Date (Publication)

2015-02-08

Date (Creation)

2015-01-08

Edition

01

Citation identifier

eea_r_3035_1_km_eunis-hab-g2-6_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

Maintenance and update frequency

Unknown

GEMET - INSPIRE themes, version 1.0

• Habitats and biotopes

GEMET

• forest

• natural area

• terrestrial ecosystem

• forest biodiversity

Keywords

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

• AAIGrid ( )

OnLine resource

Protocol	Linkage	Name
EEA:FILEPATH	https://sdi.eea.europa.eu/webdav/datastore/public/eea_r_3035_1_km_eunis-hab-g2-6_p_1940-2011_v01_r00/G2-6_ed1.asc
WWW:URL	https://sdi.eea.europa.eu/data/49e533f7-e7a0-4861-80c4-ec6e98d57bfa	Direct download

Hierarchy level

Dataset

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: 140

Regularized training gain: 2.2742

Unregularized training gain: 2.644

Iterations: 500

Training AUC: 0.975

#Test samples: 15

Test gain: 2.1767

Test AUC: 0.9567

AUC Standard Deviation: 0.0112

#Background points: 10139

bio_12_etrs2_ras contribution: 1.481

bio_15_etrs2_ras contribution: 1.5128

bio_18_etrs2_ras contribution: 3.1735

bio_4_etrs2_ras contribution: 57.3421

bio_8_etrs2_ras contribution: 14.5124

dist2water1km contribution: 0.1179

pet_he_yr contribution: 19.9521

soil_ph contribution: 0.3936

solar_1km contribution: 1.5147

bio_12_etrs2_ras permutation importance: 4.2909

bio_15_etrs2_ras permutation importance: 10.3437

bio_18_etrs2_ras permutation importance: 15.3893

bio_4_etrs2_ras permutation importance: 44.8665

bio_8_etrs2_ras permutation importance: 9.9109

dist2water1km permutation importance: 0.0855

pet_he_yr permutation importance: 14.1141

soil_ph permutation importance: 0.2741

solar_1km permutation importance: 0.725

Entropy: 6.9853

Prevalence (average of logistic output over background sites): 0.0517

Fixed cumulative value 1 cumulative threshold: 1

Fixed cumulative value 1 logistic threshold: 0.0172

Fixed cumulative value 1 area: 0.2204

Fixed cumulative value 1 training omission: 0

Fixed cumulative value 1 test omission: 0

Fixed cumulative value 1 binomial probability: 1.41E-10

Fixed cumulative value 5 cumulative threshold: 5

Fixed cumulative value 5 logistic threshold: 0.0913

Fixed cumulative value 5 area: 0.1234

Fixed cumulative value 5 training omission: 0.0143

Fixed cumulative value 5 test omission: 0.0667

Fixed cumulative value 5 binomial probability: 2.52E-12

Fixed cumulative value 10 cumulative threshold: 10

Fixed cumulative value 10 logistic threshold: 0.224

Fixed cumulative value 10 area: 0.0946

Fixed cumulative value 10 training omission: 0.0286

Fixed cumulative value 10 test omission: 0.1333

Fixed cumulative value 10 binomial probability: 4.24E-12

Minimum training presence cumulative threshold: 1.3596

Minimum training presence logistic threshold: 0.0235

Minimum training presence area: 0.2022

Minimum training presence training omission: 0

Minimum training presence test omission: 0

Minimum training presence binomial probability: 3.86E-11

10 percentile training presence cumulative threshold: 22.2207

10 percentile training presence logistic threshold: 0.3577

10 percentile training presence area: 0.0632

10 percentile training presence training omission: 0.1

10 percentile training presence test omission: 0.2667

10 percentile training presence binomial probability: 6.93E-11

Equal training sensitivity and specificity cumulative threshold: 19.1369

Equal training sensitivity and specificity logistic threshold: 0.3338

Equal training sensitivity and specificity area: 0.0695

Equal training sensitivity and specificity training omission: 0.0714

Equal training sensitivity and specificity test omission: 0.1333

Equal training sensitivity and specificity binomial probability: 8.16E-14

Maximum training sensitivity plus specificity cumulative threshold: 11.3366

Maximum training sensitivity plus specificity logistic threshold: 0.2459

Maximum training sensitivity plus specificity area: 0.09

Maximum training sensitivity plus specificity training omission: 0.0286

Maximum training sensitivity plus specificity test omission: 0.1333

Maximum training sensitivity plus specificity binomial probability: 2.26E-12

Equal test sensitivity and specificity cumulative threshold: 6.4218

Equal test sensitivity and specificity logistic threshold: 0.1369

Equal test sensitivity and specificity area: 0.1115

Equal test sensitivity and specificity training omission: 0.0214

Equal test sensitivity and specificity test omission: 0.1333

Equal test sensitivity and specificity binomial probability: 3.49E-11

Maximum test sensitivity plus specificity cumulative threshold: 2.7176

Maximum test sensitivity plus specificity logistic threshold: 0.0446

Maximum test sensitivity plus specificity area: 0.1592

Maximum test sensitivity plus specificity training omission: 0.0071

Maximum test sensitivity plus specificity test omission: 0

Maximum test sensitivity plus specificity binomial probability: 1.07E-12

Balance training omission, predicted area and threshold value cumulative threshold: 1.3596

Balance training omission, predicted area and threshold value logistic threshold: 0.0235

Balance training omission, predicted area and threshold value area: 0.2022

Balance training omission, predicted area and threshold value training omission: 0

Balance training omission, predicted area and threshold value test omission: 0

Balance training omission, predicted area and threshold value binomial probability: 3.86E-11

Equate entropy of thresholded and original distributions cumulative threshold: 7.2558

Equate entropy of thresholded and original distributions logistic threshold: 0.1631

Equate entropy of thresholded and original distributions area: 0.1065

Equate entropy of thresholded and original distributions training omission: 0.0214

Equate entropy of thresholded and original distributions test omission: 0.1333

Equate entropy of thresholded and original distributions binomial probability: 1.94E-11

Source

• EUNIS habitat type G2.6 distribution based on vegetation plot data - version 1, Jan. 2015

Metadata

File identifier

49e533f7-e7a0-4861-80c4-ec6e98d57bfa XML

Metadata language

English

Character set

UTF8

Hierarchy level

Dataset

Date stamp

2022-02-01T08:40:25.299Z

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