Article 17 web tool

Log in

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 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, Barbastella barbastellus, 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 550 N/A N/A i minimum 245 N/A N/A grids1x1 minimum
BG 6000 12500 N/A i minimum N/A N/A N/A N/A
DE 25 100 N/A i estimate 12 12 12 localities estimate
ES 208 N/A N/A i minimum N/A N/A 54 grids10x10 estimate
FR 5000 10000 N/A i mean N/A N/A N/A mean
HR N/A N/A 12 i minimum N/A N/A 12 grids1x1 minimum
IT 4500 22000 N/A i estimate N/A N/A N/A N/A
PL N/A N/A 25 i minimum N/A N/A 25 localities estimate
RO 1000 2500 N/A i minimum N/A N/A N/A N/A
SI N/A N/A 50 i minimum 50 58 N/A grids1x1 estimate
SK 50000 100000 N/A i estimate N/A N/A N/A N/A
BE 100 200 N/A i estimate N/A N/A N/A N/A
DE N/A N/A N/A i estimate 57 57 57 localities estimate
ES 47 N/A N/A i minimum N/A N/A 44 grids10x10 minimum
FR 5000 10000 N/A i mean N/A N/A N/A mean
PT N/A N/A N/A minimum N/A N/A 8 grids1x1 N/A
UK N/A N/A 5000 i estimate N/A N/A N/A N/A
BG 1000 2500 N/A i minimum N/A N/A N/A N/A
LT N/A N/A 337 i estimate N/A N/A N/A N/A
SE 5000 15000 10000 i estimate N/A N/A 405 grids1x1 estimate
AT 150 N/A N/A i minimum 184 N/A N/A grids1x1 minimum
BE 100 500 N/A i estimate 5 100 N/A iwintering estimate
BG 10000 18000 N/A i minimum N/A N/A N/A N/A
CZ 4000 6000 N/A i estimate N/A N/A N/A N/A
DE 10000 50000 20000 i estimate 919 951 935 localities estimate
DK N/A N/A N/A N/A N/A 10 localities N/A
FR 6000 10000 N/A i mean N/A N/A N/A mean
HR N/A N/A 46 i minimum N/A N/A 46 grids1x1 minimum
IT 4200 21000 N/A i estimate N/A N/A N/A N/A
LU 60 120 N/A i estimate N/A N/A N/A N/A
PL N/A N/A 6500 i minimum N/A N/A N/A N/A
RO 1000 2000 N/A i minimum N/A N/A N/A N/A
SE 3000 9000 6000 i estimate N/A N/A 241 grids1x1 estimate
SI N/A N/A 49 i minimum 49 57 N/A grids1x1 estimate
ES 12 N/A N/A i estimate N/A N/A 17 grids10x10 estimate
ES 310 N/A N/A i minimum 156 N/A N/A grids10x10 estimate
FR 5496 30229 N/A i estimate N/A N/A N/A estimate
GR N/A N/A N/A 108 508 N/A grids5x5 estimate
IT 3800 19000 N/A i estimate N/A N/A N/A N/A
PT N/A N/A N/A minimum N/A N/A 290 grids1x1 N/A
CZ 500 1000 N/A i estimate N/A N/A N/A N/A
HU N/A N/A N/A minimum N/A N/A 962 grids1x1 N/A
SK 1000 5000 N/A i estimate N/A N/A N/A N/A
NL N/A N/A N/A N/A N/A N/A N/A
LV 200 500 N/A i estimate N/A N/A N/A N/A
HR N/A N/A 2 i minimum N/A N/A 2 grids1x1 minimum
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 14800 8.58 = 550 N/A N/A i minimum b 0.51 = > Y FV = good good poor U1 U1 = U1 + noChange knowledge 11600 b 15.70
BG ALP 25200 14.60 u 25200 6000 12500 N/A i minimum b 8.59 u 10225 i Y XX u unk unk unk XX XX = U1 - noChange method 3800 b 5.14
DE ALP 4007 2.32 = 4007 25 100 N/A i estimate b 0.06 = localities Y FV = good good good FV FV = FV noChange noChange 3400 c 4.60
ES ALP 12900 7.48 + 208 N/A N/A i minimum b 0.19 = 54 grids10x10 Y U1 = good poor poor U1 U1 = U1 - noChange knowledge 4500 a 6.09
FR ALP 17000 9.85 = 5000 10000 N/A i mean c 6.97 = Y Unk FV = good good good FV FV = FV noChange noChange 10800 b 14.61
HR ALP 9700 5.62 x > N/A N/A 12 i minimum c 0.01 x >> N Y XX x poor unk poor U1 U2 x N/A N/A 7600 b 10.28
IT ALP 44100 25.56 = 4500 22000 N/A i estimate b 12.31 = > N Y FV = good good good FV U1 = U1 - noChange noChange 10200 b 13.80
PL ALP 6000 3.48 = N/A N/A 25 i minimum b 0.02 x x Unk XX x unk unk unk XX XX XX noChange noChange 1900 b 2.57
RO ALP 15300 8.87 = 1000 2500 N/A i minimum b 1.63 = Y FV = good good good FV FV = U1 = knowledge knowledge 3800 b 5.14
SI ALP 7656 4.44 = N/A N/A 50 i minimum b 0.05 u > Y XX x good poor unk U1 U1 x FV knowledge noChange 3100 b 4.19
SK ALP 15892.73 9.21 = 50000 100000 N/A i estimate c 69.67 + Y FV x good good good FV FV = XX knowledge N/A 13200 b 17.86
BE ATL 500 0.15 + >> 100 200 N/A i estimate a 1.18 + >> N N U2 u bad bad poor U2 U2 = U2 - knowledge knowledge 500 a 0.44
DE ATL 8807 2.72 + > N/A N/A N/A i estimate b 0 + x localities N Y U1 = unk unk unk XX U1 + U2 = genuine genuine 3900 c 3.41
