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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 LU

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, Mammals, Lutra lutra, All bioregions. Annexes Y, Y, N. Show all Mammals
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 1555 N/A N/A grids1x1 minimum 850 1650 N/A adults minimum
BG N/A N/A 110 grids1x1 minimum N/A N/A N/A N/A
DE 65 65 65 grids1x1 estimate 12 12 12 grids5x5 estimate
ES 119 11900 N/A grids1x1 estimate N/A N/A N/A N/A
FI 100 10000 N/A grids1x1 estimate 70 200 N/A i N/A
FR 100 200 N/A grids1x1 mean 300 1200 N/A adults mean
HR N/A N/A 56 grids1x1 minimum N/A N/A N/A N/A
PL N/A N/A 4663 grids1x1 estimate N/A N/A N/A N/A
RO 0.14 0.19 N/A grids1x1 mean N/A N/A N/A N/A
SE N/A N/A 140 grids1x1 estimate 160 240 200 i N/A
SI 21 22 N/A grids1x1 minimum N/A N/A N/A N/A
SK 1816 1816 N/A grids1x1 estimate 300 550 N/A i N/A
DE 2798 2798 2798 grids1x1 minimum 708 715 711.50 grids5x5 minimum
DK N/A N/A N/A N/A N/A 153 grids10x10 N/A
ES 605 60500 N/A grids1x1 estimate N/A N/A N/A N/A
FR 5000 15000 N/A grids1x1 estimate 7900 13500 N/A adults estimate
IE N/A N/A 2511 grids1x1 estimate 7218 10186 N/A bfemales estimate
PT N/A N/A N/A N/A N/A N/A N/A
UK N/A N/A 21441 grids1x1 minimum N/A N/A 12600 i estimate
BG N/A N/A 92 grids1x1 minimum N/A N/A N/A N/A
RO 0.14 0.19 N/A grids1x1 mean N/A N/A N/A N/A
EE N/A N/A 281 grids1x1 estimate N/A N/A N/A N/A
FI 7480 149600 N/A grids1x1 estimate 2700 3500 N/A i N/A
LT N/A N/A 1670 grids1x1 estimate 3000 4000 N/A i minimum
LV N/A N/A 2207 grids1x1 estimate 3000 4000 N/A i estimate
SE N/A N/A 1229 grids1x1 estimate 3000 4400 3700 i estimate
AT 1354 N/A N/A grids1x1 minimum 1000 1550 N/A adults minimum
BE 4 9 N/A grids1x1 estimate 5 20 N/A i minimum
BG N/A N/A 446 grids1x1 minimum N/A N/A N/A N/A
CZ N/A 3472 N/A grids1x1 estimate N/A N/A N/A N/A
DE 15903 15903 15903 grids1x1 estimate 3057 3075 3066 grids5x5 estimate
DK N/A N/A N/A N/A N/A 182 grids10x10 N/A
FR 1000 5000 N/A grids1x1 mean 3000 6000 N/A adults mean
HR N/A N/A 793 grids1x1 minimum N/A N/A N/A N/A
LU N/A N/A N/A grids1x1 estimate N/A N/A N/A N/A
PL N/A N/A 49912 grids1x1 estimate N/A N/A N/A N/A
RO 0.14 0.19 N/A grids1x1 mean N/A N/A N/A N/A
SE N/A N/A 94 grids1x1 estimate 100 150 125 i estimate
SI 165 166 N/A grids1x1 minimum N/A N/A N/A N/A
ES 2665 266500 N/A grids1x1 estimate N/A N/A N/A N/A
FR 500 1500 N/A grids1x1 estimate 1000 2000 N/A adults estimate
GR N/A N/A 69695 grids1x1 estimate 1048 1548 N/A grids5x5 estimate
HR N/A N/A 31 grids1x1 minimum N/A N/A N/A N/A
IT 4694 7234 N/A grids1x1 estimate N/A N/A N/A N/A
PT N/A N/A N/A N/A N/A N/A N/A
CZ N/A 129 N/A grids1x1 estimate N/A N/A N/A N/A
HU N/A N/A 1725 grids1x1 minimum N/A N/A N/A N/A
RO 0.14 0.19 N/A grids1x1 mean N/A N/A N/A N/A
SK 346 346 N/A grids1x1 estimate 100 300 N/A i N/A
RO 0.14 0.19 N/A grids1x1 mean N/A N/A N/A N/A
IT 100 114 N/A grids1x1 minimum 8 10 N/A i estimate
BE N/A N/A 7 grids1x1 estimate 3 7 5 i estimate
NL N/A N/A 1684 grids1x1 estimate 230 270 N/A i estimate
IT N/A N/A N/A 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 38900 15.10 + > 1555 N/A N/A grids1x1 minimum b 7.92 + Y FV = good unk good FV U1 + U1 + noChange noChange 33900 a 24.42
BG ALP 25800 10.02 = 25800 N/A N/A 110 grids1x1 minimum b 0.56 = 110 grids1x1 Y FV = good good good FV FV = FV noChange method 16400 b 11.82
