This map shows the distribution and abundance of ringed 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 map shows the distribution and abundance of harbour 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).

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 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.

The extraction of herring data set is based on: 1. http://metadata.helcom.fi/geonetwork/srv/eng/catalog.search#/metadata/a3b67a55-7c1e-469e-b692-58c4e7b79279 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 herring. Please see "lineage" section below for further details on attributes, data source, data processing, etc.

This dataset depicts the landcover in the Baltic Sea catchment area.

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.

Mudflats and sandflats not covered by seawater at low tide (according to Habitats Directive Annex I) are often devoid of vascular plants, usually coated by blue algae and diatoms. They are of particular importance as feeding grounds for wildfowl and waders. The distribution map is based on data submission by HELCOM contracting parties. Only Denmark, Germany and Estonia reported occurrences of mudflats and sandflats. Most of the submitted data is based on modelling and/or GIS analysis. Data coverage, accuracy and the methods in obtaining the data vary between countries.

This map shows probability of detection of harbour porpoise (Phocoena phocoena) in the Baltic Sea, for May – Oct. This dataset was produced by the EU LIFE+ funded SAMBAH project and maps the probability of detection of harbour porpoises in the study area, which extends from the Åland Islands in the north to the Darss and Limhamn underwater ridges in the southwest. The study area excludes areas of depths greater than 80 m. Probability of detection was modelled using General Additive Modelling and static covariates such as depth, topographic complexity, month, spatial coordinates and with time surveyed as a weight. Monthly predictions were done on a 1x1 km grid and averaged to result in seasonal distribution maps for May – Oct and Nov – Apr. This division of the year is a result of visual inspection of data and results, showing a clear separation of spatial clusters of harbour porpoises in the summer season May – Oct and a more dispersed pattern with no clear separation in Nov – Apr.

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.