5b3e4da9-4c14-498c-b20e-bc514470eab5 European Environment Agency Kongens Nytorv 6 Copenhagen K 1050 Denmark sdi@eea.europa.eu pointOfContact 2025-10-09T10:52:25.517987Z ISO 19115/19139 1.0 EPSG:3035 EUNIS Littoral biogenic habitat types (salt marshes), predicted distribution of habitat suitability - version 1, Nov. 2021 2021-11-15 publication 2019-03-01 creation 01.00 eea_r_3035_1_km_eunis-hab-m_p_1940-2017_v01_r00 DAT-137-en 10.2909/5b3e4da9-4c14-498c-b20e-bc514470eab5 This metadata corresponds to the EUNIS Littoral biogenic habitat (salt marshes) types, predicted distribution of habitat suitability dataset. Littoral habitats are those formed by animals such as worms and mussels or plants (salt marshes). The verified littoral biogenic habitat samples used are derived from the Braun-Blanquet database (http://www.sci.muni.cz/botany/vegsci/braun_blanquet.php?lang=en) which is a centralised database of vegetation plots and comprises copies of national and regional databases using a unified taxonomic reference database. The geographic extent of the distribution data are all European countries except Armenia and Azerbaijan. The modelled suitability for EUNIS saltmarsh habitat types is an indication of where conditions are favourable for the habitat type based on sample plot data (Braun-Blanquet database) and the Maxent software package. The modelled suitability map may be used as a proxy for the geographical distribution of the habitat type. However, note that it is not representing the actual distribution of the habitat type. As predictors for the suitabilty modelling not only Climate and Soil parameters have been taken into account, but also so-called RS-EVB's, Remote Sensing-enabled Essential Biodiversity Variables like Landuse, Vegetation height, Phenology, LAI(Leave Area Index) and Population density. Because the EBV's are restricted by the extent of the Remote Sensing data (EEA38 countries and the United Kingdom) the modelling result does also not go beyond this boundary. The dataset is provided both in Geodatabase and Geopackage formats. The Training map files show the modelled suitable distribution, omitting the 10% of occurrence records in the least suitable environment under the assumption that they are not representative of the overall suitable habitat distribution. The 10 percentile training presence is an arbitrary threshold which omits all regions with habitat suitability lower than the suitability values for the lowest 10% of occurrence records. European Environment Agency Kongens Nytorv 6 Copenhagen K 1050 Denmark sdi@eea.europa.eu http://www.eea.europa.eu WWW:LINK-1.0-http--link European Environment Agency public website pointOfContact European Environment Agency Environmental data centre for biodiversity Kongens Nytorv 6 Copenhagen K 1050 Denmark sdi@eea.europa.eu custodian https://sdi.eea.europa.eu/public/catalogue-graphic-overview/5b3e4da9-4c14-498c-b20e-bc514470eab5.png Habitats and biotopes GEMET - INSPIRE themes, version 1.0 2008-06-01 publication geonetwork.thesaurus.external.theme.httpinspireeceuropaeutheme-theme salt marsh habitat terrestrial ecosystem modelling natural area GEMET 2021-11-30 publication geonetwork.thesaurus.external.theme.gemet EEA38 (from 2020) United Kingdom Continents, countries, sea regions of the world. 2015-07-17T12:00:00 publication geonetwork.thesaurus.external.place.regions European Spatial scope 2019-05-22 publication geonetwork.thesaurus.external.theme.httpinspireeceuropaeumetadatacodelistSpatialScope-SpatialScope Biodiversity EEA topics 2020-09-24 publication geonetwork.thesaurus.external.theme.eea-topics 2021 1.1.4 EEA Management Plan 2021-08-25 publication geonetwork.thesaurus.local.theme.eea-mp /Métropole /Métropole/Manche mer du Nord /Métropole/Mers Celtiques /Métropole/Golfe de Gascogne /Métropole/Méditerranée occidentale Sous-regions marines geonetwork.thesaurus.local.place.dcsmm.area /Etat du Milieu/Habitats Thématiques - SIMM geonetwork.thesaurus.local.theme.simm.thematiques /Données dérivées/Données de type modèle Type de jeux de donnée - ODATIS geonetwork.thesaurus.local.theme.type_jeux_donnee no