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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, Nyctalus noctula, 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 281 N/A N/A grids1x1 minimum N/A N/A N/A N/A
BG N/A N/A 51 grids1x1 minimum N/A N/A N/A N/A
DE 1710 1710 1710 grids1x1 estimate 20 20 20 grids5x5 estimate
FR 3700 4000 N/A grids1x1 mean N/A N/A N/A mean
HR N/A N/A 11 grids1x1 minimum N/A N/A N/A N/A
IT 250 2500 N/A grids1x1 estimate N/A N/A N/A N/A
PL N/A N/A 800 grids1x1 estimate 10000 20000 N/A i estimate
RO 1500 3000 N/A grids1x1 minimum N/A N/A N/A N/A
SI 17 22 N/A grids1x1 estimate N/A N/A N/A N/A
SK 1308 1308 N/A grids1x1 estimate 746 3503 N/A i N/A
BE N/A N/A 556 grids1x1 estimate N/A N/A N/A N/A
DE 34772 34772 34772 grids1x1 minimum 460 520 490 grids5x5 minimum
DK N/A N/A N/A N/A N/A 7 localities N/A
ES 15 1500 N/A grids1x1 minimum 5 N/A N/A localities minimum
FR 40000 42000 N/A grids1x1 mean N/A N/A N/A mean
NL N/A N/A 2889 grids1x1 estimate 2000 6000 4000 i estimate
UK N/A N/A 7695 grids1x1 minimum 20600 2176000 N/A i interval
BG N/A N/A 13 grids1x1 minimum N/A N/A N/A N/A
EE N/A N/A 549 grids1x1 minimum N/A N/A N/A N/A
LT N/A N/A 673 grids1x1 minimum N/A N/A N/A N/A
LV 53000 64589 N/A grids1x1 estimate N/A N/A N/A N/A
SE N/A N/A 1820 grids1x1 estimate 60000 180000 120000 i estimate
AT 597 N/A N/A grids1x1 minimum N/A N/A N/A N/A
BE N/A N/A 113 grids1x1 estimate N/A N/A N/A N/A
BG N/A N/A 374 grids1x1 minimum N/A N/A N/A N/A
CZ 6095 6095 N/A grids1x1 estimate N/A N/A N/A N/A
DE 161975 161975 161975 grids1x1 estimate 2716 2738 2727 grids5x5 estimate
DK N/A N/A N/A N/A N/A 114 localities N/A
FR 278000 304000 N/A grids1x1 mean N/A N/A N/A mean
HR N/A N/A 94 grids1x1 minimum N/A N/A N/A N/A
IT 650 6500 N/A grids1x1 estimate N/A N/A N/A N/A
LU N/A N/A 2300 grids1x1 estimate N/A N/A N/A N/A
PL N/A N/A 27700 grids1x1 minimum 50000 75000 N/A i estimate
RO 5000 10000 N/A grids1x1 minimum N/A N/A N/A N/A
SE N/A N/A 588 grids1x1 estimate 5000 15000 10000 i estimate
SI 39 44 N/A grids1x1 estimate N/A N/A N/A N/A
ES 55 5500 N/A grids1x1 minimum 7 N/A N/A localities minimum
FR 18000 19000 N/A grids1x1 mean 38 96 N/A i mean
GR N/A N/A 66260 grids1x1 estimate 948 1634 N/A grids5x5 estimate
HR N/A N/A 15 grids1x1 minimum N/A N/A N/A N/A
IT 230 2300 N/A grids1x1 estimate N/A N/A N/A N/A
MT N/A N/A 12 grids1x1 estimate N/A N/A N/A N/A
CZ 521 521 N/A grids1x1 estimate N/A N/A N/A N/A
HU N/A N/A 1207 grids1x1 minimum N/A N/A N/A N/A
SK 861 861 N/A grids1x1 estimate 21 634 N/A i N/A
RO 250 500 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
PT N/A N/A 4 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 14200 11.35 = 281 N/A N/A grids1x1 minimum b 2.41 x x Y FV = good poor good U1 U1 = U1 x noChange knowledge 11100 b 19.93
BG ALP 20100 16.07 = 20100 N/A N/A 51 grids1x1 minimum b 0.44 = 51 grids1x1 Y FV = good good good FV FV = FV noChange method 1900 b 3.41
DE ALP 3923 3.14 = 3923 1710 1710 1710 grids1x1 estimate b 14.67 x x grids5x5 Unk XX u good unk unk XX XX x XX noChange noChange 2000 c 3.59
FR ALP 6800 5.44 = x 3700 4000 N/A grids1x1 mean c 33.03 x x Unk Unk XX = unk unk unk XX XX = FV method noChange 3100 b 5.57
HR ALP 9400 7.51 x > N/A N/A 11 grids1x1 minimum c 0.09 x >> Unk XX x good unk unk XX U2 x N/A N/A 7100 b 12.75
IT ALP 24300 19.43 = 250 2500 N/A grids1x1 estimate c 11.80 = x N Unk XX x good good unk FV FV = XX knowledge noChange 5400 b 9.69
PL ALP 8800 7.03 = x N/A N/A 800 grids1x1 estimate c 6.86 + x Y FV = unk good good FV FV + FV noChange genuine 800 b 1.44
RO ALP 10800 8.63 + 1500 3000 N/A grids1x1 minimum b 19.30 + Y FV + good good good FV FV + U1 = knowledge knowledge 3400 b 6.10
SI ALP 7656 6.12 = 7656 17 22 N/A grids1x1 estimate c 0.17 - > Y U1 - good poor poor U1 U1 - U1 - noChange noChange 1600 c 2.87
SK ALP 19116.47 15.28 + 1308 1308 N/A grids1x1 estimate c 11.22 x Y FV x good good good FV FV x U2 - knowledge N/A 19300 b 34.65
BE ATL 20200 4.32 = N/A N/A 556 grids1x1 estimate b 0.63 + Y FV x good good good FV FV = U2 - method method 12600 b 4.84
DE ATL 64773 13.87 = 34772 34772 34772 grids1x1 minimum b 39.66 = grids5x5 Y FV = good unk unk XX FV = FV noChange noChange 36500 c 14.02
DK ATL 5050 1.08 + N/A N/A N/A d 0 + x Y FV = good unk good FV FV + FV N/A N/A 800 b 0.31
ES ATL 5800 1.24 = > 15 1500 N/A grids1x1 minimum b 0.86 u 5 localities Y U2 u poor poor unk U1 U2 x U1 x knowledge noChange 1000 a 0.38
