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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, Amphibians, Rana dalmatina, All bioregions. Annexes N, Y, N. Show all Amphibians
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 123 grids1x1 minimum N/A N/A N/A N/A
BG N/A N/A 216 grids1x1 minimum N/A N/A N/A N/A
DE N/A N/A 8 grids1x1 estimate 7 7 7 localities estimate
FR 63 6000 N/A grids1x1 estimate N/A N/A N/A estimate
HR N/A N/A 39 grids1x1 minimum N/A N/A N/A N/A
IT 183 620 N/A grids1x1 estimate N/A N/A N/A N/A
RO 2 20 5 grids1x1 minimum N/A N/A N/A N/A
SI 80 87 N/A grids1x1 minimum N/A N/A N/A N/A
SK 141 141 N/A grids1x1 estimate 10000 50000 N/A i N/A
DE 205 205 205 grids1x1 minimum 205 205 205 localities minimum
ES 32 3200 N/A grids1x1 estimate N/A N/A N/A N/A
FR N/A N/A N/A estimate N/A N/A N/A estimate
BG N/A N/A 95 grids1x1 minimum N/A N/A N/A N/A
SE N/A N/A 4161 grids1x1 estimate 20000 40000 31500 i estimate
AT 421 421 N/A grids1x1 minimum N/A N/A N/A N/A
BG N/A N/A 971 grids1x1 minimum N/A N/A N/A N/A
CZ N/A N/A 4269 grids1x1 estimate N/A N/A N/A N/A
DE 23158 23158 23158 grids1x1 estimate 692 695 693.50 localities estimate
DK N/A N/A N/A estimate N/A N/A 97 localities N/A
FR 1500 11000 N/A grids1x1 minimum N/A N/A N/A minimum
HR N/A N/A 603 grids1x1 minimum N/A N/A N/A N/A
IT 974 6624 N/A grids1x1 estimate N/A N/A N/A N/A
PL N/A N/A 91 grids1x1 minimum N/A N/A 120 localities minimum
RO 2 20 5 grids1x1 minimum N/A N/A N/A N/A
SE N/A N/A 2524 grids1x1 estimate 10000 20000 15100 i estimate
SI 746 753 N/A grids1x1 minimum N/A N/A N/A N/A
ES 22 2200 N/A grids1x1 estimate N/A N/A N/A N/A
FR 1000 10000 N/A grids1x1 minimum N/A N/A N/A minimum
GR 3230 3826 N/A grids1x1 estimate N/A N/A N/A N/A
HR N/A N/A 104 grids1x1 minimum N/A N/A N/A N/A
IT 427 1496 N/A grids1x1 estimate N/A N/A N/A N/A
CZ N/A N/A 440 grids1x1 estimate N/A N/A N/A N/A
HU N/A N/A 2198 grids1x1 minimum N/A N/A N/A N/A
RO 2 20 5 grids1x1 minimum N/A N/A N/A N/A
SK 222 222 N/A grids1x1 estimate 50000 100000 N/A i N/A
RO 2 20 5 grids1x1 minimum 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 4800 3.79 x > N/A N/A 123 grids1x1 minimum c 3.04 x > Unk N U1 x poor poor poor U1 U1 x U1 x noChange genuine 4000 b 8.35
BG ALP 21200 16.73 = 21200 N/A N/A 216 grids1x1 minimum c 5.34 = 216 grids1x1 Y FV = poor poor poor U1 U1 = FV method method 3900 b 8.14
DE ALP 1224 0.97 = N/A N/A 8 grids1x1 estimate b 0.20 - > localities N Y U1 - good poor poor U1 U1 - U1 x noChange knowledge 700 c 1.46
FR ALP 8000 6.31 x 63 6000 N/A grids1x1 estimate d 74.88 x x Unk Unk XX x unk unk unk XX XX x XX noChange noChange 4600 a 9.60
HR ALP 10100 7.97 x x N/A N/A 39 grids1x1 minimum c 0.96 x x Unk XX x unk unk unk XX XX N/A N/A 2100 c 4.38
IT ALP 24500 19.33 = 183 620 N/A grids1x1 estimate b 9.92 = Y FV = good good good FV FV = FV noChange noChange 12700 b 26.51
RO ALP 45300 35.75 = 2 20 5 grids1x1 minimum b 0.12 = 5 grids1x1 Y XX = good good unk FV FV = U1 - knowledge knowledge 12000 b 25.05
SI ALP 5338 4.21 = 80 87 N/A grids1x1 minimum b 2.06 x x N Unk U1 u good unk poor U1 U1 x U1 x noChange noChange 2600 b 5.43
SK ALP 6263.15 4.94 + 141 141 N/A grids1x1 estimate c 3.48 + Y U1 x good poor poor U1 U1 = U1 = N/A N/A 5300 b 11.06
DE ATL 6845 3.02 + 205 205 205 grids1x1 minimum b 11.26 + localities Y FV + good good good FV FV + FV noChange noChange 5000 c 2.70
