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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 FI, DE, 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, Arthropods, Parnassius mnemosyne, All bioregions. Annexes N, 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 159 grids1x1 minimum N/A N/A N/A N/A
BG N/A N/A 36 grids1x1 minimum N/A N/A N/A N/A
DE 53 53 N/A grids1x1 minimum 50 50 50 localities minimum
ES 46 N/A N/A grids1x1 minimum 46 N/A N/A localities minimum
FR 138 13800 N/A grids1x1 minimum N/A N/A N/A minimum
HR N/A N/A 12 grids1x1 minimum N/A N/A N/A N/A
IT N/A N/A 463 grids1x1 estimate N/A N/A N/A N/A
PL 17 31 24 grids1x1 estimate N/A N/A N/A N/A
RO N/A N/A 11800 grids1x1 estimate N/A N/A N/A N/A
SI N/A N/A 24 grids1x1 minimum N/A N/A N/A N/A
SK 202 202 N/A grids1x1 estimate 107851 190427 N/A i N/A
BG N/A N/A 7 grids1x1 minimum N/A N/A N/A N/A
EE 81 140 N/A grids1x1 minimum N/A N/A N/A N/A
FI N/A N/A 53 grids1x1 minimum N/A N/A N/A N/A
LT N/A N/A 209 grids1x1 estimate N/A N/A N/A N/A
LV N/A N/A 119 grids1x1 estimate N/A N/A N/A N/A
SE 70 100 80 grids1x1 mean 1600 2700 1800 i mean
AT N/A N/A 83 grids1x1 estimate N/A N/A N/A N/A
BG N/A N/A 58 grids1x1 minimum N/A N/A N/A N/A
CZ N/A N/A 145 grids1x1 estimate N/A N/A N/A N/A
DE 37 38 37.50 grids1x1 estimate 37 38 37.50 localities estimate
FR 35 3500 N/A grids1x1 minimum N/A N/A N/A minimum
HR N/A N/A 29 grids1x1 minimum N/A N/A N/A N/A
IT N/A N/A 69 grids1x1 estimate N/A N/A N/A N/A
PL 40 63 52 grids1x1 estimate N/A N/A N/A N/A
RO N/A N/A 16500 grids1x1 estimate N/A N/A N/A N/A
SE 18 25 20 grids1x1 mean 25 75 30 i mean
SI 165 188 N/A grids1x1 minimum N/A N/A N/A N/A
FR 67 6700 N/A grids1x1 minimum N/A N/A N/A minimum
GR N/A N/A 12427 grids1x1 estimate 5000 10000 N/A adults estimate
HR N/A N/A 9 grids1x1 minimum N/A N/A N/A N/A
IT N/A N/A 242 grids1x1 estimate N/A N/A N/A N/A
CZ N/A N/A 31 grids1x1 estimate N/A N/A N/A N/A
HU N/A N/A 1070 grids1x1 minimum N/A N/A N/A N/A
RO N/A N/A 200 grids1x1 estimate N/A N/A N/A N/A
SK 29 29 N/A grids1x1 estimate 50520 165593 N/A i N/A
RO N/A N/A 2900 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 10900 8.12 - > N/A N/A 159 grids1x1 minimum b 0.80 - > Unk N U1 - poor poor poor U1 U1 - U1 - noChange noChange 8500 b 12.67
BG ALP 17500 13.04 = 17500 N/A N/A 36 grids1x1 minimum c 0.18 = 36 grids1x1 Y FV = unk unk unk XX FV = FV method method 2900 c 4.32
DE ALP 934 0.70 x x 53 53 N/A grids1x1 minimum c 0.27 = localities Y FV = unk good good FV FV = FV noChange noChange 900 b 1.34
ES ALP 6500 4.84 u 46 N/A N/A grids1x1 minimum c 0.23 x x Unk U1 - poor poor poor U1 U1 x U1 x knowledge knowledge 4500 a 6.71
FR ALP 29800 22.21 = 138 13800 N/A grids1x1 minimum d 35.22 = Y FV = good unk unk XX FV = FV noChange noChange 15000 b 22.35
HR ALP 6300 4.70 = N/A N/A 12 grids1x1 minimum c 0.06 x x Y XX u good unk unk XX XX N/A N/A 2600 c 3.87
IT ALP 34300 25.56 = N/A N/A 463 grids1x1 estimate a 2.34 = Y FV = good good good FV FV = FV noChange noChange 15600 b 23.25
PL ALP 4800 3.58 = 17 31 24 grids1x1 estimate b 0.12 = x Y XX u good unk unk XX XX U1 = genuine genuine 2100 b 3.13
RO ALP 11800 8.79 x N/A N/A 11800 grids1x1 estimate b 59.63 x Unk FV x unk unk unk XX FV x U1 N/A knowledge knowledge 6900 a 10.28
SI ALP 5118 3.81 = x N/A N/A 24 grids1x1 minimum c 0.12 = > Y U1 x unk unk unk XX U1 = U1 = noChange noChange 1600 b 2.38
SK ALP 6220.54 4.64 = 202 202 N/A grids1x1 estimate b 1.02 = Y U1 = good poor poor U1 U1 = FV knowledge knowledge 6500 b 9.69
