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 pressure oil slicks and spills is combination of following datasets: • Illegal oil discharges • Polluting ship accidents Illegal oil discharge data is based on airborne surveillance with remote sensing equipment in the Baltic Sea Area. The area of the detected spills in 2011–2016 was used to represent the pressure. The value of spills under 1km2 were directly given to grid cell, spills over 1km2 were buffered based on estimate spill area. For polluting ship accidents the reported oil spill volumes (m3) in years 2011-2015 were used for the pressure. Some polluting ship accidents spills were missing spilled oil volume, thus a mean of reported volumes was given to accidents with missing oil volume. Datasets were handled separately. Both layers were normalized, summed and normalized again to produce the “oil slicks and spills” pressure layer. Please see below for further details.

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

Data set represents dredging activities around the Baltic Sea. The dataset 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 map shows the distribution and abundance of grey seals across the Baltic Sea. The map was originally created for HELCOM Red list assessment of the Baltic Sea, using seal expert consultation. For the Baltic Sea Impact Index, the map was modified to represent four abundance classes, based on expert consultation. The map has been updated from the 1st version of HOLASII, based on expert consultation (HELCOM Seal EG).

This dataset contains modelled small vessel fuel consumption. This describes the geographical distribution of the fuel used by small boats. The total fuel consumption was modelled in SHEBA project to study emissions from pleasure boats. The model is based on locations and berths in marinas and leisure harbours, AIS information, statistics on fuel sale and extensive survey. For 2018 version the layer is weighted with depth, log-transformed and normalised (please see below). This dataset was also used on HOLAS 3.

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.

Concentration of nitrogen pressure layer is interpolated from annual seasonal average of total nitrogen concentrations 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).

Baltic International Trawl Survey (BITS) data (2011-2016) from ICES DATRAS database was used as a base to create a map of cod relative abundance (quarter 1 data, CPUE values per ICES subdivision). Cod = 30cm was included. For ICES rectangles surveyed by BITS, values shown are the mean CPUE per ICES subdivision based on BITS data, average for 2011-2016. For ICES rectangles not surveyed by BITS, 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 = CPUE according to BITS in the ICES rectangle with highest landings. ICES rectangles outside the BITS survey area with no reported cod landings were given the value 0. Values were first log transformed and then normalized.

This map shows the distribution and abundance of harbour porpoise across the Baltic Sea. The abundance of harbour porpoise is presented using 4 abundance classes. The classification is based on expert consultation and information from scientific literature (e.g. Sveegaard et al. 2011, Viquerat et al. 2014). The class borders are defined by expert opinion and generalizing the data gathered and modelled in SAMBAH project. For the Baltic Proper the SAMBAH results have been used to delineate the class borders: 20% probability of detection during May-October has been used to define the area of “common occurrence and reproduction”, and the 20% probability of detection during November-April has been used to define the “regular occurrence, no regular reproduction” area. Please note: The applied spatial scale includes lagoons and estuaries of the inner coastal waters (e.g. Szczecin Lagoon, Jasmund lagoon) where harbour porpoises do not or only exceptionally occur unlike the map suggests.