PhD Thesis - Grazing ecology and performance of Soay sheep · Figure 2.5: Population trends of the...

Grazing ecology, parasitism and performance of Soay sheep on St. Kilda

Owen Russell Jones

A thesis submitted in fulfilment of the requirements for the degree of Doctor of Philosophy of the University of London and for the Diploma of Imperial College

London

2003

Abstract

(1) This thesis considers several aspects of the herbivore-forage-parasite interaction of

feral Soay sheep (Ovis aries L.), their food source, and their gastro-intestinal (GI)

parasites, on the island of Hirta (St. Kilda), Scotland. The main variables that affect

performance are diet quality, parasite burden, and weather effects. The effects of

these parameters may cross generations via the effect on maternal condition and

lactation quality.

(2) A description of seasonal patterns of diet composition and quality is presented.

This is followed by an assessment of seasonality and density dependence in the

composition, and quality, of available forage, and of net primary productivity.

(3) The role of seasonality, population density, and spatial scale, in the assessment of

distribution and selectivity patterns is then considered. The spatial scale at which

the analyses are made has a huge influence on the results, and thus the scale at

which assessments are made should be carefully considered. Techniques using

hierarchical cluster analysis were employed to select appropriate spatial scales, with

promising results.

(4) After this, the effects of maternal characteristics and environmental variables on

offspring survival and birth weight are examined. Terms for weather severity and

forage quality during gestation, both remained in the models alongside population

density, thus indicating the presence of weather effects and both interference and

exploitation competition. Maternal condition and GI parasite burden were also

important factors. These results have an important bearing on the population

dynamics of the system, because juvenile recruitment is one of the key parameters

in the population dynamics of large herbivores.

(5) An experiment investigating the effects of GI parasites on foraging behaviour found

no evidence of parasite induced anorexia or of any parasite induced changes in diet

composition in Soay sheep. However, a mean intake rate estimate of 689gDM/day

was made which compares well with estimates from Scottish Blackface sheep.

(6) Finally, the consequences and limitations of these findings are assessed and areas

requiring further study are highlighted.

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Acknowledgements

I would first like to thank Scottish Natural Heritage and the National Trust for Scotland

for permission to undertake work on St. Kilda. DERA/QinetiQ/SERCo provided

essential logistical support in the form of helicopter rides and the transport of vital

equipment. I am also indebted to the members of the Soay sheep project, especially to

Tim Clutton-Brock, Steve Albon, Bryan Grenfell and Josephine Pemberton who

initiated and maintained the current phase of research. Special thanks should also go to

Jill Pilkington who, with her huge experience of “all things ‘Kilda’”, was an enormous

help with practical matters such as the logistics of getting out to the island, advice on

the feasibility of my plans, the bolusing of experimental subjects and finding AWOL

sheep. Not to mention being good company!

Thanks must also go to Mick Crawley and Iain Gordon who gave me the opportunity to

work on St. Kilda and have advised and helped with ideas, statistics and experimental

design throughout the project. I also appreciate the input of Mark Rees and Claire De

Mazancourt who commented on earlier drafts of this work and thus improved the final

product. Ian Stevenson, with whom I have shared far too much time “messing about

with sheep” in unpleasant conditions, stimulated several aspects of this work with late

night discussions in the Foot and Mouth cursed spring of 2001. He was also responsible

for the creating the “Soay sheep database”, which vastly improved the construction of

several of the datasets that I have used. Thank-you.

Much of the work here would be impossible were it not for the hard work of a large

number of volunteers who have generously given their time to help on the project since

it started. Since I started work for this PhD in 2000 other volunteers have not only

helped me out personally in various small ways, by collecting samples, locating animals

etc. but have also made my time on St. Kilda more enjoyable with their company.

I should also mention Brian Preston who first introduced me to St. Kilda in 1998, when

I helped to carry out fieldwork for his thesis. I won’t forget being forced to traverse

Conachair, Mullach Mor and Mullach Sgar on a cold rainy morning with a hangover!

At The Macaulay Institute I am grateful to Ewen Robertson for help and advice on

practical matters, and Brenda Hector and Pat Moberly for carrying out various chemical

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analyses. I also thank Bob Mayes and Stuart Lamb for their advice and assistance on the

plant wax component analysis, and Roslyn Anderson for doing an excellent job with the

aforementioned analysis.

I am also grateful to the other Ph.D. students I have met whilst undertaking this thesis,

at Silwood Park, The Macaulay Institute and elsewhere, many of whom provided

inspiration, nights out and other diversions from work. They were; Conor Doherty,

Lindsey Hewitson, Mark Hampton, Dylan Childs, Ek del Val, Josie Harrell, Ryan

Keane, Louisa Tempest, Emma Pilgrim, Heidi Cunningham and, of course, Andrea Le

Fevre, who not only read and commented on earlier drafts of this work but also put up

with my moaning when I was stressed out.

Of course, I couldn’t function nearly as well if it wasn’t for the existence of the Puff Inn

bar and its regulars, especially Kenny Kombat, Martin, Cliff, DJ, Andy Cook, Colin,

Liz, Anne, Dougie and Greg who provided (possibly too) many hours of entertainment,

moments of madness, and the occasional hangover.

Lastly, I thank my parents who have generously supported me despite hardly ever

seeing me these days! It was worthwhile in the end…

This work was funded by a CASE studentship from the Natural Environment Research

Council and The Macaulay Institute.

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Table of Contents

ABSTRACT.....................................................................................................................2

ACKNOWLEDGEMENTS............................................................................................3 TABLE OF FIGURES.........................................................................................................8 TABLE OF TABLES........................................................................................................13

CHAPTER 1 : PARASITISM, FOOD INTAKE AND THE PERFORMANCE OF UNGULATE HERBIVORES................................................................................18

1.1 DIET AND FITNESS ............................................................................................20 1.1.1 Survival ...................................................................................................20 1.1.2 Fecundity.................................................................................................21

1.2 WEATHER ........................................................................................................23 1.3 FOOD INTAKE PARAMETERS .............................................................................24 1.4 SEASONALITY ..................................................................................................26 1.5 DISTRIBUTION..................................................................................................28 1.6 PARASITE BURDEN ...........................................................................................30 1.7 OBJECTIVES .....................................................................................................32

CHAPTER 2 : INTRODUCTION TO ST. KILDA AND THE SOAY SHEEP35 2.1 THE STUDY SITE ...............................................................................................36

2.1.1 Solid geology and soil types....................................................................39 2.1.2 The plant communities ............................................................................39

2.2 THE STUDY POPULATION: SOAY SHEEP.............................................................42 2.2.1 Population dynamics...............................................................................42 2.2.2 Macro-parasites of the Soay sheep .........................................................43

2.3 WEATHER ........................................................................................................48

CHAPTER 3 : DATA COLLECTION AND STATISTICAL METHODS ......51 3.1 CORE DATA......................................................................................................52

3.1.1 Population data.......................................................................................52 3.1.2 Spatial distribution..................................................................................53 3.1.3 Morphometric data .................................................................................53 3.1.4 Parasitological data................................................................................54 3.1.5 Weather data ...........................................................................................54 3.1.6 Vegetation parameters ............................................................................55

3.2 STATISTICAL METHODS ....................................................................................56

CHAPTER 4 : SEASONALITY IN FORAGE AND DIET QUALITY OF SOAY SHEEP ON ST. KILDA ...................................................................................59

4.1 ABSTRACT........................................................................................................60 4.2 INTRODUCTION.................................................................................................60 4.3 METHODS.........................................................................................................63

4.3.1 Primary production.................................................................................63 4.3.2 Sward botanical composition and biomass.............................................65 4.3.3 Diet botanical composition .....................................................................67 4.3.4 Diet and vegetation quality .....................................................................68 4.3.5 Grazing pressure.....................................................................................69 4.3.6 Statistical methods ..................................................................................69

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4.4 RESULTS ..........................................................................................................70 4.4.1 Primary production and offtake ..............................................................70 4.4.2 Sward composition and biomass.............................................................76 4.4.3 Diet botanical composition .....................................................................87 4.4.4 Diet and forage quality ...........................................................................90

4.5 DISCUSSION .....................................................................................................92

CHAPTER 5 : THE INFLUENCE OF SEASONALITY AND SPATIAL SCALE ON THE DISTRIBUTION PATTERNS AND HABITAT USE OF SOAY SHEEP 97

5.1 ABSTRACT........................................................................................................98 5.2 INTRODUCTION.................................................................................................98 5.3 METHODS.......................................................................................................101

5.3.1 Location and habitat choice data..........................................................101 5.3.2 Assignment to heft and vegetation availability .....................................102 5.3.3 Vegetation .............................................................................................103 5.3.4 Population density.................................................................................103 5.3.5 Selectivity ..............................................................................................104 5.3.6 Matching ...............................................................................................104 5.3.7 Statistical methods ................................................................................105

5.4 RESULTS ........................................................................................................106 5.4.1 Population counts .................................................................................106 5.4.2 Vegetation composition and quality......................................................106 5.4.3 Selectivity ..............................................................................................106 5.4.4 Matching ...............................................................................................110

5.5 DISCUSSION ...................................................................................................113

CHAPTER 6 : MATERNAL AND ENVIRONMENTAL EFFECTS ON OFFSPRING BIRTH WEIGHT AND EARLY SURVIVAL .................................117

6.1 ABSTRACT......................................................................................................118 6.2 INTRODUCTION...............................................................................................118 6.3 METHODS.......................................................................................................122

6.3.1 Study area and species ..........................................................................122 6.3.2 Birth date and survival..........................................................................123 6.3.3 Morphometric measurements................................................................123 6.3.4 Parasite burden.....................................................................................124 6.3.5 Population density.................................................................................125 6.3.6 Weather variables .................................................................................125 6.3.7 Vegetation variables .............................................................................127 6.3.8 Statistical methods ................................................................................128

6.4 RESULTS ........................................................................................................130 6.4.1 Birth date...............................................................................................130 6.4.2 Birth weight...........................................................................................131 6.4.3 Sexual differences in weight gain..........................................................134 6.4.4 Survival to weaning...............................................................................135

6.5 DISCUSSION ...................................................................................................143 6.5.1 Birth weight...........................................................................................143 6.5.2 Survival to weaning...............................................................................145

6.6 CONCLUSIONS ................................................................................................148

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CHAPTER 7 : FORAGING STRATEGY AND PARASITE BURDEN OF SOAY SHEEP ON ST. KILDA .................................................................................149

7.1 ABSTRACT......................................................................................................150 7.2 INTRODUCTION...............................................................................................150 7.3 METHODS.......................................................................................................153

7.3.1 Selection and treatment.........................................................................153 7.3.2 Intake parameters .................................................................................154 7.3.3 Statistical methods ................................................................................159

7.4 RESULTS ........................................................................................................160 7.4.1 Intake rate .............................................................................................160 7.4.2 Botanical composition...........................................................................161 7.4.3 Bite rate.................................................................................................163 7.4.4 Time allocation .....................................................................................164

7.5 DISCUSSION ...................................................................................................164

CHAPTER 8 : GENERAL DISCUSSION .........................................................169

APPENDIX: THE PLANT CUTICLE WAX COMPONENT CONCENTRATIONS OF SPECIES AVAILABLE ON ST. KILDA IN AUGUST 2001...............................................................................................................................179

BIBLIOGRAPHY .......................................................................................................182

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Table of Figures

Figure 1.1: The constituent variables of daily food intake in grazing animals………...25

Figure 2.1: The St. Kilda archipelago comprising Soay, Dun, Hirta, Boreray and the sea stacks. Inset shows location of St. Kilda in relation to the west coast of Scotland. The shaded line shows the boundary of the study area (from Stevenson, 1994). ...37

Figure 2.2: Part of the study area looking south-westwards from the slopes of Conachair showing the Head Dyke, the village, with Ruaival and Dun in the background. ...38

Figure 2.3: The island of Soay, the origin of Hirta’s Soay sheep, looking north-westwards from the Mullach Bi ridge.....................................................................38

Figure 2.4: The study area on Hirta, showing the coverage of the different vegetation types. The Head Dyke is shown as a bold line, with gaps marked in red. Buildings from before 1930 are in red, the military base is depicted in grey and the cleits are represented by black dots. * represent the positions of the three automated weather stations (see sections 2.3 and 3.1.5). The contours are in feet (after Nature Conservancy 1970). ................................................................................................41

Figure 2.5: Population trends of the Soay sheep on Hirta between 1952 and 2002. The unbroken red line represents the whole island population and the broken blue line represents the population using the study area, as estimated by mark recapture techniques. The open circles represent data that is considered to be unreliable (Clutton-Brock, Grenfell et al. 2003) and filled circles represent reliable data......43

Figure 2.6: Temporal changes in strongyle L3 density in different parts of Village Bay. SIGM=Signal’s Meadow, WESM=West Meadow, WESF=West Field, MIDF=Mid Field, GUNM=Gun Meadow. Data covers the Data covers the years 1991-1998..46

Figure 2.7: Spatial differences in larval strongyle density within the study area on St. Kilda. ANLA = An Lag, GUNM=Gun Meadow, MIDF=Mid Field, OLDV=Old Village, RUAI=Ruaival, SIGM=Signal’s Meadow, WESF=West Field and WESM=West Meadow. Data covers spring in years the range 1991-1998............46

Figure 2.8: Weather variables recorded between 1999 and 2002 by automatic weather stations on St. Kilda. (a) Maximum and average wind speeds (b) average daily precipitation, (c) solar radiation, (d) mean, maximum and minimum air temperature, (e) air, grass and soil temperatures.....................................................50

Figure 3.1: An example of a box plot, see the text for an explanation. ..........................58

Figure 4.1: A set of two pyramidal grazing exclosures on the Calluna vulgaris covered slopes of Conachair. The mesh-covered exclosures have a basal area of 1.5 x 1.5m and stand 1.2m tall. They are secured to the ground using metal tent pegs............64

Figure 4.2: The approximate timings of the sampling periods used to assess offtake and productivity throughout the year.............................................................................65

Figure 4.3: Above ground net primary production, estimated using grazing exclosures, of the inbye and outbye areas on Hirta. The inbye is formerly cultivated grassland and the outbye is mainly heathland. Estimates were made during the rapid growth phase (RGP: March-August), in late-summer (between August and October) and over the winter (October-March). Error bars represent ±1s.e.m.............................72

