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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, Hypsugo savii, 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 50 N/A N/A grids1x1 minimum N/A N/A N/A N/A
BG N/A N/A 43 grids1x1 minimum N/A N/A N/A N/A
ES 53 5300 N/A grids1x1 estimate N/A N/A N/A N/A
FR 9000 12000 N/A grids1x1 mean 6 10 N/A mean
HR N/A N/A 5 grids1x1 minimum N/A N/A N/A N/A
IT 1400 28000 N/A grids1x1 estimate N/A N/A N/A N/A
SI 15 16 N/A grids1x1 estimate N/A N/A N/A N/A
ES 21 2100 N/A grids1x1 estimate N/A N/A N/A N/A
FR 3000 4000 N/A grids1x1 mean N/A N/A N/A mean
PT N/A N/A 1 grids1x1 minimum N/A N/A N/A N/A
BG N/A N/A 3 grids1x1 minimum N/A N/A N/A N/A
AT 154 N/A N/A grids1x1 minimum N/A N/A N/A N/A
BG N/A N/A 190 grids1x1 minimum N/A N/A N/A N/A
DE 14 14 14 grids1x1 estimate 14 14 14 grids1x1 estimate
FR 2000 3000 N/A grids1x1 mean 50 60 N/A colonies mean
HR N/A N/A 12 grids1x1 minimum N/A N/A N/A N/A
IT 3280 65600 N/A grids1x1 estimate N/A N/A N/A N/A
RO 500 1500 N/A grids1x1 minimum N/A N/A N/A N/A
SI 46 47 N/A grids1x1 estimate N/A N/A N/A N/A
ES N/A N/A 414 grids1x1 estimate N/A N/A 40 grids10x10 estimate
CY 31 1000 31 grids1x1 estimate N/A N/A N/A N/A
ES 240 24000 N/A grids1x1 estimate N/A N/A N/A N/A
FR 19000 20000 N/A grids1x1 mean 66934 200801 N/A i mean
GR N/A N/A 132826 grids1x1 estimate 3708 4865 N/A grids5x5 estimate
HR N/A N/A 54 grids1x1 minimum N/A N/A N/A N/A
IT 5000 75000 N/A grids1x1 estimate N/A N/A N/A N/A
MT N/A N/A 71 grids1x1 estimate N/A N/A N/A N/A
PT N/A N/A 212 grids1x1 minimum N/A N/A N/A N/A
HU N/A N/A 65 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
CZ 3 9 N/A grids1x1 estimate N/A N/A N/A N/A
CZ 5 76 N/A grids1x1 estimate N/A N/A N/A N/A
SK 555 555 N/A grids1x1 estimate 9 27 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 3000 2.58 + > 50 N/A N/A grids1x1 minimum b 0.18 + > Y FV = good good good FV U1 + U1 + noChange noChange 2300 b 5.99
BG ALP 21700 18.67 = 21700 N/A N/A 43 grids1x1 minimum b 0.15 = 43 grids1x1 Y FV = poor poor poor U1 U1 = FV method method 2100 b 5.47
ES ALP 7600 6.54 = ≈ 53 5300 N/A grids1x1 estimate b 9.56 = 5300 grids1x1 Y U1 = good good good FV U1 = U1 = knowledge knowledge 3800 a 9.90
FR ALP 16000 13.76 = ≈ 9000 12000 N/A grids1x1 mean d 37.51 = ≈ Unk Y FV = good unk good FV FV = FV noChange noChange 10100 b 26.30
HR ALP 1300 1.12 x >> N/A N/A 5 grids1x1 minimum c 0.02 x x Unk XX x unk unk unk XX U2 x N/A N/A 600 b 1.56
IT ALP 59000 50.75 = ≈ 1400 28000 N/A grids1x1 estimate c 52.52 = ≈ Y FV = good good good FV FV = FV noChange noChange 18100 b 47.14
SI ALP 7656 6.59 = 7656 15 16 N/A grids1x1 estimate c 0.06 u > Y XX x good poor unk XX U1 x XX knowledge noChange 1400 b 3.65
ES ATL 10800 70.59 = 10800 21 2100 N/A grids1x1 estimate b 23.25 = 2100 grids1x1 Y U1 = good good poor FV U1 = U1 x knowledge knowledge 1600 a 35.56
FR ATL 3100 20.26 + ≈ 3000 4000 N/A grids1x1 mean d 76.73 = ≈ Unk Y FV = good good poor U1 U1 + XX knowledge noChange 2600 b 57.78
PT ATL 1400 9.15 = 2100 N/A N/A 1 grids1x1 minimum b 0.02 x x Unk XX x good unk unk XX XX XX noChange knowledge 300 b 6.67
BG BLS 5400 100 - 5400 N/A N/A 3 grids1x1 minimum b 100 u 3 grids1x1 Unk XX x unk unk unk XX XX x FV method method 300 b 100
AT CON 2900 1.33 + > 154 N/A N/A grids1x1 minimum b 0.40 + > Y FV = good good good FV U1 + U1 + noChange noChange 2600 b 3.72
BG CON 79800 36.50 - 79800 N/A N/A 190 grids1x1 minimum b 0.50 u 190 grids1x1 Y FV = poor poor poor U1 U1 x FV method method 10200 b 14.61
DE CON 3290 1.50 + x 14 14 14 grids1x1 estimate b 0.04 + x grids1x1 Y XX x unk unk unk XX XX = N/A N/A knowledge knowledge 1000 b 1.43
FR CON 13000 5.95 + ≈ 2000 3000 N/A grids1x1 mean d 6.52 + ≈ Y Y FV = good good poor U1 U1 + FV knowledge noChange 8900 b 12.75
HR CON 2600 1.19 x >> N/A N/A 12 grids1x1 minimum b 0.03 x >> N Unk XX x unk unk poor XX U2 x N/A N/A 2500 b 3.58
IT CON 94300 43.14 = ≈ 3280 65600 N/A grids1x1 estimate c 89.79 = ≈ Y FV = good good good FV FV = FV noChange noChange 37200 b 53.30
RO CON 10100 4.62 = ≈ 500 1500 N/A grids1x1 minimum b 2.61 = ≈ Y FV = good good good FV FV = XX knowledge knowledge 4600 b 6.59
SI CON 12616 5.77 = 12616 46 47 N/A grids1x1 estimate c 0.12 u > Y XX x good poor unk XX U1 x XX knowledge noChange 2800 b 4.01
ES MAC 5700 100 = ≈ N/A N/A 414 grids1x1 estimate b 100 = 414 grids1x1 Y U1 = good good poor FV U1 = U1 + method method 4600 a 100
CY MED 9689 2.18 x ≈ 31 1000 31 grids1x1 estimate b 0.02 x ≈ Y FV = good good good FV FV x FV noChange noChange 11100 b 3.89
ES MED 99500 22.34 = ≈ 240 24000 N/A grids1x1 estimate b 5.92 = 24000 grids1x1 Y U1 = good good poor FV U1 + U1 x knowledge knowledge 24300 a 8.52
FR MED 39800 8.94 = ≈ 19000 20000 N/A grids1x1 mean d 9.52 x ≈ Y Y FV = good unk poor U1 U1 = FV knowledge knowledge 28300 b 9.92
GR MED 129331 29.04 = ≈ N/A N/A 132826 grids1x1 estimate b 64.85 = ≈ Y FV = good good good FV FV = FV noChange noChange 159300 b 55.86
HR MED 11300 2.54 x >> N/A N/A 54 grids1x1 minimum c 0.03 x >> N Unk XX x unk unk poor XX U2 x N/A N/A 11200 b 3.93
IT MED 132600 29.78 = ≈ 5000 75000 N/A grids1x1 estimate c 19.53 = ≈ Y FV = good good good FV FV = FV noChange noChange 40600 b 14.24
MT MED 71 0.02 = ≈ N/A N/A 71 grids1x1 estimate b 0.03 = ≈ N Y FV = good good good FV FV = XX knowledge knowledge 900 b 0.32
PT MED 23000 5.17 u 23000 N/A N/A 212 grids1x1 minimum b 0.10 x x Unk XX x unk unk unk XX XX U1 x knowledge noChange 9500 b 3.33
HU PAN 49644 100 + ≈ N/A N/A 65 grids1x1 minimum c 100 + ≈ Y FV = good good good FV FV + FV noChange method 5000 c 100
RO STE 1100 100 = ≈ 250 500 N/A grids1x1 minimum b 100 = ≈ Y FV = good good good FV FV = XX knowledge knowledge 700 b 100
CZ CON 1100 0 + x 3 9 N/A grids1x1 estimate a 0 + x Y FV = unk unk good XX XX XX noChange noChange 500 a 0
CZ PAN 1700 0 + x 5 76 N/A grids1x1 estimate a 0 + x Y FV = unk unk good XX XX XX noChange noChange 700 a 0
SK PAN 5078.90 0 + > 555 555 N/A grids1x1 estimate a 0 + > Y FV + good good good FV FV = XX knowledge knowledge 7500 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=

