Analysis of capercaillie brood count data: Long term analysis

Scottish Natural Heritage
Commissioned Report No. 435
Analysis of capercaillie brood count data:
Long term analysis

COMMISSIONED REPORT
Commissioned Report No. 435
Analysis of capercaillie brood count data:
Long term analysis
For further information on this report please contact:
Susan Haysom
Scottish Natural Heritage
Great Glen House
INVERNESS
IV3 8NW
Telephone: 01463-725 000
E-mail: susan.haysom@snh.gov.uk
This report should be quoted as:
Baines, D., Aebischer, N., Brown M. & Macleod, A. (2011). Analysis of capercaillie
brood count data: Long term analysis. Scottish Natural Heritage Commissioned Report
No.435.
This report, or any part of it, should not be reproduced without the permission of Scottish Natural Heritage.
This permission will not be withheld unreasonably. The views expressed by the author(s) of this report
should not be taken as the views and policies of Scottish Natural Heritage. This report is from a
partnership project with partners: Forestry Commission Scotland, the Game and Wildlife Conservation
Trust, the Royal Society for the Protection of Birds and Scottish Natural Heritage.
© Scottish Natural Heritage 2011.

COMMISSIONED REPORT
Summary
Analysis of capercaillie brood count data:
Long term analysis
Commissioned Report No. 435 (iBids n o 7822)
Contractor: The Game & Wildlife Conservation Trust
Year of publication: 2011
BACKGROUND
Capercaillie Tetrao urogallus numbers have declined in Scotland since the mid 1970s,
(Moss, 1994; Catt et al., 1998), but the last survey in 2003-04 suggested that, although
capercaillie remain seriously threatened, population size may now have stabilised at about
2000 birds (Eaton et al., 2007). The rapid decline has been linked with poor breeding
success (Moss et al., 2000) associated with both changes in weather patterns (Moss et al.,
2001) and with increases in generalist predators that may predate eggs and chicks (Baines
et al., 2004; Summers et al., 2004).
MAIN FINDINGS
Capercaillie breeding success and indices of hen density measured annually between 1991
and 2009 showed a significant decline over time. Declines in breeding success were
associated with proportionally fewer hens rearing chicks as opposed to a reduction in actual
brood size. Breeding success averaged 0.6 chicks per hen and did not differ between forest
types. Birds bred less well in forests in Perthshire towards the southern edge of their current
range, than they did in forests in Strathspey. Perthshire, together with Argyll and Moray, had
the highest declines in indices of hen density and only in the Strathspey sub-population were
densities considered stable. Breeding success (“chicks per hen” and “broods per hen”) was
strongly influenced by weather and was higher in years when there was a larger increase in
temperature in April (APRWARM), when temperatures at chick hatch time (HATCHTEMP)
were higher and when April on the whole was cooler (APRTEMP). Two weather variables;
APRTEMP and HATCHRAIN, increased over time.
The mean pine marten Martes martes index had increased 3.7-fold since 1995, and the fox
Vulpes vulpes index by 2.7-fold. Those of carrion crow Corvus corone and raptors, chiefly
buzzards Buteo buteo, showed no change. When simultaneously considering the effects of
both weather and predator explanatory variables, together with region and forest type on
breeding success, we found that “chicks per hen” and “broods per hen” varied negatively
with mean April temperature (APRTEMP) and negatively with both marten and crow indices
of abundance. “Broods per hen” was higher in years when the temperature rose more in
April (APRWARM) and when temperatures at chick hatching (HATCHTEMP) were higher,
whilst “brood size” was negatively associated with rainfall at chick hatching (HATCHRAIN)
and in forests with more crows. High indices of foxes were significantly related to the decline
in hen density indices. Increases in mammalian predators and subsequent increased
predation, together with changes in weather, and the continued presence of crows, provide
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an alternative hypothesis to that of climate change alone in explaining the reductions in
capercaillie breeding success and the population decline in Scottish forests. In the absence
of mechanisms to deal with the effects of climate change on capercaillie, it may be possible
that the decline in capercaillie can be halted and even reversed by continued improvements
in habitat management and by restoration of predator control in remaining capercaillie
strongholds.
For further information on this project contact:
Susan Haysom, Scottish Natural Heritage, Great Glen House, Inverness, IV3 8NW
Email: Susan.Haysom@snh.gov.uk Tel: 01463 725 000
For further information on the SNH Research & Technical Support Programme contact:
DSU (Policy & Advice Directorate), Scottish Natural Heritage, Great Glen House, Inverness, IV3 8NW.
Tel: 01463 725000 or pads@snh.gov.uk
ii

