Article 17 web tool

Log in
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, Miniopterus schreibersii, 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 25 N/A N/A i minimum N/A N/A 1 grids1x1 estimate
BG 10500 42000 N/A i minimum N/A N/A N/A N/A
ES 8052 N/A N/A i minimum 6 N/A N/A grids10x10 minimum
FR 11500 15000 N/A i mean N/A N/A N/A mean
HR N/A N/A 2200 i minimum N/A N/A N/A N/A
IT 1200 6000 N/A i estimate N/A N/A N/A N/A
RO 10000 20000 N/A i minimum N/A N/A N/A N/A
SI N/A N/A 1510 i minimum 11 13 N/A grids1x1 estimate
SK 41 1640 N/A i estimate N/A N/A N/A N/A
ES 12100 N/A N/A i minimum N/A N/A 115 grids10x10 estimate
FR 40000 49996 N/A i mean N/A N/A N/A mean
PT N/A N/A N/A 4 N/A N/A grids1x1 minimum
BG 600 5000 N/A i minimum N/A N/A N/A N/A
AT N/A N/A 50 i estimate N/A N/A 1 grids1x1 estimate
BG 71000 91000 N/A i minimum N/A N/A N/A N/A
FR 35000 55000 N/A i mean N/A N/A N/A mean
HR N/A N/A 31000 i minimum N/A N/A N/A N/A
IT 11000 55000 N/A i estimate N/A N/A N/A N/A
RO 3000 5000 N/A i minimum N/A N/A N/A N/A
SI N/A N/A 3438 i minimum 33 35 N/A grids1x1 estimate
CY 3000 10000 N/A i estimate N/A N/A N/A N/A
ES 305540 N/A N/A i minimum N/A N/A 556 grids10x10 estimate
FR 40154 66924 N/A i estimate N/A N/A N/A estimate
GR 23000 50000 N/A i estimate N/A N/A N/A N/A
HR N/A N/A 24000 i minimum N/A N/A N/A N/A
IT 26000 130000 N/A i estimate N/A N/A N/A N/A
PT 30000 N/A 5 i minimum N/A N/A N/A N/A
UK 400 800 800 i estimate N/A N/A N/A estimate
HU 6000 9000 N/A i estimate N/A N/A N/A N/A
SK 33 51 N/A i estimate N/A N/A N/A N/A
RO 500 1000 N/A i minimum N/A N/A N/A N/A
CZ N/A 1 N/A i 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 100 0.12 = >> 25 N/A N/A i minimum b 0.04 = >> Unk Unk XX - bad poor poor U2 U2 - U2 - noChange noChange 100 b 0.40
BG ALP 25600 30.92 = 25600 10500 42000 N/A i minimum b 37.11 = 32100 i Y FV = poor poor poor U1 U1 = U1 = noChange method 3500 b 13.83
ES ALP 2100 2.54 = ≈ 8052 N/A N/A i minimum b 11.38 x 6 grids10x10 Y FV x poor poor poor U1 U1 x U1 x noChange noChange 500 a 1.98
FR ALP 10000 12.08 - > 11500 15000 N/A i mean b 18.73 - > N Y U1 - poor unk poor U1 U2 - U2 N/A noChange noChange 3400 b 13.44
HR ALP 11900 14.37 x ≈ N/A N/A 2200 i minimum b 3.11 x > Y XX x good unk poor U1 U1 x N/A N/A 9100 b 35.97
IT ALP 13000 15.70 = ≈ 1200 6000 N/A i estimate c 5.09 - > N N U1 = good poor good U1 U1 - U2 - noChange noChange 2400 b 9.49
RO ALP 11200 13.53 = ≈ 10000 20000 N/A i minimum b 21.21 = ≈ Y FV = good good good FV FV = U1 = knowledge knowledge 3100 b 12.25
SI ALP 6858 8.28 = ≈ N/A N/A 1510 i minimum a 2.13 + ≈ Y FV = good good good FV FV + XX knowledge knowledge 1000 b 3.95
SK ALP 2034.51 2.46 - > 41 1640 N/A i estimate b 1.19 = ≈ Y FV x poor good good FV U1 x U2 - knowledge N/A 2200 b 8.70
ES ATL 34600 49.93 = ≈ 12100 N/A N/A i minimum b 21.19 = 115 grids10x10 Y U1 u poor poor poor U1 U1 = U1 x noChange noChange 11100 a 44.58
FR ATL 33800 48.77 - > 40000 49996 N/A i mean a 78.81 + > N Y U1 = poor unk poor U1 U1 = U2 - genuine noChange 13600 b 54.62
PT ATL 900 1.30 x 900 N/A N/A N/A b 0 x x Unk XX x unk unk unk XX XX XX noChange knowledge 200 b 0.80
BG BLS 9300 100 = 9300 600 5000 N/A i minimum b 100 = 3500 i Y FV = poor poor poor U1 U1 = FV method method 2100 b 100
