Article 12 web tool

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Population status and trends at the EU and Member State levels

The Article 12 web tool provides an access to EU assessments and Member States’ data compiled as part of the Habitats Directive - Article 12 reporting process. The EU assessments have been carried out in EU 27 for the period 2008-2012 and in EU28 for the period 2013-2018.

Choose period, species and if relevant sub-specific unit.

Once a selection has been made the breeding distribution of the species can be visualized in a map.

The ‘Data sheet info’ includes notes for each assessment per species.

The ‘Audit trail’ includes the methods used for the EU assessment and justifications for decisions made by the assessors.

Legend
+
Increasing
=
Stable
x
Unknown
-
Decreasing
F
Fluctuating
u
Uncertain

Codes ‘PT’, ‘ES ‘correspond to Portugal mainland (excluding Azores-PTAC and Madeira-PTMA) and Spain mainland (excluding Canary Islands-ESIC) respectively.
Similarly ‘UK’ stands for the United Kingdom of Great Britain and Northern Ireland (excluding Gibraltar-GIB).
The data from delayed delivery by Romania were not used for the EU population status assessment.

Current selection: 2013-2018, Sylvia atricapilla. Show all
Data from Member States reports
MS Breeding population Breeding distribution Winter population Breeding area from
gridded maps (km2)
Ssp. / subsp. unit
Population size Population trend Distribution size Distribution trend Population size Population trend
Min
Mouse-over texts for entries below show "Method used" reported for population size as a whole:
estimatePartial - based mainly on extrapolation from limited amount of data
estimateExpert - based mainly on expert opinion, with very limited data
absentData - insufficient or no data available
completeSurvey - complete survey or statistically robust estimate
Country Method used
AT estimatePartial
BE estimateExpert
BG estimatePartial
CZ completeSurvey
DE completeSurvey
DK estimatePartial
EE estimateExpert
ES estimatePartial
ESIC estimateExpert
FI completeSurvey
FR estimatePartial
GIB estimatePartial
GR estimatePartial
HR estimateExpert
HU completeSurvey
IE completeSurvey
IT estimateExpert
LT estimatePartial
LU estimatePartial
LV completeSurvey
NL completeSurvey
PL completeSurvey
PT estimatePartial
PTAC estimatePartial
PTMA estimatePartial
RO completeSurvey
SE estimatePartial
SI completeSurvey
SK estimatePartial
UK estimatePartial
Max
Best value Unit Type est. Change % MS ST period ST direction ST magnitude LT period LT direction LT magnitude
Mouse-over texts for entries below show "Method used" reported for distribution surface area (in km²):
estimatePartial - based mainly on extrapolation from limited amount of data
estimateExpert - based mainly on expert opinion, with very limited data
absentData - insufficient or no data available
completeSurvey - complete survey or statistically robust estimate
Country Method used
AT completeSurvey
BE completeSurvey
BG estimatePartial
CZ completeSurvey
DE estimateExpert
DK completeSurvey
EE estimatePartial
ES completeSurvey
ESIC absentData
FI completeSurvey
FR completeSurvey
GIB completeSurvey