Table of Contents
Page
BACKGROUND......................................................................................................................1
METHODS .............................................................................................................................. 3
RESULTS ............................................................................................................................. 10
DISCUSSION........................................................................................................................ 13
REFERENCES ..................................................................................................................... 17
TABLES................................................................................................................................ 21
FIGURES .............................................................................................................................. 32
Table 1. Location and characteristics of the 26 study forests
Table 2. Locations of the nearest weather stations to each forest
Table 3. Mean values of capercaillie breeding success across three forest types
Table 4. Mean values of capercaillie breeding success across six regions
Table 5. Mean decline rates of capercaillie hens
Table 6. Relationship between capercaillie breeding success, indices of hen density
and weather variables
Table 7. Trends in capercaillie breeding success and weather 1989 to 2009
Table 8. Forest specific indices of predator abundance in 1995 and 2009
Table 9. Mean predator indices in 1995 and 2009
Table 10. Correlation coefficients between predator indices in 1995 and 2009 and between
weather variables
Table 11. Relationship between capercaillie breeding success, weather and predators
Table 12. Relationship between indices of capercaillie hen density, weather and predators
Figure 1. Location of the 26 study forests
Figure 2. Mean annual capercaillie breeding success across forests from 1991 to 2009
Figure 3. Change in mean annual capercaillie index of density between 1991 and 2009
Figure 4. Trends in hen and cock numbers in 16 forests surveyed annually 2002-2009.
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ACKNOWLEDGEMENTS
We would like to thank the numerous land managers and their staff for access to their
forests. Capercaillie brood counts were also conducted by Mark Andrew, Paul Baker, Lois
Canham, Mick Canham, Norman Cobley, Kathy Fletcher, Isla Graham, Rupert Hawley,
Andrew Hoodless, David Howarth, David Lambie, Fiona Leckie, Robert Moss, Raymond
Parr, Adam Smith, Philip Warren and staff at RSPB Abernethy & Craigmore. John Woods
assisted with the collection of predator data. The weather data were kindly provided to
Melanie Brown by the UK Meteorological Office supplied through Natural Environmental
Research Council Data Centres as part of her undergraduate dissertation thesis. The lek
data were kindly provided by the Capercaillie Project Officers funded through the
Capercaillie EU LIFE Project assisted by numerous gamekeepers, foresters and volunteers.
Ron Summers (RSPB), Kenny Kortland (FCS), Susan Haysom and Megan Davies (SNH)
provided helpful comments on the draft report. Stewart A’Hara of Forest Research undertook
the DNA analysis of mammalian scats. The study was funded by Scottish Natural Heritage,
with Susan Haysom as the nominated officer, and by the Game & Wildlife Conservation
Trust.
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BACKGROUND
Capercaillie Tetrao urogallus numbers have declined in Scotland since the mid 1970s,
(Moss, 1994; Catt et al., 1998; Wilkinson et al., 2002), but the last survey in 2003-04
suggested that, although capercaillie remain seriously threatened, population size may have
stabilised at about 2000 birds (Eaton et al., 2007). The rapid decline has been linked with
poor breeding success (Moss et al., 2000) associated with changes in weather patterns
(Moss et al., 2001), increases in generalist predators that may predate eggs and chicks
(Baines et al., 2004; Summers et al., 2004a) and detrimental changes in silvicultural
practices (Moss, 1994). Simultaneous to this, mortality of full-grown birds through flying into
deer fences, has become a significant source of mortality (Catt et al., 1994; Baines &
Summers, 1997; Baines & Andrew, 2003). That the population appears to have now
stabilised is probably attributable to considerable funding being dedicated to removing or
marking deer fences and thus reducing deaths following collisions.
Previous analyses have identified several factors that influence between-year and betweenforest
variations in capercaillie breeding success in Scotland. A comparison of 14 forests
showed that birds bred more successfully where predators, particularly foxes Vulpes vulpes
and crows Corvus corone, were fewer, and where bilberry Vaccinium myrtillus, a preferred
habitat component whose leaves and berries are a key part of the diet (Storch, 1993; 1994;
Summers et al., 2004b), was more plentiful (Baines et al., 2004). Recognition of the
importance of bilberry to capercaillie has resulted in substantial collaborative habitat
management work designed to increase the amount of bilberry cover, especially within
plantation forests in Scotland. Associated improvements in predator control, disturbance
management and removing forest fences through the LIFE Nature Project "Urgent
Conservation Management for Scottish Capercaillie", the Species Action Framework and
other initiatives have sought to deliver progress towards the UKBAP targets of increasing
population size in Scotland to 5000 birds and increasing range from 40 to 45 occupied 10-
km squares by 2010. Meeting these targets has not proved possible and meeting such
optimistic targets in the future will almost certainly rely on improving breeding success. The
principal cause of poor breeding success, causing large between-year variations, relates to
weather at or around chick hatching time in June, both here in Scotland (Moss, 1985;
Summers et al. 2004a) and elsewhere in the birds’ range (Slagsvold & Grasaas, 1979). This
relationship with annual weather has been further complicated by climate change, with a
delay in natural warming during April thought to affect the timing of plant growth in spring,
essential to gravid hens, and the availability of invertebrates needed by growing chicks in
June (Moss et al., 2001; Wegge & Rolstad, in press).
Even when annual weather appears suitable for successful breeding, productivity is often
only modest and varies markedly between forests in relation to habitat quality and indices of
fox and crow abundance (Baines et al., 2004). In Scandinavia, pine martens Martes martes
have been shown to reduce capercaillie breeding success through predating both clutches
and chicks (Marcstrom et al., 1988; Kastdalen & Wegge, 1989; Kurki et al., 1997), but were
not linked to low breeding success in Scotland (Baines et al., 2004). In Scotland, pine
martens were historically persecuted by man to protect gamebirds, but legal protection within
the last 25 years has allowed martens and other predators to recover much of their former
range and abundance (Tapper, 1999).There is now evidence from one of the forests used in
this study (Forest A) that martens have become more numerous since the 1995 survey
(Summers et al., 2004a). Here, where crows and foxes are controlled, a recent study of
capercaillie nest outcomes showed that of 20 nests, pine martens predated 33 - 57%,
depending on the interpretation of the data (Summers et al., 2009). Although increasing in
numbers and range the pine marten, also a UK BAP priority species, is still rare in Scotland
with only an estimated 3,500 adults in Scotland, representing at least 95% of the British
population (Birks, 2002).
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Whilst the Summers and co-workers’ study (2009) indicates that martens can be significant
predators of capercaillie clutches, it is not known to what extent this effect occurs elsewhere
within the bird’s range in Scotland. It is likely that similar increases in marten abundance
have occurred in other forests since the mid 1990s and it has been suggested that this may
have resulted in increased levels of predation on capercaillie.
In an attempt to determine what factors influence annual breeding success of capercaillie,
this report considers the results from annual surveys of capercaillie breeding success in
Scottish forests between 1991 and 2009 (e.g. MacLeod, 2007; 2008) in relation to annual
weather variables and findings from two surveys of predators in 1995 and 2009 in forests
used by breeding capercaillie.
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METHODS
Capercaillie breeding success and density
Measures of capercaillie breeding success and indices of adult densities were obtained in
July and August from a total of 26 forests in Scotland (Table 1) (Figure 1). Data were
obtained from six regions between 1989 and 2009: Strathspey (8 forests), Aberdeenshire
(Deeside / Donside (6)), Perthshire (6), Moray (2), Easter Ross (3) and Argyll (1 forest). The
forests surveyed were classified into three types following Baines et al., (2004); (1) Open
canopy as in native pinewoods, (2) mature Scots pine Pinus sylvestris plantation, canopy
sufficiently open for some dwarf shrubs, and (3) mixed species plantation with closed
canopy, often with some clear-felled areas and restocked ground.
The number of forests surveyed in each year varied from only two in 1989 and 1990 to 20 in
2009, but in most years count data were available from 11 - 18 forests providing a total area
searched over all forests each year that ranged from 31 to 78 km 2 . Counts of hens and
accompanying chicks were conducted using pointing dogs to locate birds. Counts began at
one site (Forest L) in the first week of July, but at all other sites they began in mid-July and
all sites were completed by the end of August. The mean area searched per forest was 3.9
km 2 but search areas varied between forests from 0.6 to 11.0 km 2 . The parts of a forest
favoured for searching were those more open areas where dogs could be readily used and
were perceived to be good breeding habitat. Not all hens may breed in their first spring, but
as there was no way of distinguishing between hens that did not breed and hens that bred
but failed to rear chicks, all hens seen were included in the estimates of breeding success.
Three measures of reproductive success were calculated each year in each forest: the
proportion of hens with at least one chick was “broods per hen”, the number of chicks per
hen that had at least one chick was “brood size” and the overall measure of breeding
success was the number of “chicks per hen”, which was the total chicks divided by total hens
for each forest.
The number of hens encountered in each forest in relation to the area searched in each year
was used as an index of hen density. Typically, the same part of a forest was searched in
each year that the forest was surveyed. Where this was not the case, such as at Forest A,
where the count method also changed over time, the data were removed from analyses
relating to hen density. Annual estimates of breeding success at Forest A were however
retained as the number of hens encountered remained a high proportion of those estimated
to be present and hence representative of the forest as a whole and well above the threshold
level of 10 hens which formed an initial constraint in the selection of study forests (Baines et
al., 2004). When, such as at Forest G, the area searched differed between years, the
change in effort was taken into account when calculating indices of density. A similar index
was calculated for the number of cocks seen. Indices of hens and cock density were in turn
compared with the total numbers of cocks observed at leks (Picozzi et al., 1992) in 16
forests surveyed between 2002 and 2009, the years when lek data were available.
Weather data
Weather data were obtained for the nearest weather station to each forest for the period
1989 to 2009 (Table 2) from the UK Meteorological Office, 2009. Three weather variables;
temperature (the mean of daily maximum and daily minimum temperatures, o C), rainfall
(mean daily rainfall in mm) and rain-days (the number of days with rain) were summarised
into 10 day periods for April, May and June (1-10, 11-20 and 21-30 (or 21-31 for May)).
Weather data were restricted to these months as they coincide with the timing of hens
coming into breeding condition in April, egg laying in late April and early May and chick hatch
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in late-May and early June. Counts of hens and their chicks to determine breeding success
began at the end of the first week of July, so July weather was not considered.
The combination of weather variables and periods considered provided 27 potential
explanatory weather variables. Spurious correlations with weather become increasingly likely
as more meteorological data are included. A previous analysis of the effects of weather on
capercaillie breeding success by Moss et al. (2001) found seven weather variables, each of
10-day periods, to be significantly related to capercaillie breeding success. These were three
successive 10 day measures of mean daily April temperature, temperature in late May,
temperature in early June, the number of rain-days in late-May and number of rain-days in
early June. For our analyses, we further reduced these to four weather variables; mean April
temperature (APRTEMP), mean temperature at or around the peak time of capercaillie chick
hatching in the last eleven days of May and the first ten days of June (HATCHTEMP), the
total number of rain-days in the same period (HATCHRAIN) and, following Moss et al.
(2001), we constructed an index of April warming (APRWARM) calculated as half the
difference between the temperature in the first half of April and that in the second half of
April:- (T2 – T1) / 2. T1 = mean temperature in first 10 days of April, T2 = last 10 days.
Predator surveys
In 1995, pine marten, fox, carrion crow and raptor indices were obtained from 14 forests
where measures of capercaillie breeding success were also collected (Baines et al., 2004).
In 2009, 11 of these 14 forests were re-surveyed. These forests had predator indices from
1995, together with data on capercaillie breeding success from the 1990s and more recently.
These combinations of data allowed changes in measures of activity and distribution of pine
martens and other predators to be related to changes in measures of abundance and
breeding success of capercaillie.
Of the 14 forests from 1995, three (Forests C, E and R) were not surveyed for predators in
2009 because brood counts in them were largely discontinued in the early 1990s and the
latter forest has since been clear-felled. The 11 forests retained for the 2009 survey included
three (Forests J, K and U) where capercaillie were considered to be locally extinct. These 11
forests were supplemented with five additional forests (Forests B, N, W, X and Y), where
brood counts have been conducted in more recent years, bringing the total number of forests
surveyed in 2009 to 16. These 16 forests showed a range of forest types and exhibited a
geographical range that encompassed several Special Protection Areas which featured
capercaillie as a qualifying interest and include both core and peripheral parts of the
perceived pine marten distribution.
For each survey, approximately 10 km of unsurfaced vehicle tracks within each forest were
searched for mammal scats. Wherever possible the same 1995 routes were used in 2009.
The tracks were walked five times, initially during a clear-up round in the second half of April
to count and remove all scats, then twice in May (middle and end of the month) and twice in
June (middle and end of the month). The observer simultaneously scanned both sides of
vehicular tracks for scats at a slow walking pace. The location of all scats was recorded as a
10-figure grid reference using a GPS.
All scats were initially classified as “fox”, “marten” or “other” in the field when characteristic
elements of their morphology had not been altered by handling. Prior to handling, a digital
photograph was taken of the scat in situ with a ruler placed adjacent for scale. All scats,
other than those from dogs, were collected. In each survey year, two different observers
collected scats in the field; observers differed between 1995 and 2009. In 2009, to try to
reduce the observer error, a secondary check of identification was performed on the
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collected scats by the more experienced of the two field observers (AM), whose correct
classification during a previous survey had been verified by DNA analysis to be 88% (R.
Trout, pers. comm.).
In 2009 30% (414) of the mammalian scats collected were DNA tested to verify their
originator. Of the 305 scats that provided sufficient DNA, 77% had been correctly identified
as either fox or marten prior to DNA testing. Of those misidentified, there was a significant
tendency to over-identify scats as fox, thus under-identifying marten scats. Conversion rates
of x 0.49 for fox and x 1.30 for marten were calculated 1 , but this process had not been
conducted in 1995. Subsequent analyses of changes in mammalian predator indices over
time consider scenarios when conversion rates were either applied or not applied to both the
1995 and 2009 scat data and when rates were applied in 2009 only, but not in 1995.
Approximately 5 km of the same tracks used for scat surveys were also used as transects
along which carrion crows Corvus corone, C. cornix and raptors were counted. Bird counts
were conducted just after dawn (i.e. within 2 hours of sunrise), twice monthly in May and
June and immediately preceded the scat collection round on the same day. Sightings and
calls were given a GPS position.
DATA ANALYSIS
All analyses were conducted using GenStat version 12 (GenStat, 2009).
Analysis 1: Capercaillie breeding success and hen density
Data: 26 forests surveyed between 1991 and 2009
Type of model fitted: Generalised linear mixed model (GLMM) adjusted for over-dispersion
Dependent variable:
1.1) Chicks per hen, i.e. number of chicks in each forest in each year (Poisson error -
natural log of number of hens as the offset);
1.2) Brood size, i.e. number of chicks in each forest in each year minus the hens with
no chicks (Poisson error - natural log of number of broods as the offset);
1.3) Broods per hen, i.e. number of broods in each forest in each year divided by the
number of hens (binomial error with a logit link);
1.4) Hen density, i.e. number of hens in each forest in each year (Poisson error -
natural log of area searched as offset).
Fixed effect explanatory variables (for each model): Forest regions (6); forest type (3); year
(as continuous and factor in separate models)
Random effect explanatory variables (for each model): Forest
The differences in breeding success and indices of hen density between forests in six
Scottish regions, three forest types and between years were considered. Year was included
both as a factor and then as a continuous variable in separate models leading to a total of
eight models fitted. These analyses were conducted on all 26 forests surveyed between
1991 and 2009, but not 1989-90 when only two forests were surveyed. Significance of model
terms was tested (Type III sum of squares) using Wald tests.
Analysis 2: Relating hen and cock densities to lek counts
Data: 16 forests surveyed between 2002 and 2009
1 For details see SNH Commissioned report 415
5

