Tarnava Mare 2018 Biodiversity Survey Summary Report · 2019-03-04 · Fieldwork in 2018 was...

Tarnava Mare 2018 Biodiversity Survey Summary Report

Report editor & lead scientist: Dr Bruce Carlisle – Geography & Environmental Sciences, Northumbria University.

Science team: Zuni Askins, Kenneth Burton, Bogdan Ciortan, Sean Clough, Sian Green, Hugh Hanmer, Susan Jones, Tom Kitching, Daniela Vasilache, Kim Wallis, Lisa Wood. Project leader: Toby Farman. Assisted by: Alex Dinca, Mihaela Hojbota, Dragos Luntraru, Madalina Marian, Erik Nemeth, Alin-Marius Nicula, Madalina Petrisor, Silviu Simula, Andi Tronciu. 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 ......................................................................................................................... 6

2.2 Land use ........................................................................................................................................ 6

2.3 Grassland plants ............................................................................................................................ 6

2.4 Grassland butterflies ..................................................................................................................... 7

2.5 Birds ............................................................................................................................................... 7

2.6 Small mammals ............................................................................................................................. 8

2.7 Large mammals ............................................................................................................................. 8

2.8 Bats ................................................................................................................................................ 9

3.0 Vital statistics .................................................................................................................................. 10

3.1 Site Trends ................................................................................................................................... 18

4.0 Farmer interviews ........................................................................................................................... 19

5.0 Grassland plants .............................................................................................................................. 25

6.0 Grassland butterflies ....................................................................................................................... 30

7.0 Birds ................................................................................................................................................. 34

7.1 Point Counts ................................................................................................................................ 34

7.2 Bird ringing .................................................................................................................................. 38

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 Bats ................................................................................................................................................ 48

11.0 References ..................................................................................................................................... 49

Appendix 1 ............................................................................................................................................ 50

Appendix 2 ............................................................................................................................................ 53

Appendix 3 ............................................................................................................................................ 58

1.0 Introduction This report summarises the data gathered by Operation Wallacea’s Transylvania project during the

summer of 2018. This was the sixth 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 2018 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 2018 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 six 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 at village-level, and listing sites with 5-

year trends in plant, butterfly and bird data. Sections 4 to 10 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

As with previous years, 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.

However, there are some changes that are likely to be early warning signs of

important changes to biodiversity and need to be followed closely in coming

years.

The key messages after 6 years of survey are:

Signs of farming changes to more intense livestock farming and less

hay production:

Most villages have increased sheep numbers, particularly

lambs

There are increasing numbers of reported bear and wolf

attacks

At Viscri, the increased number and size of sheep flocks, and the

associated dogs, is impacting the ability to undertake the plant,

butterfly and bird point count

2016 signs of a general trend of declining indicator plant abundance

have not continued in 2017 or 2018

2018 was a poor year for butterfly abundance and diversity. This may

be due to the frequent wet weather.

Many grassland bird species at several villages had lower numbers in

2018 compared to 2017. But only two species have a consistent

declining trend over 5 years.

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, 2014, 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. Daia has been dropped from the project from 2018 onwards, again due to logistical

reasons. A replacement village is being planned for 2019 onwards.

Fieldwork in 2018 was undertaken over a 7 week period from 21 June to 7 August 2018, in seven

villages within the Tarnava Mare Natura 2000 site. In total, 42 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 six 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


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

2018 Nou Sasesc Mesendorf Viscri

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

2018 Viscri Crit 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

2018 Apold

The weather conditions vary from year to year. Wet weather has an impact on the number of surveys

that can be undertaken, and also has an influence on vegetation phenology and the abundance and

activity of wildlife, particularly butterflies and small mammals. The start of the 2015 fieldwork season

was particularly cool and wet, especially while surveying at Richis and Nou Sasesc. Weather in 2016

and 2017 was more “normal”. 2018 was another “wet year”.

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 six years.

An increase in the number and size of large sheep flocks, and the accompanying dogs, severely

restricted the ability to undertake surveys on Viscri’s East transect. Several bird point count sites

could not be surveyed. 5 plant and butterfly sites could only be surveyed by borrowing a four wheel

drive vehicle, rather than surveying on foot.

There are seven main survey teams covering farmer interviews, grassland plants, grassland

butterflies, birds, small mammals, large mammals and bats. Further details of the methods of each

team, and any notable alteration of methods, are given in the following sections.

2.1 Farmer interviews A fairly extensive set of farm interviews were carried out in 2018, although not as many as in 2017

and 2015 (Table 2.2). There were between 5 to 17 interviews at each village, but none at Apold. 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.

Table 2.2. Total number of interviews per year

Year 2013 2014 2015 2016 2017 2018

N interviews 41 48 153 0 137 80

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, and to

some extent 2018, 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 No further land use survey or mapping work was undertaken in 2018.

2.3 Grassland plants The plant team re-surveyed the sites from previous years, using the same methods. Apold and Crit

have now been surveyed over five years, while the other 5 villages have 6 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 2018 there was a new survey team leader. However they had

previous experience on the project from assisting with the surveys in 2017 and 2015.

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 2.3. Nearly

all plots were surveyed two or three times. Weather caused 5 sites to be surveyed just once. An

increase in the number and size of large sheep flocks at Viscri severely restricted the ability to

undertake surveys on Viscri’s East transect. So 5 Viscri sites were surveyed just once, and that was

only possible by borrowing a four wheel drive vehicle, rather than surveying on foot. 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 was used in 2015 and 2017.

The butterfly data from 2014 and 2015 contributed to the latest European Butterfly Indicator for

Grassland Species report (Van Swaay et al., 2016). The 2016 to 2018 data has been contributed to

the next report which is currently in preparation and due in 2019.

Table 2.3. Summary of how many times butterfly plots were surveyed at each village.

Village N sites Not surveyed Once 2 times 3 times N surveys

Apold 12 - - 6 6 30

Crit 18 3 3 12 1 30

Malancrav 12 1 - 11 - 22

Mesendorf 15 3 - 9 3 27

Nou Sasesc 12 - 2 10 - 24

Richis 12 - - 5 7 31

Viscri 13 - 5 2 6 27

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 2018 point count locations were very similar to those of the previous 3 years (2015 to

2017. 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. Eight of the points on Viscri’s East transect could

not be surveyed at all, due to the presence of large sheep flocks and numerous shepherd dogs. In

2018 there was a new survey team leader. However they had previous experience on the project

from assisting with the surveys from 2015 to 2017.

In addition to the point counts, the mist netting and ringing survey was continued in 2018. Three nets

were set up from dawn until about 1100 in scrub areas adjacent to farmland, across bird movement

corridors. In 2018 the mist netting and ringing took place at all 7. A new staff member led the mist

netting surveys in 2018.

The point count data has been shared with Milvus (OpenBirdMaps database) and the Ornithological

Society of Romania (Ornitodata). The ringing data is shared with Milvus.

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. From 2016 onwards, 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 are adjusted each year 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. Survey leaders have changed each year.

2.7 Large mammals The large mammal surveys commenced in 2014 and 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 each villages, 8 cameras were set up in two sets

of locations for 4 or 5 days. The cameras were placed in strategically chosen woodland locations that

seemed likely to experience frequent large mammal activity. One camera was stolen in the last week,

at Apold.

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 2018. The large

mammal survey team leader was the same as in 2017.

2.8 Bats From 2014 to 2017 a multi-method approach to bat surveying was used at each village, including

roost surveys, bat activity transect surveys, static detector surveys and mist netting. In 2018 there

was a new bat survey leader and a new survey methodology was adopted, focussing on trapping with

mist nets and one harp trap. An extensive summary report of the 2018 bat surveys gives further

details of the methodology (Kitching, 2018). The findings have been shared with Romania’s Centre

for Bat Research and Conservation.

3.0 Vital statistics

This section presents selected summary information to give a quick 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 and 3.2 summarise the farm interview data. Figure 3.1 shows the mean extent of

cultivation, hay meadows and other agricultural land use at each village from 2015 to 2018. 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. 2017 surveys showed a greater increase in “Other” than

hay or cultivation. This other category includes pasture used for livestock grazing. The “Other”

category increased at all villages except Richis. However in 2018 these increases were reversed at

most villages, so these data show no clear change in predominance of the “Other” category,

including grazing land. Changes to the hay and cultivation categories vary between villages. Despite

the greater sample sizes since 2015, 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. These sorts of differences should be watched

over the coming years, and maybe a more representative source of this information can be found.

Figure 3.1. Farm land use, showing the 2015, 2017 and 2018 mean area of cultivation, hay, and other

use. 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 number of milk cattle, ewes and lambs at each village since 2015. There

are notable differences between villages and between years. The number of sheep is much greater

than the number of milk cattle at all villages. There are large fluctuations from year to year in

numbers of sheep, which reflects the impact of exactly which farmers are interviewed in a year. So

again, despite the greater sample size since 2015, it is still felt that this may not give an accurate

picture of the number of livestock across the villages. 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 surveyed in 2018, apart from Mesendorf,

have greater numbers of lambs in 2018 than 2015. This difference needs to be monitored in the

coming years.

Figure 3.2. Farm livestock, showing mean number of lambs, ewes and milk cattle in 2015, 2017 and

2018. 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 from 2015, 2017 and 2018 (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. Two key numbers are

the increased number of sheep, particularly lambs, and the greater number of bear and wolf attacks.

However in general farmers do not state in the questionnaires that they are changing or planning to

change their farming practices.

Apold (from 2017)

increased intensification - due to less hay production and more livestock

low change potential

Crit reduced intensification – due to less cultivation, fewer livestock

reduced change potential

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

reduced change potential – all becoming more stable

Mesendorf increased intensification – due to less communal grazing, less hay production

increased change potential – favouring more silage, crops, livestock

Nou Sasesc increased intensification – more livestock, less communal grazing, more hay, but less hand-mowing


Richis slightly increased intensification – less hand-mown hay


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

medium change potential

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 and 2018 numbers have been higher, negating that

trend. No individual village has a consistent trend in indicator plant abundance over the 6 years,

although abundance at Richis does show signs of a decreasing trend. The 2017 report also suggested

this for Nou Sasesc, but 2018 abundance was the highest ever. Plant abundances at other villages

seem to be fluctuating. 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 abundance. 2016 and 2017 were good years for butterfly abundance and most villages unsurprisingly have decreased abundance in 2018. However, there has been a big decline in abundance, with Apold, Crit and Malancrav having far lower abundance than any previous year. The overall abundance across all years also was lower in 2018 compared to all previous years. This could be due to more frequent rain during the 2018 survey season, but butterfly abundance needs to be monitored closely.

Figure 3.6. Butterfly abundance per ha, for each village, for each year.

Red-backed shrike abundance is summarised in figure 3.7. In 2018, red-backed shrike numbers were

at their highest or second highest in four of the seven villages. Apold and Nou Sasesc had relatively

low numbers in 2018. The 2017 report highlighted an apparent downward trend in numbers at Nou

Sasesc. In 2018 the Nou Sasesc numbers rose slightly, meaning the trend has not continued, but has

not really been reversed either. So the Nou Sasesc trend should be monitored closely. The overall

red-backed shrike numbers seem relatively stable, or even on an upward trend.

Figure 3.7. Number of red-backed shrike per point count, per village, for 2013 to 2018.

Figure 3.8 indicates that small mammal abundance has fluctuated markedly at all villages. The

population crash in 2015 was followed by recovery in numbers at all surveyed villages in 2016 and

2017. 2017 was the most abundant small mammal year at all villages except Richis and Viscri. 2018

was a year of quite low small mammal abundance, but not as low as 2015 in any village. High

fluctuation in abundance seems to be a normal pattern, as is often the case with small mammals.

Figure 3.8. Small mammal abundance per trap night, per village, for 2013 to 2018.

The large mammal signs of presence data summarised in Figure 3.9 show that all villages had less

frequent signs in 2017 than in most earlier years, perhaps due to generally drier conditions giving

hard ground and fewer prints. Frequency of signs then increased in 2018 at all villages except Apold

and Malancrav. Signs at Malancrav seem to be on a declining trend and this needs to be closely

monitored. Mesendorf has consistently had more signs than other villages, in most years. 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 2018.

3.1 Site Trends

This section 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 6, 7 and 8 focus on.

Table 3.1 lists the sites with consistent trends. All villages have at least one site with a consistent

trend. There are many more increasing trends, than decreasing. There are no sites with consistent

trends in all three taxanomic groups. In 2017, two sites had two taxonomic groups with consistent

trends, but these have not continued in 2018.

These site trends are encouraging, as they suggest that there are no clear consistent biodiversity

declines. It will be useful to continue this analysis in future years.

Table 3.1. Sites with significant trends in indicator plant abundance, butterfly diversity, or grassland

bird abundance, in 2017 and/or 2018. First symbol for 2017, second for 2018. Green up arrow for

increase, red down arrow for decrease, dash for no significant trend.

AP01: Butterflies ↑↑ AP02: Butterflies ↑↑ AP04: Butterflies −↑ AP05: Butterflies ↑− AP09: Plants −↑ AP11: Birds −↑ CR10: Butterflies −↓ CR11: Plants −↑ MA01: Birds −↑ MA02: Butterflies ↑− MA03: Plants −↑ ME08: Butterflies ↑− ME13: Birds ↑−

NS03: Birds −↑ NS04: Butterflies ↑−, Birds ↓− NS06: Butterflies ↑↑ NS07: Plants ↓− NS08: Plants ↑− NS09: Birds −↑ RI01: Butterflies ↑− RI03: Birds ↑− RI09: Birds ↑↑ VI01: Birds ↑↑ VI02: Birds ↑↑ VI03: Birds ↑↑ VI07: Butterflies ↑− VI08: Birds ↑− VI09: Butterflies −↓ VI10: Butterflies ↑−, Birds ↑− VI12: Plants −↓

4.0 Farmer interviews

The data collected during the farm interviews over the 5 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). In 2018, a good number of interviews were conducted in most villages, but not Apold.

It is assumed that the 2015 and later data are more representative and reliable. Only the 2015 and

later data are compared in this report.

In Table 4.1, the differences between 2015 data and those for 2017 and 2018 reveal some potentially

interesting differences between villages, as well as common trends. The changes at each village can

be summarised as:

(from 2017 report, Apold: more cultivation, more other, more milk cattle, more lambs)

Crit: less cultivation, less other, fewer ewes, more lambs

Malancrav: more other, less beef cattle

Mesendorf: more cultivation, fewer ewes, fewer lambs

Nou Sasesc: more cultivation, more hay, less beef cattle, more ewes, more lambs

Richis: less other, less milk cattle, less beef cattle, more lambs

Viscri: less cultivation, more hay, more other, less beef cattle, more ewes, more lambs

It is notable that most villages seem to be increasing lamb numbers.

Also in 2018 wolf and bear attacks are reported to have increased in 5 villages and overall – but

decrease in Crit. 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, particularly lambs.

Since 2015 additional questions on mowing technique, use of communal grazing and future plans

have been 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 2018. Green – 2018 data 50% or more greater than 2017. Red – 2018 data 50% or less than 2017.

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 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)

0 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)

0 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 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)

0 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

2018 17

25.7 (10 to 50)

12 (1 to 42)

2.8 (0 to 17)

7.2 (0 to 25)

25 Jun (31 May to 03 Aug)

1.9 (0 to 14)

9.1 (0 to 50)

0.3 (0 to 3)

53.2 (0 to 500)

33.8 (0 to 400)

6.7 (0 to 70)

2.2 (0 to 7)

0.4 (0 to 2)

0 (0 to 0)

2

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)

0 3

2015 24

20.9 (3 to 50)

21.8 (3 to 80)

4.9 (0 to 18)

8.9 (1 to 60)


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)

0 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)

0 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)

0 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)

0 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

2018 14

23.5 (5 to 60)

19.6 (1 to 80)

7.4 (0 to 50)

6 (0 to 20)


6.6 (0 to 36)

9.5 (0 to 50)

0.2 (0 to 2)

49.6 (0 to 152)

16.6 (0 to 80)

0 (0 to 0)

5.4 (0 to 25)

0.5 (0 to 2)

0.1 (0 to 1)

35

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)

0 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)

0 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)

0 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

2018 16

22.8 (3 to 61)

28.1 (0 to 150)

11.2 (0 to 60)

10.4 (0 to 70)

01 Jul (15 Jun to 31 Jul)

9.6 (0 to 120)

13.1 (0 to 70)

2.8 (0 to 35)

20.2 (0 to 100)

9.1 (0 to 70)

15.8 (0 to 200)

2.6 (0 to 20)

0.9 (0 to 2)

0.3 (0 to 2)

46

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

ase

sc

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 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 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)

0 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

2018 5

15.5 (8.5 to 22)

32.1 (7 to 50)

16 (1 to 27)

18.9 (0 to 50)

19 May (01 May to 31 May)

0.4 (0 to 1)

11.4 (0 to 30)

1.7 (0 to 5)

96.2 (0 to 470)

41.6 (0 to 204)

0 (0 to 0)

1.4 (0 to 5)

0.2 (0 to 1)

2 (0 to 10)

3.5

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 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 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)

0 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

2018 12

30.3 (10 to 60)

8.9 (0 to 38)

2.8 (0 to 10)

3.5 (0 to 10)

30 May (25 Apr to 01 Jul)

1.9 (0 to 18)

1.5 (0 to 8)

0.2 (0 to 2)

47.3 (0 to 400)

24.7 (0 to 200)

0 (0 to 0)

2.5 (0 to 11)

0.9 (0 to 4)

0 (0 to 0)

3.5

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 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 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 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

2018 16

18.1 (3 to 50)

15.2 (0 to 100)

0.5 (0 to 3)

8.6 (0 to 50)


3.9 (0 to 50)

8 (0 to 45)

0.2 (0 to 2)

37.4 (0 to 107)

18.3 (0 to 100)

0.9 (0 to 7)

1.9 (0 to 8)

1.9 (0 to 12)

0 (0 to 0)

378

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)

0 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)

0 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)

0 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

2018 80

23.5 (3 to 61)

18.6 (0 to 150)

5.4 (0 to 60)

8.2 (0 to 70)

17 Jun (25 Apr to 03 Aug)

4.6 (0 to 120)

8.7 (0 to 70)

0.8 (0 to 35)

45.2 (0 to 500)

22.2 (0 to 400)

4.5 (0 to 200)

2.8 (0 to 25)

0.9 (0 to 12)

0.2 (0 to 10)

468

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. 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

2018 0.69 0.54 0.38 1.03 0.47 0.46 0.54

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

2018 1.00 0.07 1.00 0.18 0.62 0.75 0.17 0.54 0.35

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

2018 0.97 0.62 0.96 1.00 0.94 1.00 0.93

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

2018 0.40 0.71 0.66 0.41 0.61 0.35 0.55

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

2018 0.23 0.38 0.29 0.40 0.35 0.03 0.28

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

2018 0.50 0.57 0.49 0.43 0.56 0.49 0.51

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

2018 0.47 0.43 0.53 0.50 0.46 0.50 0.48

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

2018 0.41 0.43 0.53 0.50 0.46 0.50 0.47

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

2018 0.35 0.42 0.52 0.53 0.38 0.54 0.45

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

2018 0.47 0.49 0.58 0.72 0.51 0.47 0.53

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

2018 0.43 0.46 0.52 0.49 0.46 0.51 0.48

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 most of the arrow ends now lie in the lower part of the graph. So farmers have responded that not much is changing about their farming. However, the responses to other questions indicate that there are changes occurring. Of particular note are the greater numbers of sheep, and the increased frequency of wolf or bear attacks.

