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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, Pipistrellus pipistrellus, 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 1074 N/A N/A grids1x1 minimum N/A N/A N/A N/A
BG N/A N/A 57 grids1x1 minimum N/A N/A N/A N/A
DE 3747 3747 3747 grids1x1 estimate 70 70 70 grids5x5 estimate
ES 68 6800 N/A grids1x1 estimate N/A N/A N/A N/A
FR 500000 1000000 N/A grids1x1 minimum N/A N/A N/A minimum
HR N/A N/A 7 grids1x1 minimum N/A N/A N/A N/A
IT 3000 90000 N/A grids1x1 estimate N/A N/A N/A N/A
PL N/A N/A 1200 grids1x1 minimum N/A N/A N/A N/A
RO 1000 5000 N/A grids1x1 minimum N/A N/A N/A N/A
SI 138 159 N/A grids1x1 estimate N/A N/A N/A N/A
SK 1228 1228 N/A grids1x1 estimate 472 5079 N/A i N/A
BE N/A N/A 2462 grids1x1 estimate N/A N/A N/A N/A
DE 43399 43399 43399 grids1x1 minimum 416 1381 898.50 grids5x5 minimum
DK N/A N/A N/A N/A N/A 13 localities N/A
ES 275 27500 N/A grids1x1 estimate N/A N/A N/A N/A
FR 1500000 3000000 N/A grids1x1 minimum N/A N/A N/A minimum
IE N/A N/A 3637 grids1x1 minimum 1070000 2400000 N/A i estimate
NL N/A N/A 8952 grids1x1 estimate 200000 600000 400000 i estimate
PT N/A N/A 40 grids1x1 minimum N/A N/A N/A N/A
UK N/A N/A 31559 grids1x1 minimum 1100600 7843000 N/A i interval
BG N/A N/A 30 grids1x1 minimum N/A N/A N/A N/A
EE N/A N/A 121 grids1x1 minimum N/A N/A N/A N/A
LT N/A N/A 541 grids1x1 minimum N/A N/A N/A N/A
LV 45000 64589 N/A grids1x1 estimate N/A N/A N/A N/A
SE N/A N/A 47 grids1x1 estimate 588 1762 1175 i estimate
AT 449 N/A N/A grids1x1 minimum N/A N/A N/A N/A
BE N/A N/A 1300 grids1x1 minimum N/A N/A N/A N/A
BG N/A N/A 351 grids1x1 minimum N/A N/A N/A N/A
CZ 6546 6546 N/A grids1x1 estimate N/A N/A N/A N/A
DE 210428 210428 210428 grids1x1 estimate 2095 2219 2157 grids5x5 estimate
DK N/A N/A N/A N/A N/A 41 localities N/A
FR 2000000 4000000 N/A grids1x1 minimum N/A N/A N/A minimum
HR N/A N/A 31 grids1x1 minimum N/A N/A N/A N/A
IT 5000 150000 N/A grids1x1 estimate N/A N/A N/A N/A
LU N/A N/A 2787 grids1x1 estimate N/A N/A N/A N/A
PL N/A N/A 16800 grids1x1 estimate N/A N/A N/A N/A
RO 2000 5000 N/A grids1x1 minimum N/A N/A N/A N/A
SE N/A N/A 36 grids1x1 estimate 450 1350 900 i estimate
SI 100 121 N/A grids1x1 estimate N/A N/A N/A N/A
CY 15 100 15 grids1x1 estimate N/A N/A N/A N/A
ES 1528 152800 N/A grids1x1 estimate N/A N/A N/A N/A
FR 800000 1200000 N/A grids1x1 minimum 1160880 4464930 N/A i minimum
GR N/A N/A 90605 grids1x1 estimate 2317 3144 N/A grids5x5 estimate
HR N/A N/A 12 grids1x1 minimum N/A N/A N/A N/A
IT 4500 140000 N/A grids1x1 estimate N/A N/A N/A N/A
MT N/A N/A 119 grids1x1 estimate N/A N/A N/A N/A
PT N/A N/A 1101 grids1x1 minimum N/A N/A N/A N/A
CZ 196 196 N/A grids1x1 estimate N/A N/A N/A N/A
HU N/A N/A 648 grids1x1 minimum N/A N/A N/A N/A
SK 746 746 N/A grids1x1 estimate 76 375 N/A i N/A
RO 500 2000 N/A 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
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 34700 17 = 1074 N/A N/A grids1x1 minimum b 0.13 x x Y FV = good unk good FV FV = FV noChange noChange 27800 b 25.39
BG ALP 22500 11.02 = 22500 N/A N/A 57 grids1x1 minimum b 0.01 = 57 grids1x1 Y FV = unk unk unk XX FV = FV noChange method 2000 b 1.83
DE ALP 4155 2.04 = 4155 3747 3747 3747 grids1x1 estimate b 0.46 = 70 grids5x5 Y FV = good good good FV FV = FV noChange noChange 4100 c 3.74
ES ALP 9800 4.80 = > 68 6800 N/A grids1x1 estimate b 0.42 x 6800 grids1x1 Y U1 u good good poor FV U1 x U1 x noChange noChange 4500 a 4.11
