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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, Reptiles, Podarcis muralis, All bioregions. Annexes N, Y, N. Show all Reptiles
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 107 grids1x1 minimum N/A N/A N/A N/A
BG N/A N/A 378 grids1x1 minimum N/A N/A N/A N/A
DE 2 2 2 grids1x1 estimate 2 2 2 grids5x5 estimate
ES 145 14482 N/A grids1x1 estimate 10000000 50000000 37252400 i estimate
FR 500000 50000000 N/A grids1x1 minimum N/A N/A N/A minimum
HR N/A N/A 136 grids1x1 minimum N/A N/A N/A N/A
IT 2227 15575 N/A grids1x1 estimate N/A N/A N/A N/A
RO 2 50 10 grids1x1 minimum N/A N/A N/A N/A
SI 85 92 N/A grids1x1 minimum N/A N/A N/A N/A
SK 362 362 N/A grids1x1 estimate 1000 5000 N/A i N/A
BE N/A N/A 128 grids1x1 estimate N/A N/A N/A N/A
DE 12 12 12 grids1x1 estimate 1 1 1 grids5x5 estimate
ES 339 33900 N/A grids1x1 estimate 50000000 100000000 69252600 i estimate
FR 100000 50000000 N/A grids1x1 mean N/A N/A N/A mean
NL N/A N/A 15 grids1x1 estimate 300 1000 N/A i estimate
BG N/A N/A 94 grids1x1 minimum N/A N/A N/A N/A
AT N/A N/A 55 grids1x1 minimum N/A N/A N/A N/A
BE 491 925 491 grids1x1 minimum 7000 70000 30000 adults estimate
BG N/A N/A 701 grids1x1 minimum N/A N/A N/A N/A
CZ N/A N/A 5 grids1x1 estimate N/A N/A N/A N/A
DE 3688 3688 3688 grids1x1 estimate 546 556 551 grids5x5 estimate
FR 100000 50000000 N/A grids1x1 mean N/A N/A N/A mean
HR N/A N/A 296 grids1x1 minimum N/A N/A N/A N/A
IT 3943 27417 N/A grids1x1 estimate N/A N/A N/A N/A
LU 720 1391 N/A grids1x1 estimate N/A N/A N/A N/A
RO 2 50 10 grids1x1 minimum N/A N/A N/A N/A
SI 141 148 N/A grids1x1 minimum N/A N/A N/A N/A
ES 339 33900 N/A grids1x1 estimate 10000000 50000000 38297600 i estimate
FR 50000 50000000 N/A grids1x1 mean N/A N/A N/A mean
GR N/A N/A 28412 grids1x1 estimate 5000000 10000000 N/A i estimate
HR N/A N/A 157 grids1x1 minimum N/A N/A N/A N/A
IT 1662 14784 N/A grids1x1 estimate N/A N/A N/A N/A
HU N/A N/A 766 grids1x1 minimum N/A N/A N/A N/A
SK 35 35 N/A grids1x1 estimate 500 1000 N/A i N/A
RO 2 50 10 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 5700 2.81 - > N/A N/A 107 grids1x1 minimum c 0 - > Unk N U1 - poor poor poor U1 U1 - U1 x genuine genuine 4200 b 3.56
BG ALP 20500 10.11 = 20500 N/A N/A 378 grids1x1 minimum c 0 = 378 grids1x1 Y FV = unk unk unk XX FV = FV method method 5000 b 4.24
DE ALP 39 0.02 = 39 2 2 2 grids1x1 estimate b 0 = > grids5x5 N N U2 - good poor bad U2 U2 - U2 - noChange noChange 100 b 0.08
ES ALP 16700 8.24 = 145 14482 N/A grids1x1 estimate b 0.03 = 14482 grids1x1 Y FV = unk unk good XX FV x FV noChange noChange 9400 a 7.97
FR ALP 44300 21.85 = 500000 50000000 N/A grids1x1 minimum c 99.93 = < Unk Unk XX = good good good FV FV = FV noChange noChange 29800 b 25.25
HR ALP 11800 5.82 x N/A N/A 136 grids1x1 minimum c 0 x x Y FV = unk unk good XX XX N/A N/A 4300 c 3.64
IT ALP 61200 30.18 + 2227 15575 N/A grids1x1 estimate b 0.04 = Y FV = good good good FV FV + FV genuine genuine 47000 b 39.83
RO ALP 25500 12.57 = 2 50 10 grids1x1 minimum b 0 = 10 grids1x1 Y FV = good good good FV FV = U1 - knowledge knowledge 7500 b 6.36
SI ALP 7656 3.78 = 7656 85 92 N/A grids1x1 minimum c 0 x Y FV x good good good FV FV x FV noChange noChange 3300 c 2.80
SK ALP 9395.08 4.63 = 362 362 N/A grids1x1 estimate b 0 - 10000 i Y U1 - good poor poor U1 U1 - U1 = N/A N/A 7400 b 6.27
BE ATL 15300 4.39 + N/A N/A 128 grids1x1 estimate a 0 + Y FV = good good good FV FV + U1 = method method 4200 a 1.64
DE ATL 339 0.10 = 12 12 12 grids1x1 estimate b 0 = > grids5x5 N Y U1 = good poor poor U1 U1 = U1 = noChange noChange 100 b 0.04
ES ATL 43700 12.54 = 339 33900 N/A grids1x1 estimate b 0.07 + 33900 grids1x1 Y FV u good unk unk XX FV + FV noChange knowledge 31600 a 12.31
FR ATL 289000 82.92 = 100000 50000000 N/A grids1x1 mean c 99.93 = < Unk Unk XX = good good good FV FV = FV noChange noChange 220800 b 85.98
NL ATL 200 0.06 = N/A N/A 15 grids1x1 estimate a 0 + >> N Y U1 + good poor good U1 U2 + U2 + noChange noChange 100 a 0.04
BG BLS 7500 100 = 7500 N/A N/A 94 grids1x1 minimum c 100 = 94 grids1x1 Y FV = unk unk unk XX FV = FV method method 2800 b 100
AT CON 2800 0.56 - > N/A N/A 55 grids1x1 minimum c 0 - > Unk N U1 - poor good poor U1 U1 - U1 x genuine genuine 1600 b 0.48
BE CON 11500 2.29 = 491 925 491 grids1x1 minimum b 0 x N Y U1 x poor poor poor U1 U1 x U1 + noChange method 6400 a 1.93
BG CON 55300 11 = 55300 N/A N/A 701 grids1x1 minimum c 0 = 701 grids1x1 Y FV = unk unk unk XX FV = FV method method 16300 b 4.92
CZ CON 300 0.06 = N/A N/A 5 grids1x1 estimate a 0 = Y FV = good good good FV FV = FV knowledge knowledge 300 a 0.09
DE CON 39918 7.94 + 39918 3688 3688 3688 grids1x1 estimate b 0.01 + 551 grids5x5 Y FV = good good good FV FV + FV noChange noChange 26400 b 7.97
FR CON 204800 40.73 = 100000 50000000 N/A grids1x1 mean c 99.91 = < Unk Unk XX = good good good FV FV = FV noChange noChange 158100 b 47.71
HR CON 26500 5.27 x x N/A N/A 296 grids1x1 minimum c 0 x x Unk XX x unk unk unk XX XX N/A N/A 10100 c 3.05
IT CON 100900 20.07 = 3943 27417 N/A grids1x1 estimate b 0.06 = Y FV = good good good FV FV = FV noChange noChange 87600 b 26.43
LU CON 3600 0.72 = 3600 720 1391 N/A grids1x1 estimate b 0 = 1468 grids1x1 Y U1 = good poor good U1 U1 = U1 - noChange knowledge 3100 b 0.94
RO CON 45500 9.05 + 2 50 10 grids1x1 minimum b 0 + 10 grids1x1 Y FV + good good good FV FV + U1 x knowledge knowledge 16000 b 4.83
SI CON 11741 2.33 = 141 148 N/A grids1x1 minimum c 0 x Y FV x good good good FV FV x FV noChange noChange 5500 c 1.66
ES MED 66900 21.44 = 339 33900 N/A grids1x1 estimate b 0.07 u 33900 grids1x1 Y FV = poor unk poor XX XX XX noChange noChange 29900 a 15.38
FR MED 69200 22.18 = 50000 50000000 N/A grids1x1 mean c 99.79 u < Y Unk FV = good poor good U1 U1 = FV noChange genuine 58400 b 30.04
GR MED 67549.95 21.65 = N/A N/A 28412 grids1x1 estimate b 0.11 = Y FV = good good good FV FV = FV noChange noChange 30100 b 15.48
HR MED 14900 4.77 x x N/A N/A 157 grids1x1 minimum c 0 x x Unk XX x unk unk unk XX XX N/A N/A 5600 c 2.88
IT MED 93500 29.96 = 1662 14784 N/A grids1x1 estimate b 0.03 = Y FV = good good good FV FV = FV noChange noChange 70400 b 36.21
HU PAN 19307 93.88 = N/A N/A 766 grids1x1 minimum b 95.63 = Y FV = good good good FV FV = U1 = knowledge noChange 20600 b 94.93
SK PAN 1258.89 6.12 = 35 35 N/A grids1x1 estimate b 4.37 = 3000 i Y FV = good poor poor U1 U1 = U1 = N/A N/A 1100 b 5.07
RO STE 2700 100 = 2 50 10 grids1x1 minimum b 100 = 10 grids1x1 Y FV = good good good FV FV = U1 - knowledge knowledge 1200 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 2XP = 2XP = 2XP = good good good 2XP MTX = FV = nc nc FV A=