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): Apold (from 2017)

increased intensification - due to less hay production and more livestock


Crit reduced intensification – due to less cultivation, fewer livestock


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

reduced change potential – all becoming more stable

Mesendorf increased intensification – due to less communal grazing, less hay production

increased change potential – favouring more silage, crops, livestock

Nou Sasesc increased intensification – more livestock, less communal grazing, more hay, but less hand-mowing


Richis slightly increased intensification – less hand-mown hay


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

medium 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 from one year to the next. However, there are a number of signs that farming is

changing, with more sheep 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 5.1. 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.

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. Variation between years may be partly due

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

Figure 5.1. 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).

several years will be more reliable. At Crit, a year-on-year increase in the median score did not

continue in 2018. In Richis and Nou Sasesc, a year-on-year decrease halted in 2018. Indeed at Nou

Sasesc the median score and top of the IQR were particularly high in 2018.

Table 5.1 presents data on the three diversity measures and the “3-way diversity” score for each site,

for the last 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 significant

(Prho <=0.05) Spearman’s rank correlation between 3-way score and year. There is a lot of fluctuation

from one year to the next. 6 sites have consistent change in 3-way score, with 2 decreasing and 4

increasing. 2018 was a particularly good year at Malancrav, reversing widespread decreases in

diversity seen there in 2017. There are more sites with increases in indicator plant diversity (32), than

decrease (20). 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, the 2017 report

identified evidence that the botanic biodiversity at some sites of the Tarnava Mare may be declining.

This evidence is less apparent in 2018, but will continue 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 2014 2015 2016 2017 2018 2014 2015 2016 2017 2018 2014 2015 2016 2017 2018 2014 2015 2016 2017 2018

Ap

old

AP01 2.0 2.0 2.0 1.0 1.0 0.2 0.5 0.1 0.0 0.0 46.0 33.0 24.0 190.0 5.0 4.4 7.2 3.1 2.9 1.1

AP02 2.0 5.0 4.0 5.0 6.0 0.1 0.7 0.4 0.4 0.6 34.0 78.0 45.0 146.0 38.0 3.8 12.7 8.7 10.4 12.4

AP03 4.0 2.0 0.0 1.0 1.0 0.4 0.3 0.0 0.0 0.0 215.0 3.0 0.0 3.0 1.0 10.0 5.4 0.0 1.0 1.0

AP04 4.0 6.0 4.0 6.0 6.0 0.5 0.4 0.4 0.3 0.7 161.0 329.0 115.0 130.0 300.0 10.8 13.5 9.4 10.0 16.1

AP05 2.0 6.0 5.0 6.0 6.0 0.2 0.4 0.5 0.3 0.6 193.0 120.0 93.0 237.0 403.0 5.6 10.9 11.3 11.2 16.0

AP06 4.0 5.0 5.0 6.0 5.0 0.4 0.6 0.3 0.3 0.5 70.0 194.0 223.0 167.0 237.0 9.1 12.8 9.8 10.9 11.9

AP07 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 10.0 0.0 0.0 0.0 0.0 1.1 0.0 0.0 0.0 0.0

AP08 5.0 4.0 3.0 6.0 5.0 0.5 0.6 0.3 0.5 0.3 61.0 61.0 6.0 41.0 62.0 11.0 10.5 6.4 11.5 8.8

AP09 4.0 5.0 6.0 7.0 7.0 0.6 0.4 0.4 0.6 0.4 53.0 98.0 189.0 168.0 336.0 10.9 10.4 11.9 14.9 14.7

AP10 2.0 1.0 1.0 3.0 1.0 0.0 0.0 0.0 0.5 0.0 254.0 1.0 4.0 23.0 2.0 4.8 1.0 1.0 8.0 1.0

AP11 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 148.0 0.0 0.0 0.0 2.5 0.0 0.0 0.0

AP12 1.0 0.0 1.0 1.0 0.0 0.0 0.0 0.0 9.0 0.0 7.0 1.0 1.1 0.0 1.1 1.0

Cri

t

CR01 0.0 2.0 2.0 3.0 0.0 0.1 0.5 0.3 0.0 17.0 18.0 16.0 0.0 2.8 7.2 6.3

CR02 9.0 9.0 9.0 7.0 0.8 0.5 0.7 0.4 412.0 611.0 600.0 131.0 20.6 19.9 21.9 12.2

CR03 8.0 0.0 0.7 0.0 63.0 0.0 15.5 0.0

CR04 0.0 5.0 4.0 3.0 0.0 0.4 0.2 0.3 0.0 53.0 148.0 88.0 0.0 9.9 7.0 6.9

CR05 8.0 8.0 8.0 8.0 0.5 0.5 0.6 0.4 388.0 848.0 1048.0 315.0 16.7 21.0 24.2 15.0

CR06 5.0

3.0 4.0 0.1 0.1 0.1 936.0 2840.0 1790.0 15.6 0.0 32.2 23.2

CR07 5.0 4.0 5.0 6.0 0.1 0.1 0.2 0.1 1991.0 1174.0 1910.0 1246.0 25.9 16.6 25.8 19.0

CR08 2.0 6.0 7.0 5.0 0.0 0.5 0.5 0.4 71.0 581.0 726.0 180.0 3.0 16.9 19.4 10.4

CR09 0.0 8.0 4.0 6.0 0.0 0.6 0.6 0.6 0.0 945.0 526.0 815.0 0.0 23.6 14.8 20.0

CR10 4.0 2.0 2.0 3.0 0.4 0.2 0.3 0.5 58.0 6.0 16.0 4.0 8.7 3.7 5.3 8.0

CR11 2.0 3.0 3.0 2.0 0.3 0.3 0.1 0.2 4.0 12.0 16.0 20.0 4.5 6.5 4.4 4.2

CR12 4.0 2.0 5.0 4.0 0.2 0.5 0.6 0.6 75.0 15.0 102.0 177.0 7.0 6.8 12.3 11.4

CR13 5.0 5.0 6.0 6.0 0.6 0.3 0.5 0.4 285.0 1041.0 324.0 401.0 14.1 18.5 14.2 13.7

CR14 3.0 4.0 0.4 0.5 255.0 404.0 9.8 13.5

CR15 6.0 6.0 0.6 0.3 265.0 549.0 14.4 14.5

CR16 3.0 4.0 2.0 4.0 0.0 0.0 0.1 0.1 1589.0 1659.0 2742.0 1155.0 19.0 21.0 29.9 16.6

CR17 3.0 0.0 0.0 0.0 687.0 0.0 9.9 0.0

CR18 3.0 3.0 1.0 2.0 0.0 0.0 0.0 0.0 987.0 1999.0 2000.0 1252.0 13.0 23.1 21.0 14.6

Mal

ancr

av

MA01 8.0 8.0 11.0 8.0 11.0 0.5 0.7 0.5 0.7 0.8 299.0 74.0 296.0 134.0 287.0 15.8 16.0 19.4 16.3 21.6

MA02 7.0 4.0 7.0 8.0 10.0 0.3 0.1 0.7 0.6 0.6 324.0 832.0 305.0 246.0 501.0 12.8 12.9 16.6 16.4 20.6

MA03 6.0 0.0 8.0 6.0 8.0 0.5 0.0 0.6 0.5 0.5 170.0 0.0 287.0 305.0 451.0 13.2 0.0 16.7 13.6 17.7

MA04 5.0 5.0 5.0 3.0 5.0 0.5 0.5 0.4 0.0 0.6 163.0 57.0 189.0 56.0 204.0 11.7 10.7 10.5 3.9 12.7

MA05 2.0 1.0 1.0 3.0 0.2 0.0 0.0 0.3 210.0 1.0 1.0 9.0 6.0 0.0 1.0 1.0 6.4

MA06 0.0 5.0 3.0 5.0 0.0 0.5 0.1 0.3 0.0 101.0 81.0 75.0 0.0 0.0 11.3 4.4 8.3

Richness Evenness Abundance 3way

Site 2014 2015 2016 2017 2018 2014 2015 2016 2017 2018 2014 2015 2016 2017 2018 2014 2015 2016 2017 2018

MA07 4.0 2.0 3.0 2.0 1.0 0.2 0.1 0.3 0.0 0.0 82.0 38.0 172.0 87.0 73.0 7.1 3.7 7.5 3.1 1.7

MA08 4.0 0.0 4.0 3.0 4.0 0.6 0.0 0.3 0.2 0.3 39.0 0.0 147.0 48.0 61.0 10.3 0.0 8.2 5.1 7.2

MA09 3.0 5.0 6.0 6.0 5.0 0.5 0.6 0.4 0.1 0.4 11.0 247.0 279.0 129.0 139.0 7.7 13.3 13.3 8.5 10.6

MA10 3.0 3.0 2.0 0.0 1.0 0.3 0.4 0.2 0.0 0.0 24.0 9.0 9.0 0.0 1.0 5.7 7.5 4.3 0.0 1.0

MA11 2.0 6.0 6.0 2.0 0.0 0.1 0.5 0.4 0.5 0.0 8.0 120.0 123.0 2.0 0.0 3.3 12.0 11.3 7.0 0.0

Mes

end

orf

ME01 3.0 6.0 3.0 2.0 4.0 0.1 0.3 0.4 0.1 0.1 50.0 308.0 181.0 142.0 221.0 4.5 12.5 9.2 4.0 7.1

ME02 6.0 5.0 4.0 7.0 7.0 0.2 0.5 0.3 0.2 0.1 1198.0 406.0 570.0 986.0 1041.0 19.5 13.7 12.5 19.1 18.5

ME03 5.0 4.0 5.0 3.0 7.0 0.6 0.6 0.5 0.2 0.5 49.0 111.0 383.0 291.0 93.0 11.8 11.1 13.8 7.9 12.6

ME04

ME05

ME06 4.0 7.0 6.0 5.0 7.0 0.3 0.2 0.5 0.4 0.5 283.0 423.0 433.0 316.0 378.0 9.4 13.7 15.8 11.8 16.2

ME07

ME08 7.0 6.0 8.0 6.0 6.0 0.6 0.3 0.6 0.7 0.8 211.0 598.0 459.0 477.0 319.0 14.7 14.9 18.5 17.5 17.0

ME09 6.0 4.0 6.0 8.0 7.0 0.6 0.6 0.7 0.7 0.6 118.0 331.0 233.0 390.0 333.0 13.4 12.9 15.0 18.4 16.8

ME10 2.0 4.0 3.0 2.0 2.0 0.3 0.6 0.1 0.5 0.2 11.0 31.0 112.0 20.0 115.0 4.8 10.1 5.5 7.2 5.1

ME11 6.0 6.0 6.0 5.0 6.0 0.7 0.5 0.5 0.4 0.5 250.0 164.0 466.0 352.0 381.0 15.2 12.5 15.4 12.8 15.0

ME12 4.0 3.0 3.0 3.0 2.0 0.3 0.2 0.1 0.5 0.1 96.0 24.0 46.0 178.0 15.0 7.5 4.9 4.8 10.2 3.5

ME13 5.0 6.0 6.0 4.0 5.0 0.6 0.6 0.5 0.5 0.5 829.0 209.0 455.0 298.0 330.0 19.1 14.5 15.4 11.9 13.6

ME14 5.0 5.0 6.0 5.0 6.0 0.4 0.6 0.4 0.6 0.3 644.0 485.0 948.0 516.0 1087.0 15.8 16.1 19.6 16.0 19.8

ME15 1.0 3.0 1.0 2.0 2.0 0.0 0.2 0.0 0.1 0.1 21.0 17.0 33.0 24.0 30.0 1.2 4.9 1.3 3.5 3.3

No

u S

ase

sc

NS01 2.0 3.0 5.0 2.0 4.0 0.2 0.6 0.4 0.4 0.3 13.0 12.0 67.0 11.0 61.0 4.4 9.0 9.7 5.7 8.1

NS02 5.0 8.0 5.0 7.0 4.0 0.5 0.3 0.3 0.5 0.1 255.0 379.0 428.0 304.0 2534.0 12.4 14.9 12.7 14.8 30.5

NS03 1.0 0.0 1.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0 2.0 1.0 0.0 1.0 0.0 1.0

NS04 3.0 0.0 3.0 0.0 1.0 0.0 0.0 0.2 0.0 0.0 230.0 0.0 21.0 0.0 2.0 5.5 0.0 5.6 0.0 1.0

NS05 12.0 14.0 10.0 11.0 11.0 0.4 0.7 0.7 0.6 0.6 1367.0 693.0 783.0 740.0 976.0 29.3 28.0 24.4 24.3 27.0

NS06 9.0 9.0 9.0 11.0 12.0 0.5 0.7 0.6 0.4 0.4 321.0 795.0 605.0 929.0 536.0 16.9 24.2 21.5 24.7 21.6

NS07 9.0 7.0 8.0 9.0 11.0 0.4 0.6 0.6 0.2 0.7 340.0 291.0 265.0 158.0 619.0 16.5 16.0 16.9 13.0 23.8

NS08 2.0 3.0 4.0 0.0 3.0 0.2 0.4 0.5 0.0 0.0 9.0 19.0 23.0 0.0 26.0 4.3 7.4 9.4 0.0 3.3

NS09 9.0 13.0 12.0 12.0 12.0 0.4 0.7 0.6 0.7 0.6 338.0 702.0 390.0 384.0 1229.0 16.8 26.5 21.6 22.5 30.0

NS10 7.0 6.0 6.0 4.0 8.0 0.6 0.5 0.2 0.4 0.6 127.0 73.0 207.0 68.0 309.0 14.2 11.3 10.5 8.8 16.9

NS11 11.0 7.0 8.0 11.0 10.0 0.3 0.4 0.4 0.4 0.2 466.0 367.0 1135.0 528.0 1257.0 18.4 14.7 23.6 20.0 24.7

NS12 2.0 1.0 1.0 2.0 0.5 0.0 0.0 0.3 2.0 12.0 5.0 4.0 7.0 1.1 1.1 4.5

Ric

his

RI01 8.0 6.0 3.0 4.0 5.0 0.7 0.7 0.4 0.5 0.3 279.0 73.0 158.0 156.0 138.0 17.5 13.6 9.0 10.2 9.8

RI02 7.0 4.0 5.0 1.0 8.0 0.4 0.5 0.5 0.0 0.7 147.0 29.0 39.0 33.0 359.0 13.0 9.1 10.3 1.3 18.9

RI03 7.0 9.0 10.0 9.0 6.0 0.5 0.6 0.5 0.5 0.6 521.0 123.0 407.0 166.0 47.0 17.7 16.2 18.7 16.0 12.6

RI04 8.0 9.0 5.0 7.0 11.0 0.5 0.6 0.6 0.6 0.6 355.0 531.0 214.0 241.0 833.0 16.1 20.7 12.9 15.9 25.8

RI05 9.0 4.0 4.0 3.0 6.0 0.7 0.2 0.3 0.0 0.3 348.0 166.0 410.0 420.0 293.0 19.0 7.8 11.1 7.4 11.6

RI06 9.0 5.0 2.0 6.0 9.0 0.6 0.6 0.3 0.6 0.6 503.0 213.0 369.0 177.0 360.0 20.5 13.6 8.7 14.0 18.7

RI07 10.0 4.0 8.0 5.0 6.0 0.6 0.4 0.5 0.5 0.4 567.0 52.0 675.0 564.0 215.0 21.6 8.8 19.6 16.1 11.8

RI08 8.0 2.0 0.0 1.0 0.0 0.7 0.1 0.0 0.0 0.0 184.0 21.0 0.0 18.0 0.0 16.9 3.6 0.0 1.2 0.0

RI09 6.0 6.0 5.0 3.0 3.0 0.3 0.4 0.2 0.2 0.6 456.0 368.0 589.0 269.0 5.0 13.5 13.5 13.2 8.1 9.1

RI10 2.0 2.0 2.0 0.0 0.0 0.1 0.3 0.3 0.0 0.0 215.0 11.0 22.0 0.0 0.0 4.7 4.8 5.4 0.0 0.0

RI11 7.0 8.0 9.0 1.0 1.0 0.2 0.6 0.5 0.0 0.0 338.0 544.0 614.0 238.0 43.0 12.6 19.2 20.1 3.4 1.4

RI12 4.0 5.0 5.0 6.0 6.0 0.0 0.4 0.6 0.5 0.1 287.0 73.0 42.0 76.0 476.0 7.3 9.7 11.6 11.5 11.3

Vis

cri

VI01 12.0 0.0 7.0 6.0 9.0 0.5 0.0 0.6 0.4 0.3 273.0 0.0 770.0 699.0 219.0 19.6 0.0 21.2 16.7 14.6

VI02 6.0 6.0 7.0 5.0 0.5 0.6 0.5 0.7 111.0 148.0 456.0 178.0 12.0 13.6 16.7 14.1

VI03 9.0 8.0 6.0 0.0 0.7 0.7 0.3 0.0 289.0 150.0 626.0 0.0 18.9 16.9 15.4 0.0

VI04 8.0 6.0 8.0 7.0 6.0 0.4 0.2 0.4 0.1 0.3 254.0 242.0 486.0 664.0 432.0 14.8 10.2 17.2 14.7 13.1

VI05 5.0 4.0 4.0 6.0 7.0 0.1 0.1 0.4 0.3 0.1 274.0 89.0 193.0 265.0 369.0 8.4 5.9 9.8 11.5 11.4

VI06 8.0 6.0 7.0 5.0 6.0 0.5 0.7 0.5 0.4 0.2 172.0 100.0 1020.0 405.0 547.0 14.4 14.0 22.3 13.3 13.9

VI07 7.0 7.0 6.0 9.0 7.0 0.2 0.6 0.6 0.4 0.6 1120.0 384.0 365.0 1035.0 431.0 19.7 17.3 15.6 23.8 17.6

VI08 6.0 9.0 7.0 8.0 9.0 0.2 0.4 0.2 0.2 0.3 582.0 979.0 801.0 2279.0 948.0 14.0 22.4 17.2 32.4 21.7

VI09 2.0 1.0 1.0 1.0 2.0 0.2 0.0 0.0 0.0 0.1 43.0 12.0 10.0 17.0 27.0 4.3 1.1 1.1 1.2 3.0

VI10 3.0 3.0 2.0 3.0 4.0 0.1 0.2 0.2 0.2 0.3 57.0 51.0 24.0 377.0 93.0 5.0 5.9 3.9 9.3 8.3

VI11 1.0 2.0 2.0 3.0 0.0 0.0 0.1 0.0 0.5 0.0 18.0 7.0 21.0 11.0 0.0 1.2 3.5 2.7 7.7 0.0

VI12 2.0 2.0 2.0 2.0 0.0 0.1 0.1 0.1 0.5 0.0 73.0 12.0 13.0 4.0 0.0 3.6 3.0 2.9 7.0 0.0

VI13 2.0 4.0 3.0 4.0 5.0 0.3 0.3 0.1 0.5 0.1 20.0 60.0 36.0 21.0 301.0 4.7 7.4 4.7 9.0 8.9

TOTAL 24.0 21.0 21.0 23.0 21.0 0.7 0.6 0.8 0.6 0.6 26688.0 25122.0 20849.0 31190.0 29537.0 297.7 278.6 237.8 341.3 322.7

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 149 dark and light green cells compared to 163 red and orange cells. The

comparable figures for just 2017 are 32 increases and 33 decreases. This suggests a balance of

increasing and decreasing abundances. Species that experienced a consistent decline or increase

over the 5 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 2018, while some new ones have been added. The number of decline

incidences has decreased by three since 2017, while the number of increases in abundance has

increased by 5. The 2016 report identified a possible overall decline in indicator plant abundance.

