Article 17 web tool

Log in

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, Mammals, Martes martes, All bioregions. Annexes N, N, Y. 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 10000 42000 N/A i minimum 1904 N/A N/A grids1x1 minimum
BG 690 1620 N/A i minimum N/A N/A N/A N/A
DE N/A N/A N/A i estimate 40 40 40 grids10x10 estimate
ES N/A 9200 N/A i estimate 92 N/A N/A grids10x10 estimate
FI 300 1000 N/A i estimate N/A N/A N/A estimate
FR N/A N/A N/A mean 6900 27200 N/A area mean
HR N/A N/A 999 i mean N/A N/A N/A N/A
IT 227 681 N/A i estimate N/A N/A N/A N/A
PL N/A N/A 2000 i minimum N/A N/A N/A N/A
RO 6317 7019 N/A i minimum N/A N/A N/A N/A
SE 3000 5000 4000 i estimate N/A N/A N/A N/A
SI N/A N/A 500 i minimum N/A N/A 23 grids1x1 minimum
SK 10000 50000 N/A i estimate N/A N/A N/A N/A
BE 145 305 N/A i estimate 76 91 83 grids1x1 estimate
DE N/A N/A N/A i estimate 132 142 137 grids10x10 estimate
DK N/A N/A N/A N/A N/A N/A N/A
ES 176 17600 N/A i estimate 176 N/A N/A grids10x10 estimate
FR N/A N/A N/A mean 100200 181600 N/A area mean
IE 2330 3852 3043 i interval N/A N/A N/A N/A
NL 750 1500 N/A i estimate N/A N/A N/A N/A
PT N/A N/A N/A N/A N/A N/A N/A
UK 2123 9620 N/A i interval N/A N/A N/A N/A
BG N/A N/A N/A i minimum N/A N/A N/A N/A
EE 10000 50000 N/A i estimate N/A N/A N/A estimate
FI 14000 18000 N/A i estimate N/A N/A N/A N/A
LT 10000 17000 N/A i minimum N/A N/A N/A N/A
LV 21000 24000 N/A i estimate N/A N/A N/A N/A
SE 15000 35000 25000 i estimate N/A N/A N/A N/A
AT 4000 18000 N/A i minimum 1828 N/A N/A grids1x1 minimum
BE 2000 4000 N/A i estimate 690 N/A N/A grids1x1 estimate
BG 290 680 N/A i minimum N/A N/A N/A N/A
CZ 35000 45000 N/A i estimate N/A N/A N/A N/A
DE N/A N/A N/A i estimate 1382 1395 1388.50 grids10x10 estimate
DK N/A N/A N/A N/A N/A N/A N/A
FR N/A N/A N/A mean 95900 165400 N/A area mean
HR N/A N/A 3676 i mean N/A N/A N/A N/A
IT 47 141 N/A i minimum N/A N/A N/A N/A
LU 425 1575 N/A i estimate 1963 2066 N/A grids1x1 estimate
PL N/A N/A 16000 i minimum N/A N/A N/A N/A
RO 6773 7526 N/A i minimum N/A N/A N/A N/A
SE 1200 1600 1400 i estimate N/A N/A N/A N/A
SI 500 N/A N/A i minimum N/A N/A 53 grids1x1 minimum
ES N/A 15100 N/A i estimate 151 N/A N/A grids10x10 estimate
FR N/A N/A N/A mean 2400 11500 N/A area mean
GR N/A N/A N/A N/A N/A N/A N/A
HR N/A N/A 34 i mean N/A N/A N/A N/A
IT 313 939 N/A i 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 1500 2500 N/A i estimate N/A N/A N/A N/A
HU N/A N/A 14180 i estimate N/A N/A N/A N/A
RO 443 493 N/A i minimum N/A N/A N/A N/A
SK 2000 8000 N/A i estimate N/A N/A N/A N/A
RO 159 177 N/A i 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 57100 16.22 = 10000 42000 N/A i minimum c 33.75 x Y FV x good good good FV FV x FV noChange noChange 50500 b 19.03
BG ALP 22300 6.33 = 22300 690 1620 N/A i minimum b 1.50 = 1620 i Unk FV x unk unk unk XX FV = XX knowledge knowledge 12600 b 4.75
DE ALP 4155 1.18 = 4155 N/A N/A N/A i estimate b 0 = 40 grids10x10 Y FV = good good good FV FV = FV noChange noChange 4600 d 1.73
ES ALP 15200 4.32 = N/A 9200 N/A i estimate b 5.97 x 9200 i Y XX x poor poor poor U1 U1 x FV method method 7100 a 2.68
FI ALP 2200 0.62 = 300 1000 N/A i estimate b 0.84 = Y FV u good good good FV FV = FV noChange method 500 a 0.19
FR ALP 27200 7.73 = N/A N/A N/A mean b 0 = Y Unk FV = good good good FV FV = FV noChange noChange 26500 b 9.98
HR ALP 6348.92 1.80 = N/A N/A 999 i mean b 1.30 x x Y FV x good unk good FV FV x N/A N/A N/A d 0
IT ALP 42600 12.10 + 227 681 N/A i estimate b 0.59 + Y FV = good good good FV FV + FV noChange noChange 22400 b 8.44
PL ALP 13400 3.81 = N/A N/A 2000 i minimum b 2.60 = Y FV = good good good FV FV = FV noChange noChange 6700 b 2.52
RO ALP 47300 13.43 x x 6317 7019 N/A i minimum a 8.66 x x Y FV x good good good FV FV + FV N/A N/A 39200 a 14.77
SE ALP 96600 27.44 = 96600 3000 5000 4000 i estimate b 5.19 = 4000 i Y FV = good good good FV FV = FV noChange noChange 86800 b 32.71
SI ALP 7656 2.17 = 7656 N/A N/A 500 i minimum c 0.65 + Y FV = good good good FV FV + XX genuine genuine 2100 c 0.79
SK ALP 10011.10 2.84 = 10000 50000 N/A i estimate c 38.95 = Y FV x good good good FV U1 = U1 = N/A N/A 6400 c 2.41
BE ATL 18000 3.61 + 145 305 N/A i estimate b 1.17 + > N Y U1 = good poor poor U1 U1 + U2 + genuine genuine 4900 b 1.39
DE ATL 70501 14.13 = 70501 N/A N/A N/A i estimate b 0 = grids10x10 Y FV = unk good good FV FV = FV noChange noChange 13200 d 3.75
DK ATL N/A 0 = N/A N/A N/A d 0 u x Unk Unk XX u good unk unk XX XX FV N/A N/A N/A c 0