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Figure 4.4: Offtake from the inbye and outbye areas on Hirta. The inbye is formerly cultivated grassland and the outbye is mainly heathland. Estimates were made during the rapid growth phase (RGP: March-August), in late-summer (between August and October) and over the winter (October-March). Error bars represent ±1s.e.m. ...................................................................................................................73

Figure 4.5: Estimated biomass increment on the inbye and outbye areas on Hirta. The inbye is formerly cultivated grassland and the outbye is mainly heathland. Estimates were made during the rapid growth phase (RGP: March-August), in late-summer (between August and October) and over the winter (October-March). Error bars represent ±1s.e.m. ..................................................................................74

Figure 4.6: Estimated measurement error for the inbye and outbye areas on Hirta (see section 4.3.1 for details). The inbye is formerly cultivated grassland and the outbye is mainly heathland. Estimates were made during the rapid growth phase (RGP: March-August), in late-summer (between August and October) and over the winter (October-March). Error bars represent ±1s.e.m. .....................................................75

Figure 4.7: Mean annual net primary productivity of ungrazed vegetation in the outbye and inbye areas of the study area on Hirta. Error bars represent ±1s.e.m...............76

Figure 4.8: Total standing crop biomass of vegetation (gDM/m2) during spring (March) and summer (August) for each vegetation type represented in the study area on Hirta. See Table 4.1 for the species codes. Estimates were obtained from the weighted means of biomass estimates from tussock and gap plots, based on the “tussockiness” of the sward (see methods). Error bars represent ±1s.e.m..............79

Figure 4.9: Standing biomass of “quality” vegetation (gDM/m2) during spring (March) and summer (August) for each vegetation type represented in the study area on Hirta. See Table 4.1 for the species codes. Estimates were obtained from the weighted means of biomass estimates from tussock and gap plots, based on the “tussockiness” of the sward (see methods). Error bars represent ±1s.e.m..............80

Figure 4.10: Standing biomass of grass (gDM/m2) during spring (March) and summer (August) for each vegetation type represented in the study area on Hirta. See Table 4.1 for the species codes. Estimates were obtained from the weighted means of biomass estimates from tussock and gap plots, based on the “tussockiness” of the sward (see methods). Error bars represent ±1s.e.m. ...............................................81

Figure 4.11: Standing biomass of herbs (gDM/m2) during spring (March) and summer (August) for each vegetation type represented in the study area on Hirta. See Table 4.1 for the species codes. Estimates were obtained from the weighted means of biomass estimates from tussock and gap plots, based on the “tussockiness” of the sward (see methods). Error bars represent ±1s.e.m. ...............................................82

Figure 4.12: Standing biomass of DOM (gDM/m2) during spring (March) and summer (August) for each vegetation type represented in the study area on Hirta. See Table 4.1 for the species codes. Estimates were obtained from the weighted means of biomass estimates from tussock and gap plots, based on the “tussockiness” of the sward (see methods). Error bars represent ±1s.e.m. ...............................................83

Figure 4.13: Standing biomass of bryophytes (gDM/m2) during spring (March) and summer (August) for each vegetation type represented in the study area on Hirta. See Table 4.1 for the species codes. Estimates were obtained from the weighted

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means of biomass estimates from tussock and gap plots, based on the “tussockiness” of the sward (see methods). Error bars represent ±1s.e.m..............84

Figure 4.14: Standing biomass of woody C. vulgaris (gDM/m2) during spring (March) and summer (August) for each vegetation type represented in the study area on Hirta. See Table 4.1 for the species codes. Estimates were obtained from the weighted means of biomass estimates from tussock and gap plots, based on the “tussockiness” of the sward (see methods). Error bars represent ±1s.e.m..............85

Figure 4.15: Standing biomass of new-growth, young, C. vulgaris (gDM/m2) during spring (March) and summer (August) for each vegetation type represented in the study area on Hirta. See Table 4.1 for the species codes. Estimates were obtained from the weighted means of biomass estimates from tussock and gap plots, based on the “tussockiness” of the sward (see methods). Error bars represent ±1s.e.m...86

Figure 4.16: The botanical composition of the diets of Soay sheep in spring and summer as estimated using the plant faecal plant cuticle analysis technique. There were significant seasonal differences for Calluna vulgaris, Poa and bryophytes (see Table 4.15). Error bars represent ±1s.e.m. The components were Festuca spp. (FE), Calluna vulgaris (CA), Agrostis spp. (Ag), Poa spp. (Po), Holcus spp. (Ho), Lolium spp. (Lo), Nardus spp. (Na), Anthoxanthum spp. (An), Molinia spp. (Mo), Deschampsia spp. (De), Carex spp. (Cx), bryophytes (Bry) and unidentified grasses (Unk)...........................................................................................................88

Figure 4.17: The relationship between ranked availability of plant species within (a) the Head Dyke and (b) the study area and their ranked proportion representation in the diet. Availability was estimated from dry biomass in vegetation samples and diet was estimated by faecal plant cuticle analysis of faecal samples collected in spring (blue circles) and summer (red squares). The dashed line represents the line of no selection; points above this line indicate selection while points below the line indicate avoidance. Those species where there was a significant difference between availability and dietary abundance are indicated by a heavy lined symbol where those with no significant difference are plotted with a fine lined symbol. Significance was tested using a Wilcoxon test with α=0.05. The components were Festuca spp. (FE), Calluna vulgaris (CA), Agrostis spp. (Ag), Poa spp. (Po), Holcus spp. (Ho), Lolium spp. (Lo), Nardus spp. (Na), Anthoxanthum spp. (An), Molinia spp. (Mo), Deschampsia spp. (De), Carex spp. (Cx), and bryophytes (Bry). .......................................................................................................................89

Figure 4.18: Percentage faecal nitrogen content for sheep on Hirta throughout the year. “F” and “M” denote measurements from females and males respectively. The line represents the prediction from the model summarised in Table 4.18 and has the formula %FN=-0.001jd2+0.029jd+0.596, where jd=julian day and %FN=% faecal nitrogen content. The r2-value of the model is 0.482..............................................92

Figure 5.1: Boxplot showing the proportional distribution of Soay sheep during the spring and summer time amongst the seven plant community types present within the study area on Hirta (Table 4.1). For selectivity see Figure 5.2. ......................107

Figure 5.2: Selectivity in spring and summer for each of the seven plant communities (Table 4.1) and at four spatial scales ranging from the large, arbitrary scale of the study area (A), and the three progressively smaller scales (1-3) as defined by minimum area convex polygons surrounding 1,2 and 3 clusters identified using hierarchical cluster analysis. Note the large effect of spatial scale on apparent

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selectivity, especially for CA, FE and HA. Error bars represent ±1s.e.m.. Random effects were: year = 0.003, season within year = 0.003, veg. type within season within year = 0.284, scale within veg. type within season within year = 0.140, residual = 0.310.....................................................................................................109

Figure 5.3: The matching index (M) comparing the distribution of Soay sheep amongst the available plant communities during spring (Sp) and summer (Su) at four spatial scales. Perfect matching would result in a matching index of zero and the greater the value the worse the match. The spatial scales were ‘A’ (the arbitrary scale of the study area), and 1-3 (the scale as defined by minimum area convex polygons surrounding 1,2 and 3 clusters identified using hierarchical cluster analysis). Error bars represent ±1s.e.m. predicted from the LME model. The random effects were: year = 0.001, season within year = 0.107, scale within season within year = 0.181, residual = 0.160. There were significant differences between all spatial scales in the spring but in the summer there were only significant differences between A and each of the scales defined by the MCPs. There were significant differences between seasons at scales 2 and 3 only. Differences were assessed for significance using permutation tests as described in the methods. ...........................................111

Figure 5.4: The matching index (m) for each plant community type (Table 4.1), during spring (Sp) and summer (Su), at four spatial scales. Perfect matching would result in a matching index of zero and the greater the absolute value the worse the match. The spatial scales were ‘A’ (the arbitrary scale of the study area), and 1-3 (the scale as defined by minimum area convex polygons surrounding 1,2 and 3 clusters identified using hierarchical cluster analysis). Plant community types were Agrostis-Festuca grassland (AF), Calluna heath (CA), dry heath (DH), Festuca grassland (FE), Holcus-Agrostis (HA), Molinia grassland (MO) and wet heath (WH). Error bars represent ±1s.e.m. predicted from the LME model (Table 5.4)................................................................................................................................112

Figure 6.1: The relationship between age at first capture and weight at first capture. Although the line illustrates the prediction of a linear model of these two variables, the minimum adequate model also included twin status, population density, birth date and maternal weight as main effects. Detailed results are presented in Section 6.4.2 (Table 6.7). ...................................................................................................124

Figure 6.2: Frequency of births by julian birth date for tagged lambs between 1989 and 2002. The outliers with birth dates >200 were excluded from the analyses. ........131

Figure 6.3: Weights of male (open symbols/dashed line) and female (closed symbols/solid line) Soay sheep at birth, at four months and at twelve months of age. Error bars represent ±1 s.e.m.........................................................................135

Figure 6.4: The influence of (a) birth weight and population density, (b) maternal weight and population density and (c) maternal parasite burden (log10 eggs per gram of fresh faeces) on the probability of survival to weaning for Soay lambs born between 1989 and 2002. Lines represent prediction from the GLM, points represent data, error bars represent ±1 backtransformed s.e.m.............................137

Figure 6.5: The influence of (a) twin status and (b) sex on the probability of survival to weaning for Soay lambs born between 1989 and 2002. Error bars represent ± 1 backtransformed s.e.m. .........................................................................................137

Figure 6.6: The effect on survival to weaning of (a) over-winter NAO index (p=0.029) and (b) snow/sleet days in March (p<0.001). The lines represent the predictions of

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the models to which the terms have been added as a linear main effect. The numbers within the points indicate which year the data comes (e.g. 99=1999, 00=2000 etc.) from and represent the raw data. The error bars represent ±1 s.e.m................................................................................................................................140

Figure 6.7: The effect on survival to weaning of mean biomass of vegetation terms which were significant after controlling for population density. (a) biomass Calluna vulgaris (new) in the outbye, (b) density of high quality items in the outbye, (c) biomass of grass in the outbye, (d) biomass of high quality items in the outbye, (e) total biomass in the outbye, (f) total biomass minus the old-growth, woody C. vulgaris in the outbye and (g) overall total biomass. See Table 6.12 for codes. The lines represent the predictions of the models to which the terms have been added as main effects. The numbers within the points indicate which year the data comes from and represent the raw data. The error bars represent ±1 s.e.m. .142

Figure 7.1: The relationship between intake rate and body weight for Soay sheep on Hirta in August 2000. Intake rate was estimated using the n-alkane method. Females are represented with squares and males are represented with circles. Treated animals are represented with filled symbols while untreated animals are represented with open symbols. The line (formula = y=5.94 + 0.02x) represents the predictions of the linear model (Table 7.2) for which the r2-value was 0.174......161

Figure 7.2: Botanical composition (by %gDM) of the diets of Soay sheep in the study estimated using alkane/alkene concentrations in faeces and vegetation samples with the use of a non-negative least squares (NNLS) algorithm (detailed in Dove and Moore 1996). Ag=Agrostis spp., An=Anthoxanthum odoratum, Ho=Holcus spp., Fe=Festuca spp., Pl=Plantago lanceolata, Ca=Calluna vulgaris. The error bars represent ±1s.e.m...........................................................................................163

Figure 7.3: The influence of hind leg length and sex on bite rate for Soay sheep on Hirta in summer 2000. The lines represent predictions from the ANCOVA model (Table 7.3). .......................................................................................................................164

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Table of Tables

Table 2.1: Area coverage and proportion coverage of the different vegetation types within the study area. See also Figure 2.4...............................................................40

Table 2.2: The macroscopic endoparasites of the Soay sheep on St. Kilda, detailing the site of infection and main pathogenic signs for each species (from Soulsby, 1968).................................................................................................................................48

Table 4.1: The vegetation types represented within the study area on Hirta and used in this study. ................................................................................................................66

Table 4.2: A summary of the mixed effects model for above-ground net primary production (gDM/m2/month) of the inbye and outbye areas on Hirta. The inbye (Ib) is formerly cultivated grassland and the outbye (Ob) is mainly heathland. Estimates were made during the rapid growth phase (RGP: March-August), in late-summer (between August and October) and over the winter (October-March). ..................72

Table 4.3: A summary of the mixed effects model for offtake (gDM/m2/month) from the inbye and outbye areas on Hirta. The inbye (Ib) is formerly cultivated grassland and the outbye (Ob) is mainly heathland. Estimates were made during the rapid growth phase (RGP: March-August), in late-summer (between August and October) and over the winter (October-March). .....................................................73

Table 4.4: A summary of the mixed effects model for biomass increment (g/m /month) from the inbye and outbye areas on Hirta. The inbye (Ib) is formerly cultivated grassland and the outbye (Ob) is mainly heathland. Estimates were made during the rapid growth phase (RGP: March-August), in late-summer (between August and October) and over the winter (October-March).