The 2017 and 2018 data do not support this trend. In terms of total abundance across all indicator

species (the righthand column of Table 5.3), no village has a statistically significant consistent

downward trend across all years, and Crit has a consistent increase. 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 2017 report. The lower half of the table lists species

where consistent change had been identified in the 2017 report, but 2018 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

Yellow flax – Apold

Kidney vetch – Richis

Sainfoin – Apold, Crit

Mountain clover – Apold, Nou Sasesc

Sword-leaved fleabane – Crit

Large speedwell - Apold

Siberian bellflower – Apold

Yellow flax – Crit

White dwarf broom – Richis

Lady’s bedstraw – Crit, Malancrav

Crown vetch – Crit

Dorycnium – Apold, Crit, All

Wild thyme - Apold, Crit

Deptford pink – Nou Sasesc

Betony – Crit, Viscri

TOTAL - Crit

Species no longer showing consistent decline Species no longer showing consistent increase

Jurinea – Malancrav, Nou Sasesc

Large speedwell - All

Sainfoin –Richis

Lady’s bedstraw – All

Yellow scabious –Richis

Betony – Richis

Greater milkwort – Mesendorf, All

White dwarf broom – Nou Sasesc, All

Sainfoin - Viscri

Charterhouse pink – Nou Sasesc

Lady’s bedstraw – Richis

Dorycnium – Mesendorf

Deptford pink – Crit

Table 5.3. Abundance of the 10 commonest indicator species at each village. Grey: no record for two consecutive years. Dark green: >= 50% increase. Light green: >= 20% increase. Yellow: <= 20% decrease. Red: <= 50% decrease.

Village Year Sain

foin

On

ob

rych

is v

iciif

olia

Ch

arte

rho

use

Pin

k D

ian

thu

s ca

rth

usi

an

oru

m

Squ

inan

cyw

ort

A

sper

ula

cyn

an

chic

a

Mo

un

tain

Clo

ver

Trif

oliu

m m

on

tan

um

Lad

y's

Bed

stra

w

Ga

lium

ver

um

Cro

wn

Vet

ch

Co

ron

illa

ver

um

Yello

w S

cab

iou

s Sc

ab

iosa

och

role

uca

Do

rycn

ium

Do

rycn

ium

pen

tap

hyl

lum

Wild

Th

yme

Thym

us

gla

bre

scen

s

Bet

on

y St

ach

ys o

ffic

ina

lis

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

2018 133 0 750 0 103 350 1103 1893 177 0 4617

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

2018 31 1163 231 0 4957 649 43 617 6 15466 23254

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

2018 720 116 55 0 1596 175 1124 440 1815 276 6549

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

2018 483 687 340 3 2660 410 13 953 533 8277 14477

Village Year 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

Asp

eru

la c

yna

nch

ica

Mo

un

tain

Clo

ver

Trif

oliu

m m

on

tan

um

Lad

y's

Bed

stra

w

Ga

lium

ver

um

Cro

wn

Vet

ch

Co

ron

illa

ver

um

Yello

w S

cab

iou

s

Sca

bio

sa o

chro

leu

ca

Do

rycn

ium

D

ory

cniu

m p

enta

ph

yllu

m

Wild

Th

yme

Thym

us

gla

bre

scen

s

Bet

on

y

Sta

chys

off

icin

alis

TOTA

L

Nou Sasesc

2013 2327 293 373 20 1313 1943 313 2710 860 4527 16220

2014 367 413 200 3907 1807 1163 0 1797 167 580 11563

2015 880 1443 323 513 1890 1027 17 1787 50 1340 11143

2016 1535 1360 124 11 1225 2076 84 3356 775 1487 14273

2017 477 3293 223 297 1453 687 0 2210 263 7 10423

2018 1080 1980 90 0 2463 500 223 6400 497 9233 25183

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

2018 1300 403 290 113 1743 110 3 2280 663 367 9230

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

2018 3788 0 966 0 1295 228 77 4292 28 58 10908

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 1757 762 519 146 3045 966 303 1681 552 4966 15597

2018 1077 621 389 17 2117 346 370 2411 531 4811 13460

6.0 Grassland butterflies

This section reports on the 19 most abundant butterfly species, with an average abundance greater

than 10 in at least one year, 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.

Some adjustments to species naming were made during 2018 fieldwork. Assman’s, Nickerls and

heath fritillaries were combined into one heath fritillary complex as they are indistinguishable on

survey. Baton blue was renamed as Eastern baton blue. Reverdin and Idas blue numbers were

merged as they are indistinguishable in the field. The previously recorded Iolas blue numbers were

renamed as blue sp. as Iolas blue is not found in Romania. Bath white was renamed Eastern bath

white. Two species were recorded on the surveys for the first time in 2018. These are Balcan green-

veined white (Pieris balcana) and Little blue (Cupido minimus). This makes a total of 82 butterfly

species, and 2 species complexes recorded during the surveys to date.

Table 6.1 shows the species that have consistently decreased or increased over the last 5 years.

These are species with a significant Spearman’s rank correlation (Prho <= 0.05) between abundance

and year. 6 species show a total of 10 incidents of consistent increase, while 5 species show a total of

6 incidents of consistent decrease. So there are more incidents of increase rather than decline. There

are several species which were found to have a consistent increase in the 2017 report, but that trend

has not continued in 2018.

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 2017 report. The lower half of the table lists species

where consistent change had been identified in the 2017 report, but 2018 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

High brown fritillary – ME

All blues – MA

Common blue – MA

SPECIES SHOWING CONSISTENT INCREASE

Heath fritillary complex – MA, RI, All

Essex skipper – RI

Dingy skipper – ME

Wood white – AP, NS, VI

Pale clouded yellow – RI

Ringlet – AP, VI

Silver studded blue – VI

Species no longer showing consistent decline

Species no longer showing consistent increase

High brown fritillary – RI

Weaver’s fritillary – AP, ME, NS, RI, All

Small skipper – NS

Essex skipper – All

Dingy skipper – AP, CR, VI

Small white – ME

Wood white – MA, All

Small heath – AP, All

Chestnut heath –RI, All

Ringlet – CR

Silver studded blue – All

Common blue – CR, ME, RI

Osiris blue – VI, All

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.

2016 was a good year for butterfly abundance, and 2017 was even better. In 2018, most species

suffered a decline greater than 50% compared to the previous year (highlighted in red in Table 6.2).

The total number of butterflies recorded at each village should be less influenced by surveyor bias,

and natural variability in different species abundances. In 2018 Apold, Crit and Malancrav had their

lowest total butterfly abundance of any year. Overall total abundance combining all villages and all

species was also the lowest of any year.

Figure 6.1 shows each village’s butterfly diversity across the 6 years. 2018 diversity is lower than

2017 and 2016 diversity in all villages. Not only have butterfly numbers declined, those surveyed

were from a more limited range of species.

So 2018 was a poor year for butterfly abundance and diversity. This can be at least partly explained

by the frequent rainfall during the survey period. However, it will be important to keep monitoring

butterfly numbers and diversity to identify whether there is a more permanent decline.

Figure 6.1. Plot-level butterfly diversity data, summarised per 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).

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 All village per hectare abundance > 10 in at least one of the last 5 years shown here. Full species list in Appendix 2. Table in 2 parts.

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

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

Hea

th f

riti

llary

co

mp

lex

Mel

licta

ath

alia

/au

relia

/bri

tom

art

is

Smal

l ski

pp

er

Pyr

gu

s sy

lves

tris

Esse

x sk

ipp

er

Thym

elic

us

lineo

la

Din

gy s

kip

per

Eryn

nis

ta

ges

Smal

l wh

ite

Art

og

eia

ra

pa

e

Wo

od

wh

ite

Lep

tid

ea s

ina

pis

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

Dry

ad

Hip

pa

rch

ia d

rya

s

Pal

e cl

ou

ded

yel

low

Co

lias

hya

le

Rin

glet

Ap

ha

nto

pu

s h

yper

an

tus

All

blu

es

Silv

er-s

tud

ded

blu

e

Ple

bej

us

arg

us

Co

mm

on

blu

e

Po

lyo

ma

ttu

s ic

aru

s

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

TO

TAL

Ap

old

2014 5 238 9 0 0 0 0 3 11 2 32 3 17 14 3 404 218 144 21 0 0 1195

2015 2 204 2 0 0 0 0 7 2 1 33 0 49 18 10 440 130 154 62 1 92 1233

2016 2 162 5 7 0 0 0 9 29 18 45 11 29 11 18 425 122 80 65 0 148 1272

2017 0 212 5 13 0 2 0 30 23 26 56 11 21 12 15 402 197 145 87 18 184 1528

2018 0 106 2 7 0 0 0 3 5 27 26 11 22 5 19 77 28 16 31 0 35 465

Cri

t

2013 42 145 0 0 4 10 0 2 7 0 13 0 19 1 1 149 1 0 0 0 0 579

2014 113 297 8 6 1 2 41 3 1 8 15 1 39 13 1 20 15 1 2 0 0 613

2015 164 458 1 6 0 4 15 13 3 6 10 0 18 20 10 56 13 8 1 0 31 873

2016

2017 84 343 1 1 1 16 39 21 5 8 13 0 45 11 58 53 67 11 3 3 11 822

2018 1 176 0 3 0 0 0 3 0 0 9 3 9 12 11 48 73 1 11 1 5 378

Mal

ancr

av

2013 181 174 0 0 7 20 0 4 12 0 2 0 23 0 17 33 0 3 0 0 0 541

2014 22 196 2 0 0 0 5 8 26 1 19 2 35 11 8 286 61 207 7 4 0 922

2015 5 114 0 3 0 0 0 5 29 10 54 20 26 9 10 342 40 186 33 0 74 986

2016 65 215 0 0 0 4 16 35 21 17 33 0 53 26 65 207 25 44 40 20 63 1086

2017 15 115 0 10 4 2 0 21 79 12 37 10 35 5 48 185 67 83 50 5 65 935

2018 14 166 0 7 5 0 0 7 7 9 11 7 32 7 29 62 37 12 28 0 30 525

Mes

end

orf

2013 42 214 0 0 2 10 0 2 8 0 30 0 8 2 0 179 1 0 0 0 0 748

2014 216 414 29 8 4 4 55 0 1 11 26 2 1 3 4 28 19 2 2 1 0 869

2015 279 354 18 8 10 6 33 0 3 22 17 1 0 2 2 68 14 9 13 0 23 915

2016 124 177 5 15 3 32 52 5 0 14 27 0 19 10 35 27 0 5 5 0 9 658

2017 164 273 13 10 30 53 44 5 0 18 12 10 2 3 10 49 5 12 44 0 22 847

2018 142 197 3 3 5 13 13 38 5 17 7 0 19 17 57 78 110 0 2 0 2 791

Table 6.2. cont.

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

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

Hea

th f

riti

llary

co

mp

lex

Mel

licta

ath

alia

/au

relia

/bri

tom

art

is

Smal

l ski

pp

er

Pyr

gu

s sy

lves

tris

Esse

x sk

ipp

er

Thym

elic

us

lineo

la

Din

gy s

kip

per

Eryn

nis

ta

ges

Smal

l wh

ite

Art

og

eia

ra

pa

e

Wo

od

wh

ite

Lep

tid

ea s

ina

pis

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

Dry

ad

Hip

pa

rch

ia d

rya

s

Pal

e cl

ou

ded

yel

low

Co

lias

hya

le

Rin

glet

Ap

ha

nto

pu

s h

yper

an

tus

All

blu

es

Silv

er-s

tud

ded

blu

e

Ple

bej

us

arg

us

Co

mm

on

blu

e

Po

lyo

ma

ttu

s ic

aru

s

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

TO

TAL

No

u S

ases

c

2013 121 195 2 0 4 10 0 17 9 0 10 0 87 0 13 129 0 7 0 0 0 772

2014 104 168 20 0 8 1 24 0 2 3 7 3 0 0 4 74 62 3 1 0 0 536

2015 97 171 14 3 5 21 13 0 1 6 5 4 0 2 0 60 24 2 21 0 10 475

2016 85 151 3 13 8 21 71 2 18 18 10 0 3 5 75 64 21 7 20 0 5 681

2017 151 236 31 20 23 78 54 0 7 15 16 16 0 0 4 72 27 14 19 0 33 938

2018 133 113 2 0 15 14 22 3 10 25 2 0 15 8 89 36 30 2 0 2 0 617

Ric

his

2013 46 98 1 1 0 1 0 3 8 0 4 0 36 3 5 178 0 1 0 0 0 580

2014 44 98 1 0 3 0 0 0 1 1 7 2 0 0 1 34 28 3 0 0 0 239

2015 43 117 8 2 5 4 1 0 1 6 3 4 0 1 1 70 23 7 14 0 19 343

2016 73 99 8 7 15 23 21 0 13 21 10 8 0 3 3 58 21 8 3 0 12 493

2017 51 73 8 8 7 7 2 0 0 5 3 17 0 2 0 46 14 19 11 0 36 358

2018 94 65 2 4 16 22 21 0 7 23 3 0 3 5 28 43 39 3 2 0 12 460

Vis

cri

2013 23 46 0 0 0 4 0 0 3 0 21 0 0 0 0 269 1 0 0 0 0 651

2014 121 189 2 0 0 0 21 2 4 0 24 0 3 16 0 11 9 0 0 0 0 409

2015 196 269 0 1 0 3 11 4 1 1 12 0 0 27 1 42 18 7 1 1 14 614

2016 43 173 0 2 0 3 5 16 2 2 15 0 3 5 5 236 131 37 0 5 50 761

2017 123 196 0 0 0 13 30 19 2 2 21 0 0 20 3 39 69 2 3 11 2 576

2018 18 104 0 0 0 0 0 3 2 2 13 0 7 3 21 168 297 5 0 2 3 675

All

villa

ges

2013 76 145 0 0 3 9 0 5 8 0 13 0 29 1 6 156 0 2 0 0 0 645

2014 86 223 9 2 2 1 20 3 6 3 18 2 17 9 3 114 59 44 4 1 0 655

2015 114 251 6 3 3 5 10 4 5 7 18 3 18 12 5 149 36 50 17 0 41 782

2016 59 153 4 6 4 12 24 12 15 14 24 3 21 11 32 188 61 38 22 4 51 836

2017 85 214 7 7 8 24 24 17 15 11 23 8 22 9 25 118 75 38 26 5 44 863

2018 49 119 1 3 5 6 7 7 4 12 9 3 13 7 31 64 78 5 9 1 11 489

7.0 Birds

7.1 Point Counts

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. Two species were recorded on the point counts for the first time in 2018: nightjar

(Caprimulgus europaeus) and wood sandpiper (Tringa glareola).

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). In 2018, all villages apart from Crit had a lower number of birds

per point count than in 2017. Most villages have a balance of increased (dark green and light green in Table 7.2)

and decreased (red and yellow) species abundances when comparing 2018 to 2017. However, for the Mesendorf,

Nou Sasesc and Total rows there are substantially more decreases than increases in 2018.

The number of these highlighted cells illustrates the fluctuations in species numbers between 2013 and 2018. 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 those years. There was a new survey leader in 2018.

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 (4) at

particular villages. No village stands out as having more prevalent bird population changes.

So, there are many declines in bird abundance when comparing 2018 to 2017. This needs to be monitored in the

coming years. The 5-year declines in quail at Richis and Overall, and the decline in Woodlark at Malncrav need to

be watched closely. However, the five-year trends give little cause for concern. Overall, the grassland bird

populations appear in good health.

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.


Black woodpecker – AP

Common buzzard – NS

Grey-headed woodpecker – CR

Mallard – CR

Quail – RI, ALL

Red-backed shrike – NS

Serin – MA

Song thrush - AP

Tree pipit – CR

Whinchat – ALL

Willow warbler – AP

Wood pigeon – AP

Woodlark – MA, VI

Yellow wagtail – AP, ME


Barn swallow – AP

Bee-eater - CR

Black woodpecker – RI

Blackbird – CR, ALL

Blackcap – AP, ALL

Chaffinch – NS

Chiffchaff – ALL

Collared dove – NS

Collared flycatcher - NS

Feral pigeon – CR, VI

Golden oriole – RI, VI

Goldfinch – MA

Goldfinch – ME

Great tit - RI

Grey-headed woodpecker – NS

Hawfinch – MA, ME, RI

Hoopoe – AP

House sparrow - RI

Lesser spotted eagle – CR

Lesser spotted woodpecker - RI

Little owl – AP, ME

Long-tailed tit – ME

Magpie – AP

Mallard – VI

Marsh warbler – AP, RI, VI, ALL

Middle-spotted woodpecker – AP, CR, MA,

ME, RI, ALL

Pheasant – CR

Raven – MA

River warbler – CR, NS, ALL

Robin - VI

Sparrowhawk – CR, MA

Stock dove - RI

Tree pipit – RI

Treecreeper – NS, ALL

White stork - ALL

White wagtail - MA

Wood pigeon – VI

Woodlark – AP, RI

Wren – AP

Wryneck – AP, NS, ALL

Yellowhammer – 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. Table in 2 parts.

