WMS WMS end-point for individual habitat maps and models held by EMODnet Seabed Habitats. Includes all data with access limitations as "View and Download" or "View only" WMS Geoserver EMODnet JNCC EMODnet Seabed Habitats Team Joint Nature Conservation Committee Office

Joint Nature Conservation Committee

Peterborough Cambridgeshire PE1 1JY UK euseamap@jncc.gov.uk None None text/xml image/png application/atom+xml application/json;type=utfgrid application/pdf application/rss+xml application/vnd.google-earth.kml+xml application/vnd.google-earth.kml+xml;mode=networklink application/vnd.google-earth.kmz image/geotiff image/geotiff8 image/gif image/jpeg image/png; mode=8bit image/svg+xml image/tiff image/tiff8 image/vnd.jpeg-png image/vnd.jpeg-png8 text/html; subtype=openlayers text/html; subtype=openlayers2 text/html; subtype=openlayers3 text/plain application/vnd.ogc.gml text/xml application/vnd.ogc.gml/3.1.1 text/xml; subtype=gml/3.1.1 text/html application/json XML INIMAGE BLANK JSON https://gis.ices.dk/geonetwork/srv/api/records/482ecc45-8d9d-49bc-bce8-ba4009d5fad2/formatters/xml?approved=true application/vnd.iso.19139+xml eng eng WMS end-point for individual habitat maps and models held by EMODnet Seabed Habitats. Includes all data with access limitations as "View and Download" or "View only" AUTO:42001 AUTO:42002 AUTO:42003 AUTO:42004 AUTO:97001 AUTO:97002 AUTO:97003 AUTO:97004 CRS:27 CRS:83 CRS:84 EPSG:2000 EPSG:2001 EPSG:2002 EPSG:2003 EPSG:2004 EPSG:2005 EPSG:2006 EPSG:2007 EPSG:2008 EPSG:2009 EPSG:2010 EPSG:2011 EPSG:2012 EPSG:2013 EPSG:2014 EPSG:2015 EPSG:2016 EPSG:2017 EPSG:2018 EPSG:2019 EPSG:2020 EPSG:2021 EPSG:2022 EPSG:2023 EPSG:2024 EPSG:2025 EPSG:2026 EPSG:2027 EPSG:2028 EPSG:2029 EPSG:2030 EPSG:2031 EPSG:2032 EPSG:2033 EPSG:2034 EPSG:2035 EPSG:2036 EPSG:2037 EPSG:2038 EPSG:2039 EPSG:2040 EPSG:2041 EPSG:2042 EPSG:2043 EPSG:2044 EPSG:2045 EPSG:2046 EPSG:2047 EPSG:2048 EPSG:2049 EPSG:2050 EPSG:2051 EPSG:2052 EPSG:2053 EPSG:2054 EPSG:2055 EPSG:2056 EPSG:2057 EPSG:2058 EPSG:2059 EPSG:2060 EPSG:2061 EPSG:2062 EPSG:2063 EPSG:2064 EPSG:2065 EPSG:2066 EPSG:2067 EPSG:2068 EPSG:2069 EPSG:2070 EPSG:2071 EPSG:2072 EPSG:2073 EPSG:2074 EPSG:2075 EPSG:2076 EPSG:2077 EPSG:2078 EPSG:2079 EPSG:2080 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EPSG:68206405 EPSG:69036405 -180.0 180.0 -84.96424363202824 87.025 Group for the Hierarchical benthic habitat classification in the Atlantic Ocean layers. Classification of the Atlantic Ocean seabed into broad-scale benthic habitats employing a hierarchical top-down clustering approach aimed at informing Marine Spatial Planning. This work was performed at the University of Plymouth in 2021 with data provided by a wide group of partners representing the nations surrounding the Atlantic Ocean. It classifies continuous environmental data into discrete classes that can be compared to observed biogeographical patterns at various scales. It has 3 levels of classification. For ease of use, a layer is provided for each level. Level 1 has 4 classes. Level 2 has 15 classes nested within level 1. Layers indices are 2 digits (1[level1 class index]1[level 2 class index]). Level 3 has 157 classes nested within level 2 and class names have 4 digits (1digit[level1 class index]1[level 2 class index]2[level 3 class index]). Note that the classification was performed for the whole world and thus it has more classes than in the presented layer. EPSG:4326 -66.00000000002592 29.6666666666666 -62.24999999999994 83.3333333333347 text/html application/xml hierarchical_class_level1 Classification of the Atlantic Ocean seabed into broad-scale benthic habitats employing a hierarchical top-down clustering approach aimed at informing Marine Spatial Planning. This work was performed at the University of Plymouth in 2021 with data provided by a wide group of partners representing the nations surrounding the Atlantic Ocean. It classifies continuous environmental data into discrete classes that can be compared to observed biogeographical patterns at various scales. It has 3 levels of classification. For ease of use, a layer is provided for each level. Level 1 has 4 classes. Level 2 has 15 classes nested within level 1. Layers indices are 2 digits (1[level1 class index]1[level 2 class index]). Level 3 has 157 classes nested within level 2 and class names have 4 digits (1digit[level1 class index]1[level 2 class index]2[level 3 class index]). Note that the classification was performed for the whole world and thus it has more classes than in the presented layer. Level 1 shows the broadest level classifications of the region. Hierarchical_class_Level1 WCS GeoTIFF habitat marine External 528 Habitats - seabed habitats (including coastal wetlands) - collection of models EPSG:4326 CRS:84 -66.00000000002592 29.666666666619008 -62.24999999996555 83.3333333333347 text/html application/xml text/html Hierarchical_class_Level2 Classification of the Atlantic Ocean seabed into broad-scale benthic habitats employing a hierarchical top-down clustering approach aimed at informing Marine Spatial Planning. This work was performed at the University of Plymouth in 2021 with data provided by a wide group of partners representing the nations surrounding the Atlantic Ocean. It classifies continuous environmental data into discrete classes that can be compared to observed biogeographical patterns at various scales. It has 3 levels of classification. For ease of use, a layer is provided for each level. Level 1 has 4 classes. Level 2 has 15 classes nested within level 1. Layers indices are 2 digits (1[level1 class index]1[level 2 class index]). Level 3 has 157 classes nested within level 2 and class names have 4 digits (1digit[level1 class index]1[level 2 class index]2[level 3 class index]). Note that the classification was performed for the whole world and thus it has more classes than in the presented layer. Level 2 shows more detailed classifications than level 1, but less detailed than Level 3. Hierarchical_class_Level2 WCS GeoTIFF habitat marine External 528 [Do not report] EPSG:4326 CRS:84 -66.00000000000003 29.6666666666666 -62.24999999999994 83.33333333333334 text/html application/xml text/html Hierarchical_class_Level3 Classification of the Atlantic Ocean seabed into broad-scale benthic habitats employing a hierarchical top-down clustering approach aimed at informing Marine Spatial Planning. This work was performed at the University of Plymouth in 2021 with data provided by a wide group of partners representing the nations surrounding the Atlantic Ocean. It classifies continuous environmental data into discrete classes that can be compared to observed biogeographical patterns at various scales. It has 3 levels of classification. For ease of use, a layer is provided for each level. Level 1 has 4 classes. Level 2 has 15 classes nested within level 1. Layers indices are 2 digits (1[level1 class index]1[level 2 class index]). Level 3 has 157 classes nested within level 2 and class names have 4 digits (1digit[level1 class index]1[level 2 class index]2[level 3 class index]). Note that the classification was performed for the whole world and thus it has more classes than in the presented layer. Level 3 has the most detailed level of classifications. Hierarchical_class_Level3 WCS GeoTIFF marine habitat 528 External [Do not report] EPSG:4326 CRS:84 -66.00000000002592 29.666666666619008 -62.24999999996555 83.3333333333347 text/html application/xml text/html Habitat maps collated by EMODnet Seabed Habitats in the Habitats Directive Annex I classification system. Ownership of the individual maps is retained by the original owners, for more information please see the individual metadata record tied to the map, which can be seen in the query response. This service is configured for viewing individual maps. For download access to relevant maps, please use the emodnet_open_maplibrary WFS service endpoint https://ows.emodnet-seabedhabitats.eu/emodnet_open_maplibrary/wfs? Maps have been standardised to the EMODnet Seabed Habitats Habitats Directive Data Exchange format, for more information see: https://www.emodnet-seabedhabitats.eu/contribute-data/data-exchange-format/ natura 2000 habitats directive annex i habitat feature benthic ocean EPSG:3857 -11.45079614412666 65.90718041400004 -11.626113262716846 65.82520413426347 text/html application/xml Habitat maps collated by EMODnet Seabed Habitats in the Barcelona Convention classification system. Ownership of the individual maps is retained by the original owners, for more information please see the individual metadata record tied to the map, which can be seen in the query response. This service is configured for viewing individual maps. For download access to relevant maps, please use the emodnet_open_maplibrary WFS service endpoint https://ows.emodnet-seabedhabitats.eu/emodnet_open_maplibrary/wfs? Maps have been standardised to the EMODnet Seabed Habitats Translated Habitat Data Exchange format, for more information see: https://www.emodnet-seabedhabitats.eu/contribute-data/data-exchange-format/ features habitat benthic biotope ocean barcelonaconvention marine EPSG:3857 17.72333317300006 17.92643637100001 40.16566650699999 40.29484667900006 text/html application/xml Essential Fish habitat maps collated by EMODnet Seabed Habitats. Ownership of the individual maps is retained by the original owners, for more information please see the individual metadata record tied to the map, which can be seen in the query response. This service is configured for viewing individual maps. For download access to relevant maps, please use the emodnet_open_maplibrary WFS service endpoint for vector maps https://ows.emodnet-seabedhabitats.eu/emodnet_open_maplibrary/wfs? or the emodnet_open_maplibrary WCS service endpoint for vector maps https://ows.emodnet-seabedhabitats.eu/emodnet_open_maplibrary/wcs? Maps have been standardised to the EMODnet Seabed Habitats Essential Fish Habitat Data Exchange format, for more information see: https://www.emodnet-seabedhabitats.eu/contribute-data/essential-fish-habitat-map-submission-process/ features fish marine ocean EPSG:3857 -14.788729865191096 56.020044000430325 -10.64136200025605 66.43021267515824 application/xml text/html pt004002 This Persistence index of juveniles of Merluccius merluccius in the Portuguese continental coast was calculated following Colloca et al. (2015) for project Minouw. Using data from PT-IBTS (2002-2011), geostatistical methods were performed for each year. Density hot spots were outlined on the annual maps using Hot Spot Analysis (Getis-Ord Gi*) and persistente zone of undersized indivuduals was calculated for the overlapped years. More information on Technical report: Science, Technology, and Society Initiative to Minimize Unwanted Catches in European Fisheries WP1. Ecological, socioeconomic and technical characteristics of discarding fisheries Deliverable 1.2 GIS maps (juveniles) Responsible beneficiary: 1 -CSIC Contractual due date: month 24 Dissemination level: PUBLIC Report Status: FINAL Actual submission PT004002 WCS GeoTIFF External Habitats - essential fish habitats - collection of models 21 EPSG:4326 CRS:84 -10.0283 -7.4183 36.745000001 41.815000001 text/html application/xml text/html pt004003 This Persistence index of juveniles of Trachurus spp. in the Portuguese continental coast was calculated following Colloca et al. (2015) for project Minouw. Using data from PT-IBTS (2002-2011), geostatistical methods were performed for each year. Density hot spots were outlined on the annual maps using Hot Spot Analysis (Getis-Ord Gi*) and persistente zone of undersized indivuduals was calculated for the overlapped years. More information on Technical report: Science, Technology, and Society Initiative to Minimize Unwanted Catches in European Fisheries WP1. Ecological, socioeconomic and technical characteristics of discarding fisheries Deliverable 1.2 GIS maps (juveniles) Responsible beneficiary: 1 -CSIC Contractual due date: month 24 Dissemination level: PUBLIC Report Status: FINAL Actual submission PT004003 WCS GeoTIFF External Habitats - essential fish habitats - collection of models 19 EPSG:4326 CRS:84 -10.01 -7.44 36.793300001 41.813300001 text/html application/xml text/html pt004004 This Persistence index of juveniles of Mullus spp. in the Portuguese continental coast was calculated following Colloca et al. (2015) for project Minouw. Using data from PT-IBTS (2002-2011), geostatistical methods were performed for each year. Density hot spots were outlined on the annual maps using Hot Spot Analysis (Getis-Ord Gi*) and persistente zone of undersized indivuduals was calculated for the overlapped years. More information on Technical report: Science, Technology, and Society Initiative to Minimize Unwanted Catches in European Fisheries WP1. Ecological, socioeconomic and technical characteristics of discarding fisheries Deliverable 1.2 GIS maps (juveniles) Responsible beneficiary: 1 -CSIC Contractual due date: month 24 Dissemination level: PUBLIC Report Status: FINAL Actual submission PT004004 WCS GeoTIFF Habitats - essential fish habitats - collection of models External 13 EPSG:4258 CRS:84 -9.9733 -7.5533 36.83160003705442 41.43160004095315 text/html application/xml text/html pt004005 This Persistence index of juveniles of Parapenaeus longirostris. in the Portuguese continental coast was calculated following Colloca et al. (2015) for project Minouw. Using data from PT-IBTS (2002-2011), geostatistical methods were performed for each year. Density hot spots were outlined on the annual maps using Hot Spot Analysis (Getis-Ord Gi*) and persistente zone of undersized indivuduals was calculated for the overlapped years. More information on Technical report: Science, Technology, and Society Initiative to Minimize Unwanted Catches in European Fisheries WP1. Ecological, socioeconomic and technical characteristics of discarding fisheries Deliverable 1.2 GIS maps (juveniles) Responsible beneficiary: 1 -CSIC Contractual due date: month 24 Dissemination level: PUBLIC Report Status: FINAL Actual submission PT004005 WCS GeoTIFF External Habitats - essential fish habitats - collection of models 11 EPSG:4258 CRS:84 -10.028300000000002 -7.423299999999998 36.745000036956064 40.1750000401647 text/html application/xml text/html DK004008_EFH_Cod_Spawning grounds This dataset provides a map of cod spawning grounds distribution in the Inner Danish Waters. DK004008_EFH_Cod_Spawning grounds WCS GeoTIFF Habitats - essential fish habitats - collection of models External 12 EPSG:4326 CRS:84 9.52299411016039 13.572994110160304 54.12299411016062 57.9729941101604 text/html application/xml text/html DK004009_EFH_Cod_Feeding grounds This dataset provides a map of cod feeding grounds distribution in the Inner Danish Waters DK004009_EFH_Cod_Feeding grounds WCS GeoTIFF Habitats - essential fish habitats - collection of models External 12 EPSG:4326 CRS:84 9.52299411016039 13.572994110160304 54.12299411016062 57.9729941101604 text/html application/xml text/html DK004010_EFH_Cod_Nursery grounds This dataset provides a map of cod nursery grounds distribution in the Inner Danish Waters. DK004010_EFH_Cod_Nursery grounds WCS GeoTIFF Habitats - essential fish habitats - collection of models External 12 EPSG:4326 CRS:84 9.52299411016039 13.572994110160304 54.12299411016062 57.9729941101604 text/html application/xml text/html DK004011_EFH_Plaice_Spawning grounds This dataset provides a map of plaice spawning grounds distribution in the Inner Danish Waters DK004011_EFH_Plaice_Spawning grounds WCS GeoTIFF Habitats - essential fish habitats - collection of models External 12 EPSG:4326 CRS:84 9.52299411016039 13.572994110160304 54.12299411016062 57.9729941101604 text/html application/xml text/html DK004012_EFH_Plaice_Feeding grounds This dataset provides a map of plaice feeding grounds distribution in the Inner Danish Waters. DK004012_EFH_Plaice_Feeding grounds WCS GeoTIFF Habitats - essential fish habitats - collection of models External 12 EPSG:4326 CRS:84 9.52299411016039 13.572994110160304 54.12299411016062 57.9729941101604 text/html application/xml text/html DK004013_EFH_Plaice_Nursery grounds This dataset provides a map of plaice nursery grounds distribution in the Inner Danish Waters DK004013_EFH_Plaice_Nursery grounds WCS GeoTIFF Habitats - essential fish habitats - collection of models External 12 EPSG:4326 CRS:84 9.52299411016039 13.572994110160304 54.12299411016062 57.9729941101604 text/html application/xml text/html DK004014_EFH_Sole_Feeding grounds This dataset provides a map of sole feeding grounds distribution in the Inner Danish Waters. DK004014_EFH_Sole_Feeding grounds WCS GeoTIFF Habitats - essential fish habitats - collection of models External 13 EPSG:4326 CRS:84 9.52299411016039 13.572994110160304 54.12299411016062 57.9729941101604 text/html application/xml text/html DK004015_EFH_Sole_Nursery grounds This dataset provides a map of sole nursery grounds distribution in the Inner Danish Waters. DK004015_EFH_Sole_Nursery grounds WCS GeoTIFF Habitats - essential fish habitats - collection of models External 13 EPSG:4326 CRS:84 9.52299411016039 13.572994110160304 54.12299411016062 57.9729941101604 text/html application/xml text/html DK004016_EFH_Turbot_Feeding grounds This dataset provides a map of turbot feeding grounds distribution in the Inner Danish Waters. DK004016_EFH_Turbot_Feeding grounds WCS GeoTIFF Habitats - essential fish habitats - collection of models External 12 EPSG:4326 CRS:84 9.52299411016039 13.572994110160304 54.12299411016062 57.9729941101604 text/html application/xml text/html DK004017_EFH_Flounder_Spawning grounds This dataset provides a map of flounder spawning grounds distribution in the Inner Danish Waters. DK004017_EFH_Flounder_Spawning grounds WCS GeoTIFF Habitats - essential fish habitats - collection of models External 12 EPSG:4326 CRS:84 9.52299411016039 13.572994110160304 54.12299411016062 57.9729941101604 text/html application/xml text/html DK004018_EFH_Flounder_Feeding grounds This dataset provides a map of flounder feeding grounds distribution in the Inner Danish Waters. DK004018_EFH_Flounder_Feeding grounds WCS GeoTIFF Habitats - essential fish habitats - collection of models External 13 EPSG:4326 CRS:84 9.52299411016039 13.572994110160304 54.12299411016062 57.9729941101604 text/html application/xml text/html DK004019_EFH_Flounder_Nursery grounds This dataset provides a map of flounder nursery grounds distribution in the Inner Danish Waters. DK004019_EFH_Flounder_Nursery grounds WCS GeoTIFF Habitats - essential fish habitats - collection of models External 12 EPSG:4326 CRS:84 9.52299411016039 13.572994110160304 54.12299411016062 57.9729941101604 text/html application/xml text/html DK004020_EFH_Herring_Feeding grounds This dataset provides a map of herring feeding grounds distribution in the Inner Danish Waters. DK004020_EFH_Herring_Feeding grounds WCS GeoTIFF Habitats - essential fish habitats - collection of models External 12 EPSG:4326 CRS:84 9.52299411016039 13.572994110160304 54.12299411016062 57.9729941101604 text/html application/xml text/html DK004021_EFH_Herring_Nursery grounds This dataset provides a map of herring nursery grounds distribution in the Inner Danish Waters. DK004021_EFH_Herring_Nursery grounds WCS GeoTIFF Habitats - essential fish habitats - collection of models External 12 EPSG:4326 CRS:84 9.52299411016039 13.572994110160304 54.12299411016062 57.9729941101604 text/html application/xml text/html DK004022_EFH_Sprat_Feeding grounds This dataset provides a map of sprat feeding grounds distribution in the Inner Danish Waters. DK004022_EFH_Sprat_Feeding grounds WCS GeoTIFF Habitats - essential fish habitats - collection of models External 12 EPSG:4326 CRS:84 9.52299411016039 13.572994110160304 54.12299411016062 57.9729941101604 text/html application/xml text/html fi004001 Essential fish habitat (EFH) map on Potential spawning areas for cod was prepared in PanBalticScope project (co-founded by the European Maritime and Fisheries Fund of the European Union) http://www.panbalticscope.eu/ Cod (Gadus morhua) is represented by three stocks in the Baltic Sea; Eastern Baltic, Western Baltic and Kattegat cod, which is reflected in the map. “Potential spawning areas” were initially delimitated based on Hüssy (2011). In addition, the Gdansk deep as delineated by Bagge et al. (1994) was included as it sometimes contributes to reproduction of Eastern Baltic cod (Hinrichsen et al. 2016). The Gotland basin has ceased to contribute to the reproduction of cod (Hinrichsen et al. 2016). These definitions were applied in the HOLAS II project (HELCOM 2018a) based on approval by all HELCOM Contracting Parties in a review process (there referred to as ‘occasional successful spawning’ and ‘successful spawning’). Following HELCOM (2018b) additional potential spawning areas were identified by environmental thresholds for egg development and survival based on salinity and oxygen conditions (Hinrichsen et al. 