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..............................................74

Table 4.5: A summary of the mixed effects model for the estimated measurement error (g/m2) from the inbye and outbye areas on Hirta. The inbye (Ib) is formerly cultivated grassland and the outbye (Ob) is mainly heathland. Estimates were made during the rapid growth phase (RGP: March-August), in late-summer (between August and October) and over the winter (October-March)...................................75

Table 4.6: A summary of the mixed effects model for annual net primary productivity of ungrazed vegetation (gDM/m2) from the inbye and outbye areas on Hirta. The inbye is formerly cultivated grassland and the outbye is mainly heathland. ..........76

Table 4.7: Summary of the linear mixed effects model for total standing crop biomass of vegetation in relation to vegetation type and season. See Table 4.1 for the species codes. Spring samples were collected in March while summer samples were collected in August..................................................................................................79

Table 4.8: Summary of the linear mixed effects model for total standing crop biomass of “quality” vegetation in relation to vegetation type and season. See Table 4.1 for the species codes. Spring samples were collected in March while summer samples were collected in August.........................................................................................80

Table 4.9: Summary of the linear mixed effects model for the standing crop biomass of grass in relation to vegetation type and season. See Table 4.1 for the species codes. . Spring samples were collected in March while summer samples were collected in August. ....................................................................................................................81

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Table 4.10: Summary of the linear mixed effects model for the standing crop biomass of herbs in relation to vegetation type and season See Table 4.1 for the species codes. Spring samples were collected in March while summer samples were collected in August. ....................................................................................................................82

Table 4.11: Summary of the linear mixed effects model for the standing crop biomass of DOM in relation to vegetation type and season. Spring samples were collected in March while summer samples were collected in August........................................83

Table 4.12: Summary of the linear mixed effects model for the standing crop biomass of bryophytes in relation to vegetation type and season. See Table 4.1 for the species codes. Spring samples were collected in March while summer samples were collected in August..................................................................................................84

Table 4.13: Summary of the linear mixed effects model for the standing crop biomass of woody C. vulgaris in relation to vegetation type and season. See Table 4.1 for the species codes. Spring samples were collected in March while summer samples were collected in August.........................................................................................85

Table 4.14: Summary of the linear mixed effects model for the standing crop biomass of new-growth, young, C. vulgaris in relation to vegetation type and season. See Table 4.1 for the species codes. Spring samples were collected in March while summer samples were collected in August. ............................................................86

Table 4.15: The proportion of plant fragments in faecal samples from Soay sheep on Hirta in spring and summer. See also Figure 4.16 which shows the data graphically. Note that although the mean± s.e.m. values are given, the data were counts and were thus poisson distributed................................................................88

Table 4.16: Summary of the relationships between proportional representation of plant species in the diet of Soay sheep and their ranked availability (by dry biomass per unit area) within the Head Dyke. Median ranked proportion in diet along with the lower (LQ) and upper (UQ) quartiles are given alongside the ranked availability of plant species. The significance of the differences were tested using Wilcoxon tests, the results of which are also presented....................................................................90

Table 4.17: The percentage total nitrogen content of herbs and grasses in March and August. Data were obtained from samples taken in 1988, 1991 and 1992.............90

Table 4.18: Summary of the linear model for faecal nitrogen content of Soay sheep on Hirta throughout the year. Estimates and standard errors are given. P-values were derived by deletion of the term from the model and examination of the resulting change in residual deviance. Residual deviance = 55.576 on 293 d.f., r2-value = 0.482........................................................................................................................91

Table 5.1: The plant community types represented within the study area on Hirta and used in this study...................................................................................................103

Table 5.2: The proportions of sheep occupying the seven plant community types (see Table 4.1) in spring and summer. The data are skewed so the median and upper/lower quartiles are presented. .....................................................................107

Table 5.3: Ranked habitat selectivity of Soay sheep on Hirta in spring and summer at four spatial scales (1=least favoured, 7=most favoured): ‘A’ (the arbitrary scale of the study area), and 1-3 (the scale as defined by minimum area convex polygons surrounding 1,2 and 3 clusters identified using hierarchical cluster analysis)......110

14

Table 5.4: Summary of the LME model for the matching index (m) for individual plant communities for Soay sheep on Hirta at low and high population densities during spring and summer and at four spatial scales. The spatial scales were ‘A’ (the arbitrary scale of the study area), and 1-3 (the scale as defined by minimum area convex polygons surrounding 1,2 and 3 clusters identified using hierarchical cluster analysis). Plant community types were Agrostis-Festuca grassland (AF), Calluna heath (CA), dry heath (DH), Festuca grassland (FE), Holcus-Agrostis (HA), Molinia grassland (MO) and wet heath (WH)............................................113

Table 6.1: Number of animals of each sex available for this analysis. To be included, the lamb’s mother must have been caught the previous summer (for data collection purposes) and the lamb had to have been caught and weighed within 7 days of birth. Data from 2001 was not useable because of restrictions to data collection during the foot and mouth disease epidemic.........................................................123

Table 6.2: The definitions and units of the weather variables used in this study. All of the univariate data were collected using the standard methods employed by the UK Met Office (see badc.nerc.ac.uk/data/surface/). The NAO data were obtained from J.W. Hurrell (www.cgd.ucar.edu/~jhurrell/nao.stat.winter.html). ........................126

Table 6.3: The correlation coefficients of the measurements from Benbecula and Rum between January and May. Correlations are based on yearly data from 1957-2002. Significance to p<0.05 is indicated by an asterisk. See Table 6.2 for definitions and units. ......................................................................................................................126

Table 6.4: Correlation coefficients between the overwinter NAO index and the univariate weather variables recorded on Benbecula and Rum. Correlations are based on yearly data from 1957-2002. Significance to p<0.05 is indicated by an asterisk. See Table 6.2 for definitions and units ...................................................127

Table 6.5: Vegetation terms used in this study and their codes as used in this study. The areas for which the measurements were meaned, were inbye, (Ib) outbye (Ob) and overall (Ov). ..........................................................................................................127

Table 6.6: The number of male and female lambs available for analysis by conventional logistic GLM. To be included, the lamb’s mother must have been caught the previous summer (for data collection purposes) and the lamb had to have been caught and weighed within 7 days of birth. Data from 2001 was not available because of restrictions to data collection during the foot and mouth disease epidemic. To avoid non-independence of survival probabilities, a ewe could only contribute one, randomly chosen, lamb to the dataset. .........................................129

Table 6.7: Summary of the linear model for weight at first capture of Soay lambs born between 1989 and 2002 giving estimates, their standard errors and t-values. P-values were derived by deletion of the term from the model and examination of the resulting change in residual deviance. Residual deviance = 67.291 on 316 d.f., r2-value = 0.551.........................................................................................................132

Table 6.8: The effect of inclusion of weather terms as main effects on the minimum adequate model (MAM) for birth weight. The MAM had a residual deviance of 69.073. The ∆ in deviance and p-values from comparisons of the MAM (without a weather term) and a new model with the weather term fitted as a main effect. The slope of the effects (±1s.e.m.) are also given. None of the interactions between the weather term and population density were significant (p>0.025). P-values of <0.1

15

are indicated with a “.”. a = some data were unavailable for testing this term, thus the change in degrees of freedom was –26 rather than –1. ...................................133

Table 6.9: The effect of inclusion of vegetation terms on the minimum adequate model (MAM) for birth weight. Terms were added as main effects but because no vegetation data was collected until summer 1993, the model was refitted using a subset of data from 1993-2002 first. Data from 1995 and 2001 were excluded because the assessment took place in April instead of March. The resulting model had a residual deviance of 45.807. (A) The effect of the inclusion of vegetation terms on the MAM was assessed by adding the term to the MAM and examining the change in residual deviance. Slope ±1s.e.m. are given where appropriate. Significance codes: p<0.025 = **, p<0.05 = *. (B) The results of the assessment of the effects of the inclusion of the vegetation term on the population density term, assessed by removal of the population density term from the new model and checking the residual deviance. The term is assumed to have dropped out of the model if p>0.05. Density dependence was not checked because the GLM algorithm in S-plus would not iterate to a solution. Location: Ib=Inbye, Ob=outbye, Ov=Overall. DOM=dead organic matter. HQ=high quality items (live grass, live herbs and new growth Calluna vulgaris), CVW = old-growth, woody C. vulgaris................................................................................................................................134

Table 6.10: Summary of the minimum adequate generalised linear model for survival to weaning of Soay lambs born between 1989 and 2002. Coefficients are given along with their standard errors and t-values. P-values were derived by deletion of the term from the model and examination of the resulting change in residual deviance................................................................................................................................136

Table 6.11: The effect of inclusion of weather terms as main effects on the minimum adequate model (MAM) for survival to weaning which had a residual deviance of 193.430. (A) The effect of the addition of weather terms to the MAM, assessed by adding the term to the MAM and checking the change in residual deviance. Slopes ±1s.e.m. are given where appropriate. (B) The results of the assessment for density dependence, checked by adding the interaction between the weather term and population density to the new model and checking the change in residual deviance. a = term best fitted as a quadratic function y=–2.323(±0.663)+ 1.337(±0.498)2. Significance codes: p<0.05 = *, p<0.10= .. a = some data were unavailable for testing this term, thus the change in degrees of freedom was –26 rather than –1.139

Table 6.12: The effect of inclusion of vegetation terms on the minimum adequate model (MAM) for survival to weaning. Terms were added as main effects but because no vegetation data collected until summer 1993, the model was refitted using a subset of data from 1993-2002 first. Data from 1995 and 2001 were excluded because the assessment took place in April instead of March. The resulting model had a residual deviance of 113.318. (A) The effect of the inclusion of vegetation terms on the MAM was assessed by adding the term to the MAM and examining the change in residual deviance. Slopes ±1s.e.m. are given where appropriate. Significance codes: p<0.01 = ***, p<0.025 = **, p<0.05 = *. (B) The results of the assessment of the effects of the inclusion of the vegetation term on the population density term, assessed by removal of the population density term from the new model and checking the residual deviance. The term is assumed to have dropped out of the model if p>0.05. Density dependence was not checked because the GLM could not iterate to a solution. Location (Loc.): Ib=Inbye, Ob=outbye, Ov=Overall.

16

DOM=dead organic matter. HQ=high quality items (live grass, live herbs and new growth Calluna vulgaris), CVW = old-growth, woody C. vulgaris. ....................141

Table 7.1: Treatment groups and numbers in the experiment investigating foraging behaviour and parasitism of Soay sheep on Hirta in August 2001. ......................154

Table 7.2: Summary of the minimum adequate model for log intake rate of Soay sheep on Hirta in summer 2000. The r2-value was 0.174. ..............................................161

Table 7.3: Summary of the ANCOVA model for the effects of sex and hind leg length (mm) on the bite rate (bites/second) of Soay sheep of both sexes. .......................164

17

Chapter 1 – Parasitism, food intake and performance.

Chapter 1 : Parasitism, food intake and the performance

of ungulate herbivores

18


Parasitism, food intake and the performance of ungulate herbivores The term “animal performance” is ill-defined and has a number of meanings depending

on the precise context of the study. For example in some studies it may mean long-term

measures such as lifespan, survivorship or breeding success (e.g. Rodway and Regehr

1999, Mysterud, Langvatn et al. 2001, Lindstrom and Kokko 2002, Mysterud,

Steinheim et al. 2002) ), while in others it may denote short-term measures such as

growth rate (e.g. Penning, Parsons et al. 1991, Kyriazakis, Anderson et al. 1996,

French, O'Riordan et al. 2001). This thesis takes a long-term perspective, where the

importance of measures such as short-term weight gain are overshadowed by the

importance of long-term measures such as lifespan and breeding success (i.e. fitness),

which are affected by the short-term measures (Green and Rothstein 1991, Koskela

1998, Gaillard, Festa-Bianchet et al. 2000a).

An individual’s evolutionary fitness may be considered as its genetic legacy relative to

that of other individuals within the same population over the same time period and is

influenced by lifespan and fecundity (Metz, Nisbet et al. 1992). However, in this thesis,

the data are derived from individuals over different time periods and their genetic

legacies are not addressed per se. For example, Chapter 6 deals with the performance of

lambs in relation to environmental and maternal condition over a period of fourteen

years and, although their survival is assessed, their genetic legacy per se is not

addressed. Therefore, in this thesis, the term “performance” is favoured over “fitness”.

These performance measures of weight gain, survival, lifespan and breeding success are

influenced by a wide range of interacting factors including climatic conditions, nutrition

and parasite burden (Krause 1994, Post, Stenseth et al. 1997, Gaillard, Festa-Bianchet et

al. 2000c, Stephenson, Latham et al. 2000, Coltman, Pilkington et al. 2001,

Forchhammer, Clutton-Brock et al. 2001). It is the aim of this thesis to investigate in

detail the influence of nutrition and parasite burden on performance, while paying due

regard to environmental factors. This chapter introduces the major points that will be

covered in more detail in later chapters.

19


1.1 Diet and fitness

The nutrition of an animal has important implications for its survival and reproduction

(Robinson 1996, Mduma, Sinclair et al. 1999, Wallace 2000, Yokoyama, Uno et al.

2000, Schmidt and Hoi 2002). Of primary importance in this regard are growth and

condition, the interacting and consequential effects of which are now discussed.

Condition is generally understood to be a measure of the amount of metabolisable fat

reserves available to the animal (Holand 1992, Kushner 1992, Stephenson, Hendertmark

et al. 1998). In laboratory or farm-based studies this is directly measurable, using

methods such as bioelectrical impedance analysis (Kushner 1992, Thomas and Cornish

1992, Velazco, Morrill et al. 1999) or an animal condition score index (see Russel,

Doney et al. 1969). However, in practice it is rarely measurable in the field and,

therefore, weight or body-size is often substituted in ecological studies. One exception

to this is the study of Svalbard reindeer (Rangifer tarandus), which has used an

ultrasound scanner to measure back-fat depth directly, as a condition index (Stien,

Irvine et al. 2002).

Condition is influenced by diet quality and quantity (see section 1.3) and a higher

quality ad-libitum diet will enable an animal to attain a better condition. This has been

exhaustively demonstrated by animal production scientists interested in maximising

meat or milk production (e.g. Croston and Pollot 1993, Forbes 1995, Philips 2000).

1.1.1 Survival

Condition not only has implications in terms of the availability of metabolisable

reserves which can be used to aid survival in times of food shortage, but also in terms of

the prevention of heat loss as a result of subcutaneous fat insulation (Doubt 1991,

Wilson, Hustler et al. 1992, Acevedo, Meyers et al. 1997). This reduces energy

requirements at low temperatures, which also aids survival.

Body mass is closely associated with condition and is a key factor influencing survival

(and fecundity - see 1.1.2 below). A faster growth rate resulting from superior nutrition

will enable the animal to attain a larger size and, therefore, a larger volume:surface area

ratio, which would help reduce heat loss (Buffenstein, Urison et al. 1996, Murison

2001), an advantage for surviving cold winters. Large animals may also be more

20


efficient feeders on low quality food, because they tend to have longer digestive tracts

and a larger rumen, which give more time for nutrients within plant material to be

digested (Demment and Van Soest 1985).

The effect of body mass on survival has been demonstrated in a range of taxa (Arnold

and Dittami 1997, Gaillard, Andersen et al. 1998, Civantos, Salvador et al. 1999,

Unsworth, Pac et al. 1999, Zuercher, Roby et al. 1999, Festa-Bianchet, Jorgenson et al.

2000). For example, lamb mass at weaning of Bighorn sheep (Ovis canadensis) is

positively associated with over-winter survival (Festa-Bianchet, Jorgenson et al. 1997).

