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Article 17 > Species summary

Species assessments at EU biogeographical level

The Article 17 web tool provides an access to EU biogeographical and Member States’ assessments of conservation status of the habitat types and species of Community interest compiled as part of the Habitats Directive - Article 17 reporting process. These assessments have been carried out in EU25 for the period 2001-2006, in EU 27 for the period 2007-2012 and in EU28 for the period 2013-2018.

Choose a period, a group, then a species belonging to that group.
Optionally, further refine your query by selecting one of the available biogeographical regions for that species.
Once a selection has been made the conservation status can be visualised in a map view.

The 'Data sheet info' includes notes for each regional and overall assessment per species.

The 'Audit trail' includes the methods used for the EU biogeographical assessments and justifications for decisions made by the assessors.

Warning: The map does not show the distribution for sensitive species in GR

Note: Rows in italic shows data not taken into account when performing the assessments (marginal presence, occasional, extinct prior HD, information, etc)

Legend
FV
Favourable
XX
Unknown
U1
Unfavourable-Inadequate
U2
Unfavourable-Bad

Sensitive spatial information for this species is not shown in the map.

Current selection: 2013-2018, Mammals, Myotis blythii, All bioregions. Annexes Y, Y, N. Show all Mammals
Member States reports
MS Region Range (km2) Population Habitat for the species Future prospects Overall assessment Distribution
area (km2)
Surface Status
(% MS)		 Trend FRR
Min
Member State
code		 Reporting units Alternative units
Min Max Best value Unit Type of estimate Min Max Best value Unit Type of estimate
AT 50 N/A N/A i minimum 13 N/A N/A grids1x1 minimum
BG 3500 5500 N/A i minimum N/A N/A N/A N/A
ES 7 N/A N/A i minimum 7 N/A N/A grids10x10 minimum
FR 1000 5000 N/A i mean 3000 4000 N/A grids1x1 mean
HR N/A N/A 400 i minimum N/A N/A N/A N/A
IT 4500 22500 N/A i estimate N/A N/A N/A N/A
RO 5000 10000 N/A i minimum N/A N/A N/A N/A
SI N/A N/A 7 i minimum 7 8 N/A grids1x1 N/A
SK 4127 6280 N/A i estimate N/A N/A N/A N/A
ES 1000 2000 N/A i estimate 34 N/A N/A grids10x10 minimum
FR 3000 5000 N/A i mean 3900 5500 N/A grids1x1 mean
BG 100 2500 N/A i minimum N/A N/A N/A N/A
AT N/A N/A N/A 11 N/A N/A grids1x1 minimum
BG 1000 20000 N/A i minimum N/A N/A N/A N/A
FR 3000 5000 N/A i mean 3000 3500 N/A grids1x1 mean
HR N/A N/A 130 i minimum N/A N/A N/A N/A
IT 9500 48000 N/A i estimate N/A N/A N/A N/A
RO 5000 10000 N/A i minimum N/A N/A N/A N/A
SI N/A N/A 15 i minimum 15 16 N/A grids1x1 estimate
CY 65 500 N/A i estimate N/A N/A N/A N/A
ES 38546 N/A N/A i estimate 344 N/A N/A grids10x10 N/A
FR 10000 11000 N/A i mean 9000 11000 N/A grids1x1 mean
GR 10000 50000 N/A i estimate N/A N/A N/A N/A
HR N/A N/A 9250 i minimum N/A N/A N/A N/A
IT 10000 50000 N/A i estimate N/A N/A N/A N/A
PT N/A N/A N/A minimum N/A N/A 62 grids1x1 N/A
HU N/A N/A N/A minimum N/A N/A 145 grids1x1 N/A
SK 11 135 N/A i estimate N/A N/A N/A N/A
RO 200 300 N/A i minimum N/A N/A N/A N/A
CZ N/A 2 N/A i estimate N/A N/A N/A N/A
CZ N/A N/A N/A i estimate N/A N/A N/A N/A
Max
Best value Unit Type est. Method Status
(% MS)		 Trend FRP Unit Occupied
suff.		 Unoccupied
suff.		 Status Trend Range
prosp.		 Population
prosp.		 Hab. for sp.
prosp.		 Status Curr. CS Curr. CS
trend		 Prev. CS Prev. CS
trend		 Status
Nat. of ch.		 CS trend
Nat. of ch.		 Distrib. Method % MS
AT ALP 1500 1.55 - >> 50 N/A N/A i minimum c 0.15 x >> Unk Unk U1 - poor bad bad U2 U2 - U2 - genuine noChange 1300 c 4.23
BG ALP 24900 25.73 = 24900 3500 5500 N/A i minimum b 13.17 = 3500 i Y FV = poor poor poor U1 U1 = U1 = noChange method 5300 b 17.26
ES ALP 4500 4.65 = ≈ 7 N/A N/A i minimum b 0.02 x 7 i Unk XX x poor poor poor XX U1 = U2 - N/A N/A 400 a 1.30
