Spatial Variation in the Breeding Success of the Common Buzzard

Buteo buteo in relation to Habitat Type and Diet

George Swan

September 2011

A Thesis submitted in partial fulfilment of the requirements for the degree of Master of Science

and the Diploma of Imperial College London

1

Contents

List of Acronyms ........................................................................................................................................ 3

1. Introduction............................................................................................................................................. 1

1.1. Buzzards and gamebirds: defining the conflict ........................................................................... 2

1.2. Aims and Objectives ....................................................................................................................... 4

2. Background .............................................................................................................................................. 5

2.1. The Common Buzzard Buteo buteo ................................................................................................ 5

2.1.1. Taxonomy & morphology ...................................................................................................... 5

2.1.2. Distribution, population trends and current conservation status ..................................... 5

2.1.3. Reasons for reestablishment ................................................................................................... 5

2.1.4. Reproduction and survival ...................................................................................................... 6

2.1.5. Habitat and diet ........................................................................................................................ 6

2.2. Spatial variation in buzzard densities ............................................................................................ 6

2.3. The role of mammals in buzzards diet ......................................................................................... 7

2.4. The role of habitat on buzzard densities and breeding success ............................................... 8

2.4.1. Forestry ...................................................................................................................................... 8

2.4.2. Farmland.................................................................................................................................... 8

2.5 Assessing raptor diet: pellet analysis, prey remains and direct observations ........................... 9

3. Study site ................................................................................................................................................ 10

3.1. Forestry and Upland pasture ....................................................................................................... 10

3.2. Lowland farmland: Doune ........................................................................................................... 10

4. Methods ................................................................................................................................................. 12

4.1. Field data collection ...................................................................................................................... 12

4.1.1. Habitat classification .............................................................................................................. 12

4.2.1. Prey Sampling ......................................................................................................................... 12

4.2.2. Mapping buzzard nests.......................................................................................................... 14

4.1.3. Mapping core territories ........................................................................................................ 15

4.1.4. Assessing Buzzard diet .......................................................................................................... 16

4.2. Data Analysis ................................................................................................................................. 18

4.2.1. Preparatory analysis ............................................................................................................... 18

4.2.2. Variable compilation .............................................................................................................. 19

4.2.3. Exploratory univariate analysis ............................................................................................ 19

4.2.4. Investigating response type of buzzard to vole density .................................................... 20

2

4.2.5. Investigating the influence of prey size in breeding success ............................................ 20

4.2.6. Principal component analysis ............................................................................................... 20

4.2.7. Modelling ................................................................................................................................. 20

5. Results .................................................................................................................................................... 22

5.1. Exploratory analysis ...................................................................................................................... 22

5.1.1. Variation in buzzard density on a spatial scale .................................................................. 22

5.1.2. Variation in buzzard productivity on a spatial scale.......................................................... 22

5.1.3. Prey densities .......................................................................................................................... 23

5.2. Results of buzzard dietary studies ............................................................................................... 25

5.3. Investigating the type of functional response ........................................................................... 26

5.4. The influence of prey size in breeding success ......................................................................... 26

5.5. Principle component analysis ...................................................................................................... 27

5.5.1. PCA analysis: fledgling number ........................................................................................... 29

5.5.2. PCA analysis: Nearest neighbour distance ......................................................................... 30

5.6. Direct observational data ............................................................................................................. 31

6. Discussion .............................................................................................................................................. 33

6.1. Overview ........................................................................................................................................ 33

6.2. The use of principle component analysis and multivarient modelling .................................. 34

6.2.1. Breeding success ..................................................................................................................... 35

6.2.2. Density ..................................................................................................................................... 35

6.3. Cameras on nests ........................................................................................................................... 35

6.4 Study limitations and areas for future research .......................................................................... 37

6.4.1. Limitations of the methodology .......................................................................................... 37

6.4.2. The effect of weather during the hatching period ............................................................ 37

6.4.3. Limitations of the analysis .................................................................................................... 38

6.5. Resolving the conflict ................................................................................................................... 39

7. References: ............................................................................................................................................. 42

8. Appendices ............................................................................................................................................ 55

Appendix 1.The regression formula for VSI to density conversion (from Lambin et al., 2000)

................................................................................................................................................................. 55

Appendix 2. Full prey species list from all dietry analysis methods .............................................. 56

Appendix 3:Examples of prey identification from remote cameras ............................................. 57

3

Appendix 4: All detenction functions for small passerines from DISTANCE analysis ............ 58

List of Tables

Table 1: A literature review of buzzard breeding success and nearest neighbour distance ............. 7

Table 2: Categories used to classify vegetation cover within the study areas................................... 12

Table 3: The estimates of small passerine abundance from DISTANCE analysis .................. Error!

Bookmark not defined.

Table 4: Linear regressions to calculate vole density as detailed in Lambin et al., 2000. .......... Error!

Bookmark not defined.

Table 5: List of variables with descriptions ........................................................................................... 19

Table 6: Number of observations and percentage total diet of different prey items as estimated

from prey remains collection and pellet analysis .................................................................................. 26

Table 7: The results of the Principle Componet Analysis showing the loadings of habitat and

dietary variables ......................................................................................................................................... 28

Table 8: Competing generalisted liner models using the principle components to explain

fledgling number and nearest neighbour distance ............................................................................... 29

List of Figures

Figure 1: Map of study site showing all active nests. ........................................................................... 15

Figure 2: Example of buffer mapping from a forestry nest. .............................................................. 16

Figure 3: Photographs of prey remains and pellet analysis. ................................................................ 17

Figure 4: A boxplot showing the nearest neighbour distance against the habitat categories.. ...... 22

Figure 5: A boxplot showing the number of buzzard chicks against habitat type .......................... 23

Figure 6: Scatter graphs showing voles per km² against; 1) the nearest neighbour distance (left)

and; 2) the number of chicks raised to fledging (right).. ....................................................................... 25

Figure 7: Bargraphs showing the relationship between the dominant mammal species found

during the pellet analysis (A) and the prey analysis (B) against the mean number of fledglings ... 27

Figure 8: The relationship between PC1 ‘vole reliance’ and the number of chicks fledging. ........ 30

Figure 9: The relationship between ‘upland generalist’ and the nearest neighbour distance ......... 31

Figure 10: Bargraphs showing the proportions of mammals, birds and amphibians observed in all

dietary methods in forestry (top left), upland pasture (top right), lowland pasture (bottom left) and all

habitats (bottom right). ................................................................................................................................. 32

List of Acronyms VSI Vole Sign Index NND Nearest Neighbor Distance PCA Principle Component Analysis PC Principle Component

AIC Akaike Information Criterion GLM Generalized Linear Model SDRH Site Dependant Regulation Hypothesis RSPB Royal Society for the Protection of Birds.

Abstract

Widespread population increases of the Common Buzzard Buteo buteo has brought the species

back into conflict with gamebird hunters. As the first step in addressing the conflict, a basic

understanding of the factors that regulate buzzard breeding ecology is required. While variations

in prey abundance have been associated with fluctuations in raptor breeding correlates, for

generalist raptors like the buzzard the mechanisms of food limitation are poorly understood. To

address this, the productivity and breeding density of 63 buzzard pairs was studied in relation to

prey abundance, territory composition and diet across a range of different habitats in central

Scotland. Concurrently, buzzard diet was determined using the two competing methods of prey

remains and pellet analysis, while prey abundance was estimated from indices of vole sign and

distance sampling for small passerines. Nest cameras allowed for a preliminary assessment of the

prey and pellet dietary analysis methods with results suggesting that amphibian prey has been

significantly underestimated in buzzard diet. Highly significant differences were observed in

buzzard breeding success over spatial scales with low productivity being related to a reliance on

vole prey. Highly significant differences were also observed in buzzard densities with the lowest

densities being related to upland pasture and percentages of amphibians and birds. The results

suggest that buzzards may be able to reach the highest densities and productivity in lowland

pasture with access to rabbit prey. Future research is now needed to needed to assess how

densities influence predation rates on gamebirds in both an upland and lowland context.

1

Acknowledgments

Firstly, a sincere thank you goes David Anderson and Katy Freeman from the Forestry

Commission Scotland for putting up with me over the nesting season and for showing me that

raptors are not just an interest but an obsession. My thanks are also extended to Keith Burgoyne

and Mark Rafferety for the many trees that were fearlessly (Keith) and not so fearlessly (Mark)

climbed and to Mike McDonnell and Duncan Orr-Ewing for the data and company that they

provided. Collecting data from such a large number of buzzard nests is not easy and was only

made possible by the brilliant mix of enthusiasm and dedication that you guys have toward these

birds. I really enjoyed my time in Scotland and now consider myself a true ‘friend of the

buzzard’.

In terms of analysis, it would almost be easier to write all the people who didn’t give me help at

some point, but special mentions go to Rebecca Spriggs for her knowledge of R and saint like

patience, Mike Hudson for his GIS genius and to Kyle, Cian, Jeremy and Anne for not just

answering my incessant questions but actually caring about whether they gave the right answer.

I would also like to thank my two supervisors Steve Redpath and Nils Bunnefeld for the

invaluable advice that they both provided through all aspects of this project.

Finally, I would like to thank my parents for supporting me, not just through this project but

through the many unemployed years that lie ahead...

2

1. Introduction

1.1. Buzzards and gamebirds: defining the conflict

The relationship between predators and their prey has long been of interest to ecologists and

wildlife managers. Through their very nature as hunters, predators produce some of the most

controversial and polarised debates in wildlife management (reviewed in Woodroffe et al., 2005).

While predatory species enjoy an elevated position in the public conciseness, they are also vilified

by many as potential competitors for common, limited resources (Graham et al., 2004). Conflicts

between predators and man can be observed the world over with examples such as the grey wolf

Canis lupus depredation on cattle in the northwestern United States (Bangs et al., 2005), brown

bear Ursus arctos depredation on sheep in Norway (Sagør et al., 1997), jaguar Panthera onca

depredation on cattle in South America (Michalski et al., 2005) and red fox Vulpes vulpes predation

on gamebirds in Britain (Baker et al., 2006).

The attention of studies concerning raptors has often been parallel with issues related to species

management, particularly the human-predator conflict that arises when a raptor is perceived as

having the potential to reduce gamebird harvests (Thirgood et al., 2000b, Bro et al., 2006; Watson

et al., 2007; Park et al., 2008). Conflicts of this nature are particularly acute during reestablishment

or density increases (O’Toole et al., 2002; Henderson et al., 2004). Within the UK, concerns over

the impact of raptors have often been raised by parties representing the interests of gamebird

shooting (Arroyo & Viñuela, 2001; Arroyo, 2002).

Field sports play an important socioeconomic role within rural communities (Public & Corporate

Economic Consultants 2006) as well benefiting biodiversity at both a landscape (Oldfield et al.,

2003) and species level (Stoate & Szczur, 2001). However, persecution by gamekeepers (to

enhance the densities of gamebirds) has severely reduced the abundance and distribution of

many raptor species within the UK (Newton, 1979; Elliot & Avery, 1991; Brown & Stillman,

1998; Whitfield et al., 2003; Lovegrove, 2007), despite all raptor species being legally protected

under the Wildlife and Countryside Act, 1981.

The debate between the protection of economic interests (in the form of gamebirds) and the

protection of birds of prey has intensified over the last decade with repeated calls for landowners

to be granted licences to control raptor species to protect gamebirds (Green, 2011). The issue

has even attracted the attention of the European Union in the form of the ‘European Concerted

Action REGHAB’ (Reconciling Gamebird Hunting and Biodiversity) (Valkama et al., 2005).

Population increases coupled the opportunistic predatory nature of the Common Buzzard Buteo

buteo (hereafter buzzard) has brought the species back into conflict with shooting stakeholders.

3

Indeed, in a recent members poll by the Scottish Gamekeepers Association 76% of respondents

identified buzzards as having a negative effect on gamebirds (Green, 2011).This conflict arises

because buzzards are seen as either having a direct impact on the numbers of birds available to

shoot, or as causing in-direct mortality and financial loss due to disturbance (Harradine et al.,

1997; UK Raptor Working Group, 2000; Kenward et al., 2001). As a result, although the

reestablishment of the buzzard can be considered a conservation success story the species

continues to bear the brunt of the relatively high level of illegal raptor persecution occurring

within the UK (Mineau et al., 1999). In Scotland, the number of reported instances of buzzards

being deliberately poisoned and shot has been increasing over the last 20 years, (Anon. 2009;

Taylor et al., 2009).

As a generalist predator, buzzards are able to respond both functionally and numerically to

changes in prey density. The generalist predator hypothesis (GPH) states that environments with

a large selection of alternative prey species are able to support higher densities of generalist

predators as they are able to alternate between prey species according to accessibility (Hanski et

al., 1991; Bjǿrnstad et al., 1995). As gamebird species have both natural (Hudson, 1992) and

artificial (Kenward et al., 2001) fluctuations in abundance, generalist predators have the potential

of having a significant economic impact (Redpath & Thirgood, 1999; Thirgodd et al. 2000a,

2000b).

While previous studies have identified several gamebird species as being present in the diet of

buzzards (Swann & Etheridge, 1995; Kenward et al., 2001), there is a need to consider how

buzzard densities and diet are influenced by the wider landscape. Buzzards are generalist

predators living in species rich communities and yet there may be a tendency, as with other

predators, of describing their impact on gamebirds in terms of a simple single-predator single-

prey interaction (Graham et al., 2004). This could lead to illegal persecution through the

assumption that a reduction in predator numbers will lead to an increase in prey available to

shoot (Bro et al., 2006). However, to answer the question whether high densities of buzzards

impact significantly on the amount of gamebirds available to shoot, it is crucial to consider the

ways in which buzzard predation rates (their functional response) and density (their numerical

response) vary in relation to both gamebird density and the density of alternative prey species

(Park et al., 2008). Buzzards are opportunistic predators, therefore it is expected that the

proportions of prey species in their diet will vary with abundance (Tornberg & Reif, 2007).

However, given that the artificial release of gamebirds begins around July (Kenward et al., 2001),

it seems unlikely that the nesting densities of buzzards is a response to variations in gamebird

abundance and more likely that it is as a result of alternative prey abundance (Graham et al.,

1995; Swann & Etheridge, 1995). The influence of alternative (non gamebird) prey regulating

4

raptor breeding ecology on shooting estates has already been observed in relation to hen harrier

Circus cyaneus on moorland managed for red grouse Lapogus lapogus (Redpath & Thirgood 1999;

Redpath et al., 2002).

1.2. Aims and Objectives

In order to address the impact of buzzards on gamebirds more information is needed as to the

ecological details that govern buzzard densities and breeding success. To put the buzzard range

expansion into context, the question of what constrains or indeed enhances buzzard densities

and reproductive success will be addressed. The overall aim of this project is to analyse the

spatial variation in breeding ecology in the context of: (1) habitat, (2) prey abundance and, (3)

buzzard diet.

