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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.

Warning: The map does not show the distribution for sensitive species in GR, LU

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

Sensitive spatial information for this species is not shown in the map.

Current selection: 2013-2018, Mammals, Myotis emarginatus, All bioregions. Annexes Y, Y, 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 3500 i estimate 173 N/A N/A grids1x1 minimum
BG 100 700 N/A i minimum N/A N/A N/A N/A
DE N/A N/A N/A N/A N/A N/A localities N/A
ES 510 N/A 510 i estimate 22 N/A N/A grids10x10 minimum
FR 1500 2500 N/A i mean N/A N/A N/A mean
HR N/A N/A 44 i minimum N/A N/A N/A N/A
IT 2600 26000 N/A i estimate N/A N/A N/A N/A
PL N/A N/A 1350 i minimum N/A N/A N/A N/A
RO 1000 1500 N/A i minimum N/A N/A N/A N/A
SI N/A N/A 41 i minimum 41 48 N/A grids1x1 estimate
SK 1666 3713 N/A i estimate N/A N/A N/A N/A
BE 2600 3000 N/A i estimate N/A N/A N/A estimate
DE N/A N/A N/A i estimate 5 5 5 localities estimate
ES 400 N/A N/A i minimum 87 N/A N/A grids10x10 minimum
FR 30000 35000 N/A i mean N/A N/A N/A mean
NL 1000 2500 N/A i estimate N/A N/A N/A N/A
PT N/A N/A N/A minimum N/A N/A 2 grids1x1 N/A
BG 200 1500 N/A i minimum N/A N/A N/A N/A
AT N/A N/A 2900 i estimate 122 N/A N/A grids1x1 minimum
BE 2000 4000 N/A i estimate 1700 2500 N/A iwintering estimate
BG 1700 17000 N/A i minimum N/A N/A N/A N/A
CZ 4000 7000 N/A i estimate N/A N/A N/A N/A
DE 5426 6016 5686 i mean 2713 3008 2843 localities mean
FR 31200 33000 N/A i mean N/A N/A N/A mean
HR N/A N/A 4225 i minimum N/A N/A N/A N/A
IT 9000 90000 N/A i estimate N/A N/A N/A N/A
LU 3500 5000 N/A i estimate N/A N/A N/A N/A
PL N/A N/A 2600 i minimum N/A N/A N/A N/A
RO 1500 2000 N/A i minimum N/A N/A N/A N/A
SI N/A N/A 55 i minimum 55 62 N/A grids1x1 estimate
CY 275 1000 N/A i estimate N/A N/A N/A N/A
ES 5666 8538 N/A i estimate 196 N/A N/A grids10x10 minimum
FR 13705 32892 N/A i estimate N/A N/A N/A estimate
GR 5000 10000 N/A i estimate N/A N/A N/A N/A
HR N/A N/A 13270 i minimum N/A N/A N/A N/A
IT 7000 41000 N/A i estimate N/A N/A N/A N/A
PT N/A N/A N/A minimum N/A N/A 53 grids1x1 N/A
CZ 500 1500 N/A i estimate N/A N/A N/A N/A
HU N/A N/A N/A minimum N/A N/A 163 grids1x1 N/A
SK 8 216 N/A i estimate N/A N/A N/A N/A
RO 50 300 N/A i minimum N/A N/A N/A N/A
UK N/A N/A N/A 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 12100 8.42 = > N/A N/A 3500 i estimate a 13.42 - > Y U1 - good poor poor U1 U1 - U1 + noChange genuine 9300 b 18.75
BG ALP 25200 17.54 = 25200 100 700 N/A i minimum b 1.53 = 655 i Y FV = poor poor poor U1 U1 = FV method method 4100 b 8.27
DE ALP 1751 1.22 = ≈ N/A N/A N/A d 0 x x localities Y XX u good poor unk U1 U1 x XX method noChange 600 c 1.21
ES ALP 9000 6.26 = ≈ 510 N/A 510 i estimate a 1.96 = 510 i Y U1 = good poor poor U1 U1 = U2 - knowledge knowledge 1700 a 3.43
FR ALP 13700 9.54 = ≈ 1500 2500 N/A i mean b 7.67 = ≈ Y Y FV = good good unk U1 U1 = U1 = noChange noChange 5900 b 11.90
HR ALP 5800 4.04 x > N/A N/A 44 i minimum b 0.17 x >> N Unk XX x unk unk unk XX U2 x N/A N/A 4300 b 8.67
IT ALP 47400 32.99 = ≈ 2600 26000 N/A i estimate c 54.82 = ≈ Y FV = good poor poor U1 U1 = U1 - noChange noChange 8300 c 16.73
PL ALP 8300 5.78 + ≈ N/A N/A 1350 i minimum b 5.18 + x Y FV = good good unk FV FV + XX genuine genuine 2200 b 4.44
RO ALP 2100 1.46 = ≈ 1000 1500 N/A i minimum b 4.79 = ≈ Y U1 = poor poor poor U1 U1 = U1 = knowledge knowledge 1100 b 2.22
SI ALP 7656 5.33 = 7656 N/A N/A 41 i minimum a 0.16 + ≈ Y U1 - good good poor U1 U1 - U1 - noChange noChange 3200 b 6.45
SK ALP 10656.46 7.42 = ≈ 1666 3713 N/A i estimate b 10.31 + ≈ Y FV x good good good FV FV = XX knowledge knowledge 8900 b 17.94
BE ATL 15000 9.38 = >> 2600 3000 N/A i estimate a 7.48 + 1000 i N Unk XX x poor good unk U1 U2 = U2 - noChange genuine 5900 b 7.89