01/20

EEA-ETC/BD

Institution: -

Member State:

EEA-ETC/BD
EU28 ATL 2XP = 2XP = grids1x1 2XP = good good good 2XP MTX = FV = nc nc FV A=

12/19

EEA-ETC/BD

Institution: -

Member State:

EEA-ETC/BD
EU28 BLS 0MS = 0MS = 0MS = unk unk unk 0MS MTX = FV = nc nc FV A=

01/20

EEA-ETC/BD

Institution: -

Member State:

EEA-ETC/BD
EU28 CON 2XP = 2XP = 2XP x good good good 2XP MTX = FV x nc nong FV A=

02/20

EEA-ETC/BD

Institution: -

Member State:

EEA-ETC/BD
EU28 MED 2XP = 2XP x 2XP = good unk good 2XP MTX = FV nong nong XX D

01/20

EEA-ETC/BD

Institution: -

Member State:

EEA-ETC/BD
EU28 PAN 2XP = 2XP = 2XP = good good good 2XP MTX = U1 = nong nc U1 A=

02/20

EEA-ETC/BD

Institution: -

Member State:

EEA-ETC/BD
EU28 STE 2700 0MS = 2 50 10 grids1x1 0MS = grids1x1 0MS = good good good 0MS MTX = U1 - nong nong U1 A=

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.