Objective 1: Identify how prey abundance regulates the breeding ecology of buzzards.

Hypothesis a: That there is a significant relationship between the breeding

success of buzzards prey abundance estimates.

Hypothesis b: That there is a significant relationship between the density of

breeding buzzards and prey abundance.

Hypothesis c: That buzzard will display a type II response to increases in

abundance of common prey items.

Objective 2: Identify how dietary composition influences the breeding ecology of

buzzards.

Hypothesis d: Nests with access to larger prey items will raise more chicks to

fledging.

Objective 3: Undertake multivariate modelling using the results of a principle

component analysis to explore how the variables habitat and diet interact to influence

buzzard breeding ecology.

Objective 4: Investigate whether prey remains and pellet data is an accurate reflection of

buzzard diet.

5

2. Background

2.1. The Common Buzzard Buteo buteo

2.1.1. Taxonomy & morphology

The common buzzard Buteo buteo (Linnaeus, 1758) is one of 28 species of buzzard (also referred

to as ‘hawks’) recognised worldwide (Ferguson-Lees & Christie, 2001). There are currently

thought to be nine different subspecies of Buteo buteo divided into the western Buteo group and

the eastern vulpinus group, however, while morphometric analysis allows for a discrimination

between taxa, phylogenetic relationships are less clear (Kruckenhauser et al., 2004). Of the two

species of the genus Buteo to be found in the UK (the common buzzard and the rough-legged

buzzard buteo lagopus), it is only the common buzzard that is a permanent resident.

The common buzzard (hereafter buzzard) is a medium sized diurnal raptor with dark brown

plumage, a yellow cere and yellow feet. As adults they measure 51-57cm with a wingspan of 113-

128cm. Like many diurnal raptor species buzzards have reverse sexual dimorphism with the

female 5-10% taller and 27-30% heavier than the male (Snow & Perrins, 1998).

2.1.2. Distribution, population trends and current conservation status

The buzzard is the most widely distributed raptor throughout the Paleartic, one of the most

abundant of all European raptor species (Bijlsma, 1997) and the UK’s most common bird of

prey (Clements, 2002).

Until the beginning of the 19th century the buzzard was found throughout the UK but by the

early stages of the 20th century it had become restricted to only western parts of Britain (Moore,

1957). However, since the 1950’s the population has been increasing steadily; from 8-10,000 in

1970 (Tubbs, 1974), to 15-16,000 pairs in 1983 (Taylor et al., 1988) to the most recent estimate of

44-61,000 in 2000 (Clements, 2002). At present, this number is likely to be substantially higher as

the results of the British Bird Survey (Risely et. al., 2010) provide evidence of a significant and

continuing increase (63% from 1995 to 2009) across the UK. As a consequence of its large

range, increasing population trend and large population size the buzzard is evaluated as least

concern under the IUCN red list criteria (IUCN, 2010).

2.1.3. Reasons for reestablishment

The reestablishment of the buzzard across the UK has been accredited to two factors; firstly, an

increase in productivity; attributed to both the recovery of rabbit populations after a crash in the

1950’s (Moore, 1957; Sim et al., 2000) and the banning of the organochlorine pesticide

dichlorodiphenyltrichloroethane (DDT) which caused a thinning in the shells of raptor eggs

(Newton, 1979)and; secondly, higher survival rates due to a reduction in illegal persecution

gamekeepers (Taylor et al., 1988; Sim et al., 2000).

6

The fact that every county within the UK currently has breeding buzzards (Clements 2002) can

be viewed as an example of how, over the course of a relatively short time period a raptor

species can re-establish itself over a large geographical area. Studies have gone as far as to use the

success of the buzzard to provide an insight into the ability of similar re-bounding avian species

(red kites and ravens) to recolonize lost territory (Smith, 2007).

2.1.4. Reproduction and survival

Females lay a clutch of 2-4 eggs from early April through to early May over 6-9 days. After an

incubation period of 36-38 days the eggs hatch asynchronously, at intervals of 48 hours and

young spend 47-57 days in the nest before fledging (Tubbs, 1974). Immature birds will then

disperse during their first autumn or the following spring (Walls and Kenward, 1995) and finally

settle to breed in their third or fourth year. Unlike buzzards on the continent, most pairs in the

UK remain in their home range throughout the year (Walls & Kenward, 1998), although

continuous occupancy may be contingent on prey availability and mild winters (Halley, 1993).

2.1.5. Habitat and diet

Buzzards are catholic in their choice of habitat. This can be attributed to the versatile range of

hunting techniques that can be adapted to suit different habitats; (1) perching and scanning

surrounding habitat then planning onto prey; (2) soaring over open terrain before dropping onto

prey and (Hume, 2007); and (3) walking on the ground (Tubbs, 1974).As generalist predators

buzzards consume a diverse range of prey items, principally mammals (mainly voles and rabbits)

but also birds, reptiles, amphibians, insects, earthworms and carrion (Dare, 1961; Newton et al.,

1982; Graham et al., 1995; Swann & Etheridge, 1995; Sim, 2003; Selàs et al., 2007).

2.2. Spatial variation in buzzard densities

When attempting to understand spatial variation in the density of a species, it would seem

appropriate to focus on the resources that would limit the carrying capacity of an area, for

raptors, this is most often food resources (Newton, 1979). The spatial variation in food resources

for birds of prey can be seen as a function of prey abundance and accessibility, with prey

accessibility ultimately being contingent on first detection, then capture probabilities. This is

particularly apparent during the breeding period as variations in prey may determine territory

quality by influencing the ability of the females to produce eggs, the clutch size and the survival

of the brood (Lack, 1954). In this way, natural variations in the abundance and accessibility of

prey across habitat types can be expected to result in marked differences in raptor densities. Due

to its extensive distribution (Clements, 2002), this is particularly true for the buzzard and

significant differences in breeding density and fledgling success being observed across their range

(Table 1).

7

Table 1: Buzzard breeding success and nearest neighbour distance from studies in Scotland, England and Wales

Author Fledgling success

NND (km)

Location Country

Holling, 2003 2.4 - Lothian and Borders Scotland

Jardine, 2003 1.8 - Colonsay Scotland

Sim, 2003 1.7 0.8 Welsh Marches Wales

Sim et al., 2001 1.2 - West Midlands England

Austin & Houston, 1997 1.8 - Argyll Scotland

Swann & Etheridge, 1995 2.5 1.1 Moray Scotland

Swann & Etheridge, 1995 1.5 1.7 Glen Urquhart Scotland

Graham et al., 1995 - 1.9 Langholm Scotland

Dare, 1995 1.4 2.0 Snowdonia Wales

Dare, 1995 1.4 1.5 Hiraethog Wales

Newton et al., 1982 0.6 0.9 Cambrian Mountains Wales

Picozzi & Weir, 1974 1.6 - Strathspey Scotland

High abundances of buzzards have been associated with the abundance of larger prey items,

Newton (1979) for example cites an example of Skoma Island (Wales) where seven pairs nested

within an area of 3.1km feeding both rabbits and seabirds. While buzzards are territorial, it

would appear that, in the absence of human disturbance (Elliot & Avery, 1991), their density is

principally limited by prey availability (Graham et al., 1995; Reif et al., 2004). So, while it would

still be correct to describe buzzard as territorial, it is most likely that buzzards adjust their

territorial behaviour to regulate their density to the resources available (Newton 1979).

2.3. The role of mammals in buzzards diet

Previous studies into the diet of buzzards in the United Kingdom have shown that mammals

make up the majority of buzzards diet (Dare, 1961; Sim, 2003; Jardine, 2003). While most studies

have indicated that rabbits are the most significant prey species (Kenward et al., 2001; Sim 2003;

Jardine 2003), small mammals, specifically field voles, have also been shown to be a significant

component (Dare, 1961; Graham et al., 1995). In their 1995 paper on breeding success and diet

of buzzards in southern Scotland, Graham et al. described the overall importance of buzzard prey

as being lagomorph > bird > vole. However, in his summary of European breeding studies

Newton (1979) showed that breeding success of buzzards was correlated to vole abundance,

particularly in poorer territories. This was confirmed more recently in southern Scotland by

Swann & Etheridge (1995) who documented a highly significant correlation between the

breeding success of tawny owls (vole specialists).

Field vole populations experience frequent population cycles in the northern parts of their range

such as in Scotland and Scandinavia (Gibbs & Alibhai 1991; Lambin et al., 2000). While these

8

cyclical fluctuations have been documented to influence the reproductive success of both

specialist and generalist raptor species (Korpimäki & Norrdahl, 1999; Salamolard et al., 2000;

Redpath et. at., 2002; Petty & Thomas, 2003;), with generalist predators, there is the possibility of

mitigating these effects through a functional response (predating other prey species at a higher

intensity). Spidso & Selàs (1988) provided evidence that buzzard populations in Norway respond

to crashes in vole populations by dramatically increasing the proportion of birds in their diet.

This suggests that during vole population crashes, territories with access to alternative prey items

will show less variation in breeding success. In their study of buzzard habitat selection in Italy,

Sergio et al. (2005) showed that at a landscape scale buzzards prefer high levels of habitat

heterogeneity within their territories, a fact that was linked to a varied food supply. It is possible

however, that declines in several of the common prey species of generalist raptors can occur in

conjunction with one another causing even generalist predator populations to decline suddenly

(Salafsky et al., 2007).

2.4. The role of habitat on buzzard densities and breeding success

2.4.1. Forestry

Across the United Kingdom landscapes are utilised and managed for a variety of different

reasons (for example economic gain and nature conservation) and, as a result are under a variety

of pressures. Within central Scotland these pressures are most notably farming and commercial

forestry. In his early study on the status of the buzzard within the British Isles, Moore (1957)

observed that buzzards reach higher densities on farmland than within forested areas.

Commercial forestry in Scotland may affect the abundance of forest dwelling buzzards through

its effect on: (1), the number of prey; (2) the type of prey (Newton, 1979) and; (3) the availability

of prey (Sonerud, 1997). Forestry practises such as clear cutting (the harvesting of a complete

block of trees), have been recorded to cause decreases in generalist raptor species in both

Fennoscandia (Widén, 1994) and North America (Penterianni & Faivre, 1997). However, within

the UK it has been shown that clear cut and pre-thicket areas can also provide relatively high

abundances of voles to avian predators (Petty, 1999).

2.4.2. Farmland

Farming methods can also influence the density and breeding success of buzzards in many ways.

Butet et al. (2010) showed that buzzards decrease significantly with the reduction of hedgerows,

woodlands and grassland areas. This can be attributed to a decrease of prey abundance at a

landscape scale with an expansion of field margins and loss of habitat heterogeneity (Benton et

al., 2003; Petrovan et al., 2011). Similarly, grazing intensity can have significant effects on the

abundance of Cricetidae prey species; this has been demonstrated on both lowland pasture and

also on upland habitats (Evans et. al., 2006).

9

2.5 Assessing raptor diet: pellet analysis, prey remains and direct observations

Knowledge of the diet of a raptor is essential to understanding its ecology. Dietary analysis can

be achieved through several methods including: 1) monitoring individuals while hunting through

radio tracking (Kenward, 1982) or direct observations; 2) recording the prey items brought to the

nest during the breeding season (Lewis et al., 2004; Tornberg & Reif, 2007); 3) analysing raptor

stomach contents (Pietersen & Symes, 2010) and; 4) identifying prey items from pellets or faecal

samples (Tubbs & Tubbs, 1985; Korpimäki & Norrdahl, 1999; Redpath et al., 2002). The outputs

from these studies can vary from simply confirming the presence of a prey species in a diet, to

being able to quantify either the total number of individuals taken or the net energy gain

afforded by predation on a specific prey species (Seaton et al., 2008). When dietary analysis is

conducted in conjunction with demographic modelling of a prey population, more complex

ecological processes can be studied such as the impact of predators in terms of prey species

mortality (Thirgood et al., 2000a), or the functional responses of predators to fluctuations in prey

abundance (Redpath & Thirgood, 1999). In this way dietary analysis of raptors can provide

essential information to policy makers, conservationists and managers; indeed some conservation

strategies now focus on prey as an essential component for maintaining populations of avian

predators (Reynolds et al., 1992).

Assessments of buzzard diet have previously been attempted through recording either the prey

remains present around the nest site (Jardine, 2003; Sim, 2003), or through recording both prey

remains and prey items identified from pellet analysis (Graham et al., 1995; Swann & Etheridge,

1995). Often these methods are the only source of reliable data on diet as studying the prey

selection of wide ranging raptors is extremely difficult. Although these two methods form the

vast majority of all previous dietary analysis of buzzards, it is known from work on other raptor

species that both methods (pellets and prey remains) have inherent biases (Simmons et al., 1991;

Redpath et al., 2001; Tornberg & Reif, 2007). While it has been suggested that incorporating prey

and pellet results can serve to rectify the biases of both methods (Simmons et al., 1991), studies

that have addressed this since have reached different conclusions as to the accuracy of this

approach (Redpath et al., 2001; Lewis et al., 2004; Sánchez et al., 2008).

It would seem appropriate therefore, to use direct observations to account for the biases of these

two commonly used methods, something that few studies, with the exceptions of Selàs et al.

(2007) and Tornberg & Reif (2007), have so far attempted with buzzards. Indeed, Selàs’s study in

southern Norway showed that amphibians have been significantly underestimated in studies

from both pellet and prey remains. To collect direct observational data, studies have used both

remote cameras (Lewis et al., 2004) and observations from blinds (Redpath et al., 2001).

However, as well as reducing data collection time, it has been shown that cameras allow for a

10

greater proportion of prey to be identified to genus and species levels when compared to

observations from blinds (Rodgers et al., 2005). It is also the case that relatively recent advances

in digital technology have created the capability to record nest activity over longer periods of

time, allowing for cost effective observations of all prey items provisioned over a period of many

days (Bolton et al., 2007).

3. Study site

Fieldwork was conducted across 298.2km² of Stirling and the Trossachs, Central Scotland. The

area contain three dominant habitat types, forestry, upland pasture and lowland pasture. The

climate of the study area was cool and humid with a mean annual precipitation of 1344mm and

an annual mean daily temperature of 4.9°c minimum and 11.9°c maximum [Ardtalnaig

meteorological station annual means, 1971-2000, located approximately 30km north east of the

centre of the study area; Meteorological Office (www.metoffice.gov.uk)]

3.1. Forestry and Upland pasture

The upland study area was located within or around the Queen Elizabeth Forest Park, part of

Loch Lomond and The Trossachs National Park (56°10’N 4°23’W), a 167km² area consisting of

three large planted forests owned and managed by the Forestry Commission Scotland. Although

the area is predominantly planted with non-native coniferous forests, the park also contains

other habitat types including native forest, rough grassland, sheep walk and heather moorland.

