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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, Myotis daubentonii, 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 313 N/A N/A grids1x1 minimum N/A N/A N/A N/A
BG N/A N/A 9 grids1x1 minimum N/A N/A N/A N/A
DE 2139 2139 2139 grids1x1 estimate 10 10 10 localities estimate
ES 51 5100 N/A grids1x1 minimum 18 N/A N/A localities minimum
FR 90000 130000 N/A grids1x1 mean 9000 12000 N/A grids10x10 mean
HR N/A N/A 2 grids1x1 minimum N/A N/A N/A N/A
IT 430 4300 N/A grids1x1 estimate N/A N/A N/A N/A
PL N/A N/A 2000 grids1x1 minimum N/A N/A N/A N/A
RO 500 1500 N/A grids1x1 minimum N/A N/A N/A N/A
SI 31 40 N/A grids1x1 estimate N/A N/A N/A N/A
SK 699 699 N/A grids1x1 estimate 582 2278 N/A i N/A
BE N/A N/A 739 grids1x1 estimate N/A N/A N/A N/A
DE 15995 15995 15995 grids1x1 minimum 469 505 487 localities minimum
DK N/A N/A N/A N/A N/A 38 localities N/A
ES 151 15100 N/A grids1x1 estimate 104 N/A N/A localities minimum
FR 7000000 10000000 N/A grids1x1 mean 76000 80000 N/A grids10x10 mean
IE N/A N/A 1580 grids1x1 minimum 57000 79000 N/A adults estimate
NL N/A N/A 1542 grids1x1 estimate 15000 50000 25000 i estimate
PT N/A N/A 4 grids1x1 minimum N/A N/A N/A N/A
UK N/A N/A 5441 grids1x1 minimum 51000 4454000 N/A i interval
BG N/A N/A 6 grids1x1 minimum N/A N/A N/A N/A
EE N/A N/A 1135 grids1x1 minimum N/A N/A N/A N/A
FI 723 151300 N/A grids1x1 estimate N/A N/A N/A N/A
LT N/A N/A 498 grids1x1 minimum N/A N/A N/A N/A
LV 20354 39030 N/A grids1x1 estimate N/A N/A N/A N/A
SE N/A N/A 461 grids1x1 estimate 700000 2100000 1400000 i estimate
AT 333 N/A N/A grids1x1 minimum N/A N/A N/A N/A
BE N/A N/A 444 grids1x1 minimum 1100 2900 N/A iwintering estimate
BG N/A N/A 25 grids1x1 minimum N/A N/A N/A N/A
CZ 6433 6433 N/A grids1x1 estimate N/A N/A N/A N/A
DE 119141 119141 119141 grids1x1 estimate 1731 1810 1770.50 localities estimate
DK N/A N/A N/A N/A N/A 119 localities N/A
FR 590000 650000 N/A grids1x1 mean N/A N/A N/A mean
HR N/A N/A 53 grids1x1 minimum N/A N/A N/A N/A
IT 160 1600 N/A grids1x1 estimate N/A N/A N/A N/A
LU N/A N/A 1750 grids1x1 estimate N/A N/A N/A N/A
PL N/A N/A 21600 grids1x1 minimum N/A N/A N/A N/A
RO 1000 2500 N/A grids1x1 minimum N/A N/A N/A N/A
SE N/A N/A 218 grids1x1 estimate 38000 112000 75000 i estimate
SI 68 77 N/A grids1x1 estimate N/A N/A N/A N/A
ES 468 46800 N/A grids1x1 minimum 187 N/A N/A localities minimum
FR 17000 18000 N/A grids1x1 mean 183608 459021 N/A i mean
GR N/A N/A 27729 grids1x1 estimate 100 500 N/A grids5x5 estimate
IT 800 8000 N/A grids1x1 estimate N/A N/A N/A N/A
PT N/A N/A 237 grids1x1 minimum N/A N/A N/A N/A
CZ 136 136 N/A grids1x1 estimate N/A N/A N/A N/A
HU N/A N/A 464 grids1x1 minimum N/A N/A N/A N/A
SK 286 286 N/A grids1x1 estimate 19 307 N/A i N/A
RO 250 500 N/A grids1x1 minimum N/A N/A N/A N/A
HR N/A N/A 1 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 20000 13.28 = 313 N/A N/A grids1x1 minimum b 0.26 + Y FV = good good good FV FV + FV noChange genuine 16400 b 21.33
BG ALP 11400 7.57 = 11400 N/A N/A 9 grids1x1 minimum b 0.01 = 9 grids1x1 Y FV = good good good FV FV = FV noChange method 6500 b 8.45
DE ALP 3764 2.50 = 3764 2139 2139 2139 grids1x1 estimate b 1.77 x x localities Y FV = good unk good FV FV = FV noChange noChange 2700 c 3.51
ES ALP 12900 8.56 + > 51 5100 N/A grids1x1 minimum b 2.13 x 18 localities Y U1 = poor unk poor U1 U1 = U1 + noChange knowledge 3600 a 4.68