2016) during 2011-2016. Separate thresholds were used for Eastern Baltic, Western Baltic and Kattegat cod. Areas denoted “high probability spawning areas” correspond to where the initial delineations (Hüssy 2011, Bagge et al. 1994) achieve the environmental threshold values. Stocks: Kattegat cod: ICES subdivision 21, Western Baltic cod: ICES subdivisions 22-24 Eastern Baltic cod: ICES subdivisions 24 + 25-32 EFH type: Potential spawning areas Approach: Literature review combined with identification of environmental window for spawning based on: salinity and oxygen for Eastern Baltic cod, and on: salinity and depth for Western Baltic Cod and Kattegat cod Variables and thresholds: Eastern Baltic cod: Salinity > 11, Oxygen > 1.5 ml/L (annual average) Western Baltic cod and Kattegat cod: Salinity > 18, Depth >20 m Quality: The Arkona deep is functional for spawning of both the Eastern and the Western Baltic cod and in effect, the definition of the Arcona Basin as a high probability areas in the Arkona basin reflect the result for Eastern Baltic cod. The effective distribution of cod spawning areas is highly dependent on the prevailing hydrological regime, and the presence of spawning also depends on seasonally variable hydrographical conditions, such as temperature, salinity and oxygen. Seasonal differences lead to a progressive spawning season towards the east, typically starting in Kattegat and the Sound in January/February and ending in July/August in the Bornholm area. Fluctuations in temperature can delay the spawning season up to two months. It is difficult to collect egg samples to verify cod spawning, as cod eggs may drift in deep areas, and instead the level of ichthyoplankton is a main source for estimation of good environmental conditions for cod spawning. Modelling based on ichthyoplankton should be validated by comparison with distribution of running adults, to resolve the potential influence of prevailing current speed. The proposed delineations are also influenced by research on the maturity of adults and histology of gonads. The adult and juvenile cod are distributed far outside of the spawning areas depicted in the map. Attribute information: Raster value representing no spawning (0), potential spawning area (0.5) and high probability spawning area (1). References - Bagge, O, F Thurow, E Steffensen, and J Bay (1994) The Baltic cod. Dana 10:1-28 - HELCOM (2018a) State of the Baltic Sea - Second HELCOM holistic assessment 2011-2016. Baltic Sea Environment Proceedings 155 - HELCOM (2018b) 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) - Hüssy, K (2011) Review of western Baltic cod (Gadus morhua) recruitment dynamics. ICES Journal of Marine Science 68:1459-1471 - Hüssy, K, HH Hinrichsen, and B Huwer (2012) Hydrographic influence on the spawning habitat suitability of western Baltic cod (Gadus morhua). ICES Journal of Marine Science, doi:10.1093/icesjms/fss136 - Hinrichsen, HH, A Lehmann, C Petereit, A Nissling, D Ustups, U Bergström, and K. Hüssy (2016) Spawning areas of eastern Baltic cod revisited. Using hydrodynamic modelling to reveal spawning habitat suitability, egg survival probability, and connectivity patterns. Progress in Oceanography 143:13-25 SwAM (2019). Swedish Agency for Marine and Water Management. Symphony Metadata March 2019.whttps://www.havochvatten.se/download/18.67e0eb431695d86393371d86/1552566811384/bilaga-1-symphony-metadata.zip FI004001 WCS GeoTIFF Habitats - essential fish habitats - collection of models External features essential fish habitat habitat marine raster 126 EPSG:3857 CRS:84 7.534827811467941 33.864448789011114 52.49756148621375 66.43021267515823 text/html application/xml text/html fi004002 Essential fish habitat (EFH) map on Potential spawning areas for Baltic flounder was prepared in PanBalticScope project (co-founded by the European Maritime and Fisheries Fund of the European Union) http://www.panbalticscope.eu/ Baltic flounder (Platichthys solemdali) is a key species in many coastal areas of the Baltic Sea. It is the only endemic fish species of the Baltic Sea. Baltic flounder spawns in shallow coastal areas and on offshore banks, with eggs developing on the sea floor. Successful spawning may be expected at salinities down to around 5-7 (Nissling et al. 2002). ‘‘Potential spawning areas’ were initially delineated by a species distribution model (Orio et al. 2017) developed based on years 1993-1997 to consider a period with relatively better oxygen conditions, but applied with more recent data (2011-2014). The area was further delineated to encompass only areas shallower than 30 m in order to represent the demersal spawning habitat. ‘High probability spawning areas’ were identified as the sub-section encompassing salinity > 6. It should be noted that flounders in the Baltic Sea were recently separated into two species, and that spawning areas of the European flounder (Platichthys flesus) are described separately. The two data layers do not overlap and can be combined to obtain a map on spawning areas for both flounder species taken together. Stocks: ICES identifies two stocks of Baltic flounder: ICES subdivisions 26, 28 (East of Gotland and Gulf of Gdansk), and 27, 29-32 (Northern Central Baltic Sea and Northern Baltic Sea). EFH Type: Spawning areas Approach: Species distribution modelling combined with identification of environmental salinity window and depth conditions for spawning, supplemented with additional information from monitoring in Estonian waters. Variables and thresholds: Depth < 30 m, Salinity > 6 Quality: The data layer is based on species distribution modelling focusing on mature flounder at the spawning stage and should be considered a rough estimation. The data layers on environmental variables are based on modelling. Other variables than those tested in the model may also be influential. The studies from which the thresholds values for environmental variables have been obtained are based on publications conducted before the separation of Baltic flounder from European flounder but have taken the specific characteristics of the separate spawning ecotypes into account. Attribute information: Raster value representing no spawning (0), potential spawning area (0.5) and high probability spawning area (1). References: - Momigliano, P, GP Denys, H Jokinen, and J Merilä (2018) Platichthys solemdali sp. nov. (Actinopterygii, Pleuronectiformes): a new flounder species from the Baltic Sea. Frontiers in Marine Science 5:225 - Nissling, A, L Westin, and O Hjerne (2002) Reproductive success in relation to salinity for three flatfish species, dab (Limanda limanda), plaice (Pleuronectes platessa), and flounder (Pleuronectes flesus), in the brackish water Baltic Sea. ICES Journal of Marine Science 59:93-108 - Orio, A, U Bergström, M Casini, M Erlandsson, R Eschbaum, K Hüssy, A Lehmann, L Ložys, D Ustups, and A-B Florin (2017a) Characterizing and predicting the distribution of Baltic Sea flounder (Platichthys flesus) during the spawning season. Journal of Sea Research 126:46-55 - Seifert, T, F Tauber, and B Kayser (2001) A high resolution spherical grid topography of the Baltic Sea -2nd edition. Baltic sea Science Congress, Stockholm 25-29 November 2001, Poster #147 FI004002 WCS GeoTIFF Habitats - essential fish habitats - collection of models External features essential fish habitat habitat marine raster 146 EPSG:3857 CRS:84 7.534827811467941 33.864448789011114 52.49756148621375 66.43021267515823 text/html application/xml text/html fi004003 Essential fish habitat (EFH) map on Potential spawning areas for European flounder was prepared in PanBalticScope project (co-founded by the European Maritime and Fisheries Fund of the European Union) http://www.panbalticscope.eu/ European flounder (Platichthys flesus) is a key species in many coastal areas of the Baltic Sea, mainly in the central and southern sub-basins. Adults feed in shallow, coastal areas during summer and move out to deeper areas in winter, where the spawning takes place in spring. European flounder spawns above the sea floor in deep water, in areas with sufficiently high salinity for fertilization and pelagic egg development. ‘Potential spawning areas’ were initially delineated by a species distribution model (Orio et al. 2017) developed based on years 1993-1997 to consider a period with relatively better oxygen conditions, but applied with more recent data (2011-2014). The area was further delineated to encompass only areas deeper than 30 m in order to represent pelagic spawning habitat. ‘High probability spawning areas’ were identified as the sub-section encompassing salinity > 10. It should be noted that flounders in the Baltic Sea were recently separated into two species, and that spawning areas of the Baltic flounder (Platichthys solemdali) are described separately. The two data layers do not overlap and can be combined to obtain a map on spawning areas for both flounder species taken together. Stock: ICES identifies two stocks of European flounder in the Baltic Sea: ICES subdivisions 22-23 (Belt Sea and the Sound), and 24-25 (West of Bornholm and Southern Central Baltic Sea). EFH type: Spawning areas Approach: Species distribution modelling combined with identification of environmental salinity window and depth conditions for spawning. Variables and thresholds: Depth > 30 m, Salinity > 10 Quality: The data layer is based on species distribution modelling focusing on mature flounder at the spawning stage and should be considered a rough estimation. The data layers on environmental variables are based on modelling. Other variables than those tested in the model may also be influential. The studies from which the thresholds values for environmental variables have been obtained are based on publications conducted before the separation of Baltic flounder from European flounder but have taken the specific characteristics of the separate spawning ecotypes into account. Note: the map on European flounder spawning areas is currently missing information for the western Baltic Sea including the Kattegat. Attribute information: Raster value representing no spawning (0), potential spawning area (0.5) and high probability spawning area (1). References: - Momigliano, P, GP Denys, H Jokinen, and J Merilä (2018) Platichthys solemdali sp. nov. (Actinopterygii, Pleuronectiformes): a new flounder species from the Baltic Sea. Frontiers in Marine Science 5:225 - Nissling, A, L Westin, and O Hjerne (2002) Reproductive success in relation to salinity for three flatfish species, dab (Limanda limanda), plaice (Pleuronectes platessa), and flounder (Pleuronectes flesus), in the brackish water Baltic Sea. ICES Journal of Marine Science 59:93-108 - Orio, A, U Bergström, M Casini, M Erlandsson, R Eschbaum, K Hüssy, A Lehmann, L Ložys, D Ustups, and A-B Florin (2017a) Characterizing and predicting the distribution of Baltic Sea flounder (Platichthys flesus) during the spawning season. Journal of Sea Research 126:46-55 - Seifert, T, F Tauber, and B Kayser (2001) A high resolution spherical grid topography of the Baltic Sea -2nd edition. Baltic sea Science Congress, Stockholm 25-29 November 2001, Poster #147 FI004003 WCS GeoTIFF Habitats - essential fish habitats - collection of models External features essential fish habitat habitat marine raster 126 EPSG:3857 CRS:84 7.534827811467937 33.864448789011114 52.497561486213776 66.43021267515824 text/html application/xml text/html fi004004 Essential fish habitat (EFH) map on Potential nursery areas for flounders was prepared in PanBalticScope project (co-founded by the European Maritime and Fisheries Fund of the European Union) http://www.panbalticscope.eu/ The two flounder species in the Baltic Sea (Platichthys flesus and P. solemdali) have different reproductive strategies, spawning in the pelagic and in shallow waters, respectively. However, they utilize the same type nursery habitat. For both species, young of the year are found on shallow bottoms from June to September, primarily on sandy substrates. Flounder nursery areas were predicted by a generalized additive model with flounder abundance as response variable and seven map-based predictor variables. The model was based on data from available surveys of juvenile flounder in the Baltic Sea, compiled within the Pan Baltic Scope project (see HELCOM 2018a). To represent the nursery season and the current situation, only results from surveys performed in June-September during 2004-2018 were used, resulting in totally 2,114 samples. All abundance estimates were harmonized to numbers of juvenile flounder per square meter. Values for the predictor variables were extracted for each sampling point in GIS. The following environmental variables were used: salinity, wave exposure, water depth, slope of the bottom, surface temperature, bottom currents, and distance to high probability spawning area for European flounder. Stock: Baltic flounder: ICES subdivisions 26, 28 (East of Gotland and Gulf of Gdansk), and 27, 29-32 (Northern Central Baltic Sea and Northern Baltic Sea). European flounder: ICES subdivisions 22-23 (Belt Sea and the Sound), and 24-25 (West of Bornholm and Southern Central Baltic Sea).). EFH type: Nursery areas Approach: Species distribution modelling Variables and thresholds: High probability nursery areas represent areas with a predicted abundance > 0.03 juvenile flounder/m² based on the applied data sets and model. Potential nursery areas represent predicted abundance levels between 0,0001 and 0,03 juvenile flounder/m². Areas with a predicted abundance < 0.0001 juvenile flounders/m² are defined as not being flounder nursery areas. Quality: Data on juvenile flounder abundances to support the spatial model is missing from Denmark, Germany, Poland and Russia. Predictions are uncertain in these areas, and especially along the south coast of the Baltic Sea. The mix of data from different years, months and gear types may have contributed to increasing the variability in the dataset, even though a relatively high deviance explained by the model (41%) shows that it has predictive power. The spatial resolution of the predictor variables varies between 200 m and 2 km, which gives coarse predictions locally even though values are representative at an overall regional scale. The prediction is limited by a lack of accurate spatial information on surface sediments. Sandy substrates are well known as important flounder nursery habitats. Unfortunately, the only available sediment map on a Baltic Sea wide scale was too inaccurate for shallow waters where juvenile flounder occurs and was therefore excluded from the model. The map on flounder nursery areas does not make assumptions on species identity in any area. Attribute information: Raster value representing no nursery area (0), potential nursery area (0.5) and high probability nursery area (1). -999 indicates No data. References HELCOM (2018a) 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) HELCOM (2018b) HELCOM Map and Data service. Layers: mean slope and bottom currents. https://maps.helcom.fi/website/mapservice/. Accessed March 2019 HELCOM (2020) Essential fish habitats in the Baltic Sea – identification of potential spawning, recruitment and nursery areas. Lehmann, A, W Krauß, and HH Hinrichsen (2002) Effects of remote and local atmospheric forcing on circulation and upwelling in the Baltic Sea. Tellus A 54:299-316 FI004004 WCS GeoTIFF External Habitats - essential fish habitats - collection of models features essential fish habitat habitat marine raster 142 EPSG:3857 CRS:84 7.534827811467941 33.864448789011114 52.49756148621375 66.43021267515823 text/html application/xml text/html fi004005 Essential fish habitat (EFH) map on Potential spawning areas for herring was prepared in PanBalticScope project (co-founded by the European Maritime and Fisheries Fund of the European Union) http://www.panbalticscope.eu/ Herring (Clupea harengus) is widely distributed in the Baltic Sea and is common in all sub-basins. Herring feeds in the pelagic, mainly on zooplankton, and is an important prey for cod, other fish, and marine mammals. In fisheries management, herring in the Baltic Sea is sub-divided into several stocks. Herring spawns in coastal areas or offshore shallows. It has demersal eggs, which are attached to the substrate. Spawning may occur both in spring and autumn, depending on population, but spring spawning dominates today. Spawning areas of herring were identified by main habitat associations, based on existing observations of herring spawning grounds in the Baltic Sea. ‘Potential spawning areas’ were delineated based on the distribution of any of the following: modelled photic zone, photic hard bottom, charophytes, Fucus spp, Furcellaria lumbricalis, and Zostera marina, several data layers were combined due to uncertainty in the coverage of some of them, ‘High probability’ spawning areas were identified as areas where the modelled photic zone overlaps with any of the other layers. The habitat variables were identified by other existing HELCOM data layers. Stock: Herring in ICES subdivisions 22-24 (spring spawning), subdivisions 25-27, 28.2, 29 and 32, subdivision 28.1 (Gulf of Riga), subdivisions 30-31. EFH type: Spawning areas Approach: Habitat associations combined with manual corrections to eastern Gulf of Finland (Neva inlet), Curonian lagoon, Szczechin lagoon, Vistula lagoon and German waters Variables and thresholds: Distribution of Photic zone, Photic hard bottom, Charophytes, Fucus spp., Furcellaria lumbricalis, Zostera marina. The resulting data layer was corrected by removing recruitment areas in eastern Gulf of Finland (Neva inlet), Curonian lagoon, Szczechin lagoon and enhancing that of the Vistula lagoon. Quality: The data layer is mainly developed based on main habitat associations as identified from scientific literature, not actual data on herring spawning. The delineations are based on other data layers (benthic and habitat-related ecosystem components) for which mapping is not exhaustive and sampling density may vary between countries. Underlying data layers on vegetation (Fucus, Furcellaria, charophytes, Zostera) are based on inventory data and species distribution models. Information on the distribution of Furcellaria is lacking from Russia. Herring preferably spawns in shallow areas. However, the layer does not include depth as a variable, as some known spawning areas offshore would not be included if the depth restriction is used. Due to constraints in the resolution of the underlying data layer, the map also identifies areas shallower than one meter as potential spawning areas of Baltic herring. However, spawning of Baltic herring does not usually occur in such shallow depth. The map represents potential spawning areas. In addition, behavioral components and hydrographic factors influence on the actual chose of spawning site at a certain occasion. Due to these constraints, the data layer on Baltic herring spawning habitats should be considered as a rough estimation. Attribute information: Raster value representing no spawning (0), potential spawning area (0.5) and high probability spawning area (1). References - Fey, DP (2001) Differences in temperature conditions and somatic growth rate of larval and early juvenile spring spawned herring from the Vistula Lagoon, Baltic Sea manifested in the otolith size to fish size relationship. Journal of Fish Biology 58: 1257–1273 - Fey, DP, AM Lejk, P Margonski, L Szymanek, I Psuty, T Nermer, F Lempe, HV Strehlow, P Polte, D Moll, N Stybel, A Hiller, and M van Laak (2014a) Herring. An analysis of spawning ground management, ecological conditions and human impacts in Greifswald Bay, Vistula Lagoon and Hanö Bight. NMFRI, Gdynia, 177 pp. - Fey, D, A. Szkudlarek-Pawelczyk, and A. Wozniczka (2014b) Abundance and distribution of larval herring, Clupea harengus (Actinopterygii: Clupeiformes: Clupeidae) in the Pomeranian Bay, Baltic Sea as an indicator of spawning sites. Acta Ichthyologica et Piscatoria 44: 309–317 - HELCOM (2018a) HELCOM Map and Data service. Layers: mean slope and bottom currents. https://maps.helcom.fi/website/mapservice/. Accessed March 2019 - HELCOM (2018b) HELCOM Map and Data service. Layers: Seabed sediment polygon (BALANCE), Fucus distribution, Furcellaria lumbricalis distribution. https://maps.helcom.fi/website/mapservice/ Accessed 29 October 2018 - Kanstinger P, J Beher, G Grenzdörffer, C Hammer, KB Huebert, D Stepputtis, and M Peck (2018) What is left? Macrophyte meadows and Atlantic herring (Clupea harengus) spawning sites in the Greifswalder Bodden, Baltic Sea. Estuar Coast Shelf Sci 201:72-81) - Krasovskaya, N (2002) Spawning of Baltic herring in the Vistula Lagoon: Effects of environmental conditions and stock parameters. Bulletin of Sea Fish. Inst. No. 1 (155), Gdynia: 3–25 - Popiel, J (1984) On the biology of the Baltic herring. Reports of the Sea Fisheries Institute, Gdynia, vol. 19, 8-16. FI004005 WCS GeoTIFF Habitats - essential fish habitats - collection of models External features essential fish habitat habitat marine raster 195 EPSG:3857 CRS:84 7.534827811467941 33.864448789011114 52.49756148621375 66.43021267515823 text/html application/xml text/html fi004006 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 FI004006 WCS GeoTIFF Habitats - essential fish habitats - collection of models External features essential fish habitat habitat marine raster 150 EPSG:3857 CRS:84 7.534827811467941 33.864448789011114 52.49756148621375 66.43021267515823 text/html application/xml text/html fi004007 Essential fish habitat (EFH) map on Potential recruitment areas for perch was prepared in PanBalticScope project (co-founded by the European Maritime and Fisheries Fund of the European Union) http://www.panbalticscope.eu/ Perch (Perca fluviatilis) is a key species in many Baltic coastal areas. It is of freshwater origin and spawns predominantly in freshwater tributaries, close to the coastline, or in enclosed bays. Species distribution modelling have shown the importance of suitable environmental conditions for perch reproduction. Young perch has limited dispersal from its spawning area, and tagging studies have shown that most (50-95 % of the recaptures of perch are made up to 20 km from tagging site (Johnsson 1978, Böhling and Lehtonen 1985, Veneranta et al. 2011, Saks et al. 2020). 