Tarnava Mare 2017 Biodiversity Survey Summary Report
Report editor & lead scientist: Dr Bruce Carlisle – Geography & Environmental Sciences, Northumbria University.
Science team: Zuni Askins, Silvia Cojocaru, Graham Forbes, Sian Green, Paul Leafe, Chris Mackin, Cecilia Montauban, James O’Neill, Huma Pearce, Sophie Perry, Peter Thomas. Project leader: Toby Farman. Assisted by: Bogdan Ciortan, Paul Hangan, Mihaela Hojbota, Dragos Luntraru, Valentin-Ioan Marcos, Bogdan-Mihai Mehedin, Alin-Marius Nicula, Silviu Simula, Ovidiu Tanasa, Daniela Vasilache. With thanks to all the staff at Fundatia ADEPT, all the dissertation students and volunteers.
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Contents 1.0 Introduction....................................................................................................................................... 2
2.0 Methods ............................................................................................................................................ 4
2.1 Farmer interviews ......................................................................................................................... 5
2.2 Land use ........................................................................................................................................ 5
2.3 Grassland plants ............................................................................................................................ 6
2.4 Grassland butterflies ..................................................................................................................... 6
2.5 Birds ............................................................................................................................................... 7
2.6 Small mammals ............................................................................................................................. 7
2.7 Large mammals ............................................................................................................................. 7
2.8 Bats ................................................................................................................................................ 8
2.9 Orthoptera .................................................................................................................................... 8
3.0 Vital statistics .................................................................................................................................... 9
4.0 Farmer interviews ........................................................................................................................... 19
5.0 Grassland plants .............................................................................................................................. 25
6.0 Grassland butterflies ....................................................................................................................... 31
7.0 Birds ................................................................................................................................................. 35
8.0 Small mammals ............................................................................................................................... 41
9.0 Large Mammals ............................................................................................................................... 43
9.1 Camera Trap Survey .................................................................................................................... 43
9.2 Observation of large mammal signs ............................................................................................ 45
10.0 Orthoptera .................................................................................................................................... 47
11.0 Site Trends ..................................................................................................................................... 49
12.0 References ..................................................................................................................................... 50
Appendix 1 ............................................................................................................................................ 51
Appendix 2 ............................................................................................................................................ 53
Appendix 3 ............................................................................................................................................ 59
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1.0 Introduction This report summarises the data gathered by Operation Wallacea’s Transylvania project during the
summer of 2017. This was the fifth year of the project, based on an annual survey in the Tarnava
Mare Natura 2000 site to assess the effectiveness of maintaining the traditional agricultural practices
in protecting this outstanding landscape and its species. The Operation Wallacea surveys provide
annual data on a range of biodiversity and farming criteria. These data can then be used by Fundatia
ADEPT, a Romania-based NGO, to help guide their farming and conservation initiatives.
The report gives a snapshot of the 2017 situation in terms of agriculture and biodiversity. Data from
previous years are shown for comparison where appropriate. Changes in the data over a period of
several years can be used to reveal how the biodiversity of Tarnava Mare is changing, for example in
response to changing agricultural practices. Caution must be used when comparing differences
between 2017 and previous years, as there are a variety of factors which can cause the numbers to
be different, including slight changes to the methodology (see section 2), differences in the dates of
the surveys, differences in climate and weather and natural population fluctuations.
While it is still too early in the project to confidently investigate change over time, the data from the
first five years can be used to give a first warning that significant changes may be occurring, or
reassurance that the biodiversity is stable. Also the data can start to be used to investigate spatial
variation. For example, biodiversity and land use of the surveyed villages can be compared to
investigate the influence of land cover (as a function of land use) on the composition and abundance
of species.
Section 2 “Methods” outlines the fieldwork methods used. Section 3 “Vital Statistics” presents a few
key indicator figures, to give a very brief overview of the data and to compare the surveyed villages.
Sections 4 to 9 give a more detailed summary of the data gathered by each survey team.
Key messages from this year’s annual report are given on the next page.
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KEY MESSAGES
There are many substantial increases and decreases in a wide variety of taxa, as
well as taxa that have not changed.
Much of this will be natural fluctuation or “noise” in the data.
Some changes could be early warning signs of important changes to biodiversity
and need to be followed closely in coming years.
The key messages after 5 years of survey are:
Signs of farming changes to more intense livestock farming and less
hay production, now and in the future, but variation between
villages
2016 signs of a general trend of declining indicator plant abundance
have not continued in 2017
2016 and 2017 were good years for butterfly diversity, reversing
some declines seen in 2015
Generally stable or increasing grassland bird populations
Small mammal population crash seen in 2015 has been reversed in
2016 and 2017
No sites with consistent trends in plant, butterfly and bird
populations
The farming may be changing but there is no clear evidence of impact on
biodiversity yet. This can be due to a delayed response from the species, and/or
the need for several years of data to reliably identify such changes from the
“noise” of natural fluctuations and other factors.
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2.0 Methods Some adjustments to methodologies were made in the second year in response to the experience
gained during the first year of the project. See the 2014 summary report for further detail of these
adjustments. Consequently 2013 data is not always directly comparable to data from subsequent
years. The methods used in 2014 have remained the same in subsequent years to a great extent.
However, the order in which the villages were surveyed changed slightly in 2015 and 2016, with
Apold and Malancrav being switched around in 2015 and Crit not being surveyed in 2016, for
logistical reasons. The weather conditions vary from year to year. The start of the 2015 fieldwork
season was particularly cool and wet, especially while surveying at Richis and Nou Sasesc. This had an
impact on the number of surveys that could be undertaken, and also had an influence on vegetation
phenology and the abundance and activity of wildlife, particularly small mammals. Weather in 2016
and 2017 was more “normal”. Fieldwork in 2017 was undertaken over an 8 week period from 14
June to 8 August 2017, in eight villages within the Tarnava Mare Natura 2000 site. In total, 48 days
fieldwork were undertaken, with 6 days per village, although rain restricted survey work on some
days. Table 2.1 shows the villages and the respective survey dates for the five years. Note the shifts
in the villages’ survey dates.
Table 2.1. Survey schedules.
June 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
2013 Crit
2014 Richis Nou Sasesc
2015 Richis Nou Sasesc
2016 Richis No
2017 Richis (+ 15 June) Nou Sasesc Me
July 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
2013 Mesendorf Viscri Malancrav
2014 Mesendorf Viscri
2015 Mesendorf Viscri
2016 Nou Sasesc Mesendorf Viscri
2017 Mesendorf Viscri Crit
July 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31
2013 Nou Sasesc Richis Crit Viscri
2014 Crit Daia Ma
2015 Crit Daia Apold
2016 Viscri Daia Malancrav
2017 Crit Daia Malancrav
August 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
2013 Vi Mesendorf
2014 Malancrav Apold
2015 Apold
2016 Malancrav Apold
2017 Ma Apold
Much of the survey work is carried out along “the transects” which are 3 linear routes per village.
Each route was selected with the aim of traversing land covers and land uses that are representative
of the village’s surroundings. The routes are constrained by accessibility. The “central transect” is
approximately 4km long and runs along the valley floor, upstream and downstream of the village.
This transect runs through the village, usually alongside a road, near to the stream, and through
more intensely farmed land. “West” and “east” transects are approximately 6km long and each takes
a roughly semi-circular route from the valley floor up the valley sides, usually into less intensely
farmed land, meadow grassland, pasture and woodland. There have been no significant changes to
the transect locations over the five years.
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There are seven main survey teams covering farmer interviews, grassland plants and land use
mapping, grassland butterflies, birds, small mammals, large mammals and bats. An Orthoptera
survey was also undertaken this year, after a trial run in 2016. Further details of the methods of each
team, and any notable alteration of methods, are given in the following sections.
2.1 Farmer interviews An extensive set of farm interviews were carried out in 2017, for the second year (the first being
2015). In 2017 a total of 137 interviews were completed, with between 6 to 22 interviews at each
village. Very few interviews took place in 2016 due to staff injury. In 2015 a total of 153 interviews
were completed, with between 9 to 29 interviews at each village. 41 and 48 interviews were
completed in 2013 and 2014 respectively. The number of farmers interviewed varied amongst the
villages, depending on the presence and effectiveness of a local person to make contacts, the
willingness of farmers to participate, and how busy the farmers were. There was no strategy to
selecting farmers – the participants were whoever was willing and available to be interviewed. The
number of interviews in 2015 and 2017 is noticeably higher than other years. This is primarily due to
the time and persistent effort put into arranging and carrying out the interviews. The years with
small sample sizes mean year-on-year farm statistics derived from the interviews are unreliable.
However, data from the 2015 and 2017 interviews will be much more representative of each village’s
farm characteristics.
The farmer interviews involved asking a fixed set of questions covering topics such as farm
characteristics (size, age etc.), crops grown, livestock, hay cutting dates and so on. The questions
asked in 2013 and 2014 have been repeated in all subsequent years. Additional questions were
added from 2015 onwards, to investigate mowing technique, use of communal grazing and future
plans. These additional questions were actually first trialled during the second half of the 2014
season.
2.2 Land use A land use survey was first undertaken in 2013, and then repeated in 2016 at the first four villages,
and 2017 (first 5 villages). The transect routes were walked and notes were taken of the land use
types adjacent to the walked route, marking the transition points with GPS, using a fixed set of land
use types. A high resolution satellite image (Worldview 2 imagery) was also consulted and annotated
to help determine the extent of each land use and to survey areas not adjacent to the route if their
land use type was discernible from a distance.
The GPS points are being loaded into a GIS (Geographic Information System), displayed over the high
resolution satellite image, and used as a template for digitising polygons depicting the land use
around each transect. There is ongoing work to produce a land use map of the whole Tarnava Mare
Natura 2000 site, using image processing of satellite imagery. Although a preliminary map has been
produced and provided to Fundatia ADEPT, the mapping technique continues to need refinement. It
is proving challenging to distinguish between several of the land use types from satellite imagery.
Also, it can be difficult to ascertain some land uses in the field. For example, it can be difficult to tell
whether an area of grassland continues to be mown, is used for occasional low intensity grazing, or is
abandoned. Results of this analysis will be reported independently.
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2.3 Grassland plants The plant team re-surveyed the sites from previous years, using the same methods. Apold, Crit and
Daia have now been surveyed over four years, while the other 5 villages have 5 years of surveys. To
decide on locations of sites in 2014 and 2013, grassland was visually partitioned into high, medium
and low nature value (HNV, MNV, LNV) categories based on indicators such as the presence of farm
weed species, evidence of current use, shrub encroachment, and abundance and variety of
wildflowers. On each transect a minimum of six plot locations were identified with the target of 2
HNV, 2 MNV and 2 LNV plots. This was not always achieved due to the prevalence or absence of
grassland categories.
Each grassland plant plot is 50m by 5m. The surveyors walk the length of the plot counting the
number of individuals of 30 species defined as indicators of HNV dry grassland in Fundatia ADEPT’s
guide “Indicator Plants of the High Nature Value Dry Grasslands of Transylvania” (Akeroyd &
Bădărău, 2012). Betony was also counted as, although it is an indicator for damp grasslands, it is
relatively abundant and widespread on the surveyed grasslands.
The species in flower change as the fieldwork season progresses. Surveying a plot on a different date
is likely to give different results. This is of particular relevance when comparing data from different
years to assess change. Also, as the season progresses, the number of mown fields increases and the
number of fields available for survey, with standing wild flowers, decreases. This could affect the
representativeness of a village’s plant surveys, and could also affect comparisons between years if
the survey date is not similar. In 2017 there were different surveyors for the first 5 and last 3 villages.
The 2017 data for Daia is currently missing from this report as it is hiding in an unknown box in
Saschiz village.
2.4 Grassland butterflies The grassland plant plots are also used for the butterfly surveys, although they are extended to 50m
by 10m. All butterflies seen in a 5 minute walk along the length of the plot are counted. Butterfly
counts take place between 10am and 4pm, to avoid the cooler parts of the day. Butterfly counts do
not take place if it is raining. However, there still remains wide variation in the abundance of
butterflies due to weather conditions and time of day. The team aims to repeat the survey of each
site two or three times (dependant on suitable weather conditions) to reduce the impact of weather
conditions on the data. The number of times plots were surveyed is summarised in Table 2.2. Nearly
all plots were surveyed two or three times. Weather caused 7 sites to be surveyed just once. The
“Not surveyed” sites are now considered as a reserve set of sites. There is a growing set of nearby
and similar alternative plots to allow surveys even if the main site has been mowed. Each year the
butterfly survey leader has changed, although the same leader has been used in 2015 and 2017.
Table 2.2. Summary of how many times plots were surveyed at each village.
Village N sites Not surveyed Once 2 times 3 times N surveys
Apold 12 - - 12 - 24
Crit 18 2 3 12 1 30
Daia 11 - - 12 - 24
Malancrav 12 1 - 9 2 24
Mesendorf 15 3 - 7 5 29
Nou Sasesc 12 - - 3 9 33
Richis 12 - - 12 - 24
Viscri 13 1 4 4 4 24
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2.5 Birds Standing point counts were undertaken at 500m intervals along each of the three transects for each
village, giving a target of 13 point counts per east and west transect, and 9 point counts per central
transect. The 2017 point count locations were very similar to those of 2015 and 2016. Some point
counts from 2014 and 2013 were removed in 2015 due to proximity to a point on another transect.
Each point count lasted 10 minutes and all individuals seen or heard were counted. The surveys
began soon after dawn, between 0545 and 0615, and were usually completed before midday.
The time of year and amount of mown grass will affect the numbers and species of birds being
recorded. Also as the morning progresses, there is a very noticeable decrease in the amount of bird
song and activity. So, points further along a transect tend to have fewer birds. Most surveys were
repeated, walking the transect in the opposite direction to compensate for the time of day effect.
Five Apold West points, two Daia Central points, the Daia East transect, and Mesendorf South
transect were only surveyed once due to heavy rain. A few points at Malancrav and Viscri could not
be surveyed at all, or only once, due to presence of shepherd dogs. The 2017 survey leader was the
same as in 2015 and 2016. There were different surveyors in 2014 and 2013.
In addition to the point counts, the mist netting and ringing survey was continued in 2017. Three nets
were set up from dawn until about 1100 in scrub areas adjacent to farmland, across bird movement
corridors. In 2017 the mist netting and ringing took place at 6 villages, compared to all 8 villages in
2016 and 2015, and 5 villages in 2014.
A night-time corn crake survey was also continued at the first three villages. The approximate
distance and direction of corn crake calls was recorded on linear walks through potential corn crake
habitat. The survey was relatively late in the breeding season and so very few records were obtained,
which does not give an accurate estimate of corn crake abundance. Data from the corn crake survey
is not presented in this summary report.
2.6 Small mammals The small mammal survey methods were re-designed for 2014 and continued in 2015, following
limited trapping success in 2013. Cheaper plastic traps were used instead of folding Sherman traps.
The lower cost meant more traps could be bought, and replaced when stolen. Grids of 4 by 5 or
single lines of 20 traps were laid out in different habitat types (low and high nature value (LNV and
HNV) grassland, and scrub/woodland edge), dependent on characteristic and shape of the habitat
type. In 2016 and 2017, more expensive traps were used – but not as expensive as the 2013 Sherman
traps – as they are better for animal welfare and hopefully more effective at trapping small
mammals. The same basic trap grid layout was used as in 2014 and 2015, but the locations of some
trap grids were adjusted to reduce chances of trap damage or theft, and due to habitat changes from
mowing and grazing. Traps were set each evening and checked the following morning. The trap lines
/ grids were in place for at least 4 nights. Surveyors have changed each year.
2.7 Large mammals The large mammal surveys commenced in 2014 have continued in each subsequent year. Two survey
techniques are used: camera traps and observation of signs such as scat and tracks.
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Camera traps were set up in woodland locations. At Richis, 10 cameras were set up for 3 days, then
another 4 for 2 days. This first week revealed some malfunctioning cameras. In the other villages, 9
to 11 cameras were set up in two sets of locations for 4 or 5 days. The number of cameras varied due
to malfuntioning and one incident of attempted theft. The cameras were placed in strategically
chosen woodland locations that seemed likely to experience frequent large mammal activity. Unlike
previous years, no cameras were actually stolen, partly due to attaching cameras with padlocked
cables. Batteries and SD card were stolen from one camera on one occasion.
The survey of large mammal signs involved walking the east and west transects, recording sightings,
scat, tracks, digging and any other signs of large mammal presence, and GPS coordinates of their
location. The same technique and routes have been used every year from 2014 to 2017. The large
mammal survey team leader has changed every year.
2.8 Bats An extensive and systematic survey of bats has been repeated very year from 2014 to 2017. Various
methods were used at each village, including roost surveys, bat activity transect surveys, static
detector surveys and mist netting.
The bat survey results will be reported separately.
2.9 Orthoptera Surveying of Orthoptera – grasshoppers and bush crickets –was trialled in 2016. The trial ran at Nou
Sasesc and Mesendorf. The team tried different techniques to assess the diversity and abundance.
Then in 2017 Orthoptera surveys were undertaken at 5 villages (Mesendorf to Malancrav). The
technique involves arranging 5 to 8 people in a line spaced approximately 5 metres apart. Each
person then walks forward 5 steps, or 10 steps, 15 steps and so on, so that they form a diagonal line.
At that spot each person then identifies the first few Orthoptera they spot, or uses nets and pots to
capture species for later identification. After 3 minutes the surveyors re-position so that the end
result is a large X formation. The possible survey techniques are heavily constrained by the need to
minimise trampling of the hay meadow plants, so for example a sweep netting technique cannot be
used. The technique has questionable rigour and repeatability and a better approach is needed to
produce more thorough and representative data on Orthoptera communities.
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3.0 Vital statistics
This section presents selected summary information to give a concise overview of the data.
Remember that various factors can influence the data including natural fluctuations in wildlife
populations, natural variation from year to year due to changing vegetation phenology, timing of the
survey relative to the day of the year and time of day, surveyor knowledge and experience, and
sample size. The methods described above have been designed to limit these issues, while allowing a
relatively rapid biodiversity assessment across the Tarnava Mare.
Figures 3.1 to 3.3 summarise the farm interview data. Figure 3.1 shows the mean farm size for each
village’s interview respondents for each year. There is variation between years, particularly for
Mesendorf. The 2015 and 2017 data are based on a lot more interviews and can be considered more
representative. The graph illustrates that the 2014 and 2013 interviews did not give an accurate
representation of each village. Consequently, only comparisons between 2015 and 2017 are made in
this report. Richis and Viscri appear to have smaller farms than other villages.
Figure 3.1. Farm size, showing the mean total farm area for 2013 to 2017 interviewees. Village
abbreviations: AP – Apold, CR – Crit, DA – Daia, MA – Malancrav, ME – Mesendorf, NS – Nou Sasesc,
RI – Richis, VI – Viscri.
Figure 3.2 shows the mean extent of cultivation, hay meadows and other agricultural land use at
each village in 2015 and 2017. The large difference between the 2015 and 2017 data for Nou Sasesc
is probably due to a relatively small sample of 6 farmers for this village in 2017. Five villages have
larger total farmed area in 2017 than 2015, possibly a sign that farm sizes are increasing. There has
been greater increase in “Other” than hay or cultivation. This other category includes pasture used
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for livestock grazing. The “Other” category increases at all villages except Richis. Changes to the hay
and cultivation categories vary between villages. Despite the greater sample size in 2015 and 2017, it
is still felt that this may not give an accurate picture of the extent of different farming types across
the villages. This is partly due to the still limited sample size, and also the potential inaccuracy of
farmer responses. But these differences in the 2015 and 2017 data are signs that should be watched
over the coming years.
Figure 3.2. Farm land use, showing the 2015 and 2017 mean area of cultivation, hay, and other use
as shaded stacked columns. Village abbreviations: AP – Apold, CR – Crit, DA – Daia, MA – Malancrav,
ME – Mesendorf, NS – Nou Sasesc, RI – Richis, VI – Viscri.
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Figure 3.3 shows the mean number of milk cattle, ewes and lambs at each village in 2015 and 2017.
There are notable differences between villages and between years. Crit and Daia have a large
number of sheep on average. The number of sheep is much greater than the number of milk cattle at
all villages except Nou Sasesc. Nou Sasesc seems to have fewer livestock than the other villages.
Again, despite the greater sample size in 2015 and 2017, it is still felt that this may not give an
accurate picture of the number of livestock across the villages. The very large difference at Viscri
between 2015 and 2017 is probably at least partly due to sampling biases. There is large variation
amongst farms. Small traditional farms may have one or two cows and a few sheep or goats. More
specialised farms have large flocks of sheep. The results shown depend heavily on how many of these
different types of farm were included in the survey. However, all villages apart from Malancrav and
Richis have greater numbers of lambs in 2017 than 2015. This difference needs to be monitoried in
the coming years.
Figure 3.3. Farm livestock, showing mean number of lambs, ewes and milk cattle in 2015 and 2017.
Village abbreviations: AP – Apold, CR – Crit, DA – Daia, MA – Malancrav, ME – Mesendorf, NS – Nou
Sasesc, RI – Richis, VI – Viscri.
The village farming summaries listed below have been produced by compiling all of the farmer
interview responses (see section 4 for details). The previously described caveats due to limited
sampling apply here too. There are a number of signs that farming is changing, with more livestock
grazing seeming to be the most common type of change.
Apold increased intensification - due to less hay production and more livestock
low change potential
Crit reduced intensification – due to less cultivation, fewer livestock
increased change potential – favouring more silage and cultivation
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Daia slightly lower intensification – fewer livestock, more communal grazing
reduced change potential – all becoming more stable
Malancrav reduced intensification – due to reduction in all farming aspects, i.e. less farming overall
reduced change potential – all becoming more stable
Mesendorf increased intensification – due to less communal grazing, less hay production
increased change potential – favouring more silage, crops, livestock
Nou Sasesc slightly increased intensification – more livestock, less communal grazing, more hay, but less hand-mowing
increased change potential – favouring less hay, more silage
Richis slightly increased intensification – less hand-mown hay
low change potential
Viscri increased intensification – due to more livestock, more hay production
low change potential
Figures 3.4 and 3.5 summarise the grassland plant data. For each survey site, a “3-way diversity”
score has been calculated (see section 4 for details) and is summarised for each village in figure 3.4.
All villages have a wide range of “3-way diversity” scores, although this is less so for Apold. No village
has scores that are noticeably greater than other villages. At Crit the median score is tending to
increase year on year. At Daia and Nou Sasesc the median score is tending to decrease.
Figure 3.4. Site-level grassland plant survey “3-way diversity” scores, summarised for each village, for
each year. Higher scores indicate higher diversity of indicator species. In each boxplot: the horizontal
line represents the median value; the height of the box represents the inter-quartile range (IQR); the
length of the whiskers represents whichever is shorter of the maximum/minimum value or 1.5 times
the IQR; circles represent outliers (data points beyond the whisker range).
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Figure 3.5 shows the total abundance of indicator plants across the years at each village, and all
villages combined. For all villages combined the abundance decreased each year to 2016 which was a
potential cause for concern. However, in 2017 that trend was reversed. No individual village has a
consistent trend in indicator plant abundance over the 5 years. Abundance at Richis and Nou Sasesc
does show signs of a decreasing trend. This may be because the surveys at these villages are at the
start of the season, and this has become earlier over the years. Crit and Viscri seem to generally
increase in abundance. Again this may only be due to the changing dates of the surveys. Apold has
notably fewer indicator plants than other villages. Crit has notably higher numbers – this is primarily
caused by a very high amount of Betony at a few Crit sites.
Figure 3.5. Total indicator plant abundance per ha for each village, and all-village average, for each year.
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Figure 3.6 summarises the grassland butterfly diversity. There is a wide range of butterfly diversity levels at all of the villages, but less so at Viscri, Apold and Daia. All villages seemed more diverse in 2016 compared to previous years, with the exception of Crit (not surveyed). This trend seems to have continued in 2017 at Apold, Mesendorf, Nou Sasesc and Viscri, while the other villages have stayed releatively constant over the last 2 years. In 2016 Malancrav had notably higher diversity indices than other villages, but this has become more “normal” in 2017. Viscri has a consistently lower median value every year. There are no clear signs of a reduction in butterfly diversity at any of the villages.
Figure 3.6. Plot-level butterfly diversity data, summarised per village, for each year. See Figure 3.4 for explanation of the box plot elements.
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Red-backed shrike abundance is summarised in figure 3.7. Daia consistently has noticeably higher
numbers of red- backed shrike than the other villages. In all villages fewer red-backed shrike were
seen in 2015 compared to 2014. In 2016 numbers declined further in 3 villages, but in 2017 those
trends were reversed. Nou Sasesc is the only village that now has signs of a possible consistent
decline in red-backed shrike numbers. This needs to be monitored closely in future years to check
whether this is a more long-term change.
Figure 3.7. Number of red-backed shrike per point count, per village, for 2013 to 2017.
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Figure 3.8 indicates that small mammal abundance has fluctuated markedly at all villages. 2017 was
the most abundant small mammal year at all villages except Richis and Viscri. The population crash in
2015 has been followed by recovery in numbers at all surveyed villages. High fluctuation in
abundance seems to be a normal pattern, as can often be the case with small mammals.
Figure 3.8. Small mammal abundance per trap night, per village, for 2013 to 2017.
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The large mammal signs of presence data summarised in Figure 3.9 show that all villages had less
frequent signs in 2017 than 2016. This may be due to the generally drier conditions in 2017 giving
hard ground and fewer prints. Mesendorf has consistently had more signs than other villages, but
not in 2017. Nou Sasesc, Richis and Viscri consistently have fewer signs than other villages.
Figure 3.9. Signs of large mammal presence per kilometre, per village, 2014 to 2017.
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Figure 3.10 summarises the number of species of Orthopteran at each village. There are no clear
differences between the 5 villages, although Crit and Malancrav have wider variation between sites
than other villages. Mesendorf has a consistently high Orthopteran species richness.
Figure 3.10. Plot-level orthoptera species richness in 2017, summarised for each village. See Figure 3.4 for explanation of the box plot elements.
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4.0 Farmer interviews
The data collected during the farm interviews over the 4 years (2016 is excluded due to very small
sample numbers) is presented in table 4.1. Note that in both 2013 and 2014 the number of
interviews that could be conducted was low. This reduces the reliability of the data, both in terms of
comparing villages and considering changes from year to year. A lot more interviews were
conducted in 2015 and 2017, and the results differ notably from the previous years (see figure 3.1
and table 4.1). It is assumed that the 2015 and 2017 data are more representative and reliable. Only
the 2015 and 2017 data are compared in this report.
In Table 4.1, a greater than 50% difference between 2015 and 2017 is highlighted in green or red, for
an increase or decrease respectively. These highlighted cells reveal some potentially interesting
differences between villages. The changes at each village can be summarised as:
Apold: more cultivation, more other, more milk cattle, more lambs
Crit: less cultivation, less ewes, more lambs
Daia: less beef cattle, more lambs
Malancrav: more other, less beef cattle, less lambs
Mesendorf: more other, less milk cattle, less beef cattle
Nou Sasesc: more hay, more other, less beef cattle, more lambs
Richis: less other, less beef cattle, less lambs
Viscri: more hay, more other, less beef cattle, more ewes, more lambs
Also wolf and bear attacks are reported to have increased in 5 villages and overall – but decrease in
Nou Sasesc and Richis. This may be a change in the awareness and recollection of attacks amongst
the interviewees. Or this may be a real increase in wolf and bear attacks, perhaps as a result of an
increase in the number of livestock.
In 2015 and 2017 additional questions on mowing technique, use of communal grazing and future
plans were included. The farm interviews capture a wide range of information. Two index values
have been calculated to summarise this range of information and to try to pick out key differences
between villages. The data used to calculate the indices and the indices are shown in tables 4.2 and
4.3.
The intensification index is the average of the following 5 scores:
Livestock score: mean number of livestock per interviewee divided by 150 (uses the sum of
all the types of livestock recorded). More intense farming can involve larger herds/flocks.
Communal grazing score: 1 minus the proportion of interviewees who use the communal
grazing. Intensification can involve abandoning the communal grazing system and grazing
your own animals on private pasture.
Hand mown score: 1 minus the proportion of the hay area that is mown by hand. More
intense farming involves using hay cutting machinery instead of hand mowing.
Hay score: 1 minus the proportion of the total farm area that is used for hay. More intense
farming is associated with abandonment of hay meadows.
Cultivation score: the proportion of the total farm area that is used for cultivation. More
intense farming is associated with more crop cultivation.
Page 20
Table 4.1. Part 1. Farm interview results for 2013 to 2017. Green – 2017 data 50% or more greater than 2015. Red – 2017 data 50% or less than 2015.