FR ATL 177400 37.98 = 40000 42000 N/A grids1x1 mean c 46.77 = Y Y XX - good unk poor U1 U1 - U1 = noChange noChange 45900 b 17.63
NL ATL 36400 7.79 = N/A N/A 2889 grids1x1 estimate b 3.30 x >> Unk XX x good unk unk XX U2 x U2 + noChange noInfo 32300 b 12.40
UK ATL 157491 33.72 = 157491 N/A N/A 7695 grids1x1 minimum b 8.78 = Y Unk FV x good good good FV FV = FV noChange noChange 131300 b 50.42
BG BLS 7400 100 - 7400 N/A N/A 13 grids1x1 minimum b 100 = 13 grids1x1 Y FV = good good good FV U1 - U1 - method method 1000 b 100
EE BOR 32300 10.03 + N/A N/A 549 grids1x1 minimum b 0.89 - x Y FV = good good good FV U1 - FV genuine genuine 10700 a 10.03
LT BOR 65200 20.26 = N/A N/A 673 grids1x1 minimum c 1.09 u Y XX x good unk unk XX XX U1 = noInfo noInfo 28900 b 27.09
LV BOR 64589 20.07 = 64589 53000 64589 N/A grids1x1 estimate b 95.08 x 64589 grids1x1 Y U1 - good poor poor U1 U1 x U1 - noChange noChange 12100 b 11.34
SE BOR 159800 49.64 = 159800 N/A N/A 1820 grids1x1 estimate b 2.94 = 120000 i Y FV = good good good FV FV = FV noChange noChange 55000 b 51.55
AT CON 22300 2.31 = 597 N/A N/A grids1x1 minimum b 0.12 x > Y FV = good poor poor U1 U1 = U1 x noChange knowledge 16300 b 3.98
BE CON 13600 1.41 = N/A N/A 113 grids1x1 estimate a 0.02 x >> Unk XX x good poor poor U1 U2 x XX knowledge noChange 5300 b 1.29
BG CON 76200 7.89 = 76200 N/A N/A 374 grids1x1 minimum b 0.07 = 374 grids1x1 Y FV = good good good FV FV = U1 - method method 13400 b 3.27
CZ CON 82200 8.51 = 6095 6095 N/A grids1x1 estimate a 1.21 = Y FV = good good good FV FV = FV noChange noChange 55500 a 13.54
DE CON 286982 29.71 = 286982 161975 161975 161975 grids1x1 estimate b 32.27 - > grids5x5 N Unk U1 - good unk poor U1 U1 - U1 - noChange noChange 172600 c 42.11
DK CON 27402 2.84 + N/A N/A N/A d 0 + x Y FV = good unk good FV FV + FV N/A N/A 11700 b 2.85
FR CON 75300 7.80 = 278000 304000 N/A grids1x1 mean b 57.97 - Y Y FV = good bad poor U2 U2 - FV knowledge noChange 29100 b 7.10
HR CON 37000 3.83 x N/A N/A 94 grids1x1 minimum c 0.02 x >> N Unk XX x good unk poor U1 U2 x N/A N/A 35800 b 8.73
IT CON 51100 5.29 = 650 6500 N/A grids1x1 estimate c 0.71 = Y FV = good good good FV FV = FV noChange noChange 12600 b 3.07
LU CON 4000 0.41 x N/A N/A 2300 grids1x1 estimate c 0.46 - >> N N U1 - good bad poor U2 U2 - U2 - noChange genuine 3300 c 0.81
PL CON 222400 23.03 = N/A N/A 27700 grids1x1 minimum b 5.52 + x Y FV = good good good FV FV + FV noChange knowledge 27400 b 6.68
RO CON 29600 3.06 + 5000 10000 N/A grids1x1 minimum b 1.49 + Y FV + good good good FV FV + U1 = knowledge knowledge 10300 b 2.51
SE CON 25100 2.60 = 25100 N/A N/A 588 grids1x1 estimate c 0.12 = 10000 i Y FV = good good good FV FV = FV noChange noChange 14000 b 3.42
SI CON 12616 1.31 = 12616 39 44 N/A grids1x1 estimate c 0.01 - > Y U1 - good poor poor U1 U1 - U1 x noChange knowledge 2600 c 0.63
ES MED 16600 9.90 u > 55 5500 N/A grids1x1 minimum b 3.13 u 7 localities N Unk U2 u unk poor poor U1 U2 x U2 x noChange noChange 6500 a 6.37
FR MED 5000 2.98 x 18000 19000 N/A grids1x1 mean c 20.83 x x Unk Unk XX x good unk poor U1 U1 x XX knowledge noChange 2200 b 2.16
GR MED 104678 62.42 = N/A N/A 66260 grids1x1 estimate b 74.59 x x Unk XX x good poor unk U1 U1 x U1 x noChange noChange 70900 b 69.51
HR MED 19000 11.33 x > N/A N/A 15 grids1x1 minimum c 0.02 x >> N Unk XX x unk unk poor XX U2 x N/A N/A 17000 b 16.67
IT MED 22400 13.36 = x 230 2300 N/A grids1x1 estimate c 1.42 x x N Y XX x good unk unk XX XX XX knowledge noChange 5100 b 5
MT MED 12 0.01 x x N/A N/A 12 grids1x1 estimate c 0.01 x x Y FV = unk unk good XX XX FV method method 300 c 0.29
CZ PAN 5800 5.37 = 521 521 N/A grids1x1 estimate a 20.12 = Y FV = good good good FV FV = FV noChange noChange 2600 a 4.65
HU PAN 93011 86.15 = N/A N/A 1207 grids1x1 minimum c 46.62 = Y FV = good good good FV FV = FV noChange method 43700 b 78.18
SK PAN 9146.83 8.47 + 861 861 N/A grids1x1 estimate c 33.26 x Y FV + good good good FV FV = U2 - knowledge N/A 9600 b 17.17
RO STE 2800 100 + 250 500 N/A grids1x1 minimum b 100 + Y FV + good good good FV FV + U1 = knowledge knowledge 1400 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 5200 a 0
PT MED 400 0 x x N/A N/A 4 grids1x1 minimum b 0 x x Unk XX x unk unk unk XX XX N/A N/A knowledge knowledge 400 b 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 U2 = nong nong U2 D

