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        <gco:CharacterString>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 &amp; Distance to water (rivers, lakes, sea).
Statistical output of the model:
#Training samples: 65
Regularized training gain: 2.5296
Unregularized training gain: 2.8127
Iterations: 500
Training AUC: 0.9756
#Test samples: 7
Test gain: 1.6606
Test AUC: 0.9415
AUC Standard Deviation: 0.0193
#Background points: 5047
bio_12_etrs2_ras contribution: 0.8398
bio_15_etrs2_ras contribution: 13.5536
bio_18_etrs2_ras contribution: 49.1645
bio_4_etrs2_ras contribution: 2.1301
bio_8_etrs2_ras contribution: 2.3443
bld_m_sd1_1km_eu_ll contribution: 5.8124
cecsum_m_sd1_1km_eu_ll contribution: 0.14
clyppt_m_sd1_1km_eu_ll contribution: 6.2395
crfvol_m_sd1_1km_eu_ll contribution: 1.9674
dist2water1km contribution: 0.0443
orcdrc_m_sd1_1km_eu_ll contribution: 0.076
pet_he_yr contribution: 16.0585
sltppt_m_sd1_1km_eu_ll contribution: 0.3234
sndppt_m_sd1_1km_eu_ll contribution: 0.4292
solar_1km contribution: 0.8768
bio_12_etrs2_ras permutation importance: 3.5449
bio_15_etrs2_ras permutation importance: 5.8264
bio_18_etrs2_ras permutation importance: 75.1605
bio_4_etrs2_ras permutation importance: 3.813
bio_8_etrs2_ras permutation importance: 0.5864
bld_m_sd1_1km_eu_ll permutation importance: 4.6429
cecsum_m_sd1_1km_eu_ll permutation importance: 0.1711
clyppt_m_sd1_1km_eu_ll permutation importance: 2.6928
crfvol_m_sd1_1km_eu_ll permutation importance: 1.5463
dist2water1km permutation importance: 0.0366
orcdrc_m_sd1_1km_eu_ll permutation importance: 0.0151
pet_he_yr permutation importance: 0.4666
sltppt_m_sd1_1km_eu_ll permutation importance: 0
sndppt_m_sd1_1km_eu_ll permutation importance: 0.5836
solar_1km permutation importance: 0.9138
Training gain without bio_12_etrs2_ras: 2.4758
Training gain without bio_15_etrs2_ras: 2.3924
Training gain without bio_18_etrs2_ras: 2.4288
Training gain without bio_4_etrs2_ras: 2.4848
Training gain without bio_8_etrs2_ras: 2.5206
Training gain without bld_m_sd1_1km_eu_ll: 2.4941
Training gain without cecsum_m_sd1_1km_eu_ll: 2.5224
Training gain without clyppt_m_sd1_1km_eu_ll: 2.4834
Training gain without crfvol_m_sd1_1km_eu_ll: 2.491
Training gain without dist2water1km: 2.5283
Training gain without orcdrc_m_sd1_1km_eu_ll: 2.5274
Training gain without pet_he_yr: 2.521
Training gain without sltppt_m_sd1_1km_eu_ll: 2.529
Training gain without sndppt_m_sd1_1km_eu_ll: 2.5081
Training gain without solar_1km: 2.5181
Training gain with only bio_12_etrs2_ras: 0.5483
Training gain with only bio_15_etrs2_ras: 0.8073
Training gain with only bio_18_etrs2_ras: 1.7667
Training gain with only bio_4_etrs2_ras: 0.1536
Training gain with only bio_8_etrs2_ras: 0.283
Training gain with only bld_m_sd1_1km_eu_ll: 1.2828
Training gain with only cecsum_m_sd1_1km_eu_ll: 0.4173
Training gain with only clyppt_m_sd1_1km_eu_ll: 0.3192
Training gain with only crfvol_m_sd1_1km_eu_ll: 0.2936
Training gain with only dist2water1km: 0.0018
Training gain with only orcdrc_m_sd1_1km_eu_ll: 0.4465
Training gain with only pet_he_yr: 1.5753
Training gain with only sltppt_m_sd1_1km_eu_ll: 0.2119
Training gain with only sndppt_m_sd1_1km_eu_ll: 0.2175
Training gain with only solar_1km: 0.1541
Test gain without bio_12_etrs2_ras: 1.9191
