EUNIS habitat type F3.1c, 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 (Türkiye) uncertain due to lack of data for that area.

Date (Publication): 2016-07-01

Date (Creation): 2016-07-06

Edition: 01

Citation identifier: eea_r_3035_1_km_eunis-hab-f3-1c_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Status: Obsolete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

GEMET - INSPIRE themes, version 1.0

Habitats and biotopes

GEMET

tundra
natural area
terrestrial ecosystem
heathland

Keywords

Place

Europe

EEA topics

Biodiversity

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

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-f3-1c_p_1940-2011_v01_r00/F3-1c_ed1.tif
WWW:URL	https://sdi.eea.europa.eu/data/1b6b9175-8ac9-468f-888a-1b7e19bf12a1	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: 666

Regularized training gain: 1.2802

Unregularized training gain: 1.3799

Iterations: 500

Training AUC: 0.9059

#Test samples: 73

Test gain: 1.0738

Test AUC: 0.8732

AUC Standard Deviation: 0.0124

#Background points: 5666

bio_12_etrs2_ras contribution: 0.4269

bio_15_etrs2_ras contribution: 2.9731

bio_18_etrs2_ras contribution: 2.3412

bio_4_etrs2_ras contribution: 66.1064

bio_8_etrs2_ras contribution: 1.2798

bld_m_sd1_1km_eu_ll contribution: 4.9566

cecsum_m_sd1_1km_eu_ll contribution: 0.0476

clyppt_m_sd1_1km_eu_ll contribution: 1.1946

crfvol_m_sd1_1km_eu_ll contribution: 2.1861

dist2water1km contribution: 0.0257

orcdrc_m_sd1_1km_eu_ll contribution: 0.1318

pet_he_yr contribution: 6.821

phihox_m_sd1_1km_eu_ll contribution: 9.5905

sltppt_m_sd1_1km_eu_ll contribution: 0.0545

sndppt_m_sd1_1km_eu_ll contribution: 1.6297

solar_1km contribution: 0.2346

bio_12_etrs2_ras permutation importance: 2.3055

bio_15_etrs2_ras permutation importance: 3.2361

bio_18_etrs2_ras permutation importance: 2.2278

bio_4_etrs2_ras permutation importance: 55.1138

bio_8_etrs2_ras permutation importance: 0.9665

bld_m_sd1_1km_eu_ll permutation importance: 6.9569

cecsum_m_sd1_1km_eu_ll permutation importance: 0.4431

clyppt_m_sd1_1km_eu_ll permutation importance: 1.4189

crfvol_m_sd1_1km_eu_ll permutation importance: 7.1748

dist2water1km permutation importance: 0

orcdrc_m_sd1_1km_eu_ll permutation importance: 0.6062

pet_he_yr permutation importance: 8.9596

phihox_m_sd1_1km_eu_ll permutation importance: 7.6888

sltppt_m_sd1_1km_eu_ll permutation importance: 0.1921

sndppt_m_sd1_1km_eu_ll permutation importance: 2.6035

solar_1km permutation importance: 0.1064

Entropy: 7.3637

Prevalence (average of logistic output over background sites): 0.1358

Fixed cumulative value 1 cumulative threshold: 1

Fixed cumulative value 1 logistic threshold: 0.04

Fixed cumulative value 1 area: 0.3789

Fixed cumulative value 1 training omission: 0.0045

Fixed cumulative value 1 test omission: 0.0274

Fixed cumulative value 1 binomial probability: 6.88E-26

Fixed cumulative value 5 cumulative threshold: 5

Fixed cumulative value 5 logistic threshold: 0.1686

Fixed cumulative value 5 area: 0.2734

Fixed cumulative value 5 training omission: 0.0255

Fixed cumulative value 5 test omission: 0.0959

Fixed cumulative value 5 binomial probability: 5.89E-34

Fixed cumulative value 10 cumulative threshold: 10

Fixed cumulative value 10 logistic threshold: 0.2755

Fixed cumulative value 10 area: 0.2238

Fixed cumulative value 10 training omission: 0.0586

Fixed cumulative value 10 test omission: 0.1233

Fixed cumulative value 10 binomial probability: 3.71E-41

Minimum training presence cumulative threshold: 0.0459

Minimum training presence logistic threshold: 0.0014

Minimum training presence area: 0.6442

Minimum training presence training omission: 0

Minimum training presence test omission: 0

Minimum training presence binomial probability: 1.08E-10

10 percentile training presence cumulative threshold: 15.122

10 percentile training presence logistic threshold: 0.365

10 percentile training presence area: 0.1936

10 percentile training presence training omission: 0.0991

10 percentile training presence test omission: 0.1918

10 percentile training presence binomial probability: 1.32E-40

Equal training sensitivity and specificity cumulative threshold: 22.4874

Equal training sensitivity and specificity logistic threshold: 0.4269

Equal training sensitivity and specificity area: 0.1624

Equal training sensitivity and specificity training omission: 0.1622

Equal training sensitivity and specificity test omission: 0.3425

Equal training sensitivity and specificity binomial probability: 9.15E-31

Maximum training sensitivity plus specificity cumulative threshold: 9.7059

Maximum training sensitivity plus specificity logistic threshold: 0.2681

Maximum training sensitivity plus specificity area: 0.2261

Maximum training sensitivity plus specificity training omission: 0.0526

Maximum training sensitivity plus specificity test omission: 0.1233

Maximum training sensitivity plus specificity binomial probability: 1.33E-40

Equal test sensitivity and specificity cumulative threshold: 15.4509

Equal test sensitivity and specificity logistic threshold: 0.3683

Equal test sensitivity and specificity area: 0.192

Equal test sensitivity and specificity training omission: 0.1021

Equal test sensitivity and specificity test omission: 0.1918

Equal test sensitivity and specificity binomial probability: 4.77E-41

Maximum test sensitivity plus specificity cumulative threshold: 10.7774

Maximum test sensitivity plus specificity logistic threshold: 0.2889

Maximum test sensitivity plus specificity area: 0.2183

Maximum test sensitivity plus specificity training omission: 0.0646

Maximum test sensitivity plus specificity test omission: 0.1233

Maximum test sensitivity plus specificity binomial probability: 1.58E-42

Balance training omission, predicted area and threshold value cumulative threshold: 1.6988

Balance training omission, predicted area and threshold value logistic threshold: 0.0686

Balance training omission, predicted area and threshold value area: 0.3449

Balance training omission, predicted area and threshold value training omission: 0.006

Balance training omission, predicted area and threshold value test omission: 0.0411

Balance training omission, predicted area and threshold value binomial probability: 1.26E-28

Equate entropy of thresholded and original distributions cumulative threshold: 4.6521

Equate entropy of thresholded and original distributions logistic threshold: 0.1577

Equate entropy of thresholded and original distributions area: 0.2783

Equate entropy of thresholded and original distributions training omission: 0.021

Equate entropy of thresholded and original distributions test omission: 0.0959

Equate entropy of thresholded and original distributions binomial probability: 4.13E-33