Type of model fitted: Generalised linear model (GLM) with Poisson error, log link, natural log
of the area as the offset, adjusted for over-dispersion
Dependent variable:
2.1) Hen density;
2.2) Cock density.
Explanatory variables (for each model): Forest (16); count of leking cocks
For the years 2002-09 when lek data were available, the annual indices of hen and cock
abundance from brood surveys in 16 forests were compared with the total number of cocks
attending leks in spring in the same years. This was done using GLMs with Poisson error,
with hens or cocks from the brood surveys as the dependent variable and forest and the
number of leking cocks as explanatory variables (Poisson distribution, log link, adjusted for
over-dispersion) with log e area as an offset. Significance of model terms was tested (Type III
sum of squares) using likelihood-ratio tests.
Analysis 3: Relating hen densities to cock densities
Data: 16 forests surveyed between 2002 and 2009
Type of model fitted: GLM with Poisson error, log link, natural log of the area as the offset,
adjusted for over-dispersion
Dependent variable: Hen density
Explanatory variables: Forest (16); cock density; forest*cock density
Hens on brood counts were related to cocks on the same counts in a GLM with cocks as a
continuous explanatory variable, forest as a factor and an interaction of cocks*forest.
Analysis 4: Trends in the hen and cock densities
Data: 16 forests surveyed between 2002 and 2009
Type of model fitted: GLM with Poisson error, log link, natural log of the area as the offset,
adjusted for over-dispersion
Dependent variable:
4.1) Hen density;
4.2) Cock density;
4.3) Number of cocks attending leks.
Explanatory variables (for each model): Region (6); forest type (3); year (as continuous);
year*region; year*forest type
Trends in numbers of cocks and hens observed on brood counts and numbers of cocks
attending leks over time (2002-2009) were considered using GLMs with Poisson error, log
link, adjusted for over-dispersion, with each bird count (cocks or hens on brood counts, or
cocks on leks) in turn as the dependent variable, log e area as an offset, region and forest
type as factors and year as a continuous variable and interactions of year*region and
year*forest type.
Analysis 5: Weather effects and its interaction with forest type and region
Data: 25 forests surveyed between 1989 and 2009
Type of model fitted: GLMM adjusted for over-dispersion
Dependent variable:
5.1) Chicks per hen (Poisson error - natural log of number of hens as the offset);
5.2) Brood size (Poisson error - natural log of number of broods as the offset);
5.3) Broods per hen (binomial error with a logit link);
5.4) Hen density (Poisson error - natural log of area searched as offset).
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Fixed effect explanatory variables (for each model): Forest type (3); region (5); APRWARM;
APRTEMP; HATCHRAIN; HATCHTEMP; region*APRWARM; region*APRTEMP;
region*HATCHRAIN; region*HATCHTEMP;
Random effect explanatory variables (for each model): Forest
This stage of the analysis looked at the effects of weather on breeding success and how
weather interacted with forest type and region. Weather data were available for each year, in
each region other than Argyll (Forest V) and were matched to breeding data collected from a
total of 25 forests (i.e. 26 forests in analysis 1 minus Forest V) between 1989 and 2009.
To assess the effects of explanatory forest type, region, and weather on breeding success
and indices of density, GLMM were used. Variations in “chicks per hen” between forests and
years were considered by setting the number of chicks seen in each forest in each year as
the dependent variable and forest type, region, year and weather measures as fixed effects
and forest as a random effect in Poisson regressions (Poisson distribution, log link, adjusted
for over-dispersion), with the natural logarithm of the number of hens set as an offset. “Brood
size” was analysed in the same way, but excluding hens with no chicks and setting the
natural logarithm of the number of broods as an offset. “Broods per hen” was modelled using
logistic regression (binomial distribution, logit link), categorising hens as successful if they
had one or more chicks and unsuccessful if they had no chicks. Differences in indices of hen
density were modelled using hens seen in each forest in each year as the dependent
variable in Poisson regressions with the natural logarithm of the area searched in each forest
as the offset.
Analysis proceeded by fitting models that included interactions between region and the
weather variables, with subsequent models reduced by removal of non-significant weather
variables. Main effects were not allowed to be removed before any interactions that they
were included in. Region and forest type were retained in all models as we considered them
to be structural components of the design.
Analysis 6: Trends in breeding success and hen density with weather variables as
explanatory variables
Data: 25 forests surveyed between 1991 and 2009
Type of model fitted: Generalised linear mixed model (GLMM) adjusted for over-dispersion
Dependent variable:
6.1) Chicks per hen (Poisson error - natural log of number of hens as the offset);
6.2) Brood size (Poisson error - natural log of number of broods as the offset);
6.3) Broods per hen (binomial error with a logit link);
6.4) Hen density (Poisson error - natural log of area searched as offset).
Fixed effect explanatory variables (for each model): year (as continuous); weather variables
from analysis 5 as appropriate; forest region (6); forest type (3)
Random effect explanatory variables (for each model): Forest
Trends in the three measures of breeding success and hen density between 1991 and 2009
were considered by entering “year” as a continuous variable in the GLMM’s described for
analysis 4.
Analysis 7: Trends in weather variables
Data: 25 forests surveyed between 1991 and 2009
Type of model fitted: GLM with normal error and no adjustment for over-dispersion
Dependent variable:
7.1) APRWARM;
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7.2) APRTEMP;
7.3) HATCHRAIN;
7.4) HATCHTEMP.
Explanatory variables (for each model): Weather station (5); year (as continuous)
Trends in weather variables over time were considered in GLMs (all normal error, identity
link function and not adjusted for over-dispersion) with each variable in turn as the
dependent variable, weather station as a factor and year as a continuous explanatory
variable to explore the long term trends in the weather variables.
Analysis 8: Predator indices
Data: 14 forests surveyed in 1995 and 16 in 2009
Type of model fitted: Generalised linear model (GLM) with Poisson error, log link, natural log
of transect length * time interval as offset, adjusted for over-dispersion
Dependent variable:
8.1) Fox index;
8.2) Marten index;
8.3) Crow index;
8.4) Raptor index.
Explanatory variables (for each model): Forest (19); year (as factor) (2)
Subsequent analyses addressed whether indices of mammal and predatory bird activity
have changed in the same suite of forests since 1995 using generalised linear models
(GLM) with Poisson error, log link, adjusted for over-dispersion, with the number of mammal
(pine marten or fox) scats or bird (carrion crow or raptor) sightings as the dependent
variable, log e (transect length*time interval) as an offset and forest and year as factors.
Analyses were conducted first with no misidentification correction rates for mammalian
predator indices in either year, then with rates applied in both years and finally with
correction rates applied in 2009 only, and not in 1995.
Analysis 9: Correlations between predator indices and weather variables
Relationships between individual predator indices (fox, marten, carrion crow and raptors)
were compared by Pearson correlations on log e (index + 1) transformed predator data.
Relationships between the four weather variables were also compared by Pearson
correlations.
Analysis 10: Considering the effects of weather and predator simultaneously on
breeding success and hen density
Data: 14 forests surveyed between 1991 and 1995 and 16 between 2005 and 2009
Type of model fitted: Generalised linear mixed model (GLMM) adjusted for over-dispersion
Dependent variable:
10.1) Chicks per hen (Poisson error – natural log of number of hens as the offset);
10.2) Brood size (Poisson error – natural log of number of broods as the offset);
10.3) Broods per hen (binomial error with a logit link);
10.4) Hen density (Poisson error – natural log of area searched as offset).
Fixed effect explanatory variables (for each model): Forest type (3); forest region (5);
APRWARM; APRTEMP; HATCHRAIN; HATCHTEMP; Predator indices;
Random effect explanatory variables (for each model): Forest; period within forest; year
within period within forest
8