limitations to public access License CC-BY 4.0 (https://creativecommons.org/licenses/by/4.0/). Copyright holder: European Environment Agency (EEA). grid 1 biota -31.285 44.807 27.642 71.165 1940-01-01 2017-12-31 Geopackage 1 GDB https://sdi.eea.europa.eu/webdav/datastore/public/eea_r_3035_1_km_eunis-hab-m_p_1940-2017_v01_r00/ EEA:FOLDERPATH https://sdi.eea.europa.eu/data/5b3e4da9-4c14-498c-b20e-bc514470eab5 WWW:URL Direct download https://bio.discomap.eea.europa.eu/arcgis/rest/services/EUNIS/Mosaic_Saltmarshes/ImageServer ESRI:REST Suitability Layer https://bio.discomap.eea.europa.eu/arcgis/services/EUNIS/Mosaic_Saltmarshes/ImageServer/WMSServer?request=GetCapabilities&service=WMS OGC:WMS https://sdi.eea.europa.eu/data/f5946d76-bf09-4261-8f2e-6218210ae3af?path=%2FEUNIS%20habitat%20classification%20revision%20documentation WWW:URL EUNIS documentation for habitat classification https://doi.org/10.2909/5b3e4da9-4c14-498c-b20e-bc514470eab5 DOI Commission Regulation (EU) No 1089/2010 of 23 November 2010 implementing Directive 2007/2/EC of the European Parliament and of the Council as regards interoperability of spatial data sets and services 2010-12-08 publication See the referenced specification The database compiled for the Braun-Blanquet project is a compilation of various national and regional vegetation databases. The maintenance of these databases is in principle in the hands of the custodians. However, before uploading the databases into Braun-Blanquet database a quality check is performed by Alterra and Masaryk University. If possible, detected errors are corrected and reported back to the data provider. For the modelling of the habitat suitability map the Maxent software is used (http://www.cs.princeton.edu/~schapire/maxent/). The grid values in the map represent the probability (ranging from 0-1) that the cell is suitable for the habitat. The grid file represents the habitat suitability of the EUNIS type. For the modelling the widely used software Maxent for maximum entropy modelling of species’ geographic distributions was used. Maxent is a general-purpose machine-learning method with a simple and precise mathematical formulation, and has a number of aspects that make it well-suited for species distribution modelling when only presence (occurrence) data but not absence data are available (Philips et al. 2006). Because EUNIS habitats have a particular species composition, they are assumed to respond to specific ecological requirements, allowing us to generate correlative estimates of geographic distributions. Modelling habitats that have been floristically defined is a well-known procedure for ecological modelling at local scales, and a promising technique to be applied also at the continental level. The Maxent method considers presence data (known observations of a given entity) and the so-called background data. Background data comprise a set of points used to describe the environmental variation of the study area according to the available environmental layers. It is assumed that these layers represent well the most important ecological gradients on a European scale. As layers the following environmental parameters have been used: Potential Evapotranspiration, Topsoil pH, Solar radiation, Temperature Seasonality (standard deviation *100), Mean Temperature of Wettest Quarter, Annual Precipitation, Precipitation Seasonality (Coefficient of Variation), Precipitation of Warmest Quarter & Distance to water (rivers, lakes, sea) and the RS-EBV's (Remote Sensing-enabled Essential Biodiversity Variables) Inundation; occurrence, Phenology; End of Season (day number), Phenology; Length of season (days), Phenology; Low of season (day number), Phenology; NDVI mean, Phenology; NDVI seasonality, Phenology; Peak of season (day number), Phenology; Start of Season (day number), Vegetation height (m); Population density. For more information on the RS-EBV's see https://www.synbiosys.alterra.nl/nextgeoss/docs/Description_Abiotic_and_RSEBVs.pdf More information on the generation of the spatial files is provided in the documents available for download together with the dataset.