The native section of the forest was characterised by downy birch (Betula pubescens), silver birch

(Betula pendula), scots pine (Pinus sylvestris) and sessile oak (Quercus petraea). The harvested section

of the forest is characterised by coniferous trees primarily Sitka spruce (Picea sitchensis), Norway

spruce (Picea abies), European larch (Larix decidua), hybrid larch (Larix x eurolepis), scots pine and

lodgepole pine (Pinus contorta) managed on a 25-60 year rotation. The harvesting of timber creates

clear areas of successional habitats: when trees are first removed a ‘clear cut’ area is formed, this

will then be left for several years becoming grassland, after a period of time it is then planted to

become ‘pre-thicket forest’, finally it reaches the point of becoming a thicket after 12-15 years.

The Forestry Commission also creates ‘forest habitat networks’, areas of natural regeneration and

grassland left for their biodiversity value.

3.2. Lowland farmland: Doune

The lowland section of the study area was centred on the town of Doune (56°11′24″N

4°03′11″W) within the Stirling district. Around the town the area was made up of small arable

and pastoral farms, with sheep farming as the most common farming type. Most fields were

bordered by hedgerows with many small patches of native woodland and non-native forestry. In

11

the lowlands the native deciduous woodland was dominated by common oak (Quercus robur),

beech (Fagus sylvatica) and sycamore (Acer psuedoplantus).

12

4. Methods

4.1. Field data collection

4.1.1. Habitat classification

Within the study area, vegetation types were split into nine different categories following Austin

et al. (1996) and after consultation with Katie Freeman (Habitats Manager for the Trossachs

region of the Forestry Commission Scotland) (Table 2). Each habitat was then individually

sampled for prey species abundance.

Table 2: Categories used to classify vegetation cover within the study areas. Adapted from the habitat classification in Austin et al. (1996), plant names follow Clapham et al., (1981).

Habitat type Description Habitat classification

1 Mature non-native woodland

Forestry plantations after canopy closure and through to mature tree stands. Characterized by spare or near absent herb or shrub layer. The only open areas are rides between stands.

Forestry

2 Mature native woodland

Corresponds either deciduous woodland or mixed deciduous and conifer as marked on 1:25 000 scale Ordinance Survey Landranger series maps. Within the study site oak and birch were the dominant species.

Forestry

3 Clear-cut areas

Areas where stands had been cleared within the last 3 years. Sites that had been replanted with saplings within the last year were also included in this category.

Forestry

4 Pre-thicket forestry

Young forestry plantations 1-12 years old (before canopy closure). Characterized by lush herb layer and shrub birch with much open space between lines of planting.

Forestry

5 Rough grassland and natural regeneration

Characterised by uncultivated or ungrazed areas with native tree species before canopy closure. Grasses such as Molinia and bracken Pteridium aquilinum dominated. These included the Forestry Habitat Areas created by the Forestry Commission Scotland.

Upland pasture

6 Heather moorland

Characterised by cover species such as Calluna vulgaris, Vaccinium myrtillus and Nardus stricta.

Upland pasture

7 Upland farmland or 'sheepwalk'

Mountainous or hillside pasture on the boarders of the National Park. Vegetation characterised by perianal grasses such as Festuca ovina and Agrostis tenuis.

Upland pasture

8 Lowland farmland

Low lying intensively grazed or cultivated farmland. Predominantly sheep farming with some silage fields.

Lowland pasture

9 Other Rare vegetation types or land uses, for example quarries, were not included.

Unspecified

4.2.1. Prey Sampling

Scottish buzzards consume a wide variety of birds, mammals and reptiles including rabbits,

voles, corvids, galliforms, small passerines and snakes (Swann & Etheridge, 1995; Graham et al.,

1995). This generalist diet necessitates a sampling method capable of estimating both bird and

13

diurnal mammal populations concurrently. For this reason both Distance sampling and vole sign

index (VSI) sampling were chosen to be used in conjunction with one another.

4.2.1.1. Distance Sampling for prey species

The abundance of avian and mammalian prey species was assessed through an adaptation of a

transect sampling methodology recommended for distance analysis (Buckland et al., 2001) and

commonly used in the Breeding Bird Survey (BBS). Distance analysis was judged as the correct

method to obtain density estimates as it produces a detection function that compensates for

differences in detection probability across species and habitats and, as a result, can produce

density estimates representative of true population size (Buckland et al., 1993). Transects were

selected in each of the eight habitat types by overlaying a 1km by 1km grid over mapped habitats

and randomly selecting squares for sampling. Two transects were walked at each location

running parallel at between 250-500m distance. In total eight 1km transects were walked through

each of the defined habitat types between the hours of 06:00am and 10:00am. This time period

was chosen for three reasons (1) it is recommended by the BBS methodology as being when

birds are most active; (2) it has been used by previous studies for sampling lagomorph

abundance (Trout et al., 2000) and; (3) it has been noted as the time of day when buzzards are

most commonly observed, suggesting that is when they are most actively searching for prey

(Vergara, 2010). Transects were walked slowly by a single observer and numbers of individuals of

any avian or mammalian prey species, as listed by Graham (et al., 1995), Swann & Etheridge

(1995), or Selàs et al. (2007), on both sides of the transect line were recorded. Avian prey was

split into 7 different categories for ease of identification and recording: gamebirds, waders,

columbids, small passerines, thrushes, corvids and other. Transects were not conducted on days

with strong wind or heavy rain as this may have caused a reduction in activity.

4.2.1.2. VSI sampling

As voles have been shown to be an important component in the diet of buzzards (Newton,

1979; Swann & Etheridge, 1995), the relative abundance of field and bank voles was assessed

using a VSI sampling method adapted from extensive vole research conducted in the Kielder

Forest, Northern England (Lambin et al., 2000; Petty et al., 2000). The same 1km transects as

used for avian prey densities were walked with a quadrat (25m x 25m) thrown every 40m,

creating a total of 25 different VSI recordings along the transect course (200 in total for each

habitat type). Each quadrat was thoroughly examined for the presence of absence of vole sign,

specifically fresh clippings. Quadrats were then scored 0, 1 or 2 depending on the sign of

clippings present: old clippings = 1; fresh clippings = 2, making a maximum score of 50 for each

transect line. Petty (1992; 1999) provided evidence that fresh clippings are an appropriate index

indicator of vole abundance, as clippings do not remain fresh for longer than 1-2 weeks and so

14

accurately reflect recent vole activity. Although, this method does not allow a differentiation

between species, previous studies indicate that the field vole will be the most dominant species in

open habitats and the bank vole the most common in the wooded areas (Hanski et al., 2001).

4.2.2. Mapping buzzard nests

Previous nesting locations were taken from a long running program being undertaken by David

Anderson (Forestry Commission Scotland) and Duncan Orr-Ewing (RSPB Scotland). Nests

were located during the nesting period (20th April- 14th July 2011) by systematic foot searches of

all know woodland containing nesting sites. This was necessary as buzzards will often use

different nests within a single territory during different years (Tubbs, 1974). Additional nests

were located from observations of territorial birds in previously unrecorded locations. To

prevent misclassification of nesting attempts nests were only considered active if: 1) it was

observed with both a substantial build up of fresh greenery around the nest cup and a territorial

pair of birds; 2) the female was observed incubating eggs; 3) chicks were observed on the nest.

The GPS co-ordinates of all active nests were recorded at nest site. When last year’s active nest

was found to be inactive, a 500m radius around the nest was searched, if an active nest was not

located after a thorough search (a minimum of 2 visits) the territory was considered to have been

relinquished, or, if the buzzard pair was regularly seen together defending the area, it was

counted as having already failed. This is because field work began in late April when pairs may

already have abandoned nests either due to infertility or predation (Newton, 1979).

15

Figure 1: Map of study site showing all active nests.

4.1.3. Mapping core territories

Once all active sites were located (Figure 1), the nearest neighbour distance (NND) for each nest

was recorded using ArcGIS. Buzzard pairs defend an established territory while raising young

(Hodder et al., 1998), for the purpose of this study they were assumed to conduct the majority of

their hunting within the core territory surrounding the nest. To calculate core territories the

NND/2 was averaged for each of the three nest categories providing three different buffer sizes.

Once a buffer was established, the area (m²) of each of the eight habitat types within its radius

was then mapped out using digitised ordinance survey maps (1:25,000), Forestry Commission

Scotland GIS files and Google earth© images on ArcGIS (see Figure 2 for example). The areas

of habitat within territories later allowed for prey abundance estimates to be calculated for each

nest.

16

Figure 2: The habitat around 63 nests was mapped on ArcGIS and the clipped with the mean nearest neighbour distance (black circle). In this case the nest was classified as ‘Forestry dominated’ and a buffer with a radius of 550m (NND of habitat category/2) was used.

4.1.4. Assessing Buzzard diet

4.1.4.1. Pellet analysis and prey remains

An assessment of buzzard diet was made from all active nests in the study (n=38). Pellets and

prey remains were collected from within the nest cup and from plucking posts located within the

vicinity of the nest (judged as a 50m radius). Nests that had recently failed (presence of fresh

down or dead chicks) were included in this analysis but yielded few records. Prey remains were

identified and recorded for each nest individually; the number of individuals of any prey species

was recorded as the minimum number from the prey remains present, for instance, if the nest

cup comprised entirely of vole fur, the minimum number of voles would have been estimated as

being between 5 and 10. Prey remains identification and estimates of individual numbers were

made with the assistance of David Anderson, an experienced raptor researcher.

17

Figure 3 Prey remains (mole Talpa europaea and field vole Microtus agrestis) found within the nest (left), buzzard pellets before examination (right).

Pellets were analysed in the field under a crude adaption of the method used by Clarke et al.

(1997). It was assumed that each species recorded in a pellet was represented by no more than

one individual. Bird identification from pellet analysis was confined to noting the presence or

absence of any bird remains in pellets as feathers are difficult to identify to a species level

(Graham et al., 1995). In the same way rabbits Oryctolagus cuniculus and brown hares Lepus europaeus

were classed together as lagomorphs, because lagomorph hair can only be separated to species at

the microstructural level (Wolfe & Long 1997). Pellets in general were less common than prey

remains, as a consequence nests were often revisited several times to ensure that pellets were

adequately represented. While the occurrence of prey species in pellets is unlikely to be

comparative to the quantities of prey ingested, the results of prey and pellet analysis will be

useful in comparing the dietary proportions of different territories.

4.1.4.2. Direct observations

While pellet and prey remain data are easy to collect and record, several studies have shown that

direct observational data is necessary to correct inherent biases and shortcomings in dietary

analysis of raptors (Collopy, 1983; Redpath et al., 2001, Tornberg & Rief, 2007). To gather data

on buzzard diet, six Memocam© nest cameras were installed on buzzard nests, two in each

habitat category. Cameras were installed on nests chosen at random with all brood sizes

encountered in the study being represented (1 brood of three, 1 brood of two and 4 broods of

single chicks). Due to a technical malfunction, data from only one camera was gathered in the

forest dominated territories. These cameras did not record constantly but had individually

programmed motion detection triggers which were activated on the arrival of the parent bird on

the nest.

18

To identify prey, a detachable memory card within the camera units was uploaded onto a laptop

(15in screen) to view footage. Prey items were identified to the lowest taxonomic level possible,

based on morphological characteristics such as feet, tails and body shape. On occasions that the

prey item was only partly viewed or consumed quickly prey were categorised to a more general

category of family or genus. All unidentified prey items were small and consumed rapidly. For

each nest, this data was collected for 100 ‘hunting hours’ over consecutive days usually between

the hours of 06:00am and 22:30pm (when the parent bird left in the morning and returned to the

nest at night). The target of 100 hours was chosen due of time constrains and was usually

reached on the 9th day of recording. Recordings were made during June and July in the weeks

preceding fledging. This period was chosen to maximise the number of prey being brought to

the nest as both the male and female adult should be hunting constantly (Tubbs, 1974).

4.2. Data Analysis

The two measures of buzzard breeding ecology (density and breeding success) where analysed in

three stages. Firstly, a large proportion of the analysis was directed at the preparation of data for

use in models. Secondly, the data was explored with conventional univariate analysis methods.

Thirdly, a multivariate analysis was conducted using extracted values from a principle component

analysis to evaluate how the habitat and dietary variables interact to influence buzzard breeding

ecology. All analyses were considered significant if P value is <0.05.

4.2.1. Preparatory analysis

4.2.1.1. Prey densities

To produce density estimates (individuals/hectare) distance sampling data were analyzed using

the program DISTANCE (Version 6.0, Thomas et al., 2009). All data from different habitats

were tested for fit with the key functions of Uniform, Half-normal and Hazard-rate under

conventional distance sampling (Buckland et al., 2001) (see Appendix Figures 2-9). To ensure

reliable estimates of density, methods were designed to meet the three key assumptions of

distance sampling: 1) all distances were measured accurately; 2) all individuals were detected at

their initial location and; 3) all individuals on the transect line were detected. Density estimates

were determined only for small passerines as they were the only group of prey with a sufficient

sample size of >80 detections (Buckland et al., 2001). Vole abundances were determined from

the VSI sampling through a liner regression detailed in Lambin et al. (2000) (See Appendix: Table

1). As the VSI sampling period took place over both the spring and summer months both were

calculated and then averaged for each habitat type. The densities of both voles and small

passerines within the core territory of each buzzard were then calculated from the area of each

habitat type within each territory. To allow for comparable results between the different habitat

19

categories, the abundance of both voles and small passerines was calculated per km² of core

territory.

4.2.2. Variable compilation

Sixteen variables were used to describe nests comprising of five landscape variables, 5 dietary

variables from the pellet analysis and 6 dietary variables from prey remains analysis (for full list

and description see Table 5). The dietary variables had previously been reduced from twenty two

by excluding variables with only one nest record.

Table 3: Variables used in the description of buzzard nests in relation to both landscape and dietary analysis.

Variable Description

Response

NSTSUC Number of chicks raised to fledging

NND Distance of closest nesting attempt (used as a proxy for density)

Explanatory

Landscape variables

%F Percentage of habitat made up of habitat types classed under 'Forestry'

%UP Percentage of habitat made up of habitat types classed under 'Upland pasture'

%LP Percentage of habitat made up of habitat types classed under 'Lowland pasture'

VKM Voles density in an average km² of core territory

SPKM Small passerines density in an average km² of core territory

Pellet analysis variables

PLL Importance of lagomorphs in diet as estimated from pellet analysis

PLMO Importance of moles in diet as estimated from pellet analysis

PLV Importance of voles in diet as estimated from pellet analysis

PLB Importance of birds in diet as estimated from pellet analysis

PLA Importance of amphibians in diet as estimated from pellet analysis

Prey analysis variables

PRL Importance of lagomorphs in diet as estimated from prey remain analysis

PRM Importance of moles in diet as estimated from prey remain analysis

PRV Importance of voles in diet as estimated from prey remain analysis

PRB Importance of birds in diet as estimated from prey remain analysis

PRA Importance of amphibians in diet as estimated from prey remain analysis

PRC Importance of carrion in diet as estimated from prey remain analysis

4.2.3. Exploratory univariate analysis

An analysis of variance (ANOVA) was fitted to analyse the influence of habitat type on nearest

neighbour distance and laying date. Variation in productivity between habitat was explored using

a Chi squared test on nesting success (measured as 0 or 1) and an ANOVA for number of

fledglings. Further analysis on fledglings required Tukey’s Honest Significant Difference test

(Tukeys HSD).