FR ALP 9900 10.23 = ≈ 1000 5000 N/A i mean b 8.78 - > N Y U1 - poor unk poor U1 U2 - U2 - noChange noChange 4200 a 13.68
HR ALP 10900 11.26 x ≈ N/A N/A 400 i minimum b 1.17 x > N Unk U1 x unk unk poor XX U1 x N/A N/A 8100 b 26.38
IT ALP 23500 24.28 = > 4500 22500 N/A i estimate c 39.51 = ≈ Y U1 - good poor poor U1 U1 - U1 - noChange noChange 5800 b 18.89
RO ALP 11800 12.19 = ≈ 5000 10000 N/A i minimum b 21.95 = ≈ Y FV = good good good FV FV = U1 = knowledge knowledge 1700 b 5.54
SI ALP 5996 6.20 = ≈ N/A N/A 7 i minimum b 0.02 u > Y U1 u poor poor poor U1 U1 x U1 x noChange noChange 600 b 1.95
SK ALP 3781.20 3.91 = ≈ 4127 6280 N/A i estimate c 15.23 + ≈ Y FV x good good good FV FV = XX knowledge knowledge 3300 b 10.75
ES ATL 19800 77.95 = ≈ 1000 2000 N/A i estimate b 27.27 x 2000 i Y U1 x poor poor poor U1 U1 = U1 x N/A N/A 2900 a 46.03
FR ATL 5600 22.05 = > 3000 5000 N/A i mean c 72.73 = > Y U1 = poor good unk U1 U1 = U2 - knowledge noChange 3400 a 53.97
BG BLS 7800 100 = 7800 100 2500 N/A i minimum b 100 = 2000 i Y FV = unk unk unk XX FV = FV method method 1300 b 100
AT CON 1400 0.86 - >> N/A N/A N/A c 0 x >> Unk Unk U1 - poor bad bad U2 U2 - U2 - noChange noChange 1100 c 2.35
BG CON 93100 56.92 = 93100 1000 20000 N/A i minimum b 20.63 = 10000 i Y FV = poor poor poor U1 U1 = U1 = noChange noChange 20400 b 43.50
FR CON 6800 4.16 = >> 3000 5000 N/A i mean a 7.86 - >> N N U2 = poor poor unk U1 U2 + U2 - noChange noChange 3500 a 7.46
HR CON 9100 5.56 x >> N/A N/A 130 i minimum b 0.26 x >> N Unk U1 x unk poor bad U2 U2 x N/A N/A 8800 b 18.76
IT CON 33500 20.48 = > 9500 48000 N/A i estimate c 56.49 = ≈ Y U1 - good poor poor U1 U1 - U1 - noChange noChange 9700 b 20.68
RO CON 12400 7.58 = ≈ 5000 10000 N/A i minimum b 14.74 = ≈ Y FV = poor poor poor FV FV = U1 = knowledge knowledge 2400 b 5.12
SI CON 7268 4.44 = ≈ N/A N/A 15 i minimum b 0.03 u > Y U1 u good poor poor U1 U1 x U1 x noChange noChange 1000 b 2.13
CY MED 9689 2.42 x ≈ 65 500 N/A i estimate b 0.24 x x Y XX = good unk good FV XX XX noChange noChange 8800 b 3.81
ES MED 165100 41.22 = ≈ 38546 N/A N/A i estimate a 32.51 = 38546 i Y U1 = good poor poor U1 U1 = U2 - genuine genuine 37700 a 16.31
FR MED 27500 6.87 = ≈ 10000 11000 N/A i mean a 8.85 = ≈ Y N U1 - good good unk FV U1 - U2 - noChange noChange 11500 a 4.97
GR MED 123160 30.75 x ≈ 10000 50000 N/A i estimate b 25.30 x ≈ Unk XX x unk poor poor U1 U1 x U1 x noChange noChange 129900 b 56.19
HR MED 29900 7.46 x ≈ N/A N/A 9250 i minimum b 7.80 x ≈ Y U1 x good unk poor U1 U1 x N/A N/A 27900 b 12.07
IT MED 33500 8.36 = > 10000 50000 N/A i estimate c 25.30 = ≈ Y U1 - good poor poor U1 U1 - U1 - noChange noChange 10900 b 4.71
PT MED 11700 2.92 = 11700 N/A N/A N/A minimum b 0 u x Unk XX - good unk unk XX XX U2 - knowledge knowledge 4500 b 1.95
HU PAN 47706 98.53 - > N/A N/A N/A minimum a 0 - > Y U1 - poor poor poor U1 U1 - U1 - noChange noChange 8500 a 92.39
SK PAN 712.58 1.47 = ≈ 11 135 N/A i estimate c 100 + ≈ Y FV x good good good FV FV = XX knowledge knowledge 700 b 7.61
RO STE 200 100 = ≈ 200 300 N/A i minimum b 100 = ≈ Y FV = poor poor poor FV FV = U1 = knowledge knowledge 100 b 100
CZ CON 200 0 - >> N/A 2 N/A i estimate c 0 - N/ Unk XX x bad bad unk U2 U2 = U2 = noChange noChange 200 a 0
CZ PAN N/A 0 x x N/A N/A N/A i estimate a 0 x x Unk XX x unk unk unk XX XX U2 = noInfo noInfo N/A a 0
Automatic Assessments Show,Hide
EU biogeographical assessments
MS/EU28 Region Surface Status
Range		 Trend FRR Min Max Best value Unit Status
Population		 Trend FRP Unit Status
Hab. for
species		 Trend Range
prosp.		 Population
prosp.		 Hab. for sp.
prosp.		 Status
Future
prosp.		 Curr. CS Curr. CS
trend		 2012 CS 2012 CS
trend		 Status
Nat. of ch.		 CS trend
Nat. of ch.		 2001-06 status
with
backcasting		 Target 1
EU28 ALP 2XP = > i 2XP = ≈ 2XP x poor poor poor 2XP MTX = U1 = nc nc U1 D