DE ATL 2169 1.36 = >> N/A N/A N/A i estimate b 0 - >> localities N Y U1 - bad bad poor U2 U2 - U2 = noChange genuine 800 c 1.07
ES ATL 38400 24 = ≈ 400 N/A N/A i minimum b 1.07 = 1000 i Y U1 = poor poor poor U1 U1 - U1 - N/A N/A 9000 a 12.03
FR ATL 101300 63.32 = ≈ 30000 35000 N/A i mean a 86.78 + ≈ Y Y FV = good good unk FV FV + U1 + noChange noChange 57400 b 76.74
NL ATL 2500 1.56 x ≈ 1000 2500 N/A i estimate a 4.67 + ≈ N N U2 - poor good bad U2 U2 x U2 + noChange knowledge 1500 b 2.01
PT ATL 600 0.38 = 700 N/A N/A N/A minimum b 0 x x Unk XX x good unk unk XX XX U1 x knowledge noChange 200 b 0.27
BG BLS 8000 100 = 8000 200 1500 N/A i minimum b 100 = 1200 i Y FV = poor poor poor U1 U1 = FV method method 1400 b 100
AT CON 9200 2.32 = > N/A N/A 2900 i estimate a 2.40 - > Y U1 - good poor poor U1 U1 - U1 + noChange genuine 7500 b 5.64
BE CON 13300 3.35 = ≈ 2000 4000 N/A i estimate a 2.48 + > Y FV = good good poor U1 U1 + U1 + noChange noChange 6600 b 4.96
BG CON 89600 22.59 = 89600 1700 17000 N/A i minimum b 7.73 = 12000 i Y FV = poor poor poor U1 U1 = FV method method 11700 b 8.80
CZ CON 43200 10.89 + ≈ 4000 7000 N/A i estimate a 4.55 + ≈ Y FV = good good good FV FV + FV noChange noChange 18800 a 14.14
DE CON 38244 9.64 = ≈ 5426 6016 5686 i mean a 4.70 - > localities N Unk U1 - good poor poor U1 U1 - U1 - noChange noChange 11900 c 8.95
FR CON 81600 20.57 = ≈ 31200 33000 N/A i mean a 26.55 + ≈ Y Y FV = good good unk FV FV + U1 x noChange noChange 39300 b 29.55
HR CON 14600 3.68 x >> N/A N/A 4225 i minimum b 3.49 x > N Unk U1 x poor poor poor U1 U2 x N/A N/A 13900 b 10.45
IT CON 73600 18.56 = ≈ 9000 90000 N/A i estimate c 40.94 = ≈ Y FV = good poor poor U1 U1 = U1 - noChange noChange 11800 c 8.87
LU CON 3300 0.83 = ≈ 3500 5000 N/A i estimate b 3.51 = 5000 i N N U1 - good good poor U1 U1 - U1 = noChange method 2300 b 1.73
PL CON 10900 2.75 = x N/A N/A 2600 i minimum b 2.15 + x Y U1 - poor poor poor U1 U1 - U1 + noChange genuine 2800 b 2.11
RO CON 6700 1.69 = ≈ 1500 2000 N/A i minimum b 1.45 = ≈ Y U1 = poor poor poor U1 U1 = U1 = knowledge knowledge 2700 b 2.03
SI CON 12412 3.13 = ≈ N/A N/A 55 i minimum a 0.05 + ≈ N Unk U2 - good good bad U2 U2 - U1 x knowledge knowledge 3700 b 2.78
CY MED 6984 1.76 x ≈ 275 1000 N/A i estimate c 0.84 x x Y U1 = good unk poor U1 U1 x XX knowledge noChange 6500 c 2.99
ES MED 85000 21.47 = ≈ 5666 8538 N/A i estimate b 9.37 = 8538 i Y U1 = good poor poor FV U1 = U1 - knowledge knowledge 20400 a 9.40
FR MED 44400 11.22 = ≈ 13705 32892 N/A i estimate a 30.73 = ≈ Y U1 - good good poor U1 U1 - U1 = noChange noChange 22500 b 10.36
GR MED 124869 31.54 x ≈ 5000 10000 N/A i estimate b 9.89 x ≈ Unk XX x unk poor poor U1 U1 x U1 x noChange noChange 129900 b 59.83
HR MED 20100 5.08 x > N/A N/A 13270 i minimum b 17.50 x ≈ N Unk U1 x good poor poor U1 U1 x N/A N/A 19300 b 8.89
IT MED 101600 25.67 = ≈ 7000 41000 N/A i estimate c 31.66 = ≈ Y FV = good poor poor U1 U1 = U1 - noChange noChange 14600 c 6.73
PT MED 12900 3.26 = 12900 N/A N/A N/A minimum b 0 u x Unk XX - good good unk FV FV x U2 x knowledge noChange 3900 b 1.80
CZ PAN 5300 14.99 = ≈ 500 1500 N/A i estimate a 89.93 = ≈ Y FV = good good good FV FV = FV noChange noChange 1300 a 11.82
HU PAN 28509 80.62 - ≈ N/A N/A N/A minimum b 0 - > Y U1 - poor poor poor U1 U1 - FV genuine genuine 8300 b 75.45
SK PAN 1551.86 4.39 = ≈ 8 216 N/A i estimate b 10.07 + ≈ Y FV x good good good FV FV = XX knowledge knowledge 1400 b 12.73
RO STE 200 100 = ≈ 50 300 N/A i minimum b 100 = ≈ Y U1 = poor poor poor U1 U1 = U1 = knowledge knowledge 200 b 100
UK ATL 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 noChange noChange N/A d 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 143663.46 1 = < 145453.46 2GD = 2GD - 2GD MTX - U1 - nc nc U1 C

