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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 kuhlii, 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 54 N/A N/A grids1x1 minimum N/A N/A N/A N/A
ES 35 3500 N/A grids1x1 estimate N/A N/A N/A N/A
FR 32000 40000 N/A grids1x1 minimum N/A N/A N/A minimum
HR N/A N/A 3 grids1x1 minimum N/A N/A N/A N/A
IT 1700 51000 N/A grids1x1 estimate N/A N/A N/A N/A
RO 250 500 N/A grids1x1 minimum N/A N/A N/A N/A
SI 72 81 N/A grids1x1 estimate N/A N/A N/A N/A
ES 90 9000 N/A grids1x1 estimate N/A N/A N/A N/A
FR 200000 300000 N/A grids1x1 minimum N/A N/A N/A minimum
PT N/A N/A 9 grids1x1 minimum N/A N/A N/A N/A
BG N/A N/A 7 grids1x1 minimum N/A N/A N/A N/A
AT 99 N/A N/A grids1x1 minimum N/A N/A N/A N/A
BG N/A N/A 259 grids1x1 minimum N/A N/A N/A N/A
CZ 1 4 N/A grids1x1 estimate N/A N/A N/A N/A
DE 2436 2436 2436 grids1x1 mean 364 602 454 bfemales mean
FR 1200000 1500000 N/A grids1x1 minimum N/A N/A N/A minimum
HR N/A N/A 66 grids1x1 minimum N/A N/A N/A N/A
IT 5000 165000 N/A grids1x1 estimate N/A N/A N/A N/A
RO 1000 1500 N/A grids1x1 minimum N/A N/A N/A N/A
SI 247 256 N/A grids1x1 estimate N/A N/A N/A N/A
ES 17 1700 N/A grids1x1 estimate N/A N/A N/A N/A
CY 72 1000 72 grids1x1 estimate N/A N/A N/A N/A
ES 428 42800 N/A grids1x1 estimate N/A N/A N/A N/A
FR 800000 1200000 N/A grids1x1 minimum 102573 410292 N/A i minimum
GR N/A N/A 131402 grids1x1 estimate 2880 4286 N/A grids5x5 estimate
HR N/A N/A 40 grids1x1 minimum N/A N/A N/A N/A
IT 10000 250000 N/A grids1x1 estimate N/A N/A N/A N/A
MT N/A N/A 104 grids1x1 estimate N/A N/A N/A N/A
PT N/A N/A 939 grids1x1 minimum N/A N/A N/A N/A
CZ N/A 2 N/A grids1x1 estimate N/A N/A N/A N/A
HU N/A N/A 56 grids1x1 minimum N/A N/A N/A N/A
SK 184 184 N/A grids1x1 estimate 19 81 N/A i N/A
RO 500 1000 N/A grids1x1 minimum N/A N/A N/A N/A
BG N/A N/A 24 grids1x1 minimum N/A N/A N/A N/A
BE N/A N/A 2 grids1x1 estimate N/A N/A N/A N/A
UK 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 2200 2.36 = > 54 N/A N/A grids1x1 minimum b 0.08 + > Y FV = good good good FV U1 + U1 + noChange noChange 2200 b 5.95
ES ALP 8500 9.13 + 17900 35 3500 N/A grids1x1 estimate b 2.73 x 463 grids1x1 Y FV = good good good FV U2 = U1 = knowledge genuine 2400 a 6.49
FR ALP 15300 16.44 + 32000 40000 N/A grids1x1 minimum c 55.71 = < Y Y XX = good good good FV FV + U1 x noChange noChange 9600 a 25.95
HR ALP 1400 1.50 x x N/A N/A 3 grids1x1 minimum c 0 x x Unk XX x unk unk unk XX XX N/A N/A 900 b 2.43
IT ALP 54400 58.46 + 1700 51000 N/A grids1x1 estimate c 40.77 = Y FV = good good good FV FV + FV noChange noChange 17100 b 46.22
RO ALP 3600 3.87 = 250 500 N/A grids1x1 minimum b 0.58 = Y U1 = poor poor poor U1 U1 = U1 x knowledge knowledge 1300 b 3.51
SI ALP 7656 8.23 = 7656 72 81 N/A grids1x1 estimate a 0.12 = Y FV = good good good FV FV = XX knowledge knowledge 3500 b 9.46
ES ATL 17900 17.38 + 17900 90 9000 N/A grids1x1 estimate b 1.79 x 90 grids1x1 Y FV x good good good FV FV = FV genuine knowledge 8400 a 11.40
FR ATL 79700 77.38 + 200000 300000 N/A grids1x1 minimum b 98.21 = Y Y FV = good good good FV FV + FV noChange noChange 64400 a 87.38
PT ATL 5400 5.24 = 5400 N/A N/A 9 grids1x1 minimum b 0 x 5400 grids1x1 Unk XX x good unk unk XX XX XX noChange knowledge 900 c 1.22
BG BLS 4600 100 = 4600 N/A N/A 7 grids1x1 minimum b 100 = 7 grids1x1 Y FV = unk unk unk XX FV = U1 - noChange knowledge 600 b 100
AT CON 4600 1.79 + > 99 N/A N/A grids1x1 minimum b 0.01 + > Y FV = good good good FV U1 + U1 + noChange noChange 3500 b 2.80
BG CON 58000 22.52 u 58000 N/A N/A 259 grids1x1 minimum b 0.02 = 259 grids1x1 Y FV = good good good FV FV = U1 - knowledge knowledge 10600 b 8.49
CZ CON 600 0.23 + x 1 4 N/A grids1x1 estimate c 0 + x Y FV = unk unk good XX XX XX noChange noChange 500 a 0.40