Furthermore, although the survival of older males and females aged between 3 and 6

years appears to be independent of body mass, heavier yearlings and older females (7

plus) are more likely to survive the winter than their lighter counterparts (Festa-

Bianchet, Jorgenson et al. 1997). These effects are more pronounced in Soay sheep

(Ovis aries) where body mass is an important factor in the survival of all age and sex

classes, especially in severe winter conditions and when high population densities lead

to a reduced standing crop of vegetation and, therefore, reduced food availability

(Milner 1999, Milner, Albon et al. 1999, Milner, Elston et al. 1999).

Hall et al. (2001) found that the probability of post-weaning survival of grey seal

(Halichoerus grypus) pups to age one increased with body condition at weaning. As

with Bighorn sheep, there were sex related differences, with the odds of survival for

female pups over three-times higher than for males.

Although recent studies of reindeer on Svalbard have shown that parasites cause a

reduction in condition (as measured by back fat depth and body mass) and fecundity,

these effects are not enough to influence survival (Stien, Irvine et al. 2002). As noted by

Gaillard et al. (2000c), because the effect on survival is caused by the effect on

condition, a decline in condition will often be apparent even if death does not occur.

Therefore, any effect on survival is likely to be smaller than the effect on host condition

(and fecundity) and would, therefore, be more difficult to detect statistically.

1.1.2 Fecundity

Increased survivorship has a strong influence on lifetime reproductive success (LRS)

because the more breeding attempts the animal has, the more chances it has of

producing offspring. LRS may be expressed mathematically as a function of

21


reproductive lifespan (RL) and fecundity (F), where fecundity is defined as the number

of offspring surviving to breeding age (Equation 1.1).

FRLLRS ×= (1.1)

Where RL is defined as a function of lifespan (l) and age at first breeding (a) (Equation

1.2).

alRL −= (1.2)

Therefore, processes that influence survival will indirectly influence LRS. However,

breeding success may be affected by nutrition in other, subtler, ways.

The proximate effect of body size on survival has already been discussed but it also has

a direct influence on fecundity, via its effects on the male and via maternal influence.

For example, in species where males compete for mates, a larger body size is a distinct

advantage in combat (Schuett 1997, Rachlow, Berkeley et al. 1998, McElligott,

Gammell et al. 2001) and is the driving force behind sexual size dimorphism. Indeed, it

is often the case in the Soay sheep that a male will give up a consort without a struggle

when confronted by a male of significantly larger size (Stevenson and Preston,

unpublished data). Furthermore, using a molecular approach (detailed in Marshall

1998), the paternities of Soay sheep on Hirta have been established (Coltman, Bancroft

et al. 1999), and using these data Preston (unpublished data) has demonstrated that the

breeding success of normal-horned Soay rams is positively correlated with body size.

The influence of maternal condition on birth weight and early growth rate may also

influence offspring fecundity and survival. Birth-weight is positively correlated with

maternal condition (Lee, Majluf et al. 1991, Clutton-Brock, Price et al. 1992,

Robertson, Hiraiwahasegawa et al. 1992, Pomeroy, Fedak et al. 1999, Gaillard, Festa-

Bianchet et al. 2000b, Georges and Guinet 2000, Keech, Bowyer et al. 2000).

Furthermore, due to their dependence on nutrient supply from the mother in the form of

milk, the early growth rate of capital breeders1 is also influenced by maternal condition

(Penning, Parsons et al. 1995, Andersen, Gaillard et al. 2000). Because large animals

are more likely to survive than small animals, the offspring of females that are in good

condition tend to grow faster and survive better than offspring of females that are in

poor condition. Kruuk (1999) has shown that birth weight is a significant determinant of

1 Capital breeders are those which fuel reproduction from energy gained earlier and stored prior to use.

22


lifetime breeding success of male red deer (Cervus elaphus), indicating that these

effects can have long-lasting implications.

1.2 Weather

Weather can have effects on animal survival by directly influencing thermoregulatory

costs (Cook, Irwin et al. 1998, Huertas and Diaz 2001). It may also have indirect effects

via its effects on plant growth and/or parasite transmission (Todd, Levine et al. 1976,

Langvatn, Albon et al. 1996, Stromberg 1997, Douglas 2001)}. These factors may

interact to influence population dynamics and are difficult to tease apart, even using

long-term datasets.

In northern ungulates elevated thermoregulatory costs are often associated with lower

temperatures (Smith, Robbins et al. 1997, Portier, Festa-Bianchet et al. 1998, Douglas

2001, Wang, Hobbs et al. 2002), increased precipitation (Smith and Anderson 1998)

and increased wind speeds or gale frequency. However, there is also evidence that the

negative effect of precipitation is outweighed by its positive effects on plant

productivity and thus forage availability (Portier, Festa-Bianchet et al. 1998, Douglas

2001). If precipitation takes the form of snow, then the effects on survival may be due

to the covering of forage with an impenetrable layer, rather than to increased

thermoregulatory costs (Loison, Langvatn et al. 1999, Sarno, Clark et al. 1999, Crampe,

Gaillard et al. 2002).

Although most studies that have investigated the effects of weather on vital rates or

population dynamics have focused on winter/spring weather (e.g. Portier, Festa-

Bianchet et al. 1998, Coulson, Catchpole et al. 2001, Patterson and Power 2002,

Schmidt and Hoi 2002), summer weather conditions may also be important. There is

some evidence that parameters such as rainfall and temperature that influence plant

growth, and, therefore, food availability, during the summer can also affect population

growth (Wang, Hobbs et al. 2002). It is worth remembering that these effects may be

cumulative over several seasons/years (Patterson and Power 2002) and may thus not be

immediately apparent.

Recently, several studies of the Soay sheep and red deer have used the North Atlantic

oscillation (NAO) index as a composite weather variable (Clutton-Brock, Illius et al.

23


1997, Milner, Elston et al. 1999, Catchpole, Morgan et al. 2000, Forchhammer,

Clutton-Brock et al. 2001, Stenseth, Mysterud et al. 2002). This is an index based on

atmospheric pressure differences between Northern and Southern Europe and is

correlated with precipitation, temperatures and wind speeds (Hurrell 1995, Wilby,

O'Hare et al. 1997). Negative effects of weather severity upon survival have been found

and will be discussed in Chapter 6.

Parasite transmission

Parasite transmission is dependent on climatic conditions (Soulsby 1968, Stromberg

1997). The rate of development of gastrointestinal (GI) parasites from egg to infective

stage larvae is positively associated with temperature (to an asymptote) and as such

elevated temperatures will increase the rate at which infective stage larvae develop from

eggs on the sward (Soulsby 1968, Stromberg 1997). Moisture levels are also influential

because the larvae of most GI parasites rely on a film of water in which to move and

will not survive desiccation (Soulsby 1968, Stromberg 1997). As such, a period of dry

and/or cold weather can reduce transmission to hosts, which may have implications for

survival or fecundity and, therefore, population dynamics.

1.3 Food intake parameters

The role of diet as a major factor influencing ungulate life-history has already been

discussed. In the next section, the elements that make up “diet”, the main parameters of

which are forage quantity and quality, will be examined.

Quantity is determined by the length of time spent feeding, the bite rate, and the size of

the bite {Gordon, 1992 #703;Fryxell, 1991 #303}. Bite mass is itself a function of bite

area (∝ incisor arcade breadth2), bite depth and the vertical distribution of plant biomass

(Figure 1.1) (Gordon and Illius 1992).

24


Intake rate

Bite mass x Bite rate x Duration grazing

Bite volume x

x Bite depth

Figure

The c

consid

and o

includ

(CP) c

conten

Altho

seemi

value

must

1987)

Forag

wilde

(Murr

Bitearea

1.1: The constituent variables o

oncept of forage quality

er; protein, fibre, mineral

lfactory characteristics etc

e organic matter digestib

ontent and fibre. In genera

t are high while the fibre c

ugh at first glance the envi

ngly continuously and ev

of the available food is p

forage selectively in orde

.

ing at the regional scale

beest (Connochaetes taur

ay and Brown 1993).

Bulk density of grazed stratum

f daily food intake in grazing animals.

is a complex matter and there are many factors to

s, fats, sugars, starches, secondary metabolites, physical

(Arnold 1981). However, standard laboratory measures

ility (OMD), dry matter (DM) content, crude protein

l terms, diet quality is said to be higher if OMD and CP

ontent is relatively low (Fahey and Hussein 1999).

ronment of free-ranging herbivores is one where food is

enly distributed over their environment, the nutritional

atchy at a variety of scales and, therefore, herbivores

r to obtain a suitable diet (Senft, Coughenour et al.

is exemplified by migration such as occurs in the

inus) which move to exploit transient food resources

25


At the plant-community scale, herbivores will usually distribute themselves in a non-

random way so as to maximise nutrient intake by avoiding areas with poor quality

species (Hunter 1962). Selection at this scale will be dealt with in section 1.5.

Lastly, at the individual plant scale there is a negative relationship between selectivity

and intake rate; as animals become more selective they must necessarily spend more

time searching for and handling food items. Therefore, a trade-off exists between the

quality and quantity of material consumed {Fryxell, 1991 #303}.

Body size has important implications for foraging behaviour: absolute food

requirements, the inability to select small food items and gut retention time all increase

as body-weight increases (Gordon and Illius 1988b, Illius 1989). The increase in gut

size and gut retention times with body size means that larger animals are able to digest

fibrous, lower quality foods more efficiently than smaller animals (Illius and Gordon

1991). Smaller animals must, therefore, rely on a more selective foraging strategy that

allows them to ingest higher quality diets with a high cell content (Milne 1991). This is

reflected in cranial morphology and dentition: larger animals usually have bigger

mouthparts than smaller animals and this affects their selective ability in a negative

manner (Gordon and Illius 1988b). Across species, wide, flat muzzles characteristic of

larger animals (e.g. cattle) are associated with a low degree of selectivity while a

narrower, more pointed arcade (e.g. goats) is usually related to a high degree of

selectivity.

1.4 Seasonality

The quality and quantity of vegetation varies both spatially (see page 28) and

temporally. In temperate environments, the temporal variation is primarily due to the

effect of temperature and solar radiation on plant growth and phenology whereas in

tropical regions the changes are caused by the cycle of the wet and dry seasons

(Ruggiero 1992, Jhala 1997).

In temperate regions the available biomass, digestibility and crude protein content of

forage peaks in spring-summer, and the highest fibre content occurs in winter (Gordon

1989, Jiang and Hudson 1996, Chen, Ma et al. 1998, Dorgeloh, van Hoven et al. 1998,

Gonzalez-Andres and Ceresuela 1998, Gonzalez-Hernandez and Silva-Pando 1999).

26


This seasonal pattern is reversed where the variation is due to the coming of the dry-

season for example in India (Jhala 1997).

These seasonal changes in food availability and quality affect foraging behaviour. As

diet digestibility decreases and fibre content increases, the gut retention times increase

{Fryxell, 1991 #303}. Although this has the effect of increasing digestive efficiency it

does so at the cost of reducing potential intake rate due to the limits imposed by gut

capacity, and time budgets may be altered as a result. This effect has been demonstrated

in Greenland musk oxen (Ovibos moschatus) where time spent foraging decreases as

forage quality increases (Forchhammer 1995, Forchhammer and Boomsma 1995,

Schaefer and Messier 1996). At the same time, time spent ruminating is negatively

correlated with both the availability and quality of the forage (Forchhammer 1995).

During midwinter, when forage quality and quantity is at its lowest, an energy-

conserving strategy is employed and a larger proportion of time is allocated to resting.

The botanical composition of the diet also changes seasonally (Rosati and Bucher 1992,

Wansi, Pieper et al. 1992, Branch, Villarreal et al. 1994, Forchhammer and Boomsma

1995, Mohammad, Ferrando et al. 1996, Chen, Ma et al. 1998, Smith, Valdez et al.

1998, Bontti, Boo et al. 1999). This is primarily a response to changing plant species

abundance but may be due in part to the changing chemical composition of the species

and the resultant altered selection by the herbivore.

In addition, there is seasonal variation in energy requirements. The basal metabolic rate

(BMR) of temperate herbivores changes in a cyclical manner, decreasing in the winter

months and increasing during the summer (Silver, Colovos et al. 1969, Blaxter and

Boyne 1982). This is hypothesised to be an evolved response by animals to reduce the

risk of failing to meet energy requirements during predictable seasonal declines in food

availability, and is believed to be mediated by changing photoperiod (Kay 1985).

Metabolic rate also changes during the mating season, during gestation (Pekins, Smith

et al. 1998) or whilst weaning, and food requirements may change as a result. It should

be noted, however, that Iason et al. (2000) found evidence that the winter decline in

voluntary food intake was due to seasonal changes in food quality rather than biomass

availability.

27


Furthermore, any change in weather conditions will also influence energy requirements.

For example, increased heat loss caused by lower temperatures or increased wind-speed

may have to be countered with endogenous heat production. Conversely, when

temperatures are higher, animals may be forced to seek shade during the hottest part of

the day, which may limit the time available for foraging (Owen-Smith 1998).

1.5 Distribution

In free-ranging animals diet choice is reflected, in part, by their spatial distribution at

the regional and plant community scales. Much of optimal foraging theory assumes that

individuals will attempt to maximise their net energy intake rate as a way of maximising

fitness (Schoener 1971, Pyke 1984). A major model within this optimality framework is

the ideal free distribution (IFD) model of Fretwell and Lucas (1970). The three main

assumptions of this model are; (1) that the foragers have omniscient knowledge of the

quality and quantity of food in the environment, (2) that they have total freedom of

movement within the environment and (3) that all of the foragers are equal competitors

for their food resource.

In the absence of other factors such as predators or disease, animals are predicted to

tend to gravitate towards areas that provide food resources of the highest quality and

quantity. For example, Wilmshurst (1999) found that wildebeest on the Serengeti tended

to aggregate at areas of intermediate sward height where maximum nutrient intake rates

could be sustained (rather than in areas of highest biomass where the excessive sward

height limits intake rate).

Because different age, sex, weight or size of animals of the same species can have

different requirements, intra-specific segregation may occur. For example sexual

segregation has been documented in a variety of ungulate taxa (Miquelle, Peek et al.

1992, Weckerly 1993, duToit 1995, Conradt 1998, Ruckstuhl 1998, Conradt, Clutton-

Brock et al. 1999, Ginnett and Demment 1999, Ruckstuhl and Neuhaus 2000,

Weckerly, Ricca et al. 2001) and in the case of bighorn sheep (Ovis canadensis)

Ruckstuhl (1998) has suggested that differences in foraging behaviour between the

sexes in terms of time budgets and movement patterns make it difficult for males and

females to coexist in the same group hence leading to sexual segregation.

