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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, LU

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 ferrumequinum, 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 N/A N/A 145 i estimate N/A N/A 51 grids1x1 estimate
BG 2600 3600 N/A i minimum N/A N/A N/A N/A
ES 713 N/A N/A i minimum 11 N/A N/A localities minimum
FR 500 1000 N/A i mean N/A N/A N/A mean
HR N/A N/A 260 i minimum N/A N/A N/A N/A
IT 6000 15000 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 333 i minimum 75 86 N/A grids1x1 estimate
SK 331 3131 N/A i estimate N/A N/A N/A N/A
ES 450 N/A N/A i minimum 152 N/A N/A localities minimum
FR 47000 50000 N/A i mean N/A N/A N/A mean
PT N/A N/A N/A 9 N/A N/A grids1x1 minimum
UK 9245 18530 12951 i estimate N/A N/A N/A N/A
BG 1100 3500 N/A i minimum N/A N/A N/A N/A
AT 18 N/A N/A i minimum 15 N/A N/A grids1x1 minimum
BE 750 2000 750 i estimate 650 1500 650 iwintering estimate
BG 13000 18000 N/A i minimum N/A N/A N/A N/A
DE 176 398 272 i mean 88 199 136 bfemales mean
FR 15000 16000 N/A i mean N/A N/A N/A mean
HR N/A N/A 5150 i minimum N/A N/A N/A N/A
IT 15000 30000 N/A i estimate N/A N/A N/A N/A
LU 400 450 N/A i interval 163 196 N/A bfemales interval
RO 10000 15000 N/A i minimum N/A N/A N/A N/A
SI N/A N/A 577 i minimum 190 201 N/A grids1x1 estimate
CY 100 150 N/A i estimate N/A N/A N/A N/A
ES 34548 N/A N/A i minimum 329 464 N/A localities minimum
FR 22625 90502 N/A i estimate N/A N/A N/A estimate
GR 5000 10000 N/A i estimate N/A N/A N/A N/A
HR N/A N/A 4200 i minimum N/A N/A N/A N/A
IT 22000 45000 N/A i estimate N/A N/A N/A N/A
PT 4000 N/A N/A i minimum N/A N/A N/A N/A
HU 4000 10000 N/A i estimate N/A N/A N/A N/A
RO 1000 3000 N/A i minimum N/A N/A N/A N/A
SK 161 261 N/A i estimate N/A N/A N/A N/A
RO 300 500 N/A i minimum N/A N/A N/A N/A
BE 1 5 3 i estimate 1 5 3 iwintering estimate
NL N/A N/A N/A N/A N/A N/A N/A
CZ 1 5 N/A i estimate N/A N/A N/A N/A
PL N/A N/A 1 i minimum 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 3700 2.49 = >> N/A N/A 145 i estimate a 0.45 = >> Y U1 = poor bad poor U2 U2 = U2 = noChange noChange 2700 b 5.14
BG ALP 26000 17.47 = 26000 2600 3600 N/A i minimum b 9.53 = 2600 i Y FV = good good good FV FV = FV noChange method 7100 b 13.52
ES ALP 8800 5.91 = x 713 N/A N/A i minimum b 2.19 x 700 i Y U1 x good poor unk U1 U1 x U1 - noChange genuine 1400 a 2.67
FR ALP 21900 14.72 = 500 1000 N/A i mean c 2.31 = < Y U1 = good good poor U1 U1 = U1 = noChange noChange 6800 a 12.95
HR ALP 10200 6.86 x > N/A N/A 260 i minimum b 0.80 x >> N Unk XX x poor unk poor U1 U2 x N/A N/A 7600 b 14.48
IT ALP 53200 35.75 = 6000 15000 N/A i estimate c 32.28 - > N Y U1 - good poor poor U1 U1 - U1 - noChange noChange 14400 b 27.43
RO ALP 10000 6.72 = 10000 20000 N/A i minimum b 46.11 = Y FV = good good good FV FV = U1 = knowledge knowledge 2700 b 5.14
SI ALP 7622 5.12 = N/A N/A 333 i minimum a 1.02 = 80 grids1x1 N Unk U2 - good good bad U2 U2 - U2 - noChange noChange 3500 b 6.67
SK ALP 7373.81 4.96 = 331 3131 N/A i estimate b 5.32 + Y FV x good good good FV FV = U1 - knowledge knowledge 6300 b 12
ES ATL 71100 26.14 = 450 N/A N/A i minimum b 0.73 = 1500 i Y U1 = good good poor U1 U1 = U1 = noChange noChange 48200 a 32.92
FR ATL 155100 57.02 + 47000 50000 N/A i mean a 78.35 + Y Y U1 - good good poor U1 U1 = U1 x noChange noChange 68600 a 46.86
PT ATL 2800 1.03 = 2800 N/A N/A N/A b 0 x x Unk XX x good unk unk XX XX XX noChange knowledge 500 b 0.34
UK ATL 43015 15.81 + 43015 9245 18530 12951 i estimate a 20.92 + 12951 i Y FV = good good good FV FV + FV noChange noChange 29100 a 19.88
BG BLS 9200 100 = 9200 1100 3500 N/A i minimum b 100 = 1100 i Y FV = good good good FV FV = FV method method 2700 b 100
AT CON 1700 0.46 = >> 18 N/A N/A i minimum b 0.02 = >> Y U1 = bad bad poor U2 U2 = U2 = noChange noChange 1500 b 1.18
