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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.

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, Arthropods, Graphoderus bilineatus, All bioregions. Annexes Y, Y, N. Show all Arthropods
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 4 grids1x1 minimum N/A N/A N/A N/A
SK N/A 1 N/A grids1x1 estimate 100 500 N/A i N/A
DE 1 1 N/A grids1x1 mean 1 1 1 localities mean
FR 1 100 N/A grids1x1 minimum N/A N/A N/A minimum
NL N/A N/A 98 grids1x1 estimate N/A N/A 98000 i estimate
RO N/A N/A 100 grids1x1 estimate N/A N/A N/A N/A
EE N/A N/A 109 grids1x1 minimum N/A N/A N/A N/A
FI N/A N/A 44 grids1x1 minimum N/A N/A N/A N/A
LT N/A N/A 122 grids1x1 estimate N/A N/A N/A N/A
LV N/A N/A 56 grids1x1 minimum N/A N/A N/A N/A
SE 4554 6830 5692 grids1x1 estimate N/A N/A N/A N/A
AT N/A N/A N/A minimum N/A N/A N/A N/A
CZ N/A N/A 22 grids1x1 estimate N/A N/A N/A N/A
DE 98 99 99.50 grids1x1 estimate 98 99 98.50 localities estimate
DK N/A N/A N/A estimate N/A N/A 9 localities N/A
FR 5 500 N/A grids1x1 minimum N/A N/A N/A minimum
HR N/A N/A 22 grids1x1 minimum N/A N/A N/A N/A
IT 16 120 N/A grids1x1 estimate N/A N/A N/A N/A
PL N/A N/A 46 grids1x1 estimate N/A N/A N/A N/A
RO N/A N/A 200 grids1x1 estimate N/A N/A N/A N/A
SE 205 307 252 grids1x1 estimate N/A N/A N/A N/A
SI 1 3 N/A grids1x1 estimate N/A N/A N/A estimate
CZ N/A N/A 3 grids1x1 estimate N/A N/A N/A N/A
HU N/A N/A 21 grids1x1 minimum N/A N/A N/A N/A
SK 35 35 N/A grids1x1 estimate 5000 10000 N/A i N/A
IT 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 200 65.95 x >> N/A N/A 4 grids1x1 minimum b 88.89 x >> N Unk U2 x bad bad bad U2 U2 x U2 x noChange noChange 200 b 100
SK ALP 103.27 34.05 x > N/A 1 N/A grids1x1 estimate c 11.11 x Y U1 x unk bad bad U2 U2 x U2 - N/A N/A N/A b 0
DE ATL 99 3.81 = 245 1 1 N/A grids1x1 mean b 0.67 = >> localities N N U2 x bad bad poor U2 U2 = U2 + noChange method 100 a 4.17
FR ATL 100 3.85 x x 1 100 N/A grids1x1 minimum d 33.78 - >> N N U2 x bad bad bad U2 U2 x XX noChange noChange 200 b 8.33
NL ATL 2400 92.34 = N/A N/A 98 grids1x1 estimate a 65.55 = Y FV + good good good FV FV + U2 x knowledge knowledge 2100 a 87.50
RO BLS 100 100 x > N/A N/A 100 grids1x1 estimate b 100 x Unk XX x unk unk unk XX XX U1 N/A knowledge knowledge 100 a 100
EE BOR 9000 3.22 = N/A N/A 109 grids1x1 minimum b 1.81 = Y FV = good unk good FV FV = FV noChange noChange 4400 a 16.30
FI BOR 9700 3.47 = N/A N/A 44 grids1x1 minimum c 0.73 = Y FV = good good good FV FV = FV noChange method 4300 b 15.93
LT BOR 23000 8.23 = N/A N/A 122 grids1x1 estimate a 2.03 = > Y FV = good good good FV FV = XX knowledge knowledge 6400 a 23.70
LV BOR 58299 20.87 + x N/A N/A 56 grids1x1 minimum b 0.93 + 56 grids1x1 Unk XX = good good unk FV U1 + U1 x noChange knowledge N/A b 0
SE BOR 179300 64.20 = 179300 4554 6830 5692 grids1x1 estimate a 94.50 = 5700 grids1x1 Y FV = good good good FV FV = FV noChange noChange 11900 b 44.07
AT CON N/A 0 = >> N/A N/A N/A minimum d 0 x >> N N U2 = bad bad bad U2 U2 = U2 = noChange noChange N/A b 0
CZ CON 2000 4.09 + N/A N/A 22 grids1x1 estimate a 2.28 + Y U1 + good good poor U1 U1 + U2 = genuine genuine 1100 a 6.79
DE CON 7654 15.65 = >> 98 99 99.50 grids1x1 estimate a 10.32 = >> localities N Unk U1 = bad bad unk U2 U2 = U2 x noChange knowledge 4400 a 27.16
DK CON 327 0.67 = >> N/A N/A N/A estimate b 0 = >> N Unk XX x bad bad poor U2 U2 = U2 x N/A N/A 400 b 2.47
FR CON 500 1.02 x x 5 500 N/A grids1x1 minimum d 26.19 + Unk Unk XX x unk unk unk XX XX x XX noInfo noChange 600 b 3.70
HR CON 2000 4.09 x x N/A N/A 22 grids1x1 minimum c 2.28 x x Unk XX u unk unk unk XX XX N/A N/A 1300 d 8.02
IT CON 1500 3.07 - >> 16 120 N/A grids1x1 estimate c 7.05 - >> N Y U1 - poor poor poor U1 U2 - U1 x noChange knowledge 800 c 4.94
PL CON 8200 16.77 = N/A N/A 46 grids1x1 estimate c 4.77 x Y FV = good good good FV FV = FV noChange noChange 4600 c 28.40
RO CON 200 0.41 x > N/A N/A 200 grids1x1 estimate b 20.75 x Unk XX x unk unk unk XX XX U1 N/A knowledge knowledge 200 a 1.23
SE CON 24900 50.92 = 25000 205 307 252 grids1x1 estimate a 26.14 = 250 grids1x1 Y FV = good good good FV FV = FV noChange noChange 2700 b 16.67
SI CON 1615 3.30 - >> 1 3 N/A grids1x1 estimate b 0.21 - 3 grids1x1 N Unk U2 u bad bad poor U2 U2 - U2 - noChange noChange 100 b 0.62
CZ PAN 600 12.72 + > N/A N/A 3 grids1x1 estimate a 5.08 + > N Y U1 + good poor poor U1 U1 + N/A N/A genuine genuine 200 a 8.33
HU PAN 3494 74.05 = N/A N/A 21 grids1x1 minimum b 35.59 u x N Y U1 u poor poor poor U1 U1 x FV knowledge knowledge 1200 b 50
SK PAN 624.29 13.23 x > 35 35 N/A grids1x1 estimate c 59.32 x Y U1 x unk poor poor U1 U1 x U2 - knowledge N/A 1000 b 41.67
IT ALP 100 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 U1 x N/A N/A 100 c 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 303.27 2XP x >> 303.27 5 5 5 grids1x1 2XP x >> grids1x1 2XP x bad bad 0EQ MTX x U2 x nc nc U2 D