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

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

Ca

rdu

elis

ca

rdu

elis

Gre

at g

rey

shri

ke

Lan

ius

excu

bit

or

Gre

at t

it

Pa

rus

ma

jor

Ho

bb

y

Falc

o s

ub

bu

teo

Ho

op

oe

Up

up

a e

po

ps

Ho

use

sp

arro

w

Pa

sser

do

mes

ticu

s

Kes

trel

Falc

o t

inn

un

culu

s

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

2014 2.73 2.24 0.33 0.64 0.07 0 0 0.45 0 2.87 0 0 1.69 0 0 0.02 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 0.02 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 0.1 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 0.11 0.75 0.04

2018 5.27 0.87 0.18 0.55 0 0 0 0.76 0 1.62 0 0 2.35 0 0 0.13 0.62 0.09

Cri

t

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 0.24 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 0.2 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 0.53 0.02

2018 3.68 0.68 0.23 0.66 0.04 0.16 0 0.32 0 1.29 0.07 0 2.27 0 0 0 0.59 0.02

Mal

ancr

av

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 0.39 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 0.42 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 0.29 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 0.04 1 0.06

2018 5.57 0.45 0.58 0.89 0.04 0 0 0.11 0 2.08 0.04 0.02 2.26 0 0 0 0.87 0.08

Mes

end

orf

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 0.09 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 0.02

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 0.24 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 0.02 0.21 0.04

2018 2.7 0.1 0.33 1.15 0.07 0.02 0 0.16 0.03 0.85 0 0 1.98 0.03 0.03 0.02 0.08 0

No

u S

ases

c

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 0.19 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 0.31 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 0.02 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 0.33 0.1

2018 0.76 1 0.26 1.48 0.07 0 0 0.36 0 0.57 0 0.05 0.43 0 0 0 0.17 0.1

Ric

his

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 0.67 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 0.33 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 0.25 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 0.38 0.28

2018 4.17 0.45 0.6 0.95 0.74 0 0.12 0.29 0 0.88 0.05 0 2.81 0.02 0 0 0.52 0.45

Vis

cri

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 0.05 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 0.02 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 0.65 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 0.18 0.02 2.84 0.11

2018 3.52 0.46 0.07 0.21 0.52 0 0 0.3 0.04 0.5 0.04 0 3.09 0.11 0 0 1.75 0.14

TOTA

L

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 0.01 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 0.51 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 0.04 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 0.03 0.03 0.93 0.09

2018 3.75 0.55 0.33 0.82 0.22 0.03 0.02 0.33 0.01 1.12 0.03 0.01 2.23 0.02 0.01 0.02 0.66 0.13

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

ati

lis

Ro

bin

Erit

ha

cus

rub

ecu

la

Skyl

ark

Ala

ud

a a

rven

sis

Star

ling

Stu

rnu

s vu

lga

ris

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 c

itri

nel

la

Tota

l

Ap

old

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

2018 0 0.51 1.33 0.02 0.38 0 0.15 0.15 0 0 0 0.29 0.53 0.02 0 0.04 0.36 29.49

Cri

t

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

2018 0 0.45 1.96 0 0.21 0.05 4.11 0 0 0 0.05 0.27 0.25 0 0 0 0.32 28.02

Mal

ancr

av

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

2018 0 0.53 1.17 0 0.25 0 0 0.08 0 0 0 0 0.38 0.02 0 0.02 0.34 31.09

Mes

end

orf

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

2018 0 0.1 0.92 0.02 0.61 0.31 0.62 0.1 0 0 0 0.03 0.16 0 0 0 0.64 20.77

No

u S

ases

c

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

2018 0 0.43 1.1 0.1 0.33 0.07 0.38 0.07 0 0.14 0.05 0.1 0.19 0 0.02 0 0.98 19.88

Ric

his

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

2018 0 0.4 1.16 0.17 0.12 0 0.67 0.24 0 0.52 0.09 0.29 0.22 0 0.22 0.07 0.72 28.12

Vis

cri

2014 2.85 0.66 1.76 0 0.05 0.83 115.6

6 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

2018 0 0.61 1.7 0 0.29 0.27 11.27 0.05 0 0.05 0.11 0.36 0.3 0 0.07 0 0.27 56.63

TOTA

L

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

2018 0 0.43 1.34 0.04 0.31 0.1 2.52 0.1 0 0.1 0.04 0.19 0.29 0.01 0.05 0.02 0.51 30.82

7.2 Bird ringing Table 8.3 summarises the ringing surveys of 2014 to 2018. 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 and seven in 2018. The

total number of birds ringed has been gradually decreasing at most villages and overall since 2016, with a notable

drop in overall numbers in 2018. 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 7.3. 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. Table in 3 parts.

Bar

n s

wal

low

Bar

red

war

ble

r

Bee

-eat

er

Bla

ck r

ed

star

t

Bla

ck w

oo

dp

ecke

r

Bla

ckb

ird

Bla

ckca

p

Blu

e ti

t

Ch

affi

nch

Ch

iffc

haf

f

Co

al t

it

Co

llare

d f

lyca

tch

er

Co

mm

on

nig

hti

nga

le

Co

mm

on

red

star

t

Co

mm

on

wh

itet

hro

at

Co

rncr

ake

Cu

cko

o

Gar

den

war

ble

r

Go

lden

ori

ole

Go

ldfi

nch

Gre

at r

eed

war

ble

r

Gre

at s

po

tted

wo

od

pec

ker

Gre

at t

it

Gre

en w

oo

dp

ecke

r

Ap

old

2014 0 0 0 0 0 1 11 13 0 11 0 0 0 1 7 0 0 1 0 0 1 0 23 0

2015 32 0 0 0 0 1 10 7 0 4 0 1 0 0 8 0 0 2 0 0 4 2 8 4

2016 0 0 0 0 0 1 9 4 0 2 0 1 0 0 0 0 0 0 0 0 1 1 19 0

2018 0 0 0 2 0 2 4 1 0 5 0 0 0 0 2 0 0 0 0 0 0 1 11 0

Cri

t

2014 0 0 0 0 0 2 5 0 0 0 0 0 0 0 0 0 0 1 0 0 0 2 16 0

2015 0 0 0 0 0 8 5 5 3 1 0 3 0 1 0 0 0 0 0 0 0 2 24 2

2017 0 0 0 1 0 11 3 0 0 0 0 0 0 0 1 0 0 0 0 0 0 3 25 0

2018 0 0 0 0 1 1 13 0 1 1 0 0 0 0 2 0 0 0 0 0 0 1 21 0

Mal

ancr

av

2014 8 0 0 0 0 0 3 2 0 0 0 0 0 1 1 0 0 0 0 0 2 5 27 0

2015 0 0 0 0 0 3 17 2 0 2 0 0 0 0 1 0 0 1 0 0 0 2 4 0

2016 3 0 3 4 0 1 3 4 0 2 1 0 0 0 5 0 0 1 1 0 0 5 27 0

2018 0 0 0 0 0 0 1 2 0 6 0 0 0 0 1 0 0 0 0 0 0 2 27 0

Mes

end

orf

2015 0 0 0 1 0 7 1 4 0 2 0 0 0 0 25 2 0 0 0 0 0 1 3 0

2016 11 0 0 1 0 28 4 1 0 3 0 1 0 0 19 0 0 2 0 0 0 4 16 0

2017 18 0 0 1 0 3 2 1 1 0 0 1 0 0 18 0 0 0 0 1 0 0 20 0

2018 3 0 0 2 0 4 0 0 0 0 0 0 0 0 4 0 0 0 0 0 0 1 11 1

No

u S

ases

c

2015 4 2 0 0 0 3 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 14 0

2016 0 0 0 0 0 7 2 0 0 1 0 0 0 0 2 0 0 0 0 0 4 0 19 0

2017 1 4 0 0 0 8 5 2 0 2 0 0 0 0 0 0 0 0 0 0 1 3 17 2

2018 0 0 0 0 0 6 10 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 1 0

Ric

his

2015 0 0 0 0 0 0 0 0 0 0 0 0 0 0 7 0 0 0 0 0 0 0 2 0

2016 0 0 0 0 0 6 9 3 0 1 0 0 0 0 23 0 0 0 0 0 0 0 12 1

2017 0 0 0 0 0 0 11 6 0 0 0 1 0 0 25 0 0 0 0 0 0 4 27 1

2018 2 0 0 0 0 1 2 2 0 0 0 0 0 0 13 0 0 0 0 2 0 0 8 0

Vis

cri

2014 0 2 0 0 0 3 1 0 0 1 0 0 0 0 62 0 0 4 0 1 0 0 11 0

2015 5 1 0 0 0 2 1 0 0 0 0 0 0 0 41 0 0 1 0 0 0 0 0 2

2016 11 3 0 1 0 8 0 0 0 1 0 1 0 0 56 0 0 0 0 0 0 0 11 0

2017 0 2 0 1 0 0 3 0 0 1 0 0 0 0 27 0 1 1 0 0 0 1 13 0

2018 6 0 0 0 0 0 1 0 0 3 0 0 0 0 14 0 0 0 0 2 0 1 12 1

Spec

ies

Tota

l

2014 8 4 0 5 0 12 21 15 0 12 0 0 1 2 101 0 0 6 0 1 3 8 96 0

2015 179 5 0 1 0 27 39 18 3 9 0 6 0 1 107 2 0 5 0 0 4 12 61 8

2016 28 7 3 6 0 56 29 12 0 10 1 3 0 0 136 0 0 5 1 5 5 12 120 2

2017 19 6 0 3 0 25 33 9 1 4 0 2 1 0 112 0 1 2 1 2 2 11 124 3

2018 11 0 0 4 1 14 31 5 1 17 0 0 0 0 36 0 0 0 0 4 0 6 91 2

Table 7.3. cont.

Gre

enfi

nch

Gre

y-h

ead

ed w

oo

dp

ecke

r

Haw

fin

ch

Ho

bb

y

Ho

op

oe

Ho

use

sp

arro

w

Icte

rin

e w

arb

ler

Jay

Kin

gfis

her

Less

er g

rey

shri

ke

Less

er s

po

tted

wo

od

pec

ker

Less

er w

hit

eth

roat

Lin

net

Litt

le b

itte

rn

Lon

g-ea

red

ow

l

Lon

g-ta

iled

tit

Mag

pie

Mar

sh t

it

Mar

sh w

arb

ler

Mid

dle

sp

ott

ed

wo

od

pec

ker

Nu

that

ch

Qu

ail

Red

-bac

ked

sh

rike

Riv

er w

arb

ler

Ap

old

2014 0 0 0 0 0 0 1 0 0 0 0 5 0 0 0 6 0 13 5 0 0 0 0 0

2015 0 0 3 0 0 0 0 0 2 0 0 4 0 0 0 0 0 9 11 1 0 0 15 3

2016 0 0 4 0 0 0 0 1 0 0 0 1 0 0 0 0 0 4 8 0 0 0 1 2

2018 0 0 0 0 0 0 0 0 4 0 0 2 0 1 0 0 0 2 10 0 1 0 1 0

Cri

t

2014 0 0 0 0 0 2 0 0 0 0 1 1 0 0 0 0 0 3 2 0 5 0 25 1

2015 0 0 1 0 0 4 0 0 0 0 0 0 0 0 0 0 0 7 1 0 5 0 38 0

2017 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 2 1 0 0 54 0

2018 3 0 0 0 0 4 0 0 0 0 0 0 0 0 0 0 0 11 0 0 6 0 25 0

Mal

ancr

av

2014 0 0 0 0 0 0 0 4 0 0 0 0 0 0 0 0 0 10 0 1 2 0 12 0

2015 0 0 0 0 0 0 0 2 0 0 0 8 0 0 0 0 0 6 0 0 1 0 1 0

2016 0 0 0 0 0 0 0 1 0 0 0 2 0 0 0 0 0 6 0 0 2 0 4 0

2018 0 0 0 0 1 0 0 1 0 0 0 2 0 0 0 0 0 7 0 0 3 0 2 0

Mes

end

orf

2015 0 0 1 0 0 7 0 0 0 1 1 0 0 0 0 0 0 0 10 2 0 1 12 0

2016 0 0 1 0 0 18 0 4 0 1 0 5 0 0 0 0 0 2 4 1 0 0 24 0

2017 0 0 1 0 0 5 0 1 0 0 1 0 0 0 0 0 0 0 2 2 0 0 25 0

2018 2 0 2 0 0 8 0 0 0 0 0 2 0 0 0 0 0 0 0 0 4 0 19 0

No

u S

ases

c

2015 0 0 4 0 0 0 0 0 0 0 1 9 0 0 0 0 0 0 27 0 0 0 15 1

2016 0 0 2 0 0 6 0 5 0 0 0 0 0 0 0 0 0 1 8 1 0 0 13 3

2017 0 3 6 0 0 1 0 1 0 0 1 1 0 0 0 0 0 4 3 4 2 0 18 5

2018 0 0 3 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 4 0 0 0 13 0

Ric

his

2015 0 0 0 0 0 11 0 0 0 0 0 5 2 0 0 0 0 0 7 0 0 0 1 0

2016 0 0 14 0 0 0 0 0 0 0 0 5 0 0 0 0 0 7 14 0 0 0 2 0

2017 0 1 5 0 1 8 0 0 0 0 0 12 0 0 0 5 0 3 14 0 5 0 5 4

2018 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 9 0 0 0 4 1

Vis

cri

2014 0 0 0 0 0 15 0 2 0 0 0 9 1 0 1 0 0 0 37 0 0 0 12 0

2015 0 0 0 0 0 2 0 0 0 0 0 3 1 0 0 0 0 0 12 0 0 0 26 0

2016 14 0 0 0 0 1 0 0 0 7 0 4 0 0 0 0 1 0 9 0 0 0 38 0

2017 3 0 0 0 4 1 0 0 0 0 0 17 0 0 0 0 0 0 9 0 0 0 13 0

2018 0 0 0 0 0 35 0 0 0 0 0 4 1 0 0 0 1 1 19 0 0 0 3 0

Spec

ies

Tota

l

2014 2 0 0 0 0 27 2 6 0 0 1 17 1 0 5 6 0 26 70 1 7 0 92 1

2015 0 0 9 1 1 25 1 2 2 1 2 37 3 0 0 0 0 22 108 3 6 1 168 4

2016 15 0 22 0 0 26 1 11 0 8 0 18 0 0 0 0 1 23 61 2 2 0 143 5

2017 3 4 14 0 5 20 0 2 0 0 2 30 0 0 0 5 0 12 66 7 7 0 183 9

2018 5 0 5 0 1 48 0 1 4 0 0 11 1 1 0 0 1 23 42 0 14 0 67 1

Table 7.3. cont.

Ro

bin

Sco

ps

ow

l

Sed

ge w

arb

ler

Seri

n

Son

g th

rush

Spar

row

haw

k

Spo

tted

fly

catc

he

r

Star

ling

Sto

nec

hat

Thru

sh n

igh

tin

gale

Tree

pip

it

Tree

sp

arro

w

Tree

cree

per

Wh

inch

at

Wh

ite

wag

tail

Will

ow

war

ble

r

Wo

od

war

ble

r

Wo

od

lark

Wre

n

Wry

nec

k

Yello

wh

amm

er

Vill

age

Tota

l

Ap

old

2014 3 0 0 0 1 0 0 0 0 0 0 0 0 0 25 25 0 0 0 0 3 156

2015 3 0 2 1 2 0 0 0 0 1 0 0 1 0 1 5 0 0 0 0 0 147

2016 1 0 4 0 0 0 0 0 11 0 1 0 1 0 2 0 0 0 0 0 5 84

2018 1 0 2 0 2 0 0 0 4 0 0 0 0 0 0 4 11 0 0 0 0 73

Cri

t

2014 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 3 73

2015 6 0 0 0 2 0 0 0 0 0 3 1 1 0 0 0 0 0 0 0 0 123

2017 0 0 0 0 3 0 0 0 0 0 0 6 0 0 0 0 0 0 0 0 5 118

2018 5 0 0 0 3 0 0 0 0 0 1 5 0 0 0 0 0 0 0 1 6 111

Mal

ancr

av

2014 0 1 0 0 0 1 0 1 0 0 0 7 0 0 0 1 0 3 0 0 1 93

2015 2 0 0 0 0 0 0 0 0 0 0 25 0 0 0 8 3 0 0 1 0 89

2016 3 0 0 0 0 0 1 0 0 0 1 37 0 0 1 0 1 0 0 0 0 119

2018 0 0 0 0 0 0 0 0 0 0 4 9 0 0 0 9 14 0 0 0 0 91

Mes

end

orf

2015 0 2 0 0 1 0 1 0 0 0 0 26 0 19 0 0 0 0 0 0 11 141

2016 5 0 0 0 0 0 0 7 0 1 0 15 0 5 0 0 0 1 0 0 5 189

2017 1 2 0 0 0 0 0 4 0 0 1 31 0 4 0 0 0 0 0 1 2 149

2018 1 0 0 0 1 0 0 0 0 0 0 4 0 0 0 0 0 0 0 0 5 74

No

u S

ases

c

2015 1 0 0 0 1 0 0 0 1 0 1 90 0 0 0 0 0 0 0 0 0 177

2016 2 0 0 0 3 0 0 0 4 0 0 98 0 0 0 0 0 0 0 0 2 183

2017 0 0 0 0 4 0 0 0 0 0 1 62 0 0 0 0 0 0 0 1 7 168

2018 2 0 0 0 4 0 0 0 0 0 0 5 0 0 0 0 0 0 1 1 1 55

Ric

his

2015 0 0 0 0 0 0 0 0 0 0 0 6 0 0 0 0 0 0 0 0 0 41

2016 3 0 0 0 0 0 0 0 2 0 0 6 0 0 0 0 0 2 0 0 1 111

2017 0 1 0 0 0 0 1 1 11 1 1 33 0 0 0 0 0 0 0 1 2 190

2018 0 0 0 0 0 0 0 0 5 0 0 9 1 0 0 0 0 0 0 2 0 63

Vis

cri

2014 1 0 0 0 0 0 0 2 0 0 0 60 0 6 0 0 0 1 0 1 7 240

2015 0 0 0 0 0 0 0 0 0 0 0 6 0 3 0 0 1 1 0 1 8 117

2016 0 0 0 0 0 0 0 15 0 1 6 1 0 1 0 0 0 0 0 0 4 194

2017 0 0 0 0 1 0 0 0 0 0 3 10 0 3 0 0 0 0 0 0 9 123

2018 0 0 0 0 0 0 0 0 2 0 0 58 0 0 0 0 0 0 0 0 0 164

Spec

ies

Tota

l

2014 7 1 2 0 1 1 0 3 2 0 1 81 0 7 27 26 1 4 0 2 16 741

2015 12 2 3 1 6 0 1 0 1 4 8 188 2 23 2 13 4 1 0 2 23 1179

2016 16 0 5 0 4 0 1 22 18 6 8 183 1 7 3 0 1 3 0 0 17 1074

2017 1 3 2 1 11 0 1 5 12 3 8 188 0 11 0 0 0 0 0 3 26 1005

2018 9 0 2 0 10 0 0 0 11 0 5 90 1 0 0 13 25 0 1 4 12 631

8.0 Small mammals

2018 was a poor year for small mammal captures with the second lowest total number of captures

per 1000 trap nights (106) of the last 5 years. However this total was more than 5 times greater than

for 2015, the worst year (106 versus 19). 2017 had the highest capture rate with more than twice as

many small mammal captures per trap night than any of the other survey years. So it is unsurprising

that many species at at most villages are flagged in red in Table 8.1 for having a much lower than

average trapping rate in 2018. In 2016 and 2017 there was a recovery in the numbers of several

species at several villages after 2015’s extreme decrease in the number of small mammals captured.