FR ALP 17500 11.62 = 90000 130000 N/A grids1x1 mean b 90.81 = Y FV = good good good FV FV = FV noChange noChange 10900 b 14.17
HR ALP 2600 1.73 x >> N/A N/A 2 grids1x1 minimum b 0 x >> Unk XX x unk unk unk XX U2 x N/A N/A 1700 b 2.21
IT ALP 45900 30.47 = 430 4300 N/A grids1x1 estimate c 1.95 = Y U1 - good good poor U1 U1 - U1 = noChange noChange 11500 c 14.95
PL ALP 6500 4.31 = N/A N/A 2000 grids1x1 minimum c 1.65 x Y FV x good unk good FV FV x FV noChange noInfo 2000 b 2.60
RO ALP 5900 3.92 = 500 1500 N/A grids1x1 minimum b 0.83 = Y U1 = poor poor poor U1 U1 = U1 = knowledge knowledge 2000 b 2.60
SI ALP 7656 5.08 = 7656 31 40 N/A grids1x1 estimate a 0.03 u Y FV = good poor good U1 U1 = XX knowledge noChange 3000 b 3.90
SK ALP 16528.58 10.97 = 699 699 N/A grids1x1 estimate c 0.58 + Y FV x good good good FV FV = XX knowledge knowledge 16600 b 21.59
BE ATL 20700 3.63 + N/A N/A 739 grids1x1 estimate b 0.01 u x Y FV = good unk good FV FV = U2 - method method 14600 b 3.71
DE ATL 70324 12.32 = 70324 15995 15995 15995 grids1x1 minimum b 0.19 = 487 localities Y FV = good good good FV FV = FV noChange noChange 35600 c 9.04
DK ATL 11568 2.03 = N/A N/A N/A d 0 u x Y FV = good unk good FV FV x FV N/A N/A 3000 c 0.76
ES ATL 38000 6.66 = > 151 15100 N/A grids1x1 estimate b 0.09 = 104 localities Y U1 x poor poor poor U1 U1 - U1 - noChange noChange 13900 a 3.53
FR ATL 104800 18.36 = 7000000 10000000 N/A grids1x1 mean a 99.61 - Y Y FV = good poor good U1 U1 - FV noChange noChange 82400 b 20.93
IE ATL 74200 13 = 74200 N/A N/A 1580 grids1x1 minimum b 0.02 + 57000 adults Y FV = good good good FV FV + FV noChange noChange 59700 b 15.17
NL ATL 40000 7.01 = N/A N/A 1542 grids1x1 estimate b 0.02 = Y XX x good good unk FV FV = FV noChange method 37300 b 9.48
PT ATL 1800 0.32 = 1800 N/A N/A 4 grids1x1 minimum b 0 x x Unk XX x good unk unk XX XX XX noChange knowledge 400 b 0.10
UK ATL 209279 36.67 = 209279 N/A N/A 5441 grids1x1 minimum a 0.06 = Y FV = good unk good FV FV = FV noChange noChange 146700 a 37.27
BG BLS 9600 100 = 9600 N/A N/A 6 grids1x1 minimum b 100 = 6 grids1x1 Y FV = poor poor poor U1 U1 = U1 - noChange method 8000 b 100
EE BOR 49700 8.77 + N/A N/A 1135 grids1x1 minimum b 1.05 = Y FV = good good good FV FV + FV noChange noChange 24700 a 15.07
FI BOR 151300 26.69 = 723 151300 N/A grids1x1 estimate b 70.51 = Y FV x good good good FV FV = FV noChange noChange 27600 a 16.84
LT BOR 65200 11.50 x N/A N/A 498 grids1x1 minimum c 0.46 x x Unk XX x good unk unk XX XX U1 = noInfo noInfo 24200 b 14.77
LV BOR 64589 11.40 = 64589 20354 39030 N/A grids1x1 estimate c 27.54 = 20354 grids1x1 Y XX x good good unk FV FV = FV noChange noChange 9100 b 5.55
SE BOR 236000 41.64 = 236000 N/A N/A 461 grids1x1 estimate c 0.43 = 1400000 i Y FV = good good good FV FV = FV noChange noChange 78300 c 47.77
AT CON 20100 2.67 = 333 N/A N/A grids1x1 minimum b 0.04 = Y FV = good good good FV FV = FV noChange noChange 14000 b 3.10
BE CON 13700 1.82 = N/A N/A 444 grids1x1 minimum b 0.06 = Y XX x good good good FV FV = U1 - method knowledge 10100 b 2.24
BG CON 33500 4.45 = 33500 N/A N/A 25 grids1x1 minimum b 0 = 25 grids1x1 Y FV = poor poor poor U1 U1 = U1 - noChange method 23900 b 5.29
CZ CON 83900 11.14 = 6433 6433 N/A grids1x1 estimate a 0.83 = Y FV = good good good FV FV = FV noChange noChange 58900 a 13.04
DE CON 288609 38.32 = 288609 119141 119141 119141 grids1x1 estimate b 15.42 = localities Y FV = good good good FV FV = FV noChange noChange 193100 c 42.74