Interviews Years Farm
area (ha) Cultivation
(ha) Hay (ha)
First hay cut
Other (ha)
Milk cattle
Beef cattle
Ewes Lambs Goats Pigs Horses & donkeys
Buffalo Wolf and
bear attacks
Ap
old
2014 7
24.7 (6 to 40)
25 (0.75 to 90)
6.2 (0 to 15)
9 (0.75 to 20)
10 Jul (01 Jul to 08 Aug)
9.9 (0 to 60)
11.9 (0 to 45)
1.4 (0 to 4)
33.6 (0 to 120)
12.4 (0 to 80)
3.3 (0 to 18)
6 (1 to 15)
1 (0 to 2)
-- 0
2015 13
17.2 (4 to 37)
14.9 (0 to 54)
3.5 (0 to 14)
10.1 (0 to 49)
26 Jun (15 Jun to 01 Aug)
1.5 (0 to 15)
2.7 (0 to 20)
0 (0 to 0)
65.2 (6 to 294)
5.1 (0 to 20)
4.2 (0 to 37)
5.5 (0 to 15)
0.8 (0 to 4)
-- 2
2017 17
23.9 (1 to 50)
31.2 (0 to 180)
7.7 (0 to 34)
8.9 (0 to 75)
08 Jul (30 May to 01 Aug)
14.5 (0 to 71)
9.9 (0 to 107)
0 (0 to 0)
71.6 (0 to 400)
13.6 (0 to 80)
3.6 (0 to 50)
1.9 (0 to 7)
0.6 (0 to 2)
0.3 (0 to 5)
17
Cri
t
2013 11
19.9 (8 to 40)
69.3 (3.5 to 200)
12.8 (0 to 65)
24 (0 to 115)
22 May (01 Jun to 20 Jul)
32.5 (0 to 140)
19.2 (0 to 87)
9.1 (0 to 75)
308.1 (0 to 2000)
101.4 (0 to 850)
56.5 (0 to 300)
5.6 (0 to 40)
0.6 (0 to 2)
-- 6
2014 5
24 (15 to 40)
32.8 (3 to 120)
12.9 (0 to 60)
19.6 (2 to 60)
24 Jun (30 May to 01 Jul)
0.3 (0 to 1.5)
10.4 (3 to 30)
2.4 (0 to 11)
56.8 (0 to 250)
47.4 (0 to 230)
4.6 (0 to 10)
1.6 (0 to 4)
0.8 (0 to 2)
-- 0
2015 29
22.8 (1 to 95)
21.4 (0 to 100)
10.5 (0 to 60)
12.5 (1 to 50)
28 Jun (01 Jun to 01 Aug)
3.4 (0 to 42)
14.8 (0 to 100)
0.3 (0 to 3)
92.8 (0 to 1600)
1.8 (0 to 20)
13 (0 to 150)
6.9 (0 to 100)
0.7 (0 to 4)
-- 4
2017 21
23.9 (1 to 50)
15.9 (0 to 76)
2.7 (0 to 20)
9.1 (0 to 40)
19 Jun (01 May to 15 Jul)
4.2 (0 to 35)
12 (0 to 88)
0.3 (0 to 4)
38.5 (0 to 300)
5.7 (0 to 80)
5.7 (0 to 77)
2.7 (0 to 12)
0.4 (0 to 2)
0 (0 to 0)
25
Dai
a
2014 4
23.8 (8 to 42)
27 (7 to 60)
5.8 (2 to 10)
10 (5 to 20)
09 Jul (01 Jul to 20 Jul)
11.3 (0 to 45)
18.8 (1 to 45)
0.3 (0 to 1)
302.5 (0 to 1200)
150.5 (0 to 600)
26.8 (0 to 107)
9.3 (0 to 24)
0.5 (0 to 1)
-- 3
2015 24
20.9 (3 to 50)
21.8 (3 to 80)
4.9 (0 to 18)
8.9 (1 to 60)
27 Jun (15 May to 01 Aug)
8.3 (0 to 70)
14.8 (0 to 41)
6.1 (0 to 25)
92.1 (0 to 1200)
6.3 (0 to 100)
2.5 (0 to 51)
3.9 (0 to 15)
1 (0 to 3)
-- 2
2017 21
22 (2 to 50)
26.5 (2 to 100)
5.7 (0 to 20)
10.4 (1 to 97)
20 Jun (01 May to 15 Jul)
10.5 (0 to 50)
13.1 (0 to 50)
0 (0 to 0)
51 (0 to 1000)
23.9 (0 to 500)
1 (0 to 15)
3.2 (0 to 13)
1.1 (0 to 4)
0 (0 to 0)
8
Mal
ancr
av
2013 9
28.3 (2 to 80)
26.8 (3 to 50)
7.7 (1.5 to 25)
6.5 (1.5 to 20)
02 Jul (01 Jul to 10 Jul)
12.6 (0 to 40)
14 (5 to 30)
1.2 (0 to 5)
91.2 (0 to 260)
30.8 (0 to 80)
1 (0 to 4)
5.8 (0 to 26)
1 (0 to 2)
-- 6
2014 10
14.3 (2 to 30)
8.7 (0.5 to 40)
4.1 (0.5 to 10)
1.8 (0 to 5)
25 Jul (01 Jul to 15 Aug)
2.9 (0 to 25)
6.7 (1 to 40)
1.4 (0 to 10)
25.5 (0 to 170)
5.6 (0 to 35)
1.2 (0 to 9)
3.6 (0 to 20)
0.3 (0 to 1)
-- 4
2015 20
15.4 (3 to 40)
13.5 (0 to 53)
5.5 (1 to 25)
5.5 (0 to 25)
29 Jun (15 May to 01 Aug)
3.5 (0 to 50)
8.3 (0 to 31)
1.5 (0 to 10)
49.3 (0 to 500)
11.3 (0 to 80)
6.6 (0 to 93)
5.9 (0 to 32)
0.8 (0 to 3)
-- 8
2017 19
19.8 (3 to 50)
16.1 (1 to 50)
5.5 (1 to 25)
5.1 (0 to 25)
26 Jun (15 May to 30 Jul)
5.6 (0 to 32)
6.8 (0 to 25)
0.2 (0 to 3)
38.2 (0 to 300)
3.5 (0 to 30)
1.4 (0 to 25)
4.3 (0 to 30)
0.4 (0 to 2)
0.5 (0 to 5)
10
Mes
end
orf
2013 6
29.2 (6 to 100)
197.1 (0.03 to 1000)
53.7 (0 to 300)
47.5 (0 to 200)
30 Jun (30 Jun to 01 Jul)
95.9 (0 to 500)
103.5 (0 to 560)
7 (0 to 30)
54.2 (0 to 250)
46 (0 to 250)
14.3 (0 to 70)
3 (0 to 6)
1.8 (0 to 10)
-- 13
2014 6
15.3 (9 to 20)
172.3 (7 to 680)
11.5 (0 to 40)
75.8 (5 to 300)
22 Jun (01 May to 07 Jul)
85 (0 to 380)
124.3 (2 to 650)
21.5 (0 to 64)
105.8 (0 to 600)
34.2 (0 to 200)
13.7 (0 to 70)
4.8 (0 to 20)
2.8 (0 to 15)
-- 6
2015 29
17.3 (2 to 35)
16.3 (0 to 100)
5.8 (0 to 40)
10.6 (1 to 60)
25 Jun (15 May to 15 Jul)
2.8 (0 to 30)
16.1 (0 to 200)
6.8 (0 to 100)
31.1 (0 to 450)
12.6 (0 to 185)
42.1 (0 to 500)
3.2 (0 to 14)
1.5 (0 to 7)
-- 2
2017 22
27.7 (6 to 52)
32.3 (0 to 312)
5.5 (0 to 61)
8.2 (0 to 50)
22 Jun (01 Jun to 15 Jul)
18.7 (0 to 236.59)
6.8 (0 to 70)
0.1 (0 to 2)
42.6 (0 to 500)
16.6 (0 to 200)
29.7 (0 to 300)
2.4 (0 to 20)
1.5 (0 to 8)
20.7 (0 to 439)
16
Page 21
Table 4.1. Part 2.
Interviews Years Farm
area (ha) Cultivation
(ha) Hay (ha)
First hay cut
Other (ha)
Milk cattle
Beef cattle
Ewes Lambs Goats Pigs Horses & donkeys
Buffalo Wolf and
bear attacks
No
u S
ases
c
2013 4
15.5 (10 to 29)
29 (4.8 to 53)
3 (0 to 6)
4.9 (0 to 15)
01 Jul (01 Jul to 01 Jul)
21.1 (0 to 53)
5.5 (0 to 18)
2.8 (0 to 10)
14.3 (0 to 35)
6 (0 to 17)
0 (0 to 0)
2.3 (0 to 3)
0.5 (0 to 2)
-- 0
2014 3
15.7 (10 to 23)
50.3 (5 to 100)
14.3 (2 to 30)
27.3 (3 to 70)
28 May (20 May to 10 Jun)
8.7 (0 to 26)
10 (2 to 24)
4 (0 to 12)
23.3 (5 to 35)
8.3 (0 to 14)
0 (0 to 0)
2.7 (0 to 4)
0.3 (0 to 1)
-- 0
2015 11
17.9 (5 to 24)
24 (4 to 60)
10.4 (3 to 29)
9.8 (1 to 30)
30 May (15 May to 01 Jul)
3.8 (0 to 25)
14.1 (0 to 65)
7.1 (0 to 24)
10.8 (0 to 40)
6.1 (0 to 25)
0 (0 to 0)
4.2 (0 to 15)
0.7 (0 to 3)
-- 8
2017 6
19.2 (2 to 60)
49.8 (12 to 120)
8.2 (0 to 20)
19 (6 to 50)
01 Jun (01 Jun to 01 Jun)
22.7 (0 to 80)
20.2 (0 to 40)
0.2 (0 to 1)
13.7 (0 to 80)
22.2 (0 to 130)
0 (0 to 0)
1.3 (0 to 5)
0.3 (0 to 1)
1.7 (0 to 10)
0
Ric
his
2013 5
20.2 (3 to 45)
8.6 (1.5 to 16)
3.2 (0.5 to 5)
3.6 (0 to 10)
04 Jul (01 Jul to 15 Jul)
1.8 (0 to 7.5)
3.4 (1 to 6)
2 (0 to 7)
30.8 (0 to 150)
10.2 (0 to 50)
2.6 (0 to 13)
5.4 (2 to 9)
1 (0 to 2)
-- 0
2014 7
19 (6 to 44)
5.6 (2.5 to 12)
2.1 (1 to 4)
3.5 (1 to 10)
22 May (01 May to 10 Jun)
0 (0 to 0)
2.9 (0 to 10)
0.9 (0 to 4)
43.9 (0 to 300)
10.1 (0 to 70)
0 (0 to 0)
3.7 (1 to 7)
1.6 (1 to 2)
-- 0
2015 18
22.4 (1 to 50)
12.3 (0 to 70)
4.2 (0 to 15)
3.5 (0 to 13)
26 May (05 May to 01 Jun)
5 (0 to 56)
3.8 (0 to 18)
1.3 (0 to 8)
54.9 (0 to 300)
10.1 (0 to 58)
0.2 (0 to 3)
5.4 (0 to 14)
1.3 (0 to 4)
-- 2
2017 11
21 (5 to 27)
10.8 (1 to 40)
4 (1 to 20)
4.6 (0 to 20)
11 Jun (01 Jun to 01 Jul)
2.2 (0 to 19)
4.4 (0 to 30)
0.1 (0 to 1)
49.7 (0 to 400)
1.9 (0 to 20)
0 (0 to 0)
4 (0 to 9)
0.7 (0 to 2)
0 (0 to 0)
0
Vis
cri
2013 6
18.2 (6 to 25)
14.3 (5 to 28)
1.8 (0 to 3.5)
8.4 (2.5 to 25)
08 Jul (01 Jul to 30 Jul)
4.1 (0 to 14.75)
7.2 (0 to 29)
0.7 (0 to 3)
28 (0 to 60)
12.7 (0 to 40)
0 (0 to 0)
2.7 (0 to 5)
0.2 (0 to 1)
-- 0
2014 6
20.3 (2 to 50)
9.25 (5 to 23)
2.6 (0 to 10)
5.65 (2.5 to 7.4)
01 Jul (01 Jul to 01 Jul)
1 (0 to 6)
4.33 (0 to 10)
1.83 (0 to 4)
20 (0 to 76)
9.17 (0 to 30)
0 (0 to 0)
4.17 (0 to 15)
0.5 (0 to 1)
-- 0
2015 9
20.3 (14 to 25)
6.9 (1 to 16)
1.6 (1 to 3)
4.6 (1 to 7)
30 Jun (15 Jun to 07 Jul)
1 (0 to 7)
6 (0 to 10)
0.4 (0 to 3)
19.7 (0 to 55)
1.1 (0 to 4)
0 (0 to 0)
3 (0 to 8)
0.3 (0 to 1)
-- 0
2017 20
25.8 (0 to 60)
16.7 (0 to 90)
0.9 (0 to 12)
9.3 (0 to 60)
29 Jun (15 Jun to 01 Jul)
6.2 (0 to 25)
7.7 (0 to 30)
0.2 (0 to 3)
82.7 (0 to 600)
46.3 (0 to 600)
0.2 (0 to 3)
3 (0 to 20)
1.4 (0 to 8)
0 (0 to 0)
49
All
2013 41
22.5 (2 to 100)
59.3 (0 to 1000)
13.9 (0 to 300)
17.0 (0 to 200)
22 Jun (1 Jun to 30 Jul)
28.4 (0 to 500)
25.4 (0 to 560)
4.3 (0 to 75)
119.9 (0 to 2000)
44.4 (0 to 850)
17.8 (0 to 300)
4.5 (0 to 40)
0.9 (0 to 10)
-- 25
2014 48
19.2 (2 to 50)
37.8 (0.5 to 680)
6.5 (0 to 60)
17.0 (o to 300)
27 Jun (1 May to 15 Aug)
14.3 (0 to 380)
22.9 (0 to 650)
4.1 (0 to 64)
64.9 (0 to 1200)
27.9 (0 to 600)
5.1 (0 to 107)
4.4 (0 to 24)
1 (0 to 15)
-- 13
2015 153
19.5 (1 to 95)
17.1 (0 to 100)
6 (0 to 60)
8.8 (0 to 60)
21 Jun (05 May to 01 Aug)
4 (0 to 70)
11.5 (0 to 200)
3.3 (0 to 100)
58.1 (0 to 1600)
8.2 (0 to 185)
12.4 (0 to 500)
4.9 (0 to 100)
1 (0 to 7)
-- 28
2017 137
17 (2 to 40)
23.3 (0 to 312)
4.7 (0 to 61)
8.7 (0 to 97)
23 Jun (01 May to 01 Aug)
9.9 (0 to 236.59)
9.5 (0 to 107)
0.1 (0 to 4)
51.2 (0 to 1000)
17.1 (0 to 600)
6.5 (0 to 300)
2.9 (0 to 30)
0.9 (0 to 8)
3.6 (0 to 439)
125
Page 22
The change index is intended to capture how much the farming system is likely to change in the near
future towards greater intensification. The index uses questions about whether interviewees are
likely to increase or decrease various aspects of their farming, such as numbers of sheep, or area of
cultivation, or amount of hay mown by tractor for example. An “increase” response scores +1, while
a decrease response scores -1. No response or “no change” scores 0. These scores can be summed
for each village to give a village-level measure of likelihood of further intensification. If every
interviewee responded “increase” the score would be the number of interviewees. Or if everyone
responded “decrease” the score would be minus the number of interviewees. The change index is
the average of the following 4 scores:
Hay change score: based on adding together the response sums for more/less hay mown by
hand, mower and tractor. The score is re-scaled to range from 0 to 1 where 0 would
represent all interviewees saying “increase” to all types of hay cutting, and 1 would
represent all saying “decrease”.
Silage change score: based on the response sum for more/less silage production. The score is
re-scaled to range from 0 to 1 where 0 would represent all interviewees saying “decrease”,
and 1 would represent all saying “increase”.
Crop change score: same method as silage change score but using more/less crops question.
Livestock change score: based on adding together the response sums for more/less milk cattle, beef cattle and sheep. The score is re-scaled to range from 0 to 1 where 0 would represent all interviewees saying “decrease” to all the types of livestock, and 1 would represent all saying “increase”. Table 4.2. Interview data used in the calculation of village intensification and change indices.
AP CR DA MA ME NS RI VI All
Total Farm area (ha) 2015 193.2 598.39 502.45 257.1 471.72 263.95 220.56 61.99 2569.36
2017 530.3 334.8 557.3 306.5 711.3 299 118.84 317.2 3175.24
Total Cultivation Area (ha)
2015 42 198.9 107.75 99.3 93.43 114.45 75.65 11.14 742.62
2017 131.2 55.9 118.8 103.8 120.91 49 44.5 17.5 641.61
Total N livestock 2015 1053 3743 2840 1660 3251 454 1274 274 14549
2017 1729 1366 1961 1053 2646 357 669 2695 12476
Proportion using shared grazing
2015 0.50 0.68 0.58 0.58 0.66 0.73 0.76 1.00 0.66
2017 0.6 0.7 0.5 0.6 0.4 0.3 0.7 0.9 0.6
Total hay area hand mown (ha)
2015 9.5 46.5 2.5 25.5 97.18 0 24 3.5 208.68
2017 5.5 20.7 2.6 41.7 29.9 6 4 6 116.4
Total hay area (ha) 2015 131.5 300.57 195.5 87.5 298.18 107.25 55.2 41.75 1217.45
2017 152.1 190.2 217.5 97.2 179.7 114 50.34 176.1 1177.14
Sum of More/less milk cattle
2015 0 5 12 5 2 2 -7 4 23
2017 1 3 7 1 1 4 -4 -4 9
Sum of More/less beef cattle
2015 -1 1 1
2 0
3
2017 1 -2 -1 -2
Sum of More/less sheep
2015 5 7 2 10 -2 -1 -2 1 20
2017 1 2 1 1 4 -1 -2 -3 3
Sum of More/less hay by hand
2015 -1 1 2 5 3 1 -3 0 8
2017 0 -2 -4 -2 -3 -1 -12
Sum of More/less hay by hand mower
2015
0
-1 0 -1
2017 -1 -2 -1 -1 -5
Sum of More/less hay by tractor
2015 3 6 13 7 6 6 1 4 46
2017 1 5 5 1 6 2 3 -1 22
Sum of More/less silage
2015
3 1 1 2
7
2017 2 1 1 2 3 1 10
Sum of More/less crops
2015 1 0 11 8 2 4 0 0 26
2017 1 4 5 -1 5 2 0 16
Proportion inheriting farm
2015 0.91 0.92 0.78 1.00 0.96 0.82 1.00 1.00 0.92
2017 0.7 0.8 0.6 0.8 0.7 0.0 0.7 0.8 0.7
N interviews 2015 13 29 24 20 29 11 18 9 153
2017 17 21 21 19 22 6 11 20 137
Page 23
Table 4.3. The intensification and change indices for each village, and their component scores. AP CR DA MA ME NS RI VI All
Livestock score 2015 0.54 0.86 0.79 0.55 0.75 0.28 0.47 0.20 0.63
2017 0.68 0.43 0.62 0.37 0.80 0.40 0.41 0.90 0.61
Communal grazing score
2015 0.50 0.32 0.42 0.42 0.34 0.27 0.24 0.00 0.34
2017 0.41 0.29 0.48 0.37 0.59 0.67 0.27 0.11 0.38
Hand mown score 2015 0.93 0.85 0.99 0.71 0.67 1.00 0.57 0.92 0.83
2017 0.96 0.89 0.99 0.57 0.83 0.95 0.92 0.97 0.90
Hay score 2015 0.32 0.50 0.61 0.66 0.37 0.59 0.75 0.33 0.53
2017 0.71 0.43 0.61 0.68 0.75 0.62 0.58 0.44 0.63
Cultivation score 2015 0.22 0.33 0.21 0.39 0.20 0.43 0.34 0.18 0.29
2017 0.25 0.17 0.21 0.34 0.17 0.16 0.37 0.06 0.20
Hay change score 2015 0.47 0.46 0.40 0.40 0.45 0.39 0.53 0.43 0.44
2017 0.49 0.46 0.46 0.51 0.49 0.56 0.52 0.53 0.49
Silage change score 2015 0.50 0.50 0.56 0.53 0.52 0.59 0.50 0.50 0.52
2017 0.50 0.55 0.52 0.53 0.55 0.75 0.55 0.50 0.54
Crop change score 2015 0.54 0.50 0.73 0.70 0.53 0.68 0.50 0.50 0.58
2017 0.53 0.60 0.62 0.47 0.61 0.67 0.50 0.50 0.56
Livestock change score
2015 0.55 0.57 0.60 0.63 0.51 0.52 0.42 0.59 0.55
2017 0.52 0.54 0.56 0.52 0.55 0.53 0.39 0.44 0.51
Intensification Index 2015 0.50 0.57 0.60 0.55 0.47 0.52 0.47 0.33 0.52
2017 0.60 0.44 0.58 0.47 0.63 0.56 0.51 0.49 0.54
Change index 2015 0.52 0.51 0.57 0.56 0.50 0.55 0.49 0.50 0.53
2017 0.51 0.54 0.54 0.51 0.55 0.63 0.49 0.49 0.53
The intensification and change indices are visualised in figure 4.1. The two indices show some differences between the villages. Lower left areas on the diagram represent more extensive, and less changing farming practices. Upper right areas represent more intensive, and likely-to-change farming. The thicker, black horizontal and vertical lines show that several of the arrows lie at least partly in the upper right area, as do 4 villages’ arrow ends (i.e. the 2017 values).
Fig. 4.1. The intensification and change indices for each village. Village abbreviations: ALL – all villages, AP – Apold, CR – Crit, DA – Daia, MA – Malncrav, ME – Mesendorf, NS – Nou Sasesc, RI – Richis, VI – Viscri. Synthesising information from tables 4.1 to 4.3 and figure 4.1, each village can be summarised as follows (this is the same material as in Section 3 – Vital Statistics):
APAPCR
CR
DA
DA
MA
MAME
MENS
NS
RI RI
VI
VI
ALL ALL
0.45
0.5
0.55
0.6
0.65
0.3 0.4 0.5 0.6 0.7
Change I
ndex
Intensification Index
AP
CR
DA
MA
ME
NS
RI
VI
ALL
Page 24
Apold increased intensification - due to less hay production and more livestock
low change potential
Crit reduced intensification – due to less cultivation, fewer livestock
increased change potential – favouring more silage and cultivation
Daia slightly lower intensification – fewer livestock, more communal grazing
reduced change potential – all becoming more stable
Malancrav reduced intensification – due to reduction in all farming aspects, i.e. less farming overall
reduced change potential – all becoming more stable
Mesendorf increased intensification – due to less communal grazing, less hay production
increased change potential – favouring more silage, crops, livestock
Nou Sasesc slightly increased intensification – more livestock, less communal grazing, more hay, but less hand-mowing
increased change potential – favouring less hay, more silage
Richis slightly increased intensification – less hand-mown hay
low change potential
Viscri increased intensification – due to more livestock, more hay production
low change potential The calculation of the intensification and change indices is experimental. The choice of data, and calculation method may not be appropriate. The interview data may not be representative of a village as a whole due to the limited sample size. Nonetheless this data is included in this report to promote thought and discussion.
It is important to keep collecting this farm interview data in future years to be able to more reliably
confirm whether these are genuine changes in the farming practices, or due to the sampling
differences of 2015 and 2017. However, there are a number of signs that farming is changing, with
more livestock grazing seeming to be the most common type of change.
Page 25
5.0 Grassland plants
The indicator plant data for each site have been converted to three measures to characterise the
indicator species diversity and abundance. These three measures have been combined into a single
“3-way diversity” score, which is presented in Figure 3.4 of the vital statistics. The three measures
are:
A. Richness: Species richness, the number of indicator species
B. Evenness: 1 – Berger Parker dominance index
C. Abundance: Total number of individuals of each indicator species
The “3-way diversity” score is calculated as: A + 10B + C/100. This re-scales the three measures to
similar ranges of values, and then adds them together.
Figures 5.1, 5.2 and 5.3 show the richness, evenness and abundance measures for each village, and
for all 5 survey years. There is a wide range in the values of the measures across the sites at each
village. There is some variation between years. This may be due partly to variation in the date of
survey. There will also be natural fluctuation. Annual plant species change their location from year to
year, and can change from lying within a 50m by 5m plot to outside from year to year. Year on year
changes must be interpreted with caution, and longer term trends over several years will be more
reliable. The only potentially consistent trends revealed in figures 5.1 to 5.3 are Richis richness
decreasing, Nou Sasesc and Richis evenness decreasing and Viscri evenness increasing.
Figure 5.1. Site-level plant indicator species richness measure, summarised for each village, for each
year. In each boxplot: the horizontal line represents the median value; the height of the box
represents the inter-quartile range (IQR); the length of the whiskers represents whichever is shorter
of the maximum/minimum value or 1.5 times the IQR; circles represent outliers (data points beyond
the whisker range).
Page 26
Figure 5.2. Site-level plant indicator species evenness measure, summarised for each village, for each
year. See figure 4.1 for boxplot specifications.
Figure 5.3. Site-level plant indicator species abundance measure, summarised for each village, for
each year. See figure 5.1 for boxplot specifications.
Table 5.1 presents data on the three measures and the “3-way diversity” score for each site, for all 5
years. Sites with a consistent change in the 3-way score have their name highlighted in the ‘Site’
column. A consistent change in 3-way score is deemed to be present if there is a birdlife. There is a
lot of fluctuation from one year to the next. 14 sites have consistent change, with 9 decreasing and 5
increasing – but 8 of these 14 sites do not have a full 5 years’ worth of data. The TOTAL 3-way score
Page 27
consistently decreased over the first four years, but has increased to its highest level in 2017. For
many sites, 2017 had good indicator plant diversity, particularly at Apold, Mesendorf and Viscri, but
most sites at Malancrav had notably lower values than 2016. There is a lot of variability amongst the
sites, and various factors could cause changes, including weather conditions, scheduling of the
surveys, and surveyors. However, there is evidence that the botanic biodiversity at some sites of the
Tarnava Mare may be declining, and this needs to be monitored closely.
Table 5.1. Indicator plant diversity and abundance measures for each site of each village. Dark green:
>= 50% increase. Light green: >= 20% increase. Yellow: <= 20% decrease. Red: <= 50% decrease. Grey
= not surveyed.
