Physical loss pressure layer combines all human activities that cause physical loss of seabed. The pressure is given as area lost in each cell (km2). For the polygon datasets the area was assumed to be the lost area. For line and point datasets spatial extents were calculated with buffers (below in brackets). If no buffer extent is indicated, the data was reported as polygon. The human activities used for the physical loss pressure: - Bridges (2 m) - Cables (operational; 1,5 m) - Coastal defence and flood protection (area of polygon, 50 m for lines) - Dredging (capital dredging, Area of polygon or a 25/50 m buffer for <5000 m3 / >5000m3 points) - Extraction of sand and gravel - Finfish mariculture (150 m) - Harbours (polygon with 200 m buffer) - Land claim (area of polygon, 30m buffer for lines) - Marinas and leisure harbours (200 m) - Oil platforms (25 m) - Oil terminals and refineries (200 m) - Pipelines (operational; 15 m) - Shellfish mariculture (area of polygon, 150 m points) - Watercourse modification (50 m) - Wind turbines (operational; 30m point location of turbine) The datasets were first processed separately covering the whole Baltic Sea and then merged into one uniform data layer and minimizing the effect of overlapping areas. Polygon areas were clipped with coastline to remove buffered areas that reached to land.

Lagoons are expanses of shallow coastal waters, wholly or partially separated from the sea by sandbanks or shingle, or by rocks. Salinity may vary from brackish water to hypersalinity depending on rainfall, evaporation and addition of fresh seawater from storms, temporary flooding, or tidal exchange. The distribution map is based on data submission by HELCOM contracting parties. Most of the submitted data is based on modelling and/or GIS analysis. Data coverage, accuracy and the methods in obtaining the data vary between countries.

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 “circalittoral hard substrate” includes classes “Rock and other hard substrate” and “Coarse substrate” of the original data, in the circalittoral zone. The original polygon maps have been converted to 1 km x 1 km 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.

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 “circalittoral mixed substrate” includes classes “mixed sediment” of the original data, in the circalittoral zone. The original polygon maps have been converted to 1 km x 1 km 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.

The map of sprat relative abundance is mainly based on Baltic International acoustic surveys (BIAS), years 2011-2016, (ICES WGBIFS reports), reported as millions of sprat per ICES rectangle. The BIAS surveys cover almost the whole area where sprat is commonly encountered. Outside BIAS area, sprat landings data was used to complement the data. For ICES rectangles surveyed by BIAS, values shown are the mean values per ICES rectangle based on BIAS data, average for 2011-2016. For ICES rectangles not surveyed by BIAS, values are calculated as: MAX-value x Weighting factor. The weighting factor is specific to each ICES rectangle, calculated as the ratio between the commercial landings in that rectangle and the commercial landings in the ICES rectangle with highest landings (based on averages for 2011-2015). MAX-value = millions of sprat according to BIAS in the ICES rectangle with highest landings. ICES rectangles outside the BIAS survey area with no reported sprat landings were given the value 0. The abundance values / ICES rectangle were divided by the area of the rectangle to obtain values per 1km2, and then converted to 1 km x 1km grid cells. Values were first log transformed and then normalised.

The occurrence of suitable nursery habitats is crucial for maintaining fish populations (Sundblad et al. 2013). Species distribution modelling studies have shown the importance of suitable environmental conditions for pikeperch recruitment. Due to lack of coherent data on pikeperch spawning and nursery areas across the Baltic Sea countries, environmental variables were used in delineating potential recruitment areas for pikeperch. The pikeperch recruitment area presented on the map is mainly delineated by selecting areas where depth < 5 m, logged exposure < 5, salinity < 7 PSU, Secchi depth < 2 m and distance to deep (10m) water < 4km. The threshold values have been obtained from literature (Veneranta et al. 2011, Bergström et al. 2013, Sundblad et al. 2013, Kallasvuo et al. 2016). Temperature, although important for pikeperch, was left out due to high variation in timing of suitable spawning temperatures across the Baltic Sea. In Finnish coastal waters, a national pikeperch model (Kallasvuo et al. 2016) has been used, with very suitable areas for pikeperch generalized to 1 km grid. In Sweden, the areas delineated by environmental variables have been complemented with information from national interview survey (Gunnartz et al. 2011) as well as expert opinion.

Summary The following 6 categories of annual mean salinity were applied delineating the Kattegat and the Baltic Sea into regions with differences in salinity regime (fig. 15): I. Oligohaline I (< 5psu). II. Oligohaline II (5 - 7.5psu). III. Mesohaline I (7.5 - 11psu). IV. Mesohaline II (11 - 18psu). V. Polyhaline (18 - 30psu). VI. Euhaline (>30psu). Description This dataset was produced by NERI, Denmark, for the BSR INTERREG IIIB project BALANCE. Due to the stratification in the Baltic Sea it was decided to use bottom salinity for the development of the benthic marine landscapes and difference in surface to bottom salinity for the pelagic landscapes. The following 6 categories of annual mean salinity were applied delineating the Kattegat and the Baltic Sea into regions with differences in salinity regime (fig. 15): I. Oligohaline I (< 5psu). II. Oligohaline II (5 - 7.5psu). III. Mesohaline I (7.5 - 11psu). IV. Mesohaline II (11 - 18psu). V. Polyhaline (18 - 30psu). VI. Euhaline (>30psu).

Distribution of Furcellaria lumbricalis based on data submission by HELCOM contracting parties. Mainly pointwise occurrences of Furcellaria were submitted, originally gathered in national mapping and monitoring campaigns, or for scientific research purposes. From Estonian waters, a predictive model was used (200m resolution), that was converted to presence/absence using minimized difference threshold (MDT) criteria. For Poland, only confirmed occurrence of Furcellaria were included (Slupsk bansk, Rowy reef and reef at Orlowo cliff). All data (Furcellaria points and the raster presenting predicted presence of Furcellaria) were generalized to 5km x 5km grid cells.

Esker islands (according to Habitats Directive Annex I) are glaciofluvial islands consisting mainly of relatively well sorted sand, gravel or less commonly of till. Also their underwater parts are included in the habitat. The distribution map is based on data submission by HELCOM contracting parties. Only Sweden and Finland reported occurrences of esker islands. Only underwater parts are included in the datasets. The data is based on modelling and GIS analysis. Data coverage, accuracy and the methods in obtaining the data vary between countries.

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.