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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, Fish, Aspius aspius, All bioregions. Annexes N, N, Y. Show all Fish
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 158 grids1x1 estimate N/A N/A N/A N/A
PL N/A N/A 8 grids1x1 minimum N/A N/A N/A N/A
SK 100 200 N/A grids1x1 estimate 5000 30000 N/A i N/A
DE 908 908 908 grids1x1 estimate 86 120 103 grids5x5 estimate
BG N/A N/A N/A grids1x1 minimum N/A N/A N/A N/A
RO N/A N/A 145 grids1x1 minimum 10000 100000 N/A i N/A
EE N/A N/A 3179 grids1x1 estimate N/A N/A N/A N/A
FI N/A N/A 3600 grids1x1 minimum N/A N/A 102 localities minimum
LT N/A N/A 2594 grids1x1 interval N/A N/A N/A N/A
LV N/A N/A 2760 grids1x1 estimate N/A N/A N/A N/A
SE N/A N/A 18015 grids1x1 estimate 5600 11200 8960 adults estimate
AT N/A N/A 1312 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
CZ N/A N/A 55 grids1x1 estimate N/A N/A 45 grids10x10 N/A
DE 5795 5795 5795 grids1x1 minimum 493 528 510.50 grids5x5 minimum
HR N/A N/A 250 grids1x1 minimum N/A N/A N/A N/A
PL N/A N/A 7788 grids1x1 minimum N/A N/A N/A N/A
RO N/A N/A 4471 grids1x1 estimate 107804 739627 N/A i N/A
SI N/A N/A 107 grids1x1 estimate N/A N/A N/A N/A
GR N/A N/A 739 grids1x1 estimate N/A N/A N/A N/A
CZ N/A N/A 15 grids1x1 estimate N/A N/A 10 grids10x10 N/A
HU N/A N/A 1017 grids1x1 minimum N/A N/A N/A N/A
RO N/A N/A 645 grids1x1 estimate 10000 100000 N/A i N/A
SK 200 300 N/A grids1x1 estimate 50000 150000 N/A i N/A
RO N/A N/A 1813 grids1x1 mean 25713 411401 N/A i N/A
SE N/A N/A 99 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 1300 6.11 = > N/A N/A 158 grids1x1 estimate a 50 = > Y U2 + poor poor bad U2 U2 + N/A N/A noChange noChange 1200 a 6.82
PL ALP 300 1.41 = N/A N/A 8 grids1x1 minimum b 2.53 x Y FV x good good good FV FV = FV noChange noInfo N/A b 0
SK ALP 19684.07 92.48 = 100 200 N/A grids1x1 estimate c 47.47 = Y FV = good poor poor U1 U2 = FV knowledge knowledge 16400 b 93.18
DE ATL 8679 100 = 908 908 908 grids1x1 estimate b 100 = grids5x5 Y FV = good good good FV FV = FV noChange method 5000 b 100
BG BLS 100 3.57 x 100 N/A N/A N/A grids1x1 minimum c 0 x x Unk XX x poor poor poor XX XX x U1 - noInfo noInfo 100 b 2.86
RO BLS 2700 96.43 = > N/A N/A 145 grids1x1 minimum b 100 = x Y FV + poor poor poor FV FV + FV method method 3400 b 97.14
EE BOR 14400 7.42 + N/A N/A 3179 grids1x1 estimate b 10.54 + > Y U1 + good good good FV U1 + U1 = genuine genuine 11100 a 18.85
FI BOR 63000 32.44 + N/A N/A 3600 grids1x1 minimum b 11.94 + Y FV = good good good FV FV + FV noChange method 11200 a 19.02
LT BOR 64787 33.36 = N/A N/A 2594 grids1x1 interval b 8.60 = 2594 grids1x1 N Y FV = good good poor FV FV = FV knowledge knowledge 10100 b 17.15
LV BOR 23900 12.31 = N/A N/A 2760 grids1x1 estimate a 9.15 = 2760 grids1x1 Y FV = good good good FV FV = FV knowledge noChange 16700 a 28.35
SE BOR 28100 14.47 = 21800 N/A N/A 18015 grids1x1 estimate b 59.76 + 14600 adults N Y U1 + good poor poor U1 U2 + U1 + knowledge noChange 9800 a 16.64
AT CON 10000 4.70 = N/A N/A 1312 grids1x1 estimate c 6.62 - > Y U1 + good poor poor U1 U1 - U1 - noChange noChange 7600 a 5.24
BG CON 26900 12.65 = 26900 N/A N/A 41 grids1x1 minimum b 0.21 = 41 grids1x1 Y FV = good good good FV FV = U1 - noChange knowledge 11600 b 8.01
CZ CON 7900 3.72 - > N/A N/A 55 grids1x1 estimate b 0.28 - > Y U1 = good poor poor U1 U1 - U1 = noChange noChange 4400 b 3.04
DE CON 62294 29.30 = 62294 5795 5795 5795 grids1x1 minimum c 29.24 = grids5x5 Y FV = good good good FV FV = U1 + method knowledge 30700 b 21.19
HR CON 13000 6.11 = N/A N/A 250 grids1x1 minimum b 1.26 = Y FV = good good good FV FV = N/A N/A 11700 b 8.07
PL CON 43000 20.23 = N/A N/A 7788 grids1x1 minimum b 39.30 = Y FV = good good good FV FV = FV noChange noInfo 15800 b 10.90
RO CON 45000 21.17 = > N/A N/A 4471 grids1x1 estimate b 22.56 + x Y FV + poor poor poor FV FV + U1 = knowledge knowledge 60400 b 41.68
SI CON 4502 2.12 = N/A N/A 107 grids1x1 estimate c 0.54 x x Unk XX u poor unk unk XX XX U1 x knowledge knowledge 2700 b 1.86
GR MED 739 100 = N/A N/A 739 grids1x1 estimate b 100 = Unk XX x good unk unk XX XX XX noChange noChange 5500 b 100
CZ PAN 1400 2.27 = N/A N/A 15 grids1x1 estimate b 0.78 = > Y U1 = good poor poor U1 U1 = U1 = noChange noChange 700 b 1.10
HU PAN 36508 59.19 = N/A N/A 1017 grids1x1 minimum b 52.78 = Y FV = good good good FV FV = FV noChange noChange 36900 b 57.75
RO PAN 10200 16.54 + > N/A N/A 645 grids1x1 estimate b 33.47 + x Y FV = poor poor poor FV FV + U1 = knowledge knowledge 12500 b 19.56
SK PAN 13575.72 22.01 = 200 300 N/A grids1x1 estimate c 12.97 = Y FV = good good good FV U1 = FV knowledge knowledge 13800 b 21.60
RO STE 22200 100 = > N/A N/A 1813 grids1x1 mean b 100 + x Y FV + poor poor poor FV FV + U1 = knowledge knowledge 25200 b 100
SE CON 400 0 N N/ N/A N/A 99 grids1x1 estimate a 0 N N/ N/A N N/A N/A N/A N/A N/A N/A U2 = N/A N/A 100 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 ALP 21284 2XP = > 266 366 316 grids1x1 2XP = x 2XP + 2XP MTX + FV nong nong FV B2

