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EUNIS habitat type F3.1a, predicted habitat suitability - version 1, June 2016

The modelled suitability for the EUNIS habitat type 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. Note however that it is not representing the actual distribution of the habitat type.

Also note that predictions are less reliable due to data deficiency in the eastern part of Europe, and to a lesser extent to the Scandinavian countries.

Geographic restriction for plot observations: n/a

Remarks: -Prediction in eastern part of Europe (Caucasus, Türkiye) uncertain due to lack of data for that area.

Simple

Date (Publication)
2016-07-01
Date (Creation)
2016-07-06
Edition

01

Citation identifier
eea_r_3035_1_km_eunis-hab-f3-1a_p_1940-2011_v01_r00
Status
Obsolete
Point of contact
Organisation name Individual name Electronic mail address Website Role

European Environment Agency

sdi@eea.europa.eu

http://www.eea.europa.eu Point of contact

European Environment Agency

sdi@eea.europa.eu

Custodian

Point of contact

No information provided.
Maintenance and update frequency
Unknown
EEA topics
  • Biodiversity

GEMET - INSPIRE themes, version 1.0

  • Habitats and biotopes

GEMET

  • natural area

  • tundra

  • terrestrial ecosystem

  • heathland

Keywords
    Keywords
      Place
      • Europe

      Use limitation

      EEA standard re-use policy: unless otherwise indicated, re-use of content on the EEA website for commercial or non-commercial purposes is permitted free of charge, provided that the source is acknowledged ( http://www.eea.europa.eu/legal/copyright). Copyright holder: European Environment Agency (EEA).

      Access constraints
      Other restrictions
      Other constraints
      no limitations to public access
      Spatial representation type
      Grid
      Distance
      1  km
      Language of dataset
      English
      Character set
      UTF8
      Topic category
      • Biota
      N
      S
      E
      W
      thumbnail




      Begin date
      1940-01-01
      End date
      2011-12-31
      Coordinate reference system identifier
      EPSG:3035
      Distribution format
      • GeoTIFF ( )

      OnLine resource
      Protocol Linkage Name

      EEA:FILEPATH

      https://sdi.eea.europa.eu/webdav/datastore/public/eea_r_3035_1_km_eunis-hab-f3-1a_p_1940-2011_v01_r00/F3-1a_ed1.tif

      WWW:URL

      https://sdi.eea.europa.eu/data/cb23d43e-5495-4ab9-861e-1c2d13db33e2

      Direct download

      Hierarchy level
      Dataset

      Conformance result

      Title

      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

      Date (Publication)
      2010-12-08
      Explanation

      See the referenced specification

      Statement

      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).

      Statistical output of the model:

