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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

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 hipposideros, 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 16500 i estimate N/A N/A 600 grids1x1 estimate
BG 2000 2500 N/A i minimum N/A N/A N/A N/A
DE 278 468 348 i mean 139 234 174 bfemales mean
ES 575 N/A N/A i minimum 21 N/A N/A localities minimum
FR 3000 4000 N/A i mean N/A N/A N/A mean
HR N/A N/A 230 i minimum N/A N/A N/A N/A
IT 5000 30000 N/A i estimate N/A N/A N/A N/A
PL N/A N/A 6500 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 325 i minimum 325 369 N/A grids1x1 estimate
SK 1761 19480 N/A i estimate N/A N/A N/A N/A
ES 5000 N/A N/A i minimum 59 N/A N/A localities minimum
FR 28300 30000 N/A i mean N/A N/A N/A mean
IE N/A N/A 12791 i estimate N/A N/A N/A N/A
PT N/A N/A N/A N/A N/A N/A grids1x1 N/A
UK 36000 73000 50400 i estimate N/A N/A N/A N/A
BG 200 500 N/A i minimum N/A N/A N/A N/A
AT N/A N/A 1700 i estimate N/A N/A 132 grids1x1 estimate
BE 500 750 500 i estimate 200 475 200 iwintering estimate
BG 3000 6500 N/A i minimum N/A N/A N/A N/A
CZ 30000 40000 N/A i estimate N/A N/A N/A N/A
DE 12380 13586 12867 i minimum 6190 6793 6433.50 bfemales minimum
FR 39000 40000 N/A i mean N/A N/A N/A mean
HR N/A N/A 1585 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
LU N/A N/A N/A i estimate N/A N/A N/A N/A
PL N/A N/A 5500 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 542 i minimum 542 586 N/A grids1x1 N/A
CY 600 800 N/A i estimate N/A N/A N/A N/A
ES 10186 15716 N/A i minimum 270 N/A N/A localities minimum
FR 91917 183833 N/A i estimate N/A N/A N/A estimate
GR 3600 5000 N/A i estimate N/A N/A N/A N/A
HR N/A N/A 515 i minimum N/A N/A N/A N/A
IT 8000 50000 N/A i estimate N/A N/A N/A N/A
MT 200 400 N/A i estimate N/A N/A N/A estimate
PT 5000 N/A N/A i minimum N/A N/A N/A N/A
CZ 1000 2000 N/A i estimate N/A N/A N/A N/A
HU 3000 10000 N/A i estimate N/A N/A N/A N/A
RO 500 800 N/A i minimum N/A N/A N/A N/A
SK 137 1003 N/A i estimate N/A N/A N/A N/A
RO 100 200 N/A i minimum N/A N/A N/A N/A
NL N/A N/A N/A 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 27000 13.84 = > N/A N/A 16500 i estimate a 25.06 + > Y FV + good good good FV U1 + U1 + noChange noChange 21300 b 21.76
BG ALP 25900 13.28 = 25900 2000 2500 N/A i minimum b 3.42 = 2000 i Y FV = good good good FV FV = FV noChange method 8200 b 8.38
DE ALP 2997 1.54 + > 278 468 348 i mean a 0.53 + >> bfemales Y FV = unk bad good U2 U2 + U2 + noChange noChange 2000 b 2.04
ES ALP 15600 8 = > 575 N/A N/A i minimum b 0.87 x 21 localities Y U1 u poor unk poor U1 U1 - U1 - noChange noChange 4900 a 5.01
FR ALP 20500 10.51 = 3000 4000 N/A i mean a 5.32 + < Y Y XX = good good poor U1 U1 + U1 = noChange noChange 9000 a 9.19
HR ALP 11000 5.64 x N/A N/A 230 i minimum b 0.35 x >> N Unk U1 x good unk poor U1 U2 x N/A N/A 8400 b 8.58
IT ALP 41700 21.38 = 5000 30000 N/A i estimate c 26.58 - > N Y U1 - good poor poor U1 U1 - U1 - noChange noChange 12100 b 12.36
PL ALP 9100 4.66 = N/A N/A 6500 i estimate a 9.87 + Y FV u good unk poor U1 U1 = U1 + noChange knowledge 4500 a 4.60
RO ALP 12300 6.31 = 5000 10000 N/A i minimum b 11.39 = Y FV = good good good FV FV = U1 = knowledge knowledge 3300 b 3.37
SI ALP 7656 3.92 = 7656 N/A N/A 325 i minimum a 0.49 + N Unk U2 - good good bad U2 U2 - U2 x noChange knowledge 6600 b 6.74
SK ALP 21321.98 10.93 = 1761 19480 N/A i estimate b 16.13 + Y FV x good good good FV FV = U1 - knowledge knowledge 17600 b 17.98
ES ATL 68000 28.70 = > 5000 N/A N/A i minimum b 5.14 - 5000 i Y U1 = good poor poor U1 U1 - U1 - noChange noChange 45000 a 28.20
FR ATL 102400 43.22 = 28300 30000 N/A i mean a 29.95 + < Y Y FV = good good poor U1 U1 + U1 = noChange noChange 62300 a 39.04
IE ATL 11300 4.77 - 11400 N/A N/A 12791 i estimate a 13.14 + 12791 i N N U1 - poor good poor U1 U1 - FV genuine genuine 7400 a 4.64
PT ATL 1900 0.80 = 1900 N/A N/A N/A b 0 x x Unk XX x good unk unk XX XX U1 x knowledge noChange 200 b 0.13