GR estimatePartial
HR estimateExpert
HU completeSurvey
IE completeSurvey
IT estimatePartial
LT estimatePartial
LU estimatePartial
LV completeSurvey
NL completeSurvey
PL absentData
PT estimatePartial
PTAC estimatePartial
PTMA completeSurvey
RO completeSurvey
SE estimatePartial
SI absentData
SK estimatePartial
UK completeSurvey
Area
% MS ST period ST direction ST magnitude LT period LT direction LT magnitude Min
Mouse-over texts below show "Method used" reported for population size value(s):
estimatePartial - based mainly on extrapolation from limited amount of data
estimateExpert - based mainly on expert opinion, with very limited data
absentData - insufficient or no data available
completeSurvey - complete survey or statistically robust estimate
Country Method used
AT
BE
BG
CZ
DE
DK
EE
ES
ESIC
FI
FR
GIB
GR
HR
HU
IE
IT
LT
LU
LV
NL
PL
PT
PTAC
PTMA
RO
SE
SI
SK
UK
Max
Best value Unit Type est. Change % MS ST period ST direction ST magnitude LT period LT direction LT magnitude Status Distrib. % MS
AT
800000
1200000
N/A
p
estimate
noChange
2.3
2007-2018 = N/A | N/A | (5) 1981-2018 x N/A 93400 2.6 2007-2018 = N/A | N/A | (6) 1981-2018 x N/A 84600 2.4
BE
285700
476200
381000
p
estimate
genuine
0.9
2008-2018 + 23 | 42 | (32) 1973-2018 + 196 | 393 | (295) # 30659 # 0.8 2008-2018 = N/A 1973-2018 = N/A 31600 0.9
BG
500000
800000
N/A
p
estimate
noChange
1.5
2000-2018 F N/A 1980-2018 = 5 | 10 | (N/A) 102178 2.9 2000-2018 = 5 | 10 | (N/A) 1980-2018 = 5 | 10 | (N/A) 106200 3.0
CZ
1000000
2000000
N/A
p
estimate
noChange
#
3.5
2007-2018 = N/A | N/A | (0) 1982-2018 + N/A | N/A | (2) # 85500 2.4 2002-2016 = N/A | N/A | (0) 1986-2016 = N/A | N/A | (0) # 80200 2.2
DE
4650000
6150000
N/A
p
estimate
genuine
12.5
2004-2016 + 52 | 65 | (58) 1980-2016 + 41 | 180 | (N/A) 356693 9.9 2004-2016 = -10 | 10 | (N/A) 1980-2016 = -30 | 40 | (N/A) 371600 10.4
DK
N/A
N/A
486672
p
estimate
genuine
#
1.1
2006-2017 + 10.48 | 33.29 | (21.39) 1980-2017 + 151.28 | 185.67 | (167.94) # 58400 1.6 1996-2017 = N/A | N/A | (2.81) 1974-2017 = N/A | N/A | (-8.34) # 58200 1.6
EE
300000
500000
N/A
p
estimate
noChange
0.9
2007-2018 = 2 | 9 | (N/A) 1983-2018 + 26 | 39 | (N/A) 54100 1.5 2007-2018 = -2 | 0 | (N/A) 1980-2018 = 7 | 9 | (N/A) 25200 0.7
ES
2739983
3311452
N/A
p
interval
genuine
#
7.0
2007-2018 + N/A 1980-2018 + N/A 208832 5.8 2007-2018 + 5.7 | N/A | (N/A) 1980-2018 + N/A | 39.4 | (N/A) 218000 6.1
ESIC
10000
20000
N/A
p
minimum
noInfo
#
2007-2018 x N/A 1980-2018 x N/A # 6700 0.2 2007-2018 x N/A 1980-2018 x N/A # 8200 0.2
FI
122233
176568
150354
p
mean
genuine
0.3
2007-2018 + 65 | 123 | (92) 1980-2018 + 57 | 233 | (129) 118100 3.3 N/A N/A N/A 1980-2010 + 34 | 34 | (N/A) 118100 3.3
FR
5000000
8000000
N/A
p
estimate
method
15.1
2007-2018 + N/A | N/A | (11.8) 2001-2018 + N/A | N/A | (24) 549200 15.3 2007-2017 = N/A 1985-2017 = N/A 543300 15.2
GIB
101
250
250
p
estimate
noChange
2001-2018 = 0 | 0 | (0) 1980-2018 + 10 | 20 | (20) 3 2016-2018 = 0 | 0 | (0) 1980-2018 + 10 | 20 | (20) 300
GR
100000
150000
N/A
p
estimate
knowledge
0.3
2007-2018 + N/A | N/A | (199) 1980-2018 = N/A | N/A | (0) 98600 2.8 2007-2018 x N/A 1980-2018 x N/A 89900 2.5
HR
2000000
2500000
N/A
p
estimate
N/A
5.2