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 map was originally developed within the HOLAS II project (HELCOM 2018) when it was approved by all HELCOM Contracting Parties in a dedicated review process. Potential perch recruitment areas were delineated as areas with suitable condition of depth, wave exposure and salinity. Thresholds were obtained from literature, and selected to rather overestimate than underestimate the recruitment area. For the Finnish coastline, a national model was used. The map was subsequently considered by the Pan Baltic Scope project, who proposed adjustments to thresholds for some areas in Russian waters, and corrections to Estonian and German waters. Stock: Several, undefined EFH type: Recruitment areas Approach: Environmental window, national modelling approach in Finnish waters, supplemented with corrections to Estonian, German and Russian waters based on national validation with monitoring data. Variables and thresholds: Depth < 4 m (for Danish waters < 3 m), Logged exposure < 5 (For Koporo Bay and Narva Bay in Russian waters < 5.23), Salinity < 10. Based on the model for the Finnish coastline, perch recruitment areas were defined as: Unsuitable for reproduction: P(catch larvae) < 0.5, Suitable for reproduction: P(catch larvae) > 0.5, Important for reproduction: the smallest area where the expected cumulative larval abundance is 80% of the total expected abundance over study area. Quality: Recruitment area here refers to essential habitats for young-of-the-year perch (based on inventory data from spawning until the end of the first summer). The map is based on literature and environmental variables, derived from inventory data. The species distribution modelling studies, where the thresholds values for environmental variables have been obtained, are from the northern Baltic Sea. Here, the same thresholds have been applied in the southern Baltic. Also, the data layers on environmental variables are based on modelling. Due to these constraints, the data layer should be considered as a rough estimation. Attribute information: Raster value representing the potential occurrence of perch recruitment area (either 0 or 1). References - Bergström, U, G Sundblad, A-L Downie, M Snickars, C Boström, and M Lindegarth (2013) Evaluating eutrophication management scenarios in the Baltic Sea using species distribution modelling. Journal of Applied Ecology 50:680-690 - Böhling, P, and H Lehtonen (1985) Effect of environmental factors on migrations of perch (Perca fluviatilis) tagged in the coastal waters of Finland. Finnish Fisheries Research 5: 31-40 - HELCOM (2018) State of the Baltic Sea - Second HELCOM holistic assessment 2011-2016. Baltic Sea Environment Proceedings 155 - HELCOM (2020) Essential fish habitats in the Baltic Sea – identification of potential spawning, recruitment and nursery areas - Isæus, M (2004) Factors structuring Fucus communities at open and complex coastlines in the Baltic Sea. PhD thesis, Stockholm University - Johnsson, T (1978) Dispersal area of perch, Perca fluviatilis, tagged in a stream flowing into the Bothnian Sea. Aquilo, Series Zoologica 18: 62-64 - Kallasvuo, M, J Vanhatalo, and L Veneranta (2017) Modeling the spatial distribution of larval fish abundance provides essential information for management. Canadian Journal of Fisheries and Aquatic Sciences 74:636-649 - Saks, L, R Eschbaum, K Jürgens, and I Taal (2020) Ahvena ränded Liivi lahel ja Väinameres. Eesti Mereinstituut, Tartu. https://www.kalateave.ee/images/pdf¬/Uuringud/¬Ahvena_r%¬C3%A4nded_Liivi_lahel_-ja_V%C3%A4inameres.pdf - Seifert, T, F Tauber, and B Kayser (2001) A high resolution spherical grid topography of the Baltic Sea -2nd edition. Baltic sea Science Congress, Stockholm 25-29 November 2001, Poster #147 - Skovrind, M, EAF Christensen, L Jacobs, and PR Moller (2013) Marine spawning sites of Perca fluviatilis revealed by oviduct-inserted acoustic transmitters. Aquatic Biology 19:201-206 - Snickars, M, G Sundblad, A Sandström, L Ljunggren, U Bergström, G Johansson, and J Mattila (2010) Habitat selectivity of substrate spawning fish - modelling requirements of the Eurasian perch, Perca fluviatilis. Marine Ecology Progress Series 398:235-243 - Sundblad, G, Bergström, U, Sandström, A, and P Eklöv (2014) Nursery habitat availability limits adult stock sizes of predatory coastal fish. ICES Journal of Marine Science 71:672-680 - Veneranta, L, L Urho, A Lappalainen, and M Kallasvuo (2011) Turbidity characterizes reproduction areas of pikeperch (Sander lucioperca (L.)) in the northern Baltic Sea. Estuarine, Coastal and Shelf Science 95:199-206 FI004007 WCS GeoTIFF Habitats - essential fish habitats - collection of models External features essential fish habitat habitat marine raster 222 EPSG:3857 CRS:84 7.534827811467941 33.864448789011114 52.49756148621375 66.43021267515823 text/html application/xml text/html fi004008 Essential fish habitat (EFH) map on Potential recruitment areas for pikeperch was prepared in PanBalticScope project (co-founded by the European Maritime and Fisheries Fund of the European Union) http://www.panbalticscope.eu/ Pikeperch (Sander lucioperca) is a species of freshwater origin, which spawns predominantly in freshwater tributaries and has a relatively limited dispersal away from its recruitment area. 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, the distribution of pikeperch recruitment areas was delineated based on areas with suitable conditions of depth, wave exposure, salinity, water transparency (Secchi depth) and distance to deeper (10 m) waters. The threshold values were obtained from literature. Temperature, although important for pikeperch, was left out due to high variation in timing of suitable spawning temperatures across the Baltic Sea. The map on pikeperch recruitment areas was originally developed within the HOLAS II project (HELCOM 2018) when it was approved by all HELCOM Contracting Parties in a dedicated review process after correction to Swedish waters. The map was subsequently considered by the Pan Baltic Scope project, who proposed adjustments to Estonian, German, Lithuanian and Polish waters. Stock: Several, undefined EFH type: Recruitment areas Approach: Environmental window with national approach for Finnish waters, selected data points corrected for Estonian, German, Lithuanian, Polish, and Swedish waters. Variables and thresholds: Depth < 5 m, Logged exposure < 5, Salinity < 7, Secchi depth < 2, Distance to deep (10m) water < 4km. Based on the model for the Finnish coastline, pikeperch recruitment areas were defined as: Unsuitable for reproduction: P(catch larvae) < 0.5, Suitable for reproduction: P(catch larvae) > 0.5, Important for reproduction: the smallest area where the expected cumulative larval abundance is 80% of the total expected abundance over study area. Quality: Recruitment area here refers to essential habitats for young-of-the-year pikeperch (based on inventory data from spawning until the end of the first summer). The map is based on literature and environmental variables, derived from inventory data. The species distribution modelling studies, where the thresholds values for environmental variables have been obtained, are from the northern Baltic Sea. Also, the data layers on environmental variables are based on modelling. Here, same thresholds have been applied in the southern Baltic. Due to these constraints, the data layer should be considered as a rough estimation. In addition, temperature is important for pikeperch recruitment but was not included as a delineating variable due to high variation in timing. The data layer may underestimate pikeperch in Finnish waters with respect to habitats for young-of-the-year pikeperch, as it focused on newly-hatched larvae when the dispersal is more limited compared to later in the season. Attribute information: Raster value representing the potential occurrence of pikeperch reproduction area (either 0 or 1). References: - Alikas, K, and Kratzer, S (2017) Improved retrieval of Secchi depth for optically-complex waters using remote sensing data. Ecological Indicators 77: 218-227 - Bergström, U, G Sundblad, A-L Downie, M Snickars, C Boström, and M Lindegarth (2013) Evaluating eutrophication management scenarios in the Baltic Sea using species distribution modelling. Journal of Applied Ecology 50:680-690 - HELCOM (2018) State of the Baltic Sea - Second HELCOM holistic assessment 2011-2016. Baltic Sea Environment Proceedings 155 - HELCOM (2020) Essential fish habitats in the Baltic Sea – identification of potential spawning, recruitment and nursery areas - Gunnartz, U, M Lif, P Lindberg, L Ljunggren, A Sandström, and G Sundblad (2011) Kartläggning av lekområden för kommersiella fiskarter längs den svenska ostkusten - en intervjustudie (In Swedish with summary in English). Finfo 2011:3:1-42 https://www.havochvatten.se/download/18.64f5b3211343cffddb2800018015/1348912838028/finfo2011_3.pdf - Isæus, M (2004) Factors structuring Fucus communities at open and complex coastlines in the Baltic Sea. PhD thesis, Stockholm University - Kallasvuo, M, J Vanhatalo, and L Veneranta (2017) Modeling the spatial distribution of larval fish abundance provides essential information for management. Canadian Journal of Fisheries and Aquatic Sciences 74:636-649 - Seifert, T, F Tauber, and B Kayser (2001) A high resolution spherical grid topography of the Baltic Sea -2nd edition. Baltic sea Science Congress, Stockholm 25-29 November 2001, Poster #147 - Sundblad, G, Bergström, U, Sandström, A, and P Eklöv (2013) Nursery habitat availability limits adult stock sizes of predatory coastal fish. ICES Journal of Marine Science 71:672-680 - Veneranta, L, L Urho, A Lappalainen, and M Kallasvuo (2011) Turbidity characterizes reproduction areas of pikeperch (Sander lucioperca (L.)) in the northern Baltic Sea. Estuarine, Coastal and Shelf Science 95:199-206 FI004008 WCS GeoTIFF Habitats - essential fish habitats - collection of models External features essential fish habitat habitat marine raster 204 EPSG:3857 CRS:84 7.534827811467941 33.864448789011114 52.49756148621375 66.43021267515823 text/html application/xml text/html ES005001 This map shows sardine (Sardina pilchardus) Essential Fish Habitat (Spawning area) in Spanish Mediterranean waters (average predicted between 2003 and 2005). The used method can be seen in following paper: DOI 10.1007/s10750-008-9481-2 ES005001 WCS GeoTIFF features essential fish habitat habitat marine Habitats - essential fish habitats - collection of models External 523 EPSG:4326 CRS:84 -5.33223 3.6267699999999996 35.99026 42.48426 text/html application/xml text/html ES005002 This map shows sardine (Sardina pilchardus) Essential Fish Habitat (Spawning area) in Spanish Mediterranean waters. The used method can be seen in following paper:DOI 10.1007/s10750-008-9481-2 ES005002 WCS GeoTIFF features essential fish habitat habitat marine Habitats - essential fish habitats - collection of models External 518 EPSG:4326 CRS:84 -5.33223 3.6267699999999996 35.99026 42.48426 text/html application/xml text/html ES005003 This map shows sardine (Sardina pilchardus) Essential Fish Habitat (Spawning area) in Spanish Mediterranean waters. The used method can be seen in following paper:DOI 10.1007/s10750-008-9481-2 ES005003 WCS GeoTIFF features essential fish habitat habitat marine Habitats - essential fish habitats - collection of models External 518 EPSG:4326 CRS:84 -5.33223 3.6267699999999996 35.99026 42.48426 text/html application/xml text/html ES005004 This map shows sardine (Sardina pilchardus) Essential Fish Habitat (Spawning area) in Spanish Mediterranean waters. The used method can be seen in following paper:DOI 10.1007/s10750-008-9481-2 ES005004 WCS GeoTIFF features essential fish habitat habitat marine Habitats - essential fish habitats - collection of models External 518 EPSG:4326 CRS:84 -5.33223 3.6267699999999996 35.99026 42.48426 text/html application/xml text/html ES005005 This map shows anchovy (Engraulis encrasicolus) Essential Fish Habitat (Spawning area) in Spanish Mediterranean waters (average predicted between 2003 and 2005). The used method can be seen in following paper:DOI 10.1007/s10750-008-9481-2 ES005005 WCS GeoTIFF features essential fish habitat habitat marine Habitats - essential fish habitats - collection of models External 527 EPSG:4326 CRS:84 -5.33223 3.6267699999999996 35.99026 42.48426 text/html application/xml text/html ES005006 This map shows anchovy (Engraulis encrasicolus) Essential Fish Habitat (Spawning area) in Spanish Mediterranean waters (2003). The used method can be seen in following paper:DOI 10.1007/s10750-008-9481-2 ES005006 WCS GeoTIFF features essential fish habitat habitat marine Habitats - essential fish habitats - collection of models External 522 EPSG:4326 CRS:84 -5.33223 3.6267699999999996 35.99026 42.48426 text/html application/xml text/html ES005007 This map shows anchovy (Engraulis encrasicolus) Essential Fish Habitat (Spawning area) in Spanish Mediterranean waters (2004). The used method can be seen in following paper:DOI 10.1007/s10750-008-9481-2 ES005007 WCS GeoTIFF features essential fish habitat habitat marine Habitats - essential fish habitats - collection of models External 522 EPSG:4326 CRS:84 -5.33223 3.6267699999999996 35.99026 42.48426 text/html application/xml text/html ES005008 This map shows anchovy (Engraulis encrasicolus) Essential Fish Habitat (Spawning area) in Spanish Mediterranean waters (2005). The used method can be seen in following paper:DOI 10.1007/s10750-008-9481-2 ES005008 WCS GeoTIFF features essential fish habitat habitat marine Habitats - essential fish habitats - collection of models External 522 EPSG:4326 CRS:84 -5.33223 3.6267699999999996 35.99026 42.48426 text/html application/xml text/html PT005006_EFH_Sardine_SpawningGrounds Predicted map based on generalized additive models used to relate juvenile sardine presence with geographical variables and spawning grounds (egg abundance) and to model juvenile abundance PT005006_EFH_Sardine_SpawningGrounds WCS GeoTIFF 537 features essential fish habitat habitat marine External Habitats - essential fish habitats - collection of models EPSG:4326 CRS:84 -9.95 -5.95 35.8 42.05 text/html application/xml text/html PT005005_EFH_Sardine_SpawningGrounds Predicted map based on generalized additive models used to relate juvenile sardine presence with geographical variables and spawning grounds (egg abundance) and to model juvenile abundance PT005005_EFH_Sardine_SpawningGrounds WCS GeoTIFF 510 features essential fish habitat habitat marine External Habitats - essential fish habitats - collection of models EPSG:4326 CRS:84 -9.95 -5.949999999999999 35.8 42.05 text/html application/xml text/html PT005004_EFH_Sardine_SpawningGrounds Predicted map based on generalized additive models used to relate juvenile sardine presence with geographical variables and spawning grounds (egg abundance) and to model juvenile abundance PT005004_EFH_Sardine_SpawningGrounds WCS GeoTIFF 510 features essential fish habitat habitat marine External Habitats - essential fish habitats - collection of models EPSG:4326 CRS:84 -9.95 -5.949999999999999 35.8 42.05 text/html application/xml text/html PT005003_EFH_Sardine_SpawningGrounds Predicted map based on generalized additive models used to relate juvenile sardine presence with geographical variables and spawning grounds (egg abundance) and to model juvenile abundance PT005003_EFH_Sardine_SpawningGrounds WCS GeoTIFF features essential fish habitat habitat marine External 510 Habitats - essential fish habitats - collection of models EPSG:4326 CRS:84 -9.95 -5.949999999999999 35.8 42.05 text/html application/xml text/html PT005002_EFH_Sardine_SpawningGrounds Predicted map based on generalized additive models used to relate juvenile sardine presence with geographical variables and spawning grounds (egg abundance) and to model juvenile abundance PT005002_EFH_Sardine_SpawningGrounds WCS GeoTIFF features essential fish habitat habitat marine External 510 Habitats - essential fish habitats - collection of models EPSG:4326 CRS:84 -9.95 -5.949999999999999 35.8 42.05 text/html application/xml text/html PT005001_EFH_Sardine_SpawningGrounds Predicted map based on generalized additive models used to relate juvenile sardine presence with geographical variables and spawning grounds (egg abundance) and to model juvenile abundance PT005001_EFH_Sardine_SpawningGrounds WCS GeoTIFF features essential fish habitat habitat marine External 510 Habitats - essential fish habitats - collection of models EPSG:4326 CRS:84 -9.95 -5.949999999999999 35.8 42.05 text/html application/xml text/html GB000051_EFH_Haddock_SpawningGrounds The haddock (Melanogrammus aeglefinus) spawning grounds dataset is also made available in the SpatialData.gov.scot Metadata Portal. The dataset has been generated using species distribution modeling to identify the likely distribution of haddock spawning in the North Sea and West of Scotland, taking account of certain environmental influences. GB000051_EFH_Haddock_SpawningGrounds WCS GeoTIFF features essential fish habitat habitat marine External 571 Habitats - essential fish habitats - collection of models EPSG:3857 CRS:84 -4.205384596862729 10.069034051895823 49.31099883623478 61.98996297969028 text/html application/xml text/html GB000050_EFH_Whiting_SpawningGrounds The whiting (Merlangius merlangus) spawning grounds dataset is also made available in the SpatialData.gov.scot Metadata Portal. The dataset has been generated using species distribution modeling to identify the likely distribution of whiting spawning in the North Sea and West of Scotland, taking account of certain environmental influences. GB000050_EFH_Whiting_SpawningGrounds WCS GeoTIFF features essential fish habitat habitat marine External 546 Habitats - essential fish habitats - collection of models EPSG:3857 CRS:84 -4.205384596862729 10.069034051895823 49.31099883623478 61.98996297969028 text/html application/xml text/html FR005117_EFH_Sole_SpawningGrounds January spawning ground occurrence probability was modeled as part of INTERREG 3a CHARM II project (2006-2008). The gridded dataset (horizontal resolution 0.009 x 0.009 decimal degrees, i.e. approximately 800m) was created by (Carpentier et al, 2009) by fitting a Generalised Linear Model (GLM) using IBTS survey data and a series of environmental raster datasets. More information is available in the metadata of the original data source (Carpentier et al, 2009) and the CHARM II final report - see lineage for a link to these items. FR005117_EFH_Sole_SpawningGrounds WCS GeoTIFF features essential fish habitat habitat marine External 540 Habitats - essential fish habitats - collection of models EPSG:4326 CRS:84 0.0415507000720936 2.5525507000720933 49.896 51.399 text/html application/xml text/html FR005116_EFH_Plaice_SpawningGrounds January spawning ground occurrence probability was modeled as part of INTERREG 3a CHARM II project (2006-2008). The gridded dataset (horizontal resolution 0.009 x 0.009 decimal degrees, i.e. approximately 800m) was created by (Carpentier et al, 2009) by fitting a Generalised Linear Model (GLM) using IBTS survey data and a series of environmental raster datasets. More information is available in the metadata of the original data source (Carpentier et al, 2009) and the CHARM II final report - see lineage for a link to these items. FR005116_EFH_Plaice_SpawningGrounds WCS GeoTIFF features essential fish habitat habitat marine External 554 Habitats - essential fish habitats - collection of models EPSG:4326 CRS:84 0.0415507000720936 