UK ATL 53334 22.51 + 53334 36000 73000 50400 i estimate a 51.78 + 50400 i Y FV = good good good FV FV + FV noChange noChange 44700 a 28.01
BG BLS 9000 100 = 9000 200 500 N/A i minimum b 100 = 200 i Y FV = good good good FV FV = FV method method 2400 b 100
AT CON 9500 2.35 = > N/A N/A 1700 i estimate a 1.29 + > Y FV = poor good good U1 U1 + U1 - noChange knowledge 7200 b 4.22
BE CON 4520 1.12 + 500 750 500 i estimate a 0.38 + 2000 iwintering Y FV = good good good FV U2 + U2 = noChange noChange 2700 a 1.58
BG CON 94700 23.42 = 94700 3000 6500 N/A i minimum b 3.60 = 3000 i Y FV = good good good FV FV = FV method method 20500 b 12.02
CZ CON 52200 12.91 + 30000 40000 N/A i estimate a 26.53 + Y FV = good good good FV FV + FV noChange noChange 28100 a 16.48
DE CON 23114 5.72 + 27634 12380 13586 12867 i minimum a 9.75 + > bfemales N N U2 = bad poor unk U2 U2 + U2 + noChange noChange 11400 b 6.69
FR CON 102100 25.25 = 39000 40000 N/A i mean a 29.94 + < Y Y FV = good good poor U1 U1 + U1 = noChange noChange 52700 a 30.91
HR CON 11700 2.89 x >> N/A N/A 1585 i minimum b 1.20 x > N Unk U1 x poor unk poor U1 U2 x N/A N/A 10400 b 6.10
IT CON 68500 16.94 = 5000 40000 N/A i estimate c 17.05 - > Y U1 - good poor poor U1 U1 - U1 - knowledge noChange 19600 b 11.50
LU CON N/A 0 = 300 N/A N/A N/A i estimate b 0 = >> N Unk U1 x poor poor poor U1 U2 = U2 = noChange noChange N/A d 0
PL CON 9200 2.28 = N/A N/A 5500 i estimate a 4.17 + Y FV u good unk poor U1 U1 = U1 + noChange knowledge 3200 a 1.88
RO CON 16200 4.01 = 5000 10000 N/A i minimum b 5.68 = Y FV = good good good FV FV = U1 = knowledge knowledge 4700 b 2.76
SI CON 12597 3.12 = N/A N/A 542 i minimum a 0.41 + N Unk U2 - good good bad U2 U2 - U2 x noChange knowledge 10000 b 5.87
CY MED 9689 1.49 x 600 800 N/A i estimate b 0.37 x Y FV = good poor poor U1 U1 x U1 x noChange noChange 12400 b 3.35
ES MED 234000 36.02 = > 10186 15716 N/A i minimum b 6.79 u 270 localities Y U1 - good poor poor U1 U1 x U1 = noChange genuine 75000 a 20.24
FR MED 59900 9.22 + 91917 183833 N/A i estimate a 72.32 + < Y Y FV = good good poor U1 U1 + U2 = genuine noChange 35800 a 9.66
GR MED 129471 19.93 = 3600 5000 N/A i estimate b 2.26 x Y FV = good unk good FV FV x FV noChange noChange 161100 b 43.48
HR MED 25800 3.97 x N/A N/A 515 i minimum b 0.27 x >> N Unk U1 x good unk poor U1 U2 x N/A N/A 23700 b 6.40
IT MED 140300 21.59 = 8000 50000 N/A i estimate c 15.21 - > Y U1 - good poor poor U1 U1 - U1 - noChange noChange 43000 b 11.61
MT MED 41 0.01 = 200 400 N/A i estimate b 0.16 = > N Y FV = good poor good U1 U1 = U1 = noChange noChange 500 b 0.13
PT MED 50500 7.77 = 50500 5000 N/A N/A i minimum c 2.62 = 5000 i Unk XX - good good unk FV FV - XX knowledge knowledge 19000 b 5.13
CZ PAN 4900 13.84 + 1000 2000 N/A i estimate a 16.27 + Y FV = good good good FV FV + FV noChange noChange 1200 a 8
HU PAN 25669 72.51 = 3000 10000 N/A i estimate b 70.50 + Y U1 = good good poor U1 U1 + FV knowledge method 10000 b 66.67
RO PAN 1800 5.08 = 500 800 N/A i minimum b 7.05 = Y FV = good good good FV FV = U1 = knowledge knowledge 900 b 6
SK PAN 3033.60 8.57 = 137 1003 N/A i estimate b 6.18 + Y FV x good good good FV U1 = U1 - N/A N/A 2900 b 19.33
RO STE 200 100 = 100 200 N/A i minimum b 100 = Y FV = good good good FV FV = U1 = knowledge knowledge 200 b 100
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
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 good unk poor 2XP MTX = U1 = nc nc U1 D

01/20

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

01/20

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 0EQ = i 0EQ + > 0EQ x poor unk poor 0EQ MTX = U1 = nc nc U1 D

02/20

EEA-ETC/BD

Institution: -

Member State:

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

02/20

EEA-ETC/BD

Institution: -

Member State:

EEA-ETC/BD
EU28 PAN 0EQ = i 0EQ + 0EQ = good good poor 0EQ MTX - FV nong nong FV C

03/20

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.