02/20

EEA-ETC/BD

Institution: -

Member State:

EEA-ETC/BD
EU28 ATL 8679 1 = 908 grids1x1 1 = 0MS = good good good 0MS MTX = FV nc nong FV A=

02/20

EEA-ETC/BD

Institution: -

Member State:

EEA-ETC/BD
EU28 BLS 2800 1 = 145 145 145 grids1x1 0EQ = x 2XP + poor poor poor 2XP MTX + U1 = nc nong U1 B1

01/20

EEA-ETC/BD

Institution: -

Member State:

EEA-ETC/BD
EU28 BOR 194187 1 + 30148 grids1x1 2XP + >> 2XP + 2XP MTX + U1 = nong nong U1 B2

12/19

EEA-ETC/BD

Institution: -

Member State:

EEA-ETC/BD
EU28 CON 212596 1 = 19819 19819 19819 grids1x1 2XP + 2XP + good good good 2XP MTX + U1 = nong nong U1 A=

01/20

EEA-ETC/BD

Institution: -

Member State:

EEA-ETC/BD
EU28 MED 739 0MS = 739 grids1x1 0MS = 0MS x 0MS MTX x XX nc nong XX D

02/20

EEA-ETC/BD

Institution: -

Member State:

EEA-ETC/BD
EU28 PAN 61683 0EQ = 1877 1977 1977 grids1x1 2XP + > 2XP = 2XP MTX + FV nc nong FV A=

02/20

EEA-ETC/BD

Institution: -

Member State:

EEA-ETC/BD
EU28 STE 22200 0MS = > 1813 grids1x1 0MS + x 0MS + poor poor poor 2XP MTX + U1 = nc nong U1 B1

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.