ES ATL 37200 7.46 = 176 17600 N/A i estimate b 46.41 x 17600 i Y XX x poor poor poor U1 U1 x FV method method 17900 a 5.08
FR ATL 181600 36.41 = N/A N/A N/A mean b 0 = < Y Unk FV = good good unk FV FV = FV noChange noChange 183900 b 52.19
IE ATL 68500 13.73 + 38200 2330 3852 3043 i interval a 15.89 + 3043 i Y FV + good good good FV FV + FV noChange noChange 51600 a 14.64
NL ATL 24100 4.83 + 750 1500 N/A i estimate c 5.87 + Y FV + good good good FV FV + U2 + genuine noChange 21300 a 6.04
PT ATL 1400 0.28 x x N/A N/A N/A d 0 x x Unk XX u unk unk unk XX XX XX noChange N/A 700 c 0.20
UK ATL 97496 19.55 + 65998 2123 9620 N/A i interval b 30.66 + Y FV = good good good FV FV + FV noChange noChange 58900 b 16.71
BG BLS 2900 100 x 2900 N/A N/A N/A i minimum c 0 x x Unk XX x unk unk unk XX XX x XX noChange noChange 2100 b 100
EE BOR 54600 6.24 + 10000 50000 N/A i estimate c 28.04 = Y FV = good good good FV FV + FV noChange noChange 56100 a 9.68
FI BOR 311600 35.60 = 14000 18000 N/A i estimate b 14.95 = Y FV = good good good FV FV = FV noChange method 85800 a 14.80
LT BOR 64700 7.39 = 10000 17000 N/A i minimum b 12.62 = x Y FV = good good good FV FV = FV noChange noChange 68400 c 11.80
LV BOR 64589 7.38 = 21000 24000 N/A i estimate b 21.03 x 20000 i Y FV = good good good FV FV = FV noChange noChange 5700 d 0.98
SE BOR 379700 43.38 = 379700 15000 35000 25000 i estimate b 23.36 = 25000 i Y FV = good good good FV FV = FV noChange noChange 363600 b 62.73
AT CON 38100 3.60 = 4000 18000 N/A i minimum b 13.05 x Y FV x good good good FV FV x FV noChange noChange 32500 b 4.34
BE CON 15300 1.45 + 2000 4000 N/A i estimate b 3.56 + Y U1 = good good good FV U1 + U1 + noChange knowledge 11500 b 1.54
BG CON 25500 2.41 = 25500 290 680 N/A i minimum b 0.58 = 680 i Unk XX x unk unk unk XX XX = XX knowledge knowledge 7800 b 1.04
CZ CON 85800 8.11 = 35000 45000 N/A i estimate c 47.45 x x Y FV = good unk good FV FV = FV noChange noChange 77700 a 10.39
DE CON 283617 26.80 = 283617 N/A N/A N/A i estimate c 0 = grids10x10 Y FV = good unk good FV FV = FV noChange noChange 178500 d 23.86
DK CON N/A 0 = N/A N/A N/A d 0 u x Unk Unk XX u good unk unk XX XX FV N/A N/A N/A c 0
FR CON 165400 15.63 = N/A N/A N/A mean b 0 = Y Unk FV = good good good FV FV = FV noChange noChange 175000 b 23.39
HR CON 23523.91 2.22 = N/A N/A 3676 i mean b 4.36 x x Y FV x good unk good FV FV x N/A N/A N/A d 0
IT CON 16600 1.57 = x 47 141 N/A i minimum c 0.11 x x Y FV = good unk good FV FV = FV noChange noChange 4500 b 0.60
LU CON 4000 0.38 = 4000 425 1575 N/A i estimate b 1.19 = Y U1 - good good poor U1 U1 - U1 = noChange knowledge 3100 b 0.41
PL CON 308100 29.12 = N/A N/A 16000 i minimum b 18.98 = Y FV = good good good FV FV = FV noChange noChange 185600 b 24.81
RO CON 55300 5.23 x x 6773 7526 N/A i minimum a 8.48 x 7526 i Y FV x good good good FV FV + FV N/A N/A 46400 a 6.20
SE CON 24700 2.33 = 24700 1200 1600 1400 i estimate b 1.66 = 1400 i Y FV = good good good FV FV = FV noChange noChange 21500 b 2.87
SI CON 12215 1.15 = 500 N/A N/A i minimum c 0.59 + Y FV = good good good FV FV + XX genuine genuine 4000 c 0.53
ES MED 30900 25.97 = N/A 15100 N/A i estimate b 91.96 x 15100 i Y XX x poor poor poor U1 U1 x FV method method 11500 a 17.69
FR MED 11500 9.67 + N/A N/A N/A mean b 0 = Y Unk FV = good good good FV FV + FV noChange noChange 17500 b 26.92
GR MED 3801 3.19 x x N/A N/A N/A d 0 x x Unk XX = unk unk unk XX XX XX noChange noChange 4200 c 6.46
HR MED 978.35 0.82 = N/A N/A 34 i mean c 0.41 x x Unk FV x good unk good FV FV x N/A N/A N/A d 0
IT MED 70200 59 = 313 939 N/A i estimate b 7.62 = Y FV = good good good FV FV = FV noChange noChange 31100 b 47.85
PT MED 1600 1.34 x x N/A N/A N/A d 0 x x Unk XX u unk unk unk XX XX XX noChange noChange 700 c 1.08
CZ PAN 5900 8.04 = 1500 2500 N/A i estimate c 9.24 x x Y FV = good unk good FV FV = FV noChange noChange 3300 a 15.07
HU PAN 63197 86.14 = N/A N/A 14180 i estimate c 65.50 u Y U1 = good unk poor U1 U1 = XX noChange noChange 15800 c 72.15
RO PAN 2700 3.68 x x 443 493 N/A i minimum a 2.16 x 493 i Y FV x good good good FV FV + FV N/A N/A 1500 a 6.85
SK PAN 1568.05 2.14 = > 2000 8000 N/A i estimate c 23.10 = > Y U1 = good poor poor U1 U1 = U1 = N/A N/A 1300 c 5.94
RO STE 2900 100 x x 159 177 N/A i minimum a 100 x 177 i Y FV x good good good FV FV + FV N/A N/A 2500 a 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 352071 1 + ≈ 352071 38633 120019 77026 i 1 x ≈ 77026 i 2XR x good good good 2XR MTX + FV nc nong FV A=