28


Non-nutritional factors may also influence distribution. For example, animals may

avoid areas where they are annoyed by biting insects (Senft, Coughenour et al. 1987) or

they may avoid areas where they are at risk from predators (Festa-Bianchet 1988,

Barten, Bowyer et al. 2001, Ruckstuhl and Festa-Bianchet 2001) or from the ingestion

of parasite larvae {see below and \Van der Wal, 2000 #959;Hutchings, 1999 #1744}.

Thus, competition for enemy-free space may be an issue in certain situations (Holt

1977, 1984, Jeffries and Lawton 1984). However, enemy-free space is not likely to be

an important issue for the Soay sheep on Hirta because they do not experience

significant predation except for the actions of gastro-intestinal parasites.

In simple terms, if two prey species (e.g. grazing herbivores) are being preyed upon by a

single predator species (e.g. large carnivore) then the predator benefits from the

relationship with both prey species. However, the more the predator species benefits

from preying on prey species one then the more prey species two will suffer (because

the predator population will increase). Indirectly, therefore, prey species one adversely

affects prey species two and vice versa. Thus the two prey species may look like they

are competing for a limiting resource (exploitation competition) when they are

competing for the non-limiting resource of enemy-free space. Mathematical models

have shown that the coexistence of prey species under predation pressure is facilitated

by their niche differentiation (Holt 1977, 1984, Jeffries and Lawton 1984).

An important caveat is that the movement of free ranging individuals is rarely totally

free. Animals build up a spatial map of the environment in which they live and build up

a picture of the food quality of different areas by sampling. However, their knowledge is

limited to the area in which they choose, or are compelled, to live. Neighboring areas

may have better forage available but without this knowledge animals may not risk

moving from a site they know.

Therefore, it could be that the animals conform to the ideal free distribution over small

special scales (smaller than their heft2) where they have knowledge of the available

habitat that is approaching “ideal”, but not at larger spatial scales where the knowledge

2 A heft is defined as “a group of individuals using the same resources in space” (Coulson, Albon et al. 1999). These individuals may compete for resources and will frequently consist of smaller cohesive sub-groups such as mother-offspring pairs and male-male coalitions.

29


of their environment is limited. This subject will be examined in greater detail in

Chapter 5.

1.6 Parasite burden

Studies on a wide range of taxa have shown that growth, survivorship and breeding

success are all affected by parasite burden (birds (Davidar and Morton 1993, Brown,

Brown. MB et al. 1995, Siikamaki, Ratti et al. 1997), fish (Adlard and Lester 1994,

Polak 1996, Sirois and Dodson 2000) and mammals such as Soay sheep (Paterson,

Wilson et al. 1998, Coltman, Pilkington et al. 1999)). Ilmonen et al. (2000) recently

showed, by immunising breeding female pied flycatchers (Ficedula hypoleuca) with a

non-pathogenic antigen, that an immune response per se could reduce investment in

reproduction and self-maintenance. They proposed that this might be caused by an

energetic or nutritional trade-off between immune function and physical workload when

feeding young.

Infection can be exacerbated by a poor nutritional state, because reduced resource

allocation to the immune system results in a decline in resistance and resilience to

parasites. The fact that the parasite burden of sheep is negatively correlated with dietary

protein intake supports this hypothesis (van Houtert, Barger et al. 1995, van Houtert and

Sykes 1996, Theodoropoulos, Zervas et al. 1998).

Parasites have a range of physiological and behavioural effects on their hosts. The

immediate physiological effect is dependent on the biology of the host and of the

specific parasite and its life-cycle stage but often causes a reduction in condition.

The main metabolic effects of the gastrointestinal parasites of ungulates are increased

endogenous protein loss, increased mucoprotein secretion and damage to the gut tissue,

which can result in losses into the gastrointestinal tract and reduced nutrient absorption.

Blood loss and anaemia also occur and in the case of infection with the blood-feeder

Haemonchus spp., up to 10% of the circulating blood volume can be lost per day

(Parkins and Holmes 1989). Some of the material lost will be excreted in faeces but

much will be reabsorbed in the small-intestine (Rowe, Nolan et al. 1988).

Some parasites such as the abomasal Teladorsagia circumcincta and Haemonchus

contortus cause damage to the parietal cells of the abomasum, impairing secretion and

30


elevating the abomasal pH from 2-3 to 6-7. This affects digestive enzyme efficiency

and, therefore, impairs the breakdown of food (Sykes and Coop 1979).

Intestinal parasites such as Trichostrongylus colubriformis and Nematodirus battus

cause mucosal thickening and the stunting of microvilli possibly reducing the

absorption efficiencies of amino acids, fatty acids and minerals. Furthermore, calcium

and phosphorus retention is often reduced in infected animals (Sykes and Coop 1976)

These losses and inefficiencies exert a potentially heavy cost on the host. This is

because nutrients and protein synthesis are diverted away from production processes,

such as skeletal growth and muscle or fat deposition and milk production, into

homeostatic responses, such as plasma or blood protein synthesis, mucus production,

digestive tract and immune defence maintenance (Symons and Jones 1975, Jones and

Symons 1982, Symons 1985, MacRae 1993, Coop and Kyriazakis 1999a, Cosgrove and

Niezen 2000). Sykes and Coop (1976, 1977) found that the gross efficiency of

metabolisable energy was reduced by as much as 50% in lambs with sub-clinical

infections of Ostertagia (now Teladorsagia) or Trichostrongylus.

It is not surprising, therefore, given the severe metabolic costs that result from infection,

that feeding behaviour may also be affected. In fact, one of the major behavioural

symptoms of infection is a reduction in voluntary food intake (henceforth referred to as

anorexia). The phenomenon has been demonstrated in a number of vertebrate taxa

including rats (Rattus norvegicus) (Crompton, Walters et al. 1981), mice (Mus

musculus) (Vangesa and Leese 1979), reindeer (Rangifer tarandus) (Arneberg, Folstad

et al. 1996) and sheep (Ovis aries), and intake is commonly reduced by 30-60% in

sheep (Poppi, Sykes et al. 1990, van Houtert and Sykes 1996).

The underlying mechanisms of anorexia are unknown (Dynes, Poppi et al. 1998, Coop

and Kyriazakis 1999a) and the reduction in intake seems paradoxical because the

parasites impose extra metabolic and nutritional demands on their host, and thus one

might expect an increase in intake to compensate.

A number of hypotheses explaining anorexia have been advanced (see Kyriazakis

(1998) for a review). Perhaps the most intriguing hypothesis is that the anorexia is a

manifestation of the host’s attempts to forage more selectively in order to either reduce

further parasite intake or to consume plants that may make the internal environment less

31


suitable to parasites. This could be quicker and less costly than mounting an immune

response.

Few studies have demonstrated altered feeding strategies in response to intestinal

parasitism, although infected sheep have been shown to select a diet with a higher

protein content than their uninfected counterparts (Kyriazakis, Oldham et al. 1994,

Cosgrove and Niezen 2000). This is perhaps to ensure a high nutrient intake rate

without taking as many bites from vegetation that may be infected with infective

parasite larvae. Furthermore, infected sheep and other ungulates have been shown to

avoid feeding from areas contaminated with faeces which may indicate the presence of

parasites {Van der Wal, 2000 #959;Hutchings, 1999 #1019;Hutchings, 1999 #1744}

Parasite induced anorexia can be a major cost to commercial animal production (Coop

and Holmes 1996) because of its effects on animal condition. Furthermore, it is already

well known that parasites can operate as functional predators by inducing host mortality

(Gulland 1991, Albon, Stien et al. 2002) but they may also operate in subtler ways. For

example, by influencing the availability of forage via the anorexia effect (i.e. an

increase in per capita parasite burden reduces per capita food intake) or by reducing

female fecundity without necessarily causing mortality (Grenfell 1988, Grenfell 1992a,

Dobson and Crawley 1994).

1.7 Objectives

It is clear from this discussion that nutrition and parasitism both play important roles in

the ecology of ungulates at a variety of scales. The aim of this thesis is to explore the

relationships between nutrition and parasitism, using the Soay sheep of St. Kilda as a

model. In Chapter 2 a general background to the study site and the Soay sheep is

presented and then in Chapter 3 the methods that have been used to collect and analyse

the data are detailed.

Chapters that present the results of a series of investigations into the relationships

touched upon in this literature review, then follow these introductory chapters. Firstly,

in Chapter 4, a description of the seasonal changes in forage composition/quality is

presented. Also within this chapter, an analysis of the seasonal fluctuations in (1)

primary production, (2) diet quality, and (3) diet composition is presented.

32


Then, Chapter 5 addresses the plant-community scale distribution of the sheep, and the

response to seasonal changes in forage quality and abundance. Particular attention is

paid to the importance of spatial scale and the ideal free distribution which has largely

been neglected by previous literature.

This is followed by two studies at the individual animal level. The first, Chapter 6,

explores the role of weather severity, forage quantity/quality, maternal condition and

parasite burden in determining offspring performance. The influence of several of these

factors has already been explored in previous work (e.g. Langvatn, Albon et al. 1996,

Coulson, Albon et al. 1997, Portier, Festa-Bianchet et al. 1998, Smith and Anderson

1998, Coronato 1999, Douglas 2001), but the influence of weather severity, population

density and forage quantity/quality have never been addressed in the same model. In

most studies population density has been used as a surrogate for forage quality/quantity

and, as will be discussed in Chapter 6, this may not be entirely appropriate.

Then, Chapter 7 presents the results of an experiment examining the influence of

parasite burden and condition on the individual foraging behaviour of both male and

female Soay sheep. GI parasites can potentially influence herbivore population

dynamics by increasing mortality rates (Grenfell 1992b, Grenfell, Wilson et al. 1995,

Albon, Stien et al. 2002), as well as by causing parasite induced anorexia (PIA) in their

hosts (Symons 1985, Kyriazakis, Tolkamp et al. 1998) and thereby affecting grazing

pressure. Furthermore, there is some evidence that grazing animals can compensate for

PIA by altering the composition of their diet (Kyriazakis, Tolkamp et al. 1998)

PIA has been well-studied in laboratory animals (e.g. Horbury, Mercer et al. 1995,

Roberts, Hardie et al. 1999), and in tightly controlled, housed animal situations or on

simple swards (e.g. Aumont, Yvore et al. 1984, Pienaar, Basson et al. 1999, Cosgrove

and Niezen 2000). However, the phenomenon has not been studied in a free-ranging

situation on a complex sward such as exists with the Soay sheep on St. Kilda.

Therefore, this chapter aims to determine whether the GI parasites cause anorexia in

free-ranging Soay sheep and to determine whether the sheep alter their foraging patterns

in order to compensate for any reduced intake rate that may occur.

Finally, Chapter 8 discusses the main findings of the thesis and highlights areas

requiring further study.

33

Chapter 2 – St. Kilda and the Soay sheep.

34


Chapter 2 : Introduction to St. Kilda and the Soay sheep

35


Introduction to St. Kilda and the Soay sheep

The last and outmaist Ile is namit Hirtha…in this Ile is gret nowmer of scheip…This Ile is circulit on every syd with roche craggis; and na baitis may land at it but allanerly at ane place, in quhilk is ane strait and narowe entres. Sum time thair micht na pepill pas

to this Ile bot extreme dangeir of thair livis; and yit thair is na pasage to it bot quhenthe seis ar caurme bot any tempest.

Hector Boece, First Principal of Aberdeen University 1527

2.1 The study site

The study population of feral Soay sheep (Ovis aries L.) inhabit the island of Hirta

(57º49’N 08º34’W), the largest of the four islands that make up the St. Kilda

archipelago, situated approximately 70km west of the Outer Hebrides (Figure 2.1).

Hirta (637ha) is bound by cliffs on all sides except for the storm beach of Village Bay.

The island of Soay (99ha) is about 500m north-west of the Cambir and is completely

surrounded by cliffs. The island of Boreray (77ha) lies 7km to the north-east of Hirta

with its sea stacks of Stac Lee and Stac An Armin, which is famed for its gannet colony.

Village Bay is sheltered from the south by the narrow island of Dún (32 ha) and about

750m SE of its tip lies the sea stack Levenish.

The main study area on Hirta (~175ha; Figure 2.1 and Figure 2.2) is bounded by a

semicircle of steep hills; Oiseval (290m), Conachair (429m), Mullach Mór (337m) and

the Mullach Sgar ridge. This area is occupied by a variety of vegetation types that are

described in section 2.1.2. The Head Dyke wall around the main village area separates

the formerly cultivated Holcus-Agrostis pastures of the in-bye from the Calluna covered

moorlands of the outbye. The island’s original inhabitants left in 1930, taking their

blackface sheep with them and now the only other human occupants are the staff of a

radar tracking station that was established in 1957. Thus the sheep only experience

minimal disturbance from humans.

36


Stac an Armin

Stac Lee

Boreray

Village

Conachair

TheCambir

Mullach Sgar

MullachMor

Mullach Bii

Glen Bay

Village Bay

Dun

Hirta

Soay

St Kilda (57°49' N 8°34' W)

100 200 km m

St Kilda

AtlanticOcean

N Ruaival

21

km mls

Oiseval

An Lag

423•Glen Mor

Figure 2.1: The St. Kilda archipelago comprising Soay, Dun, Hirta, Boreray and the sea stacks. Inset shows location of St. Kilda in relation to the west coast of Scotland. The shaded line shows the boundary of the study area (from Stevenson, 1994).

37


Figure 2.2: Part of the study area looking south-westwards from the slopes of Conachair showing the Head Dyke, the village, with Ruaival and Dun in the background.

Figure 2.3: The island of Soay, the origin of Hirta’s Soay sheep, looking north-westwards from the Mullach Bi ridge.

38


2.1.1 Solid geology and soil types

The solid geology of St. Kilda is detailed by Harding et al. (1984) who describe the

islands as of volcanic origin and representing the remnants of a Tertiary (55-60 million

years old) volcanic complex centred between Boreray and Hirta which measured 6-7 km

in diameter.

There are three main geological associations; the Conachair acid granophyre

association, the central, and geologically varied, mixed basic association and the

ultrabasic gabbro-dolerite association that forms the Mullach Bi ridge.

The soils are generally acid and peaty and there are thick blanket-peat deposits on the

summit area between Mullach Mór and Conachair, and on the western slopes of Gleann

Mór. Intense leaching due to the relatively high precipitation is countered by the

deposition of guano, sheep dung and sea spray (Gwynne, Milner et al. 1974).