BG BLS 1100 100 u 1100 N/A N/A 7 grids1x1 minimum c 100 = 7 grids1x1 Unk FV = unk good good FV FV = FV method method 600 c 100
EE BOR 11200 11.60 + 81 140 N/A grids1x1 minimum b 19.34 + Y FV = good good good FV FV + FV noChange noChange 5100 a 26.56
FI BOR 6700 6.94 = N/A N/A 53 grids1x1 minimum b 9.27 = N N U1 = good good poor U1 U1 = U1 = noChange noChange 2600 a 13.54
LT BOR 27400 28.37 + N/A N/A 209 grids1x1 estimate b 36.57 + Y FV = good good good FV FV + U1 + genuine genuine 10200 b 53.13
LV BOR 49178 50.92 u N/A N/A 119 grids1x1 estimate b 20.82 = 119 grids1x1 Y FV = good good good FV FV = FV noChange noChange N/A b 0
SE BOR 2100 2.17 = 8000 70 100 80 grids1x1 mean b 14 = 8000 i N N U2 = bad bad poor U2 U2 = U2 = noChange noChange 1300 a 6.77
AT CON 5800 5.04 - > N/A N/A 83 grids1x1 estimate b 0.44 - > N N U1 - poor poor poor U1 U1 - U1 - noChange noChange 4600 b 10.38
BG CON 33100 28.75 u 33100 N/A N/A 58 grids1x1 minimum c 0.31 = 58 grids1x1 Unk FV = unk good good FV FV = FV method method 4500 c 10.16
CZ CON 10600 9.21 = N/A N/A 145 grids1x1 estimate a 0.77 = > Y U1 - good poor poor U1 U1 - U2 - knowledge knowledge 4400 a 9.93
DE CON 1673 1.45 = >> 37 38 37.50 grids1x1 estimate b 0.20 - >> localities N N U2 - bad bad bad U2 U2 - U2 x noChange genuine 1500 b 3.39
FR CON 9000 7.82 - > 35 3500 N/A grids1x1 minimum b 9.33 - >> Y U1 - poor poor poor U1 U2 - U2 = noChange noChange 3500 b 7.90
HR CON 6100 5.30 x x N/A N/A 29 grids1x1 minimum c 0.15 u x Unk XX u unk unk unk XX XX N/A N/A 3300 c 7.45
IT CON 13500 11.73 = N/A N/A 69 grids1x1 estimate a 0.36 x > Y FV = good unk good FV U1 = N/A N/A N/A N/A 4800 b 10.84
PL CON 11000 9.55 = 40 63 52 grids1x1 estimate b 0.27 = x Y XX u good unk unk XX XX U1 = genuine genuine 3600 b 8.13
RO CON 16500 14.33 x > N/A N/A 16500 grids1x1 estimate b 87.13 x Unk FV x unk unk unk XX FV x U1 N/A knowledge knowledge 10600 a 23.93
SE CON 400 0.35 = 2300 18 25 20 grids1x1 mean b 0.11 - 4000 i N N U2 - bad bad bad U2 U2 - U2 - noChange noChange 200 a 0.45
SI CON 7458 6.48 x x 165 188 N/A grids1x1 minimum c 0.93 - > Y U1 - unk unk poor XX U1 - U1 - noChange noChange 3300 b 7.45
FR MED 15600 19.91 - > 67 6700 N/A grids1x1 minimum b 21.07 - > Unk Unk U2 x poor poor poor U1 U2 - U1 = noChange noChange 5100 b 18.96
GR MED 34761 44.36 = N/A N/A 12427 grids1x1 estimate c 77.37 = Unk XX x good good unk FV FV = FV noChange method 12700 c 47.21
HR MED 4000 5.10 = N/A N/A 9 grids1x1 minimum c 0.06 x x Unk XX x good unk unk XX XX N/A N/A 2000 c 7.43
IT MED 24000 30.63 = N/A N/A 242 grids1x1 estimate a 1.51 x > Y FV = good unk good FV U1 = U1 - noChange noChange 7100 b 26.39
CZ PAN 2900 9.33 - >> N/A N/A 31 grids1x1 estimate a 2.33 - >> N N U2 - poor bad bad U2 U2 - U2 - noChange noChange 400 a 1.65
HU PAN 26528 85.35 = N/A N/A 1070 grids1x1 minimum b 80.45 = Y U1 u good good poor U1 U1 = U1 = noChange noChange 22300 b 91.77
RO PAN 200 0.64 x > N/A N/A 200 grids1x1 estimate b 15.04 x Unk FV x good good good FV FV x U1 N/A knowledge knowledge 100 a 0.41
SK PAN 1452.93 4.67 = 29 29 N/A grids1x1 estimate b 2.18 = Y U1 = good poor poor U1 U1 = FV knowledge knowledge 1500 b 6.17
RO STE 2900 100 x > N/A N/A 2900 grids1x1 estimate b 100 x Unk FV x unk unk unk XX FV x U1 N/A knowledge knowledge 2100 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 ALP 134172.54 2XP = > 134172.54 19788.00 19788.00 19788 grids1x1 2XP x > 2XP - 2XP MTX x U1 = nc nong U1 D