This stage of the analysis simultaneously considered the effects of weather and predators on
breeding success. Data on capercaillie breeding success were split into two periods; 1991 -
1995 and 2005 - 09, which related to the collection of the predator indices in 1995 and 2009
respectively. The selection of the range of years within these periods was a compromise
between obtaining a sufficiently robust sample of capercaillie breeding success based on
enough hens by combining years, keeping the groups of years small enough to be
representative of the year when predator indices were collected, and maximising the period
between surveys in order to maximise the independence between the two periods.
This reduced the data to information from 19 forests. Only one index value per predator was
available per period and forest so the analysis became multi-level and the GLMMs described
for analysis 5 were modified accordingly by considering forest, period within forest and year
within period within forest as the three levels of variation. By nesting, it is not assumed that
the period effect is the same across all forests. The approach adopted allows the inherent
variation in the forest*period interaction to manifest itself in the test.
Full models involving all weather variables, together with all four predator variables, were
produced. Minimal models were obtained from this by backwards stepwise elimination of
non-significant predator and weather variables from the current model, stopping when all
remaining variables were significant at P < 0.1. The minimal models were checked by
forward stepwise addition of predator and weather variables, one at a time in relation to their
significance within the current model until no further predator or weather variables were
significant at P < 0.1. The forward and backward stepwise procedures produced identical
models for each measure of capercaillie breeding success and hen density.
9

RESULTS
Capercaillie breeding success & density
From analysis 1: “Chicks per hen” averaged 0.6 between 1991 and 2009 and did not differ
between forest types (Table 3). Highest “chicks per hen” was found in Strathspey (0.86) and
lowest in Perthshire (0.37), but there was no overall difference between the six regions
considered (Table 4). “Chicks per hen” varied seven-fold between years, from 0.2 to 1.4 (χ 2 18
= 76.04, P < 0.001) (Fig. 2a). There was a significant trend for “chicks per hen” to decline
across the period of study at a rate of -3.4% (SE: 1.2) per annum (χ 2 1 = 9.34, P = 0.002) (line
fitted on a logarithmic scale from the GLMM Poisson regression). This represented an
approximate mean decline of one chick per hen every 30 years, or 0.6 chicks per hen during
the period of study. The decline in “chicks per hen” was consistent among regions and forest
types.
From analysis 1: “Brood size” did not differ between regions (table 4) and averaged 2.0
chicks. There was a significant forest type*year interaction (χ 2 34 = 83.55, P < 0.001), with
brood sizes in native pinewoods and pine plantations showing less annual variation than
those in mixed plantations. There was however no difference in brood size between forest
types over time (χ 2 2 = 0.59, P = 0.75), and “brood size” across all three forest types showed
no linear trend through time (-1.1 % (SE: 0.07 per annum) (Fig 2b).
From analysis 1: “Broods per hen” averaged 0.32 and did not differ between regions (table
4) or forest type (table 3). “Broods per hen” varied six-fold between years and ranged from
0.09 to 0.57 (χ 2 18 = 94.50, P < 0.001). There was a linear decline in “broods per hen” over
time at a rate of -4.0% (SE: 1.5) per annum (χ 2 1 = 7.63, P = 0.006) (Fig. 2c).
From analysis 1: Over the study period, hen density indices averaged 1.4 km -2 and showed
no difference either between regions (table 4) or between forest types (table 3). Hen
densities differed between years (χ 2 18 = 89.37, P