Richness Evenness Abundance 3way
Site 2013 2014 2015 2016 2017 2013 2014 2015 2016 2017 2013 2014 2015 2016 2017 2013 2014 2015 2016 2017
Ap
old
AP01
2.0 2.0 2.0 1.0
0.2 0.5 0.1 0.0
46.0 33.0 24.0 190.0
4.4 7.2 3.1 2.9
AP02
2.0 5.0 4.0 5.0
0.1 0.7 0.4 0.4
34.0 78.0 45.0 146.0
3.8 12.7 8.7 10.4
AP03
4.0 2.0 0.0 1.0
0.4 0.3 0.0 0.0
215.0 3.0 0.0 3.0
10.0 5.4 0.0 1.0
AP04
4.0 6.0 4.0 6.0
0.5 0.4 0.4 0.3
161.0 329.0 115.0 130.0
10.8 13.5 9.4 10.0
AP05
2.0 6.0 5.0 6.0
0.2 0.4 0.5 0.3
193.0 120.0 93.0 237.0
5.6 10.9 11.3 11.2
AP06
4.0 5.0 5.0 6.0
0.4 0.6 0.3 0.3
70.0 194.0 223.0 167.0
9.1 12.8 9.8 10.9
AP07
1.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0
10.0 0.0 0.0 0.0
1.1 0.0 0.0 0.0
AP08
5.0 4.0 3.0 6.0
0.5 0.6 0.3 0.5
61.0 61.0 6.0 41.0
11.0 10.5 6.4 11.5
AP09
4.0 5.0 6.0 7.0
0.6 0.4 0.4 0.6
53.0 98.0 189.0 168.0
10.9 10.4 11.9 14.9
AP10
2.0 1.0 1.0 3.0
0.0 0.0 0.0 0.5
254.0 1.0 4.0 23.0
4.8 1.0 1.0 8.0
AP11
1.0 0.0 0.0
0.0 0.0 0.0
148.0 0.0 0.0
2.5 0.0 0.0
AP12
1.0 0.0 1.0
0.0 0.0 0.0
9.0 0.0 7.0
1.1 0.0 1.1
Cri
t
CR01 4.0 0.0 2.0 2.0 0.5 0.0 0.1 0.5 31.0 0.0 17.0 18.0 9.5 0.0 2.8 7.2
CR02 4.0 9.0 9.0 9.0 0.4 0.8 0.5 0.7 50.0 412.0 611.0 600.0 8.3 20.6 19.9 21.9
CR03 4.0 8.0 0.0 0.0 0.3 0.7 0.0 0.0 57.0 63.0 0.0 0.0 7.4 15.5 0.0 0.0
CR04 2.0 0.0 5.0 4.0 0.1 0.0 0.4 0.2 14.0 0.0 53.0 148.0 2.9 0.0 9.9 7.0
CR05 7.0 8.0 8.0 8.0 0.5 0.5 0.5 0.6 1210.0 388.0 848.0 1048.0 24.4 16.7 21.0 24.2
CR06 5.0 5.0 3.0 0.1 0.1 0.1 2124.0 936.0 2840.0 27.0 15.6 0.0 32.2
CR07 3.0 5.0 4.0 5.0 0.2 0.1 0.1 0.2 2354.0 1991.0 1174.0 1910.0 28.1 25.9 16.6 25.8
CR08 5.0 2.0 6.0 7.0 0.4 0.0 0.5 0.5 228.0 71.0 581.0 726.0 11.7 3.0 16.9 19.4
CR09 5.0 0.0 8.0 4.0 0.4 0.0 0.6 0.6 548.0 0.0 945.0 526.0 14.5 0.0 23.6 14.8
CR10 0.0 4.0 2.0 2.0 0.0 0.4 0.2 0.3 0.0 58.0 6.0 16.0 0.0 8.7 3.7 5.3
CR11 4.0 2.0 3.0 3.0 0.5 0.3 0.3 0.1 34.0 4.0 12.0 16.0 9.6 4.5 6.5 4.4
CR12 4.0 4.0 2.0 5.0 0.3 0.2 0.5 0.6 49.0 75.0 15.0 102.0 7.3 7.0 6.8 12.3
CR13 5.0 5.0 5.0 6.0 0.3 0.6 0.3 0.5 254.0 285.0 1041.0 324.0 10.5 14.1 18.5 14.2
CR14 3.0 3.0 4.0 0.0 0.5 0.4 0.5 0.0 236.0 255.0 404.0 0.0 9.9 9.8 13.5 0.0
CR15 1.0 6.0 0.0 0.6 68.0 265.0 1.7 14.4
CR16 3.0 3.0 4.0 2.0 0.1 0.0 0.0 0.1 1011.0 1589.0 1659.0 2742.0 14.1 19.0 21.0 29.9
CR17 3.0 3.0 0.0 0.4 0.0 0.0 411.0 687.0 0.0 11.2 9.9 0.0
CR18 3.0 3.0 3.0 1.0 0.1 0.0 0.0 0.0 1305.0 987.0 1999.0 2000.0 17.1 13.0 23.1 21.0
Dai
a
DA01
5.0 6.0 0.0
0.7 0.5 0.0
70.0 49.0 0.0
12.4 11.4 0.0
DA02
6.0 6.0 6.0
0.4 0.6 0.5
29.0 32.0 26.0
10.4 12.6 10.9
DA03
8.0 6.0 6.0
0.6 0.4 0.3
110.0 104.0 103.0
15.4 10.7 10.5
DA04
3.0 5.0 1.0
0.2 0.2 0.0
86.0 99.0 96.0
5.5 8.4 2.0
DA05
3.0 1.0 1.0
0.1 0.0 0.0
25.0 14.0 3.0
4.1 1.1 1.0
DA06
5.0 3.0 4.0
0.5 0.3 0.6
49.0 6.0 19.0
10.2 6.4 10.0
DA07
8.0 9.0 4.0
0.7 0.5 0.6
62.0 101.0 19.0
15.9 15.4 10.5
DA08
8.0 11.0 9.0
0.7 0.6 0.6
139.0 261.0 281.0
16.3 19.1 17.3
DA09
9.0 11.0 8.0
0.5 0.7 0.8
355.0 450.0 339.0
17.5 23.0 19.0
DA10
7.0 0.0 8.0
0.4 0.0 0.3
1176.0 0.0 524.0
22.5 0.0 16.6
DA11
5.0 0.0 6.0
0.3 0.0 0.3
174.0 0.0 300.0
10.0 0.0 12.0
Mal
ancr
av
MA01 12.0 8.0 8.0 11.0 8.0 0.6 0.5 0.7 0.5 0.7 419.0 299.0 74.0 296.0 134.0 22.3 15.8 16.0 19.4 16.3
MA02 12.0 7.0 4.0 7.0 8.0 0.8 0.3 0.1 0.7 0.6 286.0 324.0 832.0 305.0 246.0 22.7 12.8 12.9 16.6 16.4
MA03 7.0 6.0 0.0 8.0 6.0 0.8 0.5 0.0 0.6 0.5 133.0 170.0 0.0 287.0 305.0 16.1 13.2 0.0 16.7 13.6
MA04 5.0 5.0 5.0 5.0 3.0 0.4 0.5 0.5 0.4 0.0 118.0 163.0 57.0 189.0 56.0 10.5 11.7 10.7 10.5 3.9
MA05 5.0 2.0 1.0 1.0 0.4 0.2 0.0 0.0 43.0 210.0 1.0 1.0 9.8 6.0 0.0 1.0 1.0
MA06 5.0 0.0 5.0 3.0 0.1 0.0 0.5 0.1 270.0 0.0 101.0 81.0 9.1 0.0 0.0 11.3 4.4
MA07 4.0 4.0 2.0 3.0 2.0 0.2 0.2 0.1 0.3 0.0 189.0 82.0 38.0 172.0 87.0 7.7 7.1 3.7 7.5 3.1
MA08 4.0 4.0 0.0 4.0 3.0 0.4 0.6 0.0 0.3 0.2 173.0 39.0 0.0 147.0 48.0 9.5 10.3 0.0 8.2 5.1
Page 28
Richness Evenness Abundance 3way
Site 2013 2014 2015 2016 2017 2013 2014 2015 2016 2017 2013 2014 2015 2016 2017 2013 2014 2015 2016 2017
MA09 2.0 3.0 5.0 6.0 6.0 0.1 0.5 0.6 0.4 0.1 134.0 11.0 247.0 279.0 129.0 4.4 7.7 13.3 13.3 8.5
MA10 0.0 3.0 3.0 2.0 0.0 0.0 0.3 0.4 0.2 0.0 0.0 24.0 9.0 9.0 0.0 0.0 5.7 7.5 4.3 0.0
MA11 8.0 2.0 6.0 6.0 2.0 0.7 0.1 0.5 0.4 0.5 210.0 8.0 120.0 123.0 2.0 17.0 3.3 12.0 11.3 7.0 M
esen
do
rf
ME01 2.0 3.0 6.0 3.0 2.0 0.2 0.1 0.3 0.4 0.1 35.0 50.0 308.0 181.0 142.0 4.4 4.5 12.5 9.2 4.0
ME02 7.0 6.0 5.0 4.0 7.0 0.0 0.2 0.5 0.3 0.2 2259.0 1198.0 406.0 570.0 986.0 30.0 19.5 13.7 12.5 19.1
ME03 5.0 5.0 4.0 5.0 3.0 0.5 0.6 0.6 0.5 0.2 64.0 49.0 111.0 383.0 291.0 10.6 11.8 11.1 13.8 7.9
ME04 3.0 0.5 42.0 8.9
ME05 4.0 0.0 227.0 6.6
ME06 5.0 4.0 7.0 6.0 5.0 0.5 0.3 0.2 0.5 0.4 526.0 283.0 423.0 433.0 316.0 15.4 9.4 13.7 15.8 11.8
ME07 2.0 0.3 20.0 5.2
ME08 6.0 7.0 6.0 8.0 6.0 0.4 0.6 0.3 0.6 0.7 167.0 211.0 598.0 459.0 477.0 12.0 14.7 14.9 18.5 17.5
ME09 6.0 6.0 4.0 6.0 8.0 0.6 0.6 0.6 0.7 0.7 613.0 118.0 331.0 233.0 390.0 18.4 13.4 12.9 15.0 18.4
ME10 1.0 2.0 4.0 3.0 2.0 0.0 0.3 0.6 0.1 0.5 16.0 11.0 31.0 112.0 20.0 1.2 4.8 10.1 5.5 7.2
ME11 4.0 6.0 6.0 6.0 5.0 0.4 0.7 0.5 0.5 0.4 154.0 250.0 164.0 466.0 352.0 9.6 15.2 12.5 15.4 12.8
ME12 2.0 4.0 3.0 3.0 3.0 0.4 0.3 0.2 0.1 0.5 72.0 96.0 24.0 46.0 178.0 6.6 7.5 4.9 4.8 10.2
ME13 5.0 5.0 6.0 6.0 4.0 0.5 0.6 0.6 0.5 0.5 655.0 829.0 209.0 455.0 298.0 16.9 19.1 14.5 15.4 11.9
ME14 6.0 5.0 5.0 6.0 5.0 0.6 0.4 0.6 0.4 0.6 1030.0 644.0 485.0 948.0 516.0 22.6 15.8 16.1 19.6 16.0
ME15 1.0 1.0 3.0 1.0 2.0 0.0 0.0 0.2 0.0 0.1 28.0 21.0 17.0 33.0 24.0 1.3 1.2 4.9 1.3 3.5
No
u S
ase
sc
NS01 5.0 2.0 3.0 5.0 2.0 0.5 0.2 0.6 0.4 0.4 260.0 13.0 12.0 67.0 11.0 12.9 4.4 9.0 9.7 5.7
NS02 7.0 5.0 8.0 5.0 7.0 0.2 0.5 0.3 0.3 0.5 1105.0 255.0 379.0 428.0 304.0 20.2 12.4 14.9 12.7 14.8
NS03 1.0 1.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 9.0 1.0 0.0 1.0 0.0 1.1 1.0 0.0 1.0 0.0
NS04 2.0 3.0 0.0 3.0 0.0 0.4 0.0 0.0 0.2 0.0 35.0 230.0 0.0 21.0 0.0 6.6 5.5 0.0 5.6 0.0
NS05 9.0 12.0 14.0 10.0 11.0 0.7 0.4 0.7 0.7 0.6 527.0 1367.0 693.0 783.0 740.0 21.2 29.3 28.0 24.4 24.3
NS06 11.0 9.0 9.0 9.0 11.0 0.6 0.5 0.7 0.6 0.4 261.0 321.0 795.0 605.0 929.0 19.4 16.9 24.2 21.5 24.7
NS07 10.0 9.0 7.0 8.0 9.0 0.7 0.4 0.6 0.6 0.2 705.0 340.0 291.0 265.0 158.0 24.1 16.5 16.0 16.9 13.0
NS08 2.0 2.0 3.0 4.0 0.0 0.2 0.2 0.4 0.5 0.0 32.0 9.0 19.0 23.0 0.0 3.9 4.3 7.4 9.4 0.0
NS09 12.0 9.0 13.0 12.0 12.0 0.6 0.4 0.7 0.6 0.7 948.0 338.0 702.0 390.0 384.0 27.1 16.8 26.5 21.6 22.5
NS10 10.0 7.0 6.0 6.0 4.0 0.6 0.6 0.5 0.2 0.4 692.0 127.0 73.0 207.0 68.0 23.3 14.2 11.3 10.5 8.8
NS11 9.0 11.0 7.0 8.0 11.0 0.5 0.3 0.4 0.4 0.4 290.0 466.0 367.0 1135.0 528.0 16.7 18.4 14.7 23.6 20.0
NS12 2.0 2.0 1.0 1.0 0.5 0.5 0.0 0.0 2.0 2.0 12.0 5.0 7.0 7.0 1.1 1.1
Ric
his
RI01 5.0 8.0 6.0 3.0 4.0 0.2 0.7 0.7 0.4 0.5 204.0 279.0 73.0 158.0 156.0 9.0 17.5 13.6 9.0 10.2
RI02 6.0 7.0 4.0 5.0 1.0 0.5 0.4 0.5 0.5 0.0 164.0 147.0 29.0 39.0 33.0 13.0 13.0 9.1 10.3 1.3
RI03 9.0 7.0 9.0 10.0 9.0 0.6 0.5 0.6 0.5 0.5 284.0 521.0 123.0 407.0 166.0 18.0 17.7 16.2 18.7 16.0
RI04 11.0 8.0 9.0 5.0 7.0 0.6 0.5 0.6 0.6 0.6 825.0 355.0 531.0 214.0 241.0 25.2 16.1 20.7 12.9 15.9
RI05 6.0 9.0 4.0 4.0 3.0 0.6 0.7 0.2 0.3 0.0 248.0 348.0 166.0 410.0 420.0 14.0 19.0 7.8 11.1 7.4
RI06 10.0 9.0 5.0 2.0 6.0 0.7 0.6 0.6 0.3 0.6 755.0 503.0 213.0 369.0 177.0 24.6 20.5 13.6 8.7 14.0
RI07 8.0 10.0 4.0 8.0 5.0 0.7 0.6 0.4 0.5 0.5 470.0 567.0 52.0 675.0 564.0 20.2 21.6 8.8 19.6 16.1
RI08 1.0 8.0 2.0 0.0 1.0 0.0 0.7 0.1 0.0 0.0 2.0 184.0 21.0 0.0 18.0 1.0 16.9 3.6 0.0 1.2
RI09 4.0 6.0 6.0 5.0 3.0 0.2 0.3 0.4 0.2 0.2 466.0 456.0 368.0 589.0 269.0 10.6 13.5 13.5 13.2 8.1
RI10 3.0 2.0 2.0 2.0 0.0 0.4 0.1 0.3 0.3 0.0 8.0 215.0 11.0 22.0 0.0 6.8 4.7 4.8 5.4 0.0
RI11 7.0 7.0 8.0 9.0 1.0 0.5 0.2 0.6 0.5 0.0 599.0 338.0 544.0 614.0 238.0 17.8 12.6 19.2 20.1 3.4
RI12 5.0 4.0 5.0 5.0 6.0 0.1 0.0 0.4 0.6 0.5 793.0 287.0 73.0 42.0 76.0 13.6 7.3 9.7 11.6 11.5
Vis
cri
VI01 7.0 12.0 0.0 7.0 6.0 0.3 0.5 0.0 0.6 0.4 385.0 273.0 0.0 770.0 699.0 13.6 19.6 0.0 21.2 16.7
VI02 5.0 6.0 6.0 7.0 0.3 0.5 0.6 0.5 199.0 111.0 148.0 456.0 9.6 12.0 13.6 16.7
VI03 4.0 9.0 8.0 6.0 0.0 0.6 0.7 0.7 0.3 0.0 11.0 289.0 150.0 626.0 0.0 10.5 18.9 16.9 15.4 0.0
VI04 6.0 8.0 6.0 8.0 7.0 0.4 0.4 0.2 0.4 0.1 403.0 254.0 242.0 486.0 664.0 13.6 14.8 10.2 17.2 14.7
VI05 2.0 5.0 4.0 4.0 6.0 0.2 0.1 0.1 0.4 0.3 5.0 274.0 89.0 193.0 265.0 4.1 8.4 5.9 9.8 11.5
VI06 4.0 8.0 6.0 7.0 5.0 0.2 0.5 0.7 0.5 0.4 210.0 172.0 100.0 1020.0 405.0 8.0 14.4 14.0 22.3 13.3
VI07 8.0 7.0 7.0 6.0 9.0 0.2 0.2 0.6 0.6 0.4 854.0 1120.0 384.0 365.0 1035.0 18.9 19.7 17.3 15.6 23.8
VI08 7.0 6.0 9.0 7.0 8.0 0.7 0.2 0.4 0.2 0.2 475.0 582.0 979.0 801.0 2279.0 18.4 14.0 22.4 17.2 32.4
VI09 1.0 2.0 1.0 1.0 1.0 0.0 0.2 0.0 0.0 0.0 22.0 43.0 12.0 10.0 17.0 1.2 4.3 1.1 1.1 1.2
VI10 2.0 3.0 3.0 2.0 3.0 0.1 0.1 0.2 0.2 0.2 21.0 57.0 51.0 24.0 377.0 3.6 5.0 5.9 3.9 9.3
VI11 1.0 1.0 2.0 2.0 3.0 0.0 0.0 0.1 0.0 0.5 9.0 18.0 7.0 21.0 11.0 1.1 1.2 3.5 2.7 7.7
VI12 1.0 2.0 2.0 2.0 2.0 0.0 0.1 0.1 0.1 0.5 1.0 73.0 12.0 13.0 4.0 1.0 3.6 3.0 2.9 7.0
VI13 0.0 2.0 4.0 3.0 4.0 0.0 0.3 0.3 0.1 0.5 0.0 20.0 60.0 36.0 21.0 0.0 4.7 7.4 4.7 9.0
TOTAL 20.0 24.0 21.0 21.0 23.0 0.6 0.7 0.6 0.8 0.6 31331.0 26688.0 25122.0 20849.0 31190.0 339.6 297.7 278.6 237.8 341.3
Page 29
Table 5.3 shows the abundance of the 10 most common indicator species that were surveyed,
totalled for each village (the equivalent data for all indicator plants is in Appendix 1). This could
potentially mask the within-site natural fluctuations in abundance and reveal more systematic
trends. However, the differences in survey date remains an influencing factor. Table 5.3 contains a
real mixture of colours, indicating variation between years, between species and between villages.
Overall, there are 133 dark and light green cells compared to 140 red and orange cells – suggesting a
balance of increasing and decreasing abundances. The comparable figures for just 2017 are 41
increases and 37 decreases. Species that experienced a consistent decline or increase over the years
are listed in Table 5.2. These are species with a significant Spearman’s rank correlation (Prho <= 0.05)
between abundance and year. Some trends identified previously have not been maintained into
2017, while some new ones have been added. The number of decline incidences has stayed the same
from 2016 to 2017, while the number of increases in abundance has increased by 2. In terms of total
abundance across all indicator species (the righthand column of Table 5.3), no village has a
statistically significant consistent trend across all years. The 2016 report identified a possible overall
decline in indicator plant abundance. The 2017 data does not support this trend. Monitoring will
continue, and with each year there can be greater certainty as to whether these are genuine trends
in wildflower abundance, or natural variation, or due to surveying artefacts such as change in survey
date or surveying staff.
Table 5.2. Species with consistent change over five years at a village or all villages combined. Bold
indicates an additional trend added since the 2016 report. The lower half of the table lists species
where consistent change had been identified in the 2016 report, but 2017 data do not continue that
trend. Underlined species are in the top 10 in terms of average annual abundance.
Species showing consistent decline Species showing consistent increase
Jurinea – Malancrav, Nou Sasesc
Large speedwell - All
Sainfoin – Apold, Richis
Lady’s bedstraw – Daia, All
Yellow scabious –Richis
Greater selfheal – Daia
Dorycnium –Daia
Sword-leaved fleabane – Crit
Deptford pink – Daia
Betony – Richis
Greater milkwort – Mesendorf, All
White dwarf broom – Nou Sasesc,
Richis, All
Sainfoin - Viscri
Charterhouse pink – Daia, Nou Sasesc
Squinancywort – Daia
Lady’s bedstraw – Richis
Dorycnium – Mesendorf
Deptford pink – Crit
Betony – Crit, Viscri
Species no longer showing consistent decline Species no longer showing consistent increase
Lady’s bedstraw –All
Crown vetch – Apold
Dorycnium – Crit
TOTAL – Apold, All
Large speedwell – Crit, Nou Sasesc
Greater milkwort – Nou Sasesc
Siberian bellflower – Malancrav
Squinancywort –Mesendorf
Yellow scabious –Viscri
Sword-leaved fleabane – Nou Sasesc,
Viscri
Page 30
Table 5.3. Abundance of the 10 commonest indicator species at each village. Grey: no record for two consecutive years. Dark green: >= 50% increase. Light green: >= 20% increase. Yellow: <= 20% decrease. Red: <= 50% decrease.
Village Year Sain
foin
On
ob
rych
is v
iciif
olia
Ch
arte
rho
use
Pin
k
Dia
nth
us
cart
hu
sia
no
rum
Squ
inan
cyw
ort
A
sper
ula
cyn
an
chic
a
Mo
un
tain
Clo
ver
Trif
oliu
m m
on
tan
um
Lad
y's
Bed
stra
w
Ga
lium
ver
um
Cro
wn
Vet
ch
Co
ron
illa
ver
um
Yello
w S
cab
iou
s
Sca
bio
sa o
chro
leu
ca
Do
rycn
ium
Do
rycn
ium
pen
tap
hyl
lum
Wild
Th
yme
Thym
us
gla
bre
scen
s
Bet
on
y
Sta
chys
off
icin
alis
TOTA
L
Apold
2014 210 47 187 0 110 513 1353 7 7 0 4180
2015 160 0 1124 0 204 468 1388 236 0 0 3668
2016 143 3 763 0 157 217 807 0 20 13 2330
2017 143 0 50 0 343 837 1367 657 93 0 3707
Crit
2013 1300 1198 193 4 3649 473 67 462 0 14764 22187
2014 169 92 323 0 2406 649 89 222 0 17554 21889
2015 523 539 320 0 3832 573 67 157 0 20429 26805
2017 334 451 494 0 5980 1843 106 474 31 27609 37946
Daia
2014 40 69 356 0 2560 233 167 764 105 2975 8273
2015 427 76 782 4 1507 542 133 631 31 467 4960
2016 400 116 811 0 1247 447 636 4 47 2364 6218
Malancrav
2013 1187 617 63 23 1133 700 287 317 993 557 6857
2014 735 76 0 0 378 491 1000 51 480 55 4836
2015 305 720 5 5 155 425 6585 35 2105 95 10745
2016 627 107 117 0 1057 687 907 440 1480 630 7640
2017 444 91 98 0 1393 131 338 0 960 22 3960
Mesendorf
2013 821 864 287 7 2694 1155 24 774 438 8351 15428
2014 538 720 331 47 3229 600 7 996 262 6545 13491
2015 1697 1010 513 93 2353 620 0 1120 80 2690 10357
2016 507 173 720 27 850 2050 23 1023 1240 7483 14397
2017 567 867 503 183 2627 1290 0 1210 503 5287 13300
Village Year Sain
foin
On
ob
rych
is v
iciif
olia
Ch
arte
rho
use
Pin
k D
ian
thu
s ca
rth
usi
an
oru
m
Squ
inan
cyw
ort
A
sper
ula
cyn
an
chic
a
Mo
un
tain
Clo
ver
Trif
oliu
m m
on
tan
um
Lad
y's
Bed
stra
w
Ga
lium
ver
um
Cro
wn
Vet
ch
Co
ron
illa
ver
um
Yello
w S
cab
iou
s
Sca
bio
sa o
chro
leu
ca
Do
rycn
ium
D
ory
cniu
m p
enta
ph
yllu
m
Wild
Th
yme
Thym
us
gla
bre
scen
s
Bet
on
y
Sta
chys
off
icin
alis
TOTA
L
Nou Sasesc
2013 2327 293 373 20 1313 1943 313 2710 860 4527 16220
2014 367 413 200 3907 1807 1163 0 1797 167 580 11563
2015 880 1443 323 513 1890 1027 17 1787 50 1340 11143
2016 1535 1360 124 11 1225 2076 84 3356 775 1487 14273
2017 477 3293 223 297 1453 687 0 2210 263 7 10423
Richis
2013 2150 193 1207 0 860 1090 1147 5827 650 197 16060
2014 1417 27 140 2193 683 1287 17 1250 3190 97 14000
2015 977 357 147 97 1037 193 0 2307 390 40 7347
2016 1300 270 70 150 2003 680 0 1810 1260 73 11797
2017 860 577 243 533 2060 220 0 817 1790 13 7860
Viscri
2013 908 0 538 0 465 1837 25 4102 0 6 7985
2014 3332 12 458 0 837 963 40 3120 25 6 10111
2015 2530 0 1470 0 877 590 97 1590 0 20 7447
2016 3930 0 2140 0 787 1610 947 4470 30 10 14550
2017 9338 3 1443 0 751 1111 43 4926 154 22 19360
All
2013 1449 527 444 9 1686 1200 310 2365 490 4734 14123
2014 851 182 249 768 1501 737 334 1026 529 3476 11043
2015 937 518 586 89 1482 555 1036 983 332 3135 10309
2016 1206 290 678 27 1047 1110 486 1586 693 1723 10172
2017 1738 755 437 145 2087 874 265 1471 542 4708 13794
Page 31
6.0 Grassland butterflies
This section reports on the 25 most abundant butterfly species, with an annual average abundance
greater than 10, as these show more reliable trends than species with few individuals observed.
Unidentified species of blue butterfly and all species of blue combined are also shown here. Data on
the full set of species are given in Appendix 2. Table 6.2 shows the abundance of each observed
butterfly species summed per village. Notable changes between years have been highlighted. These
should be interpreted with caution due to natural variability, the influence of weather during the
survey period, and changes in surveying staff.
The total number of butterflies recorded at each village should be less influenced by surveyor bias. In
2014 three villages showed notable decreases in abundance. In 2015 butterfly abundance recovered
by more than 20% in two of those villages – Richis and Viscri. But Nou Sasesc showed a further
decline. In 2016, there was a notable increase in total butterfly numbers at all villages, except
Mesendorf which experienced a 22% decrease. And 2017 was another good year for butterflies at
most villages, although Richis and Viscri had declines of greater than 20%.
Two species were recorded on the surveys for the first time in 2017. These are Scarce large blue
(Maculinea telejus) and Turquoise blue (Plebicula dorylas).
Table 6.1 shows the species that have consistently decreased or increased over the 5 years (4 years
for Apold, Crit and Daia). These are species with a significant Spearman’s rank correlation (Prho <=
0.05) between abundance and year. 19 species show a total of 50 incidents of consistent increase,
while 3 species show a total of 3 incidents of consistent decrease. So there are many more incidents
of increase rather than decline.
Butterfly biodiversity in all the surveyed villages appears to be in good health. The survey data gives
no causes for concern.
Page 32
Table 6.1. Species with consistent change over five years at a village or all villages combined. Bold
indicates an additional trend added since the 2016 report. The lower half of the table lists species
where consistent change had been identified in the 2016 report, but 2017 data do not continue that
trend. Species in red are used in the European Butterfly Indicator for Grassland Species (Van Swaay
et al., 2016)
SPECIES SHOWING CONSISTENT DECLINE
Marbled white – AP
Silver washed fritillary – NS
Essex skipper - DA
SPECIES SHOWING CONSISTENT INCREASE
High brown fritillary – RI
Weaver’s fritillary – AP, ME, NS, RI, All
Nickerls fritillary – ME, NS, RI, All
Small skipper – NS
Essex skipper – RI, All
Dingy skipper – AP, CR, VI
Small white – DA, ME
Wood white – DA, MA, NS, VI, All
Small heath – AP, DA, All
Chestnut heath –RI, All
Pale clouded yellow – DA
Ringlet – CR, DA, VI
All blues – DA
Silver studded blue – VI, All
Common blue – CR, DA, ME, RI
Short tailed blue – AP, DA, MA, ME, All
Osiris blue – DA, VI, All
Scarce swallowtail – DA, All
Map - All
Species no longer showing consistent decline
Marbled white – NS
Meadow brown – AP
Silver-studded blue – AP
Species no longer showing consistent increase
Marbled white – CR
Meadow brown – CR
Weaver’s fritillary – VI
Wood white – RI
Pale clouded yellow – CR, NS
Ringlet – AP
Common blue – VI
Blue sp. – AP, VI, All
Scarce swallowtail - AP
Page 33
Table 6.2. Grassland butterfly abundance (numbers per hectare) at each village. Grey: no sighting two years running. Dark green: >= 50% increase. Light green: >= 20% increase. Yellow: <= 20% decrease. Red: <= 50% decrease. The most abundant species with mean annual abundance > 10 shown here. Full species list in Appendix 2.
Mar
ble
d w
hit
e
Mel
anar
iga
gala
thea
Mea
do
w b
row
n
Man
iola
jurt
ina
Silv
er w
ash
ed f
riti
llary
Arg
ynn
is p
aph
ia
Hig
h b
row
n f
riti
llary
Arg
ynn
is a
dip
pe
Wea
ver'
s fr
itill
ary
Bo
lori
a d
ia
Nic
kerl
s fr
itill
ary
Mel
itae
a au
relia
Smal
l ski
pp
er
Pyr
gus
sylv
estr
is
Esse
x sk
ipp
er
Thym
elic
us
lineo
la
Larg
e sk
ipp
er
Och
lod
es v
enat
u
Din
gy s
kip
per
Eryn
nis
tag
es
Larg
e ch
equ
ered
ski
pp
er
Het
ero
pte
rus
mo
rph
eus
Smal
l wh
ite
Art
oge
ia r
apae
Wo
od
wh
ite
Lep
tid
ea s
inap
is
Smal
l hea
th
Co
eno
nym
ph
a p
amp
hilu
s
Ch
estn
ut
hea
th
Co
eno
nym
ph
a gl
ycer
ion
Dry
ad
Hip
par
chia
dry
as
Pal
e cl
ou
ded
yel
low
Co
lias
hya
le
Rin
glet
Ap
han
top
us
hyp
eran
tus
All
blu
es
Silv
er-s
tud
ded
blu
e
Ple
bej
us
argu
s
Co
mm
on
blu
e
Po
lyo
mat
tus
icar
us
Sho
rt-t
aile
d b
lue
Cu
pid
o a
rgia
des
Osi
ris
blu
e
Cu
pid
o o
siri
s
Blu
e sp
.
Lyac
aen
idae
Scar
ce s
wal
low
tail
Iph
iclid
es p
od
alir
ius
Pai
nte
d la
dy
Syn
thia
car
du
ii
Map
Ara
sch
nia
leva
na
TO
TAL
Ap
old
2014 5 238 18 9 0 0 0 0 1 3 0 11 2 32 3 17 14 3 404 218 144 21 0 0 0 3 20 1195
2015 2 204 2 2 0 0 0 0 1 7 0 2 1 33 0 49 18 10 440 130 154 62 1 92 1 0 5 1233
2016 2 162 4 5 7 0 0 0 0 9 0 29 18 45 11 29 11 18 425 122 80 65 0 148 7 12 20 1272
2017 0 212 10 5 13 0 2 0 0 30 0 23 26 56 11 21 12 15 402 197 145 87 18 184 5 0 5 1528
Cri
t
2013 42 145 4 0 0 0 10 0 5 2 0 7 0 13 0 19 1 1 149 1 0 0 0 0 3 2 0 579
2014 113 297 1 8 6 0 2 41 0 3 0 1 8 15 1 39 13 1 20 15 1 2 0 0 0 10 0 613
2015 164 458 9 1 6 0 4 15 0 13 0 3 6 10 0 18 20 10 56 13 8 1 0 31 5 3 0 873
2016
2017 84 343 4 1 1 0 16 39 0 21 0 5 8 13 0 45 11 58 53 67 11 3 3 11 4 0 0 822
Dai
a
2014 46 167 3 2 1 0 3 10 1 6 0 1 1 11 0 41 11 5 89 74 12 0 1 0 2 2 0 492
2015 71 213 0 5 3 0 2 2 0 3 0 1 3 27 0 79 12 5 226 43 86 1 1 95 4 0 0 893
2016 17 98 4 3 0 0 2 0 4 18 0 23 9 28 4 40 15 22 311 111 90 19 5 75 0 4 0 933
Mal
ancr
av
2013 181 174 2 0 0 0 20 0 8 4 0 12 0 2 0 23 0 17 33 0 3 0 0 0 1 0 0 541
2014 22 196 4 2 0 0 0 5 0 8 0 26 1 19 2 35 11 8 286 61 207 7 4 0 0 0 1 922
2015 5 114 2 0 3 0 0 0 0 5 0 29 10 54 20 26 9 10 342 40 186 33 0 74 3 0 0 986
2016 65 215 4 0 0 0 4 16 11 35 2 21 17 33 0 53 26 65 207 25 44 40 20 63 5 37 0 1086
2017 15 115 0 0 10 2 2 0 0 21 0 79 12 37 10 35 5 48 185 67 83 50 5 65 0 2 0 935
Mes
end
orf
2013 42 214 31 0 0 0 10 0 9 2 0 8 0 30 0 8 2 0 179 1 0 0 0 0 0 5 0 748
2014 216 414 8 29 8 1 4 55 5 0 1 1 11 26 2 1 3 4 28 19 2 2 1 0 0 9 0 869
2015 279 354 2 18 8 9 6 33 0 0 0 3 22 17 1 0 2 2 68 14 9 13 0 23 0 0 0 915
2016 124 177 13 5 15 3 32 52 8 5 0 0 14 27 0 19 10 35 27 0 5 5 0 9 0 0 2 658
2017 164 273 6 13 10 30 53 44 7 5 0 0 18 12 10 2 3 10 49 5 12 44 0 22 0 0 2 847
No
u S
ases
c
2013 121 195 7 2 0 0 10 0 5 17 0 9 0 10 0 87 0 13 129 0 7 0 0 0 3 1 0 772
2014 104 168 5 20 0 4 1 24 3 0 6 2 3 7 3 0 0 4 74 62 3 1 0 0 0 3 0 536
2015 97 171 0 14 3 3 21 13 1 0 3 1 6 5 4 0 2 0 60 24 2 21 0 10 0 0 3 475
2016 85 151 0 3 13 6 21 71 2 2 17 18 18 10 0 3 5 75 64 21 7 20 0 5 3 2 3 681
2017 151 236 0 31 20 23 78 54 17 0 27 7 15 16 16 0 0 4 72 27 14 19 0 33 0 0 3 938
Page 34
Table 6.2. cont.
Mar
ble
d w
hit
e
Mel
anar
iga
gala
thea
Mea
do
w b
row
n
Man
iola
jurt
ina
Silv
er w
ash
ed f
riti
llary
Arg
ynn
is p
aph
ia
Hig
h b
row
n f
riti
llary
Arg
ynn
is a
dip
pe
Wea
ver'
s fr
itill
ary
Bo
lori
a d
ia
Nic
kerl
s fr
itill
ary
Mel
itae
a au
relia
Smal
l ski
pp
er
Pyr
gus
sylv
estr
is
Esse
x sk
ipp
er
Thym
elic
us
lineo
la
Larg
e sk
ipp
er
Och
lod
es v
enat
u
Din
gy s
kip
per
Eryn
nis
tag
es
Larg
e ch
equ
ered
ski
pp
er
Het
ero
pte
rus
mo
rph
eus
Smal
l wh
ite
Art
oge
ia r
apae
Wo
od
wh
ite
Lep
tid
ea s
inap
is
Smal
l hea
th
Co
eno
nym
ph
a p
amp
hilu
s
Ch
estn
ut
hea
th
Co
eno
nym
ph
a gl
ycer
ion
Dry
ad
Hip
par
chia
dry
as
Pal
e cl
ou
ded
yel
low
Co
lias
hya
le
Rin
glet
Ap
han
top
us
hyp
eran
tus
All
blu
es
Silv
er-s
tud
ded
blu
e
Ple
bej
us
argu
s
Co
mm
on
blu
e
Po
lyo
mat
tus
icar
us
Sho
rt-t
aile
d b
lue
Cu
pid
o a
rgia
des
Osi
ris
blu
e
Cu
pid
o o
siri
s
Blu
e sp
.
