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Article 17 > Species summary

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, Muscardinus avellanarius, 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 N/A N/A 121 grids1x1 minimum 1000 3600 N/A i minimum
BG N/A N/A 9 grids1x1 minimum N/A N/A N/A N/A
DE 1354 1354 1354 grids1x1 estimate 13 13 13 grids10x10 estimate
FR 16400 16400 N/A grids1x1 estimate N/A N/A N/A estimate
HR N/A N/A 7 grids1x1 minimum N/A N/A N/A N/A
IT N/A N/A 16519 grids1x1 estimate N/A N/A N/A N/A
PL N/A N/A 1800 grids1x1 minimum N/A N/A N/A N/A
RO 24500 28000 N/A grids1x1 minimum N/A N/A N/A i N/A
SI 14 15 N/A grids1x1 minimum N/A N/A N/A N/A
SK 312 312 N/A grids1x1 estimate 1000 5000 N/A i N/A
BE N/A N/A 8 grids1x1 estimate 200 1400 N/A i estimate
DE 1512 1512 1512 grids1x1 estimate 35 36 35.50 grids10x10 estimate
FR 50000 100000 N/A grids1x1 mean N/A N/A N/A mean
NL N/A N/A 32 grids1x1 estimate 100 200 140 bfemales estimate
UK N/A N/A 4169 grids1x1 minimum 388700 2639000 N/A i interval
BG N/A N/A 2 grids1x1 minimum N/A N/A N/A N/A
EE N/A N/A N/A grids1x1 minimum N/A N/A N/A N/A
LT N/A N/A 217 grids1x1 estimate N/A N/A N/A N/A
LV N/A N/A 59 grids1x1 minimum N/A N/A N/A N/A
SE N/A N/A 53664 grids1x1 estimate 1000000 2000000 1500000 i estimate
AT N/A N/A 43 grids1x1 minimum 400 1300 N/A i minimum
BE N/A N/A 383 grids1x1 estimate 12000 75000 N/A i estimate
BG N/A N/A 25 grids1x1 minimum N/A N/A N/A N/A
CZ N/A 1395 N/A grids1x1 estimate N/A N/A N/A N/A
DE 67393 67393 67393 grids1x1 estimate 879 889 884 grids10x10 estimate
DK N/A N/A N/A N/A N/A 15 grids10x10 N/A
FR 40000 50000 N/A grids1x1 mean 73300 147800 N/A adults mean
HR N/A N/A 17 grids1x1 minimum N/A N/A N/A N/A
IT N/A N/A 16888 grids1x1 estimate N/A N/A N/A N/A
LU 1680 2253 N/A grids1x1 estimate N/A N/A N/A N/A
PL N/A N/A 13796 grids1x1 minimum N/A N/A N/A N/A
RO 51500 57000 N/A grids1x1 minimum N/A N/A N/A i N/A
SE N/A N/A 1003 grids1x1 estimate 450000 550000 500000 i estimate
SI 33 35 N/A grids1x1 minimum N/A N/A N/A N/A
FR 5000 10000 N/A grids1x1 mean 5000 10000 N/A colonies mean
GR N/A N/A 59404 grids1x1 estimate 1922 2262 N/A grids5x5 N/A
HR N/A N/A 3 grids1x1 minimum N/A N/A N/A N/A
IT N/A N/A 27254 grids1x1 estimate 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 367 grids1x1 minimum N/A N/A N/A N/A
RO 500 800 N/A grids1x1 minimum N/A N/A N/A i N/A
SK 84 84 N/A grids1x1 estimate 100 500 N/A i N/A
RO 3500 4200 N/A grids1x1 minimum N/A N/A N/A i 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 17000 16.06 x ≈ N/A N/A 121 grids1x1 minimum b 0.19 x > Y FV x good poor poor U1 U1 x FV knowledge noInfo 6000 b 10.05
BG ALP 3400 3.21 = 3400 N/A N/A 9 grids1x1 minimum b 0.01 = 9 grids1x1 Unk XX x good good unk FV FV = FV noChange method 900 b 1.51
DE ALP 2177 2.06 = ≈ 1354 1354 1354 grids1x1 estimate b 2.16 x x grids10x10 Y FV = good unk good FV FV = XX knowledge knowledge 1500 b 2.51
FR ALP 8000 7.56 = ≈ 16400 16400 N/A grids1x1 estimate d 26.12 x x Y FV = unk poor poor U1 U1 = U1 = noChange noChange 6500 a 10.89
HR ALP 500 0.47 x >> N/A N/A 7 grids1x1 minimum b 0.01 x >> Unk XX x unk unk unk XX U2 x N/A N/A N/A b 0
IT ALP 30000 28.34 = ≈ N/A N/A 16519 grids1x1 estimate b 26.31 = ≈ Y FV = good good good FV FV = FV noChange noChange 20400 b 34.17
PL ALP 7900 7.46 u ≈ N/A N/A 1800 grids1x1 minimum b 2.87 x x Y FV x good unk good FV FV x FV noChange noChange 2100 b 3.52
RO ALP 20300 19.18 = x 24500 28000 N/A grids1x1 minimum c 41.81 = 2800 grids1x1 Y FV = good good good FV FV + FV N/A N/A 14000 b 23.45
SI ALP 7656 7.23 = ≈ 14 15 N/A grids1x1 minimum c 0.02 = ≈ Y FV = good good good FV FV = FV noChange noChange 1000 c 1.68
SK ALP 8909.33 8.42 = ≈ 312 312 N/A grids1x1 estimate b 0.50 = ≈ Y FV = good good good FV FV = U1 = knowledge N/A 7300 b 12.23
BE ATL 3500 2.86 = >> N/A N/A 8 grids1x1 estimate b 0.01 + >> Y U1 - poor poor poor U1 U2 = U2 - noChange knowledge 600 b 0.64
DE ATL 4804 3.93 = ≈ 1512 1512 1512 grids1x1 estimate b 1.87 = > grids10x10 Y FV = good poor good U1 U1 = XX knowledge knowledge 3000 b 3.22
