tarnava mare 2017 biodiversity survey summary report · june to 8 august 2017, in eight villages...

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.

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

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.

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.

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.

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.

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

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.

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.

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

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.

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

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


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


Viscri increased intensification – due to more livestock, more hay production


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).

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.

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.

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.

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.

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.

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.

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.

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)


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)


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)


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)


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)


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)


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)


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)


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

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)


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)


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)


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)


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)


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)


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)


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)


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)


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)


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)


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)


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)


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

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

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

Apold increased intensification - due to less hay production and more livestock


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


Malancrav reduced intensification – due to reduction in all farming aspects, i.e. less farming overall


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


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.

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).

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

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

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

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

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

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A

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tain

Clo

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Bed

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Cro

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Yello

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Sca

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sa o

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Do

rycn

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Do

rycn

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pen

tap

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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

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Sca

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sa o

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Do

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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

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.

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

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

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

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.

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.


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


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

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

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

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

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

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

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

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.

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

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.

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)

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).

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

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 ↑

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

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

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

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

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

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

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

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

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)


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


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

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

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

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

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

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

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.


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


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

tarnava mare 2017 biodiversity survey summary report · june to 8 august 2017, in eight villages...

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