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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, Rhinolophus euryale, 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
BG 100 200 N/A i minimum N/A N/A N/A N/A
ES 1139 N/A N/A i minimum 10 N/A N/A localities minimum
FR 1500 2000 N/A i mean N/A N/A N/A mean
HR N/A N/A 688 i minimum N/A N/A N/A N/A
IT 100 1800 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
SI N/A N/A 75 i minimum 75 86 N/A grids1x1 estimate
SK 117 1103 N/A i estimate N/A N/A N/A N/A
ES 617 N/A N/A i minimum 52 N/A N/A localities minimum
FR 20900 22000 N/A i mean N/A N/A N/A mean
PT N/A N/A N/A N/A N/A 3 grids1x1 minimum
BG 1500 2500 N/A i minimum N/A N/A N/A N/A
BG 20000 22000 N/A i minimum N/A N/A N/A N/A
FR 2500 3000 N/A i mean N/A N/A N/A mean
HR N/A N/A 1090 i minimum N/A N/A N/A N/A
IT 320 9500 N/A i estimate N/A N/A N/A N/A
RO 500 1200 N/A i minimum N/A N/A N/A N/A
SI N/A N/A 440 i minimum 26 29 N/A grids1x1 N/A
CY 120 200 N/A i estimate N/A N/A N/A N/A
ES 18384 N/A N/A i minimum 89 N/A N/A localities minimum
FR 7100 7200 N/A i mean N/A N/A N/A mean
GR 2275 5000 N/A i estimate N/A N/A N/A N/A
HR N/A N/A 3125 i minimum N/A N/A N/A N/A
IT 5000 40000 N/A i estimate N/A N/A N/A N/A
PT 3000 N/A N/A i minimum N/A N/A N/A N/A
HU 4000 6000 N/A i estimate N/A N/A N/A N/A
SK 7 303 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
BG ALP 22900 47.41 = 22900 100 200 N/A i minimum b 2.45 = 100 i Y FV = good good good FV FV = U1 = method method 1600 b 14.04
ES ALP 4500 9.32 u > 1139 N/A N/A i minimum b 18.64 x 10 localities Unk U1 x poor poor good U1 U1 = U1 = noChange noChange 500 a 4.39
FR ALP 5000 10.35 = > 1500 2000 N/A i mean a 28.63 = ≈ N Y U1 = good good poor U1 U1 = U2 - knowledge noChange 1600 a 14.04
HR ALP 6600 13.67 x > N/A N/A 688 i minimum b 11.26 x > N Unk XX x unk unk poor XX U1 x N/A N/A 4500 b 39.47
IT ALP 2600 5.38 = ≈ 100 1800 N/A i estimate c 15.54 - > N Y FV = good poor good FV U2 - XX knowledge noChange 1100 b 9.65
RO ALP 1400 2.90 = ≈ 500 1000 N/A i minimum b 12.27 = ≈ Y U1 = poor poor poor U1 U1 = U1 = knowledge knowledge 100 b 0.88
SI ALP 3401 7.04 = ≈ N/A N/A 75 i minimum a 1.23 = ≈ Y FV = good good good FV FV = FV noChange noChange 400 b 3.51
SK ALP 1897.06 3.93 + ≈ 117 1103 N/A i estimate b 9.98 = ≈ Y FV = good good good FV FV = U1 - knowledge knowledge 1600 b 14.04
ES ATL 46400 74.84 = > 617 N/A N/A i minimum b 2.80 u 52 localities Y FV = good poor good U1 U1 - U1 - noChange noChange 14900 a 60.82
FR ATL 14800 23.87 + ≈ 20900 22000 N/A i mean a 97.20 + ≈ Y Y FV = good good poor U1 U1 + U2 = genuine noChange 9400 a 38.37
PT ATL 800 1.29 x 800 N/A N/A N/A b 0 x x Unk XX x unk unk unk XX XX XX noChange knowledge 200 b 0.82
BG BLS 8300 100 = 8300 1500 2500 N/A i minimum b 100 = 1500 i Y FV = unk unk unk XX FV = FV method method 1300 b 100
