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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, Rhodeus amarus, All bioregions. Annexes Y-CTC, N, N. 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 63 grids1x1 estimate N/A N/A N/A estimate
BG N/A N/A 14 grids1x1 estimate N/A N/A N/A N/A
SI N/A N/A 9 grids1x1 estimate N/A N/A N/A N/A
SK 59 59 N/A grids1x1 estimate 10000 100000 N/A i N/A
BE N/A N/A 214 grids1x1 estimate 8 3750 N/A i estimate
DE 2176 2176 2176 grids1x1 minimum 264 266 265 grids5x5 minimum
NL N/A N/A 2028 grids1x1 estimate N/A N/A N/A N/A
BG N/A N/A 47 grids1x1 estimate N/A N/A N/A N/A
RO N/A N/A 145 grids1x1 mean 100000 500000 N/A i N/A
LT N/A N/A 1676 grids1x1 minimum N/A N/A N/A N/A
LV N/A N/A 4192 grids1x1 estimate N/A N/A N/A N/A
AT N/A N/A 321 grids1x1 estimate N/A N/A N/A N/A
BE 15 1000 71 grids1x1 minimum 8000 N/A 8000 i minimum
BG N/A N/A 363 grids1x1 minimum N/A N/A N/A N/A
CZ N/A N/A 130 grids1x1 estimate N/A N/A 61 grids10x10 estimate
DE 10016 10016 10016 grids1x1 estimate 1052 1172 1112 grids5x5 estimate
FR 25000 50000 N/A grids1x1 estimate N/A N/A N/A estimate
HR N/A N/A 561 grids1x1 minimum N/A N/A N/A N/A
LU 150 250 N/A grids1x1 estimate N/A N/A N/A N/A
PL N/A N/A 17082 grids1x1 estimate N/A N/A N/A N/A
RO N/A N/A 7590 grids1x1 mean 249926 10704300 N/A i N/A
SI N/A N/A 407 grids1x1 estimate N/A N/A N/A N/A
GR N/A N/A 10646 grids1x1 estimate N/A N/A 109 grids10x10 estimate
CZ N/A N/A 99 grids1x1 estimate N/A N/A 25 grids10x10 estimate
HU N/A N/A 1596 grids1x1 minimum N/A N/A N/A N/A
RO N/A N/A 943 grids1x1 mean 100000 1000000 N/A i N/A
SK 273 273 N/A grids1x1 estimate 100000 1000000 N/A i N/A
RO N/A N/A 2198 grids1x1 estimate 83910 16213200 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 4800 18.31 = > N/A N/A 63 grids1x1 estimate a 43.45 - > Y U1 - good poor poor U1 U1 - U1 = noChange genuine 3100 a 34.07
BG ALP 15500 59.11 = 15500 N/A N/A 14 grids1x1 estimate b 9.66 = 14 grids1x1 Y FV = good good good FV FV = FV noChange knowledge 1000 b 10.99
SI ALP 1663 6.34 x x N/A N/A 9 grids1x1 estimate c 6.21 x x Unk XX x unk unk unk XX XX U1 x knowledge knowledge 1000 c 10.99
SK ALP 4259.21 16.24 = 59 59 N/A grids1x1 estimate b 40.69 = Y U1 = good poor poor U1 U1 = FV knowledge knowledge 4000 b 43.96
BE ATL 20200 20.92 = N/A N/A 214 grids1x1 estimate b 4.84 + Y FV = good good good FV FV + FV noChange noChange 8100 a 15.34
DE ATL 50441 52.25 = 50441 2176 2176 2176 grids1x1 minimum b 49.25 + 265 grids5x5 Unk XX = good good unk FV FV + FV noChange noChange 22100 b 41.86
NL ATL 25900 26.83 + N/A N/A 2028 grids1x1 estimate b 45.90 = Y FV + good good good FV FV + U1 + genuine noChange 22600 a 42.80
BG BLS 6600 70.97 = 6600 N/A N/A 47 grids1x1 estimate b 24.48 = 47 grids1x1 Y FV = good good good FV FV = FV noChange knowledge 2400 b 41.38
RO BLS 2700 29.03 = N/A N/A 145 grids1x1 mean b 75.52 = x Y FV = poor poor poor FV FV = U1 = knowledge knowledge 3400 b 58.62
LT BOR 64787 56.44 = N/A N/A 1676 grids1x1 minimum b 28.56 = 1676 grids1x1 Y FV = good good good FV FV = FV knowledge knowledge 14600 b 28.97
LV BOR 50000 43.56 + N/A N/A 4192 grids1x1 estimate a 71.44 + x Y FV = good good good FV FV + FV noChange noChange 35800 a 71.03
AT CON 16800 2.59 = N/A N/A 321 grids1x1 estimate a 0.43 - > Y U1 - good poor poor U1 U1 - U1 - noChange noChange 9300 a 2.88
BE CON 5500 0.85 = 15 1000 71 grids1x1 minimum b 0.10 u x N N U1 = good poor good U1 U1 = XX knowledge knowledge 1400 a 0.43
BG CON 67900 10.46 = 67900 N/A N/A 363 grids1x1 minimum b 0.49 = 363 grids1x1 Y FV = good good good FV FV = FV noChange knowledge 21400 b 6.62
CZ CON 9700 1.49 = > N/A N/A 130 grids1x1 estimate b 0.18 = > Y U1 = good poor poor U1 U1 = U2 = genuine noChange 5200 b 1.61
DE CON 167110 25.74 + 10016 10016 10016 grids1x1 estimate b 13.49 + grids5x5 Y FV + good good good FV FV + FV noChange noChange 75400 b 23.33
FR CON 122300 18.84 + 25000 50000 N/A grids1x1 estimate b 50.51 + Y Unk FV = good good good FV FV + FV noChange noChange 26600 b 8.23
HR CON 23900 3.68 = N/A N/A 561 grids1x1 minimum c 0.76 = Y FV = good good good FV FV = N/A N/A 27300 b 8.45
LU CON 2600 0.40 = >> 150 250 N/A grids1x1 estimate b 0.27 u >> N Unk U1 u bad bad poor U2 U2 x U1 + method method 1600 b 0.50
PL CON 123100 18.96 = N/A N/A 17082 grids1x1 estimate b 23.01 u Y FV x good good good FV FV x FV noChange knowledge 36000 b 11.14
RO CON 101900 15.70 = N/A N/A 7590 grids1x1 mean b 10.22 + x Y FV = good good good FV FV + U1 = knowledge knowledge 111300 b 34.44
SI CON 8372 1.29 = N/A N/A 407 grids1x1 estimate c 0.55 = Y FV = good good good FV FV = FV noChange noChange 7700 c 2.38
GR MED 10900 100 = N/A N/A 10646 grids1x1 estimate b 100 = Y FV = good good good FV FV = FV noChange noChange 10700 b 100
CZ PAN 3100 3.54 = N/A N/A 99 grids1x1 estimate b 3.40 + Y U1 = good good poor U1 U1 + U1 = knowledge knowledge 1800 b 2.25
HU PAN 61643 70.44 = N/A N/A 1596 grids1x1 minimum b 54.83 = Y FV = good good good FV FV = FV noChange method 53200 b 66.42
RO PAN 14200 16.23 = N/A N/A 943 grids1x1 mean b 32.39 = x Y FV = good poor good FV FV = U1 = knowledge knowledge 15700 b 19.60
SK PAN 8573.92 9.80 = 273 273 N/A grids1x1 estimate b 9.38 = Y FV = good good good FV FV = FV N/A knowledge 9400 b 11.74
RO STE 30800 100 = > N/A N/A 2198 grids1x1 estimate b 100 + x Y FV = poor poor poor FV FV + U1 = knowledge knowledge 32300 b 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 26222 2XP = > 145 145 145 grids1x1 2XP - > 2XP - good poor poor 2XP MTX - FV nong nong FV C