12/19

EEA-ETC/BD

Institution: -

Member State:

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

03/20

EEA-ETC/BD

Institution: -

Member State:

EEA-ETC/BD
EU28 BLS 0MS - x 0MS x x 0MS x unk unk unk 0MS MTX FV nong nong FV D

02/20

EEA-ETC/BD

Institution: -

Member State:

EEA-ETC/BD
EU28 CON 2XR - > grids1x1 2XR = ≈ 2XR = unk unk poor 2XR MTX - FV = nong nong FV C

03/20

EEA-ETC/BD

Institution: -

Member State:

EEA-ETC/BD
EU28 MAC 0MS = ≈ grids1x1 0MS = ≈ 414 grids1x1 0MS = good good unk 0MS MTX = U1 + nc nc U1 D

03/20

EEA-ETC/BD

Institution: -

Member State:

EEA-ETC/BD
EU28 MED 2XP = ≈ grids1x1 2XP = ≈ 2XP = good good good 2XP MTX + U1 = nong nong U1 A=

01/20

EEA-ETC/BD

Institution: -

Member State:

EEA-ETC/BD
EU28 PAN 0MS + ≈ grids1x1 0MS + ≈ 0MS = good good good 0MS 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 = XX x nong nong XX A=

02/20

EEA-ETC/BD

Institution: -

Member State:

EEA-ETC/BD
BG BLS 2XP 2XP MTX FV FV 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.