BG CON 77600 67.24 = 77600 20000 22000 N/A i minimum b 67.65 = 20000 i Y FV = unk unk unk XX FV = FV noChange method 10900 b 39.64
FR CON 4400 3.81 = ≈ 2500 3000 N/A i mean a 8.86 = < Y FV = good unk poor U1 U1 = U2 - noChange noChange 2300 a 8.36
HR CON 7800 6.76 x >> N/A N/A 1090 i minimum b 3.51 x > N Unk XX x poor unk poor U1 U2 x N/A N/A 7100 b 25.82
IT CON 12100 10.48 = ≈ 320 9500 N/A i estimate c 15.82 - >> N Y U1 = good poor good FV U2 - U2 - knowledge knowledge 4000 b 14.55
RO CON 5100 4.42 = ≈ 500 1200 N/A i minimum b 2.74 = ≈ Y U1 = poor poor poor U1 U1 = U1 = knowledge knowledge 1000 b 3.64
SI CON 8409 7.29 = ≈ N/A N/A 440 i minimum a 1.42 = 27 grids1x1 N Unk U2 - good good bad U2 U2 - U2 x noChange knowledge 2200 b 8
CY MED 8307 2.38 x x 120 200 N/A i estimate c 0.28 x x Y XX = unk unk good XX XX XX noChange noChange 10300 c 5.12
ES MED 129900 37.21 = > 18384 N/A N/A i minimum b 31.72 u 89 localities Y XX u good poor unk U1 U1 = U1 = noChange noChange 32200 a 16.02
FR MED 11400 3.27 = ≈ 7100 7200 N/A i mean a 12.34 + ≈ Y Y FV + good good poor U1 U1 + U1 + noChange noChange 6400 a 3.18
GR MED 115347 33.05 = ≈ 2275 5000 N/A i estimate b 6.28 x ≈ Unk XX x good poor unk U1 U1 x U1 x noChange noChange 113100 b 56.27
HR MED 17300 4.96 x > N/A N/A 3125 i minimum b 5.39 x > Unk XX x poor unk poor U1 U1 x N/A N/A 16500 b 8.21
IT MED 50000 14.32 = ≈ 5000 40000 N/A i estimate c 38.82 - >> N Y U1 = good poor good FV U2 - U2 - noChange noChange 17700 b 8.81
PT MED 16800 4.81 = 16800 3000 N/A N/A i minimum c 5.18 = 3000 i Unk XX - good good unk FV FV - U1 x knowledge knowledge 4800 b 2.39
HU PAN 7069 94.28 u ≈ 4000 6000 N/A i estimate a 96.99 u ≈ Y FV = good good poor U1 U1 x FV genuine method 2000 a 86.96
SK PAN 428.77 5.72 + ≈ 7 303 N/A i estimate b 3.01 + ≈ Y FV = good good good FV FV = U1 = knowledge N/A 300 b 13.04
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 = good 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 + ≈ 2XP = good poor poor 2XP MTX + U2 = gen nong U2 B1

03/20

EEA-ETC/BD

Institution: -

Member State:

EEA-ETC/BD
EU28 BLS 0MS = ≈ i 0MS = x 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 = x 2XP = unk unk unk 2XP MTX + U1 x nong nong U1 A=

01/20

EEA-ETC/BD

Institution: -

Member State:

EEA-ETC/BD
EU28 MED 2XR = ≈ i 2XR - x 2XR x good poor unk 2XR MTX = U1 x nc nong U2 B1

03/20

EEA-ETC/BD

Institution: -

Member State:

EEA-ETC/BD
EU28 PAN 0EQ x ≈ i 0EQ x ≈ 0EQ = good good poor 0EQ MTX - FV gen gen FV C

03/20

EEA-ETC/BD

Institution: -

Member State:

EEA-ETC/BD
BG ALP 2XP 2XP 2XP MTX U1 = U1 0/2

04/20

Green Balkans Federation

Institution: Green Balkans Federation

Member State: BG

Green Balkans Federation
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.