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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, Molluscs, Unio crassus, All bioregions. Annexes Y, Y, N. Show all Molluscs
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 8 N/A N/A grids1x1 minimum N/A N/A N/A N/A
BG N/A N/A N/A grids1x1 minimum N/A N/A N/A N/A
DE N/A N/A N/A N/A N/A N/A i N/A
HR N/A N/A 1 grids1x1 minimum N/A N/A 1 localities minimum
PL N/A N/A 63 grids1x1 estimate N/A N/A N/A N/A
SI 15 18 N/A grids1x1 estimate N/A N/A N/A N/A
SK 110 110 N/A grids1x1 estimate 27280 143560 N/A i N/A
BE 15 35 25 grids1x1 minimum N/A N/A N/A N/A
DE 22 30 25 grids1x1 estimate 6 6 567150 i estimate
FR N/A N/A N/A minimum N/A N/A N/A minimum
BG N/A N/A 11 grids1x1 minimum N/A N/A N/A N/A
EE N/A N/A 576 grids1x1 minimum N/A N/A N/A N/A
FI N/A N/A 241 grids1x1 minimum N/A N/A N/A N/A
LT N/A N/A 204 grids1x1 estimate 50000 500000 N/A i minimum
LV N/A N/A 99 grids1x1 minimum N/A N/A N/A N/A
SE 156 187 N/A grids1x1 estimate 1000000 3000000 N/A i estimate
AT 122 N/A N/A grids1x1 minimum N/A N/A N/A N/A
BE 174 576 174 grids1x1 minimum N/A N/A N/A N/A
BG N/A N/A 220 grids1x1 minimum N/A N/A N/A N/A
CZ N/A N/A 160 grids1x1 mean N/A N/A N/A N/A
DE 636 1264 N/A grids1x1 estimate 316 324 318.50 i estimate
DK N/A N/A N/A estimate N/A N/A 6 grids10x10 N/A
FR N/A N/A N/A minimum N/A N/A N/A minimum
HR N/A N/A 96 grids1x1 minimum N/A N/A 103 localities minimum
LU N/A N/A 41 grids1x1 minimum N/A N/A 29000 i estimate
PL N/A N/A 1126 grids1x1 estimate N/A N/A N/A N/A
RO N/A N/A 9400 grids1x1 estimate N/A N/A N/A N/A
SE 126 151 N/A grids1x1 estimate 500000 2000000 N/A i estimate
SI 133 136 N/A grids1x1 estimate N/A N/A N/A N/A
FR 1 500 N/A grids1x1 minimum N/A N/A N/A minimum
GR N/A N/A 900 grids1x1 estimate 9 15 N/A grids10x10 estimate
CZ N/A N/A 9 grids1x1 estimate N/A N/A N/A N/A
HU N/A N/A 572 grids1x1 minimum N/A N/A N/A N/A
RO N/A N/A 3000 grids1x1 estimate N/A N/A N/A N/A
SK 66 66 N/A grids1x1 estimate 520 11760 N/A i N/A
RO N/A N/A 1100 grids1x1 estimate 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 700 3.95 = 3100 8 N/A N/A grids1x1 minimum a 4.03 - >> N Y U2 - bad bad bad U2 U2 - U2 - noChange noChange 700 a 9.59
BG ALP 6900 38.94 u 6900 N/A N/A N/A grids1x1 minimum c 0 x x Y FV = poor poor poor U1 U1 x FV method method 100 c 1.37
DE ALP 1 0.01 N N/ N/A N/A N/A d 0 N N/ i N/A N N/A N/A N/A N/A N/A N/A N/A N/A noChange noChange N/A b 0
HR ALP 100 0.56 x >> N/A N/A 1 grids1x1 minimum c 0.50 x x Unk XX x unk unk unk XX U2 x N/A N/A 200 d 2.74
PL ALP 4600 25.96 = N/A N/A 63 grids1x1 estimate b 31.74 = N Unk U1 u good good poor U1 U1 = U2 - knowledge knowledge 1400 b 19.18
SI ALP 1508 8.51 - > 15 18 N/A grids1x1 estimate b 8.31 - > Y U1 - poor poor poor U1 U1 - U1 - noChange noChange 900 c 12.33
SK ALP 3912.24 22.08 + 110 110 N/A grids1x1 estimate b 55.42 = Y FV = good poor poor U1 U1 + FV knowledge knowledge 4000 b 54.79
BE ATL 1400 3.63 = 15 35 25 grids1x1 minimum b 50 u >> N N U2 = poor bad bad U2 U2 = U2 - noChange knowledge 400 a 3.03
DE ATL 1920 4.98 = >> 22 30 25 grids1x1 estimate b 50 x >> i N N U2 - poor bad unk U2 U2 x U2 - noChange method 1500 b 11.36
FR ATL 35200 91.38 = > N/A N/A N/A minimum d 0 u >> N Y XX x good good good FV U2 x U2 = noChange noChange 11300 b 85.61
BG BLS 4500 100 = 4500 N/A N/A 11 grids1x1 minimum c 100 u 11 grids1x1 Y FV = unk unk unk XX XX = FV method method 1000 c 100