20

4.2.3.1. The influence of prey abundance on breeding ecology

Analysis of the variation in prey abundance required an ANOVA to be fitted across the habitat

categories and a Tukeys HSD to observe multiple comparisons. A Pearsons product movement

correlation analysis (Pearsons) was then used to analyse the effect of prey abundance on

productivity, laying date and density.

4.2.3.2. Exploratory dietary analysis

Prey and pellet data was checked for normality before analysed using a Wilcoxon sign rank test

(Wilcoxon) to address if techniques produce significantly different results.

4.2.4. Investigating response type of buzzard to vole density

To investigate the type of response shown by buzzards to increases in vole density (Hypothesis

D) the proportion of voles in diet (estimated from pellets) was tested against the vole abundance

estimates for each territory. A generalised liner model (glm) with log link and quasipoisson errors

was fitted with proportion of voles as a quadratic element to investigate how the proportion of

voles in diet changed with vole abundance in territories.

4.2.5. Investigating the influence of prey size in breeding success

In order to explore the role of prey size in breeding success a brief analysis was conducted

concentrating on mammalian prey. Separate analyses were run for both prey remains and pellet

analysis that ranked the dominant mammal prey species in the diet of each nest depending on

size. All mammals <100g were ranked as 1 while all mammals .100g ranked as 2. An ANOVA

was then fitted for both methods.

4.2.6. Principal component analysis

A principal component analysis (PCA) was conducted on both the landscape and dietary

variables to reduce the dimensionality of the dataset. Previous studies on raptor breeding ecology

have shown that a PCA analysis can be appropriate to analyse the complex relationships within

numerous variables (Kostrzewa, 1996; Krüger & Linderström, 2001; Baines et al., 2004; Lŏhmus,

2003; Rodriguez et al., 2010). Following a reduction of the variables, loadings were used to

interpret the characteristics of the individual PCs. Only PCs with an eigenvalue >1 were be

considered for further analysis (Crawley, 2007).

4.2.7. Modelling

While the conventional univariate analysis allowed for an initial exploration of the data, model

selection, a process in which several competing hypothesis are simultaneously confronted with

data, is being increasingly promoted by environmental scientists (Burnham & Anderson, 2002;

Johnson & Omaland, 2004). Model selection, based on likelihood theory, can be used to identify

either the best single model or to make inferences based on weighted support from a set of

competing models. This is appropriate for this study as the environmental variables that govern

21

buzzard densities and breeding success are likely monotonically related to many underlying

factors as well as each other. Consequentially, it is likely to prove difficult to reject any of the

hypotheses with a single univarite test. By using the approach of model selection, several models,

each representing one hypothesis can be simultaneously evaluated in terms of support from the

landscape and dietary variables.

Two generalised liner models (with poisson errors and log link) were built using the five principle

components to explore the relationship between nesting density and breeding success with

changes in the composition of habitat and diet. The R package ‘MuMIn’ was then used to run

each model with all possible combinations of variables. From the results of this analysis the

model with the lowest AIC was selected as the best model. AIC selects the best model taking

into account the fit of the model and penalises for each extra parameter in the model (Sakamoto

et al., 1986). The delta AIC (∆i) value provided for a measure of each model relative to the best

model. A confidence set of models was obtained by extracting all model combinations with a ∆i

<2, as any models within 2∆i can be considered equally probable (Burnham & Anderson, 2002).

The final group of models were those that supported the data strongest.

22

5. Results

5.1. Exploratory analysis

5.1.1. Variation in buzzard density on a spatial scale

A total of 65 nesting attempts were recorded spanning three habitat categories: forest dominated

territories (19), upland pasture dominated territories (25) and farmland dominated territories

(22). Three nesting attempts (all upland pasture dominated) were excluded from further analysis

after a review of the data showed them to be outside the main study area (>5km). The mean

NND between all buzzard nests was found to be 0.96±0.50km (range 220-2499m, n=63) with

the forestry, upland and lowland habitat categories producing NND means of 1.1±.043km,

0.99±0.51km and 0.79±0.52.4km respectively. The distances between nearest neighbours was

tested between habitats with no significant differences observed (ANOVA,F2,60=2.08, df= 2,

p=0.13) (See Figure 4).

Figure 4: A boxplot showing the nearest neighbour distance of buzzard nests against the habitat categories. Box represents interquartile range, bold lines represent the median, dotted line

represent range, clear circles represent outliers.

5.1.2. Variation in buzzard productivity on a spatial scale

From the 63 active nests monitored, this study noted a failure rate of 43% across all habitats,

with forestry nests, upland pasture nest and lowland nests recording failure rates of 74%, 41%

and 19% respectively. A Chi-squared showed that there was a statistically significant difference

between the observed values and a uniform failure rate across the habitats (Chi-sq²= 12.88,

df=2, P<0.001).

Further exploration of productivity revealed a mean fledgling number of 0.89±0.31 across all

habitats with mean fledgling numbers of 0.26±0.45 in forestry, 0.73± 0.76 in upland and 1.41±

23

1.05 in lowland habitats. A highly significant difference was found between at least two of the

habitat types (ANOVA: F2,60=2.08, df=2, p=<0.001). A Tukeys HSD was then used to show

that there was a significant difference between lowland and forestry (diff=1.145, p=0.0001),

upland and lowland (diff=-0.68, p=0.018) but no significance between upland and forestry

(diff=0.46, p=0.168) (this is visualised in Figure 5).

Figure 5: A boxplot showing the number of buzzard chicks raised to fledging against habitat type. Box represents interquartile range, bold lines represent the median, dotted line represent

range, clear circles represent outliers.

5.1.3. Prey densities

The abundance of voles and small passerines was calculated for each of the eight habitat types

(per km²) (Tables 4 & 5). An ANOVA showed that vole abundances per km² were significantly

different across the habitat categories (F2,60=74.25, df=2, p<0.001). Significant differences were

found between lowland and forestry territories (Tukey HSD: diff=-5.13, p<0.001), upland and

lowland territories (diff=5.93, p<0.001) but no significant difference was found between upland

and forestry territories (diff=0.80, p=0.31). Following a significant result from an ANOVA of

small passerines per km² in the different habitat types (f=15.46, df=2, p<0.001), a Tukeys HSD

revealed a highly significant difference between lowland and forestry territories (diff=-1.05,

p<0.001), a significant difference between upland and lowland territories (diff=-0.65, p=0.003)

but little significance between lowland and upland (diff=-0.40,p=0.08).

24

Table 4: The estimates of small passerine abundance from DISTANCE analysis. All models

were run under a half-normal hermite polynomial distribution with observations binned and the

largest 5% truncated.

Habitat type n

Detection

probability

Encounter

rate

Dˉʰ

estimate %CV df 95% CI

Non-native

mature forestry: 93 35 60.5 3.21 14.33 13.42 2.36 4.36

Native mature

woodland: 279 68.5 30 7.21 11.59 68.61 5.72 9.07

Rough grassland: 134 20.9 76.7 5.38 15.82 8.48 3.75 7.7

Pre-thicket

forestry: 224 23.1 76.1 3.88 11.52 12.04 3.02 4.98

Clear cuts: 67 19.3 80.1 0.64 23.04 16.98 0.4 1.04

Upland pasture: 114 80.6 11.8 1.65 8.41 128.17 1.4 1.95

Heather

moorland: 121 28.6 69.8 2.5 13.31 14.16 1.88 3.31

Lowland pasture: 87 27.3 69.3 1.54 16.1 14.4 1.09 2.16

Table 5: The calculation of vole density from the regression detailed in Lambin et al., 2000 (See Appendix 1 for further details)

Habitat type VSI Spring density

Summer density

Average (Dˉʰ Estimate)

Native mature woodland 2.63 44.35 51.64 47.99

Non-native mature woodland

3.25 52.54 60.46 56.50

Rough grassland 13.63 188.45 206.85 197.65

Pre-thicket forestry 13.88 191.72 210.38 201.05

Clear-cut areas 8.25 118.04 131.01 124.52

Heather moorland 7.63 109.85 122.19 116.02

Upland farmland 8.50 121.31 134.54 127.92

Lowland farmland 2.50 42.71 49.88 46.29

To begin to address hypothesis A, vole abundance was tested against buzzard productivity,

revealing a highly significant negative relationship (Pearsons: df=61,t=-3.38,p<0.001) (Figure 6).

This result allows the null hypothesis to be rejected, however the expected positive correlation

was not found. To test hypothesis B, the influence of vole abundance was tested against a proxy

for buzzard density; NND. This revealed a significant positive correlation (df=61,t=2.28,

25

p=0.025) (Figure 6). This again allows the null hypothesis to be rejected but is converse to the

expected negative correlation.

Figure 6: Scatter graphs showing the vole abundance estimates in buzzard territories against the nearest neighbour distance (left) and the number of chicks raised to fledging (right). Regrassion lines were fitted and both the explanitory variables and the response variables were logged to

normalise the data.

The same analysis provided little evidence for an effect of abundance of small passerines in

territories on NND (Pearsons: df=61,t=0.107,p=0.915) and productivity (df=61,t=-

1.57,p=0.121).

5.2. Results of buzzard dietary studies

A total of 170 fresh prey remains (average ±1sd=4.7±2.6) and 118 fresh pellets (2.2±1.4) were

collected over the course of this study. Voles and birds formed 64.7% of buzzard diet in prey

remains and 57.8% of diet in pellet analysis (Table 4). Lagomorphs, predominantly rabbits,

(23.5% of prey, 22% of pellets) also formed a significant component of buzzard diet. The

frequency of occurrence estimated by the two methods was investigated for disparity by plotting

the medians to show that lagomorphs and birds occurred more frequently in prey remains and

voles and mole occur more frequently in the pellets. This proved significant for voles (Wilcoxon:

V=19,p<0.001), and birds (V=531, p<0.001) but not lagomorphs (V=60, p<0.68).

From the prey remains, voles where recorded the most often (29.4%) while lagomorphs made up

the second highest group of observations (23.5%). In total, 21 different species of bird in

buzzard diet including three gamebird species (Red Grouse L. lagopus scoticus, Black grouse

26

Lyrurus tetrix and Pheasant Phasianus colchicums). However of the bird species, it was corvids,

constituting 13.6% of the prey remains found, that appeared to be predated at the highest level.

From the pellets analysis, voles were the most common prey item (42.5%) followed by rabbits

(22%), birds (15.3%) and moles (9.3%). Amphibian bones were also recorded (8.5%) but did not

allow for a distinction between Common Frogs Rana temporaria and Common Toads Bufo bufo.

Table 6: Number of observations and percentage total diet of different prey items as estimated from prey remains collection and pellet analysis (for full list including direct observations see Appendix 2).

Prey remains Pellet analysis

Total % Total %

Amphibians 7 4.1 10 8.5

Reptiles 0 0 1 0.8

Birds 60 35.3 18 15.3

Red Grouse L. lagopus scoticus 1 0.6 - -

Black grouse Lyrurus tetrix 1 0.6 - -

Pheasant Phasianus colchicus 7 4.1 - -

Woodpigeon Columba palumbus 4 2.4 - -

Tawny owl Strix aluco 3 1.8 - -

Small passerine species 16 9.5 - -

Thrush Turdus species 4 2.4 - -

Corvid species 23 13.6 - -

Mammals 98 57.6 89 75.4

Hedgehog Erinaceau europaeus 1 0.6 1 0.8

Mustelid species 0 0 1 0.8

mole Talpa minor 5 2.9 11 9.3

Lagomorph species 40 23.5 26 22

Vole Cricetidae species 50 29.4 50 42.4

Mouse Apodemus species 1 0.6 0 0

Shrew Soricidae species 1 0.6 0 0

Carrion 5 2.9 0 0

Total observations/items 170 118

5.3. Investigating the type of functional response

Although the results of a generalised liner model showed that vole number estimated from

pellets increased with number of voles in the territory (t=5.007, p<0.001). When vole proportion

was added as a quadratic element, the functional response type could not be confirmed (t=1.773,

p=0.137).

5.4. The influence of prey size in breeding success

Hypothesis E was investigated by investigating the relationship between the size of the dominant

mammalian prey against fledgling number. The results indicated that size of prey has a highly

27

significant influence on the number of fledglings from both pellet analysis data (ANOVA,

F1,31=21.42,df=31,p<0.001) and prey remains data (F1,31=13.04,df=29, p<0.001) (See Figure 4).

Indeed, the mean number of fledglings raised predominantly on a diet of mammals <100g

(voles) was estimated at 0.9 from assessments made by both methods (prey and pellets).This can

be compared to the mean fledgling numbers from nests where the dominant mammal prey

species was >100g (rabbits and moles) which was estimated at 1.73 from prey remains and 2.0

from pellet analysis. These results allow the null hypothesis to be rejected.

Figure 7: Bargraphs with 95% confidence intervals attached showing the relationship between the dominant mammal species found during the pellet analysis (A) and the prey analysis (B)

against the mean number of chicks raised to fledging (mammals <100g=1, mammals >100g =2).

5.5. Principle component analysis

The PCA reduced the 16 variables to 5 factors (eigenvalues > 1) that retained 76.6% of the

original variance (Table 8). The first principle component (PC1) was clearly interpreted as being

‘vole reliant’ nests. The analysis showed high positive loadings toward all vole influenced

variables (voles abundance per km² VKM, proportion of vole in pellets PLV, and proportion of

voles in prey PRV) as well as a negative loading towards lagomorphs in prey (PRL) and

lagomorphs in pellets (PLL). PC2 was interpreted as rough upland habitat due to the high

loading of percentage upland pasture (%UP), the low loading of percentage forest (%F), and the

significant loading of bird in prey (PRB) and amphibians in pellets (PLA). PC3 was determined

to be related to a generalist pasture as the proportions of bird remains in pellets (PLB) has a

significant negative loading while proportion of mole prey (PRMO) and the proportion of

carrion prey (PRC) were positive. PC4 was related to lowland territories with a diet of rabbits as

there was a negative loading on both proportion of forest in territory (%F) and small passerines

28

per km² (SPKM) and a positive loading on the proportion of lagomorphs in prey (PRL). PC5

was taken to be reflecting dry pasture territories as there were significant loadings from both

PRMO and proportion of mole in pellets (PLMO) and prey carrion (PRC) with negative loadings

for proportion of amphibians in pellets (PLA), interestingly there was also high negative loadings

for both measures of lagomorphs in diet.

Table 7: Importance of habitat and dietary variables of buzzard with respect to each varimax-rotated factor of the principal components analysis (PCA). Factor loadings > 0.3 are indicated in bold.