Lyac
aen
idae
Scar
ce s
wal
low
tail
Iph
iclid
es p
od
alir
ius
Pai
nte
d la
dy
Syn
thia
car
du
ii
Map
Ara
sch
nia
leva
na
TO
TAL
Ric
his
2013 46 98 2 1 1 0 1 0 0 3 0 8 0 4 0 36 3 5 178 0 1 0 0 0 0 0 0 580
2014 44 98 0 1 0 3 0 0 1 0 0 1 1 7 2 0 0 1 34 28 3 0 0 0 0 2 1 239
2015 43 117 0 8 2 5 4 1 0 0 3 1 6 3 4 0 1 1 70 23 7 14 0 19 0 1 0 343
2016 73 99 2 8 7 7 23 21 0 0 17 13 21 10 8 0 3 3 58 21 8 3 0 12 2 8 2 493
2017 51 73 2 8 8 7 7 2 3 0 8 0 5 3 17 0 2 0 46 14 19 11 0 36 0 0 0 358
Vis
cri
2013 23 46 0 0 0 0 4 0 8 0 0 3 0 21 0 0 0 0 269 1 0 0 0 0 0 1 0 651
2014 121 189 1 2 0 0 0 21 0 2 0 4 0 24 0 3 16 0 11 9 0 0 0 0 1 2 0 409
2015 196 269 0 0 1 0 3 11 0 4 0 1 1 12 0 0 27 1 42 18 7 1 1 14 0 0 0 614
2016 43 173 0 0 2 0 3 5 0 16 0 2 2 15 0 3 5 5 236 131 37 0 5 50 5 2 0 761
2017 123 196 0 0 0 0 13 30 0 19 0 2 2 21 0 0 20 3 39 69 2 3 11 2 10 0 0 576
All
2013 76 145 8 0 0 0 9 0 6 5 0 8 0 13 0 29 1 6 156 0 2 0 0 0 1 1 0 645
2014 86 223 5 9 2 1 1 20 1 3 1 6 3 18 2 17 9 3 114 59 44 4 1 0 0 4 3 655
2015 114 251 2 6 3 2 5 10 0 4 1 5 7 18 3 18 12 5 149 36 50 17 0 41 2 1 1 782
2016 59 153 4 4 6 2 12 24 3 12 5 15 14 24 3 21 11 32 188 61 38 22 4 51 3 9 4 836
2017 170 426 6 15 15 15 47 49 7 33 9 30 22 46 15 44 19 50 232 148 75 51 10 86 6 0 2 1714
Page 35
7.0 Birds
This section reports on the bird species that are listed by Birdlife International (2018) as being
associated with grassland habitats, and which were observed on average at least twice per year. Data
on the full set of species are given in Appendix 3. Table 7.2 shows the abundance of each grassland
bird species per point count at each village. The abundance as a percentage of the total number of
birds throughout the season is also used to help determine if a significant change has occurred. This
percentage partly compensates for differences due to change of surveyor each year. Overall, after a
relatively low total number of birds per point count in 2015, many more birds were recorded in 2016
and 2017 (right hand column of Table 7.2). All villages apart from Apold and Richis had a higher
number of birds per point count than in 2016. There was a substantial decline in abundance of many
species at Richis in 2017 – something to monitor next year. Three species were added that had not
previously been recorded during the point counts: common redstart (Phoenicurus phoenicurus),
lapwing (Vanellus vanellus) and purple heron (Ardea Purpurea).
The number of highlighted cells illustrates the fluctuations in species numbers between 2013 and
2017. This will partly be natural variation, but also change in surveying staff. For example, in 2014
there was a fall in the number of house sparrows and tree sparrows, but an increase in sparrow sp.,
with these trends reversed in 2015. This is very probably an artefact of the different surveyors.
Likewise there is a fall in the number of middle spotted woodpecker in 2014, but rises in great
spotted woodpecker, spotted woodpecker sp. and woodpecker sp. with the trends reversed in 2015.
The same person led the point surveys in 2015, 2016 and 2017. So these effects should be reduced
for the last three years.
The species showing a consistent trend over the 5 years in certain villages or overall are shown in
Table 7.1. These are species with a significant Spearman’s rank correlation (Prho <= 0.05) between
abundance and year. There are many more instances of a grassland species showing a consistent
increase (19) than a consistent decrease (8) at particular villages. Most of the declining trends
identified in the 2016 report, using 3 or 4 years of data, have not continued over the 5 year period.
No village stands out as having more prevalent bird population changes. All villages have both
increasing and declining species. The abundance of the declining species should be monitored closely
in the villages where they have declined. However, overall the grassland bird populations appear in
good health.
Table 7.3 summarises the ringing surveys of 2014 to 2017. The mist netting and ringing only occurred
at 5 of the villages in 2014. All eight were surveyed in 2015. Seven were surveyed in 2016, six in 2017.
In 2016 and 2017, the total number of birds ringed was slightly lower than in 2015. Most of the
notable declines highlighted in red in Table 7.3 are for species with less than 10 individuals caught in
any year. Many factors can cause these numbers to fluctuate so not too much should be inferred
from any increases or declines.
Page 36
Table 7.1. Species with consistent change over five years at a village or overall. Species in red are
associated with grassland according to Birdlife International’s (2018) online species database. Bold
indicates a new entry since the previous annual report. Striked out indicates a trend that was
identified in last year’s report but no longer continues into this year.
SPECIES SHOWING CONSISTENT DECLINE
Barn swallow –DA, MA
Bee-eater – MA, RI
Black redstart - DA
Common whitethroat - DA
Cuckoo – CR, VI
Great grey shrike – DA
Hoopoe – ME, VI
Magpie - VI
Red-backed shrike – CR, NS
Whinchat – DA, ALL
White stork – ME
Willow warbler – AP
Woodlark – VI
Wryneck – RI
Yellow wagtail – AP, ME
SPECIES SHOWING CONSISTENT INCREASE
Barn swallow – AP
Bee-eater - CR
Black redstart – CR, MA, NS
Blackbird – ALL
Goldfinch – AP, MA, NS
Great tit - RI
Hoopoe – AP
House sparrow - RI
Little owl – AP
Magpie - AP
Marsh warbler – RI, VI
Raven - ME
River warbler – CR, NS, ALL
Skylark - DA
Stonechat - CR
Thrush nightingale – DA
Tree pipit – RI
White stork – AP, VI
White wagtail – VI
Woodlark – AP, RI
Wryneck - NS
Yellowhammer – CR, NS
Page 37
Table 7.2. Bird abundance per point count for more common grassland species (species listed by Birdlife International (2018) as associated with grassland, and recorded on average more than twice per year). Dark green: >= 50% increase in both abundance per point count and % of season’s total. Light green: >= 20% increase. Yellow: <= 20% decrease. Red: <= 50% decrease.
Bar
n s
wal
low
Hir
un
do
ru
stic
a
Bee
-eat
er
Mer
op
s ap
iast
er
Bla
ck r
edst
art
Ph
oen
icu
rus
och
ruro
s
Bla
ckb
ird
Turd
us
mer
ula
Co
mm
on
wh
ite
thro
at
Sylv
ia c
om
mu
nis
Co
rncr
ake
Cre
x cr
ex
Cu
cko
o
Cu
culu
s ca
no
rus
Go
ldfi
nch
Car
du
elis
car
du
elis
Gre
at g
rey
shri
ke
Lan
ius
excu
bit
or
Gre
at t
it
Par
us
maj
or
Ho
bb
y
Falc
o s
ub
bu
teo
Ho
op
oe
Up
up
a ep
op
s
Ho
use
sp
arro
w
Pas
ser
do
mes
ticu
s
Kes
trel
Falc
o t
inn
un
culu
s
Lap
win
g
Van
ellu
s va
nel
lus
Less
er g
rey
shri
ke
Lan
ius
min
or
Litt
le o
wl
Ath
ene
no
ctu
a
Mag
pie
Pic
a p
ica
Mar
sh w
arb
ler
Acr
oce
ph
alu
s p
alu
stri
s
Ap
old
2013 2.26 1.22 0 0.09 0.09 0 0 0.26 0 0.65 0.09 0 1.52 0 0 0 0 0.17 0
2014 2.73 2.24 0.33 0.64 0.07 0 0 0.45 0 2.87 0 0 1.69 0 0 0 0.02 0.15 0
2015 3.67 0.17 0.27 0.38 0 0 0 0.78 0 1.14 0 0.02 1.16 0 0 0 0.02 0.3 0
2016 7.44 3.98 0.29 0.77 0.04 0 0 1.56 0 2.06 0.04 0.06 3.79 0 0 0 0.1 0.56 0
2017 4.61 1.38 0.16 0.57 0.05 0 0 0.71 0.02 1.91 0 0.04 3.11 0 0 0 0.11 0.75 0.04
Cri
t
2013 2.47 0.07 0.1 0.37 0.08 0.03 0.07 0.32 0 0.58 0.05 0.02 1.73 0 0 0 0 0.19 0.02
2014 4.31 0.08 0.19 0.31 0.14 0.1 0.02 0.36 0 2.36 0.07 0 0.76 0 0 0 0 0.24 0.05
2015 3.67 0.23 0.2 0.45 0.03 0 0 0.28 0 0.95 0.06 0.02 2.36 0 0 0 0 0.2 0
2017 4.06 0.69 0.13 0.55 0.16 0.03 0 0.66 0 1.44 0.05 0.02 1.88 0 0 0 0 0.53 0.02
Dai
a
2014 7.1 1.17 0.21 0.69 0.38 0.03 0 0.86 0.38 2.03 0.03 0 3.72 0 0 0.03 0.14 1.76 0
2015 5.77 1.73 0.2 0.61 0.03 0 0.02 0.3 0.05 0.88 0.08 0 1.16 0 0 0 0 0.59 0.02
2016 5.63 0.2 0.11 0.88 0.02 0 0 0.91 0.05 1.57 0.07 0.23 4.13 0.04 0 0 0.02 1.3 0
2017 6.45 0.73 0.16 0.59 0.24 0 0 0.92 0 1.51 0.06 0.08 2.86 0 0.22 0.08 0.04 1.53 0.08
Mal
ancr
av
2013 4.05 0.9 0.1 0.22 0.02 0 0 0.1 0 0.32 0.02 0 4.54 0 0 0 0 0.32 0
2014 4.05 0.9 0.18 0.36 0.1 0 0.02 0.08 0 3.08 0 0.03 1.1 0 0 0 0 0.39 0.02
2015 3.75 0.73 0.23 0.35 0.02 0 0 0.22 0 1.4 0.03 0 3.82 0 0 0 0 0.42 0
2016 2.27 0.37 0.12 0.62 0.9 0.02 0.13 0.29 0 0.81 0 0 1.33 0 0 0 0 0.29 0.54
2017 4.35 0.98 0.25 0.56 0.08 0 0 0.35 0 1.88 0 0 4.46 0 0 0 0.04 1 0.06
Mes
end
orf
2013 3.62 0.01 0.04 0.31 0.12 0.03 0.07 0.18 0 0.19 0.01 0.06 2.91 0.01 0 0 0 0.04 0.01
2014 2.74 0 0.1 0.52 0.19 0.16 0 0.12 0 1.67 0 0.05 0.86 0 0 0 0 0.09 0
2015 1.7 0.02 0 0.67 0.34 0.06 0 0.13 0 0.61 0 0 2.78 0 0 0 0 0 0.02
2016 1.93 0.07 0.07 0.41 0.24 0.04 0.02 0.15 0 1.3 0.07 0 0.44 0 0 0 0 0.24 0.11
2017 3.58 0 0.31 0.37 0.19 0.08 0 0.46 0 0.77 0 0.04 3.71 0 0 0 0.02 0.21 0.04
No
u S
ases
c
2013 2.41 0.22 0 0.38 0.03 0 0 0.25 0 1.63 0.06 0 3.47 0 0 0 0 0.16 0.16
2014 2.67 0.22 0.15 1.04 0.57 0.04 0 0.3 0 1.43 0.04 0 0.3 0 0 0 0 0.19 0
2015 2.24 0.03 0.24 1.34 0.17 0 0.14 0.55 0 0.66 0.03 0 1.17 0 0 0 0 0.31 0.14
2016 3.9 0.08 0 0.52 0.04 0.1 0 0.62 0 1.29 0.06 0 4.1 0 0 0.02 0 0.17 0.02
2017 1.6 0.64 0.14 0.81 0.34 0 0 0.24 0 1.17 0.02 0 0.17 0 0 0 0 0.33 0.1
Ric
his
2013 6.38 0.81 0.03 0.27 0 0 0 0.92 0.03 0.95 0 0.05 2.7 0 0 0 0 0.89 0.14
2014 3.51 0.74 0.23 0.74 1.09 0.02 0.74 0.58 0 0.65 0 0.02 1.28 0 0 0 0 0.67 0
2015 2.08 0.31 0.08 0.96 0.46 0 0.38 0.73 0 0.79 0 0.04 1.52 0 0 0 0 0.33 0.12
2016 4.26 0.21 0.08 0.25 0.15 0.06 0.19 0.34 0 1.23 0.09 0 5.08 0.83 0 0.25 0.08 2.51 0.04
2017 2.34 0.57 0.29 1.03 0.95 0 0.81 0.45 0.02 1.4 0 0.07 2.69 0 0 0 0 0.38 0.28
Vis
cri
2013 2.47 0.32 0 0.09 0.16 0 0.1 0.26 0 0.24 0.01 0.1 6.06 0.03 0 0 0.04 2.82 0.03
2014 3.05 0.15 0.12 0.27 0.32 0.17 0.07 1.8 0.1 0.88 0.02 0.07 1.63 0 0 0.05 0 2.63 0.07
2015 2.07 0.18 0.02 0.23 0.2 0 0.02 0.16 0 0.2 0.07 0.07 0.86 0.11 0 0 0.02 1.93 0.09
2016 4.24 0.98 0.25 0.55 0.04 0 0 0.1 0 2.37 0.04 0.02 4.1 0 0 0 0 0.65 0.12
2017 2.61 0.28 0.04 0.32 0.6 0 0.04 0.49 0.02 0.7 0.02 0.16 3.74 0.02 0 0.18 0.02 2.84 0.11
Tota
l
2013 3.33 0.39 0.04 0.25 0.09 0.01 0.05 0.31 0 0.54 0.03 0.04 3.49 0.01 0 0 0.01 0.8 0.04
2014 3.51 0.65 0.18 0.55 0.32 0.06 0.09 0.48 0.04 1.91 0.02 0.02 1.22 0 0 0.01 0.01 0.61 0.02
2015 3.23 0.46 0.15 0.57 0.15 0.01 0.06 0.38 0.01 0.85 0.04 0.02 1.92 0.01 0 0 0 0.51 0.04
2016 4.2 0.79 0.13 0.57 0.2 0.03 0.05 0.56 0.01 1.51 0.05 0.05 3.27 0.13 0 0.04 0.03 0.83 0.12
2017 3.64 0.66 0.18 0.6 0.33 0.01 0.11 0.53 0.01 1.34 0.02 0.05 2.76 0 0.02 0.03 0.03 0.93 0.09
Page 38
Table 7.2. cont.
Qu
ail
Co
turn
ix c
otu
rnix
Rav
en
Co
rvu
s co
rax
Red
-bac
ked
sh
rike
Lan
ius
collu
rio
Riv
er w
arb
ler
Locu
stel
la f
luvi
atili
s
Ro
bin
Erit
hac
us
rub
ecu
la
Skyl
ark
Ala
ud
a ar
ven
sis
Star
ling
Stu
rnu
s vu
lgar
is
Sto
nec
hat
Saxo
cola
to
rqu
atu
s
Thru
sh n
igh
tin
gale
Lusc
inia
lusc
inia
Tree
pip
it
An
thu
s tr
ivia
lis
Wh
inch
at
Saxi
cola
ru
bet
ra
Wh
ite
sto
rk
Cic
on
ia c
ico
nia
Wh
ite
wag
tail
Mo
taci
lla a
lba
Will
ow
war
ble
r
Ph
yllo
sco
pu
s tr
och
ilus
Wo
od
lark
Lullu
la a
rbo
rea
Wry
nec
k
Jyn
x to
rqu
illa
Yello
wh
amm
er
Emb
eriz
a ci
trin
ella
Tota
l
Ap
old
2013 0 0.39 1.26 0 0.13 0 9.35 0 0.48 0.26 0 0 0.13 0.04 0 0 0.09 32.87
2014 0.02 0.24 1.93 0 0.11 0.02 0.02 0.07 0.11 0.11 0.16 0.11 0.47 0.02 0 0 0.07 26.73
2015 0.16 0.46 1.35 0 0.29 0 0.03 0.05 0.03 0 0 0.14 0.19 0 0 0 0.11 21.32
2016 0 0.63 2.73 0 0.73 0 7.85 0.38 0.38 0.15 0.02 0.33 0.17 0 0.02 0 0.4 56.02
2017 0 0.39 2.16 0 0.13 0 7.2 0.18 0 0 0 0.04 0.38 0 0.16 0.02 0.25 43.46
Cri
t
2013 0.02 0.53 1.71 0 0.08 0.17 1.1 0.07 0 0.14 0.14 0.2 0.15 0 0.03 0.02 0.49 18.8
2014 0 0.14 1.49 0 0 0.02 45.78 0.08 0 0.17 0.2 0.12 0.19 0 0.32 0 0.29 68.64
2015 0 0.84 1.41 0.02 0.09 0.03 0.28 0.13 0 0.03 0.02 0.16 0.11 0 0.02 0 0.25 19.66
2017 0 0.22 2.11 0.05 0.25 0.03 1.42 0 0 0 0 0.06 0.27 0 0.08 0 0.48 26.25
Dai
a
2014 0.1 0.24 4.86 0 0.03 0 15.69 0.07 0 0.07 0.79 0.03 0.38 0 0.1 0 0.48 65.69
2015 0.13 0.14 2.13 0 0.19 0.06 0.03 0.2 0.02 0.02 0.08 0.02 0.27 0 0.22 0 0.13 24.3
2016 0.05 0.52 3.8 0.02 0.54 0.14 0.29 0.2 0.21 0.13 0 0.11 0.59 0 0 0 0.14 35.25
2017 0.04 0.27 3.08 0 0.2 0.1 0.9 0.1 0.02 0 0 0.29 0.35 0 0 0 0.22 47.92
Mal
ancr
av
2013 0 0.88 0.32 0 0.07 0.02 0.39 0.05 0 0.1 0.02 0 0.12 0 0.05 0.05 0.63 24.54
2014 0 0.15 1.2 0 0.07 0.03 0.66 0.31 0 0.11 0.1 0.02 0.13 0 0.1 0 0.18 28.51
2015 0 0.22 1 0 0.25 0 0 0.07 0.15 0 0 0 0.07 0 0.02 0 0.13 26.17
2016 0 0.29 0.62 0.23 0 0.04 4.38 0.15 0 0.04 0.02 0.15 0.15 0.08 0.08 0 0.94 26.27
2017 0.02 0.35 1 0 0.31 0.02 0.02 0.06 0 0 0 0 0.23 0 0 0 0.04 39.88
Mes
end
orf
2013 0.19 0.22 0.9 0 0.26 0.21 0.21 0.03 0.04 0.07 0.03 0.31 0.25 0.07 0.04 0.01 0.44 18.41
2014 0.38 0.26 1.29 0 0.16 0.5 3.88 0.17 0 0.05 0.09 0.17 0.24 0 0.12 0 1 24.74
2015 0.08 0.39 0.5 0 0.06 0.83 0.39 0.02 0.05 0 0.02 0.02 0.41 0 0.02 0 0.58 17.41
2016 0 0.46 0.61 0.07 0.33 0 0.7 0.2 0 0.3 0 0 0.11 0 0.09 0.02 1.04 18.74
2017 0.02 0.17 0.9 0.06 1.06 0.48 1.02 0.19 0.02 0.04 0.04 0.02 0.42 0 0.02 0 0.79 26.58
No
u S
ases
c
2013 0 0.41 1.88 0 0.06 0.03 0 0.16 0 0.25 0.19 0 0.09 0 0.03 0 0.31 21.28
2014 0 1.15 1.5 0 0.02 0.02 7.15 0.3 0 0.35 0.06 0 0.2 0.04 0.35 0 0.78 29.28
2015 0 0.31 1.03 0 0.14 0 0.1 0.07 0.03 0.21 0.03 0 0.59 0 0.1 0 0.72 17.62
2016 0.02 1.48 1.33 0.04 0.65 0.54 0 0.12 0 0.19 0.06 0.1 0.25 0 0.02 0.02 0.98 27.9
2017 0 0.47 0.93 0.14 0.52 0 1.9 0.1 0 0.21 0 0.07 0.17 0 0.21 0.03 1.09 24.91
Ric
his
2013 0.03 0.3 1.35 0 0.11 0 0 0.22 0 0.11 0 0.14 0.11 0 0 0.05 0.46 30.19
2014 0.16 0.77 1.44 0 0.09 0 5.91 0.51 0 0.05 0.12 0.09 0.26 0 0 0.02 1.21 32.84
2015 0.04 0.33 0.31 0.17 0 0 4.5 0 0.02 0.21 0.04 0 0.23 0 0.06 0.02 0.63 19.79
2016 0.02 0.83 1.21 0 0.21 0.4 23.68 0.19 0 0.15 0.04 0.38 0 0 0.17 0 0.85 86.49
2017 0 0.43 0.72 0.12 0.07 0 13.02 0.09 0.02 0.33 0.03 0.22 0.31 0 0.19 0.14 1.02 41.02
Vis
cri
2013 0.06 0.13 1.06 0 0.01 1.04 1.1 0.12 0 0.07 0 0.01 0.07 0 0.12 0 0.43 55.53
2014 2.85 0.66 1.76 0 0.05 0.83 115.66 0.22 0 0.22 0.05 0.22 0.07 0 0.12 0 1.05 190.07
2015 0.09 0.05 0.95 0 0.05 1.8 0.21 0.11 0 0 0.13 0.45 0.09 0 0.05 0 0.79 35.84
2016 0 0.45 0.8 0 0.24 0 1 0.1 0 0.08 0.06 0.02 0.24 0 0 0 0.14 35.35
2017 0.23 0.23 1.04 0 0.19 1.12 29.19 0.37 0.05 0.02 0.11 0.39 0.07 0 0.04 0.02 1.04 88.42
Tota
l
2013 0.06 0.38 1.18 0 0.11 0.3 1.17 0.09 0.04 0.12 0.05 0.12 0.14 0.02 0.05 0.02 0.44 29.56
2014 0.37 0.43 1.71 0 0.07 0.17 21.53 0.21 0.01 0.14 0.16 0.09 0.23 0.01 0.14 0 0.59 52.29
2015 0.07 0.35 1.11 0.02 0.14 0.35 0.65 0.08 0.04 0.04 0.04 0.1 0.22 0 0.06 0 0.38 22.98
2016 0.01 0.66 1.59 0.05 0.38 0.16 5.37 0.19 0.08 0.15 0.03 0.15 0.22 0.01 0.05 0.01 0.64 40.65
2017 0.04 0.32 1.49 0.05 0.33 0.22 7.06 0.14 0.01 0.08 0.02 0.14 0.27 0 0.09 0.03 0.63 42.13
Page 39
Table 7.3. Number of individuals ringed for each species at each village and overall. Dark green: >= 50% increase in number of individuals. Light green: >= 20% increase.
Yellow: <= 20% decrease. Red: <= 50% decrease. Grey: none ringed two consecutive years.
Apold Crit Daia Malancrav Mesendorf Nou Sasesc Richis Viscri Species Total
2014 2015 2016 2014 2015 2017 2014 2015 2016 2017 2014 2015 2016 2015 2016 2017 2015 2016 2017 2015 2016 2017 2014 2015 2016 2017 2014 2015 2016 2017
Barn swallow 0 32 0 0 0 0 0 138 3 0 8 0 3 0 11 18 4 0 1 0 0 0 0 5 11 0 8 179 28 19
Barred Warbler 0 0 0 0 0 0 2 2 4 0 0 0 0 0 0 0 2 0 4 0 0 0 2 1 3 2 4 5 7 6
Bee-eater 0 0 0 0 0 0 0 0 0 0 0 0 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 0
Black Redstart 0 0 0 0 0 1 5 0 0 0 0 0 4 1 1 1 0 0 0 0 0 0 0 0 1 1 5 1 6 3
Blackbird 1 1 1 2 8 11 6 3 5 3 0 3 1 7 28 3 3 7 8 0 6 0 3 2 8 0 12 27 56 25
Blackcap 11 10 9 5 5 3 1 2 2 9 3 17 3 1 4 2 3 2 5 0 9 11 1 1 0 3 21 39 29 33
Blue Tit 13 7 4 0 5 0 0 0 0 0 2 2 4 4 1 1 0 0 2 0 3 6 0 0 0 0 15 18 12 9
Chaffinch 0 0 0 0 3 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 3 0 1
Chiffchaff 11 4 2 0 1 0 0 0 0 1 0 2 2 2 3 0 0 1 2 0 1 0 1 0 1 1 12 9 10 4
Coal tit 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0
Collared Flycatcher 0 1 1 0 3 0 0 2 0 0 0 0 0 0 1 1 0 0 0 0 0 1 0 0 1 0 0 6 3 2
Common Nightingale 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1
Common redstart 1 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 1 0 0
Common Whitethroat 7 8 0 0 0 1 31 25 31 41 1 1 5 25 19 18 0 2 0 7 23 25 62 41 56 27 101 107 136 112
Corncrake 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 0
Cuckoo 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1
Garden Warbler 1 2 0 1 0 0 0 1 2 1 0 1 1 0 2 0 0 0 0 0 0 0 4 1 0 1 6 5 5 2
Golden Oriole 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1
Goldfinch 0 0 0 0 0 0 0 0 5 1 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 1 0 5 2
Great Reed Warbler 1 4 1 0 0 0 0 0 0 1 2 0 0 0 0 0 0 4 1 0 0 0 0 0 0 0 3 4 5 2
Great Spotted Woodpecker 0 2 1 2 2 3 1 5 2 0 5 2 5 1 4 0 0 0 3 0 0 4 0 0 0 1 8 12 12 11
Great Tit 23 8 19 16 24 25 19 6 16 22 27 4 27 3 16 20 14 19 17 2 12 27 11 0 11 13 96 61 120 124
Green Woodpecker 0 4 0 0 2 0 0 0 1 0 0 0 0 0 0 0 0 0 2 0 1 1 0 2 0 0 0 8 2 3
Greenfinch 0 0 0 0 0 0 2 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 14 3 2 0 15 3
Grey-headed Woodpecker 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 0 0 1 0 0 0 0 0 0 0 4
Hawfinch 0 3 4 0 1 0 0 0 1 2 0 0 0 1 1 1 4 2 6 0 14 5 0 0 0 0 0 9 22 14
Hobby 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0
Hoopoe 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 4 0 1 0 5
House Sparrow 0 0 0 2 4 0 10 1 1 5 0 0 0 7 18 5 0 6 1 11 0 8 15 2 1 1 27 25 26 20
Icterine warbler 1 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 1 1 0
Jay 0 0 1 0 0 0 0 0 0 0 4 2 1 0 4 1 0 5 1 0 0 0 2 0 0 0 6 2 11 2
Kingfisher 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 0
Lesser grey shrike 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 7 0 0 1 8 0
Lesser Spotted Woodpecker 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 1 1 0 1 0 0 0 0 0 0 0 1 2 0 2
Page 40
Table 7.3. cont.
Apold Crit Daia Malancrav Mesendorf Nou Sasesc Richis Viscri Species Total
2014 2015 2016 2014 2015 2017 2014 2015 2016 2017 2014 2015 2016 2015 2016 2017 2015 2016 2017 2015 2016 2017 2014 2015 2016 2017 2014 2015 2016 2017
Lesser Whitethroat 5 4 1 1 0 0 2 8 1 0 0 8 2 0 5 0 9 0 1 5 5 12 9 3 4 17 17 37 18 30
Linnet 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 0 1 1 0 0 1 3 0 0
Long-eared owl 0 0 0 0 0 0 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 5 0 0 0
Long-tailed Tit 6 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 5 0 0 0 0 6 0 0 5
Magpie 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0
Marsh Tit 13 9 4 3 7 3 0 0 3 2 10 6 6 0 2 0 0 1 4 0 7 3 0 0 0 0 26 22 23 12
Marsh Warbler 5 11 8 2 1 2 26 40 18 36 0 0 0 10 4 2 27 8 3 7 14 14 37 12 9 9 70 108 61 66
Middle Spotted Woodpecker 0 1 0 0 0 1 0 0 0 0 1 0 0 2 1 2 0 1 4 0 0 0 0 0 0 0 1 3 2 7
Nuthatch 0 0 0 5 5 0 0 0 0 0 2 1 2 0 0 0 0 0 2 0 0 5 0 0 0 0 7 6 2 7
Quail 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0
Red-backed Shrike 0 15 1 25 38 54 43 60 61 68 12 1 4 12 24 25 15 13 18 1 2 5 12 26 38 13 92 168 143 183
River Warbler 0 3 2 1 0 0 0 0 0 0 0 0 0 0 0 0 1 3 5 0 0 4 0 0 0 0 1 4 5 9
Robin 3 3 1 3 6 0 0 0 2 0 0 2 3 0 5 1 1 2 0 0 3 0 1 0 0 0 7 12 16 1
Scops Owl 0 0 0 0 0 0 0 0 0 0 1 0 0 2 0 2 0 0 0 0 0 1 0 0 0 0 1 2 0 3
Sedge Warbler 0 2 4 0 0 0 2 1 1 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 3 5 2
Serin 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1
Song Thrush 1 2 0 0 2 3 0 0 1 3 0 0 0 1 0 0 1 3 4 0 0 0 0 0 0 1 1 6 4 11
Sparrowhawk 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0
Spotted Flycatcher 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 1 0 0 0 0 0 1 1 1
Starling 0 0 0 0 0 0 0 0 0 0 1 0 0 0 7 4 0 0 0 0 0 1 2 0 15 0 3 0 22 5
Stonechat 0 0 11 0 0 0 2 0 1 1 0 0 0 0 0 0 1 4 0 0 2 11 0 0 0 0 2 1 18 12
Thrush Nightingale 0 1 0 0 0 0 0 3 4 2 0 0 0 0 1 0 0 0 0 0 0 1 0 0 1 0 0 4 6 3
Tree Pipit 0 0 1 0 3 0 1 4 0 2 0 0 1 0 0 1 1 0 1 0 0 1 0 0 6 3 1 8 8 8
Tree Sparrow 0 0 0 0 1 6 14 34 26 46 7 25 37 26 15 31 90 98 62 6 6 33 60 6 1 10 81 188 183 188
Treecreeper 0 1 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 1 0
Whinchat 0 0 0 0 0 0 1 1 1 4 0 0 0 19 5 4 0 0 0 0 0 0 6 3 1 3 7 23 7 11
White wagtail 25 1 2 0 0 0 2 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 27 2 3 0
Willow warbler 25 5 0 0 0 0 0 0 0 0 1 8 0 0 0 0 0 0 0 0 0 0 0 0 0 0 26 13 0 0
Wood warbler 0 0 0 1 0 0 0 0 0 0 0 3 1 0 0 0 0 0 0 0 0 0 0 1 0 0 1 4 1 0
Woodlark 0 0 0 0 0 0 0 0 0 0 3 0 0 0 1 0 0 0 0 0 2 0 1 1 0 0 4 1 3 0
Wryneck 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 1 0 0 1 0 0 1 1 1 0 0 2 2 0 3
Yellowhammer 3 0 5 3 0 5 2 4 0 1 1 0 0 11 5 2 0 2 7 0 1 2 7 8 4 9 16 23 17 26
Village Total 156 147 84 73 123 118 179 344 194 256 93 89 119 141 189 149 177 183 168 41 111 190 240 117 194 123 741 1179 1074 1005
Page 41
8.0 Small mammals
2017 was a busy year for small mammal captures. In 2017 there were more than twice as many small
mammal captures per trap night than any of the previous years. In 2016 and 2017 there has been a
recovery in the numbers of several species at several villages after 2015’s extreme decrease in the
number of small mammals captured. The total number of captures per 1000 trap nights had dropped
from 154 to 19 in 2015, but rose back to 179 in 2016, and then 383 in 2017. Such a wide-spread and
general decline and then recovery is most likely due to an environmental factor such as weather
conditions (there was an extended period of cool, wet weather in 2015), or natural population
fluctuations. The greens in Table 8.1 show that it was a very good year for Yellow-necked mouse
captures in every village, good for Wood mouse and Bank vole in some villages. The reds in Table 8.1
indicate that there are also some species in several villages with reduced numbers in 2017,
particularly Common vole.
