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 sand” includes classes “Sand” and “Muddy sand” 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.

Springtime Chl-a concentration is here used as a proxy for productive surface waters. In the Baltic Sea Impact Index (BSII), areas with high springtime phytoplankton production will be given higher importance, as they are considered important areas for the Baltic Sea food web. In the current map, mean of springtime maximum weekly values (weeks 12-22, years 2003-2011) Chl-a concentration of the surface waters has been used, derived from satellite data (MERIS). Years 2003-2011 have been used, as there is no MERIS data available for years 2012-2016. The data for eastern Baltic Sea is provided by the Finnish Environment Institute (~300m resolution). Outside this high resolution data, MERIS-data downloaded from JRC-database has been used (~4 km resolution, to calculate average of maximum monthly values for April or May for 2003-2011). Both datasets were converted to 1 km x 1 km grid cells.

This layer is based on data from the BIAS project representing ambient underwater noise, modelled into a 0.5 km x 0.5 km grid, and representing sound pressure levels at 1/3 octave bands of 125 Hz exceeded at least 5% of the time. Measured and modelled acoustic data is provided as Sound Pressure Level (SPL). The time period for the data is annual values for year 2014. The selected depth interval is 0 m – bottom to represent the ambient underwater noise in the whole water column. The data were normalized setting level 0 at 92 db re 1µPa and level 1 at 127 db re 1µPa.

Distribution of eelgrass based on data submission by HELCOM contracting parties. Mainly pointwise occurrences of eelgrass were submitted, originally gathered in national mapping and monitoring campaigns, or for scientific research. Polygon data from Puck Bay (Poland) was digitized based on Polish Marine Atlas and Orlowo cliff area was added based on expert knowledge. From Estonian waters, a predictive model was used (200m resolution), that was converted to presence/absence using minimized difference threshold (MDT) criteria. All data (points, polygon and the raster presenting predicted presence of eelgrass in the Estonian waters) were generalized to 5km x 5km grid cells.

Input of impulsive anthropogenic sound includes impulsive events from 2011-2016 • Seismic surveys (HELCOM-OSPAR Registry; national data call submissions as lines in the folder of data) • Explosions (HELCOM-OSPAR Registry) • Pile driving (HELCOM-OSPAR Registry) • Airguns (HELCOM-OSPAR Registry) For the different event types, numeric intensity value was used to represent the pressure as categorized in HELCOM-OSPAR Impulsive noise registry. All nationally reported seismic surveys were given intensity values “Very low” (0.25) - Very low (0.25) - Low (0.5) - Medium (0.75) - High (1) The impact distance has not been taken into account due to the different nature of separate datasets used for the pressure layer. We acknowledge that e.g. pile driving and airguns may impact up to 20 km from the source event. The spread of the sound wave depends on the sound frequency, water salinity, temperature and density.

Mudflats and sandflats not covered by seawater at low tide (according to Habitats Directive Annex I) are often devoid of vascular plants, usually coated by blue algae and diatoms. They are of particular importance as feeding grounds for wildfowl and waders. The distribution map is based on data submission by HELCOM contracting parties. Only Denmark, Germany and Estonia reported occurrences of mudflats and sandflats. 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 “infralittoral hard substrate” includes classes “Rock and other hard substrate” and “Coarse substrate” of the original data, in the infralittoral 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.

Distribution of Fucus sp. based on data submission by HELCOM contracting parties. Mainly pointwise occurrences of Fucus 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. All data (Fucus points and the raster presenting predicted presence of Fucus) were generalized to 5km x 5km grid cells.

The extraction of Sprat data set is based on: 1. http://metadata.helcom.fi/geonetwork/srv/eng/catalog.search#/metadata/1fb1bd2d-8dff-493a-9ed3-a278aec8f371 for years 2011-2016 reported per ICES statistical rectangles (tonnes / ICES rectangle). Landing values were redistributed within each ICES rectangle by the c-square fishing effort data provided by ICES (all gears, 2011-2013). Tonnes / km² was calculated and the results were converted to 1 km x 1 km grid cells. The layer was log-transformed and normalised to produce the final pressure layer on extraction of Sprat. Please see "lineage" section below for further details on attributes, data source, data processing, etc.

Pressure layer combines all human activities that cause changes to hydrological conditions. The human activities were presented as point data which were given spatial extents (given below). The pressure value was given as the proportion of the grid cell under the pressure. The following human activities were combined into the changes to hydrological conditions layer; - Hydropower dams (a 1km2 grid cell in the river estuary was selected) - Water course modification (1 km) - Wind turbines (operational, 0.3 km, linear decline) - Oil platforms (0.5 km, linear decline) The human activity datasets were first processed separately covering the whole Baltic Sea and then summed together and overlapping areas were dissolved to remove double counting. Attenuation gradients are assigned to each layer as described above. Area effected decreases when distance from avtivity increases. Layer was normalized.