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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, Cucujus cinnaberinus, 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 21 grids1x1 minimum N/A N/A N/A N/A
BG N/A N/A 6 grids1x1 minimum N/A N/A N/A N/A
DE 60 60 60 grids1x1 estimate 8 8 8 grids5x5 estimate
HR N/A N/A 1 grids1x1 minimum N/A N/A N/A N/A
PL N/A N/A 88 grids1x1 estimate N/A N/A N/A N/A
RO N/A N/A 700 grids1x1 estimate N/A N/A N/A N/A
SI N/A N/A 10 grids1x1 estimate N/A N/A N/A N/A
SK 1264 1264 N/A grids1x1 estimate 100000 1500000 N/A i N/A
DE N/A N/A 1 grids1x1 minimum 1 1 1 grids5x5 minimum
ES N/A N/A N/A grids1x1 estimate N/A N/A N/A i estimate
BG N/A N/A 1 grids1x1 minimum N/A N/A N/A N/A
EE N/A N/A 19 grids1x1 minimum N/A N/A N/A N/A
FI N/A N/A 5 grids1x1 minimum N/A N/A N/A N/A
LT N/A N/A 63 grids1x1 estimate N/A N/A N/A N/A
LV N/A N/A 14 grids1x1 minimum N/A N/A N/A N/A
SE 50 100 75 grids1x1 estimate 100 200 150 trees estimate
AT N/A N/A 145 grids1x1 minimum N/A N/A N/A N/A
BG N/A N/A 2 grids1x1 minimum N/A N/A N/A N/A
CZ N/A N/A 414 grids1x1 estimate N/A N/A N/A N/A
DE 1828 1828 1828 grids1x1 estimate 69 71 70 grids5x5 estimate
HR N/A N/A 22 grids1x1 minimum N/A N/A N/A N/A
PL N/A N/A 288 grids1x1 estimate N/A N/A N/A N/A
RO N/A N/A 800 grids1x1 estimate N/A N/A N/A N/A
SI N/A N/A 64 grids1x1 estimate N/A N/A N/A N/A
ES N/A N/A N/A grids1x1 estimate N/A N/A N/A N/A
IT N/A N/A 30 grids1x1 mean N/A N/A N/A N/A
CZ N/A N/A 195 grids1x1 estimate N/A N/A N/A N/A
HU N/A N/A 1852 grids1x1 minimum N/A N/A N/A N/A
SK 548 548 N/A grids1x1 estimate 50000 500000 N/A i N/A
BE N/A N/A 17 grids1x1 estimate N/A N/A N/A N/A
NL 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 1500 5.01 + > N/A N/A 21 grids1x1 minimum b 0.98 + > N N U1 + poor poor poor U1 U1 + U2 x knowledge knowledge 1000 b 6.21
BG ALP 2600 8.68 = 2600 N/A N/A 6 grids1x1 minimum b 0.28 x 6 grids1x1 N Unk FV = good unk good FV FV = U1 - method method 700 b 4.35
DE ALP 650 2.17 = 60 60 60 grids1x1 estimate b 2.79 = 8 grids5x5 Y FV = good good good FV FV = FV noChange noChange 400 a 2.48
HR ALP 100 0.33 x x N/A N/A 1 grids1x1 minimum c 0.05 x x Unk XX x unk unk unk XX XX N/A N/A 100 d 0.62
PL ALP 3600 12.01 u N/A N/A 88 grids1x1 estimate b 4.09 u Unk XX u unk unk poor XX XX U1 + noChange noChange 1600 b 9.94
RO ALP 700 2.34 x > N/A N/A 700 grids1x1 estimate b 32.56 x Y FV x unk unk unk XX FV x U1 N/A knowledge knowledge 600 a 3.73
SI ALP 3213 10.72 u x N/A N/A 10 grids1x1 estimate b 0.47 x 10 grids1x1 Y FV = unk unk good XX U1 x U1 x noChange noChange 900 b 5.59
SK ALP 17600.99 58.74 = 1264 1264 N/A grids1x1 estimate b 58.79 = Y U1 = good poor poor U1 U1 = FV knowledge knowledge 10800 b 67.08
DE ATL 100 100 x x N/A N/A 1 grids1x1 minimum a 100 x x grids5x5 Unk XX = unk unk unk XX XX x N/A N/A knowledge knowledge 100 a 50
ES ATL N/A 0 = >> N/A N/A N/A grids1x1 estimate a 0 = >> Unk U2 x unk unk unk XX U2 = N/A N/A noChange noChange 100 a 50
BG BLS 100 100 = 100 N/A N/A 1 grids1x1 minimum c 100 x 1 grids1x1 N Unk FV = good unk good FV FV = U1 - method method 200 b 100
EE BOR 1100 2.28 + N/A N/A 19 grids1x1 minimum c 10.80 + > Unk U1 + poor poor poor U1 U1 + U1 = knowledge knowledge 900 b 14.75
FI BOR 700 1.45 = > N/A N/A 5 grids1x1 minimum c 2.84 = >> N N U2 - poor bad bad U2 U2 - U2 = noChange knowledge 400 b 6.56
LT BOR 8800 18.22 = > N/A N/A 63 grids1x1 estimate b 35.80 = > Y U1 - good unk poor U1 U1 - XX noInfo noInfo 3200 a 52.46
LV BOR 31893 66.04 + 31893 N/A N/A 14 grids1x1 minimum c 7.95 x 14 grids1x1 Unk U1 u good unk poor XX U1 x U2 x knowledge knowledge N/A c 0
SE BOR 5800 12.01 = 12700 50 100 75 grids1x1 estimate b 42.61 - 2000 trees N N U2 - bad bad bad U2 U2 - U2 - noChange noChange 1600 b 26.23
AT CON 6000 9.47 + N/A N/A 145 grids1x1 minimum b 4.07 + N N U1 + good good good FV U1 + U1 + knowledge knowledge 4700 b 13.78
BG CON 3900 6.16 = 3900 N/A N/A 2 grids1x1 minimum b 0.06 x 2 grids1x1 N Unk FV = good unk good FV FV = U1 - method method 700 b 2.05
CZ CON 30900 48.78 + N/A N/A 414 grids1x1 estimate a 11.62 + Y FV + good good good FV FV + FV noChange noChange 14900 a 43.70
DE CON 5761 9.09 = 1828 1828 1828 grids1x1 estimate b 51.31 + grids5x5 Unk XX x good good unk FV FV = FV noChange noChange 4300 a 12.61
HR CON 2000 3.16 x x N/A N/A 22 grids1x1 minimum c 0.62 x x Unk XX x unk unk unk XX XX N/A N/A 1400 d 4.11
PL CON 7100 11.21 u N/A N/A 288 grids1x1 estimate b 8.08 + Unk XX u poor unk poor U1 U1 x U1 + noChange method 5400 b 15.84
RO CON 800 1.26 x > N/A N/A 800 grids1x1 estimate b 22.45 x Unk XX x unk unk unk XX XX N/A N/A knowledge knowledge 800 a 2.35
SI CON 6890 10.88 u > N/A N/A 64 grids1x1 estimate b 1.80 u 70 grids1x1 Y U1 u good unk poor U1 U1 x U1 x noChange noChange 1900 b 5.57
ES MED N/A 0 = >> N/A N/A N/A grids1x1 estimate a 0 = 1 grids1x1 Unk U2 = unk unk unk XX U2 = XX noChange noChange 100 a 11.11
IT MED 800 100 = N/A N/A 30 grids1x1 mean a 100 + Y FV + good good good FV FV + U1 = noChange noChange 800 a 88.89
CZ PAN 5500 8.82 = N/A N/A 195 grids1x1 estimate a 7.51 + Y FV + good good good FV FV + FV noChange noChange 2700 a 5.18
HU PAN 49269 79 = N/A N/A 1852 grids1x1 minimum b 71.37 = Y FV = good good good FV FV = FV noChange noChange 44100 b 84.64
SK PAN 7597.91 12.18 = 548 548 N/A grids1x1 estimate c 21.12 = Y U1 = good poor poor U1 U1 = FV knowledge knowledge 5300 b 10.17
BE ATL 3200 0 + N/A N/A 17 grids1x1 estimate b 0 + > Y FV = good good poor U1 U1 + N/A N/A genuine genuine 1000 a 0
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
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 PAN 62366.91 0EQ = 2595 2595 2595 grids1x1 1 = ≈ 2595 grids1x1 2XP = good 2XP MTX = FV = nc nc FV A=

