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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.

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
Current selection: 2013-2018, Mammals, Vespertilio murinus, All bioregions. Annexes N, Y-HTL, 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 195 N/A N/A grids1x1 minimum N/A N/A N/A N/A
BG N/A N/A 55 grids1x1 minimum N/A N/A N/A N/A
DE 485 485 485 grids1x1 estimate 13 13 13 grids5x5 estimate
FR 100 500 N/A grids1x1 mean N/A N/A N/A mean
HR N/A N/A 8 grids1x1 minimum N/A N/A N/A N/A
IT 200 2000 N/A grids1x1 estimate N/A N/A N/A N/A
PL N/A N/A 1000 grids1x1 estimate N/A N/A N/A N/A
RO 100 300 N/A grids1x1 minimum N/A N/A N/A N/A
SI N/A N/A 66 grids1x1 estimate N/A N/A N/A N/A
SK 626 626 N/A grids1x1 estimate 476 518 N/A i N/A
DE 5310 5310 5310 grids1x1 estimate 64 73 68.50 grids5x5 estimate
DK N/A N/A N/A N/A N/A 9 localities N/A
NL N/A N/A 121 grids1x1 estimate 100 300 N/A i estimate
EE N/A N/A 271 grids1x1 estimate N/A N/A N/A N/A
LT N/A N/A 224 grids1x1 minimum N/A N/A N/A N/A
LV 37000 64589 N/A grids1x1 estimate N/A N/A N/A N/A
SE N/A N/A 1060 grids1x1 estimate 35000 105000 70000 i estimate
AT 123 N/A N/A grids1x1 minimum N/A N/A N/A N/A
BG N/A N/A 398 grids1x1 minimum N/A N/A N/A N/A
CZ 1614 1614 N/A grids1x1 estimate N/A N/A N/A N/A
DE 35489 35489 35489 grids1x1 estimate 795 800 797.50 grids5x5 estimate
DK N/A N/A N/A N/A N/A 42 localities N/A
FR 200 400 N/A grids1x1 mean N/A N/A N/A mean
HR N/A N/A 1 grids1x1 minimum N/A N/A N/A N/A
IT 85 850 N/A grids1x1 estimate N/A N/A N/A N/A
PL N/A N/A 5550 grids1x1 estimate N/A N/A N/A N/A
RO 200 500 N/A grids1x1 minimum N/A N/A N/A N/A
SE N/A N/A 299 grids1x1 estimate 3000 9000 6000 i estimate
SI N/A N/A 1 grids1x1 estimate N/A N/A N/A N/A
FR 20 50 N/A grids1x1 mean N/A N/A N/A mean
GR N/A N/A 35691 grids1x1 estimate 100 500 N/A grids5x5 estimate
HR N/A N/A 5 grids1x1 minimum N/A N/A N/A N/A
CZ 96 96 N/A grids1x1 estimate N/A N/A N/A N/A
HU N/A N/A 14 grids1x1 minimum N/A N/A N/A N/A
SK 192 192 N/A grids1x1 estimate 277 277 N/A i N/A
RO 100 300 N/A grids1x1 minimum N/A N/A N/A N/A
BE N/A N/A 20 grids1x1 N/A N/A N/A N/A
FR 10 20 N/A grids1x1 mean N/A N/A N/A mean
UK N/A N/A N/A N/A N/A N/A N/A
BG N/A N/A 3 grids1x1 minimum N/A N/A N/A N/A
FI N/A N/A N/A N/A N/A N/A N/A
BE N/A N/A 5 grids1x1 minimum N/A N/A N/A N/A
LU N/A N/A 2 grids1x1 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 13400 12.47 = 195 N/A N/A grids1x1 minimum b 4.83 x x Y FV = good unk poor U1 U1 = U1 = noChange noChange 10600 b 25
BG ALP 20100 18.71 = 20100 N/A N/A 55 grids1x1 minimum b 1.36 = 55 grids1x1 Y FV = good good good FV FV = FV noChange knowledge 2500 b 5.90
DE ALP 3326 3.10 = 485 485 485 grids1x1 estimate b 12.02 x x grids5x5 Y FV = good unk good FV FV = XX knowledge knowledge 1700 c 4.01
FR ALP 6500 6.05 + 100 500 N/A grids1x1 mean c 7.43 = x Unk Y FV = good unk unk XX XX + XX knowledge knowledge 2700 b 6.37
HR ALP 3000 2.79 x >> N/A N/A 8 grids1x1 minimum c 0.20 x >> Unk XX x unk unk unk XX U2 x N/A N/A 2200 b 5.19
IT ALP 23400 21.78 + x 200 2000 N/A grids1x1 estimate c 27.26 x Unk XX x good unk unk XX XX XX noChange knowledge 4100 b 9.67
PL ALP 4700 4.37 u N/A N/A 1000 grids1x1 estimate b 24.78 x x Y FV x good unk good FV FV x FV noChange noChange 1500 c 3.54
RO ALP 13000 12.10 = 100 300 N/A grids1x1 minimum b 4.96 = Y FV = good good good FV FV = U1 = knowledge knowledge 4500 b 10.61
SI ALP 7656 7.13 = 7656 N/A N/A 66 grids1x1 estimate c 1.64 x x Y XX x good unk unk XX XX XX noChange noChange 2500 c 5.90
SK ALP 12364.26 11.51 x 626 626 N/A grids1x1 estimate c 15.51 + Y FV x unk good good FV FV x XX knowledge knowledge 10100 b 23.82
DE ATL 28474 60.11 u 5310 5310 5310 grids1x1 estimate b 97.77 x x grids5x5 Unk XX x unk unk unk XX XX x XX noChange noChange 8100 c 47.09
DK ATL 8596 18.15 + N/A N/A N/A d 0 + x Unk Y FV u good unk good FV FV + N/A N/A N/A N/A 700 b 4.07