EE BOR 26600 14.84 = N/A N/A 576 grids1x1 minimum b 44.60 = Y U1 u good unk unk XX U1 = U1 x noChange noChange 11900 a 29.90
FI BOR 17600 9.82 = N/A N/A 241 grids1x1 minimum a 18.66 = Y FV = good good good FV FV = FV noChange method 7300 a 18.34
LT BOR 57534 32.09 = N/A N/A 204 grids1x1 estimate b 15.80 = > Y FV = good poor poor U1 U1 = U1 = knowledge knowledge 13800 a 34.67
LV BOR 55231 30.81 u x N/A N/A 99 grids1x1 minimum b 7.67 u x Unk U1 u unk unk poor XX U1 x U2 x method noChange N/A b 0
SE BOR 22300 12.44 = 22300 156 187 N/A grids1x1 estimate b 13.28 - 3000000 i N Y U1 - good poor poor U1 U2 - U2 = noChange knowledge 6800 a 17.09
AT CON 6100 2.14 = >> 122 N/A N/A grids1x1 minimum a 0.97 - >> N Y U2 - bad bad bad U2 U2 - U2 - noChange noChange 5100 a 3.98
BE CON 10400 3.64 + 174 576 174 grids1x1 minimum b 1.39 - >> N N U1 = poor poor poor U1 U2 - U1 x genuine genuine 4800 a 3.75
BG CON 57100 19.99 = 57100 N/A N/A 220 grids1x1 minimum c 1.75 u 220 grids1x1 Y FV = unk unk unk XX XX = FV method method 16200 c 12.66
CZ CON 20800 7.28 = N/A N/A 160 grids1x1 mean b 1.27 = > Y U2 = good poor poor U1 U2 = U2 = genuine genuine 6900 a 5.39
DE CON 52842 18.49 - > 636 1264 N/A grids1x1 estimate b 7.56 - >> i N N U2 - poor bad bad U2 U2 - U2 - noChange noChange 33700 b 26.33
DK CON 599 0.21 + > N/A N/A N/A estimate b 0 = > N N U2 = poor poor poor U1 U2 = U2 x N/A N/A 600 a 0.47
FR CON 52100 18.24 = > N/A N/A N/A minimum d 0 u >> N Y XX x good good good FV U2 x U2 = noChange noChange 15100 b 11.80
HR CON 7800 2.73 = x N/A N/A 96 grids1x1 minimum c 0.76 u x Unk XX x poor unk unk XX U1 x N/A N/A 6300 d 4.92
LU CON 800 0.28 = 2900 N/A N/A 41 grids1x1 minimum b 0.33 - >> N N U2 = bad bad poor U2 U2 - U2 - noChange genuine 100 b 0.08
PL CON 49700 17.40 = N/A N/A 1126 grids1x1 estimate b 8.96 = Y U1 u good good unk FV U1 = U1 + noChange knowledge 22700 b 17.73
RO CON 9400 3.29 = > N/A N/A 9400 grids1x1 estimate b 74.83 = Y FV = good good good FV FV = U1 N/A knowledge knowledge 8600 a 6.72
SE CON 9700 3.40 = 9700 126 151 N/A grids1x1 estimate a 1.10 - 2000000 i N N U1 - good poor poor U1 U2 - U2 = noChange knowledge 3400 a 2.66
SI CON 8372 2.93 - > 133 136 N/A grids1x1 estimate b 1.07 - > Y U1 - poor poor poor U1 U1 - U1 - noChange noChange 4500 c 3.52
FR MED 100 6.32 x >> 1 500 N/A grids1x1 minimum d 21.77 - >> N Y U1 - bad bad bad U2 U2 - U2 = N/A noChange 100 b 10
GR MED 1483 93.68 = N/A N/A 900 grids1x1 estimate b 78.23 - 15 grids10x10 Unk XX x good poor unk U1 U1 - U1 - noChange noChange 900 b 90
CZ PAN 3200 8.84 = 3200 N/A N/A 9 grids1x1 estimate a 0.25 = > N Y U2 - poor poor poor U1 U2 - U2 = genuine genuine 300 a 0.96
HU PAN 28022 77.38 = N/A N/A 572 grids1x1 minimum b 15.68 = Y FV = good good good FV FV = FV noChange method 26500 b 84.94
RO PAN 3000 8.28 = > N/A N/A 3000 grids1x1 estimate b 82.26 = Y FV = good good good FV FV = U1 N/A knowledge knowledge 2200 a 7.05
SK PAN 1989.23 5.49 = > 66 66 N/A grids1x1 estimate b 1.81 - > Y FV = poor good good FV U1 - U1 = N/A N/A 2200 b 7.05
RO STE 1100 100 - > N/A N/A 1100 grids1x1 estimate b 100 - Unk U1 - poor poor poor U1 U1 - N/A N/A knowledge knowledge 900 a 100
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 BLS 4500 0MS = 4500 11 grids1x1 1 x x grids1x1 0MS x unk unk unk 0MS MTX = XX x nc nong XX D