The soils of the in-bye fields have been highly influenced by seaweed and seabird

carcass fertilisation during former cultivation and have a deepened surface horizon

(~1.25m). They are mildly acidic and have relatively high earthworm populations and a

high nutrient content. Archaeological evidence (Emery and Morrison 1995) supports

Hornung’s contention that the soils in the floor of An Lag have also been altered by

cultivation (in Gwynne, Milner et al. 1974).

2.1.2 The plant communities

The vegetation of Hirta has been described by Gwynne et al. (1974), Poore and

Robertson (1949), Petch (1933), Turrill (1927) and McVean (1961). They cover periods

of grazing by domestic animals, a period with no grazing between 1930 and 1932 and

periods of grazing by the feral Soay sheep that were introduced in 1932.

There are a number of major plant community types within the study area (Table 2.1).

The most productive is the area of grassland on the formerly cultivated, relatively

fertile, acid and well-drained soils within the head-dyke. The community fits into U4b

of the National Vegetation Classification (NVC) system. This is a Festuca-Agrostis-

Galium saxatile grassland, sub-community Holcus lanatus – Trifolium repens.

However, in this study it is simply termed Holcus-Agrostis grassland (HA).

39


Within this community, the important grass species are Agrostis capillaris, Holcus

lanatus and Festuca rubra with occasional Anthoxanthum odoratum, Agrostis

stolonifera, A. vinealis and Poa humilis. The major herb species are Trifolium repens,

Cerastium fontanum, Plantago lanceolata, Ranunculus acris and Leontodon

autumnalis.

The area is tussocky, with the tussocks dominated by Agrostis capillaris and Holcus

lanatus whose dead grass stems are woven together to give the tussock its structure. The

close-cropped inter-tussock (or “gap”) areas are dominated by prostrate plants such as

T. repens and C. fontanum, grasses and mosses, mainly Rhytidiadelphus squarrosus,

Scleropodium purum and Pleurozium schreberi.

Outside the Head Dyke, the major vegetation types are the Calluna and Nardus

dominated wet heath (WH), dryer Calluna heath (CA) and the dry-heath (DH) of the

hillsides. Agrostis-Festuca grassland (AF) areas exist around the Head Dyke and St

Brianans’, while Abhainn Mór supports Sphagnum and Molinia dominated mires (MO).

Lastly, Gun Meadow and Ruaival are covered with the lawn-like halophytic Festuca-

Plantago community (FE). The areas and proportional cover of the study area of each

vegetation type are given in Table 2.1.

Table 2.1: Area coverage and proportion coverage of the different vegetation types within the study area. See also Figure 2.4.

Community Type Abbreviation Area (ha) Proportion

Agrostis-Festuca AF 20.86 0.117 Calluna heath CA 32.94 0.185 Wet Heath WH 45.14 0.253 Molinia MO 23.42 0.131 Festuca-Plantago FE 3.80 0.021 Holcus-Agrostis HA 22.43 0.126 Dry Heath DH 29.72 0.167

40


Figure 2.4: The study area on Hirta, showing the coverage of the different vDyke is shown as a bold line, with gaps marked in red. Buildings from bmilitary base is depicted in grey and the cleits are represented by black dots. the three automated weather stations (see sections 2.3 and 3.1.5). The contouConservancy 1970).

41

Legend

Scale: 500m

*

*

*

egetation types. The Head

efore 1930 are in red, the * represent the positions of rs are in feet (after Nature


2.2 The study population: Soay sheep

Soay sheep are the most primitive breed of sheep in western Europe, and the precise

origin of the St. Kilda population is uncertain (Campbell 1974). They may have been

introduced by Vikings as early as the 9th century AD, or they may date back to

prehistoric times (Campbell 1974). Hirta’s current population stems from a founding

group of 107 individuals introduced from Soay in 1932, two years after the St. Kildans

were evacuated along with their Blackface sheep (Campbell 1974). The population has

been the focus of biological research since the early 1960’s and have been intensively

studied since 1985 (Clutton-Brock, Price et al. 1991, 1992).

Soay sheep are similar in body proportions to mouflon and other wild sheep (Doney,

Ryder et al. 1974) but are smaller than most domestic breeds. Their leg lengths are

around 91% of Scottish black-face sheep and the hip width and body length are 75%

and 77% respectively (Doney, Ryder et al. 1974). On Hirta, adult males can reach 46kg

while adult females can reach 34kg (Soay Sheep Project, unpublished data). On

average, adult males weigh about 29kg in August while adult females weigh about 24kg

(Doney, Ryder et al. 1974). The gestation period ranges between 142 and 152 days with

a mean of 148 days (Doney, Ryder et al. 1974).

Hirta’s Soay sheep population makes an ideal subject for the study of population

dynamic processes. They suffer no predation (although parasites may act as functional

predators) and no inter-specific competition from other vertebratre herbivores such as

rabbits. Furthermore, there is no immigration or emigration and no management of the

population. Although the study area is unfenced and the sheep are, therefore, free to

move throughout the island the immigration and emigration into/from the study area are

also negligible (Coulson, Albon et al. 1999). Readers are directed to Jewell et al. (1974)

for further description of the Soay sheep and their ecology. As mentioned earlier, the

sheep only experience minimal disturbance from humans.

2.2.1 Population dynamics

The first population count was made in 1952. Subsequently, whole island population

counts have been carried out on a yearly basis every summer since 1955 (Figure 2.5).

The most striking aspect of the data are the marked fluctuations between ~600 and

42


~2000 individuals. The pattern of the fluctuations is erratic and has been the subject of a

number of papers (e.g. Clutton-Brock, Illius et al. 1997, Grenfell, Wilson et al. 1998,

Coulson, Milner-Gulland et al. 2000). The population has been observed to show

“crashes”, where a high proportion of individuals die of starvation, exacerbated by

gastrointestinal parasitism (Gulland 1992), occur in late winter/spring when a high

grazing pressure depletes the standing crop of vegetation (Clutton-Brock, Price et al.

1991, Grenfell, Price et al. 1992).

Year

Num

ber o

f she

ep (h

undr

eds)

1950 1960 1970 1980 1990 2000

0

5

10

15

20

Figure 2.5: Population trends of the Soay sheep on Hirta between 1952 and 2002. The unbroken red line represents the whole island population and the broken blue line represents the population using the study area, as estimated by mark recapture techniques. The open circles represent data that is considered to be unreliable (Clutton-Brock, Grenfell et al. 2003) and filled circles represent reliable data.

2.2.2 Macro-parasites of the Soay sheep

St. Kilda’s Soay sheep suffer from a variety of macro-parasites, most of which are

common in domesticated sheep on the mainland (Cheyne, Foster et al. 1974, Gulland

1991). They include ectoparasites, lungworms, and gastrointestinal parasites. The site of

infection and details of pathogenesis of the endoparasites are tabulated below (Table

2.2). Thirteen protozoan micro-parasites, collectively known as coccidia, and including

Eimeria spp., Cryptosporidium parvum, and Giardia duodenalis are also evident (B.

43


Craig, unpublished data). Screening for other important agricultural pathogens revealed

that they are either absent or occur at a low prevalence (Wilson, unpublished data).

Because so little is known about these pathogens they will be not be considered further

in this thesis.

The ectoparasite fauna includes both lice (Damalinia ovis) and keds (wingless flies,

Melophagus ovinus) and are usually only common on young individuals (Cheyne,

Foster et al. 1974). The keds feed on blood and heavy infestation can result in anaemia

and a reduction in host condition, while the lice mainly feed on wool and dead skin

fragments (Soulsby 1968).

There are two species of lungworm present, Dictocaulus filaria and Muellerius

capillaris. D. filaria is predominantly a parasite of lambs, the adult worms live in the

trachea and bronchi causing bronchitis and occasionally pneumonia (Soulsby 1968). M.

capillaris is more commonly associated with adult sheep. Transmission of both genera

is effected by the consumption of vegetation contaminated with larvae coughed up by

hosts (Soulsby 1968).

All but one of the gastrointestinal parasites are direct life cycle helminths, the exception

being the tapeworm Taenia hydatigena, which requires a carnivore as an intermediate

host and is presumably carried to the archipelago by gulls (Torgerson, Gulland et al.

1992, Torgerson, Pilkington et al. 1995). Between 30% and 50% of adults are infected

with this tapeworm (Gulland 1992, Torgerson, Gulland et al. 1992, Torgerson,

Pilkington et al. 1995). The remaining species include the strongyles Teladorsagia spp.

(formerly Ostertagia), Trichostrongylus spp., Chabertia ovina, Bunostomum

trigonocephalum, and Strongyloides papillosis. Others are Nematodiris spp., Tricuris

ovis, Capillaria longipes, and Moniezia expansa (Gulland 1991). Gulland (1991)

identified three species of Teladorsagia (Ostertagia); T. davtiani, T. circumcincta and

T. trifurcata. However, Braisher (1999) found no evidence for the separation of these

three species based on DNA sequences. Therefore, it is likely that there is just one

Teladorsagia species present and this will be referred to as T. circumcincta.

T. circumcincta is the dominant and most pathogenic of the gastrointestinal nematodes

(Gulland and Fox 1992) and it is on this parasite that most parasitological studies have

focussed. Its lifecycle is typical of trichostrongylids (Soulsby 1968). Adult females

44


produce eggs, which develop to the morula stage before being voided in the faeces. The

first-stage (L1) larvae emerge after between one day and several months depending on

environmental conditions. These then moult into the second-stage (L2) larvae, which in

turn moult into the infective third-stage (L3) larvae. This moult is incomplete, the L3

larvae retains the cuticle of the L2 larvae serving as protection against environmental

conditions. The development from egg to L3 is sensitive to temperature, humidity and

oxygen tension (Soulsby 1968).

The L3 larvae are ingested by the host during grazing and pass into the abomasum

where they migrate to the gastric glands after two or three days. Here they moult again

into early fourth-stage larvae (EL4) and again into the fifth-stage or immature-adult

stage. Most ingested larvae will reach mature-adult stage by day 12 and by day 16 the

mature worms emerge from the glands and attach themselves to the abomasum walls.

The worms copulate and the females lay eggs, which become apparent in the faeces at

17-18 days post-infection.

Hypobiosis, or arrested development, occasionally occurs at the EL4 stage. This allows

the larvae to remain in the mucosa for up to three months before maturing.

On St Kilda the density of the infective larval stage (L3) on the sward shows two

seasonal peaks (Figure 2.6). The first is in spring and is believed to be due to the

development of eggs deposited by immunosuppressed periparturient ewes. The second

is in mid-summer and is due to the development of eggs deposited by immunologically

naïve lambs (Wilson, Grenfell et al. 2003).

Figure 2.7 demonstrates that there are marked differences in L3 density between

different areas. This means that sheep feeding in different areas are exposed to differing

degrees of parasitological threat. This, along with the nutritional quality of the sward

may influence the grazing decisions of the sheep.

45


Month

L3 s

trong

yle

dens

ity (t

hous

ands

/gD

M)

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

0

1

2

3

4

SIGMGUNMWESMMIDFWESF

Figure 2.6: Temporal changes in strongyle L3 density in different parts of Village Bay. SIGM=Signal’s Meadow, WESM=West Meadow, WESF=West Field, MIDF=Mid Field, GUNM=Gun Meadow. Data covers the Data covers the years 1991-1998.

0

2

4

6

8

10

12

14

L3 s

trong

yle

dens

ity (h

undr

eds

of la

rvae

per

kg

vege

tatio

n)

ANLA GUNM MIDF OLDV RUAI SIGM WESF WESM

Location within study area

Figure 2.7: Spatial differences in larval strongyle density within the study area on St. Kilda. ANLA = An Lag, GUNM=Gun Meadow, MIDF=Mid Field, OLDV=Old Village, RUAI=Ruaival, SIGM=Signal’s Meadow, WESF=West Field and WESM=West Meadow. Data covers spring in years the range 1991-1998.

46


The August L3 density in Village Bay increases in a linear fashion with lamb

population size but not with the adult population (Wilson, Grenfell et al. 2003). In

n the correlation is again strongest with lamb population but the

correlation is not statistically significant (Wilson, Grenfell et al. 2003). Thus the density

of sheep has more influence on the number of L3 larvae in the summer than at other

times of the year. Wilson et al. (2003) suggest that the influence of climatic factors such

as precipitation or temperature predominate for most of the year.

Parasites, especially the strongyles, have important effects on animal condition and

consequently survival. They may also have important effects on foraging behaviour and

on diet selection and utilisation. These, effects will be discussed further in later

chapters.

For more detailed reviews of parasitism on St. Kilda, readers are referred to the book

chapters by Cheyne et al. (1974), and Wilson et al. (2003), and to Ph.D. theses by

Gulland (1991) and Boyd (1999).

Spring and Autum

47


Table 2.2: The macroscopic endoparasites of the Soay sheep on St. Kilda, detailing the site of infection and main pathogenic signs for each species (from Soulsby, 1968)

Site Species Main Pathogenic Signs

Abomasum Teladorsagia circumcincta

Abomasitis, necrosis, decreased albumin levels. Reduction in serum proteins Weight loss. Thickening of abomasal mucosa, which may become oedematous.

Bunostomum trigonocephalum Anaemia, hydraemia, oedema (leading to “bottle-jaw”). Loss of appetite, wool loss, diarrhoea. A direct blood feeder.

Capillaria longipes Tracheitis and bronchitis.

Moniezia expansa No data available.

Nematodirus battus Destruction of mucosa. Necrosis of villi. Diarrhoea and dehydration.

Strongyloides papillosis Erosion of intestinal mucosa. Anorexia, diarrhoea, anaemia and weight loss. Catarrhal enteritis of small intestine.

Small Intestine

Trichostrongylus colubriformis

Desquamation of intestinal epithelium. Shortening of red blood cell life-span, impaired erythropoiesis, reduction in amino acid pool leading to anaemia. Intermittent diarrhoea and constipation. Occasionally resides in the abomasum.

Tricuris ovis

Haemorrhagic necrosis and oedema of caecal mucosa. Haemorrhagic diarrhoea. Growth retardation.

Caecum, colon and distal ileum

Chabertia ovina Haemorrhaging.

Dictocaulus filaria Catarrhal parasitic bronchitis. Atelectasis, catarrh and pneumonia. Emphysema.

Lungs

Muellerius capillaries Necrosis and calcification of lung tissue.