BE CON 11599 3.16 + 750 2000 750 i estimate b 1.02 + 2000 iwintering Y FV x good good good FV U1 = U2 + genuine noChange 6300 a 4.95
BG CON 97400 26.57 = 97400 13000 18000 N/A i minimum b 21.18 = 13000 i Y FV = good good good FV FV = FV method method 23900 b 18.77
DE CON 9453 2.58 + 16062 176 398 272 i mean a 0.37 + >> bfemales N Y U1 = bad bad poor U2 U2 + U2 + noChange noChange 3800 b 2.99
FR CON 103000 28.09 = 15000 16000 N/A i mean a 21.18 + < Y Y XX - good good poor U1 U1 x U1 = noChange noChange 38500 a 30.24
HR CON 20600 5.62 x > N/A N/A 5150 i minimum b 7.04 x N Unk U1 x good unk poor U1 U1 x N/A N/A 19500 b 15.32
IT CON 93900 25.61 = 15000 30000 N/A i estimate c 30.74 - > Y U1 - good poor poor U1 U1 - U1 - noChange noChange 22000 b 17.28
LU CON 2500 0.68 = 400 450 N/A i interval a 0.58 + > N Unk U1 - good poor poor U1 U1 + U1 = noChange genuine 1000 a 0.79
RO CON 15100 4.12 = 10000 15000 N/A i minimum b 17.08 = Y FV = good good good FV FV = U1 = knowledge knowledge 4000 b 3.14
SI CON 11374 3.10 = N/A N/A 577 i minimum a 0.79 = 195 grids1x1 N Unk U2 - good good bad U2 U2 - U2 x noChange knowledge 6800 b 5.34
CY MED 9689 1.40 x 100 150 N/A i estimate b 0.09 x x Y U1 = good unk poor FV U1 x U1 = noChange noChange 9500 b 2.73
ES MED 266700 38.51 = 34548 N/A N/A i minimum b 24.60 u 399 localities Y U1 u good poor poor U1 U1 = U1 - noChange knowledge 77500 a 22.30
FR MED 63100 9.11 = 22625 90502 N/A i estimate b 40.28 + < Y Y U1 = good good poor U1 U1 + U2 = noChange noChange 28500 a 8.20
GR MED 128083 18.50 = 5000 10000 N/A i estimate b 5.34 x Unk XX x good poor unk U1 U1 x U1 x noChange noChange 140100 b 40.30
HR MED 20600 2.97 x N/A N/A 4200 i minimum b 2.99 x > N Unk U1 x unk unk poor XX U1 x N/A N/A 25400 b 7.31
IT MED 158300 22.86 = 22000 45000 N/A i estimate c 23.85 - > Y U1 - good poor poor U1 U1 - U2 - noChange noChange 49200 b 14.15
PT MED 46000 6.64 = 46000 4000 N/A N/A i minimum c 2.85 = 4000 i Unk XX - good good unk FV FV - U1 + knowledge knowledge 17400 b 5.01
HU PAN 28946 91.53 = 4000 10000 N/A i estimate b 76 = > Y U1 = good good poor U1 U1 = U1 - noChange method 8300 b 84.69
RO PAN 1900 6.01 = 1000 3000 N/A i minimum b 21.71 = Y FV = good good good FV FV = U1 = knowledge knowledge 700 b 7.14
SK PAN 778.05 2.46 = 161 261 N/A i estimate b 2.29 + Y FV x good good good FV FV = U1 - knowledge knowledge 800 b 8.16
RO STE 1400 100 = 300 500 N/A i minimum b 100 = Y FV = good good good FV FV = U1 = knowledge knowledge 600 b 100
BE ATL 2300 0 - >> 1 5 3 i estimate a 0 u >> Unk XX x bad bad unk U2 U2 x U2 = noChange noChange 400 a 0
NL ATL N/A 0 N N/ N/A N/A N/A N/A 0 N N/ N/A N N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A 0
CZ CON 900 0 x >> 1 5 N/A i estimate a 0 = >> Y FV = bad bad good U2 U2 = U2 = noChange noChange 600 a 0
PL CON 100 0 x x N/A N/A 1 i minimum c 0 x x Unk XX x unk unk unk XX XX XX noChange noChange 100 b 0
CZ PAN N/A 0 x x N/A N/A N/A i estimate a 0 x x Y FV = unk unk good XX XX U2 = N/A N/A 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 0EQ = i 0EQ - > 0EQ - good poor poor 0EQ MTX = U1 = nc nc U1 D

02/20

EEA-ETC/BD

Institution: -

Member State:

EEA-ETC/BD
EU28 ATL 2XP + i 2XP + 2XP - good good poor 2XP MTX = U1 = nc nc U1 D

02/20

EEA-ETC/BD

Institution: -

Member State:

EEA-ETC/BD
EU28 BLS 0MS = i 0MS = x 0MS = good good good 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 poor poor 2XP MTX = U1 = nc nc U1 D

02/20

EEA-ETC/BD

Institution: -

Member State:

EEA-ETC/BD
EU28 MED 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 PAN 0EQ = i 0EQ = 0EQ = good good poor 0EQ MTX = U1 = nc nc U1 D

02/20

EEA-ETC/BD

Institution: -

Member State:

EEA-ETC/BD
EU28 STE 0MS = i 0MS = 0MS = good good good 0MS MTX + U1 = nong nong U1 A=

01/20

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.