ES ATL 6300 2.78 = 32 3200 N/A grids1x1 estimate b 88.74 = 3200 grids1x1 Y U1 - poor good poor U1 U1 = U1 - knowledge knowledge 3000 a 1.62
FR ATL 213500 94.20 + N/A N/A N/A estimate c 0 = < Y Unk U1 - good poor poor U1 U1 x FV genuine N/A 177000 a 95.68
BG BLS 4200 100 = 4200 N/A N/A 95 grids1x1 minimum c 100 = 95 grids1x1 Y FV = poor poor poor U1 U1 = FV method method 2400 b 100
SE BOR 5900 100 = 5900 N/A N/A 4161 grids1x1 estimate b 100 + 30000 i Y U1 = good good unk FV U1 + U2 = genuine genuine 4300 b 100
AT CON 16600 2.65 = 421 421 N/A grids1x1 minimum b 0.98 x > Unk N U1 x poor poor poor U1 U1 x U1 x method noChange 11000 b 3
BG CON 73400 11.71 = 73400 N/A N/A 971 grids1x1 minimum b 2.27 = 971 grids1x1 Y FV = poor poor poor U1 U1 = FV method method 22500 b 6.14
CZ CON 68000 10.85 + N/A N/A 4269 grids1x1 estimate a 9.96 + Y FV = good good good FV FV + FV genuine genuine 48800 a 13.32
DE CON 63525 10.13 = 23158 23158 23158 grids1x1 estimate b 54.06 = localities Y FV = good good good FV FV = FV noChange noChange 36500 c 9.96
DK CON 4586 0.73 - > N/A N/A N/A estimate b 0 - > Y FV = poor poor good U1 U1 - FV N/A N/A 3900 b 1.06
FR CON 130800 20.86 u 1500 11000 N/A grids1x1 minimum c 14.59 u < Y Unk U1 - unk unk poor XX U1 x U1 x N/A N/A 100400 a 27.40
HR CON 34500 5.50 x N/A N/A 603 grids1x1 minimum c 1.41 x x Unk XX x unk unk unk XX XX N/A N/A 17100 c 4.67
IT CON 90400 14.42 = 974 6624 N/A grids1x1 estimate b 8.87 = Y FV = good good good FV FV = U1 - noChange noChange 55900 b 15.26
PL CON 10100 1.61 = N/A N/A 91 grids1x1 minimum b 0.21 = Y U1 u good good poor U1 U1 = U1 - noChange knowledge 4600 b 1.26
RO CON 118300 18.87 = 2 20 5 grids1x1 minimum b 0.01 = 5 grids1x1 Y XX = good good unk FV FV = U1 - knowledge knowledge 54600 b 14.90
SE CON 4700 0.75 = 4700 N/A N/A 2524 grids1x1 estimate c 5.89 + 15000 i Y U1 = good good unk FV U1 + U2 = genuine genuine 2400 b 0.66
SI CON 11998 1.91 = 746 753 N/A grids1x1 minimum b 1.75 x x N Unk U1 u good unk poor U1 U1 x U1 x noChange noChange 8700 b 2.37
ES MED 4500 3.35 + 22 2200 N/A grids1x1 estimate b 9.92 = 2200 grids1x1 N N U1 - good poor poor U1 U1 = U1 - knowledge noChange 900 a 1.40
FR MED 9500 7.08 = 1000 10000 N/A grids1x1 minimum c 49.09 = Y Unk FV = good poor good U1 U1 = U1 - noChange noChange 7600 a 11.80
GR MED 25946.37 19.34 = 3230 3826 N/A grids1x1 estimate b 31.49 = Y FV = good good good FV FV = FV noChange noChange 10600 b 16.46
HR MED 13700 10.21 x x N/A N/A 104 grids1x1 minimum c 0.93 x x Unk XX x unk unk unk XX XX N/A N/A 4800 c 7.45
IT MED 80500 60.01 = 427 1496 N/A grids1x1 estimate b 8.58 = Y FV = good good good FV FV = U1 - noChange noChange 40500 b 62.89
CZ PAN 6300 6.78 + N/A N/A 440 grids1x1 estimate a 15.36 + Y FV = good good good FV FV + FV genuine genuine 3300 a 4.56
HU PAN 62122 66.88 = N/A N/A 2198 grids1x1 minimum b 76.72 = Y FV = good good good FV FV = FV noChange noChange 54400 b 75.24
RO PAN 19100 20.56 = 2 20 5 grids1x1 minimum b 0.17 = 5 grids1x1 Y XX = good good unk FV FV = U1 - knowledge knowledge 9700 b 13.42
SK PAN 5367.75 5.78 = 222 222 N/A grids1x1 estimate b 7.75 = > Y U1 = good poor poor U1 U1 = U1 = N/A N/A 4900 b 6.78
RO STE 7600 100 = 2 20 5 grids1x1 minimum b 100 = 5 grids1x1 Y FV = good good good FV FV = U1 - knowledge knowledge 3400 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 126725.15 2GD = 847 7246 4048.5 grids1x1 2GD = 2GD x 2GD MTX = U1 - nc nong U1 D