DE ALP 1189 0.46 + > 65 65 65 grids1x1 estimate b 0.33 + > grids5x5 Y FV = poor poor good U1 U1 + XX knowledge noChange 1000 a 0.72
ES ALP 16600 6.45 + 119 11900 N/A grids1x1 estimate b 30.60 + 119 grids1x1 Y FV = poor good good FV FV = FV genuine genuine 8000 a 5.76
FI ALP 17900 6.95 = 100 10000 N/A grids1x1 estimate b 25.72 = Y FV = good good good FV FV = FV method method 9700 a 6.99
FR ALP 15400 5.98 + > 100 200 N/A grids1x1 mean a 0.76 + > N Y U1 = good good unk U1 U1 + U2 + genuine noChange 9700 a 6.99
HR ALP 7300 2.83 x > N/A N/A 56 grids1x1 minimum c 0.29 x x Unk XX x poor unk unk XX U1 x N/A N/A N/A a 0
PL ALP 14100 5.47 = N/A N/A 4663 grids1x1 estimate c 23.75 = Y FV = good good good FV FV = FV noChange noChange 7500 b 5.40
RO ALP 36100 14.02 = 0.14 0.19 N/A grids1x1 mean b 0 = 0.10 grids1x1 Y FV = good good good FV FV = FV knowledge knowledge 19100 b 13.76
SE ALP 52500 20.38 + 50000 N/A N/A 140 grids1x1 estimate b 0.71 + 250 i Y FV = good good good FV U1 + U1 + noChange genuine 9600 b 6.92
SI ALP 6785 2.63 = 21 22 N/A grids1x1 minimum c 0.11 x > Y U1 x good unk poor U1 U1 x U1 x noChange noChange 1200 c 0.86
SK ALP 24976.59 9.70 + > 1816 1816 N/A grids1x1 estimate b 9.25 + Y FV = good good good FV U1 + U1 + N/A N/A 22700 b 16.35
DE ATL 47946 7.57 + > 2798 2798 2798 grids1x1 minimum a 4.16 + > grids5x5 Y FV + poor poor good U1 U1 + U1 + noChange noChange 34600 a 6.83
DK ATL 13448 2.12 = N/A N/A N/A d 0 u Y FV = good good good FV FV x FV N/A N/A 13500 b 2.66
ES ATL 67700 10.69 + 605 60500 N/A grids1x1 estimate b 45.40 + 605 grids1x1 Y FV = good poor good FV FV + FV genuine genuine 53900 a 10.64
FR ATL 175625 27.74 + 5000 15000 N/A grids1x1 estimate b 14.86 + Y FV = good good unk FV FV + FV noChange noChange 103700 a 20.47
IE ATL 83600 13.21 = 83600 N/A N/A 2511 grids1x1 estimate b 3.73 + 7046 bfemales Y FV = good good good FV FV + FV noChange noChange 70000 b 13.82
PT ATL 5000 0.79 = N/A N/A N/A d 0 x x Y FV = good unk good FV FV = FV noChange noChange 2400 d 0.47
UK ATL 239701 37.87 = 239701 N/A N/A 21441 grids1x1 minimum b 31.86 = 11995 i Y FV = good good good FV FV = FV noChange genuine 228500 b 45.10
BG BLS 9700 75.19 = 9700 N/A N/A 92 grids1x1 minimum b 99.82 = 92 grids1x1 Y FV = good good good FV FV = FV noChange method 7700 b 77
RO BLS 3200 24.81 = 0.14 0.19 N/A grids1x1 mean b 0.18 = 0.10 grids1x1 Y FV = good good good FV FV = FV knowledge knowledge 2300 b 23
EE BOR 40900 5.12 = N/A N/A 281 grids1x1 estimate a 0.33 = Y FV = good good good FV FV = FV noChange noChange 20400 a 5.27
FI BOR 343700 43 = 7480 149600 N/A grids1x1 estimate b 93.58 = Y FV = good good good FV FV = FV noChange method 151000 a 39.02
LT BOR 64700 8.09 = N/A N/A 1670 grids1x1 estimate b 1.99 = Y FV = good good good FV FV = FV noChange noChange 67100 b 17.34
LV BOR 64589 8.08 = 64589 N/A N/A 2207 grids1x1 estimate b 2.63 = Y FV = good good good FV FV = FV noChange noChange 65400 a 16.90
SE BOR 285500 35.71 + 361968 N/A N/A 1229 grids1x1 estimate c 1.46 + 5200 i Y FV = good good good FV U2 + U2 + noChange genuine 83100 b 21.47
AT CON 36100 3.92 + 1354 N/A N/A grids1x1 minimum b 1.84 + Y FV = good unk good FV FV + FV noChange noChange 30400 a 5.21
BE CON 1200 0.13 + >> 4 9 N/A grids1x1 estimate b 0.01 = >> Y U1 + poor poor good U1 U2 + U2 - noChange noChange 300 b 0.05