Table 8.1. Small mammal captures per 1000 trap nights, for each species, for each village, in total and
for each habitat type (2014 value : 2015 value : 2016 value : 2017 value). Species abbreviations
shown in Table 8.2. Habitat abbreviations: L/M/HNV - low/medium/high nature value grassland; SC -
scrub; WE -woodland edge. White - zero captures. Grey – 2017 captures within 50% of average for
previous years. Dark green – 2017 captures more than 50% above average for previous years. Red -
2017 captures less than 50% of average for previous years. 2015 and 2016.
Habitat A SP AA AAM AF AS CL CS
Ap
old
LNV 0:0:0:0 40:20:13:10 0:0:0:0 0:0:88:90 50:0:25:0 0:0:0:0 0:0:0:0
MNV 0:0:0:0 40:0:0:100 0:0:0:0 0:0:13:725 90:0:50:0 0:0:0:0 10:0:0:0
SC/WE 0:0:0:0 30:20:0:0 0:0:0:0 10:50:413:440 160:40:50:10 0:0:0:10 10:0:0:0
Village Total 0:0:0:0 37:13:4:21 0:0:0:0 3:17:171:342 100:13:42:4 0:0:0:4 7:0:0:0
Cri
t
LNV 0:0:0 20:0:460 0:0:0 0:0:290 60:0:30 0:0:0 0:0:0
MNV 0:0:0 0:25:10 0:0:0 0:0:30 100:0:0 0:0:0 0:0:0
SC 0:0:0 140:25:0 0:0:0 40:0:700 90:0:60 10:0:0 0:0:0
Village Total 0:0:0 53:17:157 0:0:0 13:0:340 83:0:30 3:0:0 0:0:0
Dai
a
LNV 0:0:0:0 0:0:60:41 0:0:0:0 0:0:10:20 0:0:10:0 0:0:0:0 0:0:0:0
HNV 0:0:0:0 80:0:0:0 0:0:0:10 0:0:0:100 30:0:21:0 0:0:0:0 0:0:0:0
SC 0:0:0:0 80:0:0:10 0:0:0:0 0:0:222:560 50:0:0:10 0:0:0:0 0:0:0:0
Village Total 0:0:0:0 53:0:21:17 0:0:0:3 0:0:74:228 27:0:11:3 0:0:0:0 0:0:0:0
Mal
ancr
av
LNV 0:0:0:20 0:0:50:50 0:0:0:0 0:0:140:110 130:0:0:50 0:0:0:0 10:50:0:0
MNV 0:0:0:0 0:0:13:0 0:0:0:0 0:0:0:0 60:0:0:67 0:0:0:0 30:0:0:0
SC 0:0:0:0 40:0:0:0 0:0:0:0 0:13:0:720 150:0:0:10 0:0:0:0 10:0:0:0
Village Total 0:0:0:8 13:0:24:19 0:0:0:0 0:5:56:319 113:0:0:38 0:0:0:0 17:15:0:0
Mes
end
orf
LNV 0:0:0:0 0:0:0:0 0:0:0:0 0:0:0:8 0:0:10:8 0:0:0:0 0:0:0:0
MNV 0:0:0:0 20:0:11:92 0:0:0:0 0:0:189:83 20:0:11:58 0:0:0:0 0:0:0:0
SC/WE 0:0:0:0 0:0:80:0 0:0:0:0 0:0:50:233 80:0:50:158 0:0:0:0 0:0:0:0
Village Total 0:0:0:0 7:0:31:31 0:0:0:0 0:0:76:108 33:0:24:75 0:0:0:0 0:0:0:0
No
u S
ases
c
LNV 0:0:0:0 0:0:0:0 0:0:0:0 10:0:0:150 10:0:0:63 0:0:0:0 0:0:0:0
MNV 0:0:0:0 0:0:0:63 0:0:0:0 0:0:10:175 210:0:50:113 0:0:0:0 0:0:0:0
WE 0:0:0:0 0:0:50:25 0:0:0:0 0:0:20:531 0:0:10:185 0:0:0:0 0:0:0:0
Village Total 0:0:0:0 0:0:17:29 0:0:0:0 3:0:10:286 73:0:20:120 0:0:0:0 0:0:0:0
Ric
his
LNV 0:0:0:0 40:21:100:0 0:0:0:0 0:0:0:0 70:0:20:0 0:0:0:0 0:0:0:0
HNV 0:0:0:0 230:14:0:10 0:0:0:0 20:0:0:20 80:0:0:0 0:0:0:0 0:0:0:0
WE 0:0:0:20 160:57:70:60 0:0:0:0 60:0:0:250 360:7:20:340 0:0:0:0 0:0:0:0
Village Total 0:0:0:7 108:31:57:23 0:0:0:0 20:0:0:90 133:2:13:113 0:0:0:0 3:0:0:0
Vis
cri
LNV 0:0:0:0 0:0:13:0 0:0:0:0 0:0:0:0 0:0:0:0 0:0:0:0 0:0:0:0
MNV 0:0:0:0 0:0:42:0 0:0:0:0 0:0:0:0 0:0:28:0 0:0:0:0 0:0:0:0
SC 0:0:0:0 0:8:88:0 0:0:0:0 0:0:13:30 20:0:0:10 0:0:0:0 0:0:0:0
Village Total 0:0:0:0 0:3:47:0 0:0:0:0 0:0:4:13 7:0:9:4 0:0:0:0 0:0:0:0
Total
0:0:0:2 37:9:26:39 0:0:0:0 6:2:48:212 74:2:15:50 0:0:0:0 3:1:0:0
Page 42
Table 8.1. cont.
Habitat GG MA MAG MAR MG MM Site Total
Ap
old
LNV 0:0:0:0 20:0:0:0 0:0:0:0 20:0:100:560 0:0:0:0 0:0:0:0 130:20:250:660
MNV 0:0:0:0 20:0:13:0 20:0:0:0 270:10:63:350 0:0:0:0 0:0:0:0 450:10:138:1175
SC/WE 0:0:0:10 0:0:0:0 0:0:0:0 20:0:0:0 0:0:0:0 0:0:0:0 230:110:463:470
Village Total 0:0:0:4 13:0:4:0 7:0:0:0 103:3:54:292 0:0:0:0 0:0:0:0 270:47:283:667
Cri
t
LNV 0:0:0 40:0:0 0:0:10 40:0:0 10:0:0 0:0:0 170:0:790
MNV 0:0:0 40:0:0 0:13:10 10:0:0 0:0:0 0:0:0 150:38:50
SC 0:0:0 30:0:0 0:50:0 0:0:0 40:0:50 0:0:0 350:75:810
Village Total 0:0:0 37:0:0 0:21:7 17:0:0 17:0:17 0:0:0 223:38:550
Dai
a
LNV 0:0:0:0 10:0:0:0 10:0:0:418 10:0:80:0 0:0:0:0 0:0:0:10 30:0:160:490
HNV 0:0:0:0 20:0:0:0 0:0:0:290 20:0:42:0 0:0:0:0 0:0:0:0 150:0:63:400
SC 0:0:0:0 30:0:0:0 0:0:0:10 10:0:11:0 0:0:0:0 0:0:0:0 170:0:244:590
Village Total 0:0:0:0 20:0:0:0 3:0:0:238 13:0:46:0 0:0:0:0 0:0:0:3 117:0:154:493
Mal
ancr
av
LNV 0:0:0:0 0:0:0:0 0:0:0:0 10:0:550:0 0:0:0:0 0:0:0:0 150:50:750:230
MNV 0:0:0:0 10:0:0:0 10:0:0:0 0:0:53:17 0:0:0:0 0:0:0:0 110:0:79:83
SC 0:0:0:0 0:0:0:0 0:0:0:10 10:0:0:0 0:0:0:30 0:0:0:0 210:13:0:770
Village Total 0:0:0:0 3:0:0:0 3:0:0:4 7:0:238:4 0:0:0:12 0:0:0:0 157:20:327:404
Mes
end
orf
LNV 0:0:0:0 0:0:0:0 0:0:0:0 0:0:0:8 0:0:0:0 0:0:0:0 0:0:10:25
MNV 0:0:0:0 10:0:0:17 0:0:0:50 0:0:0:8 0:0:11:17 0:0:0:8 50:0:222:333
SC/WE 0:0:0:0 0:0:0:8 0:0:0:0 0:0:0:0 0:0:0:0 0:0:0:0 80:0:180:400
Village Total 0:0:0:0 3:0:0:0 0:0:0:17 0:0:0:6 0:0:3:6 0:0:0:3 43:0:134:253
No
u S
ases
c
LNV 0:0:0:0 10:0:10:0 0:0:0:0 10:0:0:13 0:0:0:0 0:0:0:0 40:0:10:225
MNV 0:0:0:0 0:0:0:0 20:0:10:0 0:0:0:0 0:0:0:0 0:0:0:0 230:0:70:350
WE 0:0:0:0 0:0:0:0 0:0:10:0 0:0:0:0 0:0:0:25 0:0:0:0 0:0:90:765
Village Total 0:0:0:0 3:0:3:0 7:0:7:0 3:0:0:4 0:0:0:8 0:0:0:0 90:0:57:448
Ric
his
LNV 0:0:0:0 0:0:40:0 0:43:70:0 10:0:0:0 0:0:0:0 0:0:0:0 120:64:230:0
HNV 0:0:0:0 0:0:0:0 30:7:0:0 20:0:0:0 0:0:0:0 0:0:0:0 380:21:0:30
WE 0:0:0:0 0:0:220:0 0:0:230:0 20:0:0:0 0:0:0:30 0:0:0:0 600:64:540:700
Village Total 0:0:0:0 0:0:87:0 8:17:100:0 13:0:0:0 0:0:0:10 0:0:0:0 283:50:257:243
Vis
cri
LNV 0:0:0:0 0:0:0:0 0:0:0:0 0:0:300:0 0:0:0:0 0:0:0:0 0:0:313:0
MNV 0:0:0:0 0:0:0:0 0:0:0:0 0:0:14:0 0:0:0:0 0:0:0:0 0:0:97:0
SC 0:0:0:0 10:0:0:0 0:0:0:0 0:0:213:0 0:0:0:10 0:0:0:0 30:8:313:50
Village Total 0:0:0:0 3:0:0:0 0:0:0:0 0:0:181:0 0:0:0:4 0:0:0:0 10:3:246:21
Total
0:0:0:0 10:0:13:1 4:5:15:36 19:0:59:33 2:0:0:7 0:0:0:1 154:19:179:383
Table 8.2. Small mammal species abbreviations.
Abbreviation Latin name Common name
A SP Apodemus sp.
AA Apodemus agrarius Striped field mouse
AF Apodemus flavicollis Yellow-necked mouse
AS Apodemus sylvaticus Wood mouse
AAM Arvicula amphibius Water vole
CL Crocidura leucodon Bi-coloured white-toothed shrew
CS Crocidura sauveolens Lesser white-toothed shrew
GG Glis glis Edible dormouse
MA Unidentified vole
MAG Microtus agrestis Field vole
MAR Microtus arvalis Common vole
MG Myodes glareolus Bank vole
MM Micromys minutus Harvest mouse
Page 43
9.0 Large Mammals
9.1 Camera Trap Survey
Table 9.1 summarises the large mammals recorded by the camera traps, per village. 2017 had the
greatest number of total hours of recording of the three years, and the greatest number of
individuals recorded, mainly due to a greater number of cameras being used. The overall number of
records per 24 hours was higher in 2017 than in 2016 or 2015, and increased in 6 of the 8 villages
(declined in Mesendorf and Richis). Crit and Daia had the most frequent records, wherea it was
Mesendorf and Richis in 2016. This suggests that amount of footage fluctuates quite substantially.
Roe deer Capreolus capreolus has consistently been the most frequently recorded species in all
years, and was recorded at every village every year. Two villages had a notable decrease in roe deer
recordings (red shading), while four had a notable increase (green shading). There were notable
increases in wild boar footage at most villages. Fox footage increased at some villages and decreased
at others.
The number of records is relatively low and the changes from year to year must be interpreted with
great caution. The differences should not be seen as evidence of significant shift in large mammal
populations of the studied villages.
Page 44
Table 9.1. Summary of the large mammals recorded by the camera trap survey. Records per 24 hours per
camera (records). Grey - less than 3 records in two consecutive years. Red - a 50% decrease or more. Green
- a 50% increase or more. Species abbreviations are given in Table 9.2.
CC CE FSS GG LE MF
MF/MM
MM MMS SS SV UA VV Village Total
Installation time (hr)
Apold 2014 0.41
(4) 0
(0) 0
(0) 0
(0) 0
(0) 0
(0) 0
(0) 0
(0) 0
(0) 0
(0) 0
(0) 0
(0) 0.21
(2) 0.62
(6) 233.00
2015 0.44
(8) 0.06
(1) 0
(0) 0
(0) 0
(0) 0
(0) 0
(0) 0
(0) 0.61 (11)
0.11 (2)
0 (0)
0.33 (6)
0.56 (10)
2.11 (38)
432.08
2016 0.19
(5) 0
(0) 0
(0) 0
(0) 0
(0) 0
(0) 0
(0) 0
(0) 0.08
(2) 0.38 (10)
0 (0)
0 (0)
0.19 (5)
0.83 (22)
634.39
2017 0.81 (29)
0 (0)
0 (0)
0 (0)
0 (0)
0 (0)
0 (0)
0 (0)
0 (0)
0 (0)
0 (0)
0 (0)
0 (0)
0.92 (33)
862.74
Crit 2014 0.28
(4) 0
(0) 0
(0) 0
(0) 0
(0) 0
(0) 0
(0) 0
(0) 0
(0) 0.14
(2) 0
(0) 0
(0) 0
(0) 0.42
(6) 340.90
2015 0.31
(9) 0
(0) 0
(0) 0
(0) 0
(0) 0
(0) 0.03
(1) 0
(0) 0.03
(1) 0
(0) 0
(0) 0
(0) 0.03
(1) 0.42 (12)
686.97
2017 0.34 (13)
0.05 (2)
0.08 (3)
0 (0)
0 (0)
0 (0)
0 (0)
0 (0)
0.03 (1)
1.6 (62)
0 (0)
0 (0)
0 (0)
2.12 (82)
930.10
Daia 2014 0.14
(2) 0
(0) 0
(0) 0
(0) 0
(0) 0
(0) 0
(0) 0
(0) 0
(0) 0.07
(1) 0.07
(1) 0.07
(1) 0
(0) 0.28
(4) 349.02
2015 0.58 (17)
0.1 (3)
0 (0)
0 (0)
0 (0)
0.31 (9)
0 (0)
0 (0)
0.2 (6)
0 (0)
0 (0)
0.1 (3)
0.24 (7)
1.53 (45)
706.33
2016 0.2 (9)
0 (0)
0 (0)
0 (0)
0.02 (1)
0 (0)
0.02 (1)
0 (0)
0 (0)
0.11 (5)
0 (0)
0.02 (1)
0.02 (1)
0.4 (18)
1076.85
2017 1.32 (47)
0.14 (5)
0 (0)
0 (0)
0 (0)
0 (0)
0 (0)
0 (0)
0 (0)
0.42 (15)
0 (0)
0.03 (1)
0.06 (2)
1.97 (70)
852.75
Malancrav 2014 0.74 (11)
0 (0)
0 (0)
0 (0)
0 (0)
0.07 (1)
0 (0)
0 (0)
0 (0)
0.2 (3)
0.07 (1)
0.07 (1)
0 (0)
1.08 (16)
354.85
2015 0.5
(11) 0.05
(1) 0
(0) 0
(0) 0
(0) 0
(0) 0.05
(1) 0
(0) 0.05
(1) 0.18
(4) 0
(0) 0
(0) 0.05
(1) 0.86 (19)
528.00
2016 0.1 (4)
0.18 (7)
0 (0)
0 (0)
0 (0)
0 (0)
0 (0)
0.03 (1)
0 (0)
0 (0)
0 (0)
0.03 (1)
0 (0)
0.33 (13)
952.36
2017 0.48 (17)
0 (0)
0 (0)
0 (0)
0 (0)
0 (0)
0 (0)
0 (0)
0 (0)
0 (0)
0 (0)
0 (0)
0.06 (2)
0.53 (19)
857.16
Mesendorf 2014 0.78 (19)
0 (0)
0 (0)
0.12 (3)
0.12 (3)
0 (0)
0 (0)
0.04 (1)
0 (0)
0.08 (2)
0.12 (3)
0.12 (3)
0.25 (6)
1.39 (34)
586.12
2015 0.18
(5) 0.46 (13)
0 (0)
0 (0)
0 (0)
0 (0)
0 (0)
0 (0)
0 (0)
0 (0)
0 (0)
0.07 (2)
0.11 (3)
0.81 (23)
683.22
2016 0.85 (32)
0.03 (1)
0.03 (1)
0 (0)
0 (0)
0.03 (1)
0 (0)
0 (0)
0.19 (7)
0 (0)
0.11 (4)
0.03 (1)
0.11 (4)
1.35 (51)
904.29
2017 0.3
(13) 0
(0) 0.02
(1) 0
(0) 0
(0) 0
(0) 0
(0) 0
(0) 0.09
(4) 0.14
(6) 0
(0) 0
(0) 0.05
(2) 0.63 (27)
1024.10
Nou Sasesc 2014 0.38
(6) 0
(0) 0.25
(4) 0
(0) 0
(0) 0.06
(1) 0
(0) 0
(0) 0.06
(1) 0
(0) 0
(0) 0
(0) 0.13
(2) 1.01 (16)
378.88
2015 0.21
(5) 0
(0) 0
(0) 0
(0) 0
(0) 0
(0) 0
(0) 0
(0) 0
(0) 0
(0) 0
(0) 0
(0) 0.17
(4) 0.38
(9) 572.45
2016 0.04
(1) 0
(0) 0
(0) 0
(0) 0
(0) 0.11
(3) 0
(0) 0
(0) 0
(0) 0
(0) 0
(0) 0
(0) 0.04
(1) 0.19
(5) 634.89
2017 0.4
(14) 0
(0) 0.03
(1) 0
(0) 0
(0) 0
(0) 0
(0) 0
(0) 0
(0) 0
(0) 0
(0) 0
(0) 0.17
(6) 0.6
(21) 842.22
Richis 2014 0.8
(15) 0
(0) 0
(0) 0.11
(2) 0.11
(2) 0
(0) 0
(0) 0.05
(1) 0.16
(3) 0
(0) 0
(0) 0
(0) 0.21
(4) 1.44 (27)
451.38
2015 0.34 (11)
0 (0)
0 (0)
0 (0)
0 (0)
0 (0)
0.03 (1)
0 (0)
0.09 (3)
0.03 (1)
0 (0)
0 (0)
0.06 (2)
0.56 (18)
775.32
2016 0.5 (7)
0 (0)
0 (0)
0.07 (1)
0 (0)
0.5 (7)
0 (0)
0 (0)
0 (0)
0 (0)
0 (0)
0 (0)
0 (0)
1.15 (16)
334.00
2017 0.2 (9)
0 (0)
0.02 (1)
0 (0)
0 (0)
0.11 (5)
0 (0)
0 (0)
0.02 (1)
0.09 (4)
0.02 (1)
0 (0)
0.17 (8)
0.65 (30)
1103.62
Viscri 2014 0.77 (12)
0.51 (8)
0.06 (1)
0 (0)
0 (0)
0 (0)
0 (0)
0 (0)
0 (0)
0 (0)
0 (0)
0 (0)
0.06 (1)
1.53 (24)
375.38
2015 0.54 (21)
0.1 (4)
0 (0)
0 (0)
0 (0)
0 (0)
0.05 (2)
0 (0)
0.1 (4)
0.1 (4)
0 (0)
0.03 (1)
0.05 (2)
0.98 (38)
926.00
2016 0.5
(20) 0
(0) 0.03
(1) 0
(0) 0.03
(1) 0
(0) 0
(0) 0
(0) 0.03
(1) 0
(0) 0
(0) 0
(0) 0.2 (8)
0.78 (31)
956.66
2017 0.37 (16)
0 (0)
0.09 (4)
0 (0)
0 (0)
0 (0)
0 (0)
0 (0)
0 (0)
0.37 (16)
0 (0)
0 (0)
0.02 (1)
0.9 (39)
1037.19
Species total 2014 0.57 (73)
0.06 (8)
0.04 (5)
0.04 (5)
0.04 (5)
0.02 (2)
0 (0)
0.02 (2)
0.03 (4)
0.06 (8)
0.04 (5)
0.04 (5)
0.12 (15)
1.04 (133)
3069.53
2015 0.39 (87)
0.1 (22)
0 (0)
0 (0)
0 (0)
0.04 (9)
0.02 (5)
0 (0)
0.12 (26)
0.05 (11)
0 (0)
0.05 (12)
0.14 (30)
0.91 (202)
5310.37
2016 0.34 (78)
0.03 (8)
0.01 (2)
0 (1)
0.01 (2)
0.05 (11)
0 (1)
0 (1)
0.04 (10)
0.07 (15)
0.02 (4)
0.01 (3)
0.08 (19)
0.68 (156)
5493.44
2017 0.5
(158) 0.02
(7) 0.03 (10)
0 (0)
0 (0)
0.02 (5)
0 (0)
0 (0)
0.02 (6)
0.33 (103)
0 (1)
0 (1)
0.07 (21)
1.03 (321)
7509.88
Page 45
Table 9.2. Key to large mammal names. Code Latin Species Code Latin Species
CC Capreolus capreolus Roe deer MM Martes martes Pine marten
CE Cervus elaphus Red deer MMS Meles meles European badger
EC Erinaceus concolor Eastern hedgehog MN Mustela nivalis Weasel
EE Erinaceus europaeus European hedgehog MP Mustela putorius Polecat
FSS Felis silvestris silvestris European wildcat SS Sus scrofa Wild boar
GG Glis glis Edible dormouse SV Sciurus vulgaris Red squirrel
LE Lepus europaeus Brown hare UA Ursus Arctos Brown bear
ME Mustela erminea Stoat VV Vulpes vulpes Red fox
MF Martes foina Beech marten
9.2 Observation of large mammal signs
Table 9.3 summarises the results of the large mammal transect surveys per village. There were fewer
signs overall in 2017 than in any previous years, with a notable greater than 50% decrease at every
village except Crit. Daia, Mesendorf and Malancrav have had high abundance of signs in comparison
to other villages for all three previous years. But in 2017 Apold and Crit had the highest abundances,
followed by Mesendorf. Signs of roe deer, red deer (except Viscri in 2017), badger, wild boar (except
Richis in 2016, Malancrav and Nou Sasesc in 2017) and red fox signs (except Viscri in 2014 and
Malancrav in 2017) were seen at all eight villages, in all four years. There were many more badger
signs in 2017 at 6 villages compared to 2016. Wild boar showed the reverse trend to badger at most
villages. This could be an effect of different surveyors identifying signs differently. Roe deer (6
villages), beech marten (4), and red fox (5) notably decreased overall and at four or more villages in
2017. Such species changes may again partly be due to differences in surveyor interpretation of the
signs. The large number of green- and red-shaded pairs of cells in Table 9.3 illustrates how number of
track signs fluctuates a lot between years. This is probably mostly due to natural variability in the
abundance of large mammals.
Page 46
Table 9.3. Summary of the large mammal signs observed on transect surveys – number of signs per km (number
of signs). Grey - less than 3 signs in two consecutive years. Red - a 50% decrease or more. Green - a 50% increase
or more. See table 9.2 for species abbreviations. Signs of uncertain species have been excluded from the table. Village (Transect length – km)
CC CE EC EE FSS LE ME MF MM MMS MN MP OC SS SV UA VV Village Total
Apold (13.01)
2014 0.85 (11)
0.23 (3)
0 (0)
0 (0)
0 (0)
0.15 (2)
0 (0)
0 (0)
0 (0)
0.31 (4)
0 (0)
0 (0)
0 (0)
0.54 (7)
0 (0)
0 (0)
0.31 (4)
3 (39)
2015 0.15
(2) 0.15
(2) 0
(0) 0.08
(1) 0
(0) 0
(0) 0
(0) 0.08
(1) 0.08
(1) 0.77 (10)
0 (0)
0 (0)
0 (0)
0.15 (2)
0 (0)
0.15 (2)
0.15 (2)
1.92 (25)
2016 4.61 (60)
0.38 (5)
0 (0)
0 (0)
0 (0)
0.08 (1)
0 (0)
0.15 (2)
0 (0)
0.23 (3)
0 (0)
0 (0)
0 (0)
1.92 (25)
0 (0)
0.15 (2)
0.23 (3)
7.99 (104)
2017 1.69 (22)
0.15 (2)
0 (0)
0 (0)
0 (0)
0.15 (2)
0 (0)
0.23 (3)
0 (0)
1.15 (15)
0 (0)
0 (0)
0 (0)
0.46 (6)
0 (0)
0.31 (4)
0.77 (10)
4.92 (64)
Crit (14.15)
2014 2.69 (38)
1.34 (19)
0.07 (1)
0 (0)
0 (0)
0.14 (2)
0 (0)
0 (0)
0 (0)
0.28 (4)
0 (0)
0 (0)
0 (0)
1.13 (16)
0 (0)
0.07 (1)
0.21 (3)
6.71 (95)
2015 1.98 (28)
0.42 (6)
0 (0)
0 (0)
0 (0)
0.07 (1)
0 (0)
0 (0)
0 (0)
1.13 (16)
0 (0)
0 (0)
0 (0)
0.42 (6)
0 (0)
0.42 (6)
0.07 (1)
4.66 (66)
2017 1.55 (22)
0.71 (10)
0.07 (1)
0 (0)
0.07 (1)
0.07 (1)
0 (0)
0 (0)
0 (0)
0.21 (3)
0 (0)
0 (0)
0 (0)
0.28 (4)
0 (0)
0.28 (4)
0.42 (6)
3.75 (53)
Daia (12.95)
2014 1.7
(22) 0.23
(3) 0
(0) 0
(0) 0
(0) 0
(0) 0
(0) 0
(0) 0
(0) 0.15
(2) 0
(0) 0
(0) 0
(0) 1.62 (21)
0 (0)
0.54 (7)
0.69 (9)
5.41 (70)
2015 0.62
(8) 0.46
(6) 0
(0) 0
(0) 0
(0) 0
(0) 0.08
(1) 0
(0) 0.08
(1) 2.86 (37)
0 (0)
0 (0)
0 (0)
0.46 (6)
0 (0)
0.23 (3)
0.15 (2)
5.02 (65)
2016 2.78 (36)
0.31 (4)
0 (0)
0 (0)
0 (0)
0.15 (2)
0 (0)
1.39 (18)
0 (0)
0.15 (2)
0 (0)
0 (0)
0 (0)
0.23 (3)
0 (0)
0.77 (10)
2.08 (27)
7.88 (102)
2017 0.69
(9) 0.39
(5) 0.15
(2) 0
(0) 0.08
(1) 0
(0) 0
(0) 0
(0) 0.08
(1) 0.54
(7) 0
(0) 0
(0) 0
(0) 0.39
(5) 0
(0) 0.46
(6) 0.39
(5) 3.17 (41)
Malancrav (13.38)
2014 1.72 (23)
0.22 (3)
0 (0)
0 (0)
0 (0)
0 (0)
0.07 (1)
0.07 (1)
0.07 (1)
0.45 (6)
0 (0)
0 (0)
0 (0)
0.67 (9)
0 (0)
0 (0)
0.67 (9)
4.48 (60)
2015 0.3 (4)
0.07 (1)
0 (0)
0 (0)
0.07 (1)
0 (0)
0 (0)
0.37 (5)
0.07 (1)
1.2 (16)
0 (0)
0 (0)
0 (0)
0.37 (5)
0 (0)
0.15 (2)
0.52 (7)
3.14 (42)
2016 1.72 (23)
0.22 (3)
0 (0)
0 (0)
0 (0)
0.07 (1)
0 (0)
0.15 (2)
0 (0)
0.22 (3)
0 (0)
0 (0)
0 (0)
1.42 (19)
0 (0)
0.07 (1)
0.67 (9)
5.38 (72)
2017 0.6 (8)
0.07 (1)
0.22 (3)
0 (0)
0 (0)
0 (0)
0 (0)
0.07 (1)
0.15 (2)
0.45 (6)
0 (0)
0.07 (1)
0 (0)
0 (0)
0 (0)
0.3 (4)
0 (0)
1.94 (26)
Mesendorf (12.62)
2014 1.66 (21)
1.19 (15)
0 (0)
0 (0)
0 (0)
0.08 (1)
0.16 (2)
0 (0)
0 (0)
0.4 (5)
0 (0)
0 (0)
0 (0)
0.63 (8)
0.08 (1)
0.32 (4)
0.95 (12)
6.42 (81)
2015 2.38 (30)
1.03 (13)
0.08 (1)
0.16 (2)
0 (0)
0 (0)
0 (0)
0 (0)
0 (0)
2.22 (28)
0 (0)
0 (0)
0.08 (1)
0.48 (6)
0 (0)
0 (0)
0.32 (4)
6.81 (86)
2016 3.72 (47)
1.9 (24)
0.24 (3)
0 (0)
0.08 (1)
0.08 (1)
0 (0)
0.71 (9)
0.48 (6)
0.08 (1)
0 (0)
0 (0)
0 (0)
1.82 (23)
0 (0)
0.79 (10)
0.48 (6)
10.78 (136)
2017 1.35 (17)
0.24 (3)
0 (0)
0 (0)
0.08 (1)
0.08 (1)
0 (0)
0.08 (1)
0.32 (4)
0.79 (10)
0 (0)
0 (0)
0 (0)
0.24 (3)
0 (0)
0.16 (2)
0.24 (3)
3.57 (45)
Nou Sasesc (12.10)
2014 0.91 (11)
0.25 (3)
0 (0)
0 (0)
0 (0)
0 (0)
0 (0)
0 (0)
0 (0)
0.08 (1)
0 (0)
0 (0)
0 (0)
0.5 (6)
0 (0)
0 (0)
0.41 (5)
2.15 (26)
2015 0.99 (12)
0.25 (3)
0 (0)
0.08 (1)
0 (0)
0 (0)
0 (0)
0.17 (2)
0 (0)
1.4 (17)
0 (0)
0 (0)
0 (0)
0.08 (1)
0 (0)
0 (0)
0.08 (1)
3.14 (38)
2016 0.99 (12)
0.08 (1)
0 (0)
0 (0)
0 (0)
0 (0)
0 (0)
0.25 (3)
0 (0)
0.17 (2)
0 (0)
0 (0)
0 (0)
0.41 (5)
0 (0)
0.83 (10)
1.32 (16)
4.13 (50)
2017 1.16 (14)
0.17 (2)
0 (0)
0 (0)
0 (0)
0 (0)
0 (0)
0.08 (1)
0 (0)
0.41 (5)
0 (0)
0 (0)
0 (0)
0 (0)
0 (0)
0.08 (1)
0.17 (2)
2.07 (25)
Richis (12.32)
2014 1.38 (17)
0.16 (2)
0 (0)
0 (0)
0 (0)
0 (0)
0 (0)
0.08 (1)
0 (0)
0.16 (2)
0 (0)
0 (0)
0 (0)
0.16 (2)
0 (0)
0 (0)
0.32 (4)
2.27 (28)
2015 1.54 (19)
0.41 (5)
0 (0)
0.16 (2)
0 (0)
0.08 (1)
0 (0)
0 (0)
0.08 (1)
1.22 (15)
0 (0)
0 (0)
0 (0)
0.32 (4)
0 (0)
0 (0)
0.16 (2)
4.06 (50)
2016 1.7
(21) 0.49
(6) 0
(0) 0
(0) 0
(0) 0
(0) 0
(0) 0
(0) 0
(0) 0.08
(1) 0.16
(2) 0.08
(1) 0
(0) 0
(0) 0
(0) 0.41
(5) 1.3
(16) 4.22 (52)
2017 0.49
(6) 0.16
(2) 0
(0) 0
(0) 0.08
(1) 0
(0) 0
(0) 0.08
(1) 0
(0) 0.32
(4) 0
(0) 0
(0) 0
(0) 0.08
(1) 0
(0) 0
(0) 0.41
(5) 1.62 (20)
Viscri (16.99)
2014 0.24
(4) 0.06
(1) 0
(0) 0
(0) 0
(0) 0.12
(2) 0
(0) 0
(0) 0
(0) 0
(0) 0
(0) 0
(0) 0
(0) 0.12
(2) 0
(0) 0
(0) 0
(0) 0.59 (10)
2015 1.18 (20)
1.12 (19)
0 (0)
0 (0)
0 (0)
0.06 (1)
0 (0)
0.06 (1)
0 (0)
1.71 (29)
0 (0)
0 (0)
0 (0)
0.35 (6)
0 (0)
0.12 (2)
0.35 (6)
5.12 (87)
2016 1.65 (28)
0.59 (10)
0.06 (1)
0 (0)
0 (0)
0.06 (1)
0 (0)
0.18 (3)
0 (0)
0.06 (1)
0 (0)
0 (0)
0 (0)
0.29 (5)
0 (0)
0.06 (1)
0.18 (3)
3.3 (56)
2017 0.59 (10)
0 (0)
0 (0)
0 (0)
0 (0)
0 (0)
0 (0)
0 (0)
0 (0)
0.65 (11)
0 (0)
0 (0)
0 (0)
0.18 (3)
0 (0)
0 (0)
0.18 (3)
1.59 (27)
All (107.52)
2014 1.37
(147) 0.46 (49)
0.01 (1)
0 (0)
0 (0)
0.07 (7)
0.03 (3)
0.02 (2)
0.01 (1)
0.22 (24)
0 (0)
0 (0)
0 (0)
0.66 (71)
0.01 (1)
0.11 (12)
0.43 (46)
3.8 (409)
2015 1.14
(123) 0.51 (55)
0.01 (1)
0.06 (6)
0.01 (1)
0.03 (3)
0.01 (1)
0.08 (9)
0.04 (4)
1.56 (168)
0 (0)
0 (0)
0.01 (1)
0.33 (36)
0 (0)
0.14 (15)
0.23 (25)
4.27 (459)
2016 2.11
(227) 0.49 (53)
0.04 (4)
0 (0)
0.01 (1)
0.06 (6)
0 (0)
0.34 (37)
0.06 (6)
0.12 (13)
0.02 (2)
0.01 (1)
0 (0)
0.74 (80)
0 (0)
0.36 (39)
0.74 (80)
5.28 (568)
2017 1
(108) 0.23 (25)
0.06 (6)
0 (0)
0.04 (4)
0.04 (4)
0 (0)
0.07 (7)
0.07 (7)
0.57 (61)
0 (0)
0.01 (1)
0 (0)
0.2 (22)
0 (0)
0.2 (21)
0.32 (34)
2.8 (301)
Page 47
10.0 Orthoptera In 2016 a trial orthopteran survey took place at the 12 sites at Mesendorf used by the butterfly and
botany teams. The survey recorded 13 species, and another 6 species were found in the previous
week of methodology tests. In 2017 an additional 9 species were recorded, and 6 of the 2016 species
were not found. This difference in species recorded will partly be due to the additional sites
surveyed, but also the expertise and knowledge of the Tarnava Mare grasshoppers is at an early
stage. The overall abundances of the 2017 species in Figure 10.1 show that meadow then field then
common green grasshopper are by far the most abundant species. The abundance of each species at
each village is shown in Table 10.1, with the seven most abundant sopecies being found at all 5
surveyed villages. The species richness is fairly similar at each village, with between 13 and 16
species.
