This dataset contains all HBCD in seawater monitoring station locationsas reported to HELCOM secretariat by HELCOM Contracting Parties by 2016.

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

Estuaries (according to Habitats Directive Annex I) are coastal inlets that are strongly influenced by freshwater. The distribution map is based on data submission by HELCOM contracting parties. Most of the submitted data is based on modelling, GIS analysis and/or aerial photos. Data coverage, accuracy and the methods in obtaining the data vary between countries.

Shellfish production in kilograms in 2010-2015 in geometry form: area (polygon). Swedish data covers only the permitted production in year 2015 (Swedish data in the point dataset). The data was made available by HELCOM Contracting Parties in response to data request. The data was received from Denmark (area), Germany (points) and Sweden (points). The activity was declared as not relevant in Estonia, Finland, Latvia, Lithuania, Poland and Russia. Attribute specification and units ID: Mariculture identification (DK) NR: Identification number (DK, mariculture permits) Country: Country AVG_all: A calculated average of production in 2010-2015 (Kg/year) AVG_11_15: A calculated average of shellfish production in 2011-2015 (Kg/year) 2010_Kg – 2015_Kg: An annual shellfish production (Kg) Area: Area of the shellfish mariculture (km2)

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.

Introduction of radionuclides is based on HELCOM MORS Discharge data from 2011 to 2014. The isotopes taken into account were: Cesium-137, Strontium-90, and Cobalt-60. The decay-corrected annual average of the sum of the radionuclide discharges (in Bq) were calculated for the pressure. 10 km buffer with linear decreasing function was used to represent the impact distance from the nuclear power plant outlets.

This dataset contains all heavy dioxins in biota monitoring station locations, observed matrix, biota matrix and monitored species as reported to HELCOM secretariat by HELCOM Contracting Parties by 2016.

Data for Furcellaria harvesting presenting the amount of dredged tonnes per unit area per year. The data was made available by HELCOM Contracting Parties in response to data request. The data was received from Estonia. The activity was declared as not relevant in Denmark, Germany, Finland, Latvia, Lithuania, Poland, Russia and Sweden. Attribute specification and units: Dredged_ar: Furcellaria dredging area (km2, ND = no data available) Area: Area (fishing area) within dredging occurs (km2), area do not represent actual dredging area. 2011 - 2015: Amount of dredged Furcellaria (tonnes / year) Total: Amount (Sum) of dredged Furcellaria during 2011-2015 (tonnes) Average: Calculated annual average of the amount of dredged Furcellaria (tonnes/year)

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 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.