DK CON 26586 3.53 = N/A N/A N/A d 0 u x Y FV = good unk good FV FV = FV N/A N/A 12000 c 2.66
FR CON 81500 10.82 = 590000 650000 N/A grids1x1 mean c 80.24 - Y Y FV = good poor good U1 U1 - FV noChange noChange 57100 b 12.64
HR CON 24100 3.20 x > N/A N/A 53 grids1x1 minimum b 0.01 x >> N Unk U1 x unk poor poor U1 U2 x N/A N/A 22500 b 4.98
IT CON 64800 8.60 = 160 1600 N/A grids1x1 estimate c 0.11 = Y U1 - good good poor U1 U1 - U1 = noChange noChange 14900 c 3.30
LU CON 4000 0.53 = N/A N/A 1750 grids1x1 estimate c 0.23 u Y FV = good good good FV FV = FV noChange noChange 2600 c 0.58
PL CON 65200 8.66 = N/A N/A 21600 grids1x1 minimum c 2.80 - N Y U1 - good poor poor U1 U1 - FV genuine genuine 21200 b 4.69
RO CON 9500 1.26 = 1000 2500 N/A grids1x1 minimum b 0.23 = Y U1 = poor poor poor U1 U1 = U1 = knowledge knowledge 3900 b 0.86
SE CON 25100 3.33 = 25100 N/A N/A 218 grids1x1 estimate c 0.03 = 75000 i Y FV = good good good FV FV = FV noChange noChange 13000 c 2.88
SI CON 12616 1.67 = 12616 68 77 N/A grids1x1 estimate b 0.01 u Y FV = good poor good U1 U1 = XX knowledge knowledge 4600 b 1.02
ES MED 162700 50.35 = > 468 46800 N/A grids1x1 minimum b 32.16 x 187 localities Y U1 x good unk poor U1 U1 = U1 = noChange noChange 49600 a 39.68
FR MED 34400 10.65 = 17000 18000 N/A grids1x1 mean c 23.81 - Y Y FV = good good poor U1 U1 - FV knowledge noChange 24400 b 19.52
GR MED 40925 12.67 x x N/A N/A 27729 grids1x1 estimate b 37.73 x x Unk XX x unk unk unk XX XX XX noChange noChange 29400 b 23.52
IT MED 46200 14.30 = 800 8000 N/A grids1x1 estimate c 5.99 = N Y U1 - good good poor FV U1 - U2 - noChange noChange 8700 c 6.96
PT MED 38900 12.04 = 38900 N/A N/A 237 grids1x1 minimum b 0.32 x x Unk XX x good unk unk XX XX XX noChange N/A 12900 b 10.32
CZ PAN 5800 6.99 = 136 136 N/A grids1x1 estimate a 15.35 = Y FV = good good good FV FV = FV noChange noChange 1900 a 6.88
HU PAN 71986 86.70 = N/A N/A 464 grids1x1 minimum c 52.37 u Y U1 - good good poor U1 U1 x U1 = noChange method 19300 b 69.93
SK PAN 5246.59 6.32 = 286 286 N/A grids1x1 estimate c 32.28 + Y FV x good good good FV FV = XX knowledge knowledge 6400 b 23.19
RO STE 200 100 = 250 500 N/A grids1x1 minimum b 100 = Y U1 = poor poor poor U1 U1 = U1 = knowledge knowledge 200 b 100
HR MED 900 0 x x N/A N/A 1 grids1x1 minimum c 0 x x Unk XX x unk unk poor XX XX N/A N/A 800 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 = grids1x1 2XP = 2XP = good good good 2XP MTX = FV nc nc FV A=

03/20

EEA-ETC/BD

Institution: -

Member State:

EEA-ETC/BD
EU28 ATL 2XR = grids1x1 2XR - x 2XR = good poor good 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 = poor poor poor 0MS MTX = U1 = nc nc U1 D

01/20

EEA-ETC/BD

Institution: -

Member State:

EEA-ETC/BD
EU28 BOR 2XP = grids1x1 2XP = 2XP x good good good 2XP MTX = FV = nc nc FV A=

03/20

EEA-ETC/BD

Institution: -

Member State:

EEA-ETC/BD
EU28 CON 0EQ = grids1x1 0EQ - x 0EQ = good poor unk 0EQ MTX - FV = nong nong FV C

03/20

EEA-ETC/BD

Institution: -

Member State:

EEA-ETC/BD
EU28 MED 2XP = x grids1x1 2XP x x 2XP x good unk unk 2XP MTX = XX = nc nc U1 E

02/20

EEA-ETC/BD

Institution: -

Member State:

EEA-ETC/BD
EU28 PAN 0EQ = grids1x1 0EQ x 0EQ - good good poor 0EQ MTX x XX = nong nong XX D

02/20

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.

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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.