Knowledge of Orthopterans in Tarnava Mare is at an early stage. There is the potential for this
taxanomic group to be another useful indicator of biodiversity health. But further work is needed to
refine the survey method to increase its reliability and representativeness.
Figure 10.1. Orthoptera species rank abundance (number of individuals per survey).
Page 48
Table 10.1. Orthoptera species richness and abundance per survey, averaged for each village.
2017 Total N S
pec
ies
Mea
do
w g
rass
ho
pp
er
Ch
ort
hip
pu
s p
aral
lelu
s
Fiel
d g
rass
ho
pp
er
Ch
ort
hip
pu
s b
run
neu
s
Co
mm
on
gre
en
gra
ssh
op
per
Om
oce
stu
s vi
rid
ulu
s
Stri
ped
bu
sh c
rick
et
Lep
top
hyt
es a
lbo
vitt
ata
Ro
esel
's b
ush
cri
cket
Met
rio
pte
ra r
oes
elii
Smal
l go
ld g
rass
ho
pp
er
Euth
ysti
ra b
rach
ypte
ra
Saw
-tai
led
bu
sh c
rick
et
Bar
bit
iste
s o
btu
sus
Gre
at g
ree
n b
ush
cri
cket
Tett
igo
nia
vir
idis
sim
a
War
tbit
er
Dec
ticu
s ve
rru
civo
rus
Larg
e go
ld g
rass
ho
pp
er
Ch
ryso
chra
on
dis
par
gig
ante
us
Spec
kled
bu
sh c
rick
et
Lep
top
hye
s p
un
ctat
issi
ma
Gre
y B
ush
Cri
cket
Pla
tycl
eis
alb
op
un
ctat
a
Stri
pey
-win
ged
gra
ssh
op
per
Sten
ob
oth
rus
linea
tus
Un
iden
tifi
ed s
p.
Co
neh
ead
Co
no
cep
hal
us
sp.
Sho
rt-w
inge
d c
on
ehea
d
Co
no
cep
hal
us
do
rsal
is2
Larg
e sa
w-t
aile
d b
ush
cri
cket
Po
lysa
rcu
s d
enti
cau
da
Hea
th g
rass
ho
pp
er
Ch
ort
hip
pu
s va
gan
s
Red
-win
ged
gra
ssh
op
per
Oed
ipo
da
germ
anic
a
East
ern
Gre
en
Bu
sh C
rick
et
Tett
igo
nia
cau
dat
a
Oak
bu
sh c
rick
et
Mec
on
ema
thal
assi
nu
m
Wo
od
lan
d g
rass
ho
pp
er
Om
oce
stu
s ru
fip
es
Co
mm
on
gro
un
dh
op
per
Tetr
ix u
nd
ula
ta
Crit 33.75 14 11.83 8.92 4.58 1.33 1.17 0.92 1.17 0.67 0.50 0.50 0.00 0.92 0.00 0.00 0.00 0.25 0.33 0.00 0.00 0.00 0.00 0.00 0.08
Daia 37.91 16 17.45 8.45 7.36 0.45 0.64 1.09 0.55 0.18 0.27 0.36 0.09 0.18 0.18 0.00 0.00 0.18 0.18 0.09 0.00 0.00 0.00 0.00 0.00
Malancrav 22.45 13 9.18 6.91 1.27 0.82 0.45 0.64 0.27 0.00 1.00 0.45 0.00 0.27 0.00 0.00 0.91 0.00 0.00 0.09 0.18 0.00 0.00 0.00 0.00
Mesendorf 31.40 16 16.30 4.20 1.60 1.95 1.95 0.85 0.80 0.90 0.25 0.70 0.85 0.10 0.15 0.55 0.00 0.00 0.00 0.00 0.00 0.05 0.05 0.00 0.00
Viscri 36.83 15 19.17 9.50 1.58 1.08 1.08 0.58 0.42 1.08 0.83 0.17 0.25 0.00 0.67 0.08 0.00 0.08 0.00 0.00 0.00 0.00 0.00 0.08 0.00
All 32.41 23 15.02 7.18 3.05 1.24 1.18 0.82 0.67 0.62 0.53 0.47 0.32 0.27 0.20 0.18 0.15 0.09 0.09 0.03 0.03 0.02 0.02 0.02 0.02
2016 19 Y Y Y Y Y Y Y Y Y Y Y Y
Additional 2016 species: Lesser marsh grasshopper Chorthippus albomarginatus Mottled grasshopper Myrmeleotettix maculatus Long-winged conehead Conocephalus discolor Jersey grasshopper Euchorthippus pulvinatus elegantulus Southern Oak bush cricket Meconema meridionale Dark bush cricket Pholidoptera griseoaptera
Page 49
11.0 Site Trends
This is a new section of the annual report that identifies individual survey sites that have experienced
a consistent trend in abundance of indicator plants, abundance of grassland birds, or diversity of
butterfly species. A consistent trend is identified where there is a statistically significant correlation
between the year and the plant, butterfly or bird measure (using Spearman’s rank correlation, Prho <=
0.05). The sites are those used for the plant and butterfly surveys. The bird abundance is taken from
the nearest bird point count. Bird data for some sites are excluded because there is not a suitably
close bird point count. This site level data gives more spatial detail than the village-level averages
that sections 5, 6 and 7 focus on.
Table 11.1 lists the sites with consistent trends. All villages except Crit have at least one site with a
consistent trend. There are more increasing trends, than decreasing. There are no sites with
consistent trends in all three taxanomic groups. Most sites where two taxonomic groups have
consistent trends, these trends are in conflicting directions (e.g. at DA03 plants increase but
butterflies decrease). Only two sites (DA08 and VI10) have two consistent trends in the same
direxction – both increasing in these two cases.
These site trends are encouraging. It will be useful to continue this analysis in futue years.
Table 11.1. Sites with significant trends in indicator plant abundance, butterfly diversity, or grassland
bird abundance. Green up arrow for increase, red down arrow for decrease.
Sites with significant trends
AP AP01: Butterflies ↑ AP02: Butterflies ↑ AP05: Butterflies ↑
DA DA03: Plants ↑, Butterflies ↓ DA05: Plants ↓ DA06: Butterflies ↑, Birds ↓ DA07: Butterflies ↑, Birds ↓ DA08: Plants ↑, Butterflies ↑
MA MA02: Butterflies ↑
ME ME08: Butterflies ↑ ME13: Birds ↑
NS NS04: Butterflies ↑, Birds ↓ NS06: Butterflies ↑ NS07: Plants ↓ NS08: Plants ↑
RI RI01: Butterflies ↑ RI03: Birds ↑ RI09: Birds ↑
VI VI01: Birds ↑ VI02: Birds ↑ VI03: Birds ↑ VI07: Butterflies ↑ VI08: Birds ↑ VI09: Butterflies ↓ VI10: Butterflies ↑, Birds ↑
Page 50
12.0 References
Akeroyd, J., & Bădărău, S. (2012). Indicator plants of the High Nature Value dry grasslands of Transylvania. Fundatia ADEPT Transylvania. Retrieved from http://www.fundatia-adept.org/bin/file/Wildflowers_ENG(2).pdf
Birdlife International. (2018). Data Zone - Species Search. Retrieved April 12, 2018, from http://datazone.birdlife.org/species/search
Van Swaay, C. A. M., Van Strien, A. J., Aghababyan, K., Åström, S., Botham, M., Brereton, T., … Warren, M. S. (2016). The European Butterfly Indicator for Grassland species: 1990-2015. Wageningen. Retrieved from http://www.vlindernet.nl/doc/vs2016-019_european_butterfly_indicator_1990-2015_v3.pdf
Page 51
Appendix 1 Table A1. Abundance of each indicator species at each village. Grey: no record for two consecutive years. Dark green: >= 50% increase. Light green: >= 20% increase. Yellow: <= 20% decrease. Red: <= 50% decrease. Note: Six indicator species (Adonis vernalis, Viola hirta, Orchis militaris, Dictamnus albus, Echium maculatum, Gentianopis ciliate) were not present in any site in any year, and so are not included in this table. The 10 most abundant species are underlined.
Village Year Thre
e-to
oth
ed O
rch
id
Orc
his
tri
den
tata
No
dd
ing
Sage
Sa
lvia
nu
tan
s
Juri
nea
Ju
rin
ea m
olli
s
Larg
e Sp
eed
wel
l
Ver
on
ica
au
stri
aca
Gre
ater
Milk
wo
rt
Po
lyg
ala
ma
jor
Pu
rple
vip
ers
gras
s Sc
orz
on
era
pu
rpu
rea
Hai
ry F
lax
Lin
um
hir
sutu
m
Sib
eria
n B
ellf
low
er
Ca
mp
an
ula
sib
iric
a
Yello
w F
lax
Lin
um
fla
vum
Wh
ite
Dw
arf-
Bro
om
Ch
am
aec
ytis
us
alb
us
Kid
ney
Vet
ch
An
thyl
lis v
uln
era
ria
Sain
foin
O
no
bry
chis
vic
iifo
lia
Ch
arte
rho
use
Pin
k
Dia
nth
us
cart
hu
sia
no
rum
Squ
inan
cyw
ort
A
sper
ula
cyn
an
chic
a
Mo
un
tain
Clo
ver
Trif
oliu
m m
on
tan
um
Lad
y's
Bed
stra
w
Ga
lium
ver
um
Cro
wn
Vet
ch
Co
ron
illa
ver
um
Yello
w S
cab
iou
s Sc
ab
iosa
och
role
uca
Wal
l Ger
man
der
Teu
criu
m c
ha
ma
edry
s
Gre
ater
Sel
f-h
eal
Pru
nel
la g
ran
dif
lora
Do
rycn
ium
D
ory
cniu
m p
enta
ph
yllu
m
Swo
rd-l
eave
d F
leab
ane
Inu
la e
nsi
folia
Wild
Th
yme
Thym
us
gla
bre
scen
s
Dep
tfo
rd P
ink
Dia
nth
us
arm
eria
Bet
on
y
Sta
chys
off
icin
alis
TOTA
L
Apold
2014 0 0 0 0 0 0 0 0 130 0 0 210 47 187 0 110 513 1353 0 1617 7 0 7 0 0 4180
2015 0 0 0 0 0 0 0 0 0 0 0 160 0 1124 0 204 468 1388 0 0 236 88 0 0 0 3668
2016 0 0 0 0 0 0 0 173 0 33 0 143 3 763 0 157 217 807 0 0 0 0 20 0 13 2330
2017 0 0 0 0 0 0 17 100 0 10 0 143 0 50 0 343 837 1367 67 3 657 20 93 0 0 3707
Crit
2013 0 0 0 36 0 0 0 0 0 0 0 1300 1198 193 4 3649 473 67 0 0 462 40 0 0 14764 22187
2014 0 0 0 169 0 0 0 0 0 0 0 169 92 323 0 2406 649 89 71 126 222 3 0 15 17554 21889
2015 0 0 0 301 0 0 0 0 0 0 0 523 539 320 0 3832 573 67 19 16 157 3 0 27 20429 26805
2017 0 0 0 20 300 0 0 0 0 0 0 334 451 494 0 5980 1843 106 191 66 474 0 31 46 27609 37946
Daia
2014 0 0 0 4 98 0 0 0 0 0 0 40 69 356 0 2560 233 167 0 753 764 22 105 127 2975 8273
2015 0 0 0 27 204 0 0 0 0 0 0 427 76 782 4 1507 542 133 0 44 631 53 31 31 467 4960
2016 0 0 0 0 15 0 0 73 0 0 0 400 116 811 0 1247 447 636 0 4 4 51 47 4 2364 6218
Malancrav
2013 0 0 177 0 117 0 0 0 117 0 0 1187 617 63 23 1133 700 287 0 0 317 0 993 570 557 6857
2014 0 0 22 4 0 0 0 7 0 0 0 735 76 0 0 378 491 1000 0 764 51 0 480 775 55 4836
2015 0 0 0 0 115 0 0 35 0 0 0 305 720 5 5 155 425 6585 0 40 35 75 2105 45 95 10745
2016 0 0 0 0 27 0 0 110 0 0 0 627 107 117 0 1057 687 907 67 1157 440 0 1480 230 630 7640
2017 0 305 0 0 7 0 4 4 0 0 0 444 91 98 0 1393 131 338 0 7 0 0 960 156 22 3960
Mesendorf
2013 0 0 0 12 0 0 0 2 0 0 0 821 864 287 7 2694 1155 24 0 0 774 0 438 0 8351 15428
2014 0 0 0 4 87 0 0 0 0 0 0 538 720 331 47 3229 600 7 0 0 996 0 262 124 6545 13491
2015 0 0 0 7 60 0 0 17 0 0 0 1697 1010 513 93 2353 620 0 0 0 1120 0 80 97 2690 10357
2016 0 0 0 0 97 0 0 0 0 0 0 507 173 720 27 850 2050 23 0 20 1023 120 1240 63 7483 14397
2017 0 0 0 7 113 0 0 0 0 0 0 567 867 503 183 2627 1290 0 0 0 1210 0 503 143 5287 13300
Page 52
Table A1. continued…
Village Year Thre
e-to
oth
ed O
rch
id
Orc
his
tri
den
tata
No
dd
ing
Sage
Sa
lvia
nu
tan
s
Juri
nea
Ju
rin
ea m
olli
s
Larg
e Sp
eed
wel
l
Ver
on
ica
au
stri
aca
Gre
ater
Milk
wo
rt
Po
lyg
ala
ma
jor
Pu
rple
vip
ers
gras
s Sc
orz
on
era
pu
rpu
rea
Hai
ry F
lax
Lin
um
hir
sutu
m
Sib
eria
n B
ellf
low
er
Ca
mp
an
ula
sib
iric
a
Yello
w F
lax
Lin
um
fla
vum
Wh
ite
Dw
arf-
Bro
om
Ch
am
aec
ytis
us
alb
us
Kid
ney
Vet
ch
An
thyl
lis v
uln
era
ria
Sain
foin
O
no
bry
chis
vic
iifo
lia
Ch
arte
rho
use
Pin
k
Dia
nth
us
cart
hu
sia
no
rum
Squ
inan
cyw
ort
A
sper
ula
cyn
an
chic
a
Mo
un
tain
Clo
ver
Trif
oliu
m m
on
tan
um
Lad
y's
Bed
stra
w
Ga
lium
ver
um
Cro
wn
Vet
ch
Co
ron
illa
ver
um
Yello
w S
cab
iou
s Sc
ab
iosa
och
role
uca
Wal
l Ger
man
der
Teu
criu
m c
ha
ma
edry
s
Gre
ater
Sel
f-h
eal
Pru
nel
la g
ran
dif
lora
Do
rycn
ium
D
ory
cniu
m p
enta
ph
yllu
m
Swo
rd-l
eave
d F
leab
ane
Inu
la e
nsi
folia
Wild
Th
yme
Thym
us
gla
bre
scen
s
Dep
tfo
rd P
ink
Dia
nth
us
arm
eria
Bet
on
y
Sta
chys
off
icin
alis
TOTA
L
Nou Sasesc
2013 0 0 10 0 113 0 0 7 0 0 0 2327 293 373 20 1313 1943 313 970 3 2710 0 860 437 4527 16220
2014 3 37 7 0 197 0 10 0 0 0 30 367 413 200 3907 1807 1163 0 793 80 1797 0 167 7 580 11563
2015 0 0 0 10 623 0 0 0 0 377 23 880 1443 323 513 1890 1027 17 573 193 1787 30 50 43 1340 11143
2016 0 0 0 11 782 0 0 0 215 167 0 1535 1360 124 11 1225 2076 84 829 160 3356 33 775 44 1487 14273
2017 0 0 0 0 593 0 47 0 183 410 0 477 3293 223 297 1453 687 0 170 43 2210 0 263 67 7 10423
Richis
2013 0 0 0 160 13 0 0 17 467 0 3 2150 193 1207 0 860 1090 1147 207 3 5827 1613 650 257 197 16060
2014 0 1043 0 0 267 0 243 0 3 77 617 1417 27 140 2193 683 1287 17 1407 0 1250 0 3190 43 97 14000
2015 0 0 0 0 60 37 0 0 183 347 50 977 357 147 97 1037 193 0 877 20 2307 7 390 223 40 7347
2016 0 0 17 30 20 0 0 0 723 363 127 1300 270 70 150 2003 680 0 1623 17 1810 17 1260 1243 73 11797
2017 0 0 7 0 170 0 0 0 113 420 37 860 577 243 533 2060 220 0 0 0 817 0 1790 0 13 7860
Viscri
2013 0 0 0 92 0 0 0 12 0 0 0 908 0 538 0 465 1837 25 0 0 4102 0 0 0 6 7985
2014 0 0 0 31 0 0 0 0 0 0 0 3332 12 458 0 837 963 40 148 905 3120 28 25 206 6 10111
2015 0 0 0 30 30 0 0 0 0 0 0 2530 0 1470 0 877 590 97 77 83 1590 53 0 0 20 7447
2016 0 0 0 73 0 0 0 0 0 0 0 3930 0 2140 0 787 1610 947 313 230 4470 0 30 10 10 14550
2017 0 0 0 3 871 0 0 25 49 0 0 9338 3 1443 0 751 1111 43 305 311 4926 0 154 6 22 19360
All
2013 0 0 31 50 41 0 0 6 97 0 1 1449 527 444 9 1686 1200 310 196 1 2365 276 490 211 4734 14123
2014 0 135 4 26 81 0 32 1 17 10 81 851 182 249 768 1501 737 334 302 530 1026 7 529 162 3476 11043
2015 0 0 0 47 137 5 0 6 23 90 9 937 518 586 89 1482 555 1036 193 50 983 39 332 58 3135 10309
2016 0 0 2 16 134 0 0 51 134 81 18 1206 290 678 27 1047 1110 486 405 227 1586 31 693 228 1723 10172
2017 0 44 1 4 294 0 10 18 49 120 5 1738 755 437 145 2087 874 265 105 61 1471 3 542 60 4708 13794
Page 53
Appendix 2 Table A2, part 1. Grassland butterfly abundance (numbers per hectare) at each village. Grey: no sighting two years running. Dark green: >= 50% increase. Light green: >= 20% increase. Yellow: <= 20% decrease. Red: <= 50% decrease.
Mar
ble
d w
hit
e
Mel
an
ari
ga
ga
lath
ea
Mea
do
w b
row
n
Ma
nio
la ju
rtin
a
Wal
l bro
wn
Lasi
om
ma
ta m
eger
a
Silv
er w
ash
ed f
riti
llary
Arg
ynn
is p
ap
hia
Hig
h b
row
n f
riti
llary
Arg
ynn
is a
dip
pe
Mar
ble
d f
riti
llary
Bre
nth
is d
ap
hn
e
Ass
man
's f
riti
llary
Mel
ita
ea b
rito
ma
rtis
Kn
apw
eed
fri
tilla
ry
Mel
ita
ea p
ho
ebe
Less
er m
arb
led
fri
tilla
ry
Bre
nth
is in
o
Qu
een
of
Spai
n f
riti
llary
Isso
ria
lath
on
ia
Dar
k gr
een
fri
tilla
ry
Arg
ynn
is a
gla
ja
Wea
ver'
s fr
itill
ary
Bo
lori
a d
ia
Smal
l pea
rl-b
ord
ered
fri
tilla
ry
Bo
lori
a s
elen
e
Pal
las
frit
illar
y
Arg
ynn
is la
od
ice
Pea
rl-b
ord
ered
fri
tilla
ry
Clo
ssia
na
eu
ph
rosy
ne
Spo
tted
fri
tilla
ry
Mel
ita
ea d
idym
a
Hea
th f
riti
llary
Mel
licta
ath
alia
Mar
sh f
riti
llary
Euro
dry
as
au
rin
ia
Ap
old
2014 5 238 0 18 9 0 0 0 0 0 0 0 0 0 0 0 0 0
2015 2 204 0 2 2 0 0 1 0 0 1 0 1 0 0 1 0 0
2016 2 162 0 4 5 0 0 0 0 0 0 7 2 0 0 0 0 0
2017 0 212 0 10 5 0 0 10 0 0 0 13 7 0 0 0 0 0
Cri
t
2013 42 145 0 4 0 0 0 4 0 0 0 0 0 0 0 1 4 5
2014 113 297 0 1 8 0 0 0 0 0 0 6 0 0 0 0 1 0
2015 164 458 0 9 1 0 0 0 0 0 5 6 0 0 0 2 0 0
2016
2017 84 343 0 4 1 0 0 0 1 0 0 1 0 0 0 0 1 0
Dai
a
2014 46 167 0 3 2 0 0 0 0 0 1 1 0 0 0 0 0 0
2015 71 213 0 0 5 0 0 0 0 0 0 3 3 1 0 1 2 0
2016 17 98 0 4 3 0 0 0 0 0 0 0 6 0 0 0 0 0
Mal
ancr
av
2013 181 174 0 2 0 0 0 0 2 0 0 0 0 0 0 3 5 2
2014 22 196 0 4 2 0 0 1 0 0 0 0 2 0 1 0 0 0
2015 5 114 0 2 0 0 0 0 0 0 0 3 2 0 0 0 0 0
2016 65 215 5 4 0 0 0 0 0 0 0 0 9 0 0 0 0 0
2017 15 115 0 0 0 0 0 0 0 0 2 10 2 0 0 0 2 0
Mes
end
orf
2013 42 214 0 31 0 0 0 0 0 0 0 0 0 0 0 12 0 1
2014 216 414 0 8 29 2 1 0 0 0 1 8 0 0 0 0 3 0
2015 279 354 0 2 18 1 0 0 1 0 2 8 0 0 0 1 1 0
2016 124 177 0 13 5 0 0 0 0 0 3 15 0 0 0 0 0 0
2017 164 273 0 6 13 0 0 0 2 0 2 10 0 0 0 0 0 0
No
u S
ases
c
2013 121 195 0 7 2 0 0 0 4 0 0 0 0 0 0 9 3 0
2014 104 168 0 5 20 7 1 0 0 0 1 0 0 0 0 0 3 0
2015 97 171 0 0 14 2 0 0 0 0 0 3 0 0 0 0 2 0
2016 85 151 0 0 3 4 2 0 0 0 2 13 0 0 0 3 0 0
2017 151 236 0 0 31 10 0 0 2 0 3 20 0 0 0 0 0 0
Ric
his
2013 46 98 0 2 1 0 0 0 0 0 0 1 0 0 0 4 0 0
2014 44 98 0 0 1 3 0 0 1 0 0 0 0 0 0 0 0 0
2015 43 117 0 0 8 1 0 0 0 0 0 2 0 0 0 0 0 0
2016 73 99 0 2 8 4 8 0 0 0 0 7 0 0 0 0 0 0
2017 51 73 0 2 8 0 0 0 0 0 0 8 0 0 0 0 0 0
Vis
cri
2013 23 46 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
2014 121 189 0 1 2 0 0 0 0 0 0 0 0 0 0 0 0 0
2015 196 269 0 0 0 0 0 0 0 1 2 1 0 0 0 0 0 0
2016 43 173 0 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0
2017 123 196 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
All
2013 76 145 0 8 0 0 0 1 1 0 0 0 0 0 0 5 2 1
2014 86 223 0 5 9 1 0 0 0 0 0 2 0 0 0 0 1 0
2015 114 251 0 2 6 1 0 0 0 0 1 3 1 0 0 1 1 0
2016 59 153 1 4 4 1 1 0 0 0 1 6 2 0 0 0 0 0
2017 170 426 0 6 15 2 0 3 1 0 2 15 3 0 0 0 1 0
Page 54
Table A2, part 2.
Litt
le f
riti
llary
Mel
ita
ea a
ster
ia
Nic
kerl
s fr
itill
ary
Mel
ita
ea a
ure
lia
Nio
be
frit
illar
y
Arg
ynn
is n
iob
e
Twin
-sp
ot
frit
illar
y
Bre
nth
is h
eca
te
Spo
tted
fri
tilla
ry
Mel
ita
ea d
idym
a
Frit
illar
y sp
.
Du
ke o
f B
urg
un
dy
frit
illar
y
Ha
mea
ris
luci
na
Car
din
al
Arg
ynn
is p
an
do
ra
Smal
l ski
pp
er
Pyr
gu
s sy
lves
tris
Esse
x sk
ipp
er
Thym
elic
us
lineo
la
Larg
e sk
ipp
er
Och
lod
es v
ena
tu
Gri
zzle
d s
kip
per
Pyr
gu
s m
alv
ae
Din
gy s
kip
per
Eryn
nis
ta
ges
Silv
er s
po
tted
ski
pp
er
Hes
per
ia c
om
ma
Saff
low
er s
kip
per
Pyr
gu
s ca
rth
am
i
Larg
e ch
equ
ered
ski
pp
er
Het
ero
pte
rus
mo
rph
eus
Ch
equ
ered
ski
pp
er
Ca
rter
oce
ph
alu
s p
ala
emo
n
Smal
l wh
ite
Art
og
eia
ra
pa
e
Ap
old
2014 0 0 0 0 0 0 0 0 0 0 1 0 3 1 0 0 0 11
2015 0 0 0 0 0 0 0 0 0 0 1 0 7 0 0 0 0 2
2016 0 0 0 0 0 7 0 0 0 0 0 0 9 0 0 0 0 29
2017 0 0 0 0 2 0 0 0 2 0 0 0 30 10 0 0 0 23
Cri
t
2013 0 0 0 0 0 0 0 0 10 0 5 0 2 2 0 0 0 7
2014 0 0 1 5 0 0 0 0 2 41 0 0 3 1 0 0 0 1
2015 0 0 0 3 0 0 0 0 4 15 0 0 13 0 0 0 0 3
2016
2017 0 0 3 1 4 0 0 0 16 39 0 1 21 0 0 0 0 5
Dai
a
2014 0 0 0 2 0 0 0 0 3 10 1 0 6 1 0 0 0 1
2015 0 0 0 0 0 0 0 0 2 2 0 1 3 0 0 0 0 1
2016 0 0 0 0 0 0 0 0 2 0 4 7 18 0 0 0 0 23
Mal
ancr
av
2013 2 0 0 0 0 0 0 0 20 0 8 0 4 1 0 0 0 12
2014 0 0 0 0 0 0 0 0 0 5 0 0 8 1 0 0 0 26
2015 0 0 0 0 0 0 0 0 0 0 0 0 5 5 0 0 0 29
2016 0 0 0 0 0 0 2 0 4 16 11 5 35 0 0 2 0 21
2017 0 2 0 0 2 0 0 0 2 0 0 0 21 30 0 0 0 79
Mes
end
orf
2013 1 0 0 0 0 0 0 0 10 0 9 0 2 0 1 0 0 8
2014 0 1 0 3 1 0 0 0 4 55 5 0 0 1 0 1 0 1
2015 0 9 1 5 0 0 0 0 6 33 0 0 0 0 0 0 0 3
2016 0 3 0 0 29 0 2 2 32 52 8 0 5 0 0 0 0 0
2017 0 30 3 6 3 0 2 0 53 44 7 0 5 0 0 0 0 0
No
u S
ases
c
2013 2 0 0 0 0 0 0 0 10 0 5 0 17 0 2 0 1 9
2014 0 4 0 4 0 0 0 0 1 24 3 0 0 0 0 6 0 2
2015 0 3 0 2 0 0 0 0 21 13 1 0 0 0 0 3 0 1
2016 0 6 0 0 0 0 0 0 21 71 2 0 2 0 0 17 0 18
2017 0 23 0 4 0 0 0 0 78 54 17 0 0 0 0 27 0 7
Ric
his
2013 0 0 0 0 0 0 0 0 1 0 0 0 3 0 2 0 0 8
2014 0 3 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1
2015 0 5 0 0 0 0 0 0 4 1 0 0 0 0 0 3 0 1
2016 0 7 0 0 0 0 0 0 23 21 0 0 0 0 0 17 0 13
2017 0 7 0 0 0 0 0 0 7 2 3 0 0 0 0 8 0 0
Vis
cri
2013 0 0 0 0 0 0 0 0 4 0 8 0 0 0 0 0 0 3
2014 0 0 0 0 0 0 0 0 0 21 0 0 2 0 0 0 0 4
2015 0 0 0 0 0 0 0 0 3 11 0 0 4 0 1 0 0 1
2016 0 0 0 0 2 0 0 0 3 5 0 5 16 0 0 0 0 2
2017 0 0 0 0 0 0 0 0 13 30 0 0 19 0 0 0 0 2
All
2013 1 0 0 0 0 0 0 0 9 0 6 0 5 0 1 0 0 8
2014 0 1 0 2 0 0 0 0 1 20 1 0 3 0 0 1 0 6
2015 0 2 0 1 0 0 0 0 5 10 0 0 4 1 0 1 0 5
2016 0 2 0 0 5 1 0 0 12 24 3 2 12 0 0 5 0 15
2017 0 15 2 3 3 0 0 0 47 49 7 0 33 9 0 9 0 30
Page 55
Table A2, part 3.