Peritoneal cavity Taenia hydatigena

Breakdown of liver parenchyma causing haemorrhaging. Peritonitis, ascites and fever.

2.3 Weather

The climate of St. Kilda is relatively mild in comparison to mainland Scotland due to

the warming influence of the Gulf Stream. Several papers have emphasised the

importance of weather on the population dynamics of the Soay sheep (Grenfell, Wilson

et al. 1998, Hudson and Cattadori 1999, Milner, Albon et al. 1999, Milner, Elston et al.

1999, Catchpole, Morgan et al. 2000, Coulson, Milner-Gulland et al. 2000, Coulson,

48


Catchpole et al. 2001). These have mainly used data collected from Meteorological

Of weather s n the Isle of Benbecu Kilda) and the Isle of

Rum (150km SE of St. Kilda). However, in ther

sta e ere t is from

are derived.

The average monthly wind speed does not va er, maximum recorded

speeds show tha more severe r than in the summer

(Figure 2.8 a). Precipitation and solar radiation also change throughout the year (Figure

2.8 b & c). As ex age daily sola es steadily between January

and June/July and then falls between the summ ecipitation is lowest

in late spring an aks in Oc atures

(Figure 2.8 d) rarely drop below 5 ºC even ld spells can

occasionally bring the temperature down to be d

in all the winter ber and April, averaging less than 10 days per

year. In the summer, the average temperature reaches around 12-13 ºC but warmer

spells can push the temperature close to 20 ºC. Grass and

hig between March and September and lower during autumn

and winter (Figure 2.8 e).

These seasonal fluctuations in weather para o have important

consequences for the sheep. Fluctuations in so e affect plant

growth and, the ailability. C tation and

tem have implications for heat loss a

sh

fice tation o la (50 km SE of St.

1999 and 2000 three automatic wea

tions wer cted on Hirta itself. I these that the following data summaries

ry greatly, howev

t gales are much in autumn and winte

pected, the aver r radiation ris

er and December. Pr

d summer and pe tober. Average monthly air temper

in the winter, although co

low 0 ºC and snow-lie has been recorde

months between Novem

soil temperatures are usually

her than air temperature

meters are expected t

lar radiation and temperatur

refore, the food av hanges in wind speed, precipi

perature

eep.

nd, therefore, energy requirements of the

49


Month

Win

d Sp

eed

(m/s

)

0

10

20

30

40

50

J F M A M J J A S O N D

MaximumAverage

(a) (b) (c)

Month

Mea

n Pr

ecip

itatio

n (m

m/d

ay)

0

2

4

6

8


Month

Mea

n So

lar R

adia

tion

(MJ/

m^2

/day

)

0

1

2

3

4

5

6

7

8

9

12

13

14

10

11


(d) (e)

Month

Tem

pera

ture

(deg

C)

MaxMeanMin

-5

0

5

10

15

20

25


Month

Tem

pera

ture

(deg

C

0

5

10

20

15

)


Air temperatureGrass temperatureSoil temperature

Figure 2.8: Weather variables recorded between 1999 and 2002 by automatic weather stations on St. Kilda. (a) Maximum and average wind speeds (b) average daily precipitation, (c) solar radiation, (d) mean, maximum and minimum air temperature, (e) air, grass and soil temperatures.

50

Chapter 3 – Data collection and statistical methods.

51

Chapter 3 : Data collection and statistical methods


Data collection and statistical methods

his project is part of a long-term study that has been running since the mid-1950s

hen the first population counts and body weight measurements were taken by J.M.

Boyd and the Nature Conservancy.

Since 1985, when the current phase of research began, certain “core” data have been

ollected every year. The majority of these data are collected in spring, when newborn

mbs are weighed and tagged, and in the summer when a large proportion of the study

flock is caught and morphometric measurements are taken.

I collected a portion of this core data between 1998 and 2002, but I did not design the

ethodology. Nevertheless, the data make a significant contribution to this thesis and so

I describe the methodology used. Other specific aspects of data collection are covered in

ividual chapters.

3.1 Core Data

3.1.1 Population data

Between 1985 and 2000 about 95% of lambs born within the study area were caught,

weighed and tagged within a few days of birth (Clutton-Brock, Price et al. 1992).

During this period the mother’s identity was recorded and blood samples and ear

punches were collected for genetic paternity analysis.

In 2001, due to a nationwide epidemic of foot and mouth disease (Ferguson, Donnelly

et al. 2001b, a), permission to handle animals was withdrawn by Scottish Natural

Heritage. As a result, no lambs were tagged or weighed and no genetic samples were

taken, although I carried out daily censuses to record which ewes had given birth,

whether the offspring were twins, and the sex and coat morph of the lambs.

Subsequently, in August 2001, most of the cohort were tagged and had genetic samples

taken. The identity of their mother was deduced by observation of suckling behaviour.

Therefore, for most of the 2001 cohort the only missing data was birth weight. The 2002

data collection proceeded as normal.

T

w

c

la

m

the ind

52


Mortality searches are carried out in February and March to p

relating to the dates and location of mortality. Approximately 85

rovide information

% of the animals

tagged as lambs are followed throughout their lives until they die (Clutton-Brock, Price

1955 a whole-island population count has been

carried out providing data on the total number of sheep, their age class and their coat

l of 137,937 observations. The average number of censuses

carried out by season was: Lambing (9.3 yr-1), Summer (8.4 yr-1), Rut (9.7 yr-1).

For each census three observers traversed different routes within the study area

d individual marked sheep within plant communities at grid

references to an accuracy of 100m. The three routes were fixed and between them

et al. 1992).

In the summer of every year since

and horn morphologies by a team of observers simultaneously walking three fixed

transects which encompass the whole of Hirta between them.

3.1.2 Spatial distribution

Censuses were carried out within the study area during the lambing period (April-May),

in mid summer (August), and during the rut (November-December), of each year.

Between 1985 and 2002 a total of 486 censuses were carried out (mean 27.0yr-1,

SD=6.02) giving a tota

simultaneously and locate

covered the whole study area. This yields data on the habitat utilisation and social

behaviour of the sheep.

3.1.3 Morphometric data

In August or September of most years a team of 16-18 people is assembled to catch as

many sheep as possible (mean catch for 1985-2002 = 228 animals). Morphometric

measurements including limb length and weight are taken from all those caught. Hind

leg length, measured to the nearest millimetre from the tubercalcis of the fibular tarsal

bone to the distal end of the metatarsus, gives an index of body size. Body weight,

measured to the nearest 0.1 kg using drop-scales is also an index of body size but also

includes condition. Errors from wetness of the fleece and from gut-fill cannot be

factored out.

53


In addition, sheep are sometimes caught at other times of the year and are generally

processed in the same way, but sometimes only the weights are recorded.

3.1.4 Parasitological data

faeces for several taxa:

strongyles (including Teladorsagia spp., Trichostrongylus spp., Chabertia ovina,

t m, and Strongyloides papillosis), Nematodiris spp.,

Tricuris ovis, Capillaria longipes, Dictocaulus filaria and Moniezia expansa. These egg

sity on

the sward were made using the ‘W’-pluck method (Taylor 1939). This involves walking

urement of interest is the winter North Atlantic oscillation (NAO) index,

which is a measure based on the pressure gradient between the North Atlantic and

southern Europe. This gradient is important because it affects the displacement of air

In order to assess the parasite burden of individual sheep faecal samples were collected

from ear-tagged sheep and the density of parasite eggs in the faeces was estimated using

a modified version of the McMaster technique to provide a faecal egg count (FEC)

(MAFF 1971). This gives estimates of eggs per gram of

Bunos omum trigonocephalu

counts are believed to be a reliable estimate of worm burden; the correlation of log FEC

with log worm burdens assessed by autopsy is reasonably high (r2=0.392, F1,73=47.05,

p<0.0001, Wilson (2003); see also Grenfell (1995), Boyd (1999) and Braisher (1999)).

Assessments of Nematodirus spp. and strongyle infective stage (L3) larval den

a W-shaped route across the area of interest whilst plucking up vegetation with the

thumb and forefinger until a sample of approximately 1kg has been collected. The

sample is washed in a detergent to remove the parasite larvae, which are counted and

standardised to give a count with units of larvae per kgDM of vegetation. Data covered

a variety of months in the years 1991 to 1998.

3.1.5 Weather data

Until it was closed in July 1996 the nearest Meteorological Office weather station was

situated on the island of Benbecula, approximately 50 km SE of St. Kilda. The other

nearby weather station for which appropriate data is available is on the island of Rum

(80 km SE of Benbecula). These both record standard weather data including rainfall,

solar radiation, wind-speed, barometric pressure and temperature. There is a high

correlation between Benbecula and Rum data for all the variables (see Chapter 6).

Another meas

54


from arctic regions towards the Iberian peninsula and the Azores. A high index

(gradient) strengthens the westerly winds bringing more moist air to mainland Europe.

This causes milder winters. This signifies the arrival of low- or high-pressure systems to

e ignificant implications to weather patterns. For example,

l

bient air temperature, grass temperature,

s adiation) and some over 24hrs (ambient air temperature, grass

and soil temperatures, relative humidity, wind speed and direction, precipitation, soil

Europ , which have highly s

there is a significant correlation between March rainfall and the NAO index between

December and March (Catchpole, Morgan et al. 2000). This is because March rainfall is

caused by the arrival of low-pressure systems from the Atlantic, a consequence of a

high NAO index. The reason NAO will be included in some of the analyses presented

later is that it can be regarded as the cause of the other weather variables such as

temperature, rainfall etc. Therefore, if a mechanistic approach is to be used then it is a

useful addition to the data set.

The values used in this study were obtained from J.W. Hurrell (US National Centre for

Atmospheric Research) (http://www.cgd.ucar.edu/~jhurrell/nao.stat.winter.html) and

consisted of the difference in normalised sea level pressure between Lisbon, Portuga

and Stykkisholmur, Iceland between December and March (Hurrell 1995). The data are

normalised to avoid the domination of the series by the greater variability of the

northern station.

On the 1st December 1999 an automatic weather station (AWS) was erected on Hirta, at

St Brianans. This was followed on 17th August 2000 by two additional AWSs at the

quarry and in Signals Meadow (see Figure 2.4) These stations record a variety of data,

some of which is averaged over each hour (am

wind peed and solar r

water content, solar radiation and hours of sunshine).

3.1.6 Vegetation parameters

In order to obtain data on vegetation parameters over the study area M.J. Crawley

carried out an assessment of sward characteristics in March and August of each year

between 1993 and 2002. Five transects, each with six sampling locations were assessed

on each occasion. Ten of the locations were outside the Head Dyke (the outbye area)

and twenty were within the Head Dyke (the inbye area) and each of the seven

vegetation types (Table 2.1) were represented in the sampling.

55


Furthermore, I made estimates of plant productivity at a number of sampling locations

between 2000 and 2002. The methodology for this is presented in Chapter 4.

Botanical composition by dry weight was assessed by cutting samples of the above-

ground vegetation from within two randomly placed 0.2 x 0.2m quadrats at each

location (one tussock and one inter-tussock). These samples were sorted to species

(herbs and forbs) or genus (grasses and sedges) level, before being oven dried at 80ºC,

and weighed on an electronic balance.

Mean sward height was measured by taking the mean of 25 contiguous height

measurements, spaced 0.2m apart, at each sampling location. The “tussockiness” of the

sward was assessed by categorising 30 contiguous 0.2 x 0.2m areas as being a

Ms), and linear mixed effects models (LMEs) are implemented in this

tes 2000, Crawley 2002).

stly a maximal model is fitted to the data including all

of the potential explanatory terms plus all of the interactions between them. Terms are

ther terms results in a

significant loss of explanatory power. At this point the minimum adequate model has

“tussock”, a “gap”, or “indeterminate” (see Chapter 2). The proportional representation

of each category then quantifies the “tussockiness” of the sward.

3.2 Statistical methods

In addition to standard parametric and non-parametric statistical techniques, generalized

linear models (GL

thesis. GLMs are an extension to standard multiple regression, but allow the analysis of

non-gaussian error distributions through the use of linearising link functions

(McCullagh and Nelder 1984). LMEs can deal with a wide variety of nested and pseudo

replicated data. For example, they can allow for temporal autocorrelation across

repeated measures on the same individuals and differences in the mean response

between blocks in a field experiment or differences between subjects in an experiment

or study involving repeated measures (Pinheiro and Ba

Models are fitted as follows. Fir

then removed from the model and the difference in residual deviance is assessed.

A new model, omitting the term that explains the least amount of variation, is fitted and

the two models are compared using a χ2-test (or an F-test if the model is overdispersed)

to check that the removal of the term does not result in a significant loss of explanatory

power. This process is continued until the removal of fur

56


been reached. Each deleted term is refitted to this model to ensure its lack of

explanatory power (see Crawley 2002 for more details).

Unless otherwise stated, the value of α (alpha) that was considered as significant was

0.05 for main effects and 0.025 for interactions in order to account for the greater

number of tests carried out.

All models were checked using the standard diagnostic plots. These were (1) residuals

vs. fitted values (to check for non constant variance and curvature). (2) Ordered

.).

All of the analyses were carried out using S-Plus v.6 (release 2) (Insightful Corp.).

Box plots

residuals vs. the quantiles of the standard normal distribution, to check for normality

and (3) Cook’s distance plot, which was used to examine which of the outlying points

had most influence on the parameter estimates.

Error bars, where presented, represent ±1 standard error of the mean (s.e.m

Box plots (Figure 3.1) are occasionally used in this thesis. They are often favourable to

bar plots because they summarise more information about the distribution of the data

they represent. The horizontal line shows the median, the bottom and top of the box

show the 25 and 75 percentiles (i.e. the location of the middle 50% of the data). The

horizontal line joined to the box by the dashed line (also known as the whisker) shows

1.5 times the interquartile range of the data. Points outside this range (outliers) are

shown individually as horizontal lines.

57


5452

4850

Res

pons

e va

riabl

e

Explanatory variable

A

Figure 3.1: An example of a box plot, see the text for an explanation.

58

Chapter 4 - Seasonality in forage and diet quality

59

Chapter 4 : Seasonality in forage and diet quality of Soay

sheep on St. Kilda


Seasonality in forage and diet quality of Soay sheep on St. Kilda

4.1 Abstract

Diet quality is a major factor in animal performance, and this chapter presents a

description of (1) seasonal patterns of net and total primary productivity of the

egetation on Hirta; (2) the seasonal patterns of forage composition and biomass; and

(3) the seasonal changes in the composition and quality of the diet of feral Soay sheep

v

(Ovis aries L.) on Hirta, St. Kilda.

