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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, Plecotus austriacus, 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 13 N/A N/A grids1x1 minimum N/A N/A N/A N/A
BG N/A N/A 10 grids1x1 minimum N/A N/A N/A N/A
ES 29 2900 N/A grids1x1 estimate N/A N/A N/A N/A
FR 2400 3400 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 30 300 N/A grids1x1 estimate N/A N/A N/A N/A
PL N/A N/A 200 grids1x1 minimum N/A N/A N/A N/A
RO 250 500 N/A grids1x1 minimum N/A N/A N/A N/A
SI 1 2 N/A grids1x1 estimate N/A N/A N/A N/A
SK 195 195 N/A grids1x1 estimate 250 348 N/A i N/A
BE N/A N/A 131 grids1x1 estimate 800 1600 N/A i estimate
DE 2554 2554 2554 grids1x1 estimate 28 33 30.50 grids5x5 estimate
ES 100 10000 N/A grids1x1 estimate N/A N/A N/A N/A
FR 314500 334000 N/A grids1x1 mean N/A N/A N/A mean
NL N/A N/A 103 grids1x1 estimate 350 700 525 i estimate
PT N/A N/A 4 grids1x1 minimum N/A N/A N/A N/A
UK N/A N/A 108 grids1x1 minimum 400 3000 N/A i interval
BG N/A N/A 7 grids1x1 minimum N/A N/A N/A N/A
AT 82 N/A N/A grids1x1 minimum N/A N/A N/A N/A
BE N/A N/A 175 grids1x1 minimum 5 20 N/A iwintering estimate
BG N/A N/A 48 grids1x1 minimum N/A N/A N/A N/A
CZ 1323 1323 N/A grids1x1 estimate N/A N/A N/A N/A
DE 73827 73827 73827 grids1x1 estimate 223 232 227.50 grids5x5 estimate
FR 145600 145600 N/A grids1x1 mean N/A N/A N/A mean
HR N/A N/A 25 grids1x1 minimum N/A N/A N/A N/A
IT 450 4500 N/A grids1x1 estimate N/A N/A N/A N/A
LU N/A N/A 1300 grids1x1 estimate N/A N/A N/A N/A
PL N/A N/A 10300 grids1x1 minimum N/A N/A N/A N/A
RO 250 700 N/A grids1x1 minimum N/A N/A N/A N/A
SE N/A N/A 2 grids1x1 estimate 25 75 50 i estimate
SI 24 25 N/A grids1x1 estimate N/A N/A N/A N/A
PT N/A N/A N/A minimum N/A N/A N/A N/A
ES 525 52500 N/A grids1x1 estimate N/A N/A N/A N/A
FR 12700 14300 N/A grids1x1 mean N/A N/A N/A mean
GR N/A N/A 41332 grids1x1 estimate 100 500 N/A grids5x5 estimate
IT 50 500 N/A grids1x1 estimate N/A N/A N/A N/A
PT N/A N/A 130 grids1x1 minimum N/A N/A N/A N/A
CZ 98 98 N/A grids1x1 estimate N/A N/A N/A N/A
HU N/A N/A 325 grids1x1 minimum N/A N/A N/A N/A
SK 82 82 N/A grids1x1 estimate 31 37 N/A i N/A
RO 25 60 N/A grids1x1 minimum 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 1300 2.40 x 13 N/A N/A grids1x1 minimum b 0.24 x > Y FV = good unk poor U1 U1 x U1 - noChange noInfo 4200 b 19.53
BG ALP 20700 38.24 = 20700 N/A N/A 10 grids1x1 minimum b 0.19 = 10 grids1x1 Y FV = good good good FV FV = FV noChange knowledge 1000 b 4.65
ES ALP 6700 12.38 x > 29 2900 N/A grids1x1 estimate b 27.50 x 2900 grids1x1 Y U1 x poor unk poor U1 U1 - U2 x noChange noChange 2000 a 9.30
FR ALP 3700 6.83 = 2400 3400 N/A grids1x1 mean b 54.46 x Y Y FV = good unk good FV FV = U1 = noChange noChange 2200 b 10.23
HR ALP 300 0.55 x >> N/A N/A 1 grids1x1 minimum c 0.02 x >> N Unk U1 x poor unk poor U1 U2 x N/A N/A 300 b 1.40
IT ALP 4700 8.68 = 30 300 N/A grids1x1 estimate c 3.10 - > Y FV = good poor poor U1 U1 - XX knowledge noChange 800 b 3.72
PL ALP 2200 4.06 x N/A N/A 200 grids1x1 minimum b 3.76 x x Unk XX x good unk unk XX XX XX noChange noChange 200 b 0.93
RO ALP 2100 3.88 = 250 500 N/A grids1x1 minimum b 7.04 = Y U1 = poor poor poor U1 U1 = U1 = knowledge knowledge 700 b 3.26
SI ALP 2666 4.92 x x 1 2 N/A grids1x1 estimate c 0.03 u > Y U1 - unk poor poor U1 U1 x XX knowledge noChange 200 c 0.93
SK ALP 9767.53 18.04 x 195 195 N/A grids1x1 estimate c 3.66 x Y FV x unk good good FV FV x XX knowledge N/A 9900 b 46.05
BE ATL 16500 11.95 + N/A N/A 131 grids1x1 estimate b 0.04 + > Unk XX x good good unk FV U1 = U2 x knowledge knowledge 5100 b 7.77
DE ATL 17718 12.83 = 2554 2554 2554 grids1x1 estimate c 0.77 x > grids5x5 Unk XX x unk unk unk XX U1 x U1 = noChange noInfo 3400 b 5.18