PC1 ‘Vole

reliance’

PC2 ‘Upland

generalist’

PC3 ‘Pasture

generalist’

PC4 ‘Lowland

rabbit’

PC5 ‘Dry

pasture’

Habitat variables

VKM 0.34 0.13 -0.01 0.02 -0.13

SPKM 0.28 -0.12 -0.16 -0.48 -0.13

%F 0.25 -0.26 -0.10 -0.45 0.15

%UP 0.30 0.31 -0.13 0.22 -0.16

%LP -0.37 -0.02 0.17 0.13 0.01

Dietary variables

PLL -0.29 -0.16 -0.05 -0.20 -0.26

PLMO -0.07 0.39 -0.12 -0.33 0.36

PLV 0.36 -0.11 0.23 0.06 0.04

PLB 0.04 0.17 -0.49 0.41 -0.08

PLA 0.09 0.42 0.22 0.03 -0.34

PRL -0.31 -0.18 -0.06 0.36 -0.37

PRMO 0.06 0.36 0.40 -0.28 0.41

PRV 0.34 -0.16 0.05 0.14 -0.01

PRB -0.10 0.44 -0.18 -0.15 0.38

PRA 0.20 -0.15 -0.34 0.06 -0.17

PRC 0.11 -0.13 0.49 0.23 0.36

Importance of components:

Standard deviation 2.465 1.484 1.323 1.092 1.016

Proportion of variance 0.380 0.138 0.109 0.074 0.064

Cumulative proportion 0.380 0.518 0.627 0.701 0.766

29

Table 8: Competing GLMs explaining fledgling number (with Poisson errors) and nearest neighbour distance (with quasi Poisson errors) of Buzzard in the study area. For each model, the AIC, the difference between the current model and the best model (Δ AIC), and the Akaike weights are given. Variables retained in all competing models are in bold.

Model/variables AIC Δ AIC weight

Fledgling number:

'vole reliance' 76.58 0 0.22

(Null model) 78.4 1.51 0.1

'vole reliance' + Pasture generalist 77.62 1.54 0.1

Nearest neighbour distance: (QAICc)

'vole reliance’+'upland generalist'+'dry pasture' 38.77 0 0.15

'upland generalist'+'dry pasture' 39.15 0.38 0.12

'vole reliance'+'upland generalist' 40.05 1.27 0.08

'upland generalist' 40.11 1.34 0.08

'upland pasture' + 'pasture generalist' 40.72 1.95 0.06

(Null model) 44.96 6.19 0.01

5.5.1. PCA analysis: fledgling number

When the PCA variables were tested against fledgling number the model that best explained the

differences in breeding success and against landscape and dietary variables was the PC

interpreted as ‘vole reliance’ (PC1) (Table 9). This model showed that PC1 has a significant

negative effect on the number of chicks raised to fledging (0.348±0.035,df=27,t=-3.608,p<0.01)

(Figure 8). However, the null model was within 2 Δ AIC (1.51 Δ AIC), indicating that the null

hypothesis cannot be rejected (Burnham & Anderson, 2002).

30

Figure 8: The relationship between PC1 ‘vole reliance’ and the number of chicks raised to fledging. The dotted lines represent 95% confidence intervals.

5.5.2. PCA analysis: Nearest neighbour distance

Finally, the relationship between NND and the PCA variables was explored through model

selection, this identified five models within 2 Δ AICs, of which the PC related to ‘Upland

generalist’ (PC2) featured in all. This principle component was statistically significant when

modelled against NND (0.143±0.055,df=27,t=2.61,p<0.05). The model with the lowest AIC

(see Table 9) was examined with ‘upland generalist’ (PC2) emerging as the only significant

variable (O.119±0.08, t=2.212,p<0.05) (Figure 9).

31

Figure 9: The relationship between PC2 ‘upland generalist’ and the nearest neighbour distance, measured in meters. The dotted lines represent 95% confidence intervals.

5.6. Direct observational data

Due to a low sample size (n=5), the observational data collected from the nest cameras was not

appropriate for complex quantitative analysis. However, the large number of prey records

(n=226) from the 500 hours of footage did provide the opportunity for a rough comparison of

the dietary techniques through a comparison of dietary proportions. This indicated that the

proportions of diet estimated from prey and pellets in this study may have large biases ( Figure

10).

32

Figure 10: Bargraphs showing the proportions of mammals, birds and amphibians observed in all dietary methods in forestry (top left), upland pasture (top right), lowland pasture (bottom left) and all habitats (bottom right).

In mammal species, voles only formed 20.5% of the diet, with the closest estimate coming from

prey remains (29.4%) with pellet analysis providing an estimate over double that of observed

(42.4%). Lagomorphs formed only 5.7% of overall diet, far lower than the estimates from the

other methods (prey =23.5%, pellet=22%), despite being observed as prey items on 3 out of 5

nests. The source of the biases for proportions of mammal and bird prey may well have

originated from a substantial underestimation of the perceived importance of amphibians in

buzzard diet. From all five cameras amphibians were recorded as being a key component in

buzzard diet (average 35.7%) comprising the highest proportion of prey taken in both ‘Upland

Pasture’ nests (40% and 43%). Another interesting observation was the number of reptile prey,

adders (n=4) and slow worms (n=1) were observed being brought onto 3 separate nests yet only

recorded once in both the prey and pellet analysis during the main dietary study.

33

6. Discussion

The overall aim of this project was to explore the factors that govern buzzard breeding success

and density as a first step in understanding if buzzard can have a significant impact on gamebird

harvests. The data confirms the importance of large prey items in breeding success and suggests

that upland habitat may influence breeding densities.

6.1. Overview

Understanding the temporal and spatial variation in the reproduction of raptors requires large

numbers of territories to produce reliable estimates (Newton, 1979). Reynolds et al. (2005)

estimated the minimum threshold for accurate inferences of generalist raptors to be between 60-

100 nests. Although single year studies such as this one do not allow for a comparison of

territory productivity on a temporal scale (Lŏhmus, 2003), the large sample size and the

heterogeneity of the study site permit for an interesting snap shot of how habitat and prey

availability constrain or promote buzzard productivity on a spatial scale.

The densities of buzzards observed in this study (NND mean= 0.96±0.50km) are comparable

and often higher than those reported in previous studies (See Table 1). Although the exploratory

analysis did not reveal a significant difference between the three habitat types, the results are

similar to those of Newton et al. (1982) who recorded mean NNDs in Wales to be 1.13±0.04km

in upland pasture (the observed was 0.99±0.51km) and 0.87±0.03 in lowland areas (the observed

was 0.79±0.52). In this study NND was used as a proxy for density and probably gave an

overestimate of abundance because (by definition) it did not account for the distances to other

neighbours or gaps in the distribution of breeding pairs. However, it can be assumed that

buzzard density within the study site was high as NND estimates are similar to those recorded by

Sim (2003) in a population deemed to have one of the highest densities in Europe (0.83±0.27).

Indeed, the number of nesting attempts within the study site has remained fairly constant over

the last 5 years (D. Orr-Ewing, RSPB, unpublished data) suggesting that the population is

reaching an asymptote.

In terms of general productivity, although fluctuations are common over landscapes (Swann &

Etheridge, 1995; Sim et al., 2001), this study found a surprisingly high degree of spatial

heterogeneity. In the breeding attempts for the lowland pasture for example, only 19% of nests

failed to rear young, this can be compared to the forestry nests from which a failure rate of 74%

was recorded. Buzzard populations require a recruitment standard of 1.15 young per productive

pair to ensure stability (Newton, 1979). Without taking emigration and immigration into account

34

it would appear that during this year at least, lowland pasture was the only habitat category to

exceed this, making it a probable source population. Although similar failure rates have been

reported, such low breeding success is rare in the literature (Table 1). At first glance, it would

seem contradictory to the persistence of populations to attempt to breed in areas with such low

success rates, however, a feasible explanation for this is the site dependant population regulation

hypothesis (SDRH) (Rodenhouse et al., 1997). This predicts that the high densities of buzzards

within the study area are causing individuals to inhabit increasingly poorer territories, decreasing

the per captia population growth rate.

Although the SDRH explains why buzzards attempted to lay secondary habitat, it is likely that

the effect of these areas was amplified by a poor vole year. In his early studies on avian

reproductive ecology, David Lack hypothesised that birds are stimulated into breeding by the

abundance of prey providing the energy requirements during the laying period (Lack, 1966). If

this is assumed to be correct, there may have been a decrease in vole numbers between the

beginning of the breeding season and first few weeks of hatching (the period in which most of

the chick mortality was recorded). The decrease in vole abundance over this period would not

necessarily have to have been substantial as in biomass, voles represent the lower end of prey

importance (Graham et al., 1995). Indeed, voles may only prove an adequate primary food

resource for buzzards at the higher end of their cyclical fluctuations (Lŏhmus, 2003). In an

eleven year study on habitat and vole densities in relation to buzzard breeding success Lŏhmus

(2003), observed that while buzzards in good vole habitat performed very badly in low vole years

this was accounted for overall by a high breeding success in good vole years. The breeding

success that recorded in low vole years was 0.76±0.54 SD, a result comparable to the 0.89±0.31

SD observed by this study.

6.2. Quantifying complex ecological relationships

Caution is often advised in attempting to draw inferences on the role of prey abundance in the

breeding success of raptors. Observational studies that link prey abundance to raptor

productivity often fail to take into account unforeseen factors such as prey accessibility (Preston,

1990; Widén 1994; Thirgood et al., 2002; Ontiverous et al., 2004; Salafsky, 2004; Whittingham &

Evans, 2004) or alternative prey (Smout et al., 2010). However, by using a principle component

analysis to reduce prey abundance variables, habitat variables and dietary variables over a large

number of nests it is possible to build a clearer picture of the importance of individual species. In

this study the explanatory analysis suggested three things: firstly, there was a positive correlation

with vole abundance and their proportions in diet, secondly, that with higher abundances of

voles the number of fledglings decreases, and thirdly, that a dominance of vole prey in diet may

decrease the fledgling number. The PCA analysis allowed for this complex relationship to be

35

addressed within single analyses, clearly showing how variables combine to influence buzzard

breeding ecology.

6.2.1. Correlates of breeding success

One of the most noteworthy findings from the multivariate analysis was that vole reliant pairs of

buzzards are less likely to raise multiple fledglings (Figure 8). Although this study recorded

increases of vole abundance and utilisation over a spatial scale rather than a temporal one, it

seems counterintuitive to observe a negative relationship between prey utilisation and predator

productivity. This result suggests that during this year at least it was not the abundance but the

type of prey that had the greatest influenced buzzard breeding success. These findings agree with

those of Swann and Etheridge (1995) who observed that buzzards with access to persistent

supplies of large prey items have consistently higher reproductive success than those whose diet

is dominated by small rodents. This is further confirmation of the key role that rabbits play in

the breeding success of buzzards (Tubbs, 1974; Newton, 1979; Swann & Etheridge, 1995;

Graham et al., 1995; Austin & Houston, 1997; Jardine, 2003; Sim, 2003).

6.2.2. Correlates of density

While the exploratory analysis revealed no significant difference in the NND between habitat

types, previous studies have associated buzzard density with both prey abundance (Newton,

1979) and habitat composition (Halley, 1993; Penteriani & Faivre, 1997). The modelled PC value

suggested that decreases in buzzard density are associated with increases in the proportion of

upland pasture within territories and the proportions of amphibians and birds in diet. These

results are in keeping with previous studies that have recorded low densities in upland pasture

(Newton et al., 1982; Dare & Barry, 1990) and with Spidso & Selàs (1988) who observed that

birds are a secondary prey choice after mammals.

6.3. Cameras on nests:

Analysis of prey deliveries from the nest cameras provided a more extensive description of the

diet of buzzards than the analysis of prey and pellet remains. As well as providing accurate data

on both the range and proportions of the prey species in diet, it also allowed for addition

information such as the provisioning rates, the age structure of prey and an estimation of prey

biomass. Indeed, the observed provisioning rates for the nest that recorded the largest

proportion of rabbits in prey were less than half of those recorded from the forestry nest (whose

prey was mainly amphibians and voles) despite having two more chicks, clearly showing the

advantages of prey with a larger biomass. Although provisional, the proportional data also

suggests that a simple combination of prey and pellet methods would not generate the accuracy

levels that Simmons et al., (1991) predicted. The use of nest cameras would be strongly

recommended for future research of buzzard diet.

36

In comparison to the few previous studies of buzzard diet from direct observations the nest

cameras proved highly successful at recording buzzard provisioning (Appendix 3). Tornberg and

Reif (2007) for example, compiled 92 prey deliveries from 454 hours of footage with 25 % being

unidentifiable, in contrast this study observed 263 prey deliveries in 500 hours with only 8.7%

(n=23) being unidentifiable. With the exception of small prey items being possibly consumed

instead of returned to the nest (Sonerud, 1992), the camera records created essentially true and

unbiased data on the breeding diet of buzzards. Direct observations if collected from a larger

sample over several nesting seasons would allow for the correction of false suppositions, for

instance, Sim (2003) suggested that the majority of gamebirds observed in the diet of buzzards

would have been scavenged, however, the five records of gamebirds (1 pheasant, 4 red grouse)

from the cameras were all poults with no signs of rigor mortis.

As discussed previously, the high failure rate observed in this study suggests that it was a poor

year for vole prey. In the studies of buzzard diet in northern Europe both Spidso & Selàs (1988)

and Reif et al. (2001) recorded buzzard predation on birds to be significantly higher in poor vole

years, however, the proportions of birds taken in this study are not higher than those observed

by previous studies (Dare, 1961; Graham et al., 1995; Kenward et al., 2001; Sim, 2003; Jardine,

2003). Instead, a significant and largely overlooked source of prey identified from the nest

cameras was toad and frogs (37.7% of all observations, n=96). Although Simmonds et al. (1991)

stated that amphibians are overrepresented in the prey remains of generalist raptors, the results

of this study agree with those of Selàs et al. (2007) who observed that amphibians were being

underestimated in both pellets and prey remains. Indeed, studies of buzzard diet in upland areas

rarely observe amphibians as significant (Graham et al., 1995; Swann & Etheridge, 1995; Sim et

al., 2001), for example, from a two year study of 77 nests in Wales, Sim (2003) did not record

one amphibian record from 253 prey remains.

The influence of small passerine density on buzzard breeding ecology was found to be negligible

in this study. In retrospect, the probable reason for this is was the low importance of small

passerines in spring and summer diet (3% direct observations, n=8). However, their contribution

to buzzard survivorship cannot be ruled out as fledgling dependence on nesting territories can

often extend over winter (Walls & Kenward, 1995) a period when voles are often inaccessible

and amphibians are hibernating.

37

6.4 Study limitations and areas for future research

6.4.1. Limitations of the methodology

6.4.1.1 Sampling design

The Vole Sign Index sampling and DISTANCE analysis provided coarse approximations of two

prey groups within territories, although both were limited in their accuracy by the number of

replicates. Indeed, over the 64km of transects surveyed over the study period there was not a

single observation of a rabbit, highlighting the sampling limitations of randomised sampling on

species than are not randomly dispersed by confined to a few high density sites (Thompson,

2004). If the methodology is to be repeated, a higher replicate size and the use of an automatic

range finder have been shown to improve the accuracy of density estimates (Newson et al., 2008;

Thomas et al., 2009; Ransom, 2011).

