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 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 nattereri, 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 98 N/A N/A grids1x1 minimum N/A N/A N/A N/A
BG N/A N/A 17 grids1x1 minimum N/A N/A N/A N/A
DE 1649 1649 1649 grids1x1 estimate 20 20 20 grids5x5 estimate
ES 34 3400 N/A grids1x1 estimate N/A N/A N/A N/A
FR 30000 100000 N/A grids1x1 mean N/A N/A N/A mean
HR N/A N/A 9 grids1x1 minimum N/A N/A N/A minimum
IT 270 2700 N/A grids1x1 estimate N/A N/A N/A N/A
PL N/A N/A 400 grids1x1 minimum 5000 15000 N/A i estimate
RO 250 400 N/A grids1x1 minimum N/A N/A N/A N/A
SI 14 19 N/A grids1x1 estimate N/A N/A N/A N/A
SK 147 147 N/A grids1x1 estimate 1177 2418 N/A i N/A
BE N/A N/A 312 grids1x1 estimate N/A N/A N/A N/A
DE 23586 23586 23586 grids1x1 minimum 287 313 300 grids5x5 minimum
DK N/A N/A N/A N/A N/A 1 localities N/A
ES 67 6700 N/A grids1x1 estimate N/A N/A N/A N/A
FR 300000 400000 N/A grids1x1 mean N/A N/A N/A mean
IE N/A N/A 321 grids1x1 minimum N/A N/A 464 grids10x10 minimum
NL N/A N/A 422 grids1x1 estimate 6000 24000 12000 i estimate
PT N/A N/A 2 grids1x1 minimum N/A N/A N/A N/A
UK N/A N/A 3629 grids1x1 minimum 63100 2678000 N/A i interval
BG N/A N/A 1 grids1x1 minimum N/A N/A N/A N/A
EE N/A N/A 40 grids1x1 minimum N/A N/A N/A N/A
FI 11 6600 N/A grids1x1 estimate N/A N/A N/A N/A
LT N/A N/A 33 grids1x1 minimum N/A N/A N/A N/A
LV N/A N/A 35430 grids1x1 estimate N/A N/A N/A N/A
SE N/A N/A 488 grids1x1 estimate 15000 45000 30000 i estimate
AT 79 N/A N/A grids1x1 minimum N/A N/A N/A N/A
BE N/A N/A 418 grids1x1 minimum 2000 5000 N/A iwintering estimate
BG N/A N/A 15 grids1x1 minimum N/A N/A N/A N/A
CZ 2818 2818 N/A grids1x1 estimate N/A N/A N/A N/A
DE 176667 176667 176667 grids1x1 estimate 1644 1707 1675.50 grids5x5 estimate
DK N/A N/A N/A N/A N/A 18 localities N/A
FR 66000 95000 N/A grids1x1 mean 600000 800000 N/A mean
HR N/A N/A 15 grids1x1 minimum N/A N/A N/A N/A
IT 400 4000 N/A grids1x1 estimate N/A N/A N/A N/A
LU N/A N/A 1400 grids1x1 estimate N/A N/A N/A N/A
PL N/A N/A 23700 grids1x1 estimate 15000 50000 N/A i estimate
RO 300 500 N/A grids1x1 minimum N/A N/A N/A N/A
SE N/A N/A 117 grids1x1 estimate 1000 3000 2000 i estimate
SI 20 25 N/A grids1x1 estimate N/A N/A N/A N/A
CY N/A N/A 30 grids1x1 estimate 100 300 N/A i estimate
ES 263 26300 N/A grids1x1 estimate N/A N/A N/A N/A
FR 180000 240000 N/A grids1x1 mean 12090 24181 N/A i mean
GR N/A N/A 74086 grids1x1 estimate 500 1000 N/A grids5x5 estimate
HR N/A N/A 15 grids1x1 minimum N/A N/A N/A N/A
IT 300 3000 N/A grids1x1 estimate N/A N/A N/A N/A
PT N/A N/A 170 grids1x1 minimum N/A N/A N/A N/A
CZ 39 39 N/A grids1x1 estimate N/A N/A N/A N/A
HU N/A N/A 140 grids1x1 estimate N/A N/A N/A N/A
SK 22 22 N/A grids1x1 estimate 95 851 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 8900 9.14 = > 98 N/A N/A grids1x1 minimum b 0.14 x x Y FV = good unk good FV U1 = U1 x noChange knowledge 7200 b 13.90
BG ALP 16200 16.64 = 16200 N/A N/A 17 grids1x1 minimum b 0.02 = 17 grids1x1 Y FV = poor poor poor U1 U1 = FV noChange method 11900 b 22.97
