Input of hazardous substances pressure layer is interpolated from CHASE Assessment tool concentration component. The contamination ratio values were calculated with CHASE Assessment tool for hazardous substances monitored in water, sediment and biota. Classified mean contamination ratio was used in the interpolation. Classification is based on the http://stateofthebalticsea.helcom.fi/about-helcom-and-the-assessment/downloads-and-data/. The points were interpolated to cover the entire Baltic Sea with Spline with barriers interpolation method. Please see "lineage" section below for further details on attributes, data source, data processing, etc.

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 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.

Potential cumulative impacts on benthic habitats is based on the same method than http://metadata.helcom.fi/geonetwork/srv/eng/catalog.search#/metadata/9477be37-94a9-4201-824a-f079bc27d097, but is focused on physical pressures and benthic habitats. The dataset was created based on separate analysis for potential cumulative impacts on only the benthic habitats, as these are particularly affected by physical pressures. In this case the evaluation was based on pressure layers representing http://metadata.helcom.fi/geonetwork/srv/eng/catalog.search#/metadata/ea0ef0fa-0517-40a9-866a-ce22b8948c88 and http://metadata.helcom.fi/geonetwork/srv/eng/catalog.search#/metadata/05e325f3-bc30-44a0-8f0b-995464011c82, combined with information on the distribution of eight broad benthic habitat types and five habitat-forming species (http://metadata.helcom.fi/geonetwork/srv/eng/catalog.search#/metadata/363cb353-46da-43f4-9906-7324738fe2c3, http://metadata.helcom.fi/geonetwork/srv/eng/catalog.search#/metadata/f9cc7b2c-4080-4b19-8c38-cac87955cb91, http://metadata.helcom.fi/geonetwork/srv/eng/catalog.search#/metadata/264ed572-403c-43bd-9707-345de8b9503c, http://metadata.helcom.fi/geonetwork/srv/eng/catalog.search#/metadata/822ddece-d96a-4036-9ad8-c4b599776eca and http://metadata.helcom.fi/geonetwork/srv/eng/catalog.search#/metadata/ca327bb1-d3cb-46c2-8316-f5f62f889090). The potential cumulative impacts has been estimated based on currently best available data, but spatial and temporal gaps may occur in underlying datasets. Please scroll down to "Lineage" and visit http://stateofthebalticsea.helcom.fi/cumulative-impacts/ for more info.

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 mud” includes classes “Fine mud”, “Sandy mud” and “Mud to sandy mud” 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.

Summary This dataset shows model results for the average bottom temperature in the Baltic region in the plant growth season from April to September. Description This dataset shows model results for the average bottom temperature in the Baltic region in the plant growth season from April to September.

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 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 is built from following Human activities datasets: • http://metadata.helcom.fi/geonetwork/srv/eng/catalog.search#/metadata/81c917ea-492d-48e2-9f00-e1bb7fe3e4fc • http://metadata.helcom.fi/geonetwork/srv/eng/catalog.search#/metadata/4fcd51dd-b8be-4e83-8cad-37c566782e8f The game hunting of seabirds data (see separate metadata): The total number of hunted seabirds were averaged over 2011-2015 (number of hunted seabirds / year). The area of the reporting unit was used to calculate the number of hunted seabirds / km2 and the data was converted to 1km x 1km grid. The predator control of seabirds data (see separate metadata): The total number of hunted cormorants were averaged over 2011-2015 (number of hunted cormorants / year). The area of the reporting unit was used to calculate the number of hunted cormorants / km2 and the data was converted to 1km x 1km grid. The two datasets were first separately log transformed and then summed, to get the total value for each grid cell. Zero values were given to all grid cells with no reported seabird hunting activity. The layer was normalized.

This dataset is built from the following Human activities dataset: • Hunting of seals The number of hunted seals (see separate metadata on hunting of seals) were averaged over 2011-2014 separately for grey seals, ringed seals and harbour seals (e.g. number of hunted grey seals / year). In Sweden the numbers of hunted grey seals in 2011 (74) were reported for the whole Swedish territorial waters), but here the numbers were set only to Swedish Gulf of Bothnia, as corresponding numbers were reported there in 2013 (75) and 2014 (65). The area of the reporting unit was used to calculate the number of hunted seals / km2 and the data was converted to 1km x 1km grid. For the Baltic Sea Impact Index, the values were normalized. Normalized value 0.5 was set to the level of quota for hunting of seal species in the Baltic Sea. The following quotas for hunting were used: Grey seal: 2000, Ringed seal: 350, Harbour seal 230.