2.5525507000720933 49.896 51.399 text/html application/xml text/html FR005115_EFH_Flounder_SpawningGrounds January spawning ground occurrence probability was modeled as part of INTERREG 3a CHARM II project (2006-2008). The gridded dataset (horizontal resolution 0.009 x 0.009 decimal degrees, i.e. approximately 800m) was created by (Carpentier et al, 2009) by fitting a Generalised Linear Model (GLM) using IBTS survey data and a series of environmental raster datasets. More information is available in the metadata of the original data source (Carpentier et al, 2009) and the CHARM II final report - see lineage for a link to these items. FR005115_EFH_Flounder_SpawningGrounds WCS GeoTIFF features essential fish habitat habitat marine External 553 Habitats - essential fish habitats - collection of models EPSG:4326 CRS:84 0.0415507000720936 2.5525507000720933 49.896 51.399 text/html application/xml text/html FR005114_EFH_Whiting_SpawningGrounds January spawning ground occurrence probability was modeled as part of INTERREG 3a CHARM II project (2006-2008). The gridded dataset (horizontal resolution 0.009 x 0.009 decimal degrees, i.e. approximately 800m) was created by (Carpentier et al, 2009) by fitting a Generalised Linear Model (GLM) using IBTS survey data and a series of environmental raster datasets. More information is available in the metadata of the original data source (Carpentier et al, 2009) and the CHARM II final report - see lineage for a link to these items. FR005114_EFH_Whiting_SpawningGrounds WCS GeoTIFF features essential fish habitat habitat marine External 545 Habitats - essential fish habitats - collection of models EPSG:4326 CRS:84 0.0415507000720936 2.5525507000720933 49.896 51.399 text/html application/xml text/html FR005113_EFH_Rocklings_SpawningGrounds January spawning ground occurrence probability was modeled as part of INTERREG 3a CHARM II project (2006-2008). The gridded dataset (horizontal resolution 0.009 x 0.009 decimal degrees, i.e. approximately 800m) was created by (Carpentier et al, 2009) by fitting a Generalised Linear Model (GLM) using IBTS survey data and a series of environmental raster datasets. More information is available in the metadata of the original data source (Carpentier et al, 2009) and the CHARM II final report - see lineage for a link to these items. FR005113_EFH_Rocklings_SpawningGrounds WCS GeoTIFF features essential fish habitat habitat marine External 534 Habitats - essential fish habitats - collection of models EPSG:4326 CRS:84 0.0415507000720936 2.5525507000720933 49.896 51.399 text/html application/xml text/html FR005112_EFH_Dab_SpawningGrounds January spawning ground occurrence probability was modeled as part of INTERREG 3a CHARM II project (2006-2008). The gridded dataset (horizontal resolution 0.009 x 0.009 decimal degrees, i.e. approximately 800m) was created by (Carpentier et al, 2009) by fitting a Generalised Linear Model (GLM) using IBTS survey data and a series of environmental raster datasets. More information is available in the metadata of the original data source (Carpentier et al, 2009) and the CHARM II final report - see lineage for a link to these items. FR005112_EFH_Dab_SpawningGrounds WCS GeoTIFF features essential fish habitat habitat marine External 543 Habitats - essential fish habitats - collection of models EPSG:4326 CRS:84 0.0415507000720936 2.5525507000720933 49.896 51.399 text/html application/xml text/html FR005111_EFH_Cod_SpawningGrounds January spawning ground occurrence probability was modeled as part of INTERREG 3a CHARM II project (2006-2008). The gridded dataset (horizontal resolution 0.009 x 0.009 decimal degrees, i.e. approximately 800m) was created by (Carpentier et al, 2009) by fitting a Generalised Linear Model (GLM) using IBTS survey data and a series of environmental raster datasets. More information is available in the metadata of the original data source (Carpentier et al, 2009) and the CHARM II final report - see lineage for a link to these items. FR005111_EFH_Cod_SpawningGrounds WCS GeoTIFF features essential fish habitat habitat marine External 542 Habitats - essential fish habitats - collection of models EPSG:4326 CRS:84 0.0415507000720936 2.5525507000720933 49.896 51.399 text/html application/xml text/html FR005110_EFH_Dragonets_SpawningGrounds January spawning ground occurrence probability was modeled as part of INTERREG 3a CHARM II project (2006-2008). The gridded dataset (horizontal resolution 0.009 x 0.009 decimal degrees, i.e. approximately 800m) was created by (Carpentier et al, 2009) by fitting a Generalised Linear Model (GLM) using IBTS survey data and a series of environmental raster datasets. More information is available in the metadata of the original data source (Carpentier et al, 2009) and the CHARM II final report - see lineage for a link to these items. FR005110_EFH_Dragonets_SpawningGrounds WCS GeoTIFF features essential fish habitat habitat marine External 540 Habitats - essential fish habitats - collection of models EPSG:4326 CRS:84 -0.0472530747018354 2.5357469252981644 49.896139169706416 51.399139169706416 text/html application/xml text/html FR005109_EFH_Seabass_SpawningGrounds Gridded mean probability of seabass spawning area occurence over all spawning months in the Bay of Biscay. The gridded dataset (horizontal resolution 0.05 x 0.05 decimal degrees, i.e. approximately 4.5km) was created by (Dambrine et al, 2020) by performing a geostatistical analysis of fishing data from geolocated vessels. FR005109_EFH_Seabass_SpawningGrounds WCS GeoTIFF features essential fish habitat habitat marine External 514 Habitats - essential fish habitats - collection of models EPSG:4326 CRS:84 -5.4 -1.1500000000000004 43.5 48.0 text/html application/xml text/html PT005007 Recruitment grounds of O. vulgaris identified by mapping of pre-recruit mean abundances in winter survey cruises. PT005007 WCS GeoTIFF features essential fish habitat habitat marine External Habitats - essential fish habitats - collection of models 533 EPSG:4326 CRS:84 -10.515656565656563 -7.384343434343433 36.27121212121212 42.02878787878788 text/html application/xml text/html PT005008 Recruitment grounds of O. vulgaris identified by mapping of pre-recruit mean abundances in autumn survey cruises. PT005008 WCS GeoTIFF features essential fish habitat habitat marine External 533 Habitats - essential fish habitats - collection of models EPSG:4326 CRS:84 -10.515656565656563 -7.384343434343433 36.27121212121212 42.02878787878788 text/html application/xml text/html Habitat maps collated by EMODnet Seabed Habitats in the EUNIS classification system. Ownership of the individual maps is retained by the original owners, for more information please see the individual metadata record tied to the map, which can be seen in the query response. This service is configured for viewing individual maps. For download access to relevant maps, please use the emodnet_open_maplibrary WFS service endpoint https://ows.emodnet-seabedhabitats.eu/emodnet_open_maplibrary/wfs? Maps have been standardised to the EMODnet Seabed Habitats Translated Habitat Data Exchange format, for more information see: https://www.emodnet-seabedhabitats.eu/contribute-data/data-exchange-format/ features benthic eunis habitat biotope marine ocean EPSG:3857 -32.28816802103728 60.501977887000066 -23.25073247675564 60.8648998380571 text/html application/xml Habitat maps collated by EMODnet Seabed Habitats in the HELCOM hub classification system. Ownership of the individual maps is retained by the original owners, for more information please see the individual metadata record tied to the map, which can be seen in the query response. This service is configured for viewing individual maps. For download access to relevant maps, please use the emodnet_open_maplibrary WFS service endpoint https://ows.emodnet-seabedhabitats.eu/emodnet_open_maplibrary/wfs? Maps have been standardised to the EMODnet Seabed Habitats Translated Habitat Data Exchange format, for more information see: https://www.emodnet-seabedhabitats.eu/contribute-data/data-exchange-format/ habitat benthic marine ocean helcomhub features biotype EPSG:3857 9.419315470000072 14.74799230800005 53.69363320000007 55.02076150400007 text/html application/xml Container group for EMODnet Seabed Habitats' Work Package 3 - collating rasters showing proabilities of specific habitats as part of modelling studies. EPSG:4326 -180.0 180.0 -78.6 87.025 text/html text/html text/html application/xml application/xml application/xml application/xml text/html text/html text/html text/html application/xml text/html text/html text/html application/xml application/xml application/xml application/xml application/xml text/html text/html application/xml application/xml text/html application/xml text/html text/html application/xml application/xml text/html text/html application/xml application/xml text/html text/html application/xml text/html text/html text/html application/xml text/html text/html text/html text/html text/html application/xml application/xml application/xml application/xml text/html text/html application/xml application/xml text/html text/html text/html application/xml text/html text/html application/xml application/xml application/xml application/xml text/html text/html application/xml application/xml text/html text/html application/xml application/xml application/xml application/xml text/html application/xml text/html application/xml text/html application/xml text/html application/xml text/html application/xml application/xml application/xml application/xml application/xml application/xml application/xml text/html application/xml application/xml text/html text/html application/xml application/xml application/xml application/xml application/xml text/html application/xml application/xml text/html application/xml text/html application/xml application/xml text/html text/html text/html text/html text/html text/html application/xml application/xml text/html application/xml text/html text/html text/html application/xml text/html text/html text/html application/xml application/xml application/xml application/xml application/xml text/html application/xml application/xml text/html text/html text/html text/html application/xml application/xml text/html application/xml text/html text/html application/xml application/xml text/html text/html text/html text/html text/html text/html application/xml application/xml text/html text/html text/html text/html text/html application/xml application/xml application/xml application/xml text/html application/xml text/html application/xml text/html text/html text/html application/xml text/html application/xml application/xml text/html application/xml application/xml mediseh_posidonia This dataset is an output of the “Mediterranean Sensitive Habitats” project (MEDISEH). It shows under a raster form modelled spatial distributions of Posidonia oceanica across the Mediterranean Sea. Posidonia oceanica is endemic to the Mediterranean Sea, where it is the dominant seagrass, covering about 50,000 km2 of coastal to offshore sandy and rocky areas down to depths of about 45 m. P. oceanica is a protected species according to EU legislation (Habitat directive), the Bern and Barcelona Conventions and several national legislations. While its distribution is well documented along the European shores of the western Mediterranean Sea, limited information is available about the southern shore and the eastern Mediterranean Sea. In order to bridge this information gap, one of the goals of Task 1.3 of the MEDISEH project was to model P. oceanica distribution across the whole Mediterranean basin. A Random Forest (i.e. a Machine Learning technique) was “trained” on data from regions where information was available and then used to predict the probability of occurrence of P. oceanica where needed. The raster has a spatial resolution of 0.004166 decimal degrees, and the values are in the [0,1] interval (occurrence probabilities). mediseh_posidonia_tiled WCS GeoTIFF Habitats - seabed habitats (including coastal wetlands) - collection of models External 2701 EPSG:3857 CRS:84 -6.557323000000001 42.965205499824044 29.437318067526018 47.30077599999999 text/html application/xml WCS mediseh_maerl This dataset shows modelled spatial distributions of coralligenous outcrops and mäerl beds across the Mediterranean Sea. These bioconstructions are typical Mediterranean underwater seascapes, comprising coralline algal frameworks that grow in dim light conditions. They are the result of the building activities of algal and animal constructors, counterbalanced by physical, as well as biological, eroding processes. Because of their extent, biodiversity and production, coralligenous and mäerl habitats rank among the most important ecosystems in the Mediterranean Sea, and they are considered of great significance both for fisheries and carbon regulation. mediseh_maerl_tiled WCS GeoTIFF Habitats - seabed habitats (including coastal wetlands) - collection of models External 17817 EPSG:3857 CRS:84 -6.557315834740032 42.96500914820095 29.439518903945856 47.30077271849963 text/html application/xml text/html mediseh_cora This dataset shows modelled spatial distributions of coralligenous outcrops and mäerl beds across the Mediterranean Sea. These bioconstructions are typical Mediterranean underwater seascapes, comprising coralline algal frameworks that grow in dim light conditions. They are the result of the building activities of algal and animal constructors, counterbalanced by physical, as well as biological, eroding processes. Because of their extent, biodiversity and production, coralligenous and mäerl habitats rank among the most important ecosystems in the Mediterranean Sea, and they are considered of great significance both for fisheries and carbon regulation. mediseh_cora_tiled WCS GeoTIFF Habitats - seabed habitats (including coastal wetlands) - collection of models External 15663 EPSG:3857 CRS:84 -6.557315834740004 42.96500914820098 29.4395189039463 47.30077271849997 text/html application/xml WCS lhrog10 Region: the county Rogaland Method: BRT lhrog10 WCS GeoTIFF Habitats - seabed habitats (including coastal wetlands) - collection of models External 13666 EPSG:3857 CRS:84 4.656818911127867 6.977167290008591 57.94712203930923 59.89835627994174 text/html application/xml lhoslo10 GAM kelp forest model Oslo fjord, 2010 Region: the counties Oslo, Akershus, Buskerud, Østfold, Vestfold Number of field observations: 4187 Field sampling years: 2005-2009 Prevalence: 0.04 Presence / absences: 168 /4019 Method: GAM analysis in the statistical program language R, using "look up tables" and the "Grasp Extension" to transfer the predictions to ArcView GIS 3.3. Number of predictor variables: 10 Information about the predictor variables: DEM, slope, curvature at detailed and medium spatial resolution with a 125 and 525 m moving calculating window respectively, based on a 25 m resolution DEM; wave exposure, longitude, optimal radiation index and aspect, all with 25 m resolution; and maximum current speed and slope of maximum surface speed, both with 300 m resolution but resampled to 25 m. AUC internal: 0.98. lhoslo10 WCS GeoTIFF Habitats - seabed habitats (including coastal wetlands) - collection of models External 52249 EPSG:3857 CRS:84 9.57240665776804 11.329511353505826 58.785044191073005 60.00362959603527 text/html application/xml lhnordl9 BRT kelp forest model Nordland, 2014 Region: Nordland county Number of field observations: 4331 Field sampling years: 2011, 2012 Prevalence: 13% Presence / absences: 503 / 3828 Method: BRT run in R with the R-package Dismo. (GAMs were also tested and gave similar distribution.) Number of predictor variables: 9 Information about the predictor variables: curvature at coarse and medium detailed spatial resolution with a 1025 and 525 m moving calculating window respectively, based on a 25 m resolution DEM; DEM, slope, and wave exposure (all at 25 m resolution, the predictor variables are described in Rinde et al. 2006); mean salinity, mean current speed, maximum temperature and mean temperature, all with a 800 m resolution, but resampled to 25 m. AUC internal: 0.99. lhnordl9 WCS GeoTIFF Habitats - seabed habitats (including coastal wetlands) - collection of models External 500115 EPSG:3857 CRS:84 9.532476205955183 20.866295566870146 64.88493344370302 70.45576349567023 text/html application/xml lhhord23 BRT kelp forest model Hordaland, main area, 2012 Region: The county Hordaland Number of field observations: 637 Field sampling year: 2004, 2005,2009, 2010 Prevalence: 34% Presence / absences: 215/422 Method: BRT run with the R-package Dismo. Number of predictor variables: 23 Information about the predictor variables: DEM (25 m resolution), slope, aspect, curvature at detailed, medium and coarse resolution (i.e. applying a 125, 525 and 1025 m moving calculating window respectively, based on the 25 m resolution DEM); wave exposure, latitude, longitude, and optimal radiation index, all with 25 m resolution; and maximum surface and seafloor current speed, slope of maximum surface and seafloor current speed, minimum surface and seafloor current speed, standard deviation of seafloor current speed, 10 and 90th percentile of surface and seafloor current speed, all current speed predictor variables with 200 m resolution, but resampled to 25 m. AUC independent data: 0.88 lhhord23 WCS GeoTIFF Habitats - seabed habitats (including coastal wetlands) - collection of models External 69654 EPSG:3857 CRS:84 4.710258115131029 5.681336937264231 59.58627448751801 60.642733413074964 text/html application/xml lhhord10 BRT kelp forest model Hordaland, rest area, 2012 Region: The county Hordaland Number of field observations: 637 Field sampling year: 2007, 2009 Prevalence: 34% Presence / absences: 215/422 Method: BRT run with the R-package Dismo. Number of predictor variables: 10 Information about the predictor variables: DEM (25 m resolution), slope, aspect, curvature at detailed, medium and coarse resolution (i.e. applying a 125, 525 and 1025 m moving calculating window respectively, based on the 25 m resolution DEM); wave exposure, latitude, longitude, and optimal radiation index, all with 25 m spatial resolution. AUC independent data: 0.96 lhhord10 WCS GeoTIFF Habitats - seabed habitats (including coastal wetlands) - collection of models External 14346 EPSG:3857 CRS:84 4.706536006168207 5.681208089437888 59.58592366145016 60.64272396937791 text/html application/xml lhat6 GAM kelp forest model Agder-Telemark, 2010 Region: The counties Aust-Agder and Telemark Number of field observations: 1707 Field sampling years: 2005-2008 Prevalence: 43% Presence / absences: 742/965 Method: GAM analysis in the statistical program language R, using "look up tables" and the "Grasp Extension" to transfer the predictions to ArcView GIS Number of predictor variables: 6 Information about the predictor variables: curvature at coarse and detailed resolution (i.e. applying a 1025 and 125 m moving calculating window respectively) on a DEM with 25 m resolution; DEM, wave exposure, longitude and an optimal radiation index, all at 25 m resolution. AUC internal: 0.88 lhat6 WCS GeoTIFF Habitats - seabed habitats (including coastal wetlands) - collection of models External 55101 EPSG:3857 CRS:84 5.809662135871485 10.230496227844679 57.6934091807098 59.332768827954325 text/html application/xml lami_tromso Laminaria hyperborea kelp forest is modelled for the Troms coast of northern Norway (Bekkby et al. 2013). The model was carried out in the projection UTM zone 33N. Kelp forest was defined as the dense kelp forest (see Bekkby et al. 2009), not the scattered occurrences. The model was developed based on 431 data points and GAM analyses of presence and absence points (presence being kelp forest, absence being absence of kelp at all other densities). Data were collected 2008-2009 by Akvaplan-niva, the model was run in 2011 by NIVA. The distribution model is based on depth, slope(log), wave exposure and median current speed, wave exposure being the most important variable (see Bekkby et al. 2009 for explanation of the GIS environmental layers). The input depth, slope and wave exposure models had a spatial (horizontal) distribution of 25 m, the current speed model was resampled from 500 m resolution. The output model has a spatial (horizontal) distribution of 25 m. The work was part of the National program for mapping biodiversity – coast, a program that is funded by the Ministry of Climate and Environment and the Ministry of Trade, Industry and Fisheries. The Norwegian Environment Agency is leading the project and NIVA is the scientific coordinator. lami_tromso WCS GeoTIFF Habitats - seabed habitats (including coastal wetlands) - collection of models External 672588 EPSG:3857 CRS:84 10.483416026064056 26.278718245558633 66.87272418367141 72.2489397498594 text/html application/xml csandrog10 BRT carbonate sand model Rogaland, 2015 Region: the county Rogaland