01/20

EEA-ETC/BD

Institution: -

Member State:

EEA-ETC/BD
EU28 ATL 498797 1 + ≈ 436999 5524 32877 19152 i 2GD + ≈ 19152 i 2GD + good good good 2GD MTX + FV nc nong FV A=

01/20

EEA-ETC/BD

Institution: -

Member State:

EEA-ETC/BD
EU28 BLS 2900 1 x ≈ 2900 0MS x 0MS x unk unk unk 0MS MTX x XX nc nong XX D

02/20

EEA-ETC/BD

Institution: -

Member State:

EEA-ETC/BD
EU28 BOR 875189 1 = ≈ 875189 70000 144000 107000 i 1 = ≈ 10700 i 0EQ = good good good 0EQ MTX = FV nc nong FV A=

02/20

EEA-ETC/BD

Institution: -

Member State:

EEA-ETC/BD
EU28 CON 1058155 1 = ≈ 1058155 69911 98698 84304 i 2XR x ≈ 84304 i 2XR = good good good 2XR MTX = FV nc nong FV A=

01/20

EEA-ETC/BD

Institution: -

Member State:

EEA-ETC/BD
EU28 MED 118979 1 = ≈ 118979 7897 16973 8210 i 2GD x x 2GD = unk unk unk 2GD MTX x FV = nong nong XX D

01/20

EEA-ETC/BD

Institution: -

Member State:

EEA-ETC/BD
EU28 PAN 73365 1 = ≈ 73521 18123 25173 21648 i 1 x > 21648 i 2XP = good unk poor 2XP MTX = XX nong nong XX D

01/20

EEA-ETC/BD

Institution: -

Member State:

EEA-ETC/BD
EU28 STE 2900 0MS x x 159 177 168 i 0MS x ≈ 177 i 0MS x good good good 0MS MTX + FV nc nong FV 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.