6.4.1.2. Accounting for winter weather

In several studies of breeding raptors, winter weather has had a significant impact on breeding

success (Steenhof et al., 1997; Gremel, 2001; Bloxton, 2002; Rodriguez & Bustamante, 2003).

There is evidence that weather patterns not only affect the breeding success of raptors through

direct effects such as hypothermia of chicks but also through causing declines in prey densities

(Bloxton, 2002). While the winter of 2010/11 was recorded as the coldest in 31 years with

several periods of prolonged snow cover (www.metoffice.co.uk), its effect on buzzards breeding

ecology is difficult to surmise. Snow cover can restrict the accessibility of voles to buzzards

during the winter while facilitating higher than average vole numbers during the buzzards

breeding period (Selàs, 2001a). While temperature and snow cover in the winter have been

shown to have no apparent effect on buzzard density, significant correlation has been observed

between fledgling success and winter temperatures (Kostrzewa & Kostrzewa, 1991).

6.4.1.3. Accounting for stochastic events

It is important to acknowledge the effect that weather over the hatching period has on raptor

breeding success (Dawson & Bortolotti, 2000; Selàs, 2001b;). Most mortality in buzzard chicks

occurs in the period directly after hatching (Sim, 2003) when the young birds are in down and

most vulnerable to cold and wet weather (Tubbs, 1974). During May, the month when 90% of

the chicks hatched, Glasgow (33km south) received 186mm of rain (almost double the monthly

average) (www.metoffice.gov.uk). This period of bad weather cumulated with ‘Udo’ a severe

storm on the 23rd of May that caused winds of up to 100mph across central Scotland and

brought down whole blocks of conifer forest. Severe weather patterns such has this not only

affect the breeding success of raptors through direct effects such as hypothermia of chicks but

also through causing declines in prey densities (Bloxton, 2002). It has already been observed that

precipitation during May and June may have a negative effect on buzzard broods (Selàs, 2001b)

38

and so it is fair to assume that the weather had a negative impact on the breeding success of the

buzzards in this study. Further evidence for the impact of the storm on nesting raptors within

the study area was observed within the resident red kite population (monitored by RSPB) as 11

out of 22 nesting attempts failed to rear any young, resulting in 2011 having the lowest fledgling

success since red kite recolonised the area in 1998. Of those 11 nests 7 are thought to have failed

during the period of bad weather around the 23rd of May (D. Orr-Ewing, RSPB, unpublished

data). The effect of climate on the breeding success of generalist raptors has been observed to be

magnified in years when prey abundance is already low (Lŏhmus, 2003; Salafsky et al., 2005).

6.4.1.4. Accounting for intraguild exclusion and predation

A possible constraint on buzzard density that was not accounted for within this study was that of

intraguild predation (the killing of species that use similar resources) by other avian predators. It

is possible that buzzards nest site selection was limited by the abundance of a potential predator,

in this case golden eagles, which are present in upland sections of the study area and have

previously been observed to predate buzzard (Fielding et. al., 2003). The effect of intraguild

predation by northern goshawks (Krüger, 2002) and eagle owls (Sergio et al., 2005) has already

been documented to reduce buzzard breeding densities.

6.4.2. Limitations of the analysis

6.4.2.1. The interpretation of PCA

The use of principle component analysis in this study provided the opportunity to reduce the

many habitat and dietary variables while retaining the majority of variance within them. One of

its main advantages was that it allowed, through the creation of a composite variable, highly

correlated data (such as the results of pellet and prey remains from the same prey group) to be

reflected simultaneously. In this way the use of PCA provided a useful tool in exploring the

many dimensions of the dataset. However, the interpretation of the principle components

themselves (see Table 8) had the potential to be equivocal, and care has been stressed in their

application (Crawley, 2007).

6.5. Areas for future research

6.5.1. Understanding the functional response type of buzzards to prey increases

While this study failed to draw any conclusions on the type of response exhibited by buzzards to

prey fluctuations, this is certainly an area for further study as understanding how generalists

predators respond to fluctuations in prey density is crucial to understanding their impact

(Holling, 1959). Although the functional response type has been documented for both

peregrines (type II) and hen harries (type III) in relation gamebird densities (Redpath &

Thirgood, 1999), recent literature on the subject has suggested that a ‘multispecies functional

response’ approach is more appropriate for generalist predators (Smout et al., 2010). This is

39

necessary because generalist predators depend on the density of all of their major prey species

and their response to a single species is presumably influenced by the abundance of alternative

prey. The high diversity of prey species observed in this study suggests that in order to achieve

an accurate understanding of the functional response of buzzard prey abundance several key

species will have to be accounted for. Attempts at this are likely to be further confounded as the

composition of these major prey species changes significantly over habitat types.

Another limitation of this study was its temporal scale. Longer term studies into the functional

and numerical responses of raptors allow for clearer observations of trends and responses

(Newton, 1979; Redpath & Thirgood, 1999). This appears to be particularly true for generalist

predators; indeed, Lŏhmus (2003) showed that the relationship between buzzard productivity

and landscape characteristics was distinct between years. Although limited to a single year

snapshot, the methodology of this study was designed in such a way to provide a baseline for

subsequent studies.

6.5. Educating the debate

The results of this study made two observations that are of importance to the debate concerning

the impact of buzzards on gamebirds. Firstly, that buzzards have an increased breeding success

in territories with access to larger prey. Secondly, that breeding density declines in relation to

upland habitat and secondary prey items, in this case amphibians and birds. Although it has been

suggested that buzzards in the northern sections of their range are vole specialists (Lŏhmus,

2003), the results of this study support the view that within the UK they are broad generalists,

adapting their diet and density to prey availability (Tubbs, 1974). When these findings are applied

to shooting estates which have; 1) voluntary habitat preservation and maintenance (Oldfield et al.,

2003); 2) legal predator control providing high abundances of prey species (Tharme et al., 2001;

Beja et al., 2009) and; 3) constant or annually high densities of gamebirds, it could potentially

result in highly productive and relatively dense populations of buzzards. This is of concern for

both conservationists and estate owners. For estate owners, the concern is an economic one as

high densities of generalist avian predators have been observed to have an impact on the number

of gamebirds available to harvest (Redpath & Thirgood, 1999; Thirgood et al., 2000a; 2000b). For

conservationists the concern is one of illegal persecution in that, if this predator control were to

include the illegal killing of raptors it could, in theory, create a localised sink in which high

numbers of raptors are illegally killed. This has already been suggested to be restricting buzzard

densities at local scales (Elliot & Avery, 1991; Swann & Etheridge, 1995; Gibbons et al., 1995;

Holling, 2004).

40

It would appear that the next step in resolving the conflict would be address the issue directly by

conducting research on buzzard diet within shooting estates. This would be necessary for both

upland grouse moorland and lowland pheasant estates as the results of this study suggest buzzard

densities, and therefore impact will be different for both. An accurate and unbiased assessment

of diet will be essential for any such research and of the three possible methods, data collection

through remote cameras is recommended.

41

42

7. References:

Anon. (2009). Birdcrime: Offences Against Wild Bird Legislation 2009. RSPB, Sandy.

www.rspb.org.uk/Images/birdcrime_tcm9-260567.pdf (Accessed on the 31st May 2011).

Arroyo, B. (2002). REGHAB Project (Reconciling Gamebird Hunting and Biodiversity): Conclusions

from Workshop II. Centre for Ecology and Hydrology (CEH).

http://hdl.handle.net/10261/8254 (Accessed on the 15st June 2011).

Arroyo, B., Viñuela, J. (2001). REGHAB Project (Reconciling Gamebird Hunting and Biodiversity):

Conclusions from Workshop I. Centre for Ecology and Hydrology (CEH).

http://hdl.handle.net/10261/8253 (Accessed on the 19th July 2011).

Austin, G. & Houston, D. (1997). The breeding performance of the Buzzard Buteo buteo in

Argyll, Scotland and a comparison with other areas in Britain. Bird Study, 44, pp. 146-154.

Austin, G.E., Thomas, C.J., Houston D.C. & Thompson, D.B.A. (1996). Predicting the

spatial distribution of buzzard Buteo buteo nesting areas using Geographical Information

System and remote sensing. Journal of Applied Ecology, 33, pp. 1541-1550.

Bains, D., Moss, R. & Dugan, D. (2004). Capercaillie breeding success in relation to forest

habitat and predator abundance. Journal of Applied Ecology 41, (1), pp.59-71.

Baker, P., Furlong, M., Southern, S. & Harris, S. (2006). The potential impact of red fox

Vulpes vulpes predation on agricultural landscapes in lowland Britain. Wildlife Biology, 12, (1),

pp.39-50.

Bangs, E.E., Fontaine, J.A., Jimenez, M.D., Meier, T.J., Bradley, E.H., Niemeyer, C.C.,

Smith, D.W., Mack, C.M., Asher, V. & Oakleaf. J.K. (2005). Managing wolf-human conflict

in the north-western United States. Editors: Woodroffe, R., Thirgood S., Rabinowitz, E.

People and Wildlife: Conflict or Coexistence?, Cambridge University Press, London, pp. 340–356.

Beier, P. & Drennan, J.E. (1997). Forest structure and prey abundance in foraging areas of

Northern Goshawks. Ecological applications, 7, (2), pp. 564-571.

Benton, T., Vickery, J.A. & Wilson, J.D. (2003). Farmland biodiversity: is habitat

heterogeneity the key? Trends in Ecology and Evolution, 18, (4), pp. 182-188.

Bijlsma,E. (1997). Buzzard. pp. 160-161 in (eds) Hagemeijer W.J.M., Blair M.J. The EBCC

Atlas of European breeding birds, their distribution and their abundance. T. & A.D. Poyser, London.

43

Bjǿrnstad, O.N., Falck, W. & Stenseth, N.C. (1995). A geographic gradient in small mammal

density fluctuations: a stastical modelling approach. Proceedings: Biological Sciences, 262, (1364),

pp. 127-133.

Bloxton, T.D. (2002). Prey abundance, space use, demography, and foraging habitat of

Northern Goshawks in Western Washington. Unpublished Masters Thesis, University of

Washington. http://www.ruraltech.org/pubs/theses/bloxton/bloxton_ms_thesis.pdf

(Accessed on the 21st August 2011).

Bolton, M., Butcher, N., Sharpe, F., Stevens, D. & Fisher, G. (2007). Remote monitoring of

nests using digital camera technology. Journal of Field Ornithology, 78, (2), pp. 213-220.

Bro, E., Arroyo, B. & Migot, P. (2006). Conflict between grey partridge Perdix perdix

hunting and hen harrier Circus cyaneus protection in France: a review. Wildlife Biology, 12, (3),

pp.233-247

Brown, A.F. & Stillman, R.A. (1998). The return of the Merlin to the south Pennines. Bird

Study, 45, pp.293-301.

Buckland, S .T., Anderson, D. R., Burnham. K. P., Laake, J. L., Borchers, D. L. & Thomas,

L. (2001). Introduction to distance sampling: estimating abundance of biological populations. Oxford

University Press, Oxford, UK

Buckland, S.T, Anderson,D.R., Burnham, K.P. & Laake, J.L. (1993). Distance sampling:

estimating abundance of biological populations. Chapham & Hall, London, U.K.

Burnham, K. P. & Anderson, D. R. (2002). Model Selection and Multimodel Inference: a practical

information-theoretic approach, 2nd edition. Springer-Verlag, New York

Butet, A., Michel, N., Rantier, Y., Comor, V., Hubert-Moy, L., Nabucet, J. & Delettre, Y.

(2010) Responses of common buzzard (Buteo buteo) and Eurasian kestrel (Falco tinnunculus) to

land use changes in agricultural landscapes of Western France. Agriculture, Ecosystems &

Environment, 138, (3-4), pp. 152-159.

Clapham, A.R., Tutin, T.G. & Warburg, E.F. (1981). Excursion flora of the British Isles: 3rd

Edition. Cambridge University Press, Cambridge

Clarke, R., Combridge, M. & Combridge, P. (1997). A comparison of the feeding ecology of

wintering Hen Harriers Circus cyaneus centred on two heathland areas in England. Ibis, 139,

pp. 4-18.

44

Clements, R. (2002). The common buzzard in Britain: a new population estimate. British

Birds, 95, pp. 377-383.

Collopy, M. (1983). A comparison of direct observations and collections of prey remains in

determining the diet of golden eagles. Journal of Wildlife Management, 47, pp. 360-368.

Crawley, M.J. (2007). The R Book. John Wiley & Sons Ltd, Chichester, England.

Dare, P.J. (1961). Ecological observations on a breeding population of common buzzard (Buteo buteo)

with particular reference to diet and feeding habits, PhD thesis, unpublished, University of Exeter.

Dare, P.J. & Barry, J.T. (1990). Population size, density and regularity in nest spacing of

buzzards Buteo buteo in two upland regions of North Wales. Bird Study, 37, (1), pp. 23-29.

Dare, P.J. (1995). Breeding success and territory features of Buzzards Buteo buteo in

Snowdonia and adjacent uplands of north Wales. Welsh Birds, 1, pp. 69-78.

Dawson, R.D. & Bortolotti, G.R. (2000) Reproductive success of American Kestrels: The

role of prey abundance and weather. The Condor, 102 (4) pp. 814-822.

Elliott, G.D. & Avery, M.I. (1991). A review of reports of Buzzard persecution 1975-1989.

Bird Study, 38, pp. 52-56.

Evans, D.M., Redpath, S.M., Elston, D.A., Evans, A., Mitchell, R.J. & Dennis, P. (2006) To

graze or not to graze? Sheep, voles, forestry and nature conservation in the British uplands.

Journal of Applied Ecology, 43, (3) pp.499-505.

Ferguson-Lees, J. & Christie, D. A. (2001). Raptors of the world. Houghton Mifflin, Boston.

Fielding, A.H., Haworth, P.F., Morgan, D.H., Thompson, D.B.A. & Whitfield, D.P. (2003).

The impact of Golden Eagles on a diverse bird of prey assemblage. In Thompson, D.B.A.,

Redpath, S.M., Fielding, A.H., Marquiss, M. & Galbraith, C.A. (eds) Birds of Prey in a Changing

Environment pp. 221–244. Edinburgh: The Stationery Office.

Gibbons, D. Gates, S., Green, E.R., Fuller, J.R. & Fuller, M.R. (1995). Buzzards Buteo buteo

and Ravens Corvus corax in the uplands of Britain: limits to distribution and abundance. Ibis,

137, pp.75-84.

Gipps, J.H.W. & Alibhai, S.K. (1991). Field vole Microtus agrestis. Handbook of British

Mammals, pp. 203–208. Blackwell Publishing, Oxford, UK.