ES ATL 41700 30.21 = > 100 10000 N/A grids1x1 estimate b 1.52 x 10000 grids1x1 Y U1 x poor unk poor U1 U1 - U1 - noChange noChange 9900 a 15.09
FR ATL 46500 33.68 = 314500 334000 N/A grids1x1 mean c 97.61 = Y Y FV = good good good FV FV = U1 = knowledge noChange 37000 b 56.40
NL ATL 6200 4.49 + N/A N/A 103 grids1x1 estimate a 0.03 + >> Y FV + good poor good U1 U2 + U2 + noChange noChange 5000 b 7.62
PT ATL 2100 1.52 = 2100 N/A N/A 4 grids1x1 minimum b 0 x x Unk XX x good unk unk XX XX U1 x method noChange 300 c 0.46
UK ATL 7338 5.32 u 7338 N/A N/A 108 grids1x1 minimum b 0.03 u > Unk Unk XX x unk unk unk XX U1 x U1 - noChange noInfo 4900 b 7.47
BG BLS 6400 100 u 6400 N/A N/A 7 grids1x1 minimum b 100 = 7 grids1x1 Y FV = poor poor poor U1 U1 = U1 - noChange knowledge 500 b 100
AT CON 7500 1.50 - 82 N/A N/A grids1x1 minimum b 0.03 x > Y U1 - good poor poor U1 U1 - U1 - noChange noChange 20500 b 10.04
BE CON 13200 2.64 = N/A N/A 175 grids1x1 minimum a 0.07 x > Y FV = good poor good FV U1 x U1 x noChange noChange 6400 b 3.13
BG CON 58200 11.65 u 58200 N/A N/A 48 grids1x1 minimum b 0.02 = 48 grids1x1 Y FV = poor poor poor U1 U1 = U1 - noChange method 4200 b 2.06
CZ CON 72600 14.54 - > 1323 1323 N/A grids1x1 estimate a 0.56 - >> Y FV = poor poor good XX U2 - U2 = noChange noChange 30900 a 15.13
DE CON 218185 43.68 - 223015 73827 73827 73827 grids1x1 estimate b 31.33 - >> grids5x5 N Y U1 - poor unk unk XX U2 - U1 - genuine noChange 89500 b 43.83
FR CON 25500 5.11 = 145600 145600 N/A grids1x1 mean b 61.78 = Y Y FV = good good unk FV FV = U1 = knowledge noChange 18500 b 9.06
HR CON 7500 1.50 x >> N/A N/A 25 grids1x1 minimum b 0.01 x >> N Unk U1 x poor poor poor U1 U2 x N/A N/A 7500 b 3.67
IT CON 44100 8.83 = 450 4500 N/A grids1x1 estimate c 1.05 = N Y U1 - good poor poor U1 U1 - U1 - noChange noChange 8800 b 4.31
LU CON 3600 0.72 = N/A N/A 1300 grids1x1 estimate c 0.55 - >> N N U2 - good bad bad U2 U2 - U1 x genuine genuine 3200 c 1.57
PL CON 35500 7.11 x N/A N/A 10300 grids1x1 minimum b 4.37 x x Unk XX x good unk unk XX XX XX noChange noChange 10300 b 5.04
RO CON 4000 0.80 = 250 700 N/A grids1x1 minimum b 0.20 = Y U1 = poor poor poor U1 U1 = U1 = knowledge knowledge 2400 b 1.18
SE CON 1200 0.24 u 15000 N/A N/A 2 grids1x1 estimate c 0 u 1000 i N Unk U2 x bad unk unk U2 U2 x U2 x noChange knowledge 300 b 0.15
SI CON 8375 1.68 x x 24 25 N/A grids1x1 estimate b 0.01 u > Y U1 - unk poor poor U1 U1 x U1 - noChange noInfo 1700 c 0.83
PT MAC 1400 100 x x N/A N/A N/A minimum d 0 x x Unk Unk XX x unk unk unk XX XX XX noChange noChange 1300 c 100
ES MED 222200 61.69 = > 525 52500 N/A grids1x1 estimate b 32.43 x 52500 grids1x1 Y FV x poor unk good XX U1 x U1 x noChange noChange 58400 a 43.23
FR MED 25600 7.11 = 12700 14300 N/A grids1x1 mean b 16.51 x Y Y FV = good good poor U1 U1 = U1 x noChange noChange 19600 b 14.51
GR MED 58905 16.35 x x N/A N/A 41332 grids1x1 estimate b 50.56 x x Unk XX x unk unk unk XX XX XX noChange noChange 43300 b 32.05
IT MED 34800 9.66 = > 50 500 N/A grids1x1 estimate c 0.34 = N Y U1 - good poor poor U1 U1 - U1 - noChange noChange 6300 b 4.66
PT MED 18700 5.19 = 18700 N/A N/A 130 grids1x1 minimum b 0.16 x x Unk XX x good unk unk XX XX U1 x knowledge noChange 7500 c 5.55
CZ PAN 5800 5.72 = 98 98 N/A grids1x1 estimate a 19.41 - >> Y FV = good poor good XX U2 - U2 = noChange noChange 2000 a 8.62
HU PAN 93011 91.76 = N/A N/A 325 grids1x1 minimum c 64.36 - > Y U1 u good poor poor U1 U1 x U1 - noChange method 18300 b 78.88
SK PAN 2554.23 2.52 x 82 82 N/A grids1x1 estimate c 16.24 x Y FV x unk good good FV FV x XX knowledge N/A 2900 b 12.50
RO STE 400 100 = 25 60 N/A grids1x1 minimum b 100 = Y U1 = poor poor poor U1 U1 = U1 = knowledge knowledge 200 b 100
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 54133.53 1 = 3129 7521 5325 grids1x1 2GD = 2GD = 2GD MTX = XX x nong nong XX D