Number of field observations: 633 Field sampling year: 1992, 1993, 2012 Prevalence: 43 and 22 for 50% and 85% calcium carbonate content respectively Presence / absences: 275/358 and 142/491 for 50% and 85% calcium carbonate content respectively Method: BRT run with the R-package Dismo. The grid is made up by a combination of three BRT models; two simplified models including 12 and 13 predictors for carbonate sand with 50% calcium content) and one BRT model based on 10 predictor variables for carbonate sand with 85% calcium content; all run with tree complexity equal to 5. The grid constitutes the maximum predicted probability values among these three models, which all are “smoothed” by applying a neighborhood analysis (i.e. mean of 3x3 neighbor cells) in advance of combining the models). Number of predictor variables: 10-13 Information about the predictor variables: depth, curvature at detailed, medium and coarse resolution (i.e. applying a 100, 500 and 1500 m moving calculating window respectively, based on the 25 m resolution DEM), wave exposure, slope of wave exposure, and optimal radiation index, all with 25 m resolution; minimum and average seafloor temperature, average seafloor salinity, minimum and average seafloor current speed, and slope of average seafloor current speed, the latter predictors from a hydrodynamic model with 800 m spatial resolution. AUC internal: 0.98 csandrog10 WCS GeoTIFF Habitats - seabed habitats (including coastal wetlands) - collection of models External 74392 EPSG:3857 CRS:84 4.656818911127867 6.977167290008591 57.94712203930923 59.89835627994174 text/html application/xml csandos20 BRT carbonate sand model Oslo Fjord, 2011 Region: The counties Oslo, Akershus, Buskerud, Østfold, Vestfold Number of field observations: 653 Field sampling years: 2008 Prevalence: 12% Presence / absences: 76/577 Method: BRT run in R with the R-package Dismo. Number of predictor variables: 20 Information about the predictor variables: DEM (25 m resolution), slope (two resolutions; based on a 12.5 and a 25 m DEM respectively), aspect (based on a 12.5 m DEM), curvature at detailed, medium and coarse resolution (i.e. applying a 125, 525 and 1025 m moving calculating window respectively, based on the 25 m DEM); wave exposure, longitude, and optimal radiation index, all with 25 m resolution; and maximum surface and seafloor current speed, slope of maximum surface and seafloor current speed, mean surface and seafloor current speed, standard deviation of surface and seafloor current speed, and 90th percentile of surface and seafloor current speed, all current speed predictor variables with 200 m resolution, but resampled to 25 m. AUC internal: 0.9. csandos20 WCS GeoTIFF Habitats - seabed habitats (including coastal wetlands) - collection of models External 920 EPSG:3857 CRS:84 9.989705236999145 11.248918686513685 58.900693281227966 59.402421906157834 text/html application/xml csandat10 BRT carbonate sand model Agder-Telemark, 2011 Region: The counties Aust-Agder and Telemark Number of field observations: 1848 Field sampling years: 1992 and 2008 Prevalence: 13.6% Presence / absences: 252/1596 Method: BRT run in R with the R-package Dismo. (The model predicts carbonate sand on peaks and in sheltered areas where it is unlikely to find carbonate sand. Such areas have been identified through rules set up in collaboration with geologists at Geological Survey of Norway (NGU; curvature index values >|1|, and the wave exposure index less than 10 000, cf Rinde et al. 2006) and removed from the shape layers for predicted presence of carbonate sand created from the grid layer. Number of predictor variables: 10 Information about the predictor variables: curvature at coarse, medium and detailed resolution with a 1025, 525 and 125 m moving calculating window respectively, based on a 25 m resolution DEM; DEM, slope at two resolutions (12.5 and 25 m); wave exposure, longitude, and an optimal radiation index, all with 25 m resolution; and aspect based on a 12.5 resolution DEM. AUC internal: 0.96. csandat10 WCS GeoTIFF Habitats - seabed habitats (including coastal wetlands) - collection of models External 51318 EPSG:3857 CRS:84 6.300578150431784 10.211618318667153 57.86409251099885 59.12681286470222 text/html application/xml Sacc_prob_Skagerrak The loss of Saccharina latissima kelp is modelled for the Skagerrak coast of Norway (Bekkby and Moy 2011). The model was carried out in the projection UTM zone 33N. The model shows the probability of sugar kelp loss. The model was developed based on 107 data points of absences and presences collected by NIVA. GAM analysis was carried out (presence being kelp loss, i.e. stations at which kelp is confirmed to be lost). The model was run in 2010 by NIVA. The kelp loss model is based on wave exposure and light exposure index (Bekkby and Moy 2010). The light exposure model had a spatial (horizontal) distribution of 12.5 m, the wave exposure model was resampled from 25 m resolution. The output model has a spatial (horizontal) distribution of 12.5 m. The work was part of the Coastal Monitoring Program, funded by the Norwegian Environment Agency. Sacc_prob_Skagerrak WCS GeoTIFF Habitats - seabed habitats (including coastal wetlands) - collection of models External 158045 EPSG:3857 CRS:84 6.923458940241141 11.643881179468195 57.520595598037644 60.3868067812229 text/html application/xml Sacc_loss_Skagerrak The loss of Saccharina latissima kelp is modelled for the Skagerrak coast of Norway (Bekkby and Moy 2011). The model was carried out in the projection UTM zone 33N. The model shows the probability of sugar kelp loss. The model was developed based on 107 data points of absences and presences collected by NIVA. GAM analysis was carried out (presence being kelp loss, i.e. stations at which kelp is confirmed to be lost). The model was run in 2010 by NIVA. The kelp loss model is based on wave exposure and light exposure index (Bekkby and Moy 2010). The light exposure model had a spatial (horizontal) distribution of 12.5 m, the wave exposure model was resampled from 25 m resolution. The output model has a spatial (horizontal) distribution of 12.5 m. The work was part of the Coastal Monitoring Program, funded by the Norwegian Environment Agency. Sacc_loss_Skagerrak WCS GeoTIFF Habitats - seabed habitats (including coastal wetlands) - collection of models External 5215 EPSG:3857 CRS:84 6.923459038932142 11.64391626988846 57.52060528189888 60.386806781006605 text/html application/xml Pelvetia_More Pelvetia canaliculata is modelled for the Sandoy area at the More and Romsdal coast on the West coast of Norway (Soldal et al. 2009). The model was carried out in the projection UTM zone 32N. The model was developed based on 98 data points and GAM analyses of presence and absence points (presence being seaweed regardless of density). Data were collected in 2008 and the model was run by NIVA in 2009. The distribution model is based on slope and wave exposure (the species always being on shore Soldal et al. 2009). The input slope and wave exposure models had a spatial (horizontal) distribution of 10 m. The output model has a spatial (horizontal) distribution of 10 m. The work was funded by the Research Council of Norway. Pelvetia_More WCS GeoTIFF Habitats - seabed habitats (including coastal wetlands) - collection of models External 524 EPSG:3857 CRS:84 5.553199268270463 9.178234538256808 62.36874053289029 63.625420044247335 text/html application/xml Lami_Trondelag Laminaria hyperborea kelp forest is modelled for the Trondelag coast of Norway (Bekkby et al. 2013). The model was carried out in the projection UTM zone 33N. Kelp forest was defined as the dense kelp forest (see Bekkby et al. 2009), not the scattered occurrences. The model was developed based on 1170 data points and GAM analyses of presence and absence points (presence being kelp forest, absence being absence of kelp at all other densities). Data were collected 2007-2008 by the Norwegian Institute for Water Research (NIVA) and the Institute for Marine Research (IMR), the model was run in 2009 by NIVA. The distribution model is based on depth, slope, terrain curvature, wave exposure and median current speed, wave exposure being the far most important variable (see Bekkby et al. 2009 for explanation of the GIS environmental layers). The input depth, slope, terrain curvature and wave exposure models had a spatial (horizontal) distribution of 25 m, the current speed model was resampled from 500 m resolution. The output model has a spatial (horizontal) distribution of 25 m. Analyses showed that coverage (density of kelp defined as classes) increased with predicted probability. The work was part of the National program for mapping biodiversity – coast, a program that is funded by the Ministry of Climate and Environment and the Ministry of Trade, Industry and Fisheries. The Norwegian Environment Agency is leading the project and NIVA is the scientific coordinator. Lami_Trondelag WCS GeoTIFF Habitats - seabed habitats (including coastal wetlands) - collection of models External 36785 EPSG:3857 CRS:84 3.282499050941782 13.45346727884637 61.85584059500917 66.07626002173988 text/html application/xml Lami_More Laminaria hyperborea kelp is modelled for the Sandoy area at the More and Romsdal coast on the West coast of Norway (Bekkby et al. 2009). The model was carried out in the projection UTM zone 32N. Kelp forest was defined as the dense kelp forest (see Bekkby et al. 2009), not the scattered occurrences. The model was developed based on 384 data points and GAM analyses of presence and absence points (presence being kelp regardless of density). Data were collected 2008 and the model was run by NIVA in 2009. The distribution model is based on depth, slope, terrain curvature, wave exposure and a light exposure index (Bekkby et al. 2009), depth being the most important variable, followed by terrain curvature and wave exposure (see Bekkby et al. 2009 for explanation of the GIS environmental layers). The input depth, slope, terrain curvature, wave exposure and light exposure index models had a spatial (horizontal) distribution of 10 m, the current speed model was resampled from 25 m resolution. The output model has a spatial (horizontal) distribution of 10 m. Analyses showed that coverage (density of kelp defined as classes) increased with predicted probability. The work was funded by the Research Council of Norway. Lami_More WCS GeoTIFF Habitats - seabed habitats (including coastal wetlands) - collection of models External 26304 EPSG:3857 CRS:84 5.890017784786276 6.874133585231442 62.62172790454258 62.931268246508054 text/html application/xml Carb_sand_nordland Carbonate sand deposit model in Nordland Model run in UTM zone 33N The model was run in 2014 by NIVA Data collected 2011-2013 by NGU, NIVA included absences from kelp surveys The data were collected in Nordland county, but the models covers also a small part of Troms in the North and Trøndelag in the south. 1078 datapoints, but only data down to 100 m depth and with more than 50 % carbonate content were included, resulting in 473 presences. Modelled using Maxent on presence data, validated with 139 presences and 740 absences (the validation points are spatially biased towards areas shallower than 40 m depth) Modelled the probability of finding carbonate sand with more that 50 % carbonate content (carbonate contact below 50 % is absence together with rocky seabed and other non-carbonate sand areas) Input variables: depth, slope, curvature (with both 250, 500 and 1000 m calculation window), wave exposure, maximum current speed and mean current speed Depth, slope, curvature and wave exposure had spatial resolution of 25 m, models developed by NIVA. The current speed had 200 m spatial resolution (developed from NorKyst800). Cut-off value for identifying polygon presences: 0.31 AUC=0.88 using independent validation presences and absences Carb_sand_nordland WCS GeoTIFF Habitats - seabed habitats (including coastal wetlands) - collection of models External 802292 EPSG:3857 CRS:84 8.961064178417756 18.572813139675606 64.81104569179925 69.55295801495572 text/html application/xml Carb_sand_Trondelag Carbonate sand deposits are modelled for the Trondelag coast of Norway (Bekkby et al. 2013). The model was carried out in the projection UTM zone 33N. Carbonate sand deposits were defined as having at least 50 % carbonate content. The model was developed based on 1105 data points and GAM analyses of presence and absence points. Data were collected 2007-2008 by the Geological Survey of Norway (NGU), the model was run in 2009 by NIVA. The distribution model is based on depth, wave exposure and maximum current speed, depth being the most important variable (see Bekkby et al. 2009 for explanation of the GIS environmental layers). The input depth and wave exposure models had a spatial (horizontal) distribution of 25 m, the current speed model was resampled from 500 m resolution. The output model has a spatial (horizontal) distribution of 25 m. The work was part of the National program for mapping biodiversity – coast, a program that is funded by the Ministry of Climate and Environment and the Ministry of Trade, Industry and Fisheries. The Norwegian Environment Agency is leading the project and NIVA is the scientific coordinator. Carb_sand_Trondelag WCS GeoTIFF Habitats - seabed habitats (including coastal wetlands) - collection of models External 45063 EPSG:3857 CRS:84 3.282499050941782 13.45346727884637 61.85584059500917 66.07626002173988 text/html application/xml DE300001_model Data represents the abundance of benthic community with Monoporeia affinis, Marenzelleria, Macoma balthica in the Baltic Sea. Baltic wide benthic community analysis is done based on the abundance and biomass data averaged for all sampling events in within 5 km grid cell based on the harmonised dataset that comprises data at over 7000 locations mostly sampled in period 2000-2013. Random Forest model was used for spatial interpolation.Original data can be downloaded: http://gis.ices.dk/gis/rest/services/ExternalDatasets/Benthic/MapServerFor EMODnet Seabed habitat portal the original dataset was separated as presence-absence grids per identified benthic community in the original article. DE300001 WCS GeoTIFF Habitats - seabed habitats (including coastal wetlands) - collection of models External 596 EPSG:4326 CRS:84 9.419371766 29.919653847 53.911947144 65.882766648 text/html application/xml DE300002_model Data represents the abundance of benthic community with Hydrobiidae, Pygospio elegans, Cerastoderma glaucumin the Baltic Sea. Baltic wide benthic community analysis is done based on the abundance and biomass data averaged for all sampling events in within 5 km grid cell based on the harmonised dataset that comprises data at over 7000 locations mostly sampled in period 2000-2013. Random Forest model was used for spatial interpolation.Original data can be downloaded: http://gis.ices.dk/gis/rest/services/ExternalDatasets/Benthic/MapServerFor EMODnet Seabed habitat portal the original dataset was separated as presence-absence grids per identified benthic community in the original article. DE300002 WCS GeoTIFF Habitats - seabed habitats (including coastal wetlands) - collection of models External 554 EPSG:4326 CRS:84 9.419371766 29.919653847 53.911947144 65.882766648 text/html application/xml DE300003_model Data represents the abundance of benthic community with Diastylis, Corbula gibba, Dipolydora quadrilobata, Arctica islandica, Aricidea suecica, Abra albain the Baltic Sea. Baltic wide benthic community analysis is done based on the abundance and biomass data averaged for all sampling events in within 5 km grid cell based on the harmonised dataset that comprises data at over 7000 locations mostly sampled in period 2000-2013. Random Forest model was used for spatial interpolation.Original data can be downloaded: http://gis.ices.dk/gis/rest/services/ExternalDatasets/Benthic/MapServer. For EMODnet Seabed habitat portal the original dataset was separated as presence-absence grids per identified benthic community in the original article. DE300003 WCS GeoTIFF Habitats - seabed habitats (including coastal wetlands) - collection of models External 529 EPSG:4326 CRS:84 9.419371766 29.919653847 53.911947144 65.882766648 text/html application/xml DE300004_model Data represents the abundance of benthic community with Bylgides sarsi, Pontoporeia femoratain the Baltic Sea. Baltic wide benthic community analysis is done based on the abundance and biomass data averaged for all sampling events in within 5 km grid cell based on the harmonised dataset that comprises data at over 7000 locations mostly sampled in period 2000-2013. Random Forest model was used for spatial interpolation.Original data can be downloaded: http://gis.ices.dk/gis/rest/services/ExternalDatasets/Benthic/MapServer. For EMODnet Seabed habitat portal the original dataset was separated as presence-absence grids per identified benthic community in the original article. DE300004 WCS GeoTIFF Habitats - seabed habitats (including coastal wetlands) - collection of models External 601 EPSG:4326 CRS:84 9.419371766 29.919653847 53.911947144 65.882766648 text/html application/xml DE300005_model Data represents the abundance of benthic community with Amphiura, Abra nitida, Galathowenia oculata, Ennucula tenuis, Thyasira flexuosa, Nucula nitidosa, Diplocirrus glaucusinthe Baltic Sea. Baltic wide benthic community analysis is done based on the abundance and biomass data averaged for all sampling events in within 5 km grid cell based on the harmonised dataset that comprises data at over 7000 locations mostly sampled in period 2000-2013. Random Forest model was used for spatial interpolation.Original data can be downloaded: http://gis.ices.dk/gis/rest/services/ExternalDatasets/Benthic/MapServer. For EMODnet Seabed habitat portal the original dataset was separated as presence-absence grids per identified benthic community in the original article. DE300005 WCS GeoTIFF Habitats - seabed habitats (including coastal wetlands) - collection of models External 522 EPSG:4326 CRS:84 9.419371766 29.919653847 53.911947144 65.882766648 text/html application/xml DE300006_model Data represents the abundance of benthic community with Mytilus spp., Amphibalanus improvisus in the Baltic Sea. Baltic wide benthic community analysis is done based on the abundance and biomass data averaged for all sampling events in within 5 km grid cell based on the harmonised dataset that comprises data at over 7000 locations mostly sampled in period 2000-2013. Random Forest model was used for spatial interpolation.Original data can be downloaded: http://gis.ices.dk/gis/rest/services/ExternalDatasets/Benthic/MapServer. For EMODnet Seabed habitat portal the original dataset was separated as presence-absence grids per identified benthic community in the original article. DE300006 WCS GeoTIFF Habitats - seabed habitats (including coastal wetlands) - collection of models External 509 EPSG:4326 CRS:84 9.419371766 29.919653847 53.911947144 65.882766648 text/html application/xml DE300007_model Data represents the abundance of benthic community with Phoronis, Tellina fabula, Thracia phaseolina, Ophelia borealis, Spiophanes bombyx, Branchiostoma lanceolatum, Spio arndtiin the Baltic Sea. Baltic wide benthic community analysis is done based on the abundance and biomass data averaged for all sampling events in within 5 km grid cell based on the harmonised dataset that comprises data at over 7000 locations mostly sampled in period 2000-2013. Random Forest model was used for spatial interpolation.Original data can be downloaded: http://gis.ices.dk/gis/rest/services/ExternalDatasets/Benthic/MapServer. For EMODnet Seabed habitat portal the original dataset was separated as presence-absence grids per identified benthic community in the original article. DE300007 WCS GeoTIFF Habitats - seabed habitats (including coastal wetlands) - collection of models External 517 EPSG:4326 CRS:84 9.419371766 29.919653847 53.911947144 65.882766648 text/html application/xml DE300008_model Data represents the abundance of benthic community with Tellina tenuis, Ensis directus, Haustorius arenarius, Lamprops fasciatusin the Baltic Sea. Baltic wide benthic community analysis is done based on the abundance and biomass data averaged for all sampling events in within 5 km grid cell based on the harmonised dataset that comprises data at over 7000 locations mostly sampled in period 2000-2013. Random Forest model was used for spatial interpolation.Original data can be downloaded: http://gis.ices.dk/gis/rest/services/ExternalDatasets/Benthic/MapServer. For EMODnet Seabed habitat portal the original dataset was separated as presence-absence grids per identified benthic community in the original article. DE300008 WCS GeoTIFF Habitats - seabed habitats (including coastal wetlands) - collection of models External 509 EPSG:4326 CRS:84 9.419371766 29.919653847 