Amount of hunted birds (number of birds/area) per year per area (county) is given separately for each target species: common scooter (Melanitta nigra), velvet scoter (Melanitta fusca), eider (Somateri molissima) and long tailed duck (Clangula hymalis). The data was made available by HELCOM Contracting Parties in response to data request. The data was received from Denmark, Estonia, Finland and Sweden. The activity was declared as not relevant in Germany, Latvia, Lithuania and Poland. For each species, a total number of hunted birds during the time period and a calculated average (hunted birds/year), is given. Data includes a total number (sum) of all hunted birds during the time period per county (total number of hunted birds/ county) and an average for hunted birds annually (hunted individuals/year). Velvet scoter is protected species in Sweden and Finland, and not listed as a game in Estonia. Common scoter is also protected species in Finland, thus hunting data is not available. Attribute specification and units: Country: Country AreaCode: County’s national code Area: County, unit area TOTAL: Total number of hunted birds in 2011-2015 Average: An average of hunted birds in a year (hunted birds/year) 2011_Sco – 2015_Sco: Number of hunted common scoters in 2011-2015 SUM_Sco: Total number of hunted common scoters in 2011-2015 Mean_Sco: An average number of hunted common scoters in a year (hunted individuals/year) 2011_VSco – 2015_VSco: Number of hunted velvet scoters in 2011 - 2015 SUM_Vsco: Total number of hunted velvet scoters in 2011-2015 Mean_Vsco: An average number of hunted velvet scoters in a year (hunted individuals/year) 2011_Eider – 2015_Eider: Number of hunted eiders in 2011 - 2015 SUM_Eider: Total number of hunted eiders in 2011-2015 Mean_Eider: An average number of hunted eiders in a year (hunted individuals/year) 2011_LTDuc – 2015_LTDuc: Number of hunted long tailed ducks in 2011 – 2015 SUM_LTDuck: Total number of hunted long tailed ducks in 2011-2015 Mean_LTDuc: An average number of hunted long tailed ducks in a year (hunted individuals/year) Notes: Notes regarding the data

Distribution of blue mussel based on data submission by HELCOM contracting parties. Mainly pointwise occurrences of Mytilus spp. were submitted, originally gathered in national mapping and monitoring campaigns, or for scientific research. Point data from Poland was digitized based on Polish Marine Atlas. From Lithuania, a polygon delineating reefs was used to present Mytilus occurrence. For Germany, point data was complemented with a model describing Mytilus biomass in the German marine area (Darr et al. 2014), where predicted biomasses > 1g dw/ m2 were included as presence. 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 Mytilus) were generalized to 5km x 5km grid cells.

Concentration of phosphorus pressure layer is interpolated from annual seasonal average of total phosphorus measurements from surface waters (0-10 m) extracted from ICES’s oceanographic database, database of Swedish Meteorological and Hydrological Institute, EEA’s Eionet database and Data from Gulf of Finland year 2014. The points were interpolated to cover the entire Baltic Sea with Spline with barriers interpolation method. Values were log-transformed and normalised (more detailed description below).

Data set represents dredging activities around the Baltic Sea. The data set contains information about the dredging activity and for some the type (capital, maintenance) and the year of activity as reported by HELCOM Contracting Parties in response to data request. The dredging data is missing from Denmark.

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.

The map of herring relative abundance is mainly based on Baltic International acoustic surveys (BIAS), years 2011-2016 (ICES WGBIFS reports), reported as millions of herring / ICES rectangle. Also herring landings data were 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 2011-2016). MAX-value = millions of herring according to BIAS in the ICES rectangle with highest landings. ICES rectangles outside the BIAS survey area with no reported herring landings were given the value 0. The relative abundance values in each ICES rectangle were divided by the area of the rectangle to obtain values per 1km2. If the values in small coastal ICES rectangles (outside BIAS area) became unrealistically large due to high herring landings, the value of the neighboring rectangle was given. The final layer was converted to 1 km x 1km grid cells. Values were first log transformed and normalized.

The extraction of herring data set is based on: 1. http://metadata.helcom.fi/geonetwork/srv/eng/catalog.search#/metadata/a3b67a55-7c1e-469e-b692-58c4e7b79279 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 herring. 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.

The dataset contains total landings of cod for years 2011-2016 reported per ICES statistical rectangles (tonnes / ICES rectangle) under EU Joint Research Centre’s data collection framework for fisheries data. Russian data extracted from ICES annual reports.

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.