This map presents the Special Protection Areas (SPAs) with reported breeding areas for birds. The spatial data on Special Protection Areas were gathered from the HELCOM contracting parties by Lund University, Sweden. In the data, the countries also indicated whether the sites were designated mainly due to wintering or breeding birds in the area. For Denmark, the information was obtained from standard forms for Natura 2000 sites. For Denmark, the data was updated after review process 20 February 2017. For Germany, the areas that were reported as “NA”(=information not available) were included in both breeding and wintering area maps. Many of the SPAs are both wintering and breeding areas. For the Baltic Sea Impact Index, the data was converted to 1 km x 1km grid cells.

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.

This pressure dataset is derived from three human activities datasets - Urban land use (on land) - Recreational boating and sports (updated layer for 2018 version, please see separate http://metadata.helcom.fi/geonetwork/srv/eng/catalog.search#/metadata/8c30e828-1340-4162-b7f9-254586ae32b6) - Bathing sites These data are described in more detail in separate fact sheets. Urban land use data was first converted to 1 km grid cells and expanded with 1 km. Thus, coastal urban areas extended also to the sea. These areas were given value 1 and other sea areas, value 0. Bathing sites (points) were converted to 1km grid and given value 1, rest of the sea areas were given value 0. Normalized recreational boating data was converted to 1 km grid cells. These three layers were summed to produce the layer (values from 0 to 3), after that the layer was normalized. Hunting and recreational fishing data were excluded from human disturbance layer, as they are mostly reported per country and would have resulted in overestimation of the actual pressure.

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 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.

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.

This map presents the Special Protection Areas (SPAs) with reported wintering areas for birds. The spatial data on SPAs were gathered from the HELCOM contracting parties by Lund University, Sweden. In the data, the countries also indicated whether the sites were designated mainly due to wintering or breeding birds in the area. For Denmark, the information was obtained from standard forms for Natura 2000 sites. For Denmark, the data was updated after review process 20 February 2017. For Germany, the areas that were reported as “NA”(=information not available) were included in both breeding and wintering area maps. Many of the SPAs are both wintering and breeding areas. For the Baltic Sea Impact Index, the data was converted to 1 km x 1km grid cells.

Pressure layer combines all human activities that cause physical disturbance or damage to seabed. For several human activity datasets, spatial extents were given (table below). Buffers with decreasing value rates were applied to represent the impact distance of physical disturbance. The following human activities were combined into the physical disturbance layer; - Cables (under construction, 1 km buffer) - Coastal defence and flood protection (under construction, 500 m buffer) - Deposit of dredged material (500 m buffer for points and areas) - Dredging (maintenance) (500 m buffer for points and areas) - Extraction of sand and gravel (500 m buffer) - Finfish mariculture (1 km buffer) - Fishing intensity 2011-2016 average (subsurface swept area ratio) - Furcellaria harvesting - Pipelines (0,3 km buffer) - Recreational boating and sports - Shellfish mariculture - Shipping density - Wind farms (under construction) (1 km buffer) - Wind farms (operational) (0,1 km buffer) The human activity data sets were first processed separately covering the whole Baltic Sea and then summed together. In this integration, some data layers were down-weighted to arrive at a balanced pressure layer, as described below. High pressure intensity and/or slow recovery (weighting factor 1): Coastal defence and flood protection, Deposit of dredged material, Dredging, Extraction of sand and gravel and Fishing intensity Moderate to high (Weighting factor 0,8): Pipelines and Shipping density Moderate (Weighting factor 0,6): Finfish mariculture, Shellfish mariculture and Wind farms (under construction) Low to moderate (Weighting factor 0,4): Cables Low (Weighting factor 0,2): Maerl and Furcellaria harvesting, Recreational boating and sports and Wind farms (operational) Harbours and marinas were left out from the physical disturbance pressure to avoid double counting due to their representation in the shipping density and recreational boating and sports data sets.

The pressure layer represents biological pressure caused by introduction of non-indigenous species. The data is obtained from core indicator Trend in the arrival of new non-indigenous species (BSEP 129b: http://www.helcom.fi/Lists/Publications/BSEP129B.pdf). For the Baltic Sea Impact Index, the layer was normalized.

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.