DE ALP 3548 3.65 = 3548 1649 1649 1649 grids1x1 estimate b 2.33 = grids5x5 Y FV = good good good FV FV = FV noChange noChange 1900 c 3.67
ES ALP 7200 7.40 = 34 3400 N/A grids1x1 estimate b 2.42 = 3400 grids1x1 Y U1 + good unk poor U1 U1 + U2 + knowledge knowledge 2700 a 5.21
FR ALP 12700 13.05 = 30000 100000 N/A grids1x1 mean b 91.73 + > Y Y FV = good good good FV FV + N/A N/A N/A N/A 8600 b 16.60
HR ALP 2600 2.67 x >> N/A N/A 9 grids1x1 minimum b 0.01 x >> N Unk XX x unk unk unk XX U2 x N/A N/A 2300 b 4.44
IT ALP 27900 28.67 = 270 2700 N/A grids1x1 estimate c 2.10 = Y FV = good good good FV FV = U1 - noChange noChange 7600 c 14.67
PL ALP 400 0.41 = x N/A N/A 400 grids1x1 minimum c 0.56 = x Y FV = good good good FV FV = FV noChange noChange 300 b 0.58
RO ALP 1800 1.85 = 250 400 N/A grids1x1 minimum b 0.46 = Y U1 = poor poor poor U1 U1 = U1 = knowledge knowledge 800 b 1.54
SI ALP 7656 7.87 = 7656 14 19 N/A grids1x1 estimate c 0.02 u Y XX x good poor unk U1 U1 x FV noChange noChange 1400 b 2.70
SK ALP 8425.70 8.66 - 147 147 N/A grids1x1 estimate c 0.21 x Y FV x poor good good FV FV x XX knowledge N/A 7100 b 13.71
BE ATL 20000 4.58 = N/A N/A 312 grids1x1 estimate b 0.08 u x Unk XX x good unk unk XX XX FV method method 9800 b 3.72
DE ATL 65506 15.02 = 65506 23586 23586 23586 grids1x1 minimum b 6.18 + 300 grids5x5 Y FV = good good good FV FV + FV noChange knowledge 26600 c 10.11
DK ATL 100 0.02 u > N/A N/A N/A d 0 u > Unk Unk XX u poor unk unk XX XX XX N/A N/A 100 b 0.04
ES ATL 33700 7.72 + 67 6700 N/A grids1x1 estimate b 0.89 = 6700 grids1x1 Y U1 = good poor poor U1 U1 = U1 x knowledge knowledge 6400 a 2.43
FR ATL 82200 18.84 = 300000 400000 N/A grids1x1 mean b 91.71 = Y Y FV = good good good FV FV = U1 x noChange noChange 64700 b 24.58
IE ATL 46400 10.64 = 46400 N/A N/A 321 grids1x1 minimum b 0.08 = 464 grids10x10 Y FV = good good good FV FV = FV noChange noChange 20600 b 7.83
NL ATL 20900 4.79 + N/A N/A 422 grids1x1 estimate b 0.11 + Y FV = good good unk FV FV + FV noChange method 18200 b 6.91
PT ATL 1000 0.23 = 1000 N/A N/A 2 grids1x1 minimum b 0 x x Unk XX x good unk unk XX XX U1 x knowledge noChange 200 b 0.08
UK ATL 166441 38.15 = 166441 N/A N/A 3629 grids1x1 minimum a 0.95 = Y FV = good unk good FV FV = FV noChange noChange 116600 a 44.30
BG BLS 4700 100 = 4700 N/A N/A 1 grids1x1 minimum b 100 u 1 grids1x1 Y XX = poor poor poor U1 U1 = U1 - noChange method 4200 b 100
EE BOR 6500 2.59 u > N/A N/A 40 grids1x1 minimum b 0.10 x x Unk XX x good good good FV XX FV knowledge knowledge 2200 a 5.77
FI BOR 6600 2.63 x x 11 6600 N/A grids1x1 estimate c 8.41 x x Unk XX x unk unk unk XX XX XX noChange noChange 1200 a 3.15
LT BOR 65200 25.94 = N/A N/A 33 grids1x1 minimum c 0.08 x > Unk XX x good poor unk XX U1 x U1 = method method 2500 b 6.56
LV BOR 64589 25.69 = 64589 N/A N/A 35430 grids1x1 estimate c 90.16 x Y XX x good good unk FV FV x U1 - knowledge noChange 1800 c 4.72
SE BOR 108500 43.16 + 108500 N/A N/A 488 grids1x1 estimate c 1.24 + 30000 i N Unk U1 = good poor unk U1 U1 + U2 - genuine genuine 30400 c 79.79
AT CON 5300 0.86 = > 79 N/A N/A grids1x1 minimum b 0.03 x x Y FV = good unk good FV U1 = U1 x noChange knowledge 4300 b 1.21