FR ATL 31100 25.45 - > 50000 100000 N/A grids1x1 mean d 92.91 x > N Y U1 - unk unk poor XX U2 - U2 = knowledge noChange 30000 a 32.22
NL ATL 300 0.25 = ≈ N/A N/A 32 grids1x1 estimate a 0.04 + >> N N U2 = good poor poor U1 U2 + U2 + noChange noChange 200 a 0.21
UK ATL 82516 67.51 = 82516 N/A N/A 4169 grids1x1 minimum b 5.16 - x Unk Unk XX x poor bad poor U2 U2 x U2 - noChange method 59300 a 63.69
BG BLS 200 100 = 200 N/A N/A 2 grids1x1 minimum b 100 = 2 grids1x1 Unk XX x good good unk FV FV = U1 x method method 200 b 100
EE BOR N/A 0 x x N/A N/A N/A grids1x1 minimum c 0 x x Y XX = unk unk unk XX XX XX noChange noChange N/A d 0
LT BOR 64700 43.49 = ≈ N/A N/A 217 grids1x1 estimate b 0.40 = x Y FV = good good good FV FV = FV noChange noChange 13500 b 18.42
LV BOR 19054 12.81 x x N/A N/A 59 grids1x1 minimum c 0.11 x x Unk XX x unk good unk XX XX FV knowledge noChange 4800 d 6.55
SE BOR 65000 43.70 = 65000 N/A N/A 53664 grids1x1 estimate c 99.49 u 1500000 i Y FV = good good good FV FV = FV noChange noChange 55000 b 75.03
AT CON 10300 2.34 x > N/A N/A 43 grids1x1 minimum c 0.02 x > N N U1 x poor poor poor U1 U1 - FV knowledge knowledge 3200 b 1.19
BE CON 15000 3.41 = ≈ N/A N/A 383 grids1x1 estimate b 0.19 + ≈ Y U1 = good good good FV U1 + U1 - noChange knowledge 10500 b 3.89
BG CON 7000 1.59 = 7000 N/A N/A 25 grids1x1 minimum b 0.01 = 25 grids1x1 Unk XX x good good unk FV FV = U1 x method method 2100 b 0.78
CZ CON 67500 15.37 = ≈ N/A 1395 N/A grids1x1 estimate b 0.35 x x Y FV = good unk good FV FV = FV noChange noChange 26700 a 9.90
DE CON 136301 31.03 = ≈ 67393 67393 67393 grids1x1 estimate b 33.45 = > grids10x10 N Y U1 - unk unk poor XX U1 - U1 = noChange knowledge 82100 b 30.45
DK CON 1551 0.35 - >> N/A N/A N/A c 0 - >> N Unk U2 - bad bad bad U2 U2 - U2 x N/A N/A 1500 b 0.56
FR CON 45000 10.24 = ≈ 40000 50000 N/A grids1x1 mean b 22.33 = ≈ Y Unk FV = good good poor U1 U1 = U1 = noChange noChange 42500 a 15.76
HR CON 1200 0.27 x >> N/A N/A 17 grids1x1 minimum b 0.01 x >> Unk XX x unk unk unk XX U2 x N/A N/A N/A b 0
IT CON 52000 11.84 = ≈ N/A N/A 16888 grids1x1 estimate b 8.38 = ≈ Y FV = good good good FV FV = FV noChange noChange 34900 b 12.95
LU CON 3800 0.87 = ≈ 1680 2253 N/A grids1x1 estimate b 0.98 + 2459 grids1x1 Y FV + good good good FV FV + FV noChange genuine 3000 b 1.11
PL CON 31800 7.24 u ≈ N/A N/A 13796 grids1x1 minimum b 6.85 x x Y FV x good unk good FV FV x FV noChange noChange 14000 b 5.19
RO CON 47300 10.77 = x 51500 57000 N/A grids1x1 minimum c 26.92 + 57000 grids1x1 Y FV = good good good FV FV + FV N/A N/A 41700 b 15.47
SE CON 7900 1.80 = 7900 N/A N/A 1003 grids1x1 estimate c 0.50 u 500000 i Y U1 - good good poor U1 U1 x U1 = noChange noChange 5500 b 2.04
SI CON 12616 2.87 = ≈ 33 35 N/A grids1x1 minimum c 0.02 = ≈ Y FV = good good good FV FV = FV noChange noChange 1900 c 0.70
FR MED 5700 3.80 = > 5000 10000 N/A grids1x1 mean c 7.97 = > Unk Unk U1 = good unk poor U1 U1 = XX knowledge noChange 4000 a 3.78
GR MED 95462 63.57 = ≈ N/A N/A 59404 grids1x1 estimate b 63.09 = ≈ Y FV = good good good FV FV = FV noChange noChange 64400 b 60.81
HR MED 300 0.20 x >> N/A N/A 3 grids1x1 minimum b 0 x >> Unk XX x unk unk unk XX U2 x N/A N/A N/A b 0
IT MED 48700 32.43 = ≈ N/A N/A 27254 grids1x1 estimate b 28.94 = ≈ Y FV = good good good FV FV = FV noChange noChange 37500 b 35.41
CZ PAN 2900 6.38 x x N/A 2 N/A grids1x1 estimate a 0.09 x x Unk XX = unk unk unk XX XX FV noInfo noInfo 100 a 0.31
HU PAN 38677 85.08 = ≈ N/A N/A 367 grids1x1 minimum b 33.30 = ≈ Y U1 = good good poor U1 U1 = U1 = noChange noChange 29900 b 92.28
RO PAN 2200 4.84 = x 500 800 N/A grids1x1 minimum a 58.98 = 800 grids1x1 Y FV x good good good FV FV + FV N/A N/A 1200 b 3.70
SK PAN 1684.61 3.71 = > 84 84 N/A grids1x1 estimate b 7.62 = ≈ Y U1 = good poor poor U1 U1 = U1 = N/A N/A 1200 b 3.70
RO STE 2700 100 = x 3500 4200 N/A grids1x1 minimum b 100 = 1000 grids1x1 Y FV x good good good FV FV + FV N/A N/A 2300 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 105842 2XR = ≈ grids1x1 2XR = > 2XR = good good good 2XR MTX = FV = nong nc FV D

