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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.

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

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 myotis, All bioregions. Annexes II, IV. 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 18500 i estimate 370 N/A N/A grids1x1 minimum
BG 4000 4500 N/A i minimum N/A N/A N/A N/A
DE N/A N/A N/A i estimate 2 2 2 localities estimate
ES 1000 N/A N/A i estimate 15 N/A N/A grids10x10 estimate
FR 1000 5000 N/A i mean N/A N/A N/A mean
HR N/A N/A 305 i minimum N/A N/A N/A N/A
IT 8000 80000 N/A i estimate N/A N/A N/A N/A
PL 10000 20000 N/A i estimate N/A N/A N/A N/A
RO 5000 10000 N/A i minimum N/A N/A N/A N/A
SI N/A N/A 36 i minimum 36 45 N/A grids1x1 estimate
SK 4727 22392 N/A i estimate N/A N/A N/A N/A
BE 2 10 2 i estimate 2 9 2 iwintering estimate
DE N/A N/A N/A i estimate 161 162 161.50 localities estimate
ES 443 N/A N/A i minimum 96 N/A N/A grids10x10 minimum
FR 30000 35000 N/A i mean N/A N/A N/A mean
NL 25 55 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 100 2500 N/A i minimum N/A N/A N/A N/A
AT N/A N/A 30000 i estimate N/A N/A 308 grids1x1 estimate
BE 1250 2500 1250 i estimate 900 2300 900 iwintering estimate
BG 2000 22000 N/A i minimum N/A N/A N/A N/A
CZ 40000 50000 N/A i estimate N/A N/A N/A N/A
DE 500000 1000000 823991 i estimate 1092 1105 1098.50 localities estimate
FR 72000 80000 N/A i mean N/A N/A N/A mean
HR N/A N/A 5740 i minimum N/A N/A N/A N/A
IT 6000 60000 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 50000 100000 N/A i estimate N/A N/A N/A N/A
RO 7000 10000 N/A i minimum N/A N/A N/A N/A
SE 250 750 500 i estimate N/A N/A 20 grids1x1 estimate
SI N/A N/A 95 i minimum 95 104 N/A grids1x1 estimate
ES 86344 90595 N/A i estimate 582 N/A N/A grids10x10 estimate
FR 4000 5000 N/A i mean N/A N/A N/A mean
GR 1000 5000 N/A i estimate N/A N/A N/A N/A
HR N/A N/A 11400 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
PT N/A N/A N/A minimum N/A N/A 138 grids1x1 N/A
CZ 1000 2500 N/A i estimate N/A N/A N/A N/A
HU N/A N/A N/A minimum N/A N/A 421 grids1x1 N/A
SK 94 1613 N/A i estimate 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 26200 13.45 = N/A N/A 18500 i estimate a 17.27 = Y FV = good good poor U1 U1 = U1 + noChange genuine 19000 b 21.71
BG ALP 24800 12.73 = 24800 4000 4500 N/A i minimum b 3.97 = 4000 i Y FV = poor poor poor U1 U1 = U1 = noChange noChange 5900 b 6.74
DE ALP 3671 1.88 = N/A N/A N/A i estimate a 0 = localities Y FV = good good good FV FV = FV noChange noChange 3300 c 3.77
ES ALP 6500 3.34 x 1000 N/A N/A i estimate a 0.93 x 1000 i Y XX - good poor poor U1 U1 = U1 - knowledge knowledge 1100 a 1.26
FR ALP 18000 9.24 = 1000 5000 N/A i mean a 2.80 + Y Unk FV = good good poor U1 U1 + U1 = noChange noChange 7900 a 9.03
HR ALP 11100 5.70 x N/A N/A 305 i minimum b 0.28 x >> Unk U1 x unk poor poor U1 U2 x N/A N/A 8400 b 9.60
IT ALP 47500 24.38 = > 8000 80000 N/A i estimate c 41.06 = Y U1 - good poor poor U1 U1 - U1 - noChange noChange 9500 c 10.86
PL ALP 10500 5.39 = 10000 20000 N/A i estimate c 14 x N Y U1 - good unk poor U1 U1 x FV genuine genuine 3600 b 4.11
RO ALP 17900 9.19 = 5000 10000 N/A i minimum b 7 = Y FV = good good good FV FV = U1 = knowledge knowledge 4000 b 4.57
SI ALP 7656 3.93 = N/A N/A 36 i minimum a 0.03 + N Unk U2 - good good bad U2 U2 - U2 x noChange knowledge 3200 b 3.66
SK ALP 21014.63 10.79 = 4727 22392 N/A i estimate b 12.65 + Y FV x good good good FV FV = U1 - knowledge knowledge 21600 b 24.69
BE ATL 3500 1.71 - >> 2 10 2 i estimate a 0.01 u >> Unk XX x bad bad unk U2 U2 x U2 - noChange method 600 a 0.61
DE ATL 40177 19.66 = N/A N/A N/A i estimate b 0 = > localities Unk XX x good poor unk U1 U1 = U1 + noChange knowledge 15900 c 16.22
ES ATL 38300 18.74 = 443 N/A N/A i minimum a 1.34 = 443 i Y FV = good poor poor U1 U1 = U1 - knowledge knowledge 9500 a 9.69