FR ALP 26400 12.93 = 500000 1000000 N/A grids1x1 minimum c 92.55 = < Y Y FV = good unk good FV FV = FV noChange noChange 17200 a 15.71
HR ALP 1500 0.73 x >> N/A N/A 7 grids1x1 minimum c 0 x >> Unk XX x unk unk unk XX U2 x N/A N/A 1400 b 1.28
IT ALP 63100 30.91 = 3000 90000 N/A grids1x1 estimate c 5.74 = Y FV = good good good FV FV = FV noChange noChange 25200 b 23.01
PL ALP 3000 1.47 x N/A N/A 1200 grids1x1 minimum b 0.15 x x Y FV = good good good FV FV x FV noChange noChange 1100 b 1
RO ALP 14400 7.05 = 1000 5000 N/A grids1x1 minimum b 0.37 = Y FV = good good good FV FV = U1 = knowledge knowledge 4300 b 3.93
SI ALP 7656 3.75 = 138 159 N/A grids1x1 estimate c 0.02 u > Y XX x good poor unk U1 U1 x XX knowledge noChange 4700 c 4.29
SK ALP 16899.69 8.28 = 1228 1228 N/A grids1x1 estimate b 0.15 + Y FV x good good good FV FV = XX knowledge knowledge 17200 b 15.71
BE ATL 22500 3.40 = N/A N/A 2462 grids1x1 estimate b 0.10 + Y FV = good good good FV FV + FV noChange knowledge 19300 a 3.59
DE ATL 72298 10.92 = 72298 43399 43399 43399 grids1x1 minimum c 1.84 = grids5x5 Y FV = good good good FV FV = FV noChange noChange 47100 c 8.77
DK ATL 5054 0.76 + N/A N/A N/A d 0 + x N Y U1 + good unk poor U1 U1 + FV N/A N/A 1200 b 0.22
ES ATL 50800 7.67 = > 275 27500 N/A grids1x1 estimate b 0.59 x 27500 grids1x1 Y U1 = poor unk poor FV U1 = U1 x noChange noChange 25000 a 4.65
FR ATL 145900 22.04 = 1500000 3000000 N/A grids1x1 minimum c 95.58 - > Y Y U1 - unk poor poor U1 U2 - U2 - noChange noChange 119800 a 22.30
IE ATL 79300 11.98 = 79300 N/A N/A 3637 grids1x1 minimum a 0.15 + 1070000 i Y FV = good good good FV FV + FV noChange noChange 66500 a 12.38
NL ATL 46700 7.06 = N/A N/A 8952 grids1x1 estimate b 0.38 x Y XX x good unk unk XX XX FV method noChange 43000 a 8
PT ATL 5900 0.89 = 5900 N/A N/A 40 grids1x1 minimum b 0 x 5900 grids1x1 Unk XX x good unk unk XX XX XX noChange knowledge 2100 b 0.39
UK ATL 233480 35.27 = 230973 N/A N/A 31559 grids1x1 minimum a 1.34 + i Y FV = good good good FV FV + FV noChange noChange 213300 a 39.70
BG BLS 7100 100 = 7100 N/A N/A 30 grids1x1 minimum b 100 = 30 grids1x1 Y FV = unk unk unk XX FV = FV noChange method 1500 b 100
EE BOR 20700 12.66 + N/A N/A 121 grids1x1 minimum b 0.22 u x Y FV = good good good FV FV = FV noChange noChange 5500 a 14.55
LT BOR 65200 39.88 = N/A N/A 541 grids1x1 minimum c 0.97 = x Y FV x good good good FV FV = U1 = knowledge knowledge 24800 b 65.61
LV BOR 64589 39.51 = 64589 45000 64589 N/A grids1x1 estimate c 98.72 x 64589 grids1x1 Unk XX x good unk unk XX XX XX noChange noChange 3800 b 10.05
SE BOR 13000 7.95 + 30000 N/A N/A 47 grids1x1 estimate c 0.08 + 1500 i Y FV = bad poor unk U2 U2 + U2 x noChange noChange 3700 b 9.79
AT CON 19600 2.43 = 449 N/A N/A grids1x1 minimum c 0.01 x x Y FV = good unk good FV FV = FV noChange noChange 13500 b 2.84
BE CON 15000 1.86 = N/A N/A 1300 grids1x1 minimum a 0.04 u Y FV x good good good FV FV x FV noChange noChange 11800 a 2.48
BG CON 77100 9.57 = 77100 N/A N/A 351 grids1x1 minimum b 0.01 = 351 grids1x1 Y FV = unk unk unk XX FV = FV method method 13000 b 2.74
CZ CON 82400 10.23 + 6546 6546 N/A grids1x1 estimate a 0.20 + 6546 grids1x1 Y FV = good good good FV FV + FV noChange noChange 51700 a 10.88
DE CON 290880 36.12 = 290880 210428 210428 210428 grids1x1 estimate b 6.34 = 2157 grids5x5 Y FV = good good unk FV FV = FV noChange noChange 228500 c 48.11
DK CON 11196 1.39 + N/A N/A N/A d 0 + x Y FV = good unk good FV FV + FV N/A N/A 4300 b 0.91
FR CON 103500 12.85 = 2000000 4000000 N/A grids1x1 minimum b 90.37 - > Y Y U1 - poor unk poor U1 U2 - U1 - genuine noChange 76400 a 16.08