02/20

EEA-ETC/BD

Institution: -

Member State:

EEA-ETC/BD
EU28 ATL 159969 2GD = 2GD + 2GD = 2GD MTX + U1 + nong nc U1 A=

02/20

EEA-ETC/BD

Institution: -

Member State:

EEA-ETC/BD
EU28 BLS 8000 0MS = 8000 200 1500 i 0MS = 1200 i 0MS = poor poor poor 0MS MTX = U1 = nc nc U1 D

02/20

EEA-ETC/BD

Institution: -

Member State:

EEA-ETC/BD
EU28 CON 396656 2GD = 68106 173796 120916 i 2GD + 2GD = 2GD MTX + U1 = nc nong U1 B1

02/20

EEA-ETC/BD

Institution: -

Member State:

EEA-ETC/BD
EU28 MED 395853 1 x < 397863 2GD x x 2GD x 2GD MTX x U1 x nc nc U1 D

02/20

EEA-ETC/BD

Institution: -

Member State:

EEA-ETC/BD
EU28 PAN 35360.86 1 - ≈ 35360.86 2GD - 2GD - 2GD MTX - FV = gen gen FV C

02/20

EEA-ETC/BD

Institution: -

Member State:

EEA-ETC/BD
EU28 STE 200 0MS = ≈ 50 300 i 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.