Test gain without bio_15_etrs2_ras: 1.5168
Test gain without bio_18_etrs2_ras: 1.8359
Test gain without bio_4_etrs2_ras: 1.6436
Test gain without bio_8_etrs2_ras: 1.6716
Test gain without bld_m_sd1_1km_eu_ll: 1.8202
Test gain without cecsum_m_sd1_1km_eu_ll: 1.6544
Test gain without clyppt_m_sd1_1km_eu_ll: 1.7754
Test gain without crfvol_m_sd1_1km_eu_ll: 1.6975
Test gain without dist2water1km: 1.7033
Test gain without orcdrc_m_sd1_1km_eu_ll: 1.7371
Test gain without pet_he_yr: 1.6996
Test gain without sltppt_m_sd1_1km_eu_ll: 1.6819
Test gain without sndppt_m_sd1_1km_eu_ll: 1.7197
Test gain without solar_1km: 1.6934
Test gain with only bio_12_etrs2_ras: 1.003
Test gain with only bio_15_etrs2_ras: 0.7312
Test gain with only bio_18_etrs2_ras: 1.585
Test gain with only bio_4_etrs2_ras: 0.2388
Test gain with only bio_8_etrs2_ras: 0.4073
Test gain with only bld_m_sd1_1km_eu_ll: 1.4061
Test gain with only cecsum_m_sd1_1km_eu_ll: 0.5818
Test gain with only clyppt_m_sd1_1km_eu_ll: 0.1176
Test gain with only crfvol_m_sd1_1km_eu_ll: 0.0725
Test gain with only dist2water1km: -0.0004
Test gain with only orcdrc_m_sd1_1km_eu_ll: 0.5066
Test gain with only pet_he_yr: 1.3987
Test gain with only sltppt_m_sd1_1km_eu_ll: 0.0995
Test gain with only sndppt_m_sd1_1km_eu_ll: 0.0082
Test gain with only solar_1km: 0.0367
AUC without bio_12_etrs2_ras: 0.951
AUC without bio_15_etrs2_ras: 0.9281
AUC without bio_18_etrs2_ras: 0.9508
AUC without bio_4_etrs2_ras: 0.9412
AUC without bio_8_etrs2_ras: 0.9432
AUC without bld_m_sd1_1km_eu_ll: 0.9478
AUC without cecsum_m_sd1_1km_eu_ll: 0.9422
AUC without clyppt_m_sd1_1km_eu_ll: 0.9461
AUC without crfvol_m_sd1_1km_eu_ll: 0.9411
AUC without dist2water1km: 0.9441
AUC without orcdrc_m_sd1_1km_eu_ll: 0.9439
AUC without pet_he_yr: 0.944
AUC without sltppt_m_sd1_1km_eu_ll: 0.9424
AUC without sndppt_m_sd1_1km_eu_ll: 0.9422
AUC without solar_1km: 0.9422
AUC with only bio_12_etrs2_ras: 0.8955
AUC with only bio_15_etrs2_ras: 0.832
AUC with only bio_18_etrs2_ras: 0.9267
AUC with only bio_4_etrs2_ras: 0.6579
AUC with only bio_8_etrs2_ras: 0.8095
AUC with only bld_m_sd1_1km_eu_ll: 0.9176
AUC with only cecsum_m_sd1_1km_eu_ll: 0.7989
AUC with only clyppt_m_sd1_1km_eu_ll: 0.5891
AUC with only crfvol_m_sd1_1km_eu_ll: 0.6288
AUC with only dist2water1km: 0.4837
AUC with only orcdrc_m_sd1_1km_eu_ll: 0.7726
AUC with only pet_he_yr: 0.9075
AUC with only sltppt_m_sd1_1km_eu_ll: 0.557
AUC with only sndppt_m_sd1_1km_eu_ll: 0.5491
AUC with only solar_1km: 0.5493
Entropy: 6.0094
Prevalence (average of logistic output over background sites): 0.0386
Fixed cumulative value 1 cumulative threshold: 1
Fixed cumulative value 1 logistic threshold: 0.012
Fixed cumulative value 1 area: 0.2407
Fixed cumulative value 1 training omission: 0
Fixed cumulative value 1 test omission: 0
Fixed cumulative value 1 binomial probability: 4.69E-05
Fixed cumulative value 5 cumulative threshold: 5
Fixed cumulative value 5 logistic threshold: 0.0647
Fixed cumulative value 5 area: 0.1328
Fixed cumulative value 5 training omission: 0.0308
Fixed cumulative value 5 test omission: 0.1429
Fixed cumulative value 5 binomial probability: 3.39E-05
Fixed cumulative value 10 cumulative threshold: 10
Fixed cumulative value 10 logistic threshold: 0.1269
Fixed cumulative value 10 area: 0.0933
