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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 PL

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, Arthropods, Rosalia alpina, 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 375 375 N/A grids1x1 estimate N/A N/A N/A N/A
BG N/A N/A 41 grids1x1 minimum N/A N/A N/A N/A
DE N/A N/A 50 grids1x1 estimate 32 32 32 grids5x5 estimate
ES 35 2200 N/A grids1x1 estimate 17 31 N/A localities estimate
FR 130 1300 N/A grids1x1 estimate N/A N/A N/A estimate
HR N/A N/A 47 grids1x1 minimum N/A N/A N/A N/A
IT N/A N/A 4574 grids1x1 estimate N/A N/A N/A N/A
PL N/A N/A 1876 grids1x1 estimate N/A N/A N/A N/A
RO N/A N/A 8800 grids1x1 estimate N/A N/A N/A N/A
SI 98 111 N/A grids1x1 estimate N/A N/A N/A estimate
SK 1592 1592 N/A grids1x1 estimate 12007 19861 N/A i N/A
ES 35 3500 N/A grids1x1 estimate 26 51 51 localities estimate
FR 260 1400 N/A grids1x1 estimate N/A N/A N/A estimate
BG N/A N/A 6 grids1x1 minimum N/A N/A N/A N/A
AT 50 50 N/A grids1x1 estimate N/A N/A N/A N/A
BG N/A N/A 39 grids1x1 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 N/A N/A 205 grids1x1 estimate 29 29 29 grids5x5 estimate
FR 70 350 N/A grids1x1 estimate N/A N/A N/A estimate
HR N/A N/A 149 grids1x1 minimum N/A N/A N/A N/A
IT N/A N/A 4831 grids1x1 estimate N/A N/A N/A N/A
RO N/A N/A 8500 grids1x1 estimate N/A N/A N/A N/A
SI 99 112 N/A grids1x1 estimate N/A N/A N/A N/A
ES 25 2500 N/A grids1x1 estimate 29 N/A N/A localities minimum
FR 160 1600 N/A grids1x1 estimate N/A N/A N/A estimate
GR N/A N/A 944 grids1x1 estimate 10 17 N/A grids10x10 estimate
HR N/A N/A 32 grids1x1 minimum N/A N/A N/A N/A
IT N/A N/A 8260 grids1x1 estimate N/A N/A N/A N/A
CZ N/A N/A 40 grids1x1 estimate N/A N/A N/A N/A
HU N/A N/A 667 grids1x1 minimum N/A N/A N/A N/A
SK 144 144 N/A grids1x1 estimate 5000 50000 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 10000 8.21 = > 375 375 N/A grids1x1 estimate a 1.94 x > Y U1 = poor poor poor U1 U1 = U1 x knowledge knowledge 8500 a 14.36
BG ALP 24800 20.37 = 24800 N/A N/A 41 grids1x1 minimum b 0.21 = 41 grids1x1 Y FV = good good good FV FV = FV method method 4300 b 7.26
DE ALP 1821 1.50 = N/A N/A 50 grids1x1 estimate b 0.26 = grids5x5 Y FV = good good good FV FV = U1 - knowledge method 1800 b 3.04
ES ALP 5900 4.85 + 35 2200 N/A grids1x1 estimate c 5.79 x 31 localities Y FV x unk unk good XX XX XX knowledge knowledge 1600 a 2.70
FR ALP 23600 19.38 = 130 1300 N/A grids1x1 estimate d 3.71 = Y Unk FV = good good unk FV FV = U1 - noInfo noChange 9000 b 15.20
HR ALP 8700 7.15 x > N/A N/A 47 grids1x1 minimum c 0.24 x x Unk XX x poor unk unk XX U1 x N/A N/A 2900 c 4.90
IT ALP 13400 11.01 = N/A N/A 4574 grids1x1 estimate b 23.71 + Y FV = good good good FV FV + U1 = noChange knowledge 5500 b 9.29
PL ALP 5800 4.76 x x N/A N/A 1876 grids1x1 estimate b 9.72 x x N N U1 x unk unk poor XX U1 x U1 - noChange knowledge 4700 a 7.94
RO ALP 8800 7.23 = > N/A N/A 8800 grids1x1 estimate b 45.61 = Y FV = good unk good FV FV = U1 N/A knowledge knowledge 6700 a 11.32
SI ALP 7210 5.92 = 98 111 N/A grids1x1 estimate b 0.54 u 111 grids1x1 Y U1 u good unk unk XX U1 x U1 - noChange noInfo 3900 b 6.59
SK ALP 11718 9.62 = 1592 1592 N/A grids1x1 estimate b 8.25 = Y FV = good good good FV U1 = FV knowledge knowledge 10300 b 17.40
ES ATL 11800 19.63 u x 35 3500 N/A grids1x1 estimate c 68.05 u 51 grids1x1 Y FV u unk unk good XX XX XX noChange noChange 4000 a 14.08
FR ATL 48300 80.37 = 260 1400 N/A grids1x1 estimate d 31.95 x Y Unk XX u good good unk FV FV x FV method noChange 24400 b 85.92
BG BLS 6700 100 = 6700 N/A N/A 6 grids1x1 minimum c 100 u 6 grids1x1 N Y FV = good unk good FV FV = FV method method 1300 b 100
AT CON 1300 1.04 = > 50 50 N/A grids1x1 estimate a 0.35 = > Y U1 = poor poor poor U1 U1 = U2 x noChange noChange 1000 a 2.51
BG CON 61200 49.16 = 61200 N/A N/A 39 grids1x1 minimum b 0.28 = 39 grids1x1 Y FV = good good good FV FV = FV method method 9400 b 23.56
CZ CON 1600 1.29 = N/A N/A 22 grids1x1 estimate a 0.16 = > N Y U2 - poor poor bad U2 U2 - U2 = method noChange 400 a 1
DE CON 1947 1.56 + 1947 N/A N/A 205 grids1x1 estimate b 1.45 + 18 grids5x5 Y FV = good good good FV FV + FV noChange genuine 1600 b 4.01
FR CON 6800 5.46 = x 70 350 N/A grids1x1 estimate d 1.49 x > N Unk XX u unk unk unk XX U1 x U1 = noInfo noChange 5400 b 13.53
HR CON 18900 15.18 x >> N/A N/A 149 grids1x1 minimum c 1.06 x x Unk XX x bad unk unk U2 U2 x N/A N/A 7500 c 18.80
IT CON 15500 12.45 = N/A N/A 4831 grids1x1 estimate b 34.23 + Y FV = good good good FV FV + U1 = noChange knowledge 5400 b 13.53
RO CON 8500 6.83 u > N/A N/A 8500 grids1x1 estimate b 60.23 u Unk FV u unk unk unk XX FV x U1 N/A knowledge knowledge 5700 a 14.29
SI CON 8735 7.02 = 99 112 N/A grids1x1 estimate b 0.75 u 112 grids1x1 Y U1 u good unk unk XX U1 x U1 - noChange noInfo 3500 b 8.77
ES MED 9200 11.48 + x 25 2500 N/A grids1x1 estimate c 11.10 x 29 localities Unk U1 x good unk poor XX U1 x XX noChange noChange 2200 a 8.03
FR MED 33800 42.16 = 160 1600 N/A grids1x1 estimate d 7.73 x Y FV u good good unk FV FV x U1 = noChange noChange 13600 b 49.64
GR MED 1563 1.95 = N/A N/A 944 grids1x1 estimate c 8.30 x 17 grids10x10 Unk XX x good poor unk U1 U1 x U1 x noChange noChange 1000 c 3.65
HR MED 4500 5.61 x x N/A N/A 32 grids1x1 minimum c 0.28 x x Unk XX x unk unk unk XX XX N/A N/A 1100 c 4.01
IT MED 31100 38.80 = N/A N/A 8260 grids1x1 estimate b 72.59 + Y FV = good good good FV FV + U1 = noChange knowledge 9500 b 34.67
CZ PAN 1500 10.51 + N/A N/A 40 grids1x1 estimate a 4.70 + Y U1 = good good poor U1 U1 + U1 + noChange noChange 700 a 5.07
HU PAN 11565 81.03 = N/A N/A 667 grids1x1 minimum b 78.38 = Y U1 = good good poor U1 U1 = FV knowledge knowledge 11900 b 86.23
SK PAN 1207.43 8.46 = 144 144 N/A grids1x1 estimate b 16.92 = Y FV = good good good FV U1 = FV knowledge knowledge 1200 b 8.70
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 14272.43 0EQ = 851 851 851 grids1x1 1 = ≈ 851 grids1x1 2XP = good good 2XP MTX = FV = nong nc FV D