BE CON 14300 2.31 = N/A N/A 418 grids1x1 minimum a 0.14 + Y FV = good good good FV FV + FV noChange noChange 9900 b 2.79
BG CON 24100 3.90 = 24100 N/A N/A 15 grids1x1 minimum b 0.01 = 1 grids1x1 Y FV = poor poor poor U1 U1 = U1 - noChange method 18600 b 5.24
CZ CON 80000 12.93 = 2818 2818 N/A grids1x1 estimate a 0.98 = > Y FV = good poor good FV U1 = U1 = noChange noChange 40300 a 11.35
DE CON 286945 46.38 = 176667 176667 176667 grids1x1 estimate b 61.27 + 1675 grids5x5 Y FV = good good good FV FV + FV noChange method 191500 c 53.91
DK CON 1538 0.25 = N/A N/A N/A d 0 u > Unk Y XX u good poor unk U1 U1 x XX N/A N/A 1700 b 0.48
FR CON 58800 9.50 = 66000 95000 N/A grids1x1 mean b 27.92 + Y Y FV = good good poor U1 U1 + U1 = noChange noChange 41900 b 11.80
HR CON 4800 0.78 x >> N/A N/A 15 grids1x1 minimum c 0.01 x >> N Unk XX x unk unk unk XX U2 x N/A N/A 4900 b 1.38
IT CON 30700 4.96 = 400 4000 N/A grids1x1 estimate c 0.76 = Y FV = good good good FV FV = U1 - noChange noChange 7200 c 2.03
LU CON 3700 0.60 = N/A N/A 1400 grids1x1 estimate c 0.49 u > N N U1 - good poor poor U1 U1 - U1 x noChange genuine 2800 c 0.79
PL CON 76800 12.41 = N/A N/A 23700 grids1x1 estimate c 8.22 + Y FV = good good good FV FV + FV noChange noChange 23500 b 6.62
RO CON 3000 0.48 = 300 500 N/A grids1x1 minimum b 0.14 = Y U1 = poor poor poor U1 U1 = U1 = knowledge knowledge 1400 b 0.39
SE CON 16100 2.60 = 16100 N/A N/A 117 grids1x1 estimate c 0.04 = 2000 i N Unk U1 = good poor unk U1 U1 = U2 - genuine genuine 5400 c 1.52
SI CON 12616 2.04 = 12616 20 25 N/A grids1x1 estimate c 0.01 u Y XX x good poor unk U1 U1 x FV knowledge noChange 1800 b 0.51
CY MED 8242 2.45 x N/A N/A 30 grids1x1 estimate c 0.01 x x Y U1 = good unk poor U1 U1 x FV knowledge noChange 9900 b 6.40
ES MED 131800 39.23 = 263 26300 N/A grids1x1 estimate b 4.44 + 26300 grids1x1 Y U1 + good poor poor FV U1 = U1 = genuine genuine 27600 a 17.84
FR MED 22400 6.67 = 180000 240000 N/A grids1x1 mean b 70.18 = Y Y FV = good good poor U1 U1 = XX knowledge noChange 15100 b 9.76
GR MED 108866 32.40 x N/A N/A 74086 grids1x1 estimate b 24.76 x x Unk XX x unk poor poor U1 U1 x U1 x noChange noChange 81900 b 52.94
HR MED 4700 1.40 x >> N/A N/A 15 grids1x1 minimum c 0.01 x >> N Unk XX x unk unk unk XX U2 x N/A N/A 4100 b 2.65
IT MED 25900 7.71 = 300 3000 N/A grids1x1 estimate c 0.55 = Y FV = good good good FV FV = U1 - noChange noChange 6100 c 3.94
PT MED 34100 10.15 = 34100 N/A N/A 170 grids1x1 minimum b 0.06 u x Unk XX - good unk unk XX XX U1 + knowledge knowledge 10000 b 6.46
CZ PAN 5500 12.34 = 39 39 N/A grids1x1 estimate a 19.40 = > Y FV = good poor good FV U1 = U1 = noChange noChange 1200 a 10.08
HU PAN 37341 83.78 u > N/A N/A 140 grids1x1 estimate c 69.65 u > Unk U1 u poor unk poor U1 U1 x U1 - noChange method 9100 c 76.47
SK PAN 1729.91 3.88 + 22 22 N/A grids1x1 estimate c 10.95 x Y FV x good good good FV FV x U1 - knowledge knowledge 1600 b 13.45
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 97329.70 2GD = 32888 108839 70863.5 grids1x1 2GD + 2GD = 2GD MTX + U1 x nc nong U1 B2