Effects of weather on capercaillie
From analysis 5: Analyses of “chicks per hen” and “broods per hen” found no interaction
between region, forest type and any of the weather variables. Both “chicks per hen” and
“broods per hen” were significantly related to APRWARM, HATCHTEMP (both positive) and
APRTEMP (negative) (Table 6). In other words, ”chicks per hen” and “broods per hen” were
higher in years when there was a larger temperature rise in April, when temperature in late
May / early June was higher and when April temperature was cooler. “Brood size” was not
significantly related to any of the four weather variables. The index of hen density was
positively related to APRWARM, which means that more hens were found when there had
been a larger temperature rise in April. The index of hen density was negatively related to
HATCHRAIN, so fewer hens were found when hatch time was wetter.
From analysis 6: All three measures of breeding success and hen density showed a
significant decline between 1991 and 2009 (table 7). This decline was consistent between
regions and forest types.
From analysis 7: Although predictably there were significant temperature differences in April
and at hatch time between weather stations, there were no region*year interactions for any
of the four weather variables indicating that in all cases weather patterns were consistent
across all regions considered. “Chicks per hen” and “broods per hen” were both negatively
correlated with annual variations in APRTEMP (analysis 5). There was a significant trend for
increasing April temperature over time (Table 7). Two of the variables (APRWARM and
HATCHTEMP) were positively associated with annual variation in “chicks per hen” and
“broods per hen” (analysis 5). APRWARM was also positively correlated with hen density
(analysis 5). Neither APRWARM nor HATCHTEMP showed a trend with time. HATCHRAIN,
which was not significantly correlated with any measure of breeding success but was with
the hen density (analysis 5), showed a significant increase over time. Accordingly, of the
weather variables, APRTEMP showed the most likely change over time which could readily
be predicted to explain the decline in capercaillie breeding success observed.
Effects of predators and weather on capercaillie
From analysis 8: In the 1995 survey, the number of scats found on the initial clear-up round
(x) was a good predictor of the cumulative number of scats found on the subsequent four
survey rounds (y) (n = 14, pine marten: y = 2.53 + 2.02x, r 2 = 0.76, P < 0.001, fox: y = - 1.14
+ 0.75x, r 2 = 0.91, P < 0.001). In 2009, the relationship between scats on the clear-up round
and on subsequent rounds was weaker; it was still significant for martens (n = 16, y = 8.34 +
1.39x, r 2 = 0.42, P = 0.004)), but not for fox (r 2 = 0.17, P = 0.06).
From analysis 8: Predator indices in each forest are given in Table 8. Signs of martens were
found in all but two of the forests (88%) in 2009, compared to only eight out of 14 (57%) in
1995, indicating a possible spread in range as well as a likely increase in abundance.
Predator indices from the 1995 survey of 14 forests and the 2009 survey of 16 forests are
compared in Table 9a. However when the 11 forests with predator surveys in both years are
considered; martens were found in eight forests in 1995 (73%), compared to nine in 2009
(82%). Statistical tests are based on data from the sub-set of forests that were surveyed in
both years (n = 11). There was a 3.7-fold increase in marten scats across forests (F 1,11 =
8.39, P = 0.016) and a 2.7-fold increase in fox scats (F 1,11 = 14.34, P = 0.004) (Table 9b).
These relationships remained the same irrespective of whether corrected or uncorrected
mammal indices were used. If corrected values were applied to the 2009 indices, but not to
the 1995 indices, then the level of change for martens increased (F 1,10 = 11.29, P = 0.008),
but that of fox decreased and became non-significant (F 1,10 = 1.18, P = 0.31). However,
applying the correction factor to the 2009 indices but not to the 1995 indices makes the
11

assumption that no correction was necessary in 1995, i.e. an assumption that no
misidentification occurred in 1995. Since using the scat correction factor or not made no
difference to the results, and one was not available for 1995, the scat correction factor was
not applied in any subsequent analyses. There were no changes in carrion crow or raptor
(chiefly buzzard Buteo buteo) sightings between surveys (F 1,11 = 0.01, P = 0.91 and F 1,11 =
0.85, P = 0.38 respectively). However with a mean of only one raptor sighted per 10 km of
transect, this index is relatively weak.
From analysis 9: In the 1995 survey, we found no significant correlation between the
predator indices (Table 10a). However in 2009, several of the indices were inter-correlated.
The fox index was positively correlated with that of crows, whilst marten was negatively
correlated with both crows and raptors (Table 10b). Raptors were positively correlated with
crows. There was a weak, but significant positive correlation between APRTEMP and
HATCHTEMP (Table 10c).
From analysis 10: After having controlled for region and forest type, “chicks per hen” varied
negatively with APRTEMP and with marten and crow indices (Table 11). That is, “chicks per
hen” was higher in years when April was cooler and in forests with lower marten and crow
indices.
From analysis 10: “Broods per hen” had a significant negative relationship with APRTEMP,
marten and crow and a significant positive relationship with APRWARM and HATCHTEMP
(Table 11). That is, “broods per hen” was higher in years when April was cooler, in forests
with lower marten and crow indices, in years when there was a larger temperature rise in
April and when temperatures were higher at or around chick hatching.
From analysis 10: “Brood size” had a significant negative relationship with HATCHRAIN and
crow (Table 11). That is, “brood size” was higher in years of low rainfall and in forests with
fewer crows.
From analysis 10: Indices of hen density varied between years (χ 2 11 = 63.84, P < 0.001) and
showed a decline over the period of the study (χ 2 1 = 46.25, P < 0.001) (Figure 3). Hen
density had a significant negative relationship with the fox index (Table 11). That is, hen
density was lower where there were more foxes. No weather variable significantly explained
variations in hen densities when predators were also considered.
12

DISCUSSION
Analysis of capercaillie brood count data collected annually between 1991 and 2009 showed
a temporal decline in two measures of breeding success (“chicks per hen” and “broods per
hen”) and in indices of hen densities. Breeding success did not differ between semi-natural
and two types of plantation forests, but was more than twice as low at the edge of the
current range in Perthshire as it was in the core region of Strathspey. High population
declines, as measured by changes in hen densities, were also found in Perthshire, but also
on other forests at the edge of the range in Argyll and Moray. Hen indices were relatively
stable in Strathspey, where it is considered from lek data that approximately 60% of Scottish
capercaillie now remains (K. Kortland pers. comm.). These findings are consistent with those
of Moss et al. (2000), who state that the on-going decline in capercaillie in Scotland has
been primarily due to lower breeding success. The average chicks per hen observed in this
study is 0.6 combining all forests and all years, is the lowest recorded by all of 16 previous
studies, which had a median of 1.6 chicks per hen (range 0.8 - 2.4), summarised by
Borchtchevski (1993).
Despite the data on reproductive success and density indices being widely based from 26
Scottish forests, they may not necessarily be strictly representative of the Scottish
capercaillie population. Forests selected for survey tended to be those where most
capercaillie were known to occur, or if the areas surveyed formed part of larger forests, then
parts favoured by breeding hens, usually more open parts of the forest, were surveyed.
However counts of hens during brood surveys were significantly correlated with counts of
males displaying at leks in the same forests in the same years, suggesting that trends from
brood counts were broadly representative of general regional and national trends reported
from winter transects counts in national surveys during 1992-94, 1998-99 (Catt et al., 1998;
Wilkinson et al., 2002) and including a more recent stabilisation of numbers in 2003-04
(Eaton et al., 2007). Continued declines at the edge of the range are, however, apparent not
only from our brood surveys, but also from counts of males at leks (RSPB, unpublished
data). This indicates that trends in population size can be predicted from brood surveys and
that reduced breeding success is the likely demographic mechanism for the declines
observed. Furthermore, as hen survival rates tend to be lower than those of cocks (Wegge
et al., 1987; Moss et al., 2000), reductions in numbers of hens encountered on brood counts
might be used as an early warning of impending declines in numbers of leking cocks.
A variety of mechanisms to account for reduced breeding success were reviewed by Moss
(1994 and thereafter). These included deterioration in forest habitat following changes in
silvicultural practices, reductions in the quality of chick rearing habitats following overgrazing
by red deer Cervus elaphus (Baines et al., 1994), increased predation (Baines et al., 2004;
Summers et al., 2004a) and climate change (Moss et al., 2001). In 1995, habitat data
including measures of forest structure and ground vegetation were collected from 14 forests
included in this analysis. At that time, higher capercaillie breeding success was found in
forests with more bilberry cover (Baines et al., 2004). No recent equivalent habitat data have
been collected across the same suite of forests to enable a comparison of habitat quality
through time. Both the study by Baines and co-workers (2004) and others have shown a
close positive association between capercaillie and bilberry (e.g. Storch, 1993; 1994;
Summers et al. 2004b) and have been instrumental in promoting management operations
such as selective thinning and deer reduction to favour bilberry in forests used by
capercaillie. Indeed, this management formed a key part of the recent EU LIFE Nature
Project: “Urgent Conservation Management for Scottish Capercaillie”. Accordingly, there has
been considerable expenditure aimed at improving conditions for breeding capercaillie in
many of our study forests within the last 10 years. Consequently, we surmise that it is likely
that forests are now managed better for capercaillie than they were in the 1990s and that
overall forest habitat quality is likely to be improving rather than the converse (K. Kortland,
pers. comm.).
13

It is well established that poor weather in the form of lower temperatures or higher rainfall at
or just after hatch time in June is the most important factor determining annual variations in
capercaillie breeding success (Slagsvold & Grasaas, 1979; Moss, 1985), and also that of the
related black grouse Tetrao tetrix (Summers et al., 2004). Although this study found a trend
for increased rainfall in late May or early June over time, we found that when the effect of the
predator indices were controlled for, low temperature at hatch time was a better predictor of
breeding success in capercaillie rather than rainfall. With the effect of predators controlled
for, the temperature around hatching was a predictor for broods per hen, but not chicks per
hen or brood size. The rainfall around hatching was a predictor of brood size, but not chicks
per hen or broods per hen. Moss et al. (2001) found that hens reared more chicks when
temperatures rose more in early April possibly stimulating plant growth and improving hen
nutrition and thus the viability of their chicks. Furthermore, over the period 1975 - 1999, there
had been a progressive cooling in mid-April temperatures relative to the rest of the month.
He concluded that a climatic change involving protracted spring warming could have been a
major cause of the capercaillie decline in Scotland. Repeating these types of analyses with
data from more forests over the period 1989 to 2009, we confirmed not only a positive effect
of temperature at hatch time, but also a positive effect of April warming. Both these weather
variables acted significantly upon the proportion of hens that reared broods (“broods per
hen”), but not on the number of chicks in those broods (brood size).
Unlike Moss et al. (2001) we did not find a trend through time for April warming. Instead, we
showed a significant trend for increased mean April temperature over time. Hatch in
Tetraonids and tits Parus spp. appears timed to coincide with the peak availability of
Lepidopteran larvae (Perrins, 1991; Baines et al., 1996), preferred tetraonid chick prey items
(Kastdalen & Wegge, 1985; Picozzi et al., 1999). Larvae are able to respond to earlier onset
of plant growth, but grouse and other birds are probably less flexible, although dates of onset
of breeding have advanced (McCleery & Perrins, 1998). Consequently, increasing April
temperature, as observed in this study, could bring about a mismatch in the timing of
capercaillie chick hatch and the availability of their larval prey (Baines et al. 1996; Wegge &
Rolstad, in press) thus creating more adverse conditions for chicks.
Marten and fox indices, both recognised as predators of capercaillie or their eggs and chicks
(Marcstrom et al., 1988; Kastdalen & Wegge, 1989; Wegge & Storaas, 1990; Kurki et al.,
1997; Summers et al., 2009) increased 3.7-fold and 2.7-fold respectively between 1995 and
2009. Indices of both marten and crow were significant negative explanatory variables,
together with weather, when accounting for variation in chicks per hen and broods per hen
(the crow index also explained some of the variation in brood size), whilst fox indices were
similarly negatively linked to changes in indices of hen density. This suggests a further or
revised hypothesis that may account for the decline of capercaillie in Scotland: that of
increased predation and climate change, additional to that of climate change alone. We
suggest that impacts of predation may not be mutually exclusive to those of climate change
alone, but instead they could work in parallel to them and exacerbate poor breeding success
and subsequent decline rates.
Marten and fox indices were solely derived from scat collections along forest tracks. Their
use in deriving either estimates of abundance or population size can be limited and open to
different interpretation (Webbon et al., 2004). Scat decay rates are likely to differ according
to diet, as well as seasonal and habitat differences in deposition (Davison et al., 2002) and
weather (Laing et al., 2003). Many of these potential biases were overcome by standardising
survey timing and duration to one season, one substrate type within forests and one broad
geographic area. Accordingly, we consider that by comparing 1995 and 2009 data, we
compared changes in abundance as opposed to changes in activity, but see Birks et al.,
(2004). However, the precise nature of the relationship between predator numbers and scat
density is unknown, is unlikely to be collinear and hence reported magnitudes of increase in
14

predator indices may not equate to the same levels in abundance. An increase in the marten
index across our range of forests was perhaps predictable from a similar magnitude of
increase already reported from one of the study forests (Forest A) (Summers et al., 2004;
Summers et al., 2009). Here, where crows and foxes were controlled to benefit breeding
capercaillie, a study of capercaillie nest outcomes showed that of 20 nests, pine martens
predated 33 - 57%, depending on the interpretation of the data (Summers et al., 2009). Their
study indicates that martens can be significant predators of capercaillie clutches, but did not
consider predation of chicks. As such, these data are similar to those from northern England,
where stoats Mustela erminea show a similar impact on the survival of black grouse clutches
and, together with weather, limit breeding success and attainment of BAP targets (Baines et
al., 2007). Our study confirms that increases in marten indices have occurred in other forests
over the same time period and that martens, together with crows and weather, are both
linked to lower breeding success across the 19 forests where predator indices and
capercaillie breeding data were collected.
This increase in marten index probably reflects increases in overall abundance and possibly
re-colonisation of parts of their former range in the Scottish Highlands following their legal
protection. Perhaps less predictable was the doubling of the fox index relative to 1995,
particularly when many of the sample forests had participated in the Capercaillie LIFE
Project and had received money to improve their levels of fox control. The increase in the fox
index is no longer significant if correction indices are applied in 2009, but not in 1995. This
assumes all scats were correctly identified in 1995. This is unlikely, but if so, would make
any significant relationship of fox with capercaillie invalid. Despite both marten and fox
indices increasing between surveys, their indices were not correlated. This does not readily
fit with current understanding of intra-guild predator relationships, where typically there is a
negative relationship between the abundance of larger predators such as the fox and that of
mesopredators such as martens (Lindstrom et al., 1995; Smedshaug et al., 1999) and stoats
(Warren & Baines, 2004). This is not always the case, however, and Kurki et al. (1998)
found no such negative relationship whilst Summers et al. (2004) showed an increase in the
marten index whilst fox cubs were being controlled, but adult fox numbers were maintained.
The collective evidence base suggests that increasing fox and marten abundance in Scottish
forests, together with the continued presence of crows and changes in weather, is
significantly linked with reductions in capercaillie breeding success, subsequent population
size and possibly breeding range. Numbers and distribution of several predator species in
Scotland, including both marten and fox, but also crows and some raptors, have increased
markedly in Scotland over the last 50 years (Hewson, 1984; Gibbons et al., 1994; Stone et
al., 1997). These increases corresponded with a decrease in the number of gamekeepers
employed in Scotland (Hudson 1992). Hitherto, predator numbers had been limited, legally
or illegally, by gamekeepers, whose objective has been to conserve gamebirds, particularly
red grouse Lagopus lagopus scoticus, but also capercaillie, for sport shooting (Redpath &
Thirgood, 1997; Tapper, 1999; Whitfield et al., 2004). Recent declines in red grouse and
driven grouse shooting (Barnes, 1987; McGilvray, 1995) are often associated with reductions
in gamekeeper numbers and predator management. This in turn has probably improved the
conservation status of the now protected pine marten, but has also resulted in increases in
foxes and possibly crows. Reaching the UKBAP Capercaillie Species Action Plan population
size and range targets may ultimately depend upon improving breeding success through
continued reductions in predator abundance.
It is of interest to note that Moss et al. (2001), in the absence of data on changes in predator
indices, concluded that climate change was sufficient to account for the fall in breeding
success at Forest L which, at the time, was effectively keepered. Whilst most probably
correct at the time, our data have shown a three-fold increase in the fox index at Forest L,
the arrival of pine martens and there has been a reduction in gamekeepers from three to
one. This illustrates the rapidity of some of the on-going changes in predator communities
15