BG CON 99600 10.82 = 99600 N/A N/A 446 grids1x1 minimum b 0.61 = 446 grids1x1 Y FV = good good good FV FV = FV noChange method 79700 b 13.65
CZ CON 85100 9.25 + N/A 3472 N/A grids1x1 estimate a 2.36 + Y FV = good good good FV FV + FV noChange noChange 74800 a 12.81
DE CON 143486 15.59 + > 15903 15903 15903 grids1x1 estimate a 21.66 + > grids5x5 Y FV + poor unk poor U1 U1 + U1 + noChange noChange 124000 a 21.24
DK CON 19587 2.13 + > N/A N/A N/A d 0 + > Y U1 = good unk poor U1 U1 + U2 x N/A N/A 16400 b 2.81
FR CON 83100 9.03 + 1000 5000 N/A grids1x1 mean b 4.09 + > Y Unk FV u good good unk FV U1 = U1 + noChange noChange 68000 a 11.65
HR CON 35600 3.87 x N/A N/A 793 grids1x1 minimum c 1.08 x x N Unk U1 x poor unk poor XX U1 x N/A N/A N/A a 0
LU CON N/A 0 = >> N/A N/A N/A grids1x1 estimate c 0 = >> N Unk U2 = poor poor poor U1 U2 = U2 = noChange noChange N/A d 0
PL CON 280400 30.47 = N/A N/A 49912 grids1x1 estimate c 67.99 = Y FV = good good good FV FV = FV noChange noChange 114300 b 19.58
RO CON 107100 11.64 = 0.14 0.19 N/A grids1x1 mean b 0 = 0.10 grids1x1 Y FV = good good good FV FV = FV knowledge knowledge 65100 b 11.15
SE CON 17600 1.91 + 26000 N/A N/A 94 grids1x1 estimate c 0.13 + 200 i Y FV = good good good FV U2 + U2 + noChange genuine 6100 b 1.04
SI CON 11439 1.24 = 165 166 N/A grids1x1 minimum c 0.23 = > Y U1 x good good poor U1 U1 = U1 + noChange knowledge 4800 c 0.82
ES MED 408600 64.05 + 2665 266500 N/A grids1x1 estimate b 63.70 + 2665 grids1x1 Y FV = good good good FV FV + FV genuine genuine 272800 a 60
FR MED 41600 6.52 + >> 500 1500 N/A grids1x1 estimate b 0.47 + >> Y Unk U1 - good good poor U1 U2 = U2 + N/A noChange 30600 a 6.73
GR MED 94479 14.81 + N/A N/A 69695 grids1x1 estimate b 32.99 = 1500 grids5x5 Y FV + good good good FV FV = FV noChange noChange 74900 a 16.47
HR MED 4900 0.77 x >> N/A N/A 31 grids1x1 minimum c 0.01 x x N Unk U1 x bad unk poor U2 U2 x N/A N/A N/A a 0
IT MED 33300 5.22 + 4694 7234 N/A grids1x1 estimate b 2.82 + Y FV = good good good FV FV + FV noChange genuine 32900 b 7.24
PT MED 55100 8.64 = N/A N/A N/A d 0 x x Y FV = good unk good FV FV = FV noChange N/A 43500 d 9.57
CZ PAN 5800 5.27 = N/A 129 N/A grids1x1 estimate a 3.02 + Y FV = good good good FV FV + FV noChange noChange 3200 a 3.60
HU PAN 81555 74.12 = N/A N/A 1725 grids1x1 minimum b 80.77 = Y FV = good good good FV FV = FV noChange method 71100 b 79.89
RO PAN 15100 13.72 = 0.14 0.19 N/A grids1x1 mean b 0.01 = 0.10 grids1x1 Y FV = good good good FV FV = FV knowledge knowledge 7900 b 8.88
SK PAN 7572.70 6.88 + > 346 346 N/A grids1x1 estimate b 16.20 + Y FV x good good good FV U1 = U1 = N/A N/A 6800 b 7.64
RO STE 18200 100 = 0.14 0.19 N/A grids1x1 mean b 100 = 0.10 grids1x1 Y FV = good good good FV FV = FV knowledge knowledge 13000 b 100
IT ALP 1100 0 + >> 100 114 N/A grids1x1 minimum b 0 + >> N Y FV = good good good FV U2 + U1 + noChange genuine 700 b 0
BE ATL 1900 0 + >> N/A N/A 7 grids1x1 estimate b 0 + >> N Y U1 = poor poor poor U1 U2 + U2 + noChange noChange 500 b 0
NL ATL 13900 0 + > N/A N/A 1684 grids1x1 estimate a 0 + > N Y U1 = good good good FV U1 + U2 + genuine noChange 12400 a 0
IT CON 1900 0 N N/ N/A N/A N/A N/A 0 N N/ N/A N N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A 1500 b 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 STE 18200 0MS = 0.14 0.19 grids1x1 0MS = 0.10 grids1x1 0MS = good good good 0MS MTX = FV nc nong FV A=