53.911947144 65.882766648 text/html application/xml DE300009_model DE300009 WCS GeoTIFF Habitats - seabed habitats (including coastal wetlands) - collection of models External 508 EPSG:4326 CRS:84 9.419371766 29.919653847 53.911947144 65.882766648 text/html application/xml DE300010_model Data represents the abundance of benthic community with Echinocyamus pusillus, Harmothoe, Bittium reticulatum, Oligochaeta, Alitta virens, Turritella communis, Asterias rubens in the Baltic Sea. Baltic wide benthic community analysis is done based on the abundance and biomass data averaged for all sampling events in within 5 km grid cell based on the harmonised dataset that comprises data at over 7000 locations mostly sampled in period 2000-2013. Random Forest model was used for spatial interpolation.Original data can be downloaded: http://gis.ices.dk/gis/rest/services/ExternalDatasets/Benthic/MapServer. For EMODnet Seabed habitat portal the original dataset was separated as presence-absence grids per identified benthic community in the original article. DE300010 WCS GeoTIFF Habitats - seabed habitats (including coastal wetlands) - collection of models External 508 EPSG:4326 CRS:84 9.419371766 29.919653847 53.911947144 65.882766648 text/html application/xml DK300001_model Mapping and classifying the seabed of the West Greenland continental shelf. Marine benthic habitats support a diversity of marine organisms that are both economically and intrinsically valuable. Our knowledge of the distribution of these habitats is largely incomplete, particularly in deeper water and at higher latitudes. The western continental shelf of Greenland is one example of a deep (more than 500 m) Arctic region with limited information available. This study uses an adaptation of the EUNIS seabed classification scheme to document benthic habitats in the region of the West Greenland shrimp trawl fishery from 60°N to 72°N in depths of 61–725 m. More than 2000 images collected at 224 stations between 2011 and 2015 were grouped into 7 habitat classes. A classification model was developed using environmental proxies to make habitat predictions for the entire western shelf (200–700 m below 72°N). The spatial distribution of habitats correlates with temperature and latitude. Muddy sediments appear in northern and colder areas whereas sandy and rocky areas dominate in the south. Southern regions are also warmer and have stronger currents. The Mud habitat is the most widespread, covering around a third of the study area. There is a general pattern that deep channels and basins are dominated by muddy sediments, many of which are fed by glacial sedimentation and outlets from fjords, while shallow banks and shelf have a mix of more complex habitats. This first habitat classification map of the West Greenland shelf will be a useful tool for researchers, management and conservationists. DK300001 WCS GeoTIFF Habitats - seabed habitats (including coastal wetlands) - collection of models External 21 EPSG:4326 CRS:84 -61.287802252 -44.009802252 59.41626169 72.71926169 text/html application/xml DK300002_model Mapping and classifying the seabed of the West Greenland continental shelfMarine benthic habitats support a diversity of marine organisms that are both economically and intrinsically valuable. Our knowledge of the distribution of these habitats is largely incomplete, particularly in deeper water and at higher latitudes. The western continental shelf of Greenland is one example of a deep (more than 500 m) Arctic region with limited information available. This study uses an adaptation of the EUNIS seabed classification scheme to document benthic habitats in the region of the West Greenland shrimp trawl fishery from 60°N to 72°N in depths of 61–725 m. More than 2000 images collected at 224 stations between 2011 and 2015 were grouped into 7 habitat classes. A classification model was developed using environmental proxies to make habitat predictions for the entire western shelf (200–700 m below 72°N). The spatial distribution of habitats correlates with temperature and latitude. Muddy sediments appear in northern and colder areas whereas sandy and rocky areas dominate in the south. Southern regions are also warmer and have stronger currents. The Mud habitat is the most widespread, covering around a third of the study area. There is a general pattern that deep channels and basins are dominated by muddy sediments, many of which are fed by glacial sedimentation and outlets from fjords, while shallow banks and shelf have a mix of more complex habitats. This first habitat classification map of the West Greenland shelf will be a useful tool for researchers, management and conservationists. DK300002 WCS GeoTIFF Habitats - seabed habitats (including coastal wetlands) - collection of models External 19 EPSG:4326 CRS:84 -61.287802252 -44.009802252 59.41626169 72.71926169 text/html application/xml DK300003_model Mapping and classifying the seabed of the West Greenland continental shelf. Marine benthic habitats support a diversity of marine organisms that are both economically and intrinsically valuable. Our knowledge of the distribution of these habitats is largely incomplete, particularly in deeper water and at higher latitudes. The western continental shelf of Greenland is one example of a deep (more than 500 m) Arctic region with limited information available. This study uses an adaptation of the EUNIS seabed classification scheme to document benthic habitats in the region of the West Greenland shrimp trawl fishery from 60°N to 72°N in depths of 61–725 m. More than 2000 images collected at 224 stations between 2011 and 2015 were grouped into 7 habitat classes. A classification model was developed using environmental proxies to make habitat predictions for the entire western shelf (200–700 m below 72°N). The spatial distribution of habitats correlates with temperature and latitude. Muddy sediments appear in northern and colder areas whereas sandy and rocky areas dominate in the south. Southern regions are also warmer and have stronger currents. The Mud habitat is the most widespread, covering around a third of the study area. There is a general pattern that deep channels and basins are dominated by muddy sediments, many of which are fed by glacial sedimentation and outlets from fjords, while shallow banks and shelf have a mix of more complex habitats. This first habitat classification map of the West Greenland shelf will be a useful tool for researchers, management and conservationists. DK300003 WCS GeoTIFF Habitats - seabed habitats (including coastal wetlands) - collection of models External 19 EPSG:4326 CRS:84 -61.287802252 -44.009802252 59.41626169 72.71926169 text/html application/xml DK300004_model Mapping and classifying the seabed of the West Greenland continental shelf. Marine benthic habitats support a diversity of marine organisms that are both economically and intrinsically valuable. Our knowledge of the distribution of these habitats is largely incomplete, particularly in deeper water and at higher latitudes. The western continental shelf of Greenland is one example of a deep (more than 500 m) Arctic region with limited information available. This study uses an adaptation of the EUNIS seabed classification scheme to document benthic habitats in the region of the West Greenland shrimp trawl fishery from 60°N to 72°N in depths of 61–725 m. More than 2000 images collected at 224 stations between 2011 and 2015 were grouped into 7 habitat classes. A classification model was developed using environmental proxies to make habitat predictions for the entire western shelf (200–700 m below 72°N). The spatial distribution of habitats correlates with temperature and latitude. Muddy sediments appear in northern and colder areas whereas sandy and rocky areas dominate in the south. Southern regions are also warmer and have stronger currents. The Mud habitat is the most widespread, covering around a third of the study area. There is a general pattern that deep channels and basins are dominated by muddy sediments, many of which are fed by glacial sedimentation and outlets from fjords, while shallow banks and shelf have a mix of more complex habitats. This first habitat classification map of the West Greenland shelf will be a useful tool for researchers, management and conservationists. DK300004 WCS GeoTIFF Habitats - seabed habitats (including coastal wetlands) - collection of models External 20 EPSG:4326 CRS:84 -61.287802252 -44.009802252 59.41626169 72.71926169 text/html application/xml DK300005_model Mapping and classifying the seabed of the West Greenland continental shelf. Marine benthic habitats support a diversity of marine organisms that are both economically and intrinsically valuable. Our knowledge of the distribution of these habitats is largely incomplete, particularly in deeper water and at higher latitudes. The western continental shelf of Greenland is one example of a deep (more than 500 m) Arctic region with limited information available. This study uses an adaptation of the EUNIS seabed classification scheme to document benthic habitats in the region of the West Greenland shrimp trawl fishery from 60°N to 72°N in depths of 61–725 m. More than 2000 images collected at 224 stations between 2011 and 2015 were grouped into 7 habitat classes. A classification model was developed using environmental proxies to make habitat predictions for the entire western shelf (200–700 m below 72°N). The spatial distribution of habitats correlates with temperature and latitude. Muddy sediments appear in northern and colder areas whereas sandy and rocky areas dominate in the south. Southern regions are also warmer and have stronger currents. The Mud habitat is the most widespread, covering around a third of the study area. There is a general pattern that deep channels and basins are dominated by muddy sediments, many of which are fed by glacial sedimentation and outlets from fjords, while shallow banks and shelf have a mix of more complex habitats. This first habitat classification map of the West Greenland shelf will be a useful tool for researchers, management and conservationists. DK300005 WCS GeoTIFF Habitats - seabed habitats (including coastal wetlands) - collection of models External 21 EPSG:4326 CRS:84 -61.287802252 -44.009802252 59.41626169 72.71926169 text/html application/xml DK300006_model Mapping and classifying the seabed of the West Greenland continental shelf. Marine benthic habitats support a diversity of marine organisms that are both economically and intrinsically valuable. Our knowledge of the distribution of these habitats is largely incomplete, particularly in deeper water and at higher latitudes. The western continental shelf of Greenland is one example of a deep (more than 500 m) Arctic region with limited information available. This study uses an adaptation of the EUNIS seabed classification scheme to document benthic habitats in the region of the West Greenland shrimp trawl fishery from 60°N to 72°N in depths of 61–725 m. More than 2000 images collected at 224 stations between 2011 and 2015 were grouped into 7 habitat classes. A classification model was developed using environmental proxies to make habitat predictions for the entire western shelf (200–700 m below 72°N). The spatial distribution of habitats correlates with temperature and latitude. Muddy sediments appear in northern and colder areas whereas sandy and rocky areas dominate in the south. Southern regions are also warmer and have stronger currents. The Mud habitat is the most widespread, covering around a third of the study area. There is a general pattern that deep channels and basins are dominated by muddy sediments, many of which are fed by glacial sedimentation and outlets from fjords, while shallow banks and shelf have a mix of more complex habitats. This first habitat classification map of the West Greenland shelf will be a useful tool for researchers, management and conservationists. DK300006 WCS GeoTIFF Habitats - seabed habitats (including coastal wetlands) - collection of models External 22 EPSG:4326 CRS:84 -61.287802252 -44.009802252 59.41626169 72.71926169 text/html application/xml DK300007_model Mapping and classifying the seabed of the West Greenland continental shelf. Marine benthic habitats support a diversity of marine organisms that are both economically and intrinsically valuable. Our knowledge of the distribution of these habitats is largely incomplete, particularly in deeper water and at higher latitudes. The western continental shelf of Greenland is one example of a deep (more than 500 m) Arctic region with limited information available. This study uses an adaptation of the EUNIS seabed classification scheme to document benthic habitats in the region of the West Greenland shrimp trawl fishery from 60°N to 72°N in depths of 61–725 m. More than 2000 images collected at 224 stations between 2011 and 2015 were grouped into 7 habitat classes. A classification model was developed using environmental proxies to make habitat predictions for the entire western shelf (200–700 m below 72°N). The spatial distribution of habitats correlates with temperature and latitude. Muddy sediments appear in northern and colder areas whereas sandy and rocky areas dominate in the south. Southern regions are also warmer and have stronger currents. The Mud habitat is the most widespread, covering around a third of the study area. There is a general pattern that deep channels and basins are dominated by muddy sediments, many of which are fed by glacial sedimentation and outlets from fjords, while shallow banks and shelf have a mix of more complex habitats. This first habitat classification map of the West Greenland shelf will be a useful tool for researchers, management and conservationists. DK300007 WCS GeoTIFF Habitats - seabed habitats (including coastal wetlands) - collection of models External 19 EPSG:4326 CRS:84 -61.287802252 -44.009802252 59.41626169 72.71926169 text/html application/xml FI300001_model Model describes the potential distribution range of Fucus spp in the Finnish coast. Model was produced using extensive data (~140,000 samples) on the Finnish Inventory Programme for Underwater Marine Environment (VELMU). Model was built using Boosted regression trees (BRT), and resulting models describe the probability of detecting a habitat-forming species in a cell. Environmental predictors include for instance (and are not only restricted to): bathymetry, euphotic depth, salinity, substrate, and wave exposure. As more accurate information is gained by diving than from video methods, dive data was used as the primary source for modelling with 75–90% for model training and 10–25% for validation. The secondary source, video data, was used only for species clearly identifiable from videos with additional subsets (25%) from targeted inventories. Dive and video data are limited to rather shallow depths (typically 20–30 m), leading to a situation where there are not enough samples from deep areas (below 50 m). To avoid artefacts in the models, a randomized absence dataset for areas deeper than 50 m was used during the modelling process. These points were used only as absences in macrophytes models, based on the knowledge that macrophytes do not live at such depths in the Baltic Sea due to habitat constraints and lack of light. FI300001 WCS GeoTIFF Habitats - seabed habitats (including coastal wetlands) - collection of models External 108399 EPSG:4326 CRS:84 20.580517847 27.795849857 59.674954279 65.803571709 text/html application/xml FI300002_model Model describes the potential distribution range of Mytilus trossulus x edulisin the Finnish coast. Model was produced using extensive data (~140,000 samples) on the Finnish Inventory Programme for Underwater Marine Environment (VELMU). Model was built using Boosted regression trees (BRT), and resulting models describe the probability of detecting a habitat-forming species in a cell. Environmental predictors include for instance (and are not only restricted to): bathymetry, euphotic depth, salinity, substrate, and wave exposure. As more accurate information is gained by diving than from video methods, dive data was used as the primary source for modelling with 75–90% for model training and 10–25% for validation. The secondary source, video data, was used only for species clearly identifiable from videos with additional subsets (25%) from targeted inventories. Dive and video data are limited to rather shallow depths (typically 20–30 m), leading to a situation where there are not enough samples from deep areas (below 50 m). To avoid artefacts in the models, a randomized absence dataset for areas deeper than 50 m was used during the modelling process. These points were used only as absences in macrophytes models, based on the knowledge that macrophytes do not live at such depths in the Baltic Sea due to habitat constraints and lack of light. FI300002 WCS GeoTIFF Habitats - seabed habitats (including coastal wetlands) - collection of models External 134403 EPSG:4326 CRS:84 20.580517847 27.795849857 59.674954279 65.803571709 text/html application/xml FI300003_model Model describes the potential distribution range of Potamogeton perfoliatus in the Finnish coast. Model was produced using extensive data (~140,000 samples) on the Finnish Inventory Programme for Underwater Marine Environment (VELMU). Model was built using Boosted regression trees (BRT), and resulting models describe the probability of detecting a habitat-forming species in a cell. Environmental predictors include for instance (and are not only restricted to): bathymetry, euphotic depth, salinity, substrate, and wave exposure. As more accurate information is gained by diving than from video methods, dive data was used as the primary source for modelling with 75–90% for model training and 10–25% for validation. The secondary source, video data, was used only for species clearly identifiable from videos with additional subsets (25%) from targeted inventories. Dive and video data are limited to rather shallow depths (typically 20–30 m), leading to a situation where there are not enough samples from deep areas (below 50 m). To avoid artefacts in the models, a randomized absence dataset for areas deeper than 50 m was used during the modelling process. These points were used only as absences in macrophytes models, based on the knowledge that macrophytes do not live at such depths in the Baltic Sea due to habitat constraints and lack of light. FI300003 WCS GeoTIFF Habitats - seabed habitats (including coastal wetlands) - collection of models External 112833 EPSG:4326 CRS:84 20.580517847 27.795849857 59.674954279 65.803571709 text/html application/xml FI300004_model Model describes the potential distribution range of Zostera marina in the Finnish coast. Model was produced using extensive data (~140,000 samples) on the Finnish Inventory Programme for Underwater Marine Environment (VELMU). Model was built using Boosted regression trees (BRT), and resulting models describe the probability of detecting a habitat-forming species in a cell. Environmental predictors include for instance (and are not only restricted to): bathymetry, euphotic depth, salinity, substrate, and wave exposure. As more accurate information is gained by diving than from video methods, dive data was used as the primary source for modelling with 75–90% for model training and 10–25% for validation. The secondary source, video data, was used only for species clearly identifiable from videos with additional subsets (25%) from targeted inventories. Dive and video data are limited to rather shallow depths (typically 20–30 m), leading to a situation where there are not enough samples from deep areas (below 50 m). To avoid artefacts in the models, a randomized absence dataset for areas deeper than 50 m was used during the modelling process. These points were used only as absences in macrophytes models, based on the knowledge that macrophytes do not live at such depths in the Baltic Sea due to habitat constraints and lack of light. FI300004 WCS GeoTIFF Habitats - seabed habitats (including coastal wetlands) - collection of models External 49276 EPSG:4326 CRS:84 20.580517847 27.795849857 59.674954279 65.803571709 text/html application/xml FR300001_model A habitat distribution modelling approach was used to model the spatial distribution of the two main species of kelp forests along Molène archipelagos (France). Lineage: Data represents percentage cover of kelp forest as well as biomass of the 2 main species: L. digitata and L. hyperoborea. The used approach consists in firstly establishing surveys and appropriate processing methods in order to provide a detailed underwater topography of the area and to accurately delineate hard substrates (bedrock) potentially colonized by kelp. Secondly, a habitat suitability model is fitted for each species on some carefully selected field stations, measuring kelp