02/20

EEA-ETC/BD

Institution: -

Member State:

EEA-ETC/BD
EU28 ALP 121749 1 = < 124499 17568 20916 19292 grids1x1 2GD = 2GD = 2GD MTX = U1 - nc nong U1 D

02/20

EEA-ETC/BD

Institution: -

Member State:

EEA-ETC/BD
EU28 BLS 6700 0MS = 6700 6 grids1x1 0MS x 6 grids1x1 0MS - good unk good 0MS MTX = FV = nc nc FV A=

02/20

EEA-ETC/BD

Institution: -

Member State:

EEA-ETC/BD
EU28 CON 124482 2GD = 13760 14053 1411.5 grids1x1 2GD x 2GD x 2GD MTX x U1 = nc nong U1 D

02/20

EEA-ETC/BD

Institution: -

Member State:

EEA-ETC/BD
EU28 ATL 60100 0EQ = ≈ 60100 295 4900 2597.5 grids1x1 2XP x 2XP 2XP MTX x XX x nc nc XX D

02/20

EEA-ETC/BD

Institution: -

Member State:

EEA-ETC/BD
EU28 MED 80163 1 = ≈ 80163 9421 13336 11378.5 grids1x1 2GD + 2GD = 2GD MTX + U1 = nong nong XX A=

02/20

EEA-ETC/BD

Institution: -

Member State:

EEA-ETC/BD
RO ALP 2GD 2GD MTX - U1 - U1 0/2

04/20

University of Bucharest

Institution: University of Bucharest

Member State:

University of Bucharest
EU28 CON 2GD MTX - U1 = U1 0/2

04/20

University of Bucharest

Institution: University of Bucharest

Member State:

University of Bucharest
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.