FR ATL 120800 59.11 = 30000 35000 N/A i mean a 98.53 + Y Y FV = good good poor U1 U1 + U1 x noChange noChange 70600 a 72.04
NL ATL 1300 0.64 x 25 55 N/A i estimate a 0.12 u Unk XX x unk good unk XX XX U1 + method noInfo 1200 b 1.22
PT ATL 300 0.15 = 300 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 0.20
BG BLS 8300 100 = 8300 100 2500 N/A i minimum b 100 = 1800 i Y FV = poor poor poor U1 U1 = FV method method 1100 b 100
AT CON 20500 2.47 = N/A N/A 30000 i estimate a 2.69 + Y FV = good good good FV FV + FV noChange genuine 14200 b 3.17
BE CON 13899 1.68 = 1250 2500 1250 i estimate b 0.11 + > Y FV x good good good FV U1 = U1 + noChange noChange 8500 a 1.90
BG CON 92100 11.11 = 92100 2000 22000 N/A i minimum b 1.08 = 18000 i Y FV = poor poor poor U1 U1 = U1 = noChange noChange 19200 b 4.28
CZ CON 82500 9.95 = 40000 50000 N/A i estimate a 4.03 + Y FV = good good good FV FV + FV noChange noChange 56100 a 12.52
DE CON 278452 33.59 = 278452 500000 1000000 823991 i estimate b 73.88 = localities Y U1 - good good unk FV U1 - FV genuine genuine 214400 c 47.84
FR CON 113000 13.63 = 72000 80000 N/A i mean a 6.81 + Y Y FV = good good poor U1 U1 + U1 = noChange noChange 55300 a 12.34
HR CON 27200 3.28 x > N/A N/A 5740 i minimum b 0.51 x N Unk U1 x poor unk poor U1 U1 x N/A N/A 26300 b 5.87
IT CON 69500 8.38 = > 6000 60000 N/A i estimate c 2.96 = Y U1 - good poor poor U1 U1 - U1 - noChange noChange 10700 c 2.39
LU CON 3900 0.47 = 3500 5000 N/A i estimate b 0.38 + 5000 i Y U1 = good good poor U1 U1 + U1 - noChange genuine 2900 c 0.65
PL CON 88900 10.73 + 50000 100000 N/A i estimate c 6.72 + 30000 i Y XX u good good unk FV FV = FV noChange knowledge 26300 b 5.87
RO CON 23300 2.81 = 7000 10000 N/A i minimum b 0.76 = Y FV = good good good FV FV = U1 = knowledge knowledge 7400 b 1.65
SE CON 3000 0.36 u 15000 250 750 500 i estimate c 0.04 u 1000 i Unk XX x bad bad unk U2 U2 x U2 x noChange knowledge 1200 b 0.27
SI CON 12616 1.52 = 12616 N/A N/A 95 i minimum a 0.01 + N Unk U2 - good good bad U2 U2 - U2 x noChange knowledge 5700 b 1.27
ES MED 242500 46.57 = 86344 90595 N/A i estimate a 56.40 = 90595 i Y FV = good poor poor U1 U1 = U1 = knowledge knowledge 64900 a 30.90
FR MED 21400 4.11 = 4000 5000 N/A i mean a 2.87 = Y FV = good good poor U1 U1 = U1 = noChange noChange 9900 a 4.71
GR MED 108816 20.90 x 1000 5000 N/A i estimate b 1.91 x Unk XX x unk poor poor U1 U1 x U1 x noChange noChange 86900 b 41.38
HR MED 21400 4.11 x > N/A N/A 11400 i minimum b 7.27 x N Unk U1 x good unk poor U1 U1 x N/A N/A 19600 b 9.33
IT MED 91500 17.57 = > 9000 90000 N/A i estimate c 31.55 = Y U1 - good poor poor U1 U1 - U1 - noChange noChange 19000 c 9.05
PT MED 35100 6.74 = 35100 N/A N/A N/A minimum b 0 u x Unk XX - good good unk FV FV x U1 x knowledge noChange 9700 b 4.62
CZ PAN 5800 6.70 = 1000 2500 N/A i estimate a 67.22 + Y FV = good good good FV FV + FV noChange noChange 2100 a 8.54
HU PAN 76859 88.84 - > N/A N/A N/A minimum b 0 - > Y U1 - poor poor poor U1 U1 - U1 = noChange genuine 18100 a 73.58
SK PAN 3855.99 4.46 = 94 1613 N/A i estimate b 32.78 + Y FV x good good good FV FV = U1 - knowledge N/A 4400 b 17.89
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 21014.63 1 = < 199591.63 2GD + 2GD - 2GD MTX = U1 - nc nong U1 D

02/20

EEA-ETC/BD

Institution: -

Member State:

EEA-ETC/BD
EU28 ATL 204377 1 = < 224814.7 2GD + 2GD = 2GD MTX + U1 x nc nong U1 B1

02/20

EEA-ETC/BD

Institution: -

Member State:

EEA-ETC/BD
EU28 BLS 8300 0MS = 8300 100 2500 i 0MS = 1800 i 0MS = poor poor poor 0MS MTX = FV = nong nc FV D

02/20

EEA-ETC/BD

Institution: -

Member State:

EEA-ETC/BD
EU28 CON 828867 1 = < 853537 717835 1366085 1115326 i 2XP = 2XP - 2XP MTX - U1 = nc nong U1 C

02/20

EEA-ETC/BD

Institution: -

Member State:

EEA-ETC/BD
EU28 MED 520716 1 x < 532006 2GD 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 86514.99 1 - < 94200.89 1094 4113 2603.5 i 2GD - > 2GD - 2GD MTX - U1 - nc nc U1 C

01/20

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.