AT CON 100 0.04 = >> N/A N/A 50 i estimate b 0.03 + >> Y U1 = poor poor unk U1 U2 + U2 = noChange genuine 100 b 0.14
BG CON 94600 41.30 = 94600 71000 91000 N/A i minimum b 41.02 = 71000 i Y FV = poor poor poor U1 U1 = U1 = noChange method 17000 b 24.25
FR CON 21000 9.17 = ≈ 35000 55000 N/A i mean a 22.79 + < N N U2 - good unk poor U1 U2 x U2 - noChange noChange 7500 b 10.70
HR CON 23900 10.43 x > N/A N/A 31000 i minimum b 15.70 x > N Unk U1 x good unk poor U1 U1 x N/A N/A 23500 b 33.52
IT CON 51500 22.48 + ≈ 11000 55000 N/A i estimate c 16.71 - > N N U1 = good poor poor U1 U1 = U2 - noChange noChange 9500 b 13.55
RO CON 26000 11.35 = ≈ 3000 5000 N/A i minimum b 2.03 = ≈ Y FV = good good good FV FV = U1 = knowledge knowledge 10400 b 14.84
SI CON 11943 5.21 = ≈ N/A N/A 3438 i minimum a 1.74 = ≈ Y U1 u good good poor U1 U1 = U1 x noChange knowledge 2100 b 3
CY MED 9689 1.58 x ≈ 3000 10000 N/A i estimate a 1.29 x ≈ Y FV = good good good FV FV x FV noChange noChange 11100 b 3.45
ES MED 219300 35.72 u ≈ 305540 N/A N/A i minimum b 60.52 + > Y U1 = good poor poor FV U1 = U1 x noChange knowledge 62000 a 19.28
FR MED 56700 9.24 = ≈ 40154 66924 N/A i estimate b 10.60 = > Y Y U1 = good unk unk U1 U1 = U2 - genuine noChange 25200 b 7.84
GR MED 124285 20.25 x ≈ 23000 50000 N/A i estimate b 7.23 x ≈ Unk XX x unk poor poor U1 U1 x U1 x noChange noChange 150300 b 46.75
HR MED 34600 5.64 x ≈ N/A N/A 24000 i minimum c 4.75 x > N Unk U1 x good unk poor U1 U1 x N/A N/A 32400 b 10.08
IT MED 133700 21.78 = ≈ 26000 130000 N/A i estimate c 15.45 - > N N U1 = good poor poor U1 U1 - U2 - noChange noChange 27500 b 8.55
PT MED 35600 5.80 = 35600 30000 N/A 5 i minimum b 0 = 30000 i Unk XX - good good unk FV FV - U1 + knowledge knowledge 12800 b 3.98
UK MED 5 0 = 5 400 800 800 i estimate a 0.16 = x Y FV = unk unk good XX XX U1 = knowledge noChange 200 c 0.06
HU PAN 8975 97.69 = ≈ 6000 9000 N/A i estimate a 99.44 = ≈ Y U1 = poor poor good U1 U1 = U1 + noChange method 2100 a 95.45
SK PAN 211.82 2.31 = > 33 51 N/A i estimate b 0.56 = ≈ Y FV x good good good FV U1 = U2 - knowledge N/A 100 b 4.55
RO STE 900 100 = ≈ 500 1000 N/A i minimum b 100 = ≈ Y FV = poor poor poor FV FV = U1 = knowledge knowledge 400 b 100
CZ CON 100 0 x x N/A 1 N/A i a 0 x x Y FV = unk unk good XX XX XX noChange noChange 100 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 - U2 = nong nong U2 C

03/20

EEA-ETC/BD

Institution: -

Member State:

EEA-ETC/BD
EU28 ATL 0EQ - > i 0EQ + > 0EQ x poor poor poor 0EQ MTX + U2 = gen gen U2 B1

01/20

EEA-ETC/BD

Institution: -

Member State:

EEA-ETC/BD
EU28 BLS 0MS = i 0MS = x 0MS = poor poor poor 0MS MTX - FV nong nong FV C

03/20

EEA-ETC/BD

Institution: -

Member State:

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

01/20

EEA-ETC/BD

Institution: -

Member State:

EEA-ETC/BD
EU28 MED 2XP x ≈ i 2XP + > 2XP x good poor poor 2XP MTX = U1 x nc nc U2 B1

03/20

EEA-ETC/BD

Institution: -

Member State:

EEA-ETC/BD
EU28 PAN 0EQ = ≈ i 0EQ = ≈ 0EQ = poor poor good 0EQ MTX + U2 + nong nong U2 B2

03/20

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

02/20

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.