      #Training samples: 261

      Regularized training gain: 1.451

      Unregularized training gain: 1.6818

      Iterations: 500

      Training AUC: 0.9294

      #Test samples: 28

      Test gain: 1.6294

      Test AUC: 0.9168

      AUC Standard Deviation: 0.0248

      #Background points: 5261

      bio_12_etrs2_ras contribution: 16.9278

      bio_15_etrs2_ras contribution: 1.7383

      bio_18_etrs2_ras contribution: 3.0896

      bio_4_etrs2_ras contribution: 47.2878

      bio_8_etrs2_ras contribution: 1.1727

      bld_m_sd1_1km_eu_ll contribution: 2.8954

      cecsum_m_sd1_1km_eu_ll contribution: 0.1047

      clyppt_m_sd1_1km_eu_ll contribution: 0.2259

      crfvol_m_sd1_1km_eu_ll contribution: 4.1454

      dist2water1km contribution: 0.0476

      orcdrc_m_sd1_1km_eu_ll contribution: 0.8552

      pet_he_yr contribution: 11.6802

      phihox_m_sd1_1km_eu_ll contribution: 0.3306

      sltppt_m_sd1_1km_eu_ll contribution: 0.4748

      sndppt_m_sd1_1km_eu_ll contribution: 2.8708

      solar_1km contribution: 6.1532

      bio_12_etrs2_ras permutation importance: 11.098

      bio_15_etrs2_ras permutation importance: 1.4671

      bio_18_etrs2_ras permutation importance: 3.3948

      bio_4_etrs2_ras permutation importance: 39.0025

      bio_8_etrs2_ras permutation importance: 0.9225

      bld_m_sd1_1km_eu_ll permutation importance: 2.9407

      cecsum_m_sd1_1km_eu_ll permutation importance: 0.3514

      clyppt_m_sd1_1km_eu_ll permutation importance: 1.0385

      crfvol_m_sd1_1km_eu_ll permutation importance: 13.2838

      dist2water1km permutation importance: 0.3511

      orcdrc_m_sd1_1km_eu_ll permutation importance: 2.4478

      pet_he_yr permutation importance: 15.1798

      phihox_m_sd1_1km_eu_ll permutation importance: 2.9375

      sltppt_m_sd1_1km_eu_ll permutation importance: 2.438

      sndppt_m_sd1_1km_eu_ll permutation importance: 0.9287

      solar_1km permutation importance: 2.2179

      Entropy: 7.1344

      Prevalence (average of logistic output over background sites): 0.1155

      Fixed cumulative value 1 cumulative threshold: 1

      Fixed cumulative value 1 logistic threshold: 0.0186

      Fixed cumulative value 1 area: 0.5052

      Fixed cumulative value 1 training omission: 0.0115

      Fixed cumulative value 1 test omission: 0.0357

      Fixed cumulative value 1 binomial probability: 5.91E-07

      Fixed cumulative value 5 cumulative threshold: 5

      Fixed cumulative value 5 logistic threshold: 0.0954

      Fixed cumulative value 5 area: 0.3203

      Fixed cumulative value 5 training omission: 0.0307

      Fixed cumulative value 5 test omission: 0.0357

      Fixed cumulative value 5 binomial probability: 1.40E-13

      Fixed cumulative value 10 cumulative threshold: 10

      Fixed cumulative value 10 logistic threshold: 0.1694

      Fixed cumulative value 10 area: 0.2387

      Fixed cumulative value 10 training omission: 0.0651

      Fixed cumulative value 10 test omission: 0.1071

      Fixed cumulative value 10 binomial probability: 2.35E-16

      Minimum training presence cumulative threshold: 0.5143

      Minimum training presence logistic threshold: 0.0103

      Minimum training presence area: 0.587

      Minimum training presence training omission: 0

      Minimum training presence test omission: 0.0357

      Minimum training presence binomial probability: 2.51E-05

      10 percentile training presence cumulative threshold: 18.2675

      10 percentile training presence logistic threshold: 0.2609

      10 percentile training presence area: 0.1671

      10 percentile training presence training omission: 0.0996

      10 percentile training presence test omission: 0.1786

      10 percentile training presence binomial probability: 8.34E-21

      Equal training sensitivity and specificity cumulative threshold: 22.3303

      Equal training sensitivity and specificity logistic threshold: 0.3025

      Equal training sensitivity and specificity area: 0.1426

      Equal training sensitivity and specificity training omission: 0.1418

      Equal training sensitivity and specificity test omission: 0.2143

      Equal training sensitivity and specificity binomial probability: 1.08E-22

      Maximum training sensitivity plus specificity cumulative threshold: 18.2675

      Maximum training sensitivity plus specificity logistic threshold: 0.2609

      Maximum training sensitivity plus specificity area: 0.1671

      Maximum training sensitivity plus specificity training omission: 0.0996

      Maximum training sensitivity plus specificity test omission: 0.1786

      Maximum training sensitivity plus specificity binomial probability: 8.34E-21

      Equal test sensitivity and specificity cumulative threshold: 16.6662

      Equal test sensitivity and specificity logistic threshold: 0.2475

      Equal test sensitivity and specificity area: 0.1785

      Equal test sensitivity and specificity training omission: 0.0958

      Equal test sensitivity and specificity test omission: 0.1786

      Equal test sensitivity and specificity binomial probability: 3.20E-19

      Maximum test sensitivity plus specificity cumulative threshold: 36.3306

      Maximum test sensitivity plus specificity logistic threshold: 0.4278

      Maximum test sensitivity plus specificity area: 0.0842

      Maximum test sensitivity plus specificity training omission: 0.2759

      Maximum test sensitivity plus specificity test omission: 0.2143

      Maximum test sensitivity plus specificity binomial probability: 4.69E-41

      Balance training omission, predicted area and threshold value cumulative threshold: 3.5127

      Balance training omission, predicted area and threshold value logistic threshold: 0.072

      Balance training omission, predicted area and threshold value area: 0.3592

      Balance training omission, predicted area and threshold value training omission: 0.0192

      Balance training omission, predicted area and threshold value test omission: 0.0357

      Balance training omission, predicted area and threshold value binomial probability: 1.25E-11

      Equate entropy of thresholded and original distributions cumulative threshold: 10.0342

      Equate entropy of thresholded and original distributions logistic threshold: 0.1695

      Equate entropy of thresholded and original distributions area: 0.2384

      Equate entropy of thresholded and original distributions training omission: 0.0651

      Equate entropy of thresholded and original distributions test omission: 0.1071

      Equate entropy of thresholded and original distributions binomial probability: 2.18E-16

      Source
      • EUNIS habitat type F3-1a distribution based on vegetation plot data - version 1, June 2016

      Metadata

      File identifier
      cb23d43e-5495-4ab9-861e-1c2d13db33e2 XML
      Metadata language
      English
      Character set
      UTF8
      Hierarchy level
      Dataset
      Date stamp
      2022-01-31T13:38:56.169Z
      Metadata standard name

      ISO 19115/19139

      Metadata standard version

      1.0

      Metadata author
      Organisation name Individual name Electronic mail address Website Role

      European Environment Agency

      sdi@eea.europa.eu

      Point of contact
       
       
      Access to the catalogue
      Read here the full details and access to the data.

      Overviews

      overview

      Spatial extent

      thumbnail

      Keywords

      EEA topics

      Biodiversity
      GEMET

      heathland natural area terrestrial ecosystem tundra
      GEMET - INSPIRE themes, version 1.0

      Habitats and biotopes


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