03/20

EEA-ETC/BD

Institution: -

Member State: SK

EEA-ETC/BD
EU28 ATL 226645 1 + ≈ 226645 2GD = 2GD - 2GD MTX x FV = gen nong FV C

02/20

EEA-ETC/BD

Institution: -

Member State: SK

EEA-ETC/BD
EU28 BLS 4200 0MS = 4200 95 grids1x1 0MS = 95 grids1x1 0MS = 0MS MTX = FV = nong nc FV D

02/20

EEA-ETC/BD

Institution: -

Member State: SK

EEA-ETC/BD
EU28 BOR 5900 0MS = 5900 4161 4161 4161 grids1x1 0MS + 30000 i 0MS = good good unk 0MS MTX + U2 = gen gen U2 B1

12/19

EEA-ETC/BD

Institution: -

Member State: SK

EEA-ETC/BD
EU28 CON 626909 2GD + 2GD + 2GD - 2GD MTX + U1 - nc nong U2 B1

03/20

EEA-ETC/BD

Institution: -

Member State: SK

EEA-ETC/BD
EU28 MED 134146.37 2XP = 4783 17626 11204.5 grids1x1 2XP = 2XP = 2XP MTX = U1 - nc nong FV C

03/20

EEA-ETC/BD

Institution: -

Member State: SK

EEA-ETC/BD
EU28 PAN 92889.75 1 = ≈ 92889.75 2862 2880 2865 grids1x1 1 = grids1x1 2XP = good 2XP MTX = U1 = nc nc U1 D

02/20

EEA-ETC/BD

Institution: -

Member State: SK

EEA-ETC/BD
EU28 STE 7600 0MS = ≈ 7600 2 20 5 grids1x1 0MS = 5 grids1x1 0MS = good good good 0MS MTX = U1 - nong nong U1 A=

12/19

EEA-ETC/BD

Institution: -

Member State: SK

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.