Fixed cumulative value 10 training omission: 0.0615
Fixed cumulative value 10 test omission: 0.2857
Fixed cumulative value 10 binomial probability: 1.27E-04
Minimum training presence cumulative threshold: 1.7247
Minimum training presence logistic threshold: 0.0194
Minimum training presence area: 0.2033
Minimum training presence training omission: 0
Minimum training presence test omission: 0
Minimum training presence binomial probability: 1.43E-05
10 percentile training presence cumulative threshold: 15.3937
10 percentile training presence logistic threshold: 0.1798
10 percentile training presence area: 0.0684
10 percentile training presence training omission: 0.0923
10 percentile training presence test omission: 0.2857
10 percentile training presence binomial probability: 2.79E-05
Equal training sensitivity and specificity cumulative threshold: 13.2309
Equal training sensitivity and specificity logistic threshold: 0.1564
Equal training sensitivity and specificity area: 0.0771
Equal training sensitivity and specificity training omission: 0.0769
Equal training sensitivity and specificity test omission: 0.2857
Equal training sensitivity and specificity binomial probability: 5.00E-05
Maximum training sensitivity plus specificity cumulative threshold: 11.6114
Maximum training sensitivity plus specificity logistic threshold: 0.1352
Maximum training sensitivity plus specificity area: 0.0846
Maximum training sensitivity plus specificity training omission: 0.0615
Maximum training sensitivity plus specificity test omission: 0.2857
Maximum training sensitivity plus specificity binomial probability: 7.87E-05
Equal test sensitivity and specificity cumulative threshold: 4.2418
Equal test sensitivity and specificity logistic threshold: 0.052
Equal test sensitivity and specificity area: 0.1429
Equal test sensitivity and specificity training omission: 0.0308
Equal test sensitivity and specificity test omission: 0.1429
Equal test sensitivity and specificity binomial probability: 5.22E-05
Maximum test sensitivity plus specificity cumulative threshold: 4.0498
Maximum test sensitivity plus specificity logistic threshold: 0.0507
Maximum test sensitivity plus specificity area: 0.1456
Maximum test sensitivity plus specificity training omission: 0.0308
Maximum test sensitivity plus specificity test omission: 0
Maximum test sensitivity plus specificity binomial probability: 1.39E-06
Balance training omission, predicted area and threshold value cumulative threshold: 1.7247
Balance training omission, predicted area and threshold value logistic threshold: 0.0194
Balance training omission, predicted area and threshold value area: 0.2033
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: 1.43E-05
Equate entropy of thresholded and original distributions cumulative threshold: 12.4495
Equate entropy of thresholded and original distributions logistic threshold: 0.1453
Equate entropy of thresholded and original distributions area: 0.0806
Equate entropy of thresholded and original distributions training omission: 0.0769
Equate entropy of thresholded and original distributions test omission: 0.2857
Equate entropy of thresholded and original distributions binomial probability: 6.23E-05</gco:CharacterString>
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