02/20

EEA-ETC/BD

Institution: -

Member State:

EEA-ETC/BD
EU28 BOR 48293 2XP = > 55193 151 201 176 grids1x1 2XP - 2XP - 2XP MTX - U2 - nc nc U2 C

02/20

EEA-ETC/BD

Institution: -

Member State:

EEA-ETC/BD
EU28 CON 61351 1 + < 62120 3541 3541 3541 grids1x1 2XP + 2XP x 2XP MTX + U1 - nc nong U1 B1

02/20

EEA-ETC/BD

Institution: -

Member State:

EEA-ETC/BD
EU28 MED 800 2GD = 30 grids1x1 2GD + 2GD + 2GD MTX + U2 = nong nong U2 B2

02/20

EEA-ETC/BD

Institution: -

Member State:

EEA-ETC/BD
EU28 ALP 29863.99 1 = < 30080.99 2149 2149 2149 grids1x1 2GD = 2GD = 2GD MTX = U1 - nong nong U1 D

01/20

EEA-ETC/BD

Institution: -

Member State:

EEA-ETC/BD
EU28 ATL 2GD x 2GD x 2GD x 0EQ MTX x XX x nong nc XX D

02/20

EEA-ETC/BD

Institution: -

Member State:

EEA-ETC/BD
EU28 BLS 100 0MS 100 1 grids1x1 0MS x 1 grids1x1 0MS = good unk good 0MS MTX = U1 - nong nong U1 A=

02/20

EEA-ETC/BD

Institution: -

Member State:

EEA-ETC/BD
BG BLS 2GD x 2GD x 2GD x 2GD 3GD = U1 - U1 0/2

04/20

Sofia University

Institution: Sofia University

Member State: BG

Sofia University
BG CON 2GD x 2GD x 2GD x 2GD 3GD = U1 - U1 0/2

04/20

Sofia University

Institution: Sofia University

Member State: BG

Sofia University
BG ALP 2GD x 2GD x 2GD x 2GD 3GD U1 - U1 0/2

04/20

Sofia University

Institution: Sofia University

Member State: BG

Sofia University
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.