for each assessment unit (1 km2 grid cells) and the data set covers the time period 2016-2021. Spatial Pressure and Impact Assessment (SPIA) is the framework for assessing spatial and cumulative pressures and impacts in HOLAS 3, and this analyses present a thematic assessment including only a certain subset of layers. The framework also includes results for the Baltic Sea Impact Index (full cumulative impact assessment), Baltic Sea Pressure Index (full cumulative pressure assessment), and other thematic assessments where a subset of pressure and ecosystem layers are used. For more info please - visit the HOLAS 3 website (http://stateofthebalticsea.helcom.fi/) - download the report thematic assessment of spatial distribution of pressures and impacts 2016-2021 (https://helcom.fi/post_type_publ/holas3_spa) - or check out the HELCOM SPIA online tool to make calculations for any desired combination of pressures and ecosystem layers (https://maps.helcom.fi/website/bsii/). Please scroll down to "Lineage" for a more detailed description of the methodology.

Introduction of radionuclides is based on HELCOM MORS Discharge data from 2011 to 2014. The isotopes taken into account were: Cesium-137, Strontium-90, and Cobalt-60. The decay-corrected annual average of the sum of the radionuclide discharges (in Bq) were calculated for the pressure. 10 km buffer with linear decreasing function was used to represent the impact distance from the nuclear power plant outlets.

This dataset depicts the landcover in the Baltic Sea catchment area.