01/20

EEA-ETC/BD

Institution: -

Member State:

EEA-ETC/BD
EU28 ATL 2599 2XP = 100 199 149.5 grids1x1 2XP - >> 2XP + 2XP MTX + U2 x nc nong U2 B2

02/20

EEA-ETC/BD

Institution: -

Member State:

EEA-ETC/BD
EU28 BLS 100 0MS x > 100 100 grids1x1 0MS x ≈ 100 grids1x1 0MS x unk unk unk 0MS MTX x U1 x nong nc U1 D

02/20

EEA-ETC/BD

Institution: -

Member State:

EEA-ETC/BD
EU28 BOR 279299 0EQ = ≈ 279299 4885 7161 6023 grids1x1 1 = < 6040.3 grids1x1 2XP = good 0EQ MTX = U1 x nong nong U1 A+

02/20

EEA-ETC/BD

Institution: -

Member State:

EEA-ETC/BD
EU28 CON 48896 2GD = 2GD = 2GD = 2GD MTX = U2 x nc nong U2 D

02/20

EEA-ETC/BD

Institution: -

Member State:

EEA-ETC/BD
EU28 PAN 4718.29 1 + < 4830.72 59 59 59 grids1x1 0EQ x < 59.3 grids1x1 0EQ x poor poor 0EQ MTX x U2 - nong nong U2 D

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.