02/20

EEA-ETC/BD

Institution: -

Member State:

EEA-ETC/BD
EU28 ATL 122220 2XP = ≈ grids1x1 2XP x > 2XP - poor unk poor 2XP MTX - U2 - nc nc U2 C

02/20

EEA-ETC/BD

Institution: -

Member State:

EEA-ETC/BD
EU28 BLS 200 0MS = 200 2 grids1x1 0MS = 2 grids1x1 0MS x good good unk 0MS MTX = U1 x nong nong U1 A=

01/20

EEA-ETC/BD

Institution: -

Member State:

EEA-ETC/BD
EU28 BOR 148754 2XP = ≈ grids1x1 2XP x x 2XP = good good good 2XP MTX = FV = nc nc FV A=

02/20

EEA-ETC/BD

Institution: -

Member State:

EEA-ETC/BD
EU28 CON 439268 2XP = ≈ grids1x1 2XP = > 2XP = good good good 2XP MTX - U1 = nc nong U1 C

02/20

EEA-ETC/BD

Institution: -

Member State:

EEA-ETC/BD
EU28 MED 150162 2XP = ≈ grids1x1 2XP = ≈ 2XP = good good good 2XP MTX = FV = nc nc XX A=

02/20

EEA-ETC/BD

Institution: -

Member State:

EEA-ETC/BD
EU28 PAN 45461 2XP = ≈ grids1x1 2XP = ≈ 2XP = good good poor 2XP MTX = U1 = nc nc U1 D

01/20

EEA-ETC/BD

Institution: -

Member State:

EEA-ETC/BD
EU28 STE 2700 0MS = x 3500 4200 grids1x1 0MS = 1000 grids1x1 0MS x good good good 0MS MTX + FV = nc nong FV A=

02/20

EEA-ETC/BD

Institution: -

Member State:

EEA-ETC/BD
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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.