La
rge
wh
ite
Pie
ris
bra
ssic
ae
Gre
en
-vei
ned
wh
ite
Pie
ris
na
pi
Wo
od
wh
ite
Lep
tid
ea s
ina
pis
Fen
ton
's w
oo
d w
hit
e
Lep
tid
ea m
erse
i
Bat
h w
hit
e
Po
nti
a d
ap
lidic
e
Smal
l hea
th
Co
eno
nym
ph
a p
am
ph
ilus
Ch
estn
ut
hea
th
Co
eno
nym
ph
a g
lyce
rio
n
Pea
rly
hea
th
Co
eno
nym
ph
a a
rca
nia
Dry
ad
Hip
pa
rch
ia d
rya
s
Clo
ud
ed y
ello
w
Co
lias
cro
cea
Dan
ub
e cl
ou
ded
yel
low
Co
lias
myr
mid
on
e
Pal
e cl
ou
ded
yel
low
Co
lias
hya
le
Bri
mst
on
e
Go
nep
tery
x rh
am
ni
Rin
glet
Ap
ha
nto
pu
s h
yper
an
tus
All
blu
es
Ch
alk-
hill
blu
e
Lysa
nd
ra c
ori
do
n
Mel
eage
r's
blu
e
Mel
eag
eria
da
ph
nis
Silv
er-s
tud
ded
blu
e
Ple
bej
us
arg
us
Ap
old
2014 0 0 2 0 0 32 3 0 17 3 0 14 0 3 404 0 1 218
2015 5 0 1 0 0 33 0 2 49 0 0 18 0 10 440 0 0 130
2016 0 0 18 0 0 45 11 0 29 15 0 11 0 18 425 2 0 122
2017 0 0 26 0 0 56 11 0 21 0 0 12 0 15 402 2 2 197
Cri
t
2013 1 0 0 1 0 13 0 0 19 3 0 1 0 1 149 0 139 1
2014 0 0 8 0 0 15 1 2 39 0 0 13 1 1 20 0 0 15
2015 3 0 6 0 0 10 0 0 18 0 0 20 1 10 56 0 0 13
2016
2017 0 1 8 0 0 13 0 1 45 0 0 11 0 58 53 0 0 67
Dai
a
2014 0 0 1 0 0 11 0 0 41 0 0 11 0 5 89 0 1 74
2015 0 0 3 0 0 27 0 0 79 0 0 12 1 5 226 0 0 43
2016 0 0 9 0 0 28 4 0 40 4 0 15 0 22 311 0 0 111
Mal
ancr
av
2013 0 0 0 5 0 2 0 0 23 13 0 0 0 17 33 0 15 0
2014 0 0 1 0 1 19 2 0 35 0 0 11 0 8 286 2 1 61
2015 1 0 10 0 0 54 20 0 26 0 0 9 0 10 342 9 0 40
2016 0 0 17 0 0 33 0 0 53 13 0 26 0 65 207 0 0 25
2017 0 2 12 0 0 37 10 0 35 0 0 5 0 48 185 4 2 67
Mes
end
orf
2013 1 0 0 4 0 30 0 0 8 5 0 2 0 0 179 0 174 1
2014 0 0 11 0 0 26 2 1 1 0 0 3 0 4 28 0 4 19
2015 0 1 22 0 0 17 1 7 0 3 1 2 2 2 68 1 0 14
2016 0 0 14 0 0 27 0 10 19 5 0 10 2 35 27 0 0 0
2017 0 0 18 0 0 12 10 11 2 0 0 3 0 10 49 0 2 5
No
u S
ases
c
2013 0 0 0 2 0 10 0 0 87 10 0 0 0 13 129 0 95 0
2014 0 1 3 0 0 7 3 4 0 1 0 0 0 4 74 0 0 62
2015 0 0 6 0 0 5 4 4 0 0 0 2 0 0 60 0 0 24
2016 0 0 18 0 0 10 0 12 3 2 0 5 2 75 64 0 0 21
2017 0 3 15 0 0 16 16 5 0 0 0 0 0 4 72 0 0 27
Ric
his
2013 0 0 0 1 2 4 0 0 36 7 2 3 0 5 178 0 128 0
2014 0 1 1 1 0 7 2 2 0 0 0 0 0 1 34 0 1 28
2015 0 0 6 0 0 3 4 0 0 0 0 1 1 1 70 0 0 23
2016 2 10 21 0 0 10 8 0 0 9 0 3 2 3 58 0 0 21
2017 2 2 5 0 0 3 17 0 0 0 0 2 2 0 46 0 0 14
Vis
cri
2013 2 0 0 0 0 21 0 0 0 5 1 0 0 0 269 0 265 1
2014 0 0 0 0 0 24 0 0 3 0 0 16 0 0 11 0 0 9
2015 0 0 1 0 0 12 0 0 0 0 0 27 0 1 42 0 0 18
2016 0 0 2 0 0 15 0 0 3 0 0 5 0 5 236 0 0 131
2017 2 0 2 0 2 21 0 0 0 0 0 20 2 3 39 0 0 69
All
2013 1 0 0 2 0 13 0 0 29 7 0 1 0 6 156 0 136 0
2014 0 0 3 0 0 18 2 1 17 0 0 9 0 3 114 0 1 59
2015 1 0 7 0 0 18 3 2 18 0 0 12 1 5 149 1 0 36
2016 0 1 14 0 0 24 3 3 21 7 0 11 1 32 188 0 0 61
2017 1 2 22 0 0 46 15 4 44 0 0 19 1 50 232 1 1 148
Page 56
Table A2, part 4.
M
azar
ine
blu
e
Cya
nir
is s
emia
rgu
s
Iola
s b
lue
Iola
na
iola
s
Alc
on
blu
e
Ph
eng
ari
s a
lco
n
Bat
on
blu
e
Pse
ud
op
hilo
tes
ba
ton
Rev
erd
in b
lue
Lyca
eid
es a
rgyr
og
no
mo
n
Idas
blu
e
Lyca
eid
es id
as
Ho
lly b
lue
Cel
ast
rin
a a
rgio
lus
Co
mm
on
blu
e
Po
lyo
ma
ttu
s ic
aru
s
Ch
apm
an's
blu
e
Po
lyo
mm
atu
s th
ersi
tes
Larg
e b
lue
Ma
culin
ea a
rio
n
Gre
en
-un
der
sid
e b
lue
Gla
uco
psy
che
ale
xis
East
ern
sh
ort
-tai
led
blu
e
Ever
es d
eco
lora
tus
Sho
rt-t
aile
d b
lue
Cu
pid
o a
rgia
des
Ad
on
is b
lue
Po
lyo
mm
atu
s b
ella
rgu
s
Osi
ris
blu
e
Cu
pid
o o
siri
s
Scar
ce la
rge
blu
e
Ma
culin
ea t
elej
us
Turq
uo
ise
blu
e
Ple
bic
ula
do
ryla
s
Blu
e sp
.
Lyac
aen
idae
Ap
old
2014 0 0 0 0 19 0 0 144 1 0 0 0 21 1 0 0 0 0
2015 0 1 0 0 0 0 0 154 0 0 0 0 62 0 1 0 0 92
2016 0 0 0 0 3 0 0 80 5 0 0 0 65 0 0 0 0 148
2017 0 0 0 0 0 0 2 145 2 0 0 2 87 0 18 0 2 184
Cri
t
2013 0 0 0 1 2 0 6 0 0 0 0 0 0 0 0 0 0 0
2014 0 0 0 0 0 0 0 1 0 0 0 0 2 0 0 0 0 0
2015 0 0 0 0 0 3 0 8 0 0 0 0 1 0 0 0 0 31
2016
2017 0 0 0 1 3 0 0 11 0 0 0 0 3 0 3 1 0 11
Dai
a
2014 0 0 0 0 2 0 0 12 0 0 0 0 0 0 1 0 0 0
2015 0 0 0 0 0 0 0 86 0 0 0 1 1 0 1 0 0 95
2016 0 0 0 0 0 0 0 90 7 0 0 4 19 0 5 0 0 75
Mal
ancr
av
2013 0 0 0 2 0 0 13 3 0 0 1 0 0 0 0 0 0 0
2014 0 0 0 0 4 0 0 207 0 0 0 0 7 1 4 0 0 0
2015 0 0 0 0 0 0 0 186 0 0 0 0 33 0 0 0 0 74
2016 0 0 0 0 0 0 0 44 4 4 0 8 40 0 20 0 0 63
2017 0 0 0 0 0 0 0 83 20 2 0 2 50 0 5 0 0 65
Mes
end
orf
2013 0 0 0 0 0 0 4 0 0 0 0 0 0 0 0 0 0 0
2014 0 0 0 0 0 1 0 2 0 0 0 0 2 1 1 0 0 0
2015 3 0 2 0 0 0 3 9 0 1 0 0 13 0 0 0 0 23
2016 8 0 0 0 0 0 0 5 0 0 0 0 5 0 0 0 0 9
2017 0 0 12 0 0 0 3 12 0 3 2 0 44 0 0 0 0 22
No
u S
ases
c
2013 0 0 0 0 2 1 23 7 0 2 0 0 0 0 0 0 0 0
2014 0 0 0 2 3 1 2 3 0 0 1 0 1 0 0 0 0 0
2015 0 0 0 3 0 0 0 2 0 0 0 0 21 0 0 0 0 10
2016 0 0 0 0 0 0 7 7 0 5 0 0 20 0 0 0 0 5
2017 0 2 0 0 0 0 3 14 0 3 15 0 19 0 0 0 0 33
Ric
his
2013 0 0 0 2 5 0 42 1 0 1 0 0 0 0 0 0 0 0
2014 0 0 0 0 2 0 0 3 0 0 1 0 0 0 0 0 0 0
2015 0 0 0 0 0 0 1 7 3 0 2 0 14 1 0 0 0 19
2016 0 0 0 0 0 0 3 8 0 2 6 2 3 2 0 0 0 12
2017 0 0 3 0 0 0 2 19 0 0 18 0 11 0 0 0 0 36
Vis
cri
2013 0 0 0 0 0 0 3 0 0 0 0 0 0 0 0 0 0 0
2014 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0
2015 0 0 0 0 0 0 0 7 0 0 0 2 1 0 1 0 0 14
2016 0 0 0 0 2 0 0 37 2 0 0 10 0 0 5 0 0 50
2017 0 0 0 2 0 0 0 2 0 0 0 2 3 0 11 0 0 2
All
2013 0 0 0 1 1 0 15 2 0 0 0 0 0 0 0 0 0 0
2014 0 0 0 0 4 0 0 44 0 0 0 0 4 0 1 0 0 0
2015 0 0 0 0 0 1 1 50 0 0 0 0 17 0 0 0 0 41
2016 1 0 0 0 1 0 1 38 2 1 1 3 22 0 4 0 0 51
2017 0 0 3 1 1 0 3 75 5 2 9 2 51 0 10 0 0 86
Page 57
Table A2, part 5.
Bro
wn
arg
us
Ari
cia
ag
esti
s
Soo
ty c
op
per
Lyca
ena
tit
yru
s
Scar
ce c
op
per
Heo
des
vir
ga
ure
ae
Smal
l co
pp
er
Lyca
ena
ph
laea
s
Pu
rple
-sh
ot
cop
per
Lyca
ena
alc
iph
ron
Bro
wn
hai
rstr
eak
Thec
la b
etu
lae
Gre
en
hai
rstr
eak
Ca
llop
hry
s ru
bi
Larg
e co
pp
er
Lyca
ena
dis
pa
r
Swal
low
tail
Pa
pili
o m
ach
ao
n
Scar
ce s
wal
low
tail
Iph
iclid
es p
od
alir
ius
Pai
nte
d la
dy
Syn
thia
ca
rdu
ii
Map
Ara
sch
nia
leva
na
Larg
e to
rto
ises
hel
l
Nym
ph
alis
po
lych
loro
s
Pea
cock
Ina
chis
io
Co
mm
a
Po
lyg
on
ia c
-alb
um
Red
ad
mir
al
Va
nes
s a
taa
nta
Wh
ite
adm
iral
Lim
enit
is c
am
illa
Co
mm
on
glid
er
Nep
tis
sap
ph
o
TO
TAL
Ap
old
2014 0 0 0 0 0 0 0 2 0 0 3 20 0 0 0 3 0 1 1195
2015 1 0 0 0 0 0 0 4 0 1 0 5 0 0 0 1 0 0 1233
2016 0 0 0 0 0 0 0 3 3 7 12 20 0 0 2 3 0 0 1272
2017 0 3 0 0 0 0 0 3 2 5 0 5 0 0 0 3 0 2 1528
Cri
t
2013 0 0 0 0 0 0 0 0 1 3 2 0 0 0 0 3 0 0 579
2014 0 0 0 0 0 0 0 0 0 0 10 0 0 2 0 0 0 0 613
2015 1 0 0 0 0 0 0 0 1 5 3 0 0 0 0 0 0 0 873
2016
2017 0 1 0 0 0 0 0 0 1 4 0 0 0 1 0 1 0 0 822
Dai
a
2014 0 0 0 0 0 0 0 0 0 2 2 0 0 0 1 0 0 0 492
2015 3 0 0 0 0 0 0 1 0 4 0 0 0 1 0 0 0 0 893
2016 0 2 0 0 0 0 0 2 2 0 4 0 0 0 0 0 0 0 933
Mal
ancr
av
2013 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 541
2014 1 1 2 1 0 0 0 0 0 0 0 1 0 0 0 0 0 1 922
2015 2 0 0 1 0 1 0 1 0 3 0 0 0 0 0 0 0 1 986
2016 0 13 0 0 0 0 0 0 4 5 37 0 0 0 0 9 0 2 1086
2017 0 7 0 0 0 0 0 2 2 0 2 0 0 0 0 5 0 2 935
Mes
end
orf
2013 0 0 0 0 0 0 0 0 0 0 5 0 0 0 0 1 0 0 748
2014 0 0 1 0 0 0 0 0 0 0 9 0 0 1 0 1 0 1 869
2015 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 915
2016 0 0 0 0 0 0 0 0 0 0 0 2 0 8 2 0 0 0 658
2017 0 0 0 0 0 0 0 2 2 0 0 2 0 0 0 0 0 2 847
No
u S
ases
c
2013 0 0 1 0 0 0 0 0 1 3 1 0 0 0 0 3 0 0 772
2014 0 0 6 0 0 0 0 0 0 0 3 0 0 1 1 3 1 0 536
2015 0 0 0 0 0 0 0 0 0 0 0 3 0 0 0 0 0 0 475
2016 0 0 5 0 0 0 3 0 0 3 2 3 0 3 0 8 0 0 681
2017 0 0 9 0 0 0 10 0 5 0 0 3 0 0 0 0 0 0 938
Ric
his
2013 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 580
2014 0 0 1 0 0 0 0 0 0 0 2 1 0 0 0 1 0 0 239
2015 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 343
2016 0 2 3 0 2 0 0 0 7 2 8 2 2 2 0 2 0 0 493
2017 0 0 2 0 2 0 5 0 2 0 0 0 0 0 0 0 0 0 358
Vis
cri
2013 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 1 0 0 651
2014 0 0 0 0 0 0 0 0 0 1 2 0 0 1 0 1 0 0 409
2015 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 614
2016 2 0 0 2 0 0 0 0 0 5 2 0 0 0 0 0 0 0 761
2017 0 0 0 0 0 0 0 0 2 10 0 0 0 0 0 2 0 0 576
All
2013 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 1 0 0 645
2014 0 0 1 0 0 0 0 0 0 0 4 3 0 1 0 1 0 0 655
2015 1 0 0 0 0 0 0 1 0 2 1 1 0 0 0 0 0 0 782
2016 0 2 1 0 0 0 0 1 2 3 9 4 0 2 0 3 0 0 836
2017 0 3 3 0 0 0 4 2 4 6 0 2 0 0 0 3 0 1 1714
Page 58
Table A3. Species with consistent change over five years at a village or all villages combined. Underlined
species are the most abundant species annual average > 10 – and are more reliable trends than species with
few individuals observed. Bold indicates an additional trend added since the 2016 report. The lower half of the
table lists species where consistent change had been identified in the 2016 report, but 2017 data do not
continue that trend. Species in red are used in the European Butterfly Indicator for Grassland Species (Van
Swaay et al., 2016)
SPECIES SHOWING CONSISTENT DECLINE
Marbled white – AP
Silver washed fritillary –NS
Heath fritillary –NS, All
Essex skipper - DA
Fenton’s wood white – RI, All
Meleager’s blue – RI
Reverdin blue – RI
Idas blue – NS
Holly blue –VI
Common glider – ME
SPECIES SHOWING CONSISTENT INCREASE
High brown fritillary – RI
Weaver’s fritillary – AP, ME, NS, RI, All
Small pearl bordered fritillary – AP, DA,
All
Nickerls fritillary – ME, NS, RI, All
Small skipper – NS
Essex skipper – RI, All
Grizzled skipper – DA, All
Dingy skipper – AP, CR, VI
Large chequered skipper – NS, RI
Small white – DA, ME
Large white - RI
Wood white – DA, MA, NS, VI, All
Small heath – AP, DA, All
Chestnut heath –RI, All
Pearly heath – ME, NS, All
Pale clouded yellow – DA
Brimstone – RI
Ringlet – CR, DA, VI
All blues – DA
Silver studded blue – VI, All
Common blue – CR, DA, ME, RI
Chapman’s blue – MA, All
Green underside blue – RI, All
Eastern short-tailed blue – DA, All
Short tailed blue – AP, DA, MA, ME, All
Osiris blue – DA, VI, All
Sooty copper – AP, NS, RI, All
Small copper – CR, All
Swallowtail – NS, All
Scarce swallowtail – DA, All
Map - All
Peacock – ME, NS
Species no longer showing consistent decline
Marbled white – NS
Meadow brown – AP
Heath fritillary – CR
Silver spotted skipper – CR
Bath white – All
Meleager’s blue – MA, ME, All
Silver-studded blue – AP
Red admiral - ME
Species no longer showing consistent increase
Marbled white – CR
Meadow brown – CR
Dark green fritillary – CR, ME
Weaver’s fritillary – VI
Small pearl bordered fritillary – MA
Wood white – RI
Pale clouded yellow – CR, NS
Brimstone – CR, All
Ringlet – AP
Mazarine blue – ME, All
Idas blue – CR
Common blue – VI
Eastern short-tailed blue –VI
Adonis blue – RI
Blue sp. – AP, VI, All
Large copper – DA, All
Scarce swallowtail - AP
Common glider – MA
Page 59
Appendix 3 Table A4, part 1. Bird abundance per point count for all surveyed species recorded on average more than
twice per year (rarer species listed in last part of table). Dark green: >= 50% increase in both abundance per
point count and % of season’s total. Light green: >= 20% increase. Yellow: <= 20% decrease. Red: <= 50%
decrease.
Bar
n s
wal
low
Hir
un
do
ru
stic
a
Bee
-eat
er
Mer
op
s a
pia
ster
Bla
ck r
edst
art
Ph
oen
icu
rus
och
ruro
s
Bla
ck w
oo
dp
ecke
r
Dry
oco
pu
s m
art
ius
Bla
ckb
ird
Turd
us
mer
ula
Bla
ckca
p
Sylv
ia a
tric
ap
illa
Blu
e ti
t
Cya
nis
tes
caer
ule
us
Ch
affi
nch
Frin
gill
a c
oel
ebs
Ch
iffc
haf
f
Ph
yllo
sco
pu
s co
llyb
ita
Co
al t
it
Per
ipa
rus
ate
r
Co
llare
d d
ove
Stre
pto
pel
ia d
eca
oct
o
Co
llare
d f
lyca
tch
er
Fice
du
la a
lbic
olli
s
Co
mm
on
bu
zzar
d
Bu
teo
bu
teo
Co
mm
on
gra
ssh
op
per
war
ble
r Lo
cust
ella
na
evia
Co
mm
on
wh
ite
thro
at
Sylv
ia c
om
mu
nis
Co
rn b
un
tin
g
Emb
eriz
a c
ala
nd
ra
Co
rncr
ake
Cre
x cr
ex
Cu
cko
o
Cu
culu
s ca
no
rus
Ap
old
2013 2.26 1.22 0 0.13 0.09 0 0.04 0 0.35 0 0 0 0.43 0 0.09 0 0 0
2014 2.73 2.24 0.33 0.25 0.64 0.02 0.13 0.29 0.2 0.04 0.02 0 0.93 0 0.07 0 0 0
2015 3.67 0.17 0.27 0.29 0.38 0.02 0.22 0.1 0.13 0.19 0.1 0 0.44 0 0 0 0 0
2016 7.44 3.98 0.29 0.21 0.77 0.13 0.63 0.23 0.6 0.02 0.33 0 0.48 0 0.04 0 0 0
2017 4.61 1.38 0.16 0.18 0.57 0.16 0.29 0.21 0.46 0.05 0.23 0 0.27 0 0.05 0 0 0
Cri
t
2013 2.47 0.07 0.1 0 0.37 0.17 0.08 0.29 0.05 0.02 0.03 0 0.59 0 0.08 0.12 0.03 0.07
2014 4.31 0.08 0.19 0.03 0.31 0.03 0.49 0.68 0.07 0 0.02 0.02 0.29 0 0.14 0 0.1 0.02
2015 3.67 0.23 0.2 0.05 0.45 0.14 0.11 0.25 0.08 0 0 0.05 0.53 0 0.03 0.13 0 0
2017 4.06 0.69 0.13 0 0.55 0.08 0.22 0.11 0.27 0 0 0 1 0 0.16 0 0.03 0
Dai
a
2014 7.1 1.17 0.21 0 0.69 0.07 0.34 0.21 0.07 0 0.38 0 0.66 0.03 0.38 0 0.03 0
2015 5.77 1.73 0.2 0.03 0.61 0.05 0.2 0.19 0.03 0 0.14 0 0.33 0 0.03 0 0 0.02
2016 5.63 0.2 0.11 0.07 0.88 0.05 0.64 0.04 0.23 0 0.13 0 0.57 0 0.02 0 0 0
2017 6.45 0.73 0.16 0.06 0.59 0.06 0.06 0.12 0.16 0 0.1 0 0.76 0 0.24 0 0 0
Mal
ancr
av
2013 4.05 0.9 0.1 0 0.22 0.24 0.17 0.05 0.12 0.02 0 0 0.61 0 0.02 0 0 0
2014 4.05 0.9 0.18 0.03 0.36 0.02 0.16 0.23 0.02 0 0.15 0 0.28 0 0.1 0 0 0.02
2015 3.75 0.73 0.23 0.17 0.35 0 0.05 0 0.25 0.07 0 0 0.3 0 0.02 0 0 0
2016 2.27 0.37 0.12 0 0.62 0.48 0.17 0.35 0.23 0 0.12 0 0.6 0 0.9 0 0.02 0.13
2017 4.35 0.98 0.25 0.08 0.56 0.06 0.35 0.13 0.71 0 0 0 0.27 0 0.08 0 0 0
Mes
end
orf
2013 3.62 0.01 0.04 0 0.31 0.21 0.13 0.66 0.12 0.01 0 0.03 0.37 0 0.12 0.01 0.03 0.07
2014 2.74 0 0.1 0.1 0.52 0.31 0.5 0.93 0.26 0 0.02 0 0.62 0 0.19 0 0.16 0
2015 1.7 0.02 0 0.27 0.67 0.28 0.17 1.17 0.41 0 0 0 0.52 0 0.34 0 0.06 0
2016 1.93 0.07 0.07 0.15 0.41 0.56 0.52 0.33 0.48 0.11 0 0.06 0.46 0 0.24 0 0.04 0.02
2017 3.58 0 0.31 0.13 0.37 0.63 0.08 1.23 0.4 0 0 0.12 0.33 0 0.19 0.15 0.08 0
No
u S
ases
c
2013 2.41 0.22 0 0 0.38 0.09 0.34 0.16 0.22 0.13 0 0 0.44 0 0.03 0 0 0
2014 2.67 0.22 0.15 0.11 1.04 0.52 0.39 0.17 0.76 0 0.02 0 1.06 0.26 0.57 0 0.04 0
2015 2.24 0.03 0.24 0.03 1.34 0.28 0.31 0.38 0.69 0 0 0 0.66 0 0.17 0 0 0.14
2016 3.9 0.08 0 0.02 0.52 0.1 0.42 0.46 0.46 0 0.04 0.06 0.67 0 0.04 0.02 0.1 0
2017 1.6 0.64 0.14 0.19 0.81 0.97 0.1 0.55 0.55 0 0.21 0.1 0.48 0 0.34 0.02 0 0
Ric
his
2013 6.38 0.81 0.03 0 0.27 0 0.08 0.32 0.05 0.11 0.24 0 0.68 0 0 0 0 0
2014 3.51 0.74 0.23 0 0.74 0.42 0.21 0.4 0.21 0 0.42 0 1.3 0 1.09 0 0.02 0.74
2015 2.08 0.31 0.08 0.08 0.96 0.23 0.08 0.19 0.12 0 0.21 0 0.38 0 0.46 0 0 0.38
2016 4.26 0.21 0.08 0.08 0.25 0 0.23 0.58 0.11 0 0 0 1.13 0 0.15 0.02 0.06 0.19
2017 2.34 0.57 0.29 0.09 1.03 1.12 0.28 0.31 0.22 0.05 0.36 0 0.5 0 0.95 0 0 0.81
Vis
cri
2013 2.47 0.32 0 0 0.09 0.1 0.03 0.24 0.06 0 0.03 0 0.21 0 0.16 0 0 0.1
2014 3.05 0.15 0.12 0 0.27 0 0 4.44 0.1 0 1.27 0 0.15 0 0.32 0 0.17 0.07
2015 2.07 0.18 0.02 0.04 0.23 0.04 0 0.16 0.11 0 0 0 0.82 0 0.2 0 0 0.02
2016 4.24 0.98 0.25 0.1 0.55 0.12 0.67 0.12 0.25 0.02 0 0 0.51 0 0.04 0 0 0
2017 2.61 0.28 0.04 0.07 0.32 0.02 0.11 0.88 0.16 0 0.02 0 0.33 0 0.6 0.07 0 0.04
Tota
l
2013 3.33 0.39 0.04 0.01 0.25 0.13 0.12 0.3 0.11 0.03 0.04 0.01 0.45 0 0.09 0.02 0.01 0.05
2014 3.51 0.65 0.18 0.07 0.55 0.17 0.28 0.83 0.21 0 0.23 0 0.63 0.04 0.32 0 0.06 0.09
2015 3.23 0.46 0.15 0.13 0.57 0.12 0.13 0.31 0.19 0.04 0.06 0.01 0.48 0 0.15 0.02 0.01 0.06
2016 4.2 0.79 0.13 0.09 0.57 0.2 0.47 0.3 0.34 0.02 0.08 0.02 0.63 0 0.2 0.01 0.03 0.05
2017 3.64 0.66 0.18 0.1 0.6 0.4 0.19 0.44 0.36 0.01 0.12 0.03 0.5 0 0.33 0.03 0.01 0.11
Page 60
Table A4, part 2.
Fe
ral p
igeo
n
Co
lum
ba
livi
a (
do
mes
t.)