ES ATL 26100 8.05 = 47 N/A N/A i minimum b 0.37 = 44 grids10x10 Y U1 = good poor poor U1 U1 = U1 - noChange knowledge 3500 a 3.06
FR ATL 211500 65.26 = 5000 10000 N/A i mean b 59.07 = Y Unk XX = good good unk FV FV = U1 = knowledge knowledge 64100 b 56.08
PT ATL 3000 0.93 = 4100 N/A N/A N/A minimum b 0 x x Unk XX x good unk unk XX XX XX noChange knowledge 600 b 0.52
UK ATL 74189 22.89 = 74189 N/A N/A 5000 i estimate c 39.38 x x Unk Unk XX x good unk unk XX XX x XX noChange noChange 41700 a 36.48
BG BLS 8400 100 = 8400 1000 2500 N/A i minimum b 100 = 1500 i Y FV = poor poor poor U1 U1 = U1 - noChange method 1000 b 100
LT BOR 65200 54.65 = N/A N/A 337 i estimate b 3.26 x > Y U1 = unk unk unk XX U1 = U1 = N/A N/A 10800 b 42.19
SE BOR 54100 45.35 = 54100 5000 15000 10000 i estimate c 96.74 = 10000 i N Unk FV x good poor unk FV FV = U2 - genuine genuine 14800 c 57.81
AT CON 9100 1.28 = > 150 N/A N/A i minimum c 0.20 = > Y FV = good good poor U1 U1 = U1 x noChange knowledge 6700 b 2.23
BE CON 1000 0.14 + >> 100 500 N/A i estimate a 0.40 + >> Y FV = good good good FV U2 + U2 + noChange noChange 1000 a 0.33
BG CON 85500 11.99 = 85500 10000 18000 N/A i minimum b 18.86 = 12000 i Y FV = poor poor poor U1 U1 = U1 - noChange method 6200 b 2.06
CZ CON 81000 11.36 = 4000 6000 N/A i estimate a 6.74 = > Y FV = good poor good FV U1 = U1 = noChange noChange 43500 a 14.45
DE CON 233805 32.78 + 10000 50000 20000 i estimate b 26.94 + > localities N Unk U1 - good poor unk U1 U1 = U1 = noChange noChange 109100 c 36.25
DK CON 1581 0.22 = N/A N/A N/A d 0 u x Unk Y XX u good unk unk XX XX U1 x N/A N/A 1400 b 0.47
FR CON 69500 9.74 = 6000 10000 N/A i mean b 10.78 = Y Unk FV = good good poor U1 U1 = U1 = noChange noChange 48400 b 16.08
HR CON 31100 4.36 x > N/A N/A 46 i minimum c 0.06 x > N Y U1 x poor unk poor U1 U1 x N/A N/A 30100 b 10
IT CON 57100 8.01 = 4200 21000 N/A i estimate c 16.97 = > N Y U1 - good good poor FV U1 - U2 - noChange noChange 8400 b 2.79
LU CON 500 0.07 + > 60 120 N/A i estimate b 0.12 + > N Unk U2 u good poor poor U1 U2 + U2 x noChange genuine 300 b 0.10
PL CON 91600 12.84 = N/A N/A 6500 i minimum a 8.76 = N Unk U1 u good unk poor U1 U1 = U1 x noChange knowledge 28000 b 9.30
RO CON 23200 3.25 = 1000 2000 N/A i minimum b 2.02 = Y FV = good poor good FV FV = U1 = knowledge knowledge 6600 b 2.19
SE CON 15700 2.20 + 15700 3000 9000 6000 i estimate c 8.08 + 6000 i Y FV = good unk unk XX FV + U2 + genuine genuine 7300 c 2.43
SI CON 12616 1.77 = N/A N/A 49 i minimum b 0.07 u > Y XX x good poor unk U1 U1 x FV knowledge noChange 4000 b 1.33
ES MAC 2700 100 = 12 N/A N/A i estimate a 100 + 17 grids10x10 Y U1 + poor poor poor U1 U1 = U1 + noChange knowledge 1800 a 100
ES MED 60900 22.66 + 310 N/A N/A i minimum b 1.05 = 310 grids10x10 Y U1 = good poor poor U1 U1 = U1 - noChange knowledge 14500 a 14.26
FR MED 25200 9.38 = 5496 30229 N/A i estimate b 60.40 x < Y FV = poor unk poor U1 U1 = U1 = noChange noChange 16200 b 15.93
GR MED 75598 28.12 x x N/A N/A N/A b 0 x x N Unk U1 x unk poor poor U1 U1 x U1 x noChange noChange 48800 b 47.98
IT MED 71100 26.45 = > 3800 19000 N/A i estimate c 38.55 = > N Y U1 - good good good FV U1 - U2 - noChange noChange 8700 b 8.55
PT MED 36000 13.39 = 36300 N/A N/A N/A minimum b 0 x x Unk XX x good unk unk XX XX U1 x knowledge noChange 13500 b 13.27
CZ PAN 5800 11.05 = 500 1000 N/A i estimate a 20 = > Y FV = good poor good FV U1 = U1 = noChange noChange 1500 a 5.10
HU PAN 40982 78.11 x N/A N/A N/A minimum b 0 x Y U1 u poor poor poor U1 U1 x U1 - noChange method 23500 b 79.93
SK PAN 5686.65 10.84 + 1000 5000 N/A i estimate c 80 x Y FV x good good good FV FV x XX knowledge N/A 4400 b 14.97
NL 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 N/A N/A N/A N/A 0
LV BOR 2900 0 = x 200 500 N/A i estimate c 0 x Y U1 - good unk poor U1 U1 - XX knowledge knowledge 2100 b 0
HR MED 5500 0 x x N/A N/A 2 i minimum c 0 x x Unk XX x unk unk unk XX XX N/A N/A 4300 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 BOR 2GD = 2GD x > 2GD x 2GD MTX = U2 gen nong U2 B1