02/20

EEA-ETC/BD

Institution: -

Member State:

EEA-ETC/BD
EU28 MED 2XP + grids1x1 2XP + 2XP = good good good 2XP MTX + FV = nc nong XX A=

02/20

EEA-ETC/BD

Institution: -

Member State:

EEA-ETC/BD
EU28 PAN 2XP = 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 ALP 2XP + grids1x1 2XP = grids1x1 2XP = good good good 2XP MTX + U1 + nong nc U1 A=

02/20

EEA-ETC/BD

Institution: -

Member State:

EEA-ETC/BD
EU28 ATL 2XP = grids1x1 2XP + > 2XP = good poor good 2XP MTX + FV nc nong U1 A=

02/20

EEA-ETC/BD

Institution: -

Member State:

EEA-ETC/BD
EU28 BLS 0EQ = grids1x1 0EQ = grids1x1 0EQ = good good good 0EQ MTX + U1 x gen nong U1 A+

03/20

EEA-ETC/BD

Institution: -

Member State:

EEA-ETC/BD
EU28 BOR 2XP = grids1x1 2XP = grids1x1 0EQ = good good good 0EQ MTX = U2 + nong nong U2 A=

03/20

EEA-ETC/BD

Institution: -

Member State:

EEA-ETC/BD
EU28 CON 2XP = grids1x1 2XP = > 2XP = good good good 2XP MTX = U1 + nc nong U1 D

02/20

EEA-ETC/BD

Institution: -

Member State:

EEA-ETC/BD
BG CON 2XP 2XP 2XP MTX U1 + U1 0/2

04/20

Bulgarian Biodiversity Foundation

Institution: Bulgarian Biodiversity Foundation

Member State: BG

Bulgarian Biodiversity Foundation
BG ALP 2XP > i 2XP poor 2XP MTX U1 + U1 0/2

04/20

Bulgarian Biodiversity Foundation

Institution: Bulgarian Biodiversity Foundation

Member State: BG

Bulgarian Biodiversity Foundation
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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.