DE CON 11663 4.53 + 11663 2436 2436 2436 grids1x1 mean b 0.17 + bfemales Y FV = good good good FV FV + FV noChange noChange 4200 b 3.36
FR CON 41700 16.19 + 1200000 1500000 N/A grids1x1 minimum b 93.79 - Y FV = good unk good FV FV - FV knowledge noChange 29500 a 23.62
HR CON 15500 6.02 x >> N/A N/A 66 grids1x1 minimum b 0 x >> N Unk U1 x poor unk poor U1 U2 x N/A N/A 14900 b 11.93
IT CON 94100 36.53 = 5000 165000 N/A grids1x1 estimate c 5.91 = Y FV = good good good FV FV = FV noChange noChange 45400 b 36.35
RO CON 18800 7.30 + 1000 1500 N/A grids1x1 minimum b 0.09 + Y U1 + poor poor poor U1 U1 + U1 x knowledge knowledge 8100 b 6.49
SI CON 12616 4.90 = 12616 247 256 N/A grids1x1 estimate a 0.02 = Y FV = good good good FV FV = XX knowledge knowledge 8200 b 6.57
ES MAC 2300 100 = 2300 17 1700 N/A grids1x1 estimate b 100 x 17 grids1x1 Unk XX x good good poor FV FV = FV noChange noChange 200 a 100
CY MED 9689 1.65 x 72 1000 72 grids1x1 estimate b 0.01 x Y FV = good good good FV FV x FV noChange noChange 13300 b 3.93
ES MED 163100 27.81 + 163100 428 42800 N/A grids1x1 estimate b 1.68 x 428 grids1x1 Y FV = good good good FV FV = FV noChange noChange 44300 a 13.10
FR MED 43300 7.38 = 800000 1200000 N/A grids1x1 minimum c 77.87 = Y FV = good good poor U1 U1 = U1 = noChange noChange 32000 a 9.46
GR MED 130175 22.19 = N/A N/A 131402 grids1x1 estimate b 10.23 = Y FV = good good good FV FV = FV noChange noChange 157600 b 46.61
HR MED 9700 1.65 x >> N/A N/A 40 grids1x1 minimum c 0 x >> N Unk U1 x unk unk poor XX U2 x N/A N/A 9500 b 2.81
IT MED 154800 26.39 = 10000 250000 N/A grids1x1 estimate c 10.12 = Y FV = good good good FV FV = FV noChange noChange 50100 b 14.82
MT MED 409 0.07 = N/A N/A 104 grids1x1 estimate b 0.01 = N Y FV = good good good FV FV = FV noChange knowledge 1000 b 0.30
PT MED 75400 12.85 = 75400 N/A N/A 939 grids1x1 minimum b 0.07 x 75400 grids1x1 Y FV = good good good FV FV = FV noChange knowledge 30300 c 8.96
CZ PAN 300 0.66 + x N/A 2 N/A grids1x1 estimate c 0.41 + x Y FV = unk unk good XX XX N/A N/A knowledge knowledge 100 a 1.56
HU PAN 44315 96.78 + N/A N/A 56 grids1x1 minimum c 23.24 + Y FV + good good good FV FV + FV noChange method 4100 b 64.06
SK PAN 1176.61 2.57 + > 184 184 N/A grids1x1 estimate a 76.35 + > Y FV + good good good FV FV = XX knowledge knowledge 2200 b 34.38
RO STE 4200 100 + 500 1000 N/A grids1x1 minimum b 100 + Y U1 + poor poor poor U1 U1 + U1 = knowledge knowledge 1800 b 100
BG ALP 18900 0 u 18900 N/A N/A 24 grids1x1 minimum b 0 = 24 grids1x1 Y FV = unk unk unk XX XX = XX knowledge knowledge 1000 b 0
BE ATL 100 0 N N/ N/A N/A 2 grids1x1 estimate 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 100 a 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
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 2XR + grids1x1 2XR = 2XR = good good good 2XR MTX + U1 x nong nong U1 A=

03/20

EEA-ETC/BD

Institution: -

Member State:

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

02/20

EEA-ETC/BD

Institution: -

Member State:

EEA-ETC/BD
EU28 BLS 0MS = grids1x1 0MS = x 0MS = unk unk unk 0MS MTX + U1 - nong nong U1 A=

02/20

EEA-ETC/BD

Institution: -

Member State:

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

03/20

EEA-ETC/BD

Institution: -

Member State:

EEA-ETC/BD
EU28 MAC 0MS = grids1x1 0MS x 0MS x good good poor 0MS MTX = FV nc nc FV A=

03/20

EEA-ETC/BD

Institution: -

Member State:

EEA-ETC/BD
EU28 MED 2XR + grids1x1 2XR = 2XR = good good good 2XR MTX = FV nc nc U1 A+

03/20

EEA-ETC/BD

Institution: -

Member State:

EEA-ETC/BD
EU28 PAN 2XR + x grids1x1 2XR + x 2XR + good good good 2XR MTX = FV nc nc FV A=

03/20

EEA-ETC/BD

Institution: -

Member State:

EEA-ETC/BD
EU28 STE 0MS + grids1x1 0MS + 0MS + poor poor poor 0MS MTX = U1 = nc nc U1 D

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.