and their management in Scottish forests. Whilst climate control is outside of our immediate
direct influence, it may be possible that the decline in capercaillie can be halted and even
reversed by continued improvements in habitat management and by restoration of predator
control in remaining capercaillie strongholds. The link between a rare predator and a rare
prey species such as pine marten and capercaillie is likely to be a contentious issue. Better
understanding of factors influencing marten numbers will be called for, including habitat
requirements, prey availability and their relationship with fox numbers. Aspects of this will be
considered in a forthcoming study by the RSPB at their Abernethy Forest Reserve (J. Wilson
pers. comm.). However, it is also likely that there will be calls for culls of martens to protect
wildlife and the evidence to support such an option needs to be robust and carefully
considered. The data presented here are statistical associations from which we hypothesise
that martens may be impacting upon capercaillie. Carefully designed predator removal
experiments in select forests or other management possibilities involving aversion or
diversionary feeding, may need to be instigated for compelling evidence of cause and effect.
16

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19

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20

TABLES
Table 1. Location and characteristics of the 26 forest areas surveyed for capercaillie
breeding success over the period 1989-2009, and for predator indices in either 1995 and / or
2009. Forest type: 1, open canopy as in native pinewoods; 2, mature Scots pine plantation,
canopy sufficiently open for some dwarf shrubs; 3, mixed species plantation with closed
canopy, often with some clear-felled areas and restocked ground.
___________________________________________________________________
Capercaillie
Forest Region Forest type Predator survey count years
A Strathspey 1 1995, 2009 1989-2009
B Perthshire 3 2009 2001-2009
C Strathspey 1 1995 1991-93, 2005-09
D Perthshire 3 - 2001-2009
E Aberdeenshire 1 1995 1991-94, 98-2001
F Easter Ross 2 - 2003-2006
G Strathspey 2 1995, 2009 1991-2009
H Moray 3 1995, 2009 1991-93, 2001-09
I Perthshire 2 1995, 2009 1991-2009
J Perthshire 2 1995, 2009 1991-2001, 2009
K Perthshire 3 1995, 2009 1991-2000, 2009
L Aberdeenshire 1 1995, 2009 1989-2009
M Aberdeenshire 2 - 2001-2009
N Strathspey 2 2009 2002-2009
O Strathspey 2 - 1993-98, 2002-05
P Aberdeenshire 2 - 2001-2009
Q Strathspey 3 1995, 2009 1992-2000,2005-9
R Perthshire 3 1995 1991-1993
S Strathspey 1 1995, 2009 1992-99, 2001-09
T Moray 2 - 2002-2006
U Aberdeenshire 1 1995, 2009 1991-2007, 2009
V Argyll 1 - 2000-2009
W Easter Ross 3 2009 2000-2009
X Easter Ross 3 2009 2000-2009
Y Aberdeenshire 2 2009 2003-2009
Z Strathspey 1 1995, 2009 1992-2009
21

Table 2. Locations of nearest weather stations to each of the forests surveyed for
capercaillie
__________________________________________________________________
Region Weather Station Forests Years covered
__________________________________________________________________
Aberdeenshire Balmoral E 1991-1994
Braemar E 2001
P 2001-2009
Aboyne L 1991-2009
M 2001-2009
U 1991-2009
Y 2003-2009
Moray Nairn All in region 1991-2009
Perthshire Ardtalnaig B 2001-2009
D 2001-2009
I 1991-2009
Strathallan J 1991-2009
K 1991-2009
R 1991-1993
Easter Ross Nairn All in region 2000-2009
Strathspey Aviemore All in region 1991-2009
_________________________________________________________________
22

Table 3. Mean values of capercaillie breeding success and indices of hen density (+ SE )
across three forest types between 1991 and 2009 predicted from GLMM output adjusted for
region, forest type and year. Wald statistic (χ 2 ) tests the null hypothesis of no difference
between forest types.
_______________________________________________________________________
Forests Chicks per hen Broods size Broods per hen Hens km -2
_______________________________________________________________________
Native pinewoods 8 0.44 + 0.10 2.10 + 0.17 0.22 + 0.06 0.92 + 0.49
Scots pine plantations 10 0.64 + 0.14 2.02 + 0.16 0.36 + 0.08 1.34 + 0.70
Mixed plantations 8 0.69 + 0.16 2.10 + 0.19 0.39 + 0.09 1.76 + 0.86
χ 2 2 =2.48 χ 2 2 = 0.74 χ 2 2 = 3.09 χ 2 2 = 2.18
P = 0.33 P = 0.50 P = 0.25 P = 0.37
_______________________________________________________________________
23

Table 4. Mean values of capercaillie breeding success and indices of hen density (+ SE)
across six Scottish regions between 1991 and 2009 predicted from GLMM output adjusted
for region, forest type and year. Wald statistic (χ 2 ) tests the null hypothesis of no difference
between regions.
______________________________________________________________________
Forests Chicks per hen Broods size Broods per hen Hens km -2
______________________________________________________________________
Strathspey 8 0.86 + 0.14 2.34 + 0.13 0.41 + 0.06 1.92 + 0.80
Aberdeenshire 6 0.69 + 0.18 1.88 + 0.17 0.42 + 0.09 1.62 + 0.80
Perthshire 6 0.37 + 0.09 1.87 + 0.18 0.21 + 0.06 0.94 + 0.48
Moray 2 0.55 + 0.21 2.59 + 0.42 0.22 + 0.11 0.77 + 0.88
Easter Ross 3 0.47 + 0.18 1.94 + 0.28 0.26 + 0.12 0.66 + 0.52
Argyll 1 0.67 + 0.36 1.92 + 0.44 0.39 + 0.20 3.19 + 3.56
χ 2 5 = 7.17 χ 2 5 = 2.01 χ 2 5 = 3.91 χ 2 5 = 3.03
P = 0.27 P = 0.10 P = 0.58 P = 0.70
_____________________________________________________________________
24

Table 5. Mean percentage decline rates of capercaillie (hens km -2 annum -1 ) (+ SE) for each
of six Scottish regions between 1991 and 2009. Wald statistic (χ 2 ) tests the null hypothesis
of no difference between regions.
_______________________________________________________________________
Region
Percentage annual decline + SE
_______________________________________________________________________
Strathspey -1.3 + 0.9
Aberdeenshire -13.0 + 1.3
Perthshire -16.4 + 2.0
Moray -16.2 + 2.8
Easter Ross -8.8 + 4.3
Argyll -23.0 + 6.5
χ 2 5 = 106.11
P < 0.001
_____________________________________________________________________
25

Table 6. Relationship between capercaillie breeding success, an index of hen density (hens
km -2 ) and weather variables 1989 - 2009. Parameter estimates are mean slopes (SE) from
GLMMs adjusted for region and forest type.
_______________________________________________________________
Dependent
Parameter
variable Fixed term estimate χ 2 1 P___
Chicks/hen APRTEMP -0.172 (0.061) 7.85 0.006
APRWARM 0.095 (0.030) 9.89 0.002
HATCHTEMP 0.141 (0.056) 6.24 0.013
HATCHRAIN 0.044 (0.043) 1.03 0.31
Brood size APRTEMP -0.044 (0.029) 2.42 0.12
APRWARM 0.017 (0.014) 1.52 0.22
HATCHTEMP 0.011 (0.023) 0.22 0.64
HATCHRAIN -0.009 (0.018) 0.23 0.64
Broods/hen APRTEMP -0.195 (0.073) 7.16 0.008
APRWARM 0.105 (0.036) 8.36 0.004
HATCHTEMP 0.204 (0.071) 8.20 0.005
HATCHRAIN 0.095 (0.053) 3.31 0.07
Hen density APRTEMP -0.060 (0.042) 1.96 0.16
APRWARM 0.045 (0.021) 4.55 0.034
HATCHTEMP -0.030 (0.044) 0.47 0.50
HATCHRAIN -0.074 (0.028) 7.08 0.008
________________________________________________________________________
26