presence/absence and biomass. Predictive maps are produced, based on hard substrate areas previously delineated. Type of occurrence data used: In situ data were acquired in the period from late summer to early autumn. Information on the presence/absence of Laminaria species was acquired by towing a high definition video. Species-specific biomass were sampled at low tide for the intertidal areas and by Scuba diving for the sub-tidal areas. Environmental covariates/explanatory variables: Presence–absence of L. digitata distribution was best determined through the combined effects of depth, sediment proximity along current direction, benthic position index (BPI), immersion rate and winter temperature. The sub-model for biomass of L. digitata where present, was predicted using the additional contribution of several variables, with light being the most important (55.62% of deviance explained) and its interactions withwave exposure and spring temperature. Total suspended matter contributed little and only then through the interaction with light. The best model that explained 78.89% of deviance for the presence of L. hyperborea included depth, winter temperature, sediment proximity along current direction and BPI Biomass where L. hyperborea was present was mainly modeled by the same predictors as presence/absence Algorithm/modelling approach: Kelp biological response (presence/absence or biomass) was estimated using Generalized Additive Models (GAM) FR300001 WCS GeoTIFF Habitats - seabed habitats (including coastal wetlands) - collection of models External 4381 EPSG:4326 CRS:84 -5.087043113 -4.78603324 48.284535541 48.460820575 text/html application/xml FR300002_model A habitat distribution modelling approach was used to model the spatial distribution of the two main species of kelp forests along Molène archipelagos (France). Lineage: Data represents percentage cover of kelp forest as well as biomass of the 2 main species: L. digitata and L. hyperoborea. The used approach consists in firstly establishing surveys and appropriate processing methods in order to provide a detailed underwater topography of the area and to accurately delineate hard substrates (bedrock) potentially colonized by kelp. Secondly, a habitat suitability model is fitted for each species on some carefully selected field stations, measuring kelp presence/absence and biomass. Predictive maps are produced, based on hard substrate areas previously delineated. Type of occurrence data used: In situ data were acquired in the period from late summer to early autumn. Information on the presence/absence of Laminaria species was acquired by towing a high definition video. Species-specific biomass were sampled at low tide for the intertidal areas and by Scuba diving for the sub-tidal areas. Environmental covariates/explanatory variables: Presence–absence of L. digitata distribution was best determined through the combined effects of depth, sediment proximity along current direction, benthic position index (BPI), immersion rate and winter temperature. The sub-model for biomass of L. digitata where present, was predicted using the additional contribution of several variables, with light being the most important (55.62% of deviance explained) and its interactions withwave exposure and spring temperature. Total suspended matter contributed little and only then through the interaction with light. The best model that explained 78.89% of deviance for the presence of L. hyperborea included depth, winter temperature, sediment proximity along current direction and BPI Biomass where L. hyperborea was present was mainly modeled by the same predictors as presence/absence Algorithm/modelling approach: Kelp biological response (presence/absence or biomass) was estimated using Generalized Additive Models (GAM) FR300002 WCS GeoTIFF Habitats - seabed habitats (including coastal wetlands) - collection of models External 4703 EPSG:4326 CRS:84 -5.087043113 -4.78603324 48.284535541 48.460820575 text/html application/xml FR300003_model A habitat distribution modelling approach was used to model the spatial distribution of kelp forests within the Bay of Morlaix (France). Lineage: Data represents presence-absence prediction of kelp forest. Biological ground truth data were integrated with high resolution environmental datasets to develop statistical model that accurately predict the structure of Laminaria forests within the Bay of Morlaix. As a direct management output, high-resolution map (25 m2 grid) was produced. Type of occurrence data used: Forest occurrence (presence or absence),representative across the full range of environmental gradients, was sampled through a combination of underwater video surveys and direct diver observations. Environmental covariates/explanatory variables: The probability of kelp forest occurrence and its standard deviation was predicted using an additive multiple regression of water depth, light availability, significant wave height and sediment proximity. Algorithm/modelling approach: Kelp biological response (presence/absence) was estimated using Generalized Additive Models (GAM) FR300003 WCS GeoTIFF Habitats - seabed habitats (including coastal wetlands) - collection of models External 2840 EPSG:4326 CRS:84 -4.076344636 -3.778158118 48.670096902 48.773045812 text/html application/xml FR300004_model A habitat distribution modelling approach was used to model the spatial distribution of kelp forests along Britanny’s coast (France). Lineage: Data represents percentage cover of kelp forest. The Habitat model was generated from kelp forest presence/absence determined from acoustic surveys of laminarial algae. KdPar, sea surface temperature, depth and current speed were used as predictive variables. Habitat model represents only the areas where kelp forests may occur, not the areas confirmed in field observations. Type of occurrence data used: Acoustic surveys of kelp forest were carried out in spring 2005-2006-2007 in 10 study areas along the coast of Brittany. A specific algorithm was developed for automatic detection of presence/absence of kelp forest from the acoustic data. Environmental covariates/explanatory variables: Covariates used were: - KPAR (proxy of light availability): spatial resolution 1km, averaged values (temporal coverage 7 years -1998-2004-, temporal resolution 1 week) - Sea surface temperature: spatial resolution 1km, averaged values (temporal coverage 20 years, temporal resolution 1 week) - Tidal current velocity: spatial resolution 300m, maximum values (temporal coverage one mean spring tide) Algorithm/modelling approach: A stepwise multiple regression with a backward selection of variables was used to predict values of kelp frequency (%)For EMODNet Seabed habitats portal, the dataset was scaled between 0-1. Original data is 0-100. FR300004 WCS GeoTIFF Habitats - seabed habitats (including coastal wetlands) - collection of models External 308 EPSG:4326 CRS:84 -5.149595522 -0.3552987319999996 47.48960483 49.745545542 text/html application/xml GB300001_model Predicitive habitat model of Lophelia pertusa distribution in Hatton Bank and George Bligh Bank GB300001 WCS GeoTIFF Habitats - seabed habitats (including coastal wetlands) - collection of models External 835 EPSG:4326 CRS:84 -20.034432771 -12.698502771 57.344334573 59.602128573 text/html application/xml GB300002_model GB300002 WCS GeoTIFF Habitats - seabed habitats (including coastal wetlands) - collection of models External 64 EPSG:4326 CRS:84 -11.131832416 3.102937854000002 49.223234507 61.554748585 text/html application/xml GB300003_model GB300003 WCS GeoTIFF Habitats - seabed habitats (including coastal wetlands) - collection of models External 64 EPSG:4326 CRS:84 -11.131832416 3.102937854000002 49.223234507 61.554748585 text/html application/xml GB300004_model GB300004 WCS GeoTIFF Habitats - seabed habitats (including coastal wetlands) - collection of models External 63 EPSG:4326 CRS:84 -11.131832416 3.102937854000002 49.223234507 61.554748585 text/html application/xml GB300005_model GB300005 WCS GeoTIFF Habitats - seabed habitats (including coastal wetlands) - collection of models External 63 EPSG:4326 CRS:84 -11.131832416 3.102937854000002 49.223234507 61.554748585 text/html application/xml GB300006_model GB300006 WCS GeoTIFF Habitats - seabed habitats (including coastal wetlands) - collection of models External 62 EPSG:4326 CRS:84 -11.131832416 3.102937854000002 49.223234507 61.554748585 text/html application/xml GB300007_model GB300007 WCS GeoTIFF Habitats - seabed habitats (including coastal wetlands) - collection of models External 64 EPSG:4326 CRS:84 -11.131832416 3.102937854000002 49.223234507 61.554748585 text/html application/xml GB300008_model GB300008 WCS GeoTIFF Habitats - seabed habitats (including coastal wetlands) - collection of models External 62 EPSG:4326 CRS:84 -11.131832416 3.102937854000002 49.223234507 61.554748585 text/html application/xml GB300009_model GB300009 WCS GeoTIFF Habitats - seabed habitats (including coastal wetlands) - collection of models External 62 EPSG:4326 CRS:84 -11.131832416 3.102937854000002 49.223234507 61.554748585 text/html application/xml GB300010_model GB300010 WCS GeoTIFF Habitats - seabed habitats (including coastal wetlands) - collection of models External 60 EPSG:4326 CRS:84 -11.131832416 3.102937854000002 49.223234507 61.554748585 text/html application/xml GB300011_model GB300011 WCS GeoTIFF Habitats - seabed habitats (including coastal wetlands) - collection of models External 62 EPSG:4326 CRS:84 -11.131832416 3.102937854000002 49.223234507 61.554748585 text/html application/xml GB300012_model GB300012 WCS GeoTIFF Habitats - seabed habitats (including coastal wetlands) - collection of models External 62 EPSG:4326 CRS:84 -11.131832416 3.102937854000002 49.223234507 61.554748585 text/html application/xml GB300013_model GB300013 WCS GeoTIFF Habitats - seabed habitats (including coastal wetlands) - collection of models External 60 EPSG:4326 CRS:84 -11.131832416 3.102937854000002 49.223234507 61.554748585 text/html application/xml GB300014_model GB300014 WCS GeoTIFF Habitats - seabed habitats (including coastal wetlands) - collection of models External 60 EPSG:4326 CRS:84 -11.131832416 3.102937854000002 49.223234507 61.554748585 text/html application/xml GB300015_model GB300015 WCS GeoTIFF Habitats - seabed habitats (including coastal wetlands) - collection of models External 2243 EPSG:4326 CRS:84 -180.0 180.0 -72.0 70.0 text/html application/xml text/html GB300016_model GB300016 WCS GeoTIFF Habitats - seabed habitats (including coastal wetlands) - collection of models External 2510 EPSG:4326 CRS:84 -180.0 180.0 -72.0 70.0 text/html application/xml text/html GB300017_model GB300017 WCS GeoTIFF Habitats - seabed habitats (including coastal wetlands) - collection of models External 2188 EPSG:4326 CRS:84 -180.0 180.0 -72.0 70.0 text/html application/xml text/html GB300018_model GB300018 WCS GeoTIFF Habitats - seabed habitats (including coastal wetlands) - collection of models External 2007 EPSG:4326 CRS:84 -180.0 180.0 -72.0 70.0 text/html application/xml text/html GB300019_model GB300019 WCS GeoTIFF Habitats - seabed habitats (including coastal wetlands) - collection of models External 2442 EPSG:4326 CRS:84 -180.0 180.0 -72.0 70.0 text/html application/xml text/html GB300020_model GB300020 WCS GeoTIFF Habitats - seabed habitats (including coastal wetlands) - collection of models External 1518 EPSG:4326 CRS:84 -180.0 180.0 -72.0 70.0 text/html application/xml text/html GB300021_model GB300021 WCS GeoTIFF Habitats - seabed habitats (including coastal wetlands) - collection of models External 1310 EPSG:4326 CRS:84 -180.0 180.0 -72.0 70.0 text/html application/xml text/html GB300022_model GB300022 WCS GeoTIFF Habitats - seabed habitats (including coastal wetlands) - collection of models External 588205 EPSG:4326 CRS:84 -180.0 180.0 -78.6 68.0 text/html application/xml text/html IE004000_model IE004000 WCS GeoTIFF Habitats - seabed habitats (including coastal wetlands) - collection of models External 84743 EPSG:4326 CRS:84 -71.42502517 19.644974829999995 37.578380131 81.918380131 text/html application/xml IE004001_model IE004001 WCS GeoTIFF Habitats - seabed habitats (including coastal wetlands) - collection of models External 908 EPSG:4326 CRS:84 -24.254548009 -5.999548008999998 48.16497807 60.15747807 text/html application/xml IE004002_model IE004002 WCS GeoTIFF Habitats - seabed habitats (including coastal wetlands) - collection of models External 1076 EPSG:4326 CRS:84 -22.383668588 -7.143668588000001 48.851780411 60.049280411 text/html application/xml IE004003_model IE004003 WCS GeoTIFF Habitats - seabed habitats (including coastal wetlands) - collection of models External 2464 EPSG:4326 CRS:84 -24.209548009 -6.554548009000001 48.16497807 60.11247807 text/html application/xml NO300001_model Data represents the distribution of seagrass (eelgrass, Zostera marina) in an area on the West coast of Norway. The model can be inquired via e-mail to trine.bekkby@niva.no. NO300001 WCS GeoTIFF Habitats - seabed habitats (including coastal wetlands) - collection of models External 13581 EPSG:4326 CRS:84 6.225371738 6.761523193 62.671697372 62.913013928 text/html application/xml Corine_2018_Coastal_Wetlands_WGS84 This dataset provides a map of coastal wetlands by RAMSAR classifications. The map is produced from Copernicus Corine Land Cover CLC2018 data. Corine_2018_Coastal_Wetlands_WGS84 WCS GeoTIFF Habitats - seabed habitats (including coastal wetlands) - collection of models External features habitat marine raster 18407 EPSG:4326 CRS:84 -56.50514190170442 72.90618750111624 24.28417702528799 72.66478252575735 text/html application/xml text/html do004006 This map represents fine habitat complexity of the marine environment off the eastern end of the Dominican Republic, produced by The Nature Conservancy (TNC), the Asner Lab of Arizona State University, and Arizona State University's Global Airborne Observatory (GAO). DO004006 WCS GeoTIFF Habitats - seabed habitats (including coastal wetlands) - collection of models External features habitat marine raster 815378 EPSG:32619 CRS:84 -69.19495298022102 -68.30410108950214 18.052134442686736 18.75224950995145 text/html application/xml do004007 This map represents percentage of live coral cover of the eastern end of the Dominican Republic, produced by The Nature Conservancy (TNC), the Asner Lab of Arizona State University, and Arizona State University's Global Airborne Observatory (GAO). DO004007 WCS GeoTIFF Habitats - seabed habitats (including coastal wetlands) - collection of models External features habitat marine raster 325338 EPSG:32619 CRS:84 -69.19495298022102 -68.30410108950214 18.052134442686736 18.75224950995145 text/html application/xml vi004011 This map represents bathymetry of Saint Croix in the U.S. Virgin Islands, produced by The Nature Conservancy (TNC), the Asner Lab of Arizona State University, and Arizona State University's Global Airborne Observatory (GAO). VI004011 WCS GeoTIFF Habitats - seabed habitats (including coastal wetlands) - collection of models External features habitat extent raster 213112 EPSG:32620 CRS:84 -64.9126471921306 -64.55281032823771 17.663751801669836 17.825024134424716 text/html application/xml vi004012 This map represents fine habitat complexity of the marine environment of Saint Croix in the U.S. Virgin Islands, produced by The Nature Conservancy (TNC), the Asner Lab of Arizona State University, and Arizona State University's Global Airborne Observatory (GAO). VI004012 WCS GeoTIFF Habitats - seabed habitats (including coastal wetlands) - collection of models External features habitat marine raster 219681 EPSG:32620 CRS:84 -64.9126471921306 -64.55281032823771 17.663751801669836 17.825024134424716 text/html application/xml vi004013 This map represents live coral coverage around Saint Croix in the U.S. Virgin Islands, produced by The Nature Conservancy (TNC), the Asner Lab of Arizona State University, and Arizona State University's Global Airborne Observatory (GAO). VI004013 WCS GeoTIFF Habitats - seabed habitats (including coastal wetlands) - collection of models External features habitat marine raster 101651 EPSG:32620 CRS:84 -64.9126471921306 -64.5532721619711 17.663751801669836 17.825020456732968 text/html application/xml do004002 This map represents algal cover around the Dominican Republic, produced by The Nature Conservancy (TNC), the Asner Lab of Arizona State University, and Arizona State University's Global Airborne Observatory (GAO). This layer is a percentage layer that has been converted to integers through multiplication by a factor of 100. DO004002 WCS GeoTIFF Habitats - seabed habitats (including coastal wetlands) - collection of models External 609839 raster marine EPSG:3857 CRS:84 -69.20805200112801 -68.29618488535507 18.036471114114576 18.7811728851541 text/html application/xml do004003 This map represents seagrass cover around the Dominican Republic, produced by The Nature Conservancy (TNC), the Asner Lab of Arizona State University, and Arizona State University's Global Airborne Observatory (GAO). This layer is a percentage layer that has been converted to integers through multiplication by a factor of 100. DO004003 WCS GeoTIFF raster marine habitat Habitats - seabed habitats (including coastal wetlands) - collection of models External 407417 EPSG:3857 CRS:84 -69.20805153990233 -68.29618338397452 18.0364721908906 18.781173885145197 text/html application/xml do004004 This map represents sand cover around the Dominican Republic, produced by The Nature Conservancy (TNC), the Asner Lab of Arizona State University, and Arizona State University's Global Airborne Observatory (GAO). This layer is a percentage layer that has been converted to integers through multiplication by a factor of 100. DO004004 WCS GeoTIFF raster marine habitat 451408 External Habitats - seabed habitats (including coastal wetlands) - collection of models EPSG:3857 CRS:84 -69.20805200112801 -68.29618488535507 18.036471114114576 18.7811728851541 text/html application/xml vi004008 This map represents algal cover around St. Croix in the U.S. Virgin Islands, produced by The Nature Conservancy (TNC), the Asner Lab of Arizona State University, and Arizona State University's Global Airborne Observatory (GAO). This layer is a percentage layer that has been converted to integers through multiplication by a factor of 100. VI004008 WCS GeoTIFF raster marine habitat Habitats - seabed habitats (including coastal wetlands) - collection of models 101429 External EPSG:3857 CRS:84 -64.91749204098859 -64.5477212300986 17.650473506383307 17.843712363449583 text/html application/xml vi004009 This map represents seagrass cover around St. Croix in the U.S. Virgin Islands, produced by The Nature Conservancy (TNC), the Asner Lab of Arizona State University, and Arizona State University's Global Airborne Observatory (GAO). This layer is a percentage layer that has been converted to integers through multiplication by a factor of 100. VI004009 WCS GeoTIFF Habitats - seabed habitats (including coastal wetlands) - collection of models External 90516 raster marine habitat EPSG:3857 CRS:84 -64.91749279770448 -64.54771589826284 17.650472678658698 17.84371244706268 text/html application/xml vi004010 This map represents sand cover around St. Croix in the U.S. Virgin Islands, produced by The Nature Conservancy (TNC), the Asner Lab of Arizona State University, and Arizona State University's Global Airborne Observatory (GAO). This layer is a percentage layer that has been converted to integers through multiplication by a factor of 100. VI004010 WCS GeoTIFF marine raster habitat Habitats - seabed habitats (including coastal wetlands) - collection of models External 103377 EPSG:3857 CRS:84 -64.91749204098859 -64.5477212300986 17.650473506383307 17.843712363449583 text/html application/xml LAMpred_Nordic Point data on Laminaria hyperborea, collected from different sources in the Nordic countries, were used to model the kelp forest distribution by fitting boosted regression trees to the compiled data. The predictors were distance to shore, slope, curvature, aspect, salinity, temperature, light, current speed, sea ice concentration and wave fetch. The aim of the project was to model the distribution of kelp forests in the Nordic countries in order to identify their ecosystem functions and services, including their role in the carbon cycle. LAMpred_Nordic WCS GeoTIFF raster marine habitat Habitats - seabed habitats (including coastal wetlands) - collection of models External 142409 EPSG:4326 CRS:84 -75.0 36.474999999999994 54.3666666666667 87.025 text/html application/xml WCS SACpred_Nordic Point data on Saccharina latissima, collected from different sources in the Nordic countries, were used to model the kelp forest distribution by fitting boosted regression trees to the compiled data. The predictors were distance to shore, slope, curvature, aspect, salinity, temperature, light, current