02/20

EEA-ETC/BD

Institution: -

Member State:

EEA-ETC/BD
EU28 ATL 436247 1 = < 436257 2GD = 2GD = 2GD MTX = U1 x nong nong U1 A=

02/20

EEA-ETC/BD

Institution: -

Member State:

EEA-ETC/BD
EU28 BLS 4700 0MS = 4700 1 grids1x1 0MS x 1 0MS = poor poor poor 0MS MTX = U1 - nc nong U1 D

02/20

EEA-ETC/BD

Institution: -

Member State:

EEA-ETC/BD
EU28 BOR 251389 1 + < 252039 36002 42591 39296.5 2GD + 2GD = 2GD MTX + U2 - gen gen U2 B1

02/20

EEA-ETC/BD

Institution: -

Member State:

EEA-ETC/BD
EU28 CON 618699 1 = ≈ 619229 2GD + 2GD = 2GD MTX + U1 = nong nong U1 A=

02/20

EEA-ETC/BD

Institution: -

Member State:

EEA-ETC/BD
EU28 MED 336008 1 x ≈ 336008 2GD x 2GD x 2GD MTX x U1 x nc nc U1 D

01/20

EEA-ETC/BD

Institution: -

Member State:

EEA-ETC/BD
EU28 PAN 44570.91 1 x < 48305.01 201 201 201 grids1x1 1 x < 2018.9 2XP 2XP MTX x U1 - nc nong 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.