2007-2018 x N/A 1980-2018 x N/A 56561 1.6 2007-2018 = N/A 1980-2018 x N/A 79500 2.2
HU
1056000
1104000
N/A
p
interval
knowledge
#
2.5
2007-2018 + 11 | 33 | (N/A) 1980-2018 + 73 | 110 | (N/A) # 93030 2.6 2007-2018 = N/A 1980-2018 = N/A 94600 2.6
IE
366335
855733
586216
i
interval
genuine
#
N/A
2006-2016 + 340.7 | 458.6 | (396.5) 1980-2016 x N/A 75400 2.1 2006-2016 + N/A | N/A | (129) 1972-2016 + N/A | N/A | (738.2) # 74400 2.1
IT
2000000
5000000
N/A
p
estimate
noChange
8.1
2000-2014 + 10 | 15 | (N/A) 1993-2018 = N/A 331200 9.2 2007-2018 = N/A 1993-2018 + 5 | 10 | (N/A) 328400 9.2
LT
200000
350000
N/A
p
estimate
genuine
0.6
2013-2018 + 5 | 10 | (N/A) 1980-2018 = 0 | 0 | (N/A) 73500 2.0 2006-2018 = 0 | 0 | (N/A) 1980-2018 = 0 | 0 | (N/A) 71100 2.0
LU
25000
30000
N/A
p
estimate
noChange
#
2007-2018 = 0 | 0 | (N/A) 1980-2018 = N/A 1517 # 2007-2018 = 0 | 0 | (N/A) 1980-2018 = 0 | 0 | (N/A) 2400
LV
283086
370453
323836
p
interval
method
0.8
2005-2018 + 57.1 | 139.4 | (94.2) 1995-2018 + 128.21 | 463.55 | (N/A) 66500 1.9 2000-2013 = N/A | N/A | (5) 1980-2017 + N/A | N/A | (24) 64700 1.8
NL
300000
500000
N/A
p
estimate
knowledge
0.9
2006-2017 + 48 | 61 | (55) 1984-2017 + 194 | 263 | (226) 44700 1.2 2000-2015 = N/A | N/A | (-0.22) 1977-2015 = N/A | N/A | (6.17) 40400 1.1
PL
4799000
5346000
N/A
p
interval
genuine
11.7
2007-2018 + 11 | 23 | (17) 1980-2018 x N/A # N/A N/A 2007-2018 x N/A 1980-2018 x N/A N/A N/A
PT
1000000
5000000
N/A
p
estimate
noChange
#
6.9
2004-2018 + N/A 1980-2018 + N/A # 80700 # 2.2 2005-2018 = N/A | N/A | (-7) 1980-2018 = N/A # 79300 2.2
PTAC
349284
819438
493190
p
mean
method
#
1.1
2007-2017 - 0.1 | 50 | (N/A) 1980-2018 x N/A # 7400 # 0.2 2008-2018 = N/A 1980-2018 x N/A # 7400 0.2
PTMA
50000
100000
N/A
p
estimate
noChange
#
0.2
2008-2018 + 56 | 92 | (N/A) 1980-2018 = N/A 1800 2001-2018 = N/A | N/A | (1) 1980-2018 x N/A | N/A | (1) 1800
RO
2130766
2639637
N/A
p
interval
method
5.5
2008-2018 u 0 | 5 | (N/A) 1980-2018 x N/A 222800 6.2 2007-2018 = N/A 1980-2018 = N/A 218800 6.1
SE
1366000
1503000
1440000
p
estimate
genuine
3.3
2007-2018 + 10 | 21 | (16) 1980-2018 + 131 | 172 | (150) 451800 12.6 2007-2018 + 5 | 15 | (10) 1980-2018 + 10 | 30 | (20) 451600 12.7
SI
569000
781000
N/A
p
estimate
genuine
1.6
2008-2018 - N/A | N/A | (-13.2) 1980-2018 x N/A 14100 # 0.4 2007-2018 x N/A 1980-2018 = N/A | N/A | (8) # 12100 0.3
SK
800000
1000000
N/A
p
estimate
noChange
2.1
2007-2018 = N/A 1980-2018 = N/A 49020 1.4 2007-2018 = N/A 1980-2018 = N/A 52200 1.5
UK
N/A
N/A
1665895
p
estimate
genuine
#
3.9
2004-2016 + N/A | N/A | (70.25) 1980-2016 + N/A | N/A | (248.34) # 255200 7.1 1989-2009 + 20.09000 | 20.09000 | (N/A) 1970-2009 + 20.28 | 20.28 | (N/A) 253700 7.1
EU population status assessments
Breeding population Breeding distribution Winter population EU population
status
Contribution
to Target 1
Season Previous status Ssp. / subsp. unit
Population size (min) Population size (max) Unit Short-term trend Long-term trend Area Short-term trend Long-term trend Population size (min) Population size (max) Unit Short-term trend Long-term trend Breeding Wintering
EU28 32800000 51100000 p + + 3360000 NE NE Secure A B Secure Sylvia atricapilla