02/20

EEA-ETC/BD

Institution: -

Member State: SE

EEA-ETC/BD
EU28 BOR 179265 1 = ≈ 179265 1276.00 1307.00 1291.50 grids1x1 1 = > 1291.50 grids1x1 2XP x good poor poor 2XP MTX = U1 = nc nc U1 D

02/20

EEA-ETC/BD

Institution: -

Member State: SE

EEA-ETC/BD
EU28 ALP 17720.24000 1 = > 197.00 200.00 198.50 grids1x1 1 - > grids1x1 2XP - poor poor poor 2XP MTX = U1 - nc nong U1 D

02/20

EEA-ETC/BD

Institution: -

Member State: SE

EEA-ETC/BD
EU28 MED 1583 1 = 1150.50 grids1x1 1 - >> grids1x1 2XP - bad bad bad 2XP MTX - U2 - nc nc XX C

02/20

EEA-ETC/BD

Institution: -

Member State: SE

EEA-ETC/BD
EU28 PAN 2XP 3647 grids1x1 1 = > grids1x1 2XP = good good good 2XP MTX - U1 - nc nc U1 C

02/20

EEA-ETC/BD

Institution: -

Member State: SE

EEA-ETC/BD
EU28 STE 1100 0MS - > 1100.00 1100.00 1100 grids1x1 0MS - > grids1x1 0MS - poor poor poor 0MS MTX - XX x nong nong C

04/20

EEA-ETC/BD

Institution: -

Member State:

EEA-ETC/BD
EU28 CON 285713 2GD = 287813 12234.00 13292.00 12562 grids1x1 2GD - >> grids1x1 2GD - poor bad poor 2GD 3GD - U2 - nc nc U2 C

02/20

EEA-ETC/BD

Institution: -

Member State: SE

EEA-ETC/BD
EU28 ATL 38520 1 = > 37.00 65.00 50 grids1x1 0EQ x >> grids1x1 2XP x good bad bad 2XP MTX x U2 - nc nong U2 D

03/20

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.