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. 2018 was again a relatively wet year, and weather has probably

caused the low capture rates. Signs of a recovery in numbers will be looked for in 2019. Taxa with

such high fluctuations in numbers need more years of monitoring to be able to clearly identify

population trends.

Table 8.1. Small mammal captures per 1000 trap nights, for each species, for each village, in total and

for each habitat type (2014 : 2015 : 2016 : 2017 : 2018). 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 – 2018 captures within 50% of average for previous

years. Dark green – 2018 captures more than 50% above average for previous years. Red - 2018

captures less than 50% of average for previous years. Table in 2 parts.

A SP AA AAM AF AS CL CS GG

Ap

old

LNV 0:0:0:0:0 40:20:13:10:0 0:0:0:0:0 0:0:88:90:0 50:0:25:0:0 0:0:0:0:0 0:0:0:0:0 0:0:0:0:0

MNV 0:0:0:0:0 40:0:0:100:0 0:0:0:0:0 0:0:13:725:83 90:0:50:0:8 0:0:0:0:0 10:0:0:0:0 0:0:0:0:0

SC/WE 0:0:0:0:0 30:20:0:0:0 0:0:0:0:0 10:50:413:440:50 160:40:50:10:0 0:0:0:10:0 10:0:0:0:0 0:0:0:10:25

Total 0:0:0:0:0 37:13:4:21:0 0:0:0:0:0 3:17:171:342:50 100:13:42:4:3 0:0:0:4:0 7:0:0:0:0 0:0:0:4:9

Cri

t

LNV 0:0:0:0:0 20:0:0:460:40 0:0:0:0:0 0:0:0:290:0 60:0:0:30:0 0:0:0:0:0 0:0:0:0:0 0:0:0:0:0

MNV 0:0:0:0:0 0:25:0:10:8 0:0:0:0:0 0:0:0:30:33 100:0:0:0:0 0:0:0:0:0 0:0:0:0:0 0:0:0:0:0

SC 0:0:0:0:0 140:25:0:0:0 0:0:0:0:0 40:0:0:700:267 90:0:0:60:8 10:0:0:0:0 0:0:0:0:0 0:0:0:0:0

Total 0:0:0:0:0 53:17:0:157:15 0:0:0:0:0 13:0:0:340:106 83:0:0:30:3 3:0:0:0:0 0:0:0:0:0 0:0:0:0:0

Mal

ancr

av LNV 0:0:0:20:0 0:0:50:50:0 0:0:0:0:0 0:0:140:110:0 130:0:0:50:0 0:0:0:0:0 10:50:0:0:0 0:0:0:0:0

MNV 0:0:0:0:0 0:0:13:0:0 0:0:0:0:0 0:0:0:0:120 60:0:0:67:10 0:0:0:0:0 30:0:0:0:0 0:0:0:0:0

SC 0:0:0:0:0 40:0:0:0:0 0:0:0:0:0 0:13:0:720:392 150:0:0:10:8 0:0:0:0:8 10:0:0:0:0 0:0:0:0:0

Total 0:0:0:8:0 13:0:24:19:0 0:0:0:0:0 0:5:56:319:184 113:0:0:38:6 0:0:0:0:3 17:15:0:0:0 0:0:0:0:0

Mes

end

orf

LNV 0:0:0:0:0 0:0:0:0:0 0:0:0:0:0 0:0:0:8:0 0:0:10:8:0 0:0:0:0:0 0:0:0:0:0 0:0:0:0:0

MNV 0:0:0:0:0 20:0:11:92:0 0:0:0:0:0 0:0:189:83:0 20:0:11:58:0 0:0:0:0:0 0:0:0:0:0 0:0:0:0:0

SC/WE 0:0:0:0:0 0:0:80:0:0 0:0:0:0:0 0:0:50:233:8 80:0:50:158:17 0:0:0:0:0 0:0:0:0:0 0:0:0:0:0

Total 0:0:0:0:0 7:0:31:31:0 0:0:0:8:0 0:0:76:108:3 33:0:24:75:6 0:0:0:0:0 0:0:0:0:0 0:0:0:0:0

No

u S

ases

c

LNV 0:0:0:0:0 0:0:0:0:0 0:0:0:0:0 10:0:0:150:10 10:0:0:63:30 0:0:0:0:0 0:0:0:0:0 0:0:0:0:0

MNV 0:0:0:0:0 0:0:0:63:13 0:0:0:0:0 0:0:10:175:0 210:0:50:113:0 0:0:0:0:0 0:0:0:0:0 0:0:0:0:0

WE 0:0:0:0:0 0:0:50:25:0 0:0:0:0:0 0:0:20:531:250 0:0:10:185:110 0:0:0:0:0 0:0:0:0:0 0:0:0:0:0

Total 0:0:0:0:0 0:0:17:29:4 0:0:0:0:0 3:0:10:286:93 73:0:20:120:50 0:0:0:0:0 0:0:0:0:0 0:0:0:0:0

Ric

his

LNV 0:0:0:0:0 40:21:100:0:0 0:0:0:0:0 0:0:0:0:0 70:0:20:0:0 0:0:0:0:0 0:0:0:0:0 0:0:0:0:0

HNV 0:0:0:0:0 230:14:0:10:0 0:0:0:0:0 20:0:0:20:0 80:0:0:0:0 0:0:0:0:0 0:0:0:0:0 0:0:0:0:0

WE 0:0:0:20:0 160:57:70:60:0 0:0:0:0:0 60:0:0:250:33 360:7:20:340:67 0:0:0:0:0 0:0:0:0:0 0:0:0:0:0

Total 0:0:0:7:0 108:31:57:23:0 0:0:0:0:0 20:0:0:90:11 133:2:13:113:22 0:0:0:0:0 3:0:0:0:0 0:0:0:0:0

Vis

cri

LNV 0:0:0:0:0 0:0:13:0:0 0:0:0:0:0 0:0:0:0:0 0:0:0:0:0 0:0:0:0:0 0:0:0:0:0 0:0:0:0:0

MNV 0:0:0:0:0 0:0:42:0:26 0:0:0:0:0 0:0:0:0:26 0:0:28:0:0 0:0:0:0:0 0:0:0:0:0 0:0:0:0:0

SC 0:0:0:0:0 0:8:88:0:0 0:0:0:0:0 0:0:13:30:0 20:0:0:10:0 0:0:0:0:0 0:0:0:0:0 0:0:0:0:0

Total 0:0:0:0:0 0:3:47:0:6 0:0:0:0:0 0:0:4:13:6 7:0:9:4:0 0:0:0:0:0 0:0:0:0:0 0:0:0:0:0

Total

0:0:0:2:0 37:9:26:39:4 0:0:0:0:0 6:2:48:212:63 74:2:15:50:12 0:0:0:0:0 3:1:0:0:0 0:0:0:0:1

Table 8.1. cont.

MA MAG MAR MG MM SA Site Total

Ap

old

LNV 20:0:0:0:0 0:0:0:0:0

20:0:100:560:0

0:0:0:0:0 0:0:0:0:0 0:0:0:0:0 130:20:250:660:0

MNV 20:0:13:0:0 20:0:0:0:0 270:10:63:350:0

0:0:0:0:0 0:0:0:0:0 0:0:0:0:0 450:10:138:1175:92

SC/WE 0:0:0:0:0 0:0:0:0:0 20:0:0:0:0 0:0:0:0:0 0:0:0:0:0 0:0:0:0:0 230:110:463:470:75

Total 13:0:4:0:0 7:0:0:0:0 103:3:54:292:0

0:0:0:0:0 0:0:0:0:0 0:0:0:0:0 270:47:283:667:63

Cri

t

LNV 40:0:0:0:0 0:0:0:10:80 40:0:0:0:20 10:0:0:0:0 0:0:0:0:0 0:0:0:0:0 170:0:0:790:140

MNV 40:0:0:0:0 0:13:0:10:0 10:0:0:0:0 0:0:0:0:0 0:0:0:0:0 0:0:0:0:0 150:38:0:50:42

SC 30:0:0:0:0 0:50:0:0:0 0:0:0:0:0 40:0:0:50:8 0:0:0:0:0 0:0:0:0:0 350:75:0:810:283

Total 37:0:0:0:0 0:21:0:7:24 17:0:0:0:6 17:0:0:17:3 0:0:0:0:0 0:0:0:0:0 223:38:0:550:156

Mal

ancr

av LNV 0:0:0:0:0 0:0:0:0:0 10:0:550:0:0 0:0:0:0:0 0:0:0:0:0 0:0:0:0:10 150:50:750:230:10

MNV 10:0:0:0:0 10:0:0:0:10 0:0:53:17:0 0:0:0:0:0 0:0:0:0:0 0:0:0:0:0 110:0:79:83:140

SC 0:0:0:0:0 0:0:0:10:0 10:0:0:0:0 0:0:0:30:0 0:0:0:0:0 0:0:0:0:0 210:13:0:770:408

Total 3:0:0:0:0 3:0:0:4:3 7:0:238:4:0 0:0:0:12:0 0:0:0:0:0 0:0:0:0:3 157:20:327:404:200

Mes

end

orf

LNV 0:0:0:0:0 0:0:0:0:0 0:0:0:8:0 0:0:0:0:0 0:0:0:0:0 0:0:0:0:0 0:0:10:25:0

MNV 10:0:0:17:0 0:0:0:50:0 0:0:0:8:0 0:0:11:17:0 0:0:0:8:0 0:0:0:0:0 50:0:222:333:0

SC/WE 0:0:0:8:0 0:0:0:0:0 0:0:0:0:0 0:0:0:0:0 0:0:0:0:0 0:0:0:0:0 80:0:180:400:25

Total 3:0:0:0:0 0:0:0:17:0 0:0:0:6:0 0:0:3:6:0 0:0:0:3:0 0:0:0:0:0 43:0:134:253:9

No

u S

ases

c

LNV 10:0:10:0:0 0:0:0:0:0 10:0:0:13:0 0:0:0:0:0 0:0:0:0:0 0:0:0:0:0 40:0:10:225:40

MNV 0:0:0:0:0 20:0:10:0:0 0:0:0:0:13 0:0:0:0:0 0:0:0:0:0 0:0:0:0:13 230:0:70:350:38

WE 0:0:0:0:0 0:0:10:0:0 0:0:0:0:0 0:0:0:25:50 0:0:0:0:0 0:0:0:0:0 0:0:90:765:410

Total 3:0:3:0:0 7:0:7:0:0 3:0:0:4:4 0:0:0:8:18 0:0:0:0:0 0:0:0:0:4 90:0:57:448:171

Ric

his

LNV 0:0:40:0:0 0:43:70:0:25 10:0:0:0:125 0:0:0:0:0 0:0:0:0:0 0:0:0:0:0 120:64:230:0:150

HNV 0:0:0:0:0 30:7:0:0:0 20:0:0:0:0 0:0:0:0:0 0:0:0:0:0 0:0:0:0:0 380:21:0:30:0

WE 0:0:220:0:0 0:0:230:0:0 20:0:0:0:8 0:0:0:30:0 0:0:0:0:0 0:0:0:0:25 600:64:540:700:133

Total 0:0:87:0:0 8:17:100:0:8 13:0:0:0:44 0:0:0:10:0 0:0:0:0:0 0:0:0:0:8 283:50:257:243:94

Vis

cri

LNV 0:0:0:0:0 0:0:0:0:0 0:0:300:0:0 0:0:0:0:0 0:0:0:0:0 0:0:0:0:0 0:0:313:0:0

MNV 0:0:0:0:0 0:0:0:0:105 0:0:14:0:79 0:0:0:0:0 0:0:0:0:0 0:0:0:0:0 0:0:97:0:237

SC 10:0:0:0:0 0:0:0:0:0 0:0:213:0:0 0:0:0:10:8 0:0:0:0:0 0:0:0:0:0 30:8:313:50:8

Total 3:0:0:0:0 0:0:0:0:26 0:0:181:0:19 0:0:0:4:3 0:0:0:0:0 0:0:0:0:0 10:3:246:21:61

Total

10:0:13:1:0 4:5:15:36:9 19:0:59:33:11

2:0:0:7:3 0:0:0:1:0 0:0:0:0:2 154:19:179:383:106

Table 8.2. Small mammal species abbreviations.

Abbreviation Latin name Common name

A SP Apodemus sp. Unidentified Apodemus

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

SA Sorex araneus Common shrew

9.0 Large Mammals

9.1 Camera Trap Survey

Table 9.1 summarises the large mammals recorded by the camera traps, per village. The total

installation hours across the 2018 season was similar to that for 2015 and 2016, but lower than 2017.

However, 2018 had the greatest overall number of records per 24 hours per camera. There was

variation between villages in frequency of records with Apold, Crit, Malancrav and Mesndorf having a

lower frequency than 2017, while Nou Sasesc, Richis and Viscri increased. Richs and Viscri had the

most frequent records, whereas it was Crit and Daia in 2017, and 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 has continued to be recorded at every village every year. Three villages had a notable

decrease in roe deer recordings (red shading), while two had a notable increase (green shading).

There was a low incidence of wild boar footage at all villages except Richis. Fox footage increased at

five villages and decreased at two. There was more bear footage overall in 2018 than any previous

year, with five villages having an increase on 2017, and only Malancrav and Mesendorf having no

bear footage.

The number of records is relatively low and the changes from year to year must be interpreted with

great caution. On their own, these 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.

SPECIES 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

2018 0.22 (6)

0 (0)

0.04 (1)

0 (0)

0 (0)

0 (0)

0.04 (1)

0 (0)

0.04 (1)

0 (0)

0 (0)

0.18 (5)

0.04 (1)

0.55 (15) 654.72

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

2018 0.19 (6)

0 (0)

0.03 (1)

0 (0)

0 (0)

0 (0)

0.06 (2)

0 (0)

0.1 (3)

0.25 (8)

0 (0)

0.13 (4)

0.32 (10)

1.08 (34) 755.62

Mala

ncra

v

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

2018 0.26 (8)

0 (0)

0.03 (1)

0 (0)

0.03 (1)

0 (0)

0.03 (1)

0 (0)

0 (0)

0 (0)

0 (0)

0 (0)

0.13 (4)

0.51 (16) 751.60

Mesen

dorf

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

2018 0.28 (9)

0 (0)

0 (0)

0 (0)

0 (0)

0 (0)

0 (0)

0 (0)

0.19 (6)

0 (0)

0 (0)

0 (0)

0.09 (3)

0.57 (18) 762.63

Nou S

asesc

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

2018 0.52 (16)

0 (0)

0 (0)

0 (0)

0 (0)

0 (0)

0 (0)

0 (0)

0.03 (1)

0 (0)

0 (0)

0.03 (1)

0.06 (2)

0.68 (21) 739.78

Ric

his

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

2018 0.74 (27)

0.03 (1)

0.03 (1)

0 (0)

0 (0)

0 (0)

0 (0)

0 (0)

0.03 (1)

1.49 (54)

0 (0)

0.08 (3)

0.11 (4)

2.56 (93) 871.43

Vis

cri

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

2018 1.06 (33)

0.1 (3)

0 (0)

0 (0)

0.06 (2)

0 (0)

0 (0)

0 (0)

0.03 (1)

0 (0)

0 (0)

0.06 (2)

0.16 (5)

1.5 (47) 749.80

Specie

s to

tal

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

2018 0.48 (105)

0.02 (4)

0.02 (4)

0 (0)

0.01 (3)

0 (0)

0.02 (4)

0 (0)

0.06 (13)

0.28 (62)

0 (0)

0.07 (15)

0.13 (29)

1.11 (244) 5285.58

Table 9.2. Key to large mammal names. Code Latin Species Code Latin Species

CC Capreolus capreolus Roe deer MF Martes foina Beech marten

CE Cervus elaphus Red deer MM Martes martes Pine marten

EC Erinaceus concolor Eastern hedgehog MMS Meles meles European badger

EE Erinaceus europaeus European hedgehog MN Mustela nivalis Weasel

FC Felis catus Feral cat 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

9.2 Observation of large mammal signs

Table 9.3 summarises the results of the large mammal transect surveys per village. After a low in

2017, there was a greater overall number of signs in 2018, but not as many as 2015 and 2016. There

was an increase on 2017 numbers at all villages except Apold and Malancrav. Mesendorf continues

to have the highest frequency of signs, after a one year dip in 2017. Crit also has a relatively high

number of signs. The number of signs at Malancrav seems to be on a consistent downward trend

over the years.

Signs of roe deer, badger, wild boar (except Richis in 2016, Malancrav in 2017 and 2018 and Nou

Sasesc in 2017) and red fox (except Viscri in 2014 and Malancrav in 2017 and 2018) were seen at all

seven villages, in all five years. 2018 was the first year when signs of red deer were not observed at

any village. There were more signs of roe deer at all villages in 2018 compared to 2017. The number

of badger and bear signs was similar in 2018 and 2017. Wild boar signs decreased or stayed low at all

villages. Fox signs increased at 5 villages compared to 2017. For previous years such species changes

may partly be due to differences in surveyor interpretation of the signs. Howeever in the 2017 and

2018 surveys were led by the same person. 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. Weather conditions also

influence the visibility and longevity of the signs.