Net primary productivity and offtake rates,

grazing exclosures between 20 rable to those measured

elsewhere. They differ between vegetation types and are highest on the formerly

cultivated Holcus-Agrostis swards within the Head Dyke and lowest on the Calluna

vulgaris heath. Primary production fluctuates throughout the year, peaking in summer

and still occurs throughout the winter months, although at lower levels. Annual net

productivity of the inbye is estimated to be 681±126gDM/m2.

Sward botanical composition and standing crop biomass were assessed using repeated

sampling on fixed transects since 1993 and also differ seasonally and between

vegetation types. The biomass of high quality food items was highest in summer, and on

the formerly cultivated Holcus-Agrostis swards within the Head Dyke. These swards

also had the highest proportion of grasses and herbs. Although the C. vulgaris heath

had the highest standing crop biomass, a high proportion was made up of woody old-

growth C. vulgaris. The proportion of bryophyte and dead organic matter in the sward

was highest in spring for the favoured habitats. These seasonal changes were reflected

in the composition and quality of the sheep diets. Diet quality, determined by faecal

nitrogen content, was lowest in the late winter and peaked in summer.

4.2 Introduction

Diet quality is a major factor influencing animal performance (see Chapter 1).

Therefore, before investigating large-scale animal distribution patterns (in Chapter 5), it

measured using wire mesh temporary

00 and 2003, are compa

60


is first appropriate to describe the characteristics of the available swards togethe

the responses of the animals to th

terms of diet composition and quality.

r with

e changing vegetation biomass and composition in

ponents of sward characteristics are species composition and the rate

. All of these may affect the suitability of a location

meters including rainfall,

Ceresuela 1998,

onzalez-Hernandez and Silva-Pando 1999).

y also be affected by grazing pressure, which has been shown to

alter the competitive relationships of species (“competitor release”) {Crawley, 1983

The two major com

of primary production, which, in natural conditions, fluctuate both spatially and

seasonally (Fitter and Hay 1987).

The spatial variation in plant species composition is usually caused by spatial

differences in myriad environmental conditions including soil texture, water and

nutrient availability and soil acidity

for a particular species (Fitter and Hay 1987). The classification of areas of vegetation

into distinct vegetation types is a convenient system for the purposes of analysis.

Although the boundaries between the resultant vegetation types are often not distinctly

delineated and are merely abstractions drawn from continuous variation (sensu

Whittaker 1960), on Hirta the boundaries are unusually sharp.

The patterns of temporal variation, or “seasonality”, in sward composition and primary

productivity are caused by seasonal changes in weather para

temperature and the amount of solar radiation. These alterations can potentially affect

the competitive balance of coexisting species and thus cause a change in their relative

abundance. In temperate environments, the seasonality is largely due to the effect of

temperature and solar radiation on plant growth and phenology, whereas in tropical

regions the changes are dominated by the cycling of the wet and dry seasons (Ruggiero

1992, Jhala 1997).

In temperate regions, the available biomass, digestibility and crude protein content of

forage peaks during the rapid growth phase in spring-summer, and the highest fibre

content occurs in late winter (Gordon 1989, Jiang and Hudson 1996, Chen, Ma et al.

1998, Dorgeloh, van Hoven et al. 1998, Gonzalez-Andres and

G

Species composition ma

#1374}. Work by Tuke {, 2001 #1464} and Crawley et al. {, 2003 #603} has

demonstrated this effect on Hirta. Evidence for grazing pressure altering the rate of

61


primary production is equivocal. Many of the studies that have reported the

“stimulation” of growth by grazing have often failed to account for below ground

biomass (Crawley 1983).

Although there is evidence that large herbivores select a diet with a species composition

that is proportionally different from the composition of the forage (Edwards, Newman

et al. 1996a), the seasonal changes in forage composition are often reflected by altered

diet composition (Rosati and Bucher 1992, Wansi, Pieper et al. 1992, Branch, Villarreal

of C. vulgaris

e predictions of the ideal free distribution

et al. 1994, Forchhammer and Boomsma 1995, Mohammad, Ferrando et al. 1996, Chen,

Ma et al. 1998, Smith, Valdez et al. 1998, Bontti, Boo et al. 1999). Although this is

principally a response to changing plant species availability, it may also relate to the

changing chemical composition, and physical characteristics of the species. In a study of

sheep in Scotland, Salt et al. (1994) found that between May and September, the

percentage of grasses in the diet decreased from 74% to 10% while the percentage of

Calluna vulgaris increased from 1% to 77%. They concluded that this was probably

because of changes in grass abundance because the nutritional quality

shoots was highest in June and July.

These small-scale preferences of sheep for certain plant species result in large-scale

distribution patterns. Previous work on Scottish hill sheep distribution by Hunter (1962)

showed that vegetation types on “mull” soils (such as Agrostis-Festuca, and Holcus-

Agrostis swards) were intensively grazed throughout the year while those on “mor”

soils (heath and bog) were only grazed lightly and mostly in the winter. Bakker et al.

(1983) also found that sheep preferred grasslands in summer and heath in the

winter.These results are consistent with th

model (Fretwell and Lucas 1970).

The purpose of this chapter is to present descriptions of the botanical composition of the

vegetation communities within the study area on Hirta. Assessments will also be made

of the seasonal changes in these characteristics and of the parallel changes in the

botanical composition and quality of the diet of the sheep.

62


4.3 Methods

Data were collected from the Village Bay area of the island of Hirta, part of Scotland’s

St. Kilda archipelago (57º49’N 08º34’W) situated approximately 70km west of the

sward), An Lag (wet heath (WH)), Conachair

prevent sheep from grazing from it. The exclosures

Outer Hebrides. The island is home to a free-ranging and feral population of Soay sheep

(Ovis aries L.), which are the only important vertebrate herbivores on the island. The

vegetation communities within the study area on Hirta have been classified into seven

distinct types (Table 4.1). Descriptions of both the sheep population and the study site

are presented in greater detail in Chapter 2.

4.3.1 Primary production

Primary productivity and offtake estimates from representative sampling areas of

vegetation were made between 2000 and 2003. Both inbye areas (within the Head

Dyke), and outbye areas (outside the Head Dyke) were represented. The inbye areas

were the Holcus-Agrostis (HA) swards of Mid Field and West Meadow and the

Agrostis-Festuca (AF) sward of St Columba’s. The outbye areas were in Gun Meadow

(a lawn-like Festuca-Plantago (FE)

(Calluna (CA) heath) and Glen Mór (Molinia (MO) grassland). Thus all but one of the

vegetation types (Table 4.1) found within the study area were represented (dry heath

(DH) was omitted).

Within each sampling area, three pairs of tussock and gap plots (see section 2.1.1) with

an area of 0.2 x 0.2m were selected and randomly assigned as the “initial sample”,

“ungrazed” and “grazed” plots. The position of the “grazed” and “ungrazed” plots were

marked with 4-inch nails at the corners of the quadrat and an exclosure was then erected

over the “ungrazed” plot in order to

were pyramidal, with a basal area of 1.5 x 1.5m and standing 1.3m high (Figure 4.1).

63


Figure 4.1: A set of two pyramidal grazing exclosures on the Calluna vulgaris covered slopes of Conachair. The mesh-covered exclosures have a basal area of 1.5 x 1.5m and stand 1.2m tall. They are secured to the ground using metal tent pegs.

The “initial sample” plot provided an estimate of the initial biomass condition for both

the “grazed” and “ungrazed” plots (w1). After a 2-3 month period of growth, the

vegetation within the ungrazed plots was plucked down using the forefinger and thumb

until the vegetation within them resembled the vegetation in the grazed plots. This

Finally, three new plots with similar initial conditions were selected and the process was

repeated, with care taken to avoid self-shading effects (Cebrian and Duarte 1994). Two

replicates were used in each of six locations and the measurements were carried out so

as to provide an assessment of offtake and production over three parts of the year: the

rapid growth phase (RGP), between August and October and over-winter (Figure 4.2).

“pluck down” gave an estimate of offtake (w2). The remaining vegetation within both

the ungrazed, and grazed plots (w3 and w4 respectively) was cut with scissors to leave a

1cm stubble. All of the samples were sorted into woody old-growth Calluna vulgaris,

green new-growth C. vulgaris and other vegetation (comprising grass, herbs, dead

organic matter and bryophytes). Samples were oven-dried at 80ºC for 48hr before

weighing.

64


65

The amount of biomass production that did not include grazing will here be termed the

biomass increment (BI), while the amount consumed by the sheep will be termed

offtake (OT). Both BI and OT can be calculated from the values obtained from the

sampling protocol (above) using Equations 4.1 and 4.2. Above-ground net primary

production (ANPP) is the sum of BI and OT (Equation 4.3). It does not include

respiration and biomass turnover (decomposition). The biomass of woody C. vulgaris

was excluded from the calculations because it is slow growing and is not consumed by

the sheep.

13 wwBI −= (4.1)

2wOT = (4.2)

BIwANPP += 2 (4.3)

Because w3 and w4 should be identical, the difference between them gives an estimate of

magnitude and direction the measurement error introduced to the offtake and

NPP while a negative error would indicate the

opposite.

productivity estimates (Equation 4.4). A positive error would indicate an underestimate

of offtake or an overestimate of A

43 wwE −= (4.4)

Figure 4.2: The approximate timings of the sampling periods used to assess offtake and productivity throughout the year.

The protocol has also been used to estimate productivity on similar sward types on the

Letterewe estate in Scotland (Milner, Alexander et al. 2002) and, to some extent on St.

Kilda (Tuk

FebJanDecNovOctSepAugJulJunMayAprMar FebJanDecNovOctSepAugJulJunMayAprMar

Rapid growth phase (RGP) Over-winterSummer

e, 2001). They found the method to be satisfactory for the measurement of

In order to obtain data on vegetation parameters over the study area M.J. Crawley

carried out an assessment of sward characteristics in March and August of each year

production and offtake from relatively productive swards, but where the sward

heterogeneity exceeded productivity (e.g. Calluna and wet heath), the estimation

utilisation rates were impossible (Milner, Alexander et al. 2002).

4.3.2 Sward botanical composition and biomass


between 1993 and 2002. Five transects, each with six sampling locations were assessed

on each occasion. All of the seven vegetation types (Table 4.1) were represented in the

sampling.

Table 4.1: The vegetation types represented within the study area on Hirta and used in this study. Vegetation Type Code

Agrostis-Festuca grassland AF Holcus-Agrostis grassland HA Festuca-Plantago sward FE Calluna heath CA Dry heath DH Wet heath Molinia gra

WH ssland MO

ne tussock and one inter-tussock). These samples were sorted to species

(herbs and forbs) or genus (grasses and sedges) level, before being oven dried at 80ºC,

nd weighed on an electronic balance.

or the purposes of this study, these biomass data were combined to give summary

iomass values for grass, herbs, bryophytes, dead organic matter (DOM), new growth

alluna vulgaris, old growth C. vulgaris and total biomass.

” of the sward was assessed by categorising 30 contiguous 0.2 x 0.2m

Botanical composition and “tussockiness”

Botanical composition by dry weight was assessed by cutting samples of the above-

ground vegetation from within two randomly placed 0.2 x 0.2m quadrats at each

location (o

a

F

b

C

The “tussockiness

areas as being a “tussock”, a “gap”, or “indeterminate” (see Chapter 2). The

proportional representation of each category then quantifies the tussockiness of the

sward. The weighted biomass (wB) of a particular sward component could then be

calculated by taking the means of biomass in tussocks (bT) and in gaps (bG) and

weighting them by proportion cover of gaps (pG) and tussocks (pT) (Equation 4.5).

⎟⎟⎠

⎞⎜⎜⎝

+⎟⎟⎠

⎜⎜⎝

×+

=pG

bGpTpG

wB⎛

×+

⎞⎛bT

pTpTpG

(4.5)

66


4.3.3 Diet botanical composition

Faecal plant cuticle analysis

Data concerning the botanical composition of the diets were collected using the faecal

plant cuticle analysis (FPCA arks and chek 1968). The cuticles of

plants are much less digestibl th nt and, as such, fragments can

remain identifiable after pass estiv ract of an animal thus allowing

the quantification of diet composition from the faeces. The identification of the cuticular

fragments is based on epider acteristics incl g cell size and shape, the cell

wall type (thick, thin, smooth tted), sto and guard cell characteristics,

e shape of silica bodies, the type and distribution of hairs, the presence of hooks and

papillae as well as other features such as striations and surface markings (Baumgartner

Faecal samples were collected in spring (March) and summer (August) from

move small particles. This

and a coverslip placed on top.

The slides were examined under a phase-contrast binocular microscope at a

agnification of 100x (or 200x for closer examination). Successive systematic traverses

of the slides were made until at least 100 epidermal fragments were identified. The

) method (Sp Male

e than other parts of e pla

ing through the dig e t

mal char udin

, corrugated, pi mata

th

and Martin 1939, Milner and Gwynne 1974).

The use of these techniques is well suited to studies of free-ranging animals since it

causes minimal disturbance, and has proved to be useful over a wide range of taxa

(Putman 1984). The technique I used was a modification of Sparks and Malechek’s

(1968) method as follows.

individually tagged animals within the study area. They were then freeze-dried, ground

to a powder using a coffee grinder, and washed through a 1mm mesh screen to remove

large fragments and then through a 0.2mm mesh screen to re

standardised the fragment size to between 0.2mm and 1mm. The sample was then

soaked for 5 minutes in 5ml of concentrated HNO3 in a test tube, in order to remove

pigment from the fragments and aid identification. The sample was made up to 100ml

with distilled water and boiled for 2-3mins to complete the clearing process.

The mixture was placed in a round-bottomed bowl and, while stirring, a sample was

taken using a plastic pipette. Three drops of the substance was then placed on a slide

m

67


relative abundance of each category gave an estimate of botanical composition. The

assumptions of this technique are as follows:

• That the rates of digestion are equal for all plant species.

iable.

stimated using faecal nitrogen content (percentage of dry

993 were

mples were analysed for total nitrogen content at The

• That there is a 1:1 correlation between number of fragments and the dry weight

consumed for all plant species.

• Some fragments are likely to be destroyed, or rendered unidentifiable by the

preparation process. Therefore, these methods assume that all plