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.

The occurrence of suitable nursery habitats is crucial for maintaining fish populations (Sundblad et al. 2013). For perch, species distribution modelling studies (Snickars et al. 2010, Bergström et al. 2013, Sundblad et al. 2013) have shown the importance of suitable environmental conditions for reproduction. Due to lack of coherent data on perch spawning and nursery areas across the Baltic Sea countries, environmental variables were used in delineating potential recruitment areas for perch. The distribution area or perch recruitment is delineated by selecting areas where depth < 4 m (For Danish waters < 3 m), logged exposure < 5 (exposure model described in Isæus 2004), and salinity < 10 PSU. The threshold values have been obtained from literature (Snickars et al. 2010, Bergström et al. 2013, Skovrind et al. 2013, Sundblad et al. 2013). Relatively “loose” thresholds have been used, to rather overestimate than underestimate the recruitment area (precautionary approach). Along the Finnish coastline a national model has been used (Kallasvuo et al. 2016), with suitable environments for perch recruitment generalized to 1 km x 1 km grid.

The map of herring relative abundance is mainly based on Baltic International acoustic surveys (BIAS), years 2011-2016 (ICES WGBIFS reports), reported as millions of herring / ICES rectangle. Also herring landings data were 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 2011-2016). MAX-value = millions of herring according to BIAS in the ICES rectangle with highest landings. ICES rectangles outside the BIAS survey area with no reported herring landings were given the value 0. The relative abundance values in each ICES rectangle were divided by the area of the rectangle to obtain values per 1km2. If the values in small coastal ICES rectangles (outside BIAS area) became unrealistically large due to high herring landings, the value of the neighboring rectangle was given. The final layer was converted to 1 km x 1km grid cells. Values were first log transformed and normalized.

Distribution of eelgrass based on data submission by HELCOM contracting parties. Mainly pointwise occurrences of eelgrass were submitted, originally gathered in national mapping and monitoring campaigns, or for scientific research. Polygon data from Puck Bay (Poland) was digitized based on Polish Marine Atlas and Orlowo cliff area was added based on expert knowledge. 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 eelgrass in the Estonian waters) were generalized to 5km x 5km grid cells.

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.

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.

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.

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

This map shows the distribution and abundance of harbour porpoise across the Baltic Sea. The ecosystem component maps on mammals distribution were drafted by EG MAMA harbour porpoise and seals distribution teams. The dataset was created to be used in the HELCOM Third Holistic Assessment of the Ecosystem health of the Baltic Sea. The methodology report can be found in https://dce2.au.dk/pub/TR240.pdf

The extraction of Sprat data set is based on: 1. http://metadata.helcom.fi/geonetwork/srv/eng/catalog.search#/metadata/1fb1bd2d-8dff-493a-9ed3-a278aec8f371 for years 2011-2016 reported per ICES statistical rectangles (tonnes / ICES rectangle). Landing values were redistributed within each ICES rectangle by the c-square fishing effort data provided by ICES (all gears, 2011-2013). Tonnes / km² was calculated and the results were converted to 1 km x 1 km grid cells. The layer was log-transformed and normalised to produce the final pressure layer on extraction of Sprat. Please see "lineage" section below for further details on attributes, data source, data processing, etc.