03/20

EEA-ETC/BD

Institution: -

Member State:

EEA-ETC/BD
EU28 CON 0EQ + 0EQ + > 0EQ - good poor poor 0EQ MTX = U1 - nc nc U1 D

03/20

EEA-ETC/BD

Institution: -

Member State:

EEA-ETC/BD
EU28 MAC 0MS = 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 MED 2XR + i 2XR x x 2XR - unk unk poor 2XR MTX - U1 x nc nong U2 B1

01/20

EEA-ETC/BD

Institution: -

Member State:

EEA-ETC/BD
EU28 PAN 2XR x 2XR x > 2XR x poor poor poor 2XR MTX = U1 = nc nc U1 D

02/20

EEA-ETC/BD

Institution: -

Member State:

EEA-ETC/BD
EU28 ALP 2XR = > 2XR + > 2XR x good unk poor 2XR MTX = U1 - nc nc U1 D

04/20

EEA-ETC/BD

Institution: -

Member State:

EEA-ETC/BD
EU28 ATL 2XR = 2XR x x 2XR x good unk unk 2XR MTX U2 = nong nong U2 D

12/19

EEA-ETC/BD

Institution: -

Member State:

EEA-ETC/BD
EU28 BLS 0MS = i 0MS = x 0MS = poor poor poor 0MS MTX = U1 = nc nc U1 D

01/20

EEA-ETC/BD

Institution: -

Member State:

EEA-ETC/BD
BG ALP 2XP 2XP 2XP MTX U1 - U1 0/2

04/20

Green Balkans Federation

Institution: Green Balkans Federation

Member State: BG

Green Balkans Federation
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.