45

Graham, I., Redpath, S. & Thirgood, S. (1995). The diet and breeding density of Common

Buzzards Buteo buteo in relation to indices of prey abundance. Bird Study, 42, (2), pp. 165-173.

Graham, K., Beckerman, A.P. & Thirgood, S.(2004). Human-predator-prey conflicts:

ecological correlates, prey losses and patterns of management. Biological Conservation, 122 ,(2),

pp.159-171.

Green, S. (2011). Change the law on buzzards. Country Life (August Edition) p. 57.

Gremel, S. (2001). Spotted Owl monitoring in Olympic National Park: 2001 annual report. U.S. Dept.

Int., National Park Service, Olympic National Park, Port Angeles, WA.

http://www.reo.gov/monitoring/reports/nso/N110202_4_OLYNPS01.pdf (Accessed on

the 22nd July 2011).

Halley, D.J. (1993). Population changes and territorial distribution of Common Buzzards

Buteo buteo in the Central Highlands. Bird Study, 40, (1), pp. 24-30.

Hanski, I., Hansson, L. & Henttonen, H. (1991). Specialist predators, generalist predators,

and the microtine rodent cycle. Journal of Animal Ecology, 60, pp. 353-367.

Hanski, I., Henttonen, H., Korpimäki, E., Oksanen, L. & Turchin, P. (2001). Small-rodent

dynamics and predation. Ecology, 82, (6), pp. 1505-1520.

Harradine, J., Reynolds, N. & Laws, T. (1997). Raptors and Gamebirds: A Survey of Game

Managers Affected by Raptors. British Association for Shooting and Conservation, Rossett, UK.

Henderson,I., Parrott, D. & Moore, N. (2004). Racing pigeons- impact of raptor predation. A report

to Scottish Natural Heritage, Central Science Laboratory,

Hodder, K.H., Kenward, R.E., Walls, S.S. & Clarke, R.T. (1998). Estimating core ranges: a

comparison of techniques using the common buzzard (Buteo buteo). Journal of Raptor

Research, 32, (2), pp. 82-89.

Holling, C.S. (1959). Some characteristics of simple types of predation and parasitism.

Canadian Entomologist, 91, pp. 385-398.

Holling, M. (2003). Buzzards (Buteo buteo) on the increase in Lothian and Borders, Scotland.

In Thompson, D.B.A., Redpath, S.M., Fielding, A.H., Marquiss, M. & Galbraith, C.A. (eds)

Birds of Prey in a Changing Environment pp. 173-178. Edinburgh: The Stationery Office.

46

Hudson, P.J. (1992). Grouse in Space and Time- the Population Biology of a Managed

Gamebird. The Game Conservancy Trust, Fordingbrige, Hampshire

Hume, R. (2007). Complete birds of Britain and Europe (revised and updated). Dorling Kingersley

Limited, London.

IUCN (2010). Buteo buteo. IUCN Red List of Threatened Species. Version 2010.4.

<www.iucnredlist.org> . Downloaded on 05 June 2011.

Jardine, D.C. (2003). Buzzards (Buteo buteo) on Colonsay 1990-2000: numbers and breeding

performance. pp.179-182. In Thompson, D.B.A., Redpath, S.M., Fielding, A.H., Marquiss,

M. & Galbraith, C.A. (eds). Birds of Prey in a Changing Environment Edinburgh: The Stationery

Office.

Johnson, J.B. & Omaland, K.S. (2004) Model selection in ecology and evolution. Trends in

Ecology and Evolution, 19, (2), pp.101-108.

Kenward, R.E. (1982). Goshawk hunting behaviour, and range size as a function of food

and habitat availability. Journal of Applied Ecology, 51, pp. 69-80.

Kenward, R., Hall, D., Walls, S. & Hodder, K. (2001). Factors affecting predation by

buzzards Buteo buteo on released pheasants Phasianus colchicus. Journal of Applied Ecology, 38, (4),

pp. 813-822.

Korpimäki E. & Norrdahl, K. (1991). Numerical and functional responses of kestrels, short-

eared owls, and long-eared owls to vole densities, Ecology 72 (3) pp. 814-826.

Kostrzewa, R. & Kostrzewa, A. (1991).Winter weather, spring and summer density, and

subsequent breeding success of Eurasian Kestrel, Common Buzzard and Northern

Goshawks. The Auk, 108, pp. 342-347.

Kostrzewa, A. (1996). A comparative study of nest-site occupancy and breeding

performance as indicators for nesting-habitat quality in three European raptor species.

Ethology, Ecology & Evolution, 8, pp. 1-18.

Kruckenhauser, L., Haring, E., Pinsker, W., Riesing, M. J., Winkler, H., Wink, M. &

Gamauf, A. (2004). Genetic vs. morphological differentiation of Old World buzzards (genus

Buteo, Accipitridae). Zoologica Scripta, 33, pp. 197–211.

Krüger, O. & Linderström, J. (2001). Habitat heterogeneity affects population growth rate in

goshawk Accipiter gentilis. Journal of Animal Ecology, 70, (2), pp.173-181.

47

Krüger, O. (2002). Interactions between common buzzard Buteo buteo and goshawk Accipiter

gentilis: trade-offs revealed by a field experiment. Oikos, 96, pp. 441–452.

Lack, D. (1954). The natural regulation of animal numbers. Oxford University Press, Oxford.

Lack, D. (1966). Population studies of birds. Oxford University Press, Oxford.

Lambin, X., Petty, S. & Mackinnon, J. (2000). Cyclic dynamics in field vole populations and

generalist predation. Journal of Animal Ecology, 69, pp. 106-118.

Lewis, S.B., Fuller, M.R. & Titus, K. (2004). A comparison of 3 methods for assessing raptor

diet during the breeding season. Wildlife Society Bulletin, 32, pp.373-385.

Lŏhmus, A. (2003). Are certain habitats better every year? A review and a case study on

birds of prey. Ecography, 26, (5), pp. 545-552.

Lovegrove, R. (2007). Silent fields: the long decline of a nation’s wildlife. Oxford University Press,

Oxford.

Michalski, F., Boulhosa, L.P., Faria, A. & Peres, C.A. (2005). Human-wildlife conflicts in a

fragmented Amazonian forest landscape: determinants of large felid depredation on

livestock. Animal Conservation, 9, pp.179-188.

Mineau, P., Fletcher, M.R., Glaser, L.C., Thomas, N.J., Brassard, C., Wilson, L.K., Elliott,

J.E., Lyon, L.A., Henny, C.J., Bollinger, T. & Porter, S.L. (1999). Poisoning of raptors with

organophosphorus and carbamate pesticides with emphasis in Canada US and UK. Journal of

Raptor Research, 33, pp. 1-37.

Moore, N.W. (1957). The past and present status of the buzzard in the British Isles. British

Birds, 69, pp. 193-201.

Newson, E.N., Evans, K.L., Noble, D.G., Greenwood, J.J.D. & Gaston, K.J. (2008) Use of

distance sampling to improve estimates of national population sizes for common and

widespread breeding birds in the UK. Journal of Applied Ecology, 45, pp. 1330-1338.

Newton, I. (1979). The population ecology of raptors. T. & A.D. Poyser, Berkhamsted, UK.

Newton, I., Davis, P.E. & Davis, J.E. (1982). Ravens and buzzards in relation to sheep-

farming and forestry in Wales. Journal of Applied Ecology, 19, pp.681-706.

O’Toole, L., Fielding, A.H. & Haworth, P.F. (2002). Re-introduction of the golden eagle

into the Republic of Ireland. Biological Conservation, 103, (3), pp. 303-312.

48

Oldfield, T.E.E., Smith, R.J., Harrop, S.R. & Leader-Williams, N. (2003) Field sports and

conservation in the United Kingdom. Nature, 423, pp. 531-533.

Ontiveros, D., Pleguezuelos, J. & Caro, J. (2004). Prey density, prey detectability and food

habits: the case of Bonelli’s eagle and the conservation measures. Biological Conservation, 123,

(1), pp. 19-25.

Park, K.J., Graham, K.E., Calladine, J. & Wernham, C.W. (2008). Impacts of birds of prey

on gamebirds within the UK: a review. Ibis, 150, (1), pp. 9-26.

Penteriani, V. & Faivre, B. (1997). Breeding density and landscape-level habitat selection of

common buzzards (Buteo buteo) in a mountain area (Abruzzo Apennines, Italy). Journal of

Raptor Research, 31, (3), pp. 208-212.

Petrovan, S.O., Barrio, I.C., Ward A.I. & Wheeler, P.M. (2011). Farming for pests? Local

and landscape-scale effects of grassland management on rabbit densities. European Journal of

Wildlife Research, 57, (1), pp. 27-34.

Petty, S. J. (1992). Ecology of the tawny owl Strix aluco in the spruce forests of Northumberland and

Argyll. PhD thesis, Open University.

Petty, S.J. (1999). Diet of tawny owls (Strix aluco) in relation to field vole (Microtus agrestis)

abundance in a conifer forest in northern England. Journal of Zoology, 248, pp. 451–465.

Petty, S.J. &Thomas, C.J. (2003). Distribution, numbers and breeding performance of tawny

owls (Strix aluco). in relation to clear-cutting in a conifer forest in northern England. Birds of

Prey in a Changing Environment (eds) Thompson, D.B.A., Redpath S.M., Fielding A.H.,

Marquiss M., Galbraith, C.A.). pp. 111-129. The Stationary Office, Edinburgh, UK.

Picozzi, N. & Wier, D. (1974). Breeding biology of the Buzzard in Speyside. British Birds, 67,

pp.199-210.

Pietersen, D.W. & Symes, C.T. (2010). Assessing the diet of the Amur Falcon Falco amurensis

and Lesser Kestrel Falco naumanni using stomach contents analysis. Ostrich, Journal of African

Ornithology, 81, (1), pp.3 9-44.

Preston, C. (1990). Distribution of raptor foraging in relation to prey biomass and habitat

structure. The Condor, 92, pp. 107-112.

49

Public & Corporate Economic Consultants. (2006). The Economic and Environmental Impact

of Sporting Shooting. Commissioned report for British Association for Shooting &

Conservation, the Countryside Alliance and the Country Land and Business Association, in

association with Game Conservancy Trust. Cambridge, UK.

http://www.shootingfacts.co.uk/ (Accessed on the 25th August 2011).

Ransom, J.I. (2011) Customizing a rangefinder for community-based wildlife conservation

initiatives. Biodiversity Conservation, 20, pp.1603-1609

Redpath, S. & Thirgood, S. (1999). Numerical and functional responses in generalist

predators: hen harriers and peregrines on Scottish grouse moors. Journal of Animal Ecology, 68,

pp. 879-892.

Redpath, S., Clarke, R., Madders, M. & Thirgood, S. (2001). Assessing raptor diet:

comparing pellets, prey remains, and observational data at hen harrier nests. The Condor, 103,

pp. 184-188.

Redpath, S.M., Thirgood, S.J. & Clarke, R. (2002). Field vole Microtus agrestis abundance and

hen harrier Circus cyaneus diet and breeding in Scotland. Ibis, 144, pp. E33–E38.

Reif, V., Tornberg, R., Jungell S. & Korpimaki, E. (2001). Diet variation of common

buzzards in Finland supports the alternative prey hypothesis. Ecography, 24, pp. 267-274.

Reif, V, Jungell, S., Korpimaki, E., Tornberg, R., Mykra, S. (2004) Numerical response of

common buzzards and the predation rate of amin and alternative prey under fluctuating food

conditions. Annales Zoologici Fennici, 41 pp.599-607.

Reynolds, R.T., Graham, R.T., Reiser, M.H., Bassett, P.L., Kennedy, D.A., Boyce,D.A.,

Goodwin, G., Smith, R. & Fisher, E.l. (1992). Management recommendations for the Northern

Goshawks in the southwestern United States. United States Forest Service, General Technical

Report, RM-217.

Reynolds, R.T., Wiens, D.J., Joy, S.M. & Salafsky, S.R. (2005). Sampling considerations for

the demographic and habitat studies of Northern Goshawks. Journal of Raptor Research, 39, (3),

pp.274-285.

Risely, K., Baillie, S.R., Eaton, M.A., Joys, A.C., Musgrove, A.J., Noble, D.G., Renwick, A.R.

& Wright, L.J. (2010). The Breeding Birds Survey 2009. BTO Research Report 559. British Trust for

Ornithology, Thetford.

50

Rodenhouse, N.L., Sherry, T.W. & Holmes, R.T. (1997) Site- dependant regulation of

population size: a new synthesis. Ecology, 78, (7), pp. 2025-2042.

Roderiguez, C. & Bustamante, J. (2003). The effect of weather on Lesser Kestrel breeding

success: can climate change explain population declines? Journal of Animal Ecology, 72, (5),

pp.793-810.

Rodgers, A.S., DeStefano, S. & Ingraldi, M.F. (2005). Quantifying northern Goshawk diets

using remote cameras and observations for blinds. Journal of Raptor Research, 39, (3), pp. 303-

309.

Rodriguez, B., Siverio, F., Rodríguez, A., Siverio, M. Hernadez, J.J. & Figuerola, J. (2010).

Density, habitat selection and breeding biology of Common Buzzards Buteo buteo in an insular

environment. Bird Study, 57, pp. 75-83.

Sagør, J.T., Swenson, J.E. & Røskaft, E. (1997). Compatibility of brown bear Ursus arctos

and free-ranging sheep in Norway. Biological Conservation, 81, (1), pp.91-95.

Sakamoto, Y., Ishiguro, M. & Kitagawa, G. (1986). Akaike information criterion statistics. KTK

Scientific Publishers, Tokyo.

Salafsky, S.R. (2004). Covariation between prey abundance and Northern Goshawk fecundity on the

Kaibab Plateau, Arizona. MSc Thesis, Colorado State University, Fort Collins, Colorado.

Salafsky, S.R., Reynolds, R.T. & Noon, B.R. (2005). Patterns of temporal variation in

Goshawk reproduction and prey resources. Journal of Raptor Research, 39 ,(3), pp.237-246.

Salafsky, S.R., Reynolds, R.T., Noon, B.R. & Wiens, J.A. (2007) Reproductive responses of

Northern Goshawks to variable prey populations. The Journal of Wildlife Management, 71,

(&) pp.2274-2283.

Salamolard, M., Butet, A., Leroux, A. & Bretagnolle. V. (2000) Resonces of an avian

predator to variation in prey density at a temperate latitude. Ecology, 81, pp. 2428-2441.

Sánchez, R., Margalida, A., González, M.L. & Oria, J. (2008). Biases in diet sampling

methods in the Spanish Imperial Eagle Aquila adalberti. Ornis Fennica, 85, pp. 82-89.

Seaton, R., Hyde, N., Holland, J.D., Minot, E.O. & Springett, B.P. (2008). Breeding season

diet and prey selection of the New Zealand Falcon (Falco novaeseelandiae) in a plantation forest.