NL ATL 10300 21.74 x N/A N/A 121 grids1x1 estimate b 2.23 x x N N U1 x good unk poor U1 U1 x U2 = genuine noChange 8400 b 48.84
EE BOR 16200 5.23 + N/A N/A 271 grids1x1 estimate b 0.52 + x Y FV = good good good FV FV + FV noChange noChange 6000 a 8.52
LT BOR 65200 21.05 = N/A N/A 224 grids1x1 minimum c 0.43 x x Unk FV = good unk good FV FV = FV knowledge knowledge 14900 b 21.16
LV BOR 64589 20.85 = 64589 37000 64589 N/A grids1x1 estimate b 97.03 x 64589 grids1x1 Y FV = good good good FV FV = XX knowledge knowledge 7800 b 11.08
SE BOR 163800 52.87 + 163800 N/A N/A 1060 grids1x1 estimate c 2.02 + 70000 i Y FV = good good good FV FV + U2 = genuine genuine 41700 b 59.23
AT CON 7700 1.42 = 123 N/A N/A grids1x1 minimum b 0.28 x x Y FV = good unk poor U1 U1 = U1 = noChange noChange 5600 b 3.36
BG CON 61000 11.24 u 61000 N/A N/A 398 grids1x1 minimum b 0.89 = 398 grids1x1 Y FV = poor poor poor U1 U1 = U1 - noChange knowledge 12100 b 7.26
CZ CON 73400 13.53 = 1614 1614 N/A grids1x1 estimate a 3.62 = Y FV = good good good FV FV = FV noChange noChange 27300 a 16.38
DE CON 262915 48.47 - 267224 35489 35489 35489 grids1x1 estimate b 79.59 x x grids5x5 N Unk U1 x poor unk unk XX U1 x XX knowledge noChange 76100 c 45.65
DK CON 18449 3.40 = N/A N/A N/A d 0 u x Y FV = good unk good FV FV x FV N/A N/A 4600 b 2.76
FR CON 2100 0.39 + 200 400 N/A grids1x1 mean c 0.67 x x Unk Y FV = good unk unk XX XX = U1 x noChange noChange 2200 b 1.32
HR CON 900 0.17 x >> N/A N/A 1 grids1x1 minimum c 0 x >> Unk XX x unk unk unk XX U2 x N/A N/A 900 b 0.54
IT CON 7800 1.44 + x 85 850 N/A grids1x1 estimate c 1.05 x Unk XX x good unk unk XX XX XX noChange knowledge 1500 b 0.90
PL CON 29800 5.49 = N/A N/A 5550 grids1x1 estimate c 12.45 x x Y FV x good good good FV FV x XX knowledge noInfo 10500 c 6.30
RO CON 40700 7.50 = 200 500 N/A grids1x1 minimum b 0.78 = Y FV = good good good FV FV = U1 = knowledge knowledge 16200 b 9.72
SE CON 25100 4.63 = 25100 N/A N/A 299 grids1x1 estimate c 0.67 = 6000 i Y FV = good good good FV FV = U2 = genuine genuine 9600 b 5.76
SI CON 12616 2.33 = 12616 N/A N/A 1 grids1x1 estimate c 0 x x Y XX x good unk unk XX XX XX noChange noChange 100 c 0.06
FR MED 850 1.47 x x 20 50 N/A grids1x1 mean c 0.10 x x Unk Unk XX x unk unk unk XX XX x XX noChange noChange 400 b 0.95
GR MED 51745 89.53 x x N/A N/A 35691 grids1x1 estimate b 99.89 x x N Unk U1 x unk poor poor U1 U1 x U1 x noChange noChange 37300 b 88.60
HR MED 5200 9 x >> N/A N/A 5 grids1x1 minimum c 0.01 x >> N Unk XX x poor poor poor U1 U2 x N/A N/A 4400 b 10.45
CZ PAN 4900 4.86 = 96 96 N/A grids1x1 estimate a 31.79 = Y FV = good good good FV FV = FV noChange noChange 1200 a 24
HU PAN 93011 92.27 = N/A N/A 14 grids1x1 minimum c 4.64 x Y FV x good unk good FV FV x FV noChange method 900 c 18
SK PAN 2887.40 2.86 x 192 192 N/A grids1x1 estimate c 63.58 + Y FV x unk good good FV FV x XX knowledge knowledge 2900 b 58
RO STE 3300 100 = 100 300 N/A grids1x1 minimum b 100 = Y FV = good good good FV FV = U1 = knowledge knowledge 1700 b 100
BE ATL 3200 0 N N/ N/A N/A 20 grids1x1 b 0 N N/ XX N N/A N/A N/A XX XX N/A N/A N/A N/A 1100 b 0
FR ATL 900 0 x x 10 20 N/A grids1x1 mean c 0 x x Unk Unk XX x unk unk unk XX XX x N/A N/A noChange noChange 700 b 0
UK 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 noChange noChange N/A d 0
BG BLS 1700 0 = 1700 N/A N/A 3 grids1x1 minimum b 0 u 3 grids1x1 Y FV = unk unk unk XX XX = XX knowledge knowledge 300 b 0
FI BOR 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 2300 a 0
BE CON 1000 0 N N/ N/A N/A 5 grids1x1 minimum N/A 0 N N/ XX N N/A N/A N/A XX XX N/A N/A N/A N/A 300 b 0
LU CON 200 0 x x N/A N/A 2 grids1x1 estimate c 0 x x Unk XX x unk unk unk XX XX XX noChange noChange 200 c 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 + x grids1x1 2XP x x 2XP x good unk unk 2XP MTX = XX = nc nc XX D