02/20

EEA-ETC/BD

Institution: -

Member State:

EEA-ETC/BD
EU28 BLS 1100 0MS x 1100 7 grids1x1 0MS = 7 grids1x1 0MS = unk good good 0MS MTX = FV = nc nc FV A=

02/20

EEA-ETC/BD

Institution: -

Member State:

EEA-ETC/BD
EU28 BOR 96578 2XP + ≈ 102478 532.00 571.50 571.50 grids1x1 2XP + 2XP = 2XP MTX + U1 + gen nc U1 A+

02/20

EEA-ETC/BD

Institution: -

Member State:

EEA-ETC/BD
EU28 CON 115131 2XP - >> 17179.00 20698.00 18937.50 grids1x1 2XP x >> 2XP - 2XP MTX x U1 - nong nong U1 A=

02/20

EEA-ETC/BD

Institution: -

Member State:

EEA-ETC/BD
EU28 MED 78361 2XP = > 78361 12745.00 19378.00 16061.50 grids1x1 2XP = > 2XP x 2XP MTX = XX - nong nong XX A=

03/20

EEA-ETC/BD

Institution: -

Member State:

EEA-ETC/BD
EU28 PAN 31080.93 2XP = >> 31080.93 1330.00 1330.00 1330 grids1x1 2XP = >> 2XP x good 2XP MTX = U1 = nc nc U1 D

02/20

EEA-ETC/BD

Institution: -

Member State:

EEA-ETC/BD
EU28 STE 2900 0MS x > 2900 grids1x1 0MS x 0MS x unk unk unk 0MS MTX x U1 x nong nc U1 A=

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.