02/20

EEA-ETC/BD

Institution: -

Member State:

EEA-ETC/BD
EU28 ATL 96541 0EQ = 4418 4418 4418 grids1x1 0EQ + 2XP + good good good 2XP MTX + FV nc nong U1 A+

02/20

EEA-ETC/BD

Institution: -

Member State:

EEA-ETC/BD
EU28 BLS 9300 0EQ = 192 192 192 grids1x1 2XP = 2XP = poor poor poor 2XP MTX = FV nong nong FV D

02/20

EEA-ETC/BD

Institution: -

Member State:

EEA-ETC/BD
EU28 BOR 114787 0EQ + 5868 5868 5868 grids1x1 0EQ + grids1x1 0EQ = good good good 0EQ MTX + FV nc nong FV A=

02/20

EEA-ETC/BD

Institution: -

Member State:

EEA-ETC/BD
EU28 CON 649182 2GD + 61635 87720 74241 grids1x1 2GD + 2GD + good good good 2GD MTX + FV nc nong U2 A+

02/20

EEA-ETC/BD

Institution: -

Member State:

EEA-ETC/BD
EU28 MED 10900 0MS = 10646 grids1x1 0MS = 0MS = good good good 0MS MTX = FV nc nong FV A=

02/20

EEA-ETC/BD

Institution: -

Member State:

EEA-ETC/BD
EU28 PAN 87517 0MS = 2911 2911 2911 grids1x1 0MS = 2XP = good good good 2XP MTX = FV nc nong FV A=

02/20

EEA-ETC/BD

Institution: -

Member State:

EEA-ETC/BD
EU28 STE 30800 0MS = 2198 grids1x1 0MS + x 0MS = poor poor poor 2XP MTX + U1 = nc nong U1 B1

12/19

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.