speed, sea ice concentration and wave fetch. SACpred_Nordic WCS GeoTIFF marine habitat raster Habitats - seabed habitats (including coastal wetlands) - collection of models External 142463 EPSG:4326 CRS:84 -75.0 36.474999999999994 54.3666666666667 87.025 text/html application/xml WCS Modiolus_habitatsuitabilitymodel_uk_50 This dataset was produced using an ensemble modelling approach, utilising the random forest algorithm to predict habitat suitability of Modiolus modiolus beds across the UK. Habitat suitability models require two types of input data, presence/absence data (also known as response variables) and environmental datasets (also known as predictor variables). The output of the model describes the probability of habitat occurrence as a percentage, with the overall resolution of the dataset being 300x300m. Another output shows the standard deviation of the predictive values of the habitat suitability model. This describes the variability of the habitat suitability models derived from fifty iterations using a random forests approach. The standard deviation can be used to assess the level of confidence, however this does not provide a full picture and should only be used as an indicator. The predictor variables used within the model include: Depth to seabed Slope of seabed Kinetic energy at the seabed due to currents Kinetic energy at the seabed due to waves Seabed Substrate Absolute maximum of annual temperatures at the seabed (over 30-year period) Absolute minimum of seasonal salinity Modiolus_habitatsuitabilitymodel_uk_50 WCS GeoTIFF Habitats - seabed habitats (including coastal wetlands) - collection of models False External Modiolus_habitatsuitabilitymodel_uk_50 17648 EPSG:3035 CRS:84 -16.252082640406925 4.260642924389332 48.140834666773536 61.878745737279594 text/html application/xml text/html Sabellaria_habitatsuitabilitymodel_uk_50 This dataset was produced using an ensemble modelling approach, utilising the random forest algorithm to predict habitat suitability of Sabellaria spinulosa across the UK. Habitat suitability models require two types of input data, presence/absence data (also known as response variables) and environmental datasets (also known as predictor variables). The output of the model describes the probability of habitat occurrence as a percentage, with the overall resolution of the dataset being 300 x 300 m. Another output shows the standard deviation of the predictive values of the habitat suitability model. This describes the variability of the habitat suitability models derived from fifty iterations using a random forests approach. The standard deviation can be used to assess the level of confidence, however this does not provide a full picture and should only be used as an indicator. The predictor variables used within the model include: Depth to seabed Slope of seabed Light attenuation coefficient of photosynthetic active radiation (Kd(PAR)) Kinetic energy at the seabed due to currents Kinetic energy at the seabed due to waves Seabed Substrate Mean of annual temperature at the seabed (over 30-year period) Absolute minimum of seasonal salinity Sabellaria_habitatsuitabilitymodel_uk_50 WCS GeoTIFF Habitats - seabed habitats (including coastal wetlands) - collection of models False External Sabellaria_habitatsuitabilitymodel_uk_50 9261 EPSG:3035 CRS:84 -12.96985414960067 4.202897918276176 48.973478756690646 61.08467660839321 text/html application/xml text/html Zostera_habitatsuitabilitymodel_uk_50 This dataset was produced using an ensemble modelling approach, utilising the random forest algorithm to predict habitat suitability of Zostera marina beds across the UK. Habitat suitability models require two types of input data, presence/absence data (also known as response variables) and environmental datasets (also known as predictor variables). The output of the model describes the probability of habitat occurrence as a percentage, with the overall resolution of the dataset being 300 x 30 0m. Another output shows the standard deviation of the predictive values of the habitat suitability model. This describes the variability of the habitat suitability models derived from fifty iterations using a random forests approach. The standard deviation can be used to assess the level of confidence, however this does not provide a full picture and should only be used as an indicator. The predictor variables used within the model include: Depth to seabed Slope of seabed PAR at the Seabed Kinetic energy at the seabed due to currents Kinetic energy at the seabed due to waves Seabed Substrate Mean of annual minima temperature at the seabed (over 30-year period) Absolute minimum of seasonal salinity Zostera_habitatsuitabilitymodel_uk_50 WCS GeoTIFF Habitats - seabed habitats (including coastal wetlands) - collection of models False External Zostera_habitatsuitabilitymodel_uk_50 1565 EPSG:3035 CRS:84 -39.032299623679386 5.6737505893886215 42.65372982727621 67.58846646582079 text/html application/xml text/html Habitat maps collated by EMODnet Seabed Habitats in the MSFD Benthic Broad Habitat Types classification system. Ownership of the individual maps is retained by the original owners, for more information please see the individual metadata record tied to the map, which can be seen in the query response. This service is configured for viewing individual maps. For download access to relevant maps, please use the emodnet_open_maplibrary WFS service endpoint https://ows.emodnet-seabedhabitats.eu/emodnet_open_maplibrary/wfs? Maps have been standardised to the EMODnet Seabed Habitats Translated Habitat Data Exchange format, for more information see: https://www.emodnet-seabedhabitats.eu/contribute-data/data-exchange-format/ features benthic habitat biotype marine ocean msfd EPSG:3857 9.419315470000072 14.74799230800005 53.69363320000007 55.02076150400007 text/html application/xml Habitat maps collated by EMODnet Seabed Habitats in classification systems other than EUNIS and Habitats Directive Annex I. Ownership of the individual maps is retained by the original owners, for more information please see the individual metadata record tied to the map, which can be seen in the query response. This service is configured for viewing individual maps. For download access to relevant maps, please use the emodnet_open_maplibrary WFS service endpoint https://ows.emodnet-seabedhabitats.eu/emodnet_open_maplibrary/wfs? Maps have been standardised to the EMODnet Seabed Habitats Original Habitat Data Exchange format, for more information see: https://www.emodnet-seabedhabitats.eu/contribute-data/data-exchange-format/ features benthic eunis habitat biotope marine ocean EPSG:3857 -37.21620247566273 62.5077933736843 -84.96424363202824 71.00564000048564 text/html application/xml BS004012 This map shows benthic habitats surrounding the Bahamas. It is produced and owned by The Nature Conservancy (TNC) and derived from PlanetScope Dove Classic imagery. This map is part of the The Nature Conservancy Caribbean Benthic Habitat dataset. BS004012 WCS GeoTIFF Habitats - seabed habitats (including coastal wetlands) - collection of classified maps 265312 External EPSG:3035 CRS:84 -37.21620247566273 -27.663979861418788 15.294812377610786 24.94677918531907 text/html application/xml Habitat maps collated by EMODnet Seabed Habitats in the RAMSAR classification system. Ownership of the individual maps is retained by the original owners, for more information please see the individual metadata record tied to the map, which can be seen in the query response. This service is configured for viewing individual maps. For download access to relevant maps, please use the emodnet_open_maplibrary WFS service endpoint https://ows.emodnet-seabedhabitats.eu/emodnet_open_maplibrary/wfs? Maps have been standardised to the EMODnet Seabed Habitats Translated Habitat Data Exchange format, for more information see: https://emodnet.ec.europa.eu/sites/emodnet.ec.europa.eu/files/public/emodnetseabedhabitats_guidance_dataexchangeformats_v4.pdf feature habitat benthic ramsar marine ocean EPSG:3857 14.396664412105228 65.81946129900008 -61.77965238747082 28.623635173899242 text/html application/xml Non_hierarchical_class Classification of the seabed in the Atlantic Ocean into broad-scale benthic habitats employing a non-hierarchical top-down clustering approach aimed at informing Marine Spatial Planning. This work was performed at the University of Plymouth in 2021 with data provided by a wide group of partners representing the nations surrounding the Atlantic Ocean. It classifies continuous environmental data into discrete classes that can be compared to observed biogeographical patterns at various scales. It has 3 levels of classification. The numbers in the raster layer correspond to individual classes. Description of these classes is given in McQuaid, K.A. et al. (2023). Non_hierarchical_class WCS GeoTIFF habitat marine External 514 Habitats - seabed habitats (including coastal wetlands) - collection of models EPSG:4326 CRS:84 -66.00000000000003 29.6666666666666 -62.24999999999994 83.33333333333334 text/html application/xml text/html density_of_syringammina_fragillissima_at_fine_scale Fine scale density map of the xenophyophores Syringammina fragillissima (ind. m-2) at a 1200m deep station off Rockall Bank (North East Atlantic) by The University of Plymouth for the Deeplinks project. The drivers of the distribution of this species are poorly known despise its Vulnerable Marine Ecosystems (VME) status and more knowledge on its biology is needed to efficiently manage its conservation. A Convolutional Neural Network was used to measure its density in 58,147 images collected by the AUV autosub6000 during the JC136 cruise in May 2016. Several environmental data layers (topographic and oceanographic) at 2.5 x 2.5 m resolution were collected by the AUV during the same cruise and used in the modelling. Random Forest regression was used to predict the density of the target species in the extent of the environmental layer representing around 3 km2. Citation: Piechaud, N.; Howell, K.; University of Plymouth (UoP), United Kingdom; (2023): Modeled density map of Syringammina fragillissima at a fine scale from the Rockall Bank in North East Atlantic between 1981-2010. https://marineinfo.org/doc/dataset/8141 density_of_syringammina_fragillissima_at_fine_scale WCS GeoTIFF marine habitat External Habitats - seabed habitats (including coastal wetlands) - collection of models 1 EPSG:32628 CRS:84 -13.577366382816892 -13.519057461192375 58.37582363551167 58.39950394940393 text/html application/xml text/html distribution_of_acanella_arbuscula_assemblage Species distribution models (GAM, Maxent and Random Forest ensemble) predicting the distribution of Acanella arbuscula assemblage in the Celtic Sea. This community is considered ecologically coherent according to the cluster analysis conducted by Parry et al. (2015) on image sample. Modelling its distribution complements existing work on their definition and offers a representation of the extent of the areas of the North East Atlantic where they can occur based on the best available knowledge. This work was performed at the University of Plymouth in 2021. Citation: Graves K.; Howell, K.; University of Plymouth (UoP), United Kingdom; (2023): Modelled distribution map of Acanella arbuscula assemblage in the North East Atlantic in 2021. https://marineinfo.org/doc/dataset/8227 distribution_of_acanella_arbuscula_assemblage WCS GeoTIFF habitat marine External Habitats - seabed habitats (including coastal wetlands) - collection of models 1 EPSG:4326 CRS:84 -34.670493943195595 14.199276906079788 45.71717449496695 65.1152400730628 text/html application/xml text/html distribution_of_discrete_lophelia_pertusa_colonies Species distribution models (GAM, Maxent and Random Forest ensemble) predicting the distribution of discrete Lophelia pertusa - Desmophylum pertusum colonies assemblage in the Celtic Sea. This community is considered ecologically coherent according to the cluster analysis conducted by Parry et al. (2015) on image samples. Modelling its distribution complements existing work on their definition and offers a representation of the extent of the areas of the North East Atlantic where they can occur based on the best available knowledge. This work was performed at the University of Plymouth in 2021. Citation: Graves K.; Howell, K.; University of Plymouth (UoP), United Kingdom; (2023): Modelled distribution map of discrete Lophelia pertusa colonies in the North East Atlantic in 2021. https://marineinfo.org/doc/dataset/8243 distribution_of_discrete_lophelia_pertusa_colonies WCS GeoTIFF habitat marine External Habitats - seabed habitats (including coastal wetlands) - collection of models 1 EPSG:4326 CRS:84 -34.670493943195595 14.199276906079788 45.71717449496695 65.1152400730628 text/html application/xml text/html distribution_of_kophobelemnon_stelliferum_assemblage Modelled density of the seapen Kophobelemnon stelliferum in the North East Atlantic. The Random Forest density model trained on data collected by an ROV was constrained by an ensemble of Maxent and Random Forest presence-absence model trained on a larger dataset also collected by an ROV. This species provides structural complexity in an environment where it is lacking and, thus, promotes higher biodiversity where they settle. They are vulnerable to mechanical disturbance of the sediment by fishing gear and a better understanding of their distribution will lead to better management of their population. This work was performed at the University of Plymouth in 2021. Citation: Rix J.; Graves K.; Piechaud N.; Howell, K.; University of Plymouth (UoP), United Kingdom; (2023): Modeled distribution map of Kophobelemnon stelliferum assemblage in the North East Atlantic in 2021. https://marineinfo.org/doc/dataset/8251 distribution_of_kophobelemnon_stelliferum_assemblage WCS GeoTIFF marine habitat External Habitats - seabed habitats (including coastal wetlands) - collection of models 1 EPSG:4326 CRS:84 -34.670493943195595 14.201905726458989 45.71717449496695 65.11786889344198 text/html application/xml text/html distribution_of_lophelia_reef Species distribution model (Maxent) predicting the distribution of a Vulnerable Marine Ecosystems (VME): the reef-forming Scleractinian coral Desmophyllum pertusum. This species is a VME indicator taxa and forms habitat that enhance deep-sea diversity (Ross and Howell, 2013). Maps of the likely distribution of the habitat formed by this species will enable efficient Marine Spatial planning to facilitate their conservation. This work was performed at the University of Plymouth in 2021. This GIS layer is linked to the layer for Pheronema aggregations. See full metadata for further details. Citation: Bridges, A.; Howell, K.; University of Plymouth (UoP), United Kingdom; (2023): Modeled distribution map of Lophelia Reef and Pheronema aggregations in the North East Atlantic in 2022. https://marineinfo.org/doc/dataset/8213 distribution_of_lophelia_reef WCS GeoTIFF marine habitat External Habitats - seabed habitats (including coastal wetlands) - collection of models 1 EPSG:4326 CRS:84 -22.520086150546295 6.909557994566022 48.272387903546594 62.03426258864373 text/html application/xml text/html distribution_of_mixed_cold_water_coral_communities Species distribution models (Random Forest) predicting the distribution of mixed cold-water coral community (Coral Garden) assemblage in the Celtic Sea. This community is considered ecologically coherent according to the cluster analysis conducted by Parry et al. (2015) on image sample. Modelling its distribution complements existing work on their definition and offers a representation of the extent of the areas of the North East Atlantic where they can occur based on the best available knowledge. This work was performed at the University of Plymouth in 2021. Citation: Graves K.; Howell, K.; University of Plymouth (UoP), United Kingdom; (2023): Modeled distribution map of mixed cold-water coral community in the Northeast Atlantic in 2021. https://marineinfo.org/doc/dataset/8254 distribution_of_mixed_cold_water_coral_communities WCS GeoTIFF marine habitat External Habitats - seabed habitats (including coastal wetlands) - collection of models 1 EPSG:4326 CRS:84 -34.670493943195595 14.199276906079788 45.71717449496695 65.1152400730628 text/html application/xml text/html distribution_of_pheronema_aggregations Species distribution model (Maxent) predicting the distribution of a Vulnerable Marine Ecosystems (VME): the aggregations forming Hexactinellid sponge Pheronema carpenteri. This species is a VME indicator taxa and forms habitat that enhance deep-sea diversity (Ross and Howell, 2013). Maps of the likely distribution of the habitat formed by this species will enable efficient Marine Spatial planning to facilitate their conservation. This work was performed at the University of Plymouth in 2021. This GIS layer is linked to the layer for Lophelia Reef distribution. See full metadata for further details. Citation: Bridges, A.; Howell, K.; University of Plymouth (UoP), United Kingdom; (2023): Modeled distribution map of Lophelia Reef and Pheronema aggregations in the North East Atlantic in 2022. https://marineinfo.org/doc/dataset/8213 distribution_of_pheronema_aggregations WCS GeoTIFF marine habitat External Habitats - seabed habitats (including coastal wetlands) - collection of models 1 EPSG:4326 CRS:84 -22.520086150546295 6.909557994566022 48.272387903546594 62.03426258864373 text/html application/xml text/html distribution_of_solenosmilia_variabilis_reef Species distribution models (GAM, Maxent and Random Forest ensemble) predicting the distribution of Solenosmilia variabilis reef assemblage in the Celtic Sea. This community is considered ecologically coherent according to the cluster analysis conducted by Parry et al. (2015) on image sample. Modelling its distribution complements existing work on their definition and offers a representation of the extent of the areas of the North East Atlantic where they can occur based on the best available knowledge. This work was performed at the University of Plymouth in 2021. Citation: Graves K.; Howell, K.; University of Plymouth (UoP), United Kingdom; (2023): Modeled distribution map of Solenosmilia variabilis reef in the Northeast Atlantic in 2021. https://marineinfo.org/doc/dataset/8255 distribution_of_solenosmilia_variabilis_reef WCS GeoTIFF marine habitat External Habitats - seabed habitats (including coastal wetlands) - collection of models 1 EPSG:4326 CRS:84 -34.670493943195595 14.199276906079788 45.71717449496695 65.1152400730628 text/html application/xml text/html distribution_of_solitary_scleractinian_fields Species distribution models (GAM, Maxent and Random Forest ensemble) predicting the distribution of Solitary Scleractinian fields assemblage in the Celtic Sea. This community is considered ecologically coherent according to the cluster analysis conducted by Parry et al. (2015) on image sample. Modelling its distribution complements existing work on their definition and offers a representation of the extent of the areas of the north-east Atlantic where they can occur based on the best available knowledge. This work was performed at the University of Plymouth in 2021. Citation: Graves K.; Howell, K.; University of Plymouth (UoP), United Kingdom; (2023): Modeled distribution map of Solitary Scleractinian fields in the Northeast Atlantic in 2021. https://marineinfo.org/doc/dataset/8225 distribution_of_solitary_scleractinian_fields WCS GeoTIFF marine habitat External Habitats - seabed habitats (including coastal wetlands) - collection of models 1 EPSG:4326 CRS:84 -34.670493943195595 14.199276906079788 45.71717449496695 65.1152400730628 text/html application/xml text/html distribution_of_syringammina_fragilissima_fields Species distribution models (GAM, Maxent and Random Forest ensemble) predicting the distribution of Syringammina fragilissima fields assemblage in the North East Atlantic. This community is considered ecologically coherent according to the cluster analysis conducted by Parry et al. (2015) on image sample. Modelling its distribution complements existing work on their definition and offers a representation of the extent of the areas of the North East Atlantic where they can occur based on the best available knowledge. This work was performed at the University of Plymouth in 2021. Citation: Graves, K.; Howell, K.; University of Plymouth (UoP), United Kingdom; (2023): Modelled distribution map of Syringammina fragilissima fields in the North East Atlantic in 2021. https://marineinfo.org/doc/dataset/8219 distribution_of_syringammina_fragilissima_fields WCS GeoTIFF marine habitat External Habitats - seabed habitats (including coastal wetlands) - collection of models 1 EPSG:4326 CRS:84 -34.670493943195595 14.199276906079788 45.71717449496695 65.1152400730628 text/html application/xml text/html