Table 9.3. 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. In two parts. Village

(Transect length – km) CC CE EC EE FC FC/FSS FSS LE ME MF MM

Apold (13.01)

2014 0.85 (11) 0.23 (3) 0 (0) 0 (0) 0 (0) 0 (0) 0 (0) 0.15 (2) 0 (0) 0 (0) 0 (0)

2015 0.15 (2) 0.15 (2) 0 (0) 0.08 (1) 0 (0) 0 (0) 0 (0) 0 (0) 0 (0) 0.08 (1) 0.08 (1)

2016 4.61 (60) 0.38 (5) 0 (0) 0 (0) 0 (0) 0 (0) 0 (0) 0.08 (1) 0 (0) 0.15 (2) 0 (0)

2017 1.69 (22) 0.15 (2) 0 (0) 0 (0) 0 (0) 0 (0) 0 (0) 0.15 (2) 0 (0) 0.23 (3) 0 (0)

2018 2.46 (32) 0 (0) 0 (0) 0 (0) 0 (0) 0 (0) 0 (0) 0 (0) 0 (0) 0 (0) 0 (0)

Crit (14.15)

2014 2.69 (38) 1.34 (19) 0.07 (1) 0 (0) 0 (0) 0 (0) 0 (0) 0.14 (2) 0 (0) 0 (0) 0 (0)

2015 1.98 (28) 0.42 (6) 0 (0) 0 (0) 0 (0) 0 (0) 0 (0) 0.07 (1) 0 (0) 0 (0) 0 (0)

2017 1.55 (22) 0.71 (10) 0.07 (1) 0 (0) 0 (0) 0 (0) 0.07 (1) 0.07 (1) 0 (0) 0 (0) 0 (0)

2018 2.69 (38) 0 (0) 0 (0) 0 (0) 0.07 (1) 0.14 (2) 0.07 (1) 0.21 (3) 0 (0) 0 (0) 0 (0)

Malancrav (13.38)

2014 1.72 (23) 0.22 (3) 0 (0) 0 (0) 0 (0) 0 (0) 0 (0) 0 (0) 0.07 (1) 0.07 (1) 0.07 (1)

2015 0.3 (4) 0.07 (1) 0 (0) 0 (0) 0 (0) 0 (0) 0.07 (1) 0 (0) 0 (0) 0.37 (5) 0.07 (1)

2016 1.72 (23) 0.22 (3) 0 (0) 0 (0) 0 (0) 0 (0) 0 (0) 0.07 (1) 0 (0) 0.15 (2) 0 (0)

2017 0.6 (8) 0.07 (1) 0.22 (3) 0 (0) 0 (0) 0 (0) 0 (0) 0 (0) 0 (0) 0.07 (1) 0.15 (2)

2018 0.82 (11) 0 (0) 0.07 (1) 0 (0) 0 (0) 0 (0) 0 (0) 0.07 (1) 0 (0) 0 (0) 0 (0)

Mesendorf (12.62)

2014 1.66 (21) 1.19 (15) 0 (0) 0 (0) 0 (0) 0 (0) 0 (0) 0.08 (1) 0.16 (2) 0 (0) 0 (0)

2015 2.38 (30) 1.03 (13) 0.08 (1) 0.16 (2) 0 (0) 0 (0) 0 (0) 0 (0) 0 (0) 0 (0) 0 (0)

2016 3.72 (47) 1.9 (24) 0.24 (3) 0 (0) 0 (0) 0 (0) 0.08 (1) 0.08 (1) 0 (0) 0.71 (9) 0.48 (6)

2017 1.35 (17) 0.24 (3) 0 (0) 0 (0) 0 (0) 0 (0) 0.08 (1) 0.08 (1) 0 (0) 0.08 (1) 0.32 (4)

2018 3.33 (42) 0 (0) 0.24 (3) 0 (0) 0 (0) 0 (0) 0.08 (1) 0.08 (1) 0 (0) 0 (0) 0 (0)

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 (0) 0 (0)

2015 0.99 (12) 0.25 (3) 0 (0) 0.08 (1) 0 (0) 0 (0) 0 (0) 0 (0) 0 (0) 0.17 (2) 0 (0)

2016 0.99 (12) 0.08 (1) 0 (0) 0 (0) 0 (0) 0 (0) 0 (0) 0 (0) 0 (0) 0.25 (3) 0 (0)

2017 1.16 (14) 0.17 (2) 0 (0) 0 (0) 0 (0) 0 (0) 0 (0) 0 (0) 0 (0) 0.08 (1) 0 (0)

2018 1.57 (19) 0 (0) 0.17 (2) 0 (0) 0 (0) 0 (0) 0 (0) 0 (0) 0 (0) 0 (0) 0 (0)

Richis (12.32)

2014 1.38 (17) 0.16 (2) 0 (0) 0 (0) 0 (0) 0 (0) 0 (0) 0 (0) 0 (0) 0.08 (1) 0 (0)

2015 1.54 (19) 0.41 (5) 0 (0) 0.16 (2) 0 (0) 0 (0) 0 (0) 0.08 (1) 0 (0) 0 (0) 0.08 (1)

2016 1.7 (21) 0.49 (6) 0 (0) 0 (0) 0 (0) 0 (0) 0 (0) 0 (0) 0 (0) 0 (0) 0 (0)

2017 0.49 (6) 0.16 (2) 0 (0) 0 (0) 0 (0) 0 (0) 0.08 (1) 0 (0) 0 (0) 0.08 (1) 0 (0)

2018 1.95 (24) 0 (0) 0 (0) 0 (0) 0 (0) 0 (0) 0 (0) 0.08 (1) 0 (0) 0 (0) 0.08 (1)

Viscri (16.99)

2014 0.24 (4) 0.06 (1) 0 (0) 0 (0) 0 (0) 0 (0) 0 (0) 0.12 (2) 0 (0) 0 (0) 0 (0)

2015 1.18 (20) 1.12 (19) 0 (0) 0 (0) 0 (0) 0 (0) 0 (0) 0.06 (1) 0 (0) 0.06 (1) 0 (0)

2016 1.65 (28) 0.59 (10) 0.06 (1) 0 (0) 0 (0) 0 (0) 0 (0) 0.06 (1) 0 (0) 0.18 (3) 0 (0)

2017 0.59 (10) 0 (0) 0 (0) 0 (0) 0 (0) 0 (0) 0 (0) 0 (0) 0 (0) 0 (0) 0 (0)

2018 1.94 (33) 0 (0) 0 (0) 0 (0) 0 (0) 0 (0) 0 (0) 0 (0) 0 (0) 0 (0) 0 (0)

Species Total

(107.52)

2014 1.37 (147) 0.46 (49) 0.01 (1) 0 (0) 0 (0) 0 (0) 0 (0) 0.07 (7) 0.03 (3) 0.02 (2) 0.01 (1)

2015 1.14 (123) 0.51 (55) 0.01 (1) 0.06 (6) 0 (0) 0 (0) 0.01 (1) 0.03 (3) 0.01 (1) 0.08 (9) 0.04 (4)

2016 2.11 (227) 0.49 (53) 0.04 (4) 0 (0) 0 (0) 0 (0) 0.01 (1) 0.06 (6) 0 (0) 0.34 (37) 0.06 (6)

2017 1 (108) 0.23 (25) 0.06 (6) 0 (0) 0 (0) 0 (0) 0.04 (4) 0.04 (4) 0 (0) 0.07 (7) 0.07 (7)

2018 1.85 (199) 0 (0) 0.06 (6) 0 (0) 0.01 (1) 0.02 (2) 0.02 (2) 0.06 (6) 0 (0) 0 (0) 0.01 (1)

Table 9.3 cont. Village

(Transect length – km) MM/MF MMS MN MP OC SS SV UA VV

Village Total

Apold (13.01)

2014 0.08 (1) 0.31 (4) 0 (0) 0 (0) 0 (0) 0.54 (7) 0 (0) 0 (0) 0.31 (4) 3 (39)

2015 0 (0) 0.77 (10) 0 (0) 0 (0) 0 (0) 0.15 (2) 0 (0) 0.15 (2) 0.15 (2) 1.92 (25)

2016 0.08 (1) 0.23 (3) 0 (0) 0 (0) 0 (0) 1.92 (25) 0 (0) 0.15 (2) 0.23 (3) 7.99 (104)

2017 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)

2018 0 (0) 0.61 (8) 0 (0) 0 (0) 0 (0) 0.38 (5) 0 (0) 0.23 (3) 0.38 (5) 4.07 (53)

Crit (14.15)

2014 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 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 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)

2018 0.07 (1) 1.55 (22) 0 (0) 0 (0) 0 (0) 0.14 (2) 0 (0) 0.35 (5) 0.85 (12) 6.22 (88)

Malancrav (13.38)

2014 0 (0) 0.45 (6) 0 (0) 0 (0) 0 (0) 0.67 (9) 0 (0) 0 (0) 0.67 (9) 4.48 (60)

2015 0 (0) 1.2 (16) 0 (0) 0 (0) 0 (0) 0.37 (5) 0 (0) 0.15 (2) 0.52 (7) 3.14 (42)

2016 0.15 (2) 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 (0) 0.45 (6) 0 (0) 0.07 (1) 0 (0) 0 (0) 0 (0) 0.3 (4) 0 (0) 1.94 (26)

2018 0.37 (5) 0.07 (1) 0 (0) 0.07 (1) 0 (0) 0 (0) 0 (0) 0 (0) 0 (0) 1.49 (20)

Mesendorf (12.62)

2014 0.32 (4) 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 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 0.32 (4) 0.08 (1) 0 (0) 0 (0) 0 (0) 1.82 (23) 0 (0) 0.79 (10) 0.48 (6) 10.78 (136)

2017 0 (0) 0.79 (10) 0 (0) 0 (0) 0 (0) 0.24 (3) 0 (0) 0.16 (2) 0.24 (3) 3.57 (45)

2018 0.48 (6) 1.03 (13) 0 (0) 0 (0) 0 (0) 0.08 (1) 0 (0) 0.24 (3) 0.95 (12) 6.5 (82)

Nou Sasesc (12.10)

2014 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 (0) 1.4 (17) 0 (0) 0 (0) 0 (0) 0.08 (1) 0 (0) 0 (0) 0.08 (1) 3.14 (38)

2016 0.08 (1) 0.17 (2) 0 (0) 0 (0) 0 (0) 0.41 (5) 0 (0) 0.83 (10) 1.32 (16) 4.13 (50)

2017 0 (0) 0.41 (5) 0 (0) 0 (0) 0 (0) 0 (0) 0 (0) 0.08 (1) 0.17 (2) 2.07 (25)

2018 0.33 (4) 0.5 (6) 0 (0) 0 (0) 0 (0) 0.08 (1) 0 (0) 0.08 (1) 0.25 (3) 2.98 (36)

Richis (12.32)

2014 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 0 (0) 1.22 (15) 0 (0) 0 (0) 0 (0) 0.32 (4) 0 (0) 0 (0) 0.16 (2) 4.06 (50)

2016 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 (0) 0.32 (4) 0 (0) 0 (0) 0 (0) 0.08 (1) 0 (0) 0 (0) 0.41 (5) 1.62 (20)

2018 0.16 (2) 0.16 (2) 0 (0) 0 (0) 0 (0) 0.08 (1) 0 (0) 0.16 (2) 0.97 (12) 3.65 (45)

Viscri (16.99)

2014 0.06 (1) 0 (0) 0 (0) 0 (0) 0 (0) 0.12 (2) 0 (0) 0 (0) 0 (0) 0.59 (10)

2015 0.18 (3) 1.71 (29) 0 (0) 0 (0) 0 (0) 0.35 (6) 0 (0) 0.12 (2) 0.35 (6) 5.12 (87)

2016 0.18 (3) 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 (0) 0.65 (11) 0 (0) 0 (0) 0 (0) 0.18 (3) 0 (0) 0 (0) 0.18 (3) 1.59 (27)

2018 0.06 (1) 0.59 (10) 0 (0) 0 (0) 0 (0) 0.12 (2) 0 (0) 0.24 (4) 0.59 (10) 3.53 (60)

Species Total

(107.52)

2014 0.07 (7) 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 0.03 (3) 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 0.1 (11) 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 0 (0) 0.57 (61) 0 (0) 0.01 (1) 0 (0) 0.2 (22) 0 (0) 0.2 (21) 0.32 (34) 2.8 (301)

2018 0.18 (19) 0.58 (62) 0 (0) 0.01 (1) 0 (0) 0.11 (12) 0 (0) 0.17 (18) 0.5 (54) 3.57 (384)

10.0 Bats Fully detailed results from the 2018 bat surveys are given in Kitching (2018). A brief summary is given

here. There were 218 hours and 12 minutes of trapping effort over 38 trapping surveys. All surveys

employed one harp trap, with a variable number and size of mist nets also employed. 245 individual

bats from 15 species were captured. Table 10.1 summarises the captures at each village. Figure 10.2

shows the proportion of the total captures contributed by each species.

Table 10.1. Total bat captures and species richness for each village.

Village Captures Species

Richis 5 4

Nou Sasesc 36 7

Mesendorf 38 8

Viscri 79 6

Crit 26 5

Malancrav 22 6

Apold 37 9

Figure 10.1. Percentage contribution of each species to total captures at each village.

An additional 7 species were identified (with less certainty) from acoustic recordings. This suggests a

mixed approach is best for estimating the species present. The consistency of the trapping method

and controlled sampling effort make this technique suitable to be repeated each year and allow

monitoring of relative abundance of bat species.

11.0 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

Kitching, T. (2018). Bats of the Târnava Mare region of Transylvania : a summary report from 2018. 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

Juri

nea

mo

llis

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

C

ha

ma

ecyt

isu

s a

lbu

s

Kid

ney

Vet

ch

An

thyl

lis v

uln

era

ria

Sain

foin

On

ob

rych

is v

iciif

olia

Ch

arte

rho

use

Pin

k D

ian

thu

s ca

rth

usi

an

oru

m

Squ

inan

cyw

ort

A

sper

ula

cyn

an

chic

a

Mo

un

tain

Clo

ver

Trif

oliu

m m

on

tan

um

Lad

y's

Bed

stra

w

Ga

lium

ver

um

Cro

wn

Vet

ch

Co

ron

illa

ver

um

Yello

w S

cab

iou

s 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 St

ach

ys o

ffic

ina

lis

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

2018 0 0 0 13 0 0 0 83 0 0 0 133 0 750 0 103 350 1103 0 10 1893 0 177 0 0 4617

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

2018 0 0 0 51 0 0 0 0 6 0 0 31 1163 231 0 4957 649 43 0 34 617 0 6 0 15466 23254

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

2017 0 0 0 7 873 0 0 0 0 0 0 135 55 578 7 6709 644 265 0 0 1473 0 69 7 1804 12625

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

2018 0 0 29 0 58 0 0 4 0 0 0 720 116 55 0 1596 175 1124 7 0 440 0 1815 135 276 6549

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

Juri

nea

mo

llis

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

C

ha

ma

ecyt

isu

s a

lbu

s

Kid

ney

Vet

ch

An

thyl

lis v

uln

era

ria

Sain

foin

On

ob

rych

is v

iciif

olia

Ch

arte

rho

use

Pin

k D

ian

thu

s ca

rth

usi

an

oru

m

Squ

inan

cyw

ort

A

sper

ula

cyn

an

chic

a

Mo

un

tain

Clo

ver

Trif

oliu

m m

on

tan

um

Lad

y's

Bed

stra

w

Ga

lium

ver

um

Cro

wn

Vet

ch

Co

ron

illa

ver

um

Yello

w S

cab

iou

s 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 St

ach

ys o

ffic

ina

lis

TOTA

L

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

2018 0 0 0 23 3 0 0 0 0 0 0 483 687 340 3 2660 410 13 0 43 953 0 533 47 8277 14477

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

2018 0 0 0 0 193 0 0 0 0 13 23 1080 1980 90 0 2463 500 223 570 1780 6400 0 497 137 9233 25183

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

2018 0 0 0 7 57 0 223 0 0 540 20 1300 403 290 113 1743 110 3 810 30 2280 0 663 270 367 9230

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

2018 0 0 0 37 80 0 0 12 18 0 0 3788 0 966 0 1295 228 77 0 3 4292 0 28 25 58 10908

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

Juri

nea

mo

llis

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

C

ha

ma

ecyt

isu

s a

lbu

s

Kid

ney

Vet

ch

An

thyl

lis v

uln

era

ria

Sain

foin

On

ob

rych

is v

iciif

olia

Ch

arte

rho

use

Pin

k D

ian

thu

s ca

rth

usi

an

oru

m

Squ

inan

cyw

ort

A

sper

ula

cyn

an

chic

a

Mo

un

tain

Clo

ver

Trif

oliu

m m

on

tan

um

Lad

y's

Bed

stra

w

Ga

lium

ver

um

Cro

wn

Vet

ch

Co

ron

illa

ver

um

Yello

w S

cab

iou

s 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 St

ach

ys o

ffic

ina

lis

TOTA

L

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 5 418 0 10 18 49 120 5 1757 762 519 146 3045 966 303 105 61 1681 3 552 61 4966 15597

2018 0 0 4 19 56 0 32 14 3 79 6 1077 621 389 17 2117 346 370 198 272 2411 0 531 87 4811 13460

Appendix 2 Table A2, part 1. Butterfly abundance 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

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

co

mp

lex

Mel

licta

ath

alia

/au

relia

/bri

tom

art

is

Mar

sh f

riti

llary

Euro

dry

as

au

rin

ia

Nio

be

frit

illar

y

Arg

ynn

is n

iob

e

Twin

-sp

ot

frit

illar

y

Bre

nth

is h

eca

te

Ap

old

2014 5 238 0 18 9 0 0 0 0 0 0 0 0 0 0 0 0 0 0

2015 2 204 0 2 2 0 1 0 0 1 0 1 0 0 1 0 0 0 0

2016 2 162 0 4 5 0 0 0 0 0 7 2 0 0 0 0 0 0 0

2017 0 212 0 10 5 0 10 0 0 0 13 7 0 0 0 0 0 0 0

2018 0 106 0 8 2 0 0 0 0 0 7 0 0 0 0 0 0 0 0

Cri

t

2013 42 145 0 4 0 0 4 0 0 0 0 0 0 0 1 4 5 0 0

2014 113 297 0 1 8 0 0 0 0 0 6 0 0 0 0 1 0 1 5

2015 164 458 0 9 1 0 0 0 0 5 6 0 0 0 2 0 0 0 3

2016

2017 84 343 0 4 1 0 0 1 0 0 1 0 0 0 0 1 0 3 1

2018 1 176 0 0 0 0 0 0 0 0 3 0 0 0 0 0 0 0 0

Dai

a

2014 46 167 0 3 2 0 0 0 0 1 1 0 0 0 0 0 0 0 2

2015 71 213 0 0 5 0 0 0 0 0 3 3 1 0 1 2 0 0 0

2016 17 98 0 4 3 0 0 0 0 0 0 6 0 0 0 0 0 0 0

2017 85 212 0 3 0 0 2 0 0 2 0 4 0 0 0 0 0 0 0

Mal

ancr

av

2013 181 174 0 2 0 0 0 2 0 0 0 0 0 0 3 7 2 0 0

2014 22 196 0 4 2 0 1 0 0 0 0 2 0 1 0 0 0 0 0

2015 5 114 0 2 0 0 0 0 0 0 3 2 0 0 0 0 0 0 0

2016 65 215 5 4 0 0 0 0 0 0 0 9 0 0 0 0 0 0 0

2017 15 115 0 0 0 0 0 0 0 2 10 2 0 0 0 4 0 0 0

2018 14 166 0 0 0 0 0 0 0 0 7 0 0 0 0 5 0 0 0

Mes

end

orf

2013 42 214 0 31 0 0 0 0 0 0 0 0 0 0 12 2 1 0 0

2014 216 414 0 8 29 2 0 0 0 1 8 0 0 0 0 4 0 0 3

2015 279 354 0 2 18 1 0 1 0 2 8 0 0 0 1 10 0 1 5

2016 124 177 0 13 5 0 0 0 0 3 15 0 0 0 0 3 0 0 0

2017 164 273 0 6 13 0 0 2 0 2 10 0 0 0 0 30 0 3 6

2018 142 197 0 7 3 0 0 0 0 0 3 0 2 0 0 5 0 0 0

No

u S

ases

c

2013 121 195 0 7 2 0 0 4 0 0 0 0 0 0 9 4 0 0 0

2014 104 168 0 5 20 7 0 0 0 1 0 0 0 0 0 8 0 0 4

2015 97 171 0 0 14 2 0 0 0 0 3 0 0 0 0 5 0 0 2

2016 85 151 0 0 3 4 0 0 0 2 13 0 0 0 3 8 0 0 0

2017 151 236 0 0 31 10 0 2 0 3 20 0 0 0 0 23 0 0 4

2018 133 113 0 5 2 0 0 0 2 2 0 0 2 0 0 15 0 0 2

Ric

his

2013 46 98 0 2 1 0 0 0 0 0 1 0 0 0 4 0 0 0 0

2014 44 98 0 0 1 3 0 1 0 0 0 0 0 0 0 3 0 0 0

2015 43 117 0 0 8 1 0 0 0 0 2 0 0 0 0 5 0 0 0

2016 73 99 0 2 8 4 0 0 0 0 7 0 0 0 0 15 0 0 0

2017 51 73 0 2 8 0 0 0 0 0 8 0 0 0 0 7 0 0 0

2018 94 65 3 3 2 0 0 0 3 0 4 0 0 0 0 16 0 0 0

Vis

cri

2013 23 46 0 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 0

2015 196 269 0 0 0 0 0 0 1 2 1 0 0 0 0 0 0 0 0

2016 43 173 0 0 0 0 0 0 0 0 2 0 0 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 0

2018 18 104 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

All

villa

ges

2013 76 145 0 8 0 0 1 1 0 0 0 0 0 0 5 3 1 0 0

2014 86 223 0 5 9 1 0 0 0 0 2 0 0 0 0 2 0 0 2

2015 114 251 0 2 6 1 0 0 0 1 3 1 0 0 1 3 0 0 1

2016 59 153 1 4 4 1 0 0 0 1 6 2 0 0 0 4 0 0 0

2017 85 214 0 3 7 1 1 1 0 1 7 1 0 0 0 8 0 1 1

2018 49 119 0 3 1 0 0 0 1 0 3 0 0 0 0 5 0 0 0

Table A2, part 2.