HR CON 8200 1.02 x >> N/A N/A 31 grids1x1 minimum c 0 x >> N Unk U1 x unk unk poor XX U2 x N/A N/A 8000 b 1.68
IT CON 90000 11.17 = 5000 150000 N/A grids1x1 estimate c 2.33 = Y FV = good good good FV FV = FV noChange noChange 28000 b 5.89
LU CON 4000 0.50 = N/A N/A 2787 grids1x1 estimate c 0.08 u 2787 grids1x1 Y FV = good unk unk XX FV - FV method genuine 2600 c 0.55
PL CON 44300 5.50 x N/A N/A 16800 grids1x1 estimate b 0.51 x x Y FV = good good good FV FV x FV noChange noChange 16600 b 3.49
RO CON 35400 4.40 = 2000 5000 N/A grids1x1 minimum b 0.11 = Y FV = good good good FV FV = U1 = knowledge knowledge 12100 b 2.55
SE CON 11200 1.39 + 15000 N/A N/A 36 grids1x1 estimate c 0 + 1000 i Y FV = bad bad unk U2 U2 + U2 x genuine genuine 3200 b 0.67
SI CON 12616 1.57 = 100 121 N/A grids1x1 estimate c 0 u > Y XX x good poor unk U1 U1 x XX knowledge noChange 5300 c 1.12
CY MED 9689 1.42 x 15 100 15 grids1x1 estimate b 0 x Y FV = good good good FV FV x FV noChange noChange 13400 b 3.55
ES MED 300700 43.97 = > 1528 152800 N/A grids1x1 estimate b 6.22 x 152800 grids1x1 Y U1 = poor good poor FV U1 = U1 x noChange noChange 155800 a 41.33
FR MED 48400 7.08 = 800000 1200000 N/A grids1x1 minimum c 80.56 x Y Y FV = good unk poor U1 U1 = U1 = noChange noChange 35400 a 9.39
GR MED 114202 16.70 = N/A N/A 90605 grids1x1 estimate b 7.30 x Y FV = good unk good FV FV x FV noChange noChange 99800 b 26.47
HR MED 3300 0.48 x >> N/A N/A 12 grids1x1 minimum c 0 x >> N Unk U1 x unk unk poor XX U2 x N/A N/A 3000 b 0.80
IT MED 132600 19.39 = 4500 140000 N/A grids1x1 estimate c 5.82 = Y FV = good good good FV FV = FV noChange noChange 37300 b 9.89
MT MED 409 0.06 = N/A N/A 119 grids1x1 estimate b 0.01 = N Y FV = good good good FV FV = FV noChange knowledge 1000 b 0.27
PT MED 74500 10.89 = 74500 N/A N/A 1101 grids1x1 minimum b 0.09 x 74500 grids1x1 Y FV = good good good FV FV = FV noChange knowledge 31300 b 8.30
CZ PAN 5500 5.17 = 5500 196 196 N/A grids1x1 estimate a 12.33 = 196 grids1x1 Y FV = good good good FV FV = FV noChange noChange 2100 a 7.09
HU PAN 93011 87.50 = N/A N/A 648 grids1x1 minimum c 40.75 u Y FV = good good good FV FV = FV noChange genuine 18800 b 63.51
SK PAN 7789.25 7.33 = 746 746 N/A grids1x1 estimate b 46.92 + Y FV x good good good FV FV = XX knowledge knowledge 8700 b 29.39
RO STE 2400 100 + 500 2000 N/A grids1x1 minimum b 100 + Y FV + good good good FV FV + U1 = knowledge knowledge 900 b 100
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 2800 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 = grids1x1 2XP = 2XP = good good good 2XP MTX XX = nong nong XX A=

12/19

EEA-ETC/BD

Institution: -

Member State:

EEA-ETC/BD
EU28 ATL 2XR = 2XR - x 2XR - good poor poor 2XR MTX - FV nong nong FV C

03/20

EEA-ETC/BD

Institution: -

Member State:

EEA-ETC/BD
EU28 BLS 0MS = grids1x1 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 BOR 2XR = grids1x1 2XR x x 2XR x unk unk unk 2XR MTX x U1 = nong nong U1 D

02/20

EEA-ETC/BD

Institution: -

Member State:

EEA-ETC/BD
EU28 CON 2XR = grids1x1 2XR - x 2XR = unk unk poor 2XR MTX - FV = nong nong FV C

03/20

EEA-ETC/BD

Institution: -

Member State:

EEA-ETC/BD
EU28 MED 2XR = > grids1x1 2XR x 2XR = poor good poor 2XR MTX = U1 = nc nc U1 D

02/20

EEA-ETC/BD

Institution: -

Member State:

EEA-ETC/BD
EU28 PAN 0EQ = grids1x1 0EQ + 0EQ = good good good 0EQ MTX = FV = nc nc FV A=

12/19

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=

12/19

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.