Journal of Raptor Research, 42, (4), pp.256-264.

51

Selàs, V. (2001a). Predation on reptiles and birds by the common buzzard, Buteo buteo, in

relation to changes in its main prey, voles. Canadian Journal of Zoology, 79, (11), pp.2086-2093.

Selàs, V. (2001b). Breeding density and brood size of the common buzzard Buteo buteo in

relation to snow cover in spring. Ardea, 89, (3), pp.471-479.

Selàs, V., Tveiten, R. & Aanonsen, O. M. (2007). Diet of Common Buzzards (Buteo buteo). in

southern Norway determined from prey remains and video recordings. Ornis Fennica, 84, pp.

97-104.

Sergio, F., Scandolara, C., Marchesi, L., Pedrini, P. & Penteriani, V. (2005). Effect of agro-

forestry and landscape changes on common buzzards (Buteo buteo) in the Alps: implications

for conservation. Animal Conservation, 8, (1), pp. 17-25.

Sim, I.M.W., Campbell. L., Pain, D. & Wilson, J.D. (2000). Correlates of the population

increase of Common Buzzards Buteo buteo in the West Midlands between 1983 and 1996. Bird

Study, 47, pp. 154-164.

Sim, I.M.W., Cross, A.V., Lamacraft, D.L. & Pain, D.J. (2001) Correlates of Common

Buzzard Buteo buteo density and breeding success in the West Midlands. Bird Study, 48, pp.

317-329.

Sim, I.M.W. (2003). Land use, common buzzards (buteo buteo) and rabbit (Oryctolagus cuniculus)

in the Welsh Marches. pp. 351-371 In Thompson, D.B.A., Redpath, S.M., Fielding, A.H.,

Marquiss, M. & Galbraith, C.A. (eds). Birds of Prey in a Changing Environment. Edinburgh: The

Stationery Office.

Simmons, R.E., Avery, D.M. & Avery, G. (1991). Biases in diets determined from pellets

and remains: correction factors for a mammal and bird-eating raptor. Journal Raptor Research,

25, (3), pp, 63-67.

Smith, M., C. (2007). The return of the common buzzard to Warwickshire and it’s possible use as an

indicator for the return of the common raven and the red kite. Unpublished thesis, University of

Manchester, Manchester.

Smout, S., Asseburg, C., Matthiopoulos, J., Fernández, C., Redpath, S., Thirgood, S. &

Harwood, J. (2010). The Functional Response of a Generalist Predator. PLoS ONE 5, (5):

e10761. doi:10.1371/journal.pone.00761

Snow, D.W. & Perrins C.M. (1998). The Birds of the Western Palearctic Concise Edition. Oxford

University Press, Oxford, UK.

52

Sonerud, G.A. (1992). Functional responses of birds of prey: Biases due to the load-size

effect in central place foragers. Oikos, 63, (2), pp. 223-232.

Sonerud, G.A. (1997). Hawk owls in Fennoscandia: population fluctuations, effects of

modern forestry, and recommendations on improving foraging habitats. Journal of Raptor

Research, 31, (2), pp. 167-174.

Spidso, T.K. & Selàs, V. (1988) Prey selection and breeding success in the common buzzard

Buteo buteo in relation to small rodent cycles in southern Norway. Fauna Novegica, 11, (2),

pp.61-66.

Steenhof, K., Kochert, M.N. & McDonald, T.L. (1997). Interactive effects of prey and

weather on golden eagle reproduction. Journal of Animal Ecology, 66, pp.350-362.

Stoate, C. & Szczur, J. (2001). Could game management have a role in the conservation of

farmland passerines? A case study from a Leicestershire farm. Bird Study, 48, (3), pp. 279-292.

Swann, R.L. & Etheridge, B. (1995). A comparison of breeding success and prey of the

Common Buzzard in two areas of northern Scotland. Bird Study, 42, (2), pp. 37-43.

Taylor, K., Hudson R. & Horne G. (1988). Buzzard breeding distribution and abundance in

Britain and Northern Ireland in 1983. Bird Study, 35, pp. 109-118.

Taylor, M.J., Sharp, E.A. & Giela, A. (2009). A Report of Investigations in Scotland: Pesticide

Poisoning of Animals in 2008. Science and Advice for Scottish Agriculture (SASA)., Chemistry

Branch, Roddinglaw Road, Edinburgh, Scotland.

Thirgood, S. J., Redpath, S.M. & Graham, I.M. (2003). What determines the foraging

distribution of raptors on heather moorland? Oikos, 100, pp.15-24.

Thirgood, S.J., Redpath, S.M., Campbell, S. & Smith, A. (2002). Do habitat characteristics

influence predation on red grouse? Journal of Applied Ecology, 39, pp.217-225.

Thirgood, S.J., Redpath, S.M., Haydon, D.T., Rothery, P., Newton, I. & Hudson, P.J.

(2000a) Habitat loss and raptor predation: disentangling long and short term causes of red

grouse decline. Proceedings of the Royal Society London B, 267, pp.651-656

Thirgood, S.J., Redpath,S.M., Rothery, P. & Aebischer, N.J. (2000b). Raptor predation and

population limitation in red grouse. Journal of Applied Ecology, 69, (3), pp.504-516.

Thomas, L., Laake, J.L., Rexstad, E., Strindberg, S., Marques, F.F.C., Buckland, S.T.,

Borchers, D.L., Anderson, D.R., Burnham, K.P., Burt, M.L., Hedley, S.L., Pollard, J.H.,

53

Bishop, J.R.B. & Marques, T.A. (2009). Distance 6.0. Research Unit for Wildlife Population

Assessment, University of St. Andrews, UK.

Thompson, W.L. (2004). Sampling rare or elusive species; concepts, designs and techniques

for estimating population parameters. (Ed) Island Press, London.

Tornberg, R. & Reif, V. (2007). Assessing the diet of birds of prey: a comparison of prey

items found in nests and images. Ornis Fennica, 84, pp. 21-31.

Trout, R., Langton, S., Smith, G. & Haines-Young, R. (2000). Factors affecting the

abundance of rabbits (Oryctolagus cuniculus) in England and Wales. Journal of the Zoological Society

of London, 252, pp. 227-238.

Tubbs, C. (1972). Analysis of nest record cards for the Buzzard. Bird Study, 19, (2), pp. 97-

104.

Tubbs, C. (1974). The buzzard. David & Charles, Newton Abbot.

Tubbs, C.R., Tubbs, J.M. (1985). Buzzards and land use in the New Forest, Hampshire,

England. Biological Conservation, 31, pp. 41-65.

UK Raptor Working Group, (2000). UK Raptor Working Group, Report of the UK Raptor

Working Group. DETR/JNCC, Bristol, UK.

Valkama, J., Korpimaki, E., Arroyo, B., Beja, P., Bretagnolle, V., Bro, E., Kenward, R.E.,

Maǹosa, S., Redpath, S.M., Thirgood, S.J. & Viǹuela, J. (2005). Birds of prey as limiting

factors of gamebird populations in Europe: a review. Biological Review, 80, pp. 171-203.

Vergara, P. (2010). Time-of-day bias in diurnal raptor abundance and richness estimated by

road surveys. Revista Catalana d’Ornitologia. 26, pp. 22-30.

Walls, S.S. & Kenward, R.E. (1995). Movements of radio-tagged Common Buzzards Buteo

buteo in their first year. Ibis, 137, pp.177-182

Walls, S.S. & Kenward, R.E. (1998). Movements of radio-tagged Buzzard Buteo buteo in early

life. Ibis, 140, pp. 561-568.

Watson, M., Aebischer, N.J., Potts, G.R. & Ewald, J.A. (2007). The relative effects of raptor

predation and shooting on overwinter mortality of grey partridges in the United Kingdom.

Journal of Applied Ecology, 44, pp. 972-982

54

Whitfield, D.P., Fieldling, A.H., McLeod, D.R.A. & Haworth, P.F. (2003).The effects of

persecution on age of breeding and territory occupation in golden eagles in Scotland.

Biological Conservation 118, (2), pp.249-259

Whittingham, M.J. & Evans, K.L. (2004). The effects of habitat structure on predation risk

of birds in agricultural landscapes. Ibis, 146, (2), pp.210-220

Widén, P. (1994). Habitat quality for raptors: a field experiment. Journal of Avian Biology, 25,

pp. 219-223.

Woodroffe, R., Thirgood, S. & Rabinowitz, A. (2005). People and Wildlife: Conflict or

Coexistence? Cambridge University Press.

Wolfe, A. & Long, A. (1997). Distinguishing between hair fibres of the rabbit and the

mountain hare in scats of the Red Fox. Journal of Zoology (ZSL), 242, pp. 370-375.

55

8. Appendix

Appendix 1.

Table 1: Liner regressions between the vole sign indices (VSI) and live trapping estimation of

density. Values in brackets are standard errors of parameter estimates. Taken from Lambin et al.,

2000.

Season Regression n R² F P

Vole density

Spring (March-May) Nha¯¹=13.10(1.76)*VSI+9.96(11.6) 36 0.62 55.12 <0.001

Summer (June to August) Nha¯¹=14.11(1.33)*VSI+14.60(9.34) 48 0.71 111.76 <0.001

56

Appendix 2. Full prey species list from direct observations, prey remains and pellet

analysis methods

Direct Obs. Prey Pellet Total % Total % Total %

Amphibians 94 35.7 7 4.1 10 8.5 Common Toad Bufo bufo 57 21.7 6 3.5 - - Common Frog Rana temporaria 37 14.1 1 0.6 - - Reptiles 5 1.9 0 0.0 1 0.8 Slow-worm Anguis fragilis 1 0.4 0 0 - - Adder Vipera berus 4 1.5 0 0 - - Birds 31 11.8 60 35.3 18 15.3 Teal Anas crecca 1 0.4 0 0 - - Red Grouse L. lagopus scoticus 4 1.5 1 0.6 - - Black grouse Lyrurus tetrix 0 0 1 0.6 - - Pheasant Phasianus colchicus 1 0.4 7 4.1 - - Unidentified wader 1 0.4 0 0 - - Woodpigeon Columba palumbus 0 0 4 2.4 - - Tawny owl Strix aluco 0 0 3 1.8 - - Great Spotted Woodpecker Dendrocopos major 0 0 1 0.6 - - Chaffinch Fringilla coelebs 0 0 5 2.9 - - Willow warbler Phylloscopus trochilus 0 0 1 0.6 - - Medow pipet Anthus pratensis 4 1.5 3 1.8 - - Dunnock Prunella modularis 0 0 1 0.6 - - Great Tit Parus major 0 0 1 0.6 - - Coal Tit Parus ater 0 0 1 0.6 - - Robin Erithacus rubecula 0 0 1 0.6 - - Unidentified Small Passerine 8 3.0 3 1.8 - - Blackbird Turdus merula 3 1.1 1 0.6 - - Mistle Thrush Turdus viscivorus 4 1.5 2 1.2 - - Song Thrush Turdus philomelos 0 0 1 0.6 - - Jay Garrulus glandarius 0 0 1 0.6 - - Magpie Pica Pica 0 0 2 1.2 - - Jackdaw Corvus monedula 0 0 8 4.7 - - Crow Corvus corone 5 1.9 10 5.9 - - Rook Corvus frugilegus 0 0 2 1.2 - - Mammals 110 41.8 98 57.6 89 75.4 Hedgehog Erinaceau europaeus 0 0 1 0.6 1 0.8 Weasel Mustela nivalis 4 1.5 0 0 1 0.8 Stoat Mustela erminea 2 0.8 0 0 0 0 mole Talpa minor 18 6.8 5 2.9 11 9.3 Rabbit Oryctolagus cuniculus 15 5.7 39 22.9 - - Brown Hare Lepus europaeus 0 0 1 0.6 - - Unidentified lagomorph sp. 0 0 - - 26 22.0 Water Vole Arvicola terrestris 1 0.4 - - - - Unidentified Cricetidae species 54 20.5 50 29.4 50 42.4 Unidentified Apodemus species 8 3.0 1 0.6 0 0 Unidentified Soricidae species 8 3.0 1 0.6 0 0 Carrion 0 0 5 2.9 0 0 Sheep Ovis aries Roe Deer Capreolus capreolus Red Fox Vulpes vulpes

0 0 0

0 0 0

2 2 1

1.2 1.2 0.6

0 0 0

0 0 0

Unidentified prey 23 8.7 0 0 0 0

57

Total observations/items 263 170 118

Appendix 3: Still images from one of the nest cameras (Upland pasture C2). Wood mouse

Apodemus sylvaticus (top left), Unidentified duck sp. (top right), Mole Talpa minor (middle left),

Weasel Mustela nivalis (middle right), Common Frog Rana temporaria (bottom left), Common Toad

Bufo bufo (bottom right).

58

Appendix 4:

Non-native mature forestry: Detection probability of small passerine birds as function of the

distance to the observer (from line transect data in non-native forestry habitat, n=93). Columns -

number of detections in different distance classes, Line - Fit detection function (Software

Distance 6.0). (Chi²=2.98, df=2,P=0.23)

Rough grassland: Detection probability of small passerine birds as function of the distance to

the observer (from line transect data in rough grassland habitat, n=134). Columns - number of

detections in different distance classes, line - Fit detection function (Software Distance 6.0).

(Chi²=0.067, df=2,P=0.97)

59

Upland pasture: Detection probability of small passerine birds as function of the distance to

the observer (from line transect data in upland pasture habitat, n=114). Columns - number of

detections in different distance classes, line - Fit detection function (Software Distance 6.0).

(Chi²=0.72, df=4,P=0.95)

Pre-thicket forestry: Detection probability of small passerine birds as function of the distance

to the observer (from line transect data in pre-thicket forestry habitat, n=224). Columns -

number of detections in different distance classes, line - Fit detection function (Software

Distance 6.0). (Chi²=8.47, df=2, P=0.014)

60

Heather moorland: Detection probability of small passerine birds as function of the distance to

the observer (from line transect data in heather moorland habitat, n=121). Columns - number of

detections in different distance classes, line - Fit detection function (Software Distance 6.0).

(Chi²=3.21, df=3,P=0.359)

Clear cut: Detection probability of small passerine birds as function of the distance to the

observer (from line transect data in clear cut habitat, n=67). Columns - number of detections in

different distance classes, line - Fit detection function (Software Distance 6.0). (Chi²=1.39,

df=3,P=0.706)

61

Lowland pasture: Detection probability of small passerine birds as function of the distance to

the observer (from line transect data in lowland pasture habitat, n=87). Columns - number of

detections in different distance classes, line - Fit detection function (Software Distance 6.0).Chi

squared goodness of fit test: Chi²=2.44, df=3,P=0.48)

Native woodland: Detection probability of small passerine birds as function of the distance to

the observer (from line transect data in native woodland habitat, n=279). Columns - number of

detections in different distance classes, line - Fit detection function (Software Distance 6.0).

(Chi²=3.95, df=2, P=0.14)