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

Larg

e w

hit

e

Pie

ris

bra

ssic

ae

Gre

en

-vei

ned

wh

ite

Pie

ris

na

pi

Bal

can

gre

en

-vei

ned

wh

ite

Pie

ris

ba

lca

na

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

Ap

old

2014 0 0 0 0 0 0 1 0 3 1 0 0 0 11 0 0 0 2 0

2015 0 0 0 0 0 0 1 0 7 0 0 0 0 2 5 0 0 1 0

2016 0 7 0 0 0 0 0 0 9 0 0 0 0 29 0 0 0 18 0

2017 2 0 0 0 2 0 0 0 30 10 0 0 0 23 0 0 0 26 0

2018 0 0 0 0 0 0 0 0 3 3 0 0 0 5 2 0 2 27 0

Cri

t

2013 0 0 0 0 10 0 5 0 2 2 0 0 0 7 1 0 0 0 1

2014 0 0 0 0 2 41 0 0 3 1 0 0 0 1 0 0 0 8 0

2015 0 0 0 0 4 15 0 0 13 0 0 0 0 3 3 0 0 6 0

2016

2017 4 0 0 0 16 39 0 1 21 0 0 0 0 5 0 1 0 8 0

2018 0 0 0 0 0 0 0 1 3 1 0 0 0 0 1 0 0 0 0

Dai

a

2014 0 0 0 0 3 10 1 0 6 1 0 0 0 1 0 0 0 1 0

2015 0 0 0 0 2 2 0 1 3 0 0 0 0 1 0 0 0 3 0

2016 0 0 0 0 2 0 4 7 18 0 0 0 0 23 0 0 0 9 0

2017 2 0 0 0 18 18 0 0 39 0 0 0 0 15 0 0 0 5 0

Mal

ancr

av

2013 0 0 0 0 20 0 8 0 4 1 0 0 0 12 0 0 0 0 5

2014 0 0 0 0 0 5 0 0 8 1 0 0 0 26 0 0 0 1 0

2015 0 0 0 0 0 0 0 0 5 5 0 0 0 29 1 0 0 10 0

2016 0 0 2 0 4 16 11 5 35 0 0 2 0 21 0 0 0 17 0

2017 2 0 0 0 2 0 0 0 21 30 0 0 0 79 0 2 0 12 0

2018 0 0 0 0 0 0 0 0 7 13 0 0 0 7 2 0 0 9 0

Mes

end

orf

2013 0 0 0 0 10 0 9 0 2 0 1 0 0 8 1 0 0 0 4

2014 1 0 0 0 4 55 5 0 0 1 0 1 0 1 0 0 0 11 0

2015 0 0 0 0 6 33 0 0 0 0 0 0 0 3 0 1 0 22 0

2016 29 0 2 2 32 52 8 0 5 0 0 0 0 0 0 0 0 14 0

2017 3 0 2 0 53 44 7 0 5 0 0 0 0 0 0 0 0 18 0

2018 12 0 3 0 13 13 2 0 38 0 0 0 0 5 2 0 0 17 0

No

u S

ases

c

2013 0 0 0 0 10 0 5 0 17 0 2 0 1 9 0 0 0 0 2

2014 0 0 0 0 1 24 3 0 0 0 0 6 0 2 0 1 0 3 0

2015 0 0 0 0 21 13 1 0 0 0 0 3 0 1 0 0 0 6 0

2016 0 0 0 0 21 71 2 0 2 0 0 17 0 18 0 0 0 18 0

2017 0 0 0 0 78 54 17 0 0 0 0 27 0 7 0 3 0 15 0

2018 0 0 2 0 14 22 17 0 3 0 0 0 0 10 0 0 0 25 0

Ric

his

2013 0 0 0 0 1 0 0 0 3 0 2 0 0 8 0 0 0 0 1

2014 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 1 0 1 1

2015 0 0 0 0 4 1 0 0 0 0 0 3 0 1 0 0 0 6 0

2016 0 0 0 0 23 21 0 0 0 0 0 17 0 13 2 10 0 21 0

2017 0 0 0 0 7 2 3 0 0 0 0 8 0 0 2 2 0 5 0

2018 0 3 0 0 22 21 3 2 0 0 0 3 0 7 0 2 0 23 0

Vis

cri

2013 0 0 0 0 4 0 8 0 0 0 0 0 0 3 2 0 0 0 0

2014 0 0 0 0 0 21 0 0 2 0 0 0 0 4 0 0 0 0 0

2015 0 0 0 0 3 11 0 0 4 0 1 0 0 1 0 0 0 1 0

2016 2 0 0 0 3 5 0 5 16 0 0 0 0 2 0 0 0 2 0

2017 0 0 0 0 13 30 0 0 19 0 0 0 0 2 2 0 0 2 0

2018 0 2 0 0 0 0 0 0 3 0 0 0 0 2 0 0 0 2 0

All

villa

ges

2013 0 0 0 0 9 0 6 0 5 0 1 0 0 8 1 0 0 0 2

2014 0 0 0 0 1 20 1 0 3 0 0 1 0 6 0 0 0 3 0

2015 0 0 0 0 5 10 0 0 4 1 0 1 0 5 1 0 0 7 0

2016 5 1 0 0 12 24 3 2 12 0 0 5 0 15 0 1 0 14 0

2017 2 0 0 0 24 24 3 0 17 5 0 4 0 15 0 1 0 11 0

2018 1 1 1 0 6 7 3 0 7 2 0 0 0 4 1 0 0 12 0

Table A2, part 3.

East

ern

bat

h w

hit

e

Po

nti

a e

du

sa

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

Maz

arin

e b

lue

Cya

nir

is s

emia

rgu

s

Alc

on

blu

e

Ph

eng

ari

s a

lco

n

East

ern

bat

on

blu

e

Pse

ud

op

hilo

tes

vicr

am

a

sch

iffe

rmu

elle

ri

Idas

/Rev

erd

in b

lue

Lyca

eid

es id

as/

arg

yro

gn

om

on

Ho

lly b

lue

Cel

ast

rin

a a

rgio

lus

Ap

old

2014 0 32 3 0 17 3 0 14 0 3 404 0 1 218 0 0 0 19 0

2015 0 33 0 2 49 0 0 18 0 10 440 0 0 130 0 0 0 0 0

2016 0 45 11 0 29 15 0 11 0 18 425 2 0 122 0 0 0 3 0

2017 0 56 11 0 21 0 0 12 0 15 402 2 2 197 0 0 0 0 2

2018 0 26 11 0 22 0 0 5 0 19 77 0 0 28 0 0 2 15 0

Cri

t

2013 0 13 0 0 19 3 0 1 0 1 149 0 139 1 0 0 1 2 6

2014 0 15 1 2 39 0 0 13 1 1 20 0 0 15 0 0 0 0 0

2015 0 10 0 0 18 0 0 20 1 10 56 0 0 13 0 0 0 0 0

2016

2017 0 13 0 1 45 0 0 11 0 58 53 0 0 67 0 0 1 3 0

2018 0 9 3 0 9 0 0 12 0 11 48 0 0 73 0 0 0 5 0

Dai

a

2014 0 11 0 0 41 0 0 11 0 5 89 0 1 74 0 0 0 2 0

2015 0 27 0 0 79 0 0 12 1 5 226 0 0 43 0 0 0 0 0

2016 0 28 4 0 40 4 0 15 0 22 311 0 0 111 0 0 0 0 0

2017 0 35 0 0 69 0 0 21 0 55 122 0 0 160 0 0 0 0 2

Mal

ancr

av

2013 0 2 0 0 23 13 0 0 0 17 33 0 15 0 0 0 2 0 13

2014 1 19 2 0 35 0 0 11 0 8 286 2 1 61 0 0 0 4 0

2015 0 54 20 0 26 0 0 9 0 10 342 9 0 40 0 0 0 0 0

2016 0 33 0 0 53 13 0 26 0 65 207 0 0 25 0 0 0 0 0

2017 0 37 10 0 35 0 0 5 0 48 185 4 2 67 0 0 0 0 0

2018 0 11 7 0 32 2 0 7 0 29 62 11 2 37 0 0 0 13 0

Mes

end

orf

2013 0 30 0 0 8 5 0 2 0 0 179 0 174 1 0 0 0 0 4

2014 0 26 2 1 1 0 0 3 0 4 28 0 4 19 0 0 0 0 0

2015 0 17 1 7 0 3 1 2 2 2 68 1 0 14 3 2 0 0 3

2016 0 27 0 10 19 5 0 10 2 35 27 0 0 0 8 0 0 0 0

2017 0 12 10 11 2 0 0 3 0 10 49 0 2 5 0 12 0 0 3

2018 0 7 0 3 19 0 0 17 0 57 78 0 7 110 0 2 0 0 3

No

u S

ases

c

2013 0 10 0 0 87 10 0 0 0 13 129 0 95 0 0 0 0 2 23

2014 0 7 3 4 0 1 0 0 0 4 74 0 0 62 0 0 2 3 2

2015 0 5 4 4 0 0 0 2 0 0 60 0 0 24 0 0 3 0 0

2016 0 10 0 12 3 2 0 5 2 75 64 0 0 21 0 0 0 0 7

2017 0 16 16 5 0 0 0 0 0 4 72 0 0 27 0 0 0 0 3

2018 0 2 0 2 15 0 0 8 0 89 36 0 15 30 0 0 13 2 2

Ric

his

2013 2 4 0 0 36 7 2 3 0 5 178 0 128 0 0 0 2 5 42

2014 0 7 2 2 0 0 0 0 0 1 34 0 1 28 0 0 0 2 0

2015 0 3 4 0 0 0 0 1 1 1 70 0 0 23 0 0 0 0 1

2016 0 10 8 0 0 9 0 3 2 3 58 0 0 21 0 0 0 0 3

2017 0 3 17 0 0 0 0 2 2 0 46 0 0 14 0 3 0 0 2

2018 0 3 0 3 3 0 0 5 0 28 43 0 7 39 0 0 8 0 10

Vis

cri

2013 0 21 0 0 0 5 1 0 0 0 269 0 265 1 0 0 0 0 3

2014 0 24 0 0 3 0 0 16 0 0 11 0 0 9 0 0 0 1 1

2015 0 12 0 0 0 0 0 27 0 1 42 0 0 18 0 0 0 0 0

2016 0 15 0 0 3 0 0 5 0 5 236 0 0 131 0 0 0 2 0

2017 2 21 0 0 0 0 0 20 2 3 39 0 0 69 0 0 2 0 0

2018 0 13 0 0 7 0 0 3 0 21 168 0 0 297 0 0 13 0 0

All

villa

ges

2013 0 13 0 0 29 7 0 1 0 6 156 0 136 0 0 0 1 1 15

2014 0 18 2 1 17 0 0 9 0 3 114 0 1 59 0 0 0 4 0

2015 0 18 3 2 18 0 0 12 1 5 149 1 0 36 0 0 0 0 1

2016 0 24 3 3 21 7 0 11 1 32 188 0 0 61 1 0 0 1 1

2017 0 23 8 2 22 0 0 9 0 25 118 1 1 75 0 2 0 0 1

2018 0 9 3 1 13 0 0 7 0 31 64 1 4 78 0 0 4 4 2

Table A2, part 4.

C

om

mo

n b

lue

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

Litt

le b

lue

Cu

pid

o m

inim

us

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

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

Ap

old

2014 144 1 0 0 0 21 0 1 0 0 0 0 0 0 0 0 0 0 0

2015 154 0 0 0 0 62 0 0 1 0 0 92 1 0 0 0 0 0 0

2016 80 5 0 0 0 65 0 0 0 0 0 148 0 0 0 0 0 0 0

2017 145 2 0 0 2 87 0 0 18 0 2 184 0 3 0 0 0 0 0

2018 16 0 0 0 0 31 0 0 0 0 0 35 0 0 0 0 0 0 0

Cri

t

2013 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

2014 1 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0

2015 8 0 0 0 0 1 0 0 0 0 0 31 1 0 0 0 0 0 0

2016

2017 11 0 0 0 0 3 0 0 3 1 0 11 0 1 0 0 0 0 0

2018 1 0 0 0 0 11 0 0 1 0 0 5 0 1 0 0 0 0 0

Dai

a

2014 12 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0

2015 86 0 0 0 1 1 0 0 1 0 0 95 3 0 0 0 0 0 0

2016 90 7 0 0 4 19 0 0 5 0 0 75 0 2 0 0 0 0 0

2017 31 0 0 0 5 0 0 0 5 0 0 9 0 0 0 0 0 0 0

Mal

ancr

av

2013 3 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

2014 207 0 0 0 0 7 0 1 4 0 0 0 1 1 2 1 0 0 0

2015 186 0 0 0 0 33 0 0 0 0 0 74 2 0 0 1 0 1 0

2016 44 4 4 0 8 40 0 0 20 0 0 63 0 13 0 0 0 0 0

2017 83 20 2 0 2 50 0 0 5 0 0 65 0 7 0 0 0 0 0

2018 12 2 7 0 0 28 2 0 0 0 0 30 0 0 0 0 0 0 0

Mes

end

orf

2013 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

2014 2 0 0 0 0 2 0 1 1 0 0 0 0 0 1 0 0 0 0

2015 9 0 1 0 0 13 0 0 0 0 0 23 0 0 0 0 0 0 0

2016 5 0 0 0 0 5 0 0 0 0 0 9 0 0 0 0 0 0 0

2017 12 0 3 2 0 44 0 0 0 0 0 22 0 0 0 0 0 0 0

2018 0 0 5 0 8 2 2 0 0 0 0 2 0 2 0 0 0 0 0

No

u S

ases

c

2013 7 0 2 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0

2014 3 0 0 1 0 1 0 0 0 0 0 0 0 0 6 0 0 0 0

2015 2 0 0 0 0 21 0 0 0 0 0 10 0 0 0 0 0 0 0

2016 7 0 5 0 0 20 0 0 0 0 0 5 0 0 5 0 0 0 3

2017 14 0 3 15 0 19 0 0 0 0 0 33 0 0 9 0 0 0 10

2018 2 0 10 0 7 0 3 0 2 0 0 0 0 3 2 3 0 0 3

Ric

his

2013 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

2014 3 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0

2015 7 3 0 2 0 14 0 1 0 0 0 19 0 0 1 0 0 0 0

2016 8 0 2 6 2 3 0 2 0 0 0 12 0 2 3 0 2 0 0

2017 19 0 0 18 0 11 0 0 0 0 0 36 0 0 2 0 2 0 5

2018 3 0 3 0 3 2 2 0 0 0 0 12 2 0 3 0 0 0 0

Vis

cri

2013 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

2014 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

2015 7 0 0 0 2 1 0 0 1 0 0 14 0 0 0 2 0 0 0

2016 37 2 0 0 10 0 0 0 5 0 0 50 2 0 0 2 0 0 0

2017 2 0 0 0 2 3 0 0 11 0 0 2 0 0 0 0 0 0 0

2018 5 0 0 0 10 0 0 0 2 0 0 3 0 2 0 0 0 0 0

All

villa

ge

2013 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

2014 44 0 0 0 0 4 0 0 1 0 0 0 0 0 1 0 0 0 0

2015 50 0 0 0 0 17 0 0 0 0 0 41 1 0 0 0 0 0 0

2016 38 2 1 1 3 22 0 0 4 0 0 51 0 2 1 0 0 0 0

2017 38 2 1 4 1 26 0 0 5 0 0 44 0 1 1 0 0 0 2

2018 5 0 3 0 3 9 1 0 1 0 0 11 0 1 1 0 0 0 0

Table A2, part 5.