02/20

EEA-ETC/BD

Institution: -

Member State:

EEA-ETC/BD
EU28 ATL 2XP = grids1x1 2XP = 2XP x good unk unk 2XP MTX U1 = nong nong U1 D

12/19

EEA-ETC/BD

Institution: -

Member State:

EEA-ETC/BD
EU28 BLS 0MS - x grids1x1 0MS = x 0MS = good good good 0MS MTX = U1 = nc nc U1 D

02/20

EEA-ETC/BD

Institution: -

Member State:

EEA-ETC/BD
EU28 BOR 2XP = grids1x1 2XP x x 2XP - good poor poor 2XP MTX - FV = gen gen FV C

02/20

EEA-ETC/BD

Institution: -

Member State:

EEA-ETC/BD
EU28 CON 2XR = 2XR - > 2XR - good unk poor 2XR MTX = U1 = nc nc U1 D

02/20

EEA-ETC/BD

Institution: -

Member State:

EEA-ETC/BD
EU28 MED 2XR = grids1x1 2XR x x 2XR x good poor poor 2XR MTX = U1 x nc nc U2 B1

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
BG CON 2XP 2XP 2XP MTX U1 = U1 0/2

04/20

Green Balkans Federation

Institution: Green Balkans Federation

Member State: BG

Green Balkans Federation
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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.