The extraction of cod pressure layer is based on two datasets: 1. http://metadata.helcom.fi/geonetwork/srv/eng/catalog.search#/metadata/7a1389b3-382a-487f-8888-ac45c94c5a97 for years 2011-2016 reported per ICES statistical rectangles (tonnes / ICES rectangle). 2. http://metadata.helcom.fi/geonetwork/srv/eng/catalog.search#/metadata/debeafcd-948b-4455-88ae-7a3d1618f5a8 from ICES recreational fisheries reports for 2011-2016, reported per country (only coastal areas included). Landing values were redistributed within each ICES rectangle by the c-square fishing effort data provided by ICES (all gears, 2011-2013). Tonnes / km² were calculated for both data sets and the results were converted to 1 km x 1 km grid cells. The layers were summed together, log-transformed and normalised to produce the final pressure layer on extraction of cod. 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 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.

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.

Esker islands (according to Habitats Directive Annex I) are glaciofluvial islands consisting mainly of relatively well sorted sand, gravel or less commonly of till. Also their underwater parts are included in the habitat. The distribution map is based on data submission by HELCOM contracting parties. Only Sweden and Finland reported occurrences of esker islands. Only underwater parts are included in the datasets. The data is based on modelling and GIS analysis. Data coverage, accuracy and the methods in obtaining the data vary between countries.

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.

Essential fish habitat (EFH) map on Potential spawning areas for sprat was prepared in PanBalticScope project (co-founded by the European Maritime and Fisheries Fund of the European Union) http://www.panbalticscope.eu/ Sprat (Sprattus sprattus) occurs in the entire Baltic Sea, and mainly in open sea areas. It is assessed as a single stock in the Baltic Sea within fisheries management. Sprat eggs are pelagic, and sprat spawning is well known from the deep basins in the central Baltic, where it typically occurs from February to August. Further north, spawning starts later in the year, and is less certain. Recent fisheries surveys indicate that sprat spawning does no longer occur in the Gulf of Finland. Sprat spawning areas were delineated using environmental variables due to lack of coherent field data across the Baltic Sea countries. “Potential sprat spawning areas” were delineated as areas with salinity > 6 and water depth > 30 m, but for the Arcona basin depth > 20 m was used (Grauman, 1980, Bauman et al. 2006, Voss et al. 2012). “High probability spawning areas” were delineated for areas deeper than 70 m. Stock: Sprat in subdivisions 22-32 (ICES) EFH type: Potential spawning areas Approach: Environmental envelope, corrected for areas 20-40 m south of Bornholm. Variables and thresholds: Potential spawning area: Depth > 30 m, Salinity > 6 (annual average) High probability spawning area: Depth >70 m, Salinity > 6 (annual average) Quality: The map is based on literature and environmental variables, not actual data on sprat spawning. The map might overestimate the spawning area west and north of Gotland. The data layers on environmental variables are based on modelling. Attribute information: Raster value representing no spawning (0), potential spawning area (0.5) and high probability spawning area (1). References: - Baumann, H, H Hinrichsen, C Mollmann, F Koster, A Malzahn, and A Temming (2006) Recruitment variability in Baltic Sea sprat (Sprattus sprattus) in tightly coupled to temperature and transport patterns affecting the larval and early juvenile stages. Canadian Journal of Fisheries and Aquatic Science 63:2191-2201 - Grauman GB (1980) Long term changes in the abundance data of eggs and larvae of sprat in the Baltic Sea. Fisheries research in the Baltic Sea, Riga. 15:138-150 (in Russian) - HELCOM (2018) Outcome of the regional expert workshop on essential fish habitats, organized by Pan Baltic Scope project and HELCOM (HELCOM Pan Baltic Scope EFH WS 1-2018) - Voss R, MA Peck, HH Hinrichsen, C Clemmesen, H Baumann, D Stepputis, M Bernreuther, JO Schmidt, A Temming, and FW Köster (2012) Recruitment processes in Baltic sprat - A re-evaluation of GLOBEC Germany hypotheses. Progress in Oceanography 107:61-79

Distribution of Charophytes (Chara spp., Nitella spp., Nitellopsis spp., Tolypella spp.) mainly based on data submission by HELCOM contracting parties. Submitted point data was originally gathered in national mapping and monitoring campaigns, or for scientific research. Also scientific publications were used to complement the data (in Curonian, Vistula and Szczechin lagoons, see reference list). Polygon data from Poland was digitized based on Polish Marine Atlas. 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 Charophytes) were generalized to 5km x 5km grid cells.

Summary Model results for the distribution of where at least 1% available light touches the seabed (the photic zone) and non-photic zone in the Baltic Sea based on 1% mean annual irradiance Description This dataset shows model results forthe distribution of where at least 1% available light touches the seabed (the photic zone) and non-photic zone in the Baltic Sea based on 1% mean annual irradiance. From an ecological point of view, available light is one of the primary physical parameters influencing and structuring the biological communities in the marine environment, as it is the driving force behind the primary production by providing the energy for the photosynthesis - energy that ultimately is transferred to other organisms not capable of photosynthesis. The depth of the photic zone is traditionally defined, for benthic plants, as the depth where 1% of the surface irradiance (as measured just below the water surface) is available for photosynthesis. Only two intervals based on light regime were used in the dataset, because they reflect the significant ecological difference between the shallow water depth with the presence of submerged aquatic vegetation, and the deeper waters where fauna (and bacteria) dominate diversity of species, abundance, and biomass. The intervals are: I. The photic zone (where at least 1% of the available light touches the seabed). II. The non-photic zone.The measurements of Secchi Depth used for producing this dataset are not evenly distributed and some areas in the Baltic Proper, Gulf of Riga and southern Baltic are not well covered.

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.

Input of heat pressure dataset contains delta heat values from warm water discharge of - Discharge of warm water from nuclear power plants - Fossil fuel energy production. The Discharge of warm water from nuclear power plants was requested from HELCOM Contracting Parties. Average temperature change of the cooling water (°C) and amount of cooling water (m3) was used to calculate the heat load in TWh. No data on heat load was available for the Leningrad nuclear power plant; therefore the average heat load (TWh) of discharge of warm water from nuclear power plants was given. No heat load information was available for fossil fuel energy production sites. Heat load of 1 (TWh) was given to all production sites, based on average heat load of individual production site in Helsinki from recent years. 1 km buffer was used for both datasets with steep decline around the outlet. Overlapping areas were summed. Heat load from both layers were summed and the layer was normalized.