La

rge

cop

per

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 2 0 0 3 20 0 0 0 3 0 1 1195

2015 4 0 1 0 5 0 0 0 1 0 0 1233

2016 3 3 7 12 20 0 0 2 3 0 0 1272

2017 3 2 5 0 5 0 0 0 3 0 2 1528

2018 0 2 0 0 5 0 0 2 7 0 0 465

Cri

t

2013 0 1 3 2 0 0 0 0 3 0 0 579

2014 0 0 0 10 0 0 2 0 0 0 0 613

2015 0 1 5 3 0 0 0 0 0 0 0 873

2016

2017 0 1 4 0 0 0 1 0 1 0 0 822

2018 1 0 1 0 0 0 0 0 0 0 0 378

Dai

a

2014 0 0 2 2 0 0 0 1 0 0 0 492

2015 1 0 4 0 0 0 1 0 0 0 0 893

2016 2 2 0 4 0 0 0 0 0 0 0 933

2017 2 0 6 0 0 0 0 0 0 0 0 928

Mal

ancr

av

2013 0 0 1 0 0 0 0 0 0 0 0 541

2014 0 0 0 0 1 0 0 0 0 0 1 922

2015 1 0 3 0 0 0 0 0 0 0 1 986

2016 0 4 5 37 0 0 0 0 9 0 2 1086

2017 2 2 0 2 0 0 0 0 5 0 2 935

2018 0 0 0 0 0 0 0 0 0 0 0 525

Mes

end

orf

2013 0 0 0 5 0 0 0 0 1 0 0 748

2014 0 0 0 9 0 0 1 0 1 0 1 869

2015 0 1 0 0 0 0 0 0 0 0 0 915

2016 0 0 0 0 2 0 8 2 0 0 0 658

2017 2 2 0 0 2 0 0 0 0 0 2 847

2018 0 0 5 0 0 0 0 0 0 0 2 791

No

u S

ases

c

2013 0 1 3 1 0 0 0 0 3 0 0 772

2014 0 0 0 3 0 0 1 1 3 1 0 536

2015 0 0 0 0 3 0 0 0 0 0 0 475

2016 0 0 3 2 3 0 3 0 8 0 0 681

2017 0 5 0 0 3 0 0 0 0 0 0 938

2018 0 0 0 0 0 0 0 0 2 0 2 617

Ric

his

2013 0 0 0 0 0 0 0 0 0 0 0 580

2014 0 0 0 2 1 0 0 0 1 0 0 239

2015 0 0 0 1 0 0 0 0 0 0 0 343

2016 0 7 2 8 2 2 2 0 2 0 0 493

2017 0 2 0 0 0 0 0 0 0 0 0 358

2018 0 2 0 0 0 0 0 0 2 0 0 460

Vis

cri

2013 0 0 0 1 0 0 1 0 1 0 0 651

2014 0 0 1 2 0 0 1 0 1 0 0 409

2015 0 0 0 0 0 0 0 0 0 0 0 614

2016 0 0 5 2 0 0 0 0 0 0 0 761

2017 0 2 10 0 0 0 0 0 2 0 0 576

2018 0 0 2 0 0 0 0 0 0 0 0 675

All

villa

ges

2013 0 0 1 1 0 0 0 0 1 0 0 645

2014 0 0 0 4 3 0 1 0 1 0 0 655

2015 1 0 2 1 1 0 0 0 0 0 0 782

2016 1 2 3 9 4 0 2 0 3 0 0 836

2017 1 2 3 0 1 0 0 0 1 0 1 863

2018 0 0 1 0 1 0 0 0 1 0 0 489

Appendix 3 Table A4, part 1. Bird abundance per point count for all 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

Locu

stel

la n

aev

ia

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

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

2018 5.27 0.87 0.18 0.07 0.55 0.15 0.35 0.16 0.42 0 0.13 0 0.78 0 0 0 0 0

Cri

t

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

2018 3.68 0.68 0.23 0.11 0.66 0.14 0.27 0.36 0.25 0 0 0.02 0.75 0 0.04 0 0.16 0

Mal

ancr

av

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

2018 5.57 0.45 0.58 0.25 0.89 0.13 0.25 0.32 0.53 0 0.19 0.08 0.85 0 0.04 0 0 0

Mes

end

orf

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

2018 2.7 0.1 0.33 0.08 1.15 0.61 0.05 1.57 0.3 0 0.03 0 0.46 0 0.07 0.08 0.02 0

No

u S

ases

c

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

2018 0.76 1 0.26 0.14 1.48 1.29 0.21 0.69 1.02 0 0.21 0.07 0.4 0 0.07 0 0 0

Ric

his

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

2018 4.17 0.45 0.6 0.09 0.95 0.9 0.07 0.26 0.26 0 0.21 0 0.47 0 0.74 0.02 0 0.12

Vis

cri

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

2018 3.52 0.46 0.07 0.09 0.21 0.7 0.2 0.23 0.07 0 0.02 0 0.7 0 0.52 0 0 0

TOTA

L

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

2018 3.75 0.55 0.33 0.12 0.82 0.54 0.19 0.52 0.38 0 0.11 0.02 0.63 0 0.22 0.02 0.03 0.02

Table A4, part 2.

Fera

l pig

eon

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 ep

op

s

Ho

use

mar

tin

Del

ich

on

urb

ica

Ho

use

sp

arro

w

Pas

ser

do

mes

ticu

s

Jack

daw

Co

rvu

s m

on

edu

la

Ap

old

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

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

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

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

2018 1.55 0 0.15 0.76 0 0.65 1.62 1.11 0.04 0 0.4 0 0.09 0 0 1.8 2.35 0

Cri

t

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

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

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

2018 1.11 0 0.5 0.32 0 0.36 1.29 0.93 0.21 0.02 0.46 0.07 0 0.13 0 1 2.27 0

Mal

ancr

av

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

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

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

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

2018 2.49 0 0.04 0.11 0 1 2.08 1.38 0.02 0.17 0.53 0.04 0 0 0.02 1.68 2.26 0

Mes

end

orf

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

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

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

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

2018 0.46 0.03 0.69 0.16 0.03 0.48 0.85 0.57 0 0.18 0.44 0 0 0.21 0 0.2 1.98 0

No

u S

ases

c

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

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

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

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

2018 0.07 0 0.52 0.36 0 0.36 0.57 0.69 0.12 0.19 0.36 0 0.02 0.14 0.05 0.07 0.43 0

Ric

his

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

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

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

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

2018 1 0 0.69 0.29 0 0.26 0.88 0.66 0.17 0.03 0.53 0.05 0 2.12 0 0.17 2.81 0

Vis

cri

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

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

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

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

2018 5.52 0 0.79 0.3 0.04 0.34 0.5 0.63 0.13 0.04 0.14 0.04 0 3.04 0 0.23 3.09 0

TOTA

L

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

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

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

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

2018 1.78 0.01 0.49 0.33 0.01 0.49 1.12 0.85 0.1 0.09 0.41 0.03 0.02 0.84 0.01 0.74 2.23 0

Table A4, part 3. Ja

y

Ga

rru

lus

gla

nd

ari

us

Kes

trel

Falc

o t

inn

un

culu

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

Ap

old

2014 1.2 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

2015 0.43 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

2016 1.21 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

2017 0.54 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

2018 0.76 0 0 0.05 0.07 0.02 0.04 0.13 0.11 0.62 0 0.6 0.09 0.22 0 1.13 0.05 0

Cri

t

2014 0.59 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

2015 0.38 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

2017 0.34 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

2018 0.54 0 0 0.13 0 0 0.05 0 0 0.59 0 0.66 0.02 0.43 0 0.36 0.09 0

Mal

ancr

av

2014 0.82 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

2015 0.82 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

2016 0.02 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

2017 0.85 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

2018 1.08 0 0 0 0 0.06 0.06 0 0.3 0.87 0 0.49 0.08 0.3 0 0.43 0.08 0

Mes

end

orf

2014 0.26 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

2015 0.16 0 0 0.11 0 0.03 0 0 0 0 0 0.03 0.02 0.17 0 0.44 0 0.08

2016 0.76 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

2017 0.27 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

2018 0.38 0.03 0.03 0 0.02 0.07 0 0.02 0.05 0.08 0 0.23 0 0.3 0 0.51 0 0

No

u S

ases

c

2014 0.41 0 0 0 0 0.09 0 0 0 0.19 0 0.56 0 0 0 0.87 0.04 0

2015 0.07 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

2016 0.42 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

2017 0.38 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

2018 0.36 0 0 0 0 0.02 0 0 0 0.17 0 0.55 0.1 0.19 0.12 0.52 0.21 0

Ric

his

2014 0.26 0 0 0 0 0.05 0.07 0 0 0.67 0 0 0 0 0 0.35 0.09 0.16

2015 0.08 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

2016 0.23 0.83 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

2017 0.17 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

2018 0.14 0.02 0 0 0.03 0.03 0.02 0 0 0.52 0 0.17 0.45 0.17 0.02 0.14 0.09 0

Vis

cri

2014 0.07 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

2015 0.07 0.11 0 0 0 0.02 0 0.02 0 1.93 0 0 0.09 0.13 0 0.09 0 0.09

2016 1.27 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

2017 0.33 0.02 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

2018 0.13 0.11 0 0.04 0 0.11 0.09 0 0.07 1.75 0.14 0.16 0.14 0.23 0.04 0.16 0.11 0

TOTA

L

2014 0.52 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

2015 0.32 0.01 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

2016 0.63 0.13 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

2017 0.39 0 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

2018 0.48 0.02 0.01 0.03 0.02 0.04 0.04 0.02 0.08 0.66 0.02 0.4 0.13 0.27 0.02 0.46 0.08 0

Table A4, part 4. R

aven

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

Riv

er w

arb

ler

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

Ap

old

2014 0.24 1.93 0 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

2015 0.46 1.35 0 0 0.29 0 0.02 0 0.05 0 0 0.03 0.13 0.05 0.03 0 1.89 0.21

2016 0.63 2.73 0 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

2017 0.39 2.16 0 0 0.13 0 0.02 0 0 0 0.02 7.2 0.3 0.18 0 0 1.79 0.2

2018 0.51 1.33 0 0.02 0.38 0 0 0 0 0.04 0.02 0.15 0.22 0.15 0 0 0.85 0.24

Cri

t

2014 0.14 1.49 0.08 0 0 0 0 0.02 0.02 0 0.03 45.78 0.05 0.08 0 0.17 0.03 0.03

2015 0.84 1.41 0 0.02 0.09 0 0 0.03 0.05 0 0 0.28 0.06 0.13 0 0.03 0.14 0

2017 0.22 2.11 0 0.05 0.25 0 0 0.03 0.02 0.03 0 1.42 0.14 0 0 0 0.73 0.08

2018 0.45 1.96 0 0 0.21 0 0 0.05 0.02 0.05 0.02 4.11 0.13 0 0 0 0.38 0.2

Mal

ancr

av

2014 0.15 1.2 0.02 0 0.07 0 0.07 0.03 0 0 0 0.66 0.13 0.31 0 0.11 0.08 0.02

2015 0.22 1 0 0 0.25 0 0.05 0 0 0 0.02 0 0.13 0.07 0.15 0 1.28 0.05

2016 0.29 0.62 0 0.23 0 0 0 0.04 0.04 0 0.08 4.38 0.19 0.15 0 0.04 1.69 0

2017 0.35 1 0 0 0.31 0 0 0.02 0.02 0.02 0 0.02 0.17 0.06 0 0 1.75 0.04

2018 0.53 1.17 0 0 0.25 0 0 0 0 0.08 0.02 0 0.04 0.08 0 0 1.47 0.04

Mes

end

orf

2014 0.26 1.29 0.09 0 0.16 0 0 0.5 0.03 0 0 3.88 0.12 0.17 0 0.05 0.22 0.03

2015 0.39 0.5 0 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

2016 0.46 0.61 0 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

2017 0.17 0.9 0 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

2018 0.1 0.92 0 0.02 0.61 0 0 0.31 0.1 0 0 0.62 0.33 0.1 0 0 0.38 0.31

No

u S

ases

c

2014 1.15 1.5 0.09 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

2015 0.31 1.03 0 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

2016 1.48 1.33 0 0.04 0.65 0 0 0.54 0 0 0 0 0.38 0.12 0 0.19 0.27 0.12

2017 0.47 0.93 0 0.14 0.52 0 0 0 0.22 0 0 1.9 0.28 0.1 0 0.21 1.5 0.21

2018 0.43 1.1 0 0.1 0.33 0 0 0.07 0.31 0 0 0.38 0.43 0.07 0 0.14 0.17 0.21

Ric

his

2014 0.77 1.44 0.12 0 0.09 0 0 0 0.09 0 0 5.91 0 0.51 0 0.05 2.63 0

2015 0.33 0.31 0 0.17 0 0 0 0 0.08 0 0 4.5 0.02 0 0.02 0.21 0.52 0

2016 0.83 1.21 0 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

2017 0.43 0.72 0 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

2018 0.4 1.16 0 0.17 0.12 0 0 0 0.36 0 0.02 0.67 0.28 0.24 0 0.52 1.16 0

Vis

cri

2014 0.66 1.76 0.15 0 0.05 7.37 0 0.83 0 0 0 115.66 0.05 0.22 0 0.22 1.1 0

2015 0.05 0.95 0 0 0.05 15.5 0 1.8 0.13 0 0 0.21 0 0.11 0 0 2.59 0

2016 0.45 0.8 0 0 0.24 0 0 0 0.04 0.04 0.02 1 0.02 0.1 0 0.08 2.88 0.12

2017 0.23 1.04 0 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

2018 0.61 1.7 0 0 0.29 12.38 0 0.27 0.14 0 0 11.27 0 0.05 0 0.05 2.89 0.05

TOTA

L

2014 0.43 1.71 0.07 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

2015 0.35 1.11 0 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

2016 0.66 1.59 0 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

2017 0.32 1.49 0 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

2018 0.43 1.34 0 0.04 0.31 1.82 0 0.1 0.13 0.02 0.01 2.52 0.2 0.1 0 0.1 1.06 0.15

Table A4, part 5. Tu

rtle

do

ve

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

Wo

od

pig

eon

Co

lum

ba

pa

lum

ba

s

Wo

od

war

ble

r

Ph

yllo

sco

pu

s si

bila

trix

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

2014 0 0.16 0.11 0.47 0.02 1.05 0 0 0.02 0 0.02 0.07 26.73 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

Lapwing Vanellus vanellus

Marsh harrier Circus circus

Meadow pipit Anthus pratensis

Montagu's harrier Circus pygargus

Nightjar Caprimulgus europaeus

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

Swift Apus apus

Tawny owl Strix aluco

Tawny pipit Anthus campestris

Water rail Rallus aquaticus

White-backed woodpecker Dendrocopos

leucotos

Wood sandpiper Tringa glareola

2015 0.05 0 0.14 0.19 0 1.27 0 0 0.05 0 0 0.11 21.32

2016 0.06 0.02 0.33 0.17 0 0.71 0 0.02 0.1 0 0 0.4 56.02

2017 0.04 0 0.04 0.38 0 0.66 0 0.16 0.09 0.02 0 0.25 43.46

2018 0.05 0 0.29 0.53 0.02 0.6 0.05 0 0.16 0.04 0 0.36 29.49

Cri

t

2014 0.03 0.2 0.12 0.19 0 0.12 0 0.32 0 0 0 0.29 68.64

2015 0.13 0.02 0.16 0.11 0 0.19 0.02 0.02 0 0 0 0.25 19.66

2017 0.08 0 0.06 0.27 0 0.19 0 0.08 0 0 0 0.48 26.25

2018 0.05 0.05 0.27 0.25 0 0.61 0 0 0 0 0 0.32 28.02

Mal

ancr

av

2014 0 0.1 0.02 0.13 0 0.7 0 0.1 0.02 0 0 0.18 28.51

2015 0.02 0 0 0.07 0 0.57 0 0.02 0 0 0 0.13 26.17

2016 0.35 0.02 0.15 0.15 0.08 0.1 0 0.08 0 0 0 0.94 26.27

2017 0.02 0 0 0.23 0 0.75 0 0 0.06 0 0 0.04 39.88

2018 0.04 0 0 0.38 0.02 0.81 0.06 0 0.04 0.02 0 0.34 31.09

Mes

end

orf

2014 0 0.09 0.17 0.24 0 0.36 0.02 0.12 0 0 0.02 1 24.74

2015 0.02 0.02 0.02 0.41 0 0.23 0 0.02 0.05 0 0 0.58 17.41

2016 0.09 0 0 0.11 0 0.44 0 0.09 0.04 0.02 0 1.04 18.74

2017 0.04 0.04 0.02 0.42 0 0.5 0 0.02 0.02 0 0 0.79 26.58

2018 0.03 0 0.03 0.16 0 0.48 0 0 0.08 0 0 0.64 20.77

No

u S

ases

c

2014 0.06 0.06 0 0.2 0.04 0.43 0 0.35 0 0 0 0.78 29.28

2015 0.03 0.03 0 0.59 0 0.31 0 0.1 0 0 0 0.72 17.62

2016 0.1 0.06 0.1 0.25 0 0.37 0 0.02 0.17 0.02 0 0.98 27.9

2017 0.14 0 0.07 0.17 0 0.55 0 0.21 0.07 0.03 0 1.09 24.91

2018 0.12 0.05 0.1 0.19 0 0.67 0 0.02 0.14 0 0 0.98 19.88

Ric

his

2014 0.23 0.12 0.09 0.26 0 0.44 0 0 0 0.02 0 1.21 32.84

2015 0.06 0.04 0 0.23 0 0.27 0 0.06 0 0.02 0 0.63 19.79

2016 0.09 0.04 0.38 0 0 1.34 0 0.17 0 0 0.17 0.85 86.49

2017 0.12 0.03 0.22 0.31 0 0.14 0 0.19 0 0.14 0 1.02 41.02

2018 0.02 0.09 0.29 0.22 0 0.45 0 0.22 0.03 0.07 0 0.72 28.12

Vis

cri

2014 0.05 0.05 0.22 0.07 0 0.39 0 0.12 0 0 0 1.05 190.07

2015 0.09 0.13 0.45 0.09 0 0.46 0 0.05 0 0 0.05 0.79 35.84

2016 0 0.06 0.02 0.24 0 0.61 0.02 0 0 0 0 0.14 35.35

2017 0.23 0.11 0.39 0.07 0 1.32 0 0.04 0 0.02 0.07 1.04 88.42

2018 0.05 0.11 0.36 0.3 0 0.79 0 0.07 0 0 0.13 0.27 56.63

TOTA

L

2014 0.06 0.16 0.09 0.23 0.01 0.5 0 0.14 0 0 0 0.59 52.29

2015 0.09 0.04 0.1 0.22 0 0.49 0 0.06 0.02 0 0.01 0.38 22.98

2016 0.1 0.03 0.15 0.22 0.01 0.59 0.01 0.05 0.07 0.01 0.02 0.64 40.65

2017 0.1 0.02 0.14 0.27 0 0.54 0 0.09 0.03 0.03 0.01 0.63 42.13

2018 0.05 0.04 0.19 0.29 0.01 0.62 0.02 0.05 0.06 0.02 0.02 0.51 30.82

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 2018 Biodiversity Survey Summary Report · 2019-03-04 · Fieldwork in 2018 was...

Documents

Transcript of Tarnava Mare 2018 Biodiversity Survey Summary Report · 2019-03-04 · Fieldwork in 2018 was...