Table 7. Trends in capercaillie breeding success and indices of hen density (mean annual %
change) and weather measurements (1991-2009). Parameter estimates for capercaillie
breeding success and weather variables are mean slopes (+ SE) from GLMMs involving
region and forest type, and GLMs including weather station and year respectively.
___________________________________________________________
Slope + SE χ 2 1 P
Capercaillie_____________________________________________
Chicks per hen -3.4 % + 1.2 9.34 0.002
Broods per hen -4.0 % + 1.5 7.63 0.006
Mean brood size -1.1 % + 0.7 4.00 0.047
Density (hens km -2 ) -6.5 % + 0.7 63.67

Table 8. Indices of mammalian and avian predators from forest transects walked between
April and June in 1995 (from Baines et al. 2004) and 2009 in the forest areas where
capercaillie brood counts were conducted. Values for fox and pine marten are mean scats
10 km -1 day -1 10 2 and exclude the clearance round. Those for crows and raptors are mean
observations 10 km -1 visit -1 .
_________________________________________________________________________
Fox Marten Crow Raptor
Forest 1995 2009 1995 2009 1995 2009 1995 2009
A 9.3 57.5 7.0 221.9 0.9 1.5 0 0
B - 54.6 - 31.4 - 0 - 2.0
C 0 - 0 - 2.5 - 2.8 -
E 5.1 - 0 - - - -
G 13.4 36.2 4.5 158.3 1.2 1.7 0.7 0
H 8.7 45.5 36.0 39.0 1.4 4.4 0.2 1.3
I 9.0 25.1 47.8 40.2 0.9 1.2 0.4 1.2
J 1.8 43.9 3.5 0 0.8 5.5 2.1 3.9
K 60.5 86.1 1.8 4.8 5.4 4.2 1.1 1.9
L 4.9 14.9 0 93.3 0.7 0 0.2 0
N - 29.2 - 53.0 - 0 - 0.9
Q 11.0 5.4 26.5 5.4 0.3 1.4 0 0
R 125.1 - 0 - 5.7 - 0.9 -
S 11.1 17.2 25.9 31.9 0 0 0.5 0
U 19.8 141.1 0 0 1.3 11.3 0.2 2.4
W - 105.7 - 120.8 - 2.5 - 0
X - 12.1 - 46.9 - 2.0 - 0.5
Y - 6.4 - 42.8 - 2.6 - 0.6
Z 8.1 5.7 0 30.3 1.3 0 0.2 0
_________________________________________________________________________
28

Table 9a. Predator indices, (means + 1SE) from 14 forests used by breeding capercaillie in
1995 and 16 forests in 2009. Mammal indices are scats 10 km -1 day -1 x 100 and exclude
scats from the clear-up round. Bird indices are sightings 10 km -1 visit -1 .
_________________________________________________________________________
1995 (n = 14 forests) 2009 (n = 16 forests)
Predator Forests with sign Abundance Forests with sign Abundance
Index
Index
_________________________________________________________________________
Fox 13 (93%) 21.8 + 9.0 16 (100%) 42.9 + 9.7
Pine marten 8 (57%) 12.0 + 4.6 14 (88%) 57.8 + 15.4
Carrion crow 13 (93%) 3.1 + 0.8 11 (69%) 2.4 + 0.7
Raptors 12 (86%) 1.7 + 0.6 9 (56%) 0.9 + 0.3
_________________________________________________________________________
Table 9b. Predator indices, (means + 1 SE) from 11 forests used by breeding capercaillie
surveyed in both 1995 and 2009. Mammal indices are scats 10 km -1 day -1 x 100 and exclude
scats from the clear-up round. Bird indices are sightings 10 km -1 visit -1 .
_________________________________________________________________________
1995 2009
Predator Forests with sign Abundance Forests with sign Abundance
Index
Index
_________________________________________________________________________
Fox 11 (100%) 15.9 + 5.2 11 (100%) 43.5 + 12.2
Pine marten 8 (73%) 15.3 + 5.5 9 (82%) 57.3 + 21.8
Carrion crow 10 (91%) 2.7 + 0.7 8 (73%) 2.9 + 1.0
Raptors 9 (82%) 1.5 + 0.7 5 (45%) 1.0 + 0.4
_________________________________________________________________________
29

Table 10. Correlations between a) predator indices (log e (index + 1) in 1995 (14 forests), b)
predator indices in 2009 (16 forests) and c) weather variables (n = 21). Values are Pearson
correlation coefficients. Significant relationships are given in bold, * P < 0.05, ** P < 0.01.
_____________________________________________________________________
a) 1995
Fox -
Marten 0.15 -
Fox Marten Crow Raptor__________
Crow 0.34 -0.35 -
Raptor -0.34 -0.17 0.30 -
_______________________________________________________________
b) 2009
Fox Marten Crow Raptor__________
Fox -
Marten -0.28 -
Crow 0.50 * -0.62 * -
Raptor 0.47 -0.74 * 0.52 * -
_______________________________________________________________
c) Weather variables
HATCHRAIN HATCHTEMP APRTEMP
APRWARM
HATCHRAIN -
HATCHTEMP -0.41 -
APRTEMP 0.03 0.44 * -
APRWARM 0.09 -0.26 -0.17 -
_______________________________________________________________
30

Table 11. Relationship between capercaillie breeding success and indices of hen density
with weather variables and predator indices for the years 1991 – 95 and 2005 - 2009.
Parameter estimates are mean slopes (SE) from GLMMs including region and forest type.
______________________________________________________________________
Dependent
Parameter
variable Fixed term estimate χ 2 1 Prob_
Chicks/hen APRTEMP -0.186 (0.083) 4.96 0.028
APRWARM 0.119 (0.069) 2.94 0.09
HATCHTEMP 0.133 (0.076) 3.07 0.09
Marten -0.006 (0.002) 8.83 0.004
Crow -0.178 (0.057) 9.61 0.005
Brood size HATCHRAIN -0.057 (0.021) 7.36 0.008
Crow -0.059 (0.022) 7.49 0.008
Raptor 0.047 (0.027) 2.97 0.09
Broods/hen APRTEMP -0.234 (0.101) 5.32 0.023
APRWARM 0.178 (0.085) 4.38 0.039
HATCHTEMP 0.209 (0.100) 4.38 0.040
Marten -0.006 (0.002) 6.11 0.017
Crow -0.177 (0.079) 5.05 0.036
Hens km -2 Crow -0.178 (0.108) 2.74 0.11
Fox -0.024 (0.009) 7.20 0.014
______________________________________________________________________
31

FIGURES
Figure 1. Locations of the 26 forests in which capercaillie were surveyed within the years
1991-2009.
32

Figure 2. Annual capercaillie breeding success (chicks per hen, broods per hen and brood
size) measured from 11 - 20 forests per year between 1991 and 2009. Values are means +
SE back transformed estimates from a GLMM including region, forest and year.
2.0
Mean chicks per hen + SE
1.5
1.0
0.5
0.0
0.8
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
YEAR
2002
2003
2004
2005
2006
2007
2008
2009
3
Mean broods per hen + SE
0.6
0.4
0.2
0.0
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
YEAR
2002
2003
2004
2005
2006
2007
2008
2009
Mean brood size + SE
2
1
0
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
YEAR
2002
2003
2004
2005
2006
2007
2008
2009
33

Figure 3. Change in mean annual capercaillie density (hens km -2 ) between 1991 and 2009.
Indices of density are estimated from Poisson regressions.
3.5
2.8
Hen density
2.1
1.4
0.7
0.0
1991 1997 2003 2009
YEAR
34

Figure 4. Trends in the total numbers of hen capercaillie seen on annual brood counts and
the number of cocks observed attending spring leks in the same 16 forests between 2002
and 2009.
120
TOTAL BIRDS
80
40
0
2001 2003 2005 2007 2009
YEAR
COCKS
HENS
35

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