02/20

EEA-ETC/BD

Institution: -

Member State:

EEA-ETC/BD
EU28 ATL 2XP = > i 2XP x > 2XP x poor poor poor 2XP MTX + U2 = nong nong U2 B2

03/20

EEA-ETC/BD

Institution: -

Member State:

EEA-ETC/BD
EU28 BLS 0MS = ≈ i 0MS = ≈ 0MS = unk unk unk 0MS MTX = FV nc nc FV A=

03/20

EEA-ETC/BD

Institution: -

Member State:

EEA-ETC/BD
EU28 CON 2XP = > i 2XP = > 2XP - poor poor poor 2XP MTX = U1 = nc nc U1 D

02/20

EEA-ETC/BD

Institution: -

Member State:

EEA-ETC/BD
EU28 MED 0EQ = ≈ i 0EQ = ≈ 0EQ x good poor poor 0EQ MTX = U1 x nc nc U2 B1

02/20

EEA-ETC/BD

Institution: -

Member State:

EEA-ETC/BD
EU28 PAN 2XR - > 2XR + > 2XR - poor poor poor 2XR MTX = U1 = nc nc U1 D

02/20

EEA-ETC/BD

Institution: -

Member State:

EEA-ETC/BD
EU28 STE 0MS = ≈ i 0MS = ≈ 0MS = poor poor poor 0MS MTX = U1 = nc nc U1 D

12/19

EEA-ETC/BD

Institution: -

Member State:

EEA-ETC/BD
The current dataset is readonly, so you cannot add a conclusion.

Legal notice: A minimum amount of personal data (including cases of submitted comments during the public consultation) is stored in the web tool. These data are necessary for the functioning of the tool and are only accessible to tool administrators.

The distribution data for France (2013 – 2018 reporting) were corrected after the official submission of the Article 17 reports by France. The maps displayed via this web tool take into account these corrections, while the values under Distribution area (km2) used for the EU biogeographical assessment are based on the original Article 17 report submitted by France. More details are provided in the feedback part of the reporting envelope on CDR.