02/20

EEA-ETC/BD

Institution: -

Member State:

EEA-ETC/BD
EU28 ATL 0EQ x grids1x1 0EQ x x 0EQ x unk unk unk 0EQ MTX U2 = nong nong U2 D

02/20

EEA-ETC/BD

Institution: -

Member State:

EEA-ETC/BD
EU28 BLS 0MS = grids1x1 0MS x x 0MS = unk unk unk 0MS MTX x x nong nong D

02/20

EEA-ETC/BD

Institution: -

Member State:

EEA-ETC/BD
EU28 BOR 0EQ + grids1x1 0EQ x x 0EQ = good good good 0EQ MTX + U2 = nong nong U2 A=

02/20

EEA-ETC/BD

Institution: -

Member State:

EEA-ETC/BD
EU28 CON 0EQ - x grids1x1 0EQ x x 0EQ x unk unk unk 0EQ MTX x XX = nong nong XX D

02/20

EEA-ETC/BD

Institution: -

Member State:

EEA-ETC/BD
EU28 MED 0EQ x x grids1x1 0EQ x x 0EQ x unk poor poor 0EQ MTX = U1 x nc nc XX D

02/20

EEA-ETC/BD

Institution: -

Member State:

EEA-ETC/BD
EU28 PAN 2XP = x grids1x1 2XP + 2XP x good good good 2XP MTX XX = nong nong XX A=

02/20

EEA-ETC/BD

Institution: -

Member State:

EEA-ETC/BD
EU28 STE 0MS = grids1x1 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.