02/20

EEA-ETC/BD

Institution: -

Member State:

EEA-ETC/BD
EU28 ATL 138056 1 = > 2GD + 2GD = 2GD MTX = U1 = nc nc U1 D

02/20

EEA-ETC/BD

Institution: -

Member State:

EEA-ETC/BD
EU28 BLS 6400 0MS x 6400 7 grids1x1 0MS = 7 grids1x1 0MS = poor poor poor 0MS MTX = U1 - nc nong U1 D

02/20

EEA-ETC/BD

Institution: -

Member State:

EEA-ETC/BD
EU28 CON 499460 1 - 2GD - 2GD - 2GD MTX - U1 - gen nc U1 C

01/20

EEA-ETC/BD

Institution: -

Member State:

EEA-ETC/BD
EU28 MAC 1400 0MS x x 0MS x x 0MS x unk unk unk 0MS MTX x XX x nc nc XX D

02/20

EEA-ETC/BD

Institution: -

Member State:

EEA-ETC/BD
EU28 MED 360205 1 = 54737 108762 81749.5 grids1x1 2GD x 2GD x 2GD MTX x XX x nong nc U1 D

02/20

EEA-ETC/BD

Institution: -

Member State:

EEA-ETC/BD
EU28 PAN 101365.23 0EQ = 505 505 505 grids1x1 2XP - >> 2XP x 2XP MTX x U1 - nc nong U1 D

02/20

EEA-ETC/BD

Institution: -

Member State:

EEA-ETC/BD
EU28 STE 400 0MS = 25 60 42.5 grids1x1 0MS = 0MS = poor poor poor 0MS MTX = U1 = nc nc U1 D

02/20

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.