Gar
den
war
ble
r
Sylv
ia b
ori
n
Go
lden
ori
ole
Ori
olu
s o
rio
lus
Go
ldfi
nch
Ca
rdu
elis
ca
rdu
elis
Gre
at g
rey
shri
ke
Lan
ius
excu
bit
or
Gre
at s
po
tted
wo
od
pec
ker
Den
dro
cop
os
ma
jor
Gre
at t
it
Pa
rus
ma
jor
Gre
en
wo
od
pec
ker
Pic
us
viri
dis
Gre
en
fin
ch
Ch
lori
s ch
lori
s
Gre
y-h
ead
ed w
oo
dp
ecke
r
Pic
us
can
us
Haw
fin
ch
Co
cco
thra
ust
es c
occ
oth
rau
stes
Ho
bb
y
Falc
o s
ub
bu
teo
Ho
ney
bu
zzar
d
Per
nis
ap
ivo
rus
Ho
od
ed c
row
Co
rvu
s co
rnix
Ho
op
oe
Up
up
a e
po
ps
Ho
use
mar
tin
Del
ich
on
urb
ica
Ho
use
sp
arro
w
Pa
sser
do
mes
ticu
s
Jack
daw
Co
rvu
s m
on
edu
la
Jay
Ga
rru
lus
gla
nd
ari
us
Kes
trel
Falc
o t
inn
un
culu
s
Ap
old
2013 0 0 0.13 0.26 0 0.13 0.65 0.3 0 0 0.83 0.09 0 0.04 0 0.65 1.52 0 1 0
2014 0.4 0 0.15 0.45 0 0.82 2.87 0.67 0.02 0.02 0.69 0 0.04 0 0 0.53 1.69 0 1.2 0
2015 1.29 0 0.41 0.78 0 0.3 1.14 0.68 0.1 0.02 0.76 0 0 0 0.02 0.03 1.16 0 0.43 0
2016 4.19 0 0.56 1.56 0 0.75 2.06 1.13 0.38 0.02 0.96 0.04 0.23 0 0.06 2.79 3.79 0 1.21 0
2017 4.02 0 0.25 0.71 0.02 0.79 1.91 0.91 0.04 0.02 1.68 0 0.07 0.02 0.04 2.55 3.11 0 0.54 0
Cri
t
2013 0.41 0 0.53 0.32 0 0.03 0.58 0.2 0.05 0.05 0.39 0.05 0 0.24 0.02 1.64 1.73 0 0.37 0
2014 0.05 0 0.8 0.36 0 0.8 2.36 0.42 0.54 0.05 0.03 0.07 0 0.39 0 0.41 0.76 0 0.59 0
2015 0.16 0 1.16 0.28 0 0.27 0.95 0.61 0.13 0.05 0.28 0.06 0.06 0.02 0.02 1.92 2.36 0 0.38 0
2017 0.25 0 0.78 0.66 0 0.28 1.44 0.94 0.23 0.03 0.61 0.05 0.09 0.31 0.02 1.86 1.88 0 0.34 0
Dai
a
2014 2.52 0 0.69 0.86 0.38 0.79 2.03 0.14 0.45 0.21 0 0.03 0 0 0 2 3.72 0 0.38 0
2015 0.17 0 0.77 0.3 0.05 0.22 0.88 0.48 0.33 0.02 0.34 0.08 0 0.27 0 1.06 1.16 0 0.36 0
2016 1.05 0 0.73 0.91 0.05 0.73 1.57 0.96 0.43 0.07 0.79 0.07 0.09 0.36 0.23 0.21 4.13 0.02 0.59 0.04
2017 1.71 0 0.8 0.92 0 0.41 1.51 1.04 0.76 0.06 1.92 0.06 0 2.12 0.08 0.31 2.86 0 0.29 0
Mal
ancr
av
2013 1.46 0 0.68 0.1 0 0 0.32 1 0.05 0 0.2 0.02 0.02 0 0 0.17 4.54 0 0.32 0
2014 0.98 0 0.18 0.08 0 1.1 3.08 0.39 0.39 0.23 0 0 0.07 0 0.03 1.64 1.1 0 0.82 0
2015 0.57 0 0.35 0.22 0 0.42 1.4 1.03 0.02 0.03 0.27 0.03 0 0 0 6.05 3.82 0 0.82 0
2016 1.88 0 1.1 0.29 0 0.29 0.81 0.85 0.04 0.06 0.27 0 0 1.19 0 0.33 1.33 0 0.02 0
2017 3.98 0 0.19 0.35 0 0.83 1.88 1.5 0.04 0.02 1.21 0 0 0 0 8.29 4.46 0.04 0.85 0
Mes
end
orf
2013 0.03 0 0.34 0.18 0 0 0.19 0.25 0 0 0.46 0.01 0.01 0.12 0.06 0.01 2.91 0 0.32 0.01
2014 0.36 0 1.07 0.12 0 0.83 1.67 0.16 0.14 0.29 0.07 0 0.05 0 0.05 0 0.86 0 0.26 0
2015 0.94 0 0.64 0.13 0 0.28 0.61 0.42 0.06 0.03 0.38 0 0 0.16 0 0.05 2.78 0 0.16 0
2016 0.26 0 0.52 0.15 0 0.31 1.3 0.91 0.11 0.3 0.41 0.07 0.02 0.04 0 0.22 0.44 0.04 0.76 0
2017 0.5 0.08 0.79 0.46 0 0.5 0.77 0.67 0.1 0.15 0.81 0 0.02 0.5 0.04 0.02 3.71 0 0.27 0
No
u S
ases
c
2013 0.09 0 0.38 0.25 0 0.19 1.63 0.78 0.09 0 0.22 0.06 0 0 0 0.13 3.47 0 1.34 0
2014 0.61 0 0.94 0.3 0 0.28 1.43 0.83 0.3 0.11 0 0.04 0.02 0.02 0 0 0.3 0 0.41 0
2015 0.17 0 0.34 0.55 0 0.21 0.66 0.28 0 0.17 0.31 0.03 0 0.1 0 0.1 1.17 0 0.07 0
2016 0 0.06 0.88 0.62 0 0.87 1.29 1.17 0.15 0.12 0.71 0.06 0.13 0.62 0 0 4.1 0 0.42 0
2017 0.79 0.07 0.9 0.24 0 0.38 1.17 0.62 0.1 0.19 1.02 0.02 0 0 0 0.09 0.17 0 0.38 0
Ric
his
2013 0 0 0.84 0.92 0.03 0.49 0.95 0.49 0.35 0.11 2.08 0 0 0.41 0.05 1.08 2.7 0 0.19 0
2014 0.26 0 0.26 0.58 0 0.09 0.65 0.44 0.6 0.07 0.09 0 0 0.77 0.02 0.3 1.28 0 0.26 0
2015 0.33 0 0.5 0.73 0 0.13 0.79 0.1 0.08 0.02 0.02 0 0 0.96 0.04 0.1 1.52 0 0.08 0
2016 3.74 0 0.53 0.34 0 0.47 1.23 0.36 0.34 0.04 0.43 0.09 0.02 10.49 0 0.47 5.08 0.04 0.23 0.83
2017 0.91 0 0.66 0.45 0.02 0.52 1.4 0.43 0.28 0.16 0.5 0 0.03 1.69 0.07 0.26 2.69 0 0.17 0
Vis
cri
2013 0.69 0 0.34 0.26 0 0.1 0.24 0.16 0 0 0.06 0.01 0.03 6.79 0.1 0.4 6.06 0.66 0.04 0.03
2014 1.05 0.05 0.54 1.8 0.1 0.24 0.88 0.12 0.22 0.02 0 0.02 0 31.37 0.07 0 1.63 0 0.07 0
2015 1.34 0 0.45 0.16 0 0.13 0.2 0.32 0.11 0.02 0 0.07 0 2.21 0.07 0 0.86 0 0.07 0.11
2016 3 0 0.59 0.1 0 0.61 2.37 1.67 0.04 0.06 1.06 0.04 0 0 0.02 3.29 4.1 0 1.27 0
2017 2.79 0 0.77 0.49 0.02 0.37 0.7 0.33 0.09 0.05 1.19 0.02 0 3.3 0.16 0.04 3.74 0 0.33 0.02
Tota
l
2013 0.41 0 0.46 0.31 0 0.11 0.54 0.4 0.06 0.02 0.52 0.03 0.01 1.52 0.04 0.58 3.49 0.14 0.41 0.01
2014 0.65 0 0.57 0.48 0.04 0.63 1.91 0.41 0.32 0.12 0.12 0.02 0.02 3.28 0.02 0.55 1.22 0 0.52 0
2015 0.65 0 0.6 0.38 0.01 0.25 0.85 0.52 0.11 0.04 0.31 0.04 0.01 0.45 0.02 1.25 1.92 0 0.32 0.01
2016 1.98 0.01 0.7 0.56 0.01 0.57 1.51 1 0.21 0.1 0.66 0.05 0.07 1.84 0.05 1.01 3.27 0.01 0.63 0.13
2017 1.81 0.02 0.65 0.53 0.01 0.5 1.34 0.79 0.2 0.09 1.09 0.02 0.03 0.99 0.05 1.58 2.76 0 0.39 0
Page 61
Table A4, part 3. La
pw
ing
Va
nel
lus
van
ellu
s
Less
er g
rey
shri
ke
Lan
ius
min
or
Less
er s
po
tted
eag
le
Aq
uila
po
ma
rin
a
Less
er s
po
tted
wo
od
pec
ker
Den
dro
cop
os
min
or
Less
er w
hit
eth
roat
Sylv
ia c
urr
uca
Lin
net
Ca
rdu
elis
ca
nn
ab
ina
Litt
le o
wl
Ath
ene
no
ctu
a
Lon
g-ta
iled
tit
Aeg
ith
alo
s ca
ud
atu
s
Mag
pie
Pic
a p
ica
Mal
lard
An
as
pla
tyrh
ynch
os
Mar
sh t
it
Po
ecile
pa
lust
ris
Mar
sh w
arb
ler
Acr
oce
ph
alu
s p
alu
stri
s
Mid
dle
sp
ott
ed w
oo
dp
ecke
r
Den
dro
cop
us
med
ius
Mis
tle
thru
sh
Turd
us
visc
ivo
rus
Nu
that
ch
Sitt
a e
uro
pa
ea
Ph
easa
nt
Ph
asi
an
us
colc
hic
us
Qu
ail
Co
turn
ix c
otu
rnix
Rav
en
Co
rvu
s co
rax
Red
-bac
ked
sh
rike
Lan
ius
collu
rio
Ree
d w
arb
ler
Acr
oce
ph
alu
s sc
irp
ace
us
Ap
old
2013 0 0 0.17 0.09 0.22 0.09 0 0.3 0.17 0 0.78 0 0.39 0 1.3 0.04 0 0.39 1.26 0
2014 0 0 0.07 0.07 0.04 0.16 0.02 0.27 0.15 0 0.78 0 0.02 0.05 1.89 0.04 0.02 0.24 1.93 0
2015 0 0 0.02 0.03 0 0 0.02 0 0.3 0 0.24 0 0.17 0.48 0.44 0.05 0.16 0.46 1.35 0
2016 0 0 0.15 0.06 0 0.13 0.1 0 0.56 0 0.73 0 0.21 0 0.98 0 0 0.63 2.73 0
2017 0 0 0.02 0.07 0.04 0.05 0.11 0 0.75 0.11 1.41 0.04 0.29 0 1.23 0.05 0 0.39 2.16 0
Cri
t
2013 0 0 0.07 0.05 0 0.15 0 0.25 0.19 0 0.31 0.02 0.34 0.02 0.61 0.03 0.02 0.53 1.71 0
2014 0 0 0 0.03 0.05 0.05 0 0 0.24 0.02 0.76 0.05 0.14 0 1.29 0.02 0 0.14 1.49 0.08
2015 0 0 0.06 0.03 0 0.06 0 0.03 0.2 0.02 0.22 0 0.14 0.02 0.3 0.02 0 0.84 1.41 0
2017 0 0 0.11 0.05 0 0.16 0 0.13 0.53 0 0.78 0.02 0.27 0.02 0.88 0.05 0 0.22 2.11 0
Dai
a
2014 0 0.03 0 0.1 0.14 0 0.14 0.28 1.76 0 1 0 0 0 0.48 0.07 0.1 0.24 4.86 0.07
2015 0 0 0.05 0.08 0 0.06 0 0.47 0.59 0.05 0.09 0.02 0.17 0.02 0.39 0.14 0.13 0.14 2.13 0
2016 0 0 0.04 0.09 0.14 0.27 0.02 0.2 1.3 0.02 0.29 0 0.21 0 0.64 0.05 0.05 0.52 3.8 0
2017 0.22 0.08 0.04 0.02 0.04 0.12 0.04 0.06 1.53 0 0.47 0.08 0.18 0 0.39 0.08 0.04 0.27 3.08 0
Mal
ancr
av
2013 0 0 0 0.07 0 0 0 0.12 0.32 0 0.41 0 0.27 0 0.37 0 0 0.88 0.32 0
2014 0 0 0.02 0.08 0 0.05 0 0.1 0.39 0 1.15 0.02 0.05 0 1.38 0.03 0 0.15 1.2 0.02
2015 0 0 0 0.02 0.07 0.02 0 0.1 0.42 0.02 0.23 0 0.15 0 0.2 0 0 0.22 1 0
2016 0 0 0 0.08 0.02 0.04 0 0 0.29 0 0.04 0.54 0.15 0 0.38 0.13 0 0.29 0.62 0
2017 0 0 0 0.04 0.02 0.13 0.04 0.25 1 0 0.71 0.06 0.29 0 0.92 0 0.02 0.35 1 0
Mes
end
orf
2013 0 0 0.1 0.03 0.01 0.04 0 0 0.04 0 0.5 0.01 0.72 0.01 0.84 0 0.19 0.22 0.9 0
2014 0 0 0.03 0 0.1 0 0 0 0.09 0 0.36 0 0.07 0 0.95 0.02 0.38 0.26 1.29 0.09
2015 0 0 0.11 0 0.03 0 0 0 0 0 0.03 0.02 0.17 0 0.44 0 0.08 0.39 0.5 0
2016 0 0 0 0.07 0 0.04 0 0 0.24 0 0.28 0.11 0.22 0 0.59 0.06 0 0.46 0.61 0
2017 0 0 0.04 0.1 0 0.12 0.02 0.02 0.21 0 0.67 0.04 0.42 0 0.75 0.02 0.02 0.17 0.9 0
No
u S
ases
c
2013 0 0 0 0.06 0.03 0 0 0.25 0.16 0 0.53 0.16 0.41 0 1 0.06 0 0.41 1.88 0
2014 0 0 0 0 0.09 0 0 0 0.19 0 0.56 0 0 0 0.87 0.04 0 1.15 1.5 0.09
2015 0 0 0 0.03 0.14 0 0 0 0.31 0 0.24 0.14 0.07 0 0.24 0.24 0 0.31 1.03 0
2016 0 0.02 0.19 0.15 0 0.08 0 0.25 0.17 0 0.27 0.02 0.52 0.13 0.75 0.06 0.02 1.48 1.33 0
2017 0 0 0 0.05 0.03 0.07 0 0.05 0.33 0 0.98 0.1 0.43 0 0.86 0.33 0 0.47 0.93 0
Ric
his
2013 0 0 0.05 0 0.03 0.08 0 0 0.89 0 0.51 0.14 0.22 0 0.46 0.03 0.03 0.3 1.35 0
2014 0 0 0 0 0.05 0.07 0 0 0.67 0 0 0 0 0 0.35 0.09 0.16 0.77 1.44 0.12
2015 0 0 0 0 0.06 0.06 0 0 0.33 0 0.17 0.12 0.02 0 0.21 0.23 0.04 0.33 0.31 0
2016 0 0.25 0.15 0.02 0.04 0.09 0.08 0.04 2.51 0 0.06 0.04 0.13 0 0.26 0 0.02 0.83 1.21 0
2017 0 0 0 0.02 0.05 0.14 0 0.12 0.38 0 0.22 0.28 0.21 0 0.48 0.19 0 0.43 0.72 0
Vis
cri
2013 0 0 0.1 0.03 0 0.12 0.04 0 2.82 0 0.09 0.03 0.06 0 0.19 0.01 0.06 0.13 1.06 0
2014 0 0.05 0.05 0 1.15 0.05 0 0 2.63 0 0.1 0.07 0 0.02 0.05 0.24 2.85 0.66 1.76 0.15
2015 0 0 0 0 0.02 0 0.02 0 1.93 0 0 0.09 0.13 0 0.09 0 0.09 0.05 0.95 0
2016 0 0 0 0.1 0.04 0.27 0 0.08 0.65 0 0.27 0.12 0.16 0 0.59 0.02 0 0.45 0.8 0
2017 0 0.18 0.07 0.02 0.04 0.21 0.02 0 2.84 0.04 0.18 0.11 0.12 0 0.18 0.07 0.23 0.23 1.04 0
Tota
l
2013 0 0 0.07 0.04 0.02 0.08 0.01 0.11 0.8 0 0.39 0.04 0.35 0.01 0.61 0.02 0.06 0.38 1.18 0
2014 0 0.01 0.02 0.03 0.17 0.05 0.01 0.07 0.61 0 0.59 0.02 0.04 0.01 0.97 0.06 0.37 0.43 1.71 0.07
2015 0 0 0.03 0.02 0.03 0.03 0 0.08 0.51 0.01 0.15 0.04 0.13 0.07 0.3 0.07 0.07 0.35 1.11 0
2016 0 0.04 0.07 0.08 0.04 0.13 0.03 0.08 0.83 0 0.27 0.12 0.23 0.02 0.6 0.05 0.01 0.66 1.59 0
2017 0.02 0.03 0.04 0.05 0.03 0.12 0.03 0.08 0.93 0.02 0.68 0.09 0.28 0 0.71 0.1 0.04 0.32 1.49 0
Page 62
Table A4, part 4. R
iver
war
ble
r
Locu
stel
la f
luvi
ati
lis
Ro
bin
Erit
ha
cus
rub
ecu
la
Ro
ok
Co
rvu
s fr
ug
ileg
us
Seri
n
Seri
nu
s se
rin
us
Skyl
ark
Ala
ud
a a
rven
sis
Son
g th
rush
Turd
us
ph
ilom
elo
s
Spar
row
haw
k
Acc
ipit
er n
isu
s
Spo
tted
fly
catc
her
Mu
scic
ap
a s
tria
ta
Star
ling
Stu
rnu
s vu
lga
ris
Sto
ck d
ove
Co
lum
ba
oen
as
Sto
nec
hat
Saxo
cola
to
rqu
atu
s
Thru
sh n
igh
tin
gale
Lusc
inia
lusc
inia
Tree
pip
it
An
thu
s tr
ivia
lis
Tree
sp
arro
w
Pa
sser
mo
nta
nu
s
Tree
cree
per
Cer
thia
fa
mili
ari
s
Turt
le d
ove
Stre
pto
pel
ia t
urt
ur
Wh
inch
at
Saxi
cola
ru
bet
ra
Wh
ite
sto
rk
Cic
on
ia c
ico
nia
Wh
ite
wag
tail
Mo
taci
lla a
lba
Will
ow
war
ble
r
Ph
yllo
sco
pu
s tr
och
ilus
Ap
old
2013 0 0.13 0 0 0 0.04 0.04 0.17 9.35 0.57 0 0.48 0.26 2.13 0 0 0 0 0.13 0.04
2014 0 0.11 0 0 0.02 0.04 0.04 0.05 0.02 0.05 0.07 0.11 0.11 0.62 0.04 0 0.16 0.11 0.47 0.02
2015 0 0.29 0 0.02 0 0.05 0 0 0.03 0.13 0.05 0.03 0 1.89 0.21 0.05 0 0.14 0.19 0
2016 0 0.73 0 0 0 0.02 0.04 0 7.85 0.85 0.38 0.38 0.15 2.29 0.29 0.06 0.02 0.33 0.17 0
2017 0 0.13 0 0.02 0 0 0 0.02 7.2 0.3 0.18 0 0 1.79 0.2 0.04 0 0.04 0.38 0
Cri
t
2013 0 0.08 0 0 0.17 0.12 0 0 1.1 0.17 0.07 0 0.14 0.1 0 0.07 0.14 0.2 0.15 0
2014 0 0 0 0 0.02 0.02 0 0.03 45.78 0.05 0.08 0 0.17 0.03 0.03 0.03 0.2 0.12 0.19 0
2015 0.02 0.09 0 0 0.03 0.05 0 0 0.28 0.06 0.13 0 0.03 0.14 0 0.13 0.02 0.16 0.11 0
2017 0.05 0.25 0 0 0.03 0.02 0.03 0 1.42 0.14 0 0 0 0.73 0.08 0.08 0 0.06 0.27 0
Dai
a
2014 0 0.03 0 0.03 0 0 0 0 15.69 0.79 0.07 0 0.07 5.21 0 0.24 0.79 0.03 0.38 0
2015 0 0.19 0 0.03 0.06 0 0 0 0.03 0.13 0.2 0.02 0.02 1.66 0.08 0.31 0.08 0.02 0.27 0
2016 0.02 0.54 0 0.04 0.14 0 0.02 0 0.29 0.36 0.2 0.21 0.13 2.27 0.18 0 0 0.11 0.59 0
2017 0 0.2 10.63 0.02 0.1 0 0.04 0 0.9 0.31 0.1 0.02 0 3.1 0.1 0.12 0 0.29 0.35 0
Mal
ancr
av
2013 0 0.07 0 0.05 0.02 0.05 0.05 0 0.39 0.05 0.05 0 0.1 3.95 0 0 0.02 0 0.12 0
2014 0 0.07 0 0.07 0.03 0 0 0 0.66 0.13 0.31 0 0.11 0.08 0.02 0 0.1 0.02 0.13 0
2015 0 0.25 0 0.05 0 0 0 0.02 0 0.13 0.07 0.15 0 1.28 0.05 0.02 0 0 0.07 0
2016 0.23 0 0 0 0.04 0.04 0 0.08 4.38 0.19 0.15 0 0.04 1.69 0 0.35 0.02 0.15 0.15 0.08
2017 0 0.31 0 0 0.02 0.02 0.02 0 0.02 0.17 0.06 0 0 1.75 0.04 0.02 0 0 0.23 0
Mes
end
orf
2013 0 0.26 0 0 0.21 0.03 0 0.13 0.21 0.1 0.03 0.04 0.07 0.49 0.01 0.01 0.03 0.31 0.25 0.07
2014 0 0.16 0 0 0.5 0.03 0 0 3.88 0.12 0.17 0 0.05 0.22 0.03 0 0.09 0.17 0.24 0
2015 0 0.06 0 0 0.83 0.23 0.02 0 0.39 0.08 0.02 0.05 0 0.28 0.08 0.02 0.02 0.02 0.41 0
2016 0.07 0.33 0.02 0 0 0.06 0 0 0.7 0.22 0.2 0 0.3 0.93 0.02 0.09 0 0 0.11 0
2017 0.06 1.06 0 0 0.48 0.08 0 0 1.02 0.38 0.19 0.02 0.04 0.73 0.17 0.04 0.04 0.02 0.42 0
No
u S
ases
c
2013 0 0.06 0 0 0.03 0 0.03 0.03 0 0.13 0.16 0 0.25 1.75 0 0 0.19 0 0.09 0
2014 0 0.02 0 0 0.02 0.06 0 0.02 7.15 0.07 0.3 0 0.35 0.22 0.02 0.06 0.06 0 0.2 0.04
2015 0 0.14 0 0.14 0 0.31 0.03 0 0.1 0.69 0.07 0.03 0.21 0.59 0.03 0.03 0.03 0 0.59 0
2016 0.04 0.65 0 0 0.54 0 0 0 0 0.38 0.12 0 0.19 0.27 0.12 0.1 0.06 0.1 0.25 0
2017 0.14 0.52 0 0 0 0.22 0 0 1.9 0.28 0.1 0 0.21 1.5 0.21 0.14 0 0.07 0.17 0
Ric
his
2013 0 0.11 0 0 0 0 0 0 0 0.03 0.22 0 0.11 4.86 0 0.11 0 0.14 0.11 0
2014 0 0.09 0 0 0 0.09 0 0 5.91 0 0.51 0 0.05 2.63 0 0.23 0.12 0.09 0.26 0
2015 0.17 0 0 0 0 0.08 0 0 4.5 0.02 0 0.02 0.21 0.52 0 0.06 0.04 0 0.23 0
2016 0 0.21 19.19 0 0.4 0 0.06 0 23.68 0.11 0.19 0 0.15 1.66 0.02 0.09 0.04 0.38 0 0
2017 0.12 0.07 0.02 0.03 0 0.55 0 0 13.02 0.36 0.09 0.02 0.33 1.83 0.02 0.12 0.03 0.22 0.31 0
Vis
cri
2013 0 0.01 21.47 0 1.04 0.03 0 0 1.1 0.26 0.12 0 0.07 4.93 0 0.03 0 0.01 0.07 0
2014 0 0.05 7.37 0 0.83 0 0 0 115.66 0.05 0.22 0 0.22 1.1 0 0.05 0.05 0.22 0.07 0
2015 0 0.05 15.5 0 1.8 0.13 0 0 0.21 0 0.11 0 0 2.59 0 0.09 0.13 0.45 0.09 0
2016 0 0.24 0 0 0 0.04 0.04 0.02 1 0.02 0.1 0 0.08 2.88 0.12 0 0.06 0.02 0.24 0
2017 0 0.19 25.95 0 1.12 0.02 0 0 29.19 0.21 0.37 0.05 0.02 2.46 0.07 0.23 0.11 0.39 0.07 0
Tota
l
2013 0 0.11 4.45 0.01 0.3 0.04 0.01 0.04 1.17 0.17 0.09 0.04 0.12 2.5 0 0.03 0.05 0.12 0.14 0.02
2014 0 0.07 0.74 0.01 0.17 0.03 0 0.01 21.53 0.12 0.21 0.01 0.14 0.92 0.02 0.06 0.16 0.09 0.23 0.01
2015 0.02 0.14 1.92 0.02 0.35 0.09 0 0 0.65 0.12 0.08 0.04 0.04 1.15 0.06 0.09 0.04 0.1 0.22 0
2016 0.05 0.38 2.78 0.01 0.16 0.02 0.02 0.01 5.37 0.3 0.19 0.08 0.15 1.7 0.1 0.1 0.03 0.15 0.22 0.01
2017 0.05 0.33 4.53 0.01 0.22 0.12 0.01 0 7.06 0.27 0.14 0.01 0.08 1.71 0.11 0.1 0.02 0.14 0.27 0
Page 63
Table A4, part 5. W
oo
d p
igeo
n
Co
lum
ba
pa
lum
ba
s
Wo
od
lark
Lullu
la a
rbo
rea
Wre
n
Tro
glo
dyt
es t
rog
lod
ytes
Wry
nec
k
Jyn
x to
rqu
illa
Yello
w w
agta
il
Mo
taci
lla f
lava
Yello
wh
amm
er
Emb
eriz
a c
itri
nel
la
Tota
l
Rare species (on average 2 or less records per year)
Ap
old
2013 1.65 0 0 0 0.04 0.09 32.87 Barred warbler Sylvia nisoria
Black stork Ciconia nigra
Bullfinch Pyrrhula pyrrhula
Common kingfisher Alcedo atthis
Common nightingale Luscinia megarhynchos
Common Redstart Phoenicurus phoenicurus
Gadwall Anas strepera
Goldcrest Regulus regulus
Goshawk Accipiter gentilis
Great reed warbler Acrocephalus arundinaceus
Grey heron Ardea cinerea
Grey wagtail Motacilla cinerea
Icterine warbler Hippolais icterina
Marsh harrier Circus circus
Meadow pipit Anthus pratensis
Montagu's harrier Circus pygargus
Olivacious warbler Iduna pallida
Purple Heron Ardea Purpurea
Red-breasted flycatcher Ficedula parva
Sand Martin Riparia riparia
Scops owl Otus scops
Sedge warbler Acrocephalus schoenobaenus
Steppe buzzard Buteo buteo vulpinus
Tawny owl Strix aluco
Tawny pipit Anthus campestris
Water rail Rallus aquaticus
White-backed woodpecker Dendrocopos leucotos
Wood warbler Phylloscopus sibilatrix
2014 1.05 0 0.02 0 0.02 0.07 26.73
2015 1.27 0 0.05 0 0 0.11 21.32
2016 0.71 0.02 0.1 0 0 0.4 56.02
2017 0.66 0.16 0.09 0.02 0 0.25 43.46
Cri
t
2013 0.19 0.03 0 0.02 0.02 0.49 18.8
2014 0.12 0.32 0 0 0 0.29 68.64
2015 0.19 0.02 0 0 0 0.25 19.66
2017 0.19 0.08 0 0 0 0.48 26.25
Dai
a
2014 0.62 0.1 0 0 0 0.48 65.69
2015 0.48 0.22 0.05 0 0 0.13 24.3
2016 0.59 0 0.14 0 0 0.14 35.25
2017 0.22 0 0.04 0 0 0.22 47.92
Mal
ancr
av
2013 0.49 0.05 0 0.05 0 0.63 24.54
2014 0.7 0.1 0.02 0 0 0.18 28.51
2015 0.57 0.02 0 0 0 0.13 26.17
2016 0.1 0.08 0 0 0 0.94 26.27
2017 0.75 0 0.06 0 0 0.04 39.88
Mes
end
orf
2013 0.21 0.04 0.01 0.01 0.04 0.44 18.41
2014 0.36 0.12 0 0 0.02 1 24.74
2015 0.23 0.02 0.05 0 0 0.58 17.41
2016 0.44 0.09 0.04 0.02 0 1.04 18.74
2017 0.5 0.02 0.02 0 0 0.79 26.58
No
u S
ases
c
2013 0.16 0.03 0.03 0 0 0.31 21.28
2014 0.43 0.35 0 0 0 0.78 29.28
2015 0.31 0.1 0 0 0 0.72 17.62
2016 0.37 0.02 0.17 0.02 0 0.98 27.9
2017 0.55 0.21 0.07 0.03 0 1.09 24.91
Ric
his
2013 0.22 0 0 0.05 0 0.46 30.19
2014 0.44 0 0 0.02 0 1.21 32.84
2015 0.27 0.06 0 0.02 0 0.63 19.79
2016 1.34 0.17 0 0 0.17 0.85 86.49
2017 0.14 0.19 0 0.14 0 1.02 41.02
Vis
cri
2013 1.01 0.12 0 0 0.21 0.43 55.53
2014 0.39 0.12 0 0 0 1.05 190.07
2015 0.46 0.05 0 0 0.05 0.79 35.84
2016 0.61 0 0 0 0 0.14 35.35
2017 1.32 0.04 0 0.02 0.07 1.04 88.42
Tota
l
2013 0.5 0.05 0.01 0.02 0.06 0.44 29.56
2014 0.5 0.14 0 0 0 0.59 52.29
2015 0.49 0.06 0.02 0 0.01 0.38 22.98
2016 0.59 0.05 0.07 0.01 0.02 0.64 40.65
2017 0.54 0.09 0.03 0.03 0.01 0.63 42.13
Page 64
Table A5. Species with consistent change over five years at a village or overall. Species in red are associated
with grassland according to Birdlife International’s (2018) online species database. Bold indicates a new entry
since the previous annual report. Striked out indicates a trend that was identified in last year’s report but no
longer continues into this year.
SPECIES SHOWING CONSISTENT DECLINE
Barn swallow –DA, MA
Bee-eater – MA, RI
Black redstart - DA
Chaffinch – DA
Collared dove – CR, DA
Common whitethroat - DA
Cuckoo – CR, VI
Great grey shrike – DA
Hoopoe – ME, VI
House martin - DA
Lesser spotted eagle – VI
Lesser whitethroat – AP
Long-tailed tit – AP, MA
Magpie - VI
Red-backed shrike – CR, NS
Spotted flycatcher – AP, NS
Whinchat – DA, ALL
White stork – ME
Willow warbler – AP
Wood pigeon – AP
Wood warbler – ME
Woodlark – VI
Wryneck – RI
Yellow wagtail – AP, ME
SPECIES SHOWING CONSISTENT INCREASE
Barn swallow – AP
Bee-eater - CR
Black redstart – CR, MA, NS
Black woodpecker – CR, DA, RI, VI
Blackbird – ALL
Blackcap – AP
Blue tit – AP, RI
Chaffinch – NS
Chiffchaff – CR, ME, VI, ALL
Coal tit – AP
Collared dove – AP
Collared flycatcher – CR, NS
Corn bunting – NS
Feral pigeon – AP, RI, VI, ALL
Garden warbler – NS
Golden oriole – AP, CR, RI, VI, ALL
Goldfinch – AP, MA, NS
Great spotted woodpecker – RI
Great tit - RI
Green woodpecker – AP, CR, DA, TOTAL
Greenfinch – AP
Grey-headed woodpecker – NS, VI
Hawfinch – DA, MA, NS, RI
Honey buzzard – CR, RI
Hooded crow – DA, NS, RI
Hoopoe – AP
House sparrow - RI
Jay – VI
Lesser spotted woodpecker - RI
Lesser whitethroat – NS
Linnet – DA, RI
Little owl – AP
Long-tailed tit - RI
Magpie - AP
Marsh tit – RI
Marsh warbler – RI, VI
Middle spotted woodpecker – DA, RI
Raven - ME
River warbler – CR, NS, ALL
Robin – DA, VI
Skylark - DA
Sparrowhawk – DA
Spotted flycatcher – MA
Stock dove – MA, RI
Stonechat - CR
Thrush nightingale – DA
Tree pipit – RI
Tree creeper – AP, DA, NS, RI, ALL
Turtle dove – AP, MA, NS, ALL
White stork – AP, VI
White wagtail – VI
Wood pigeon - ME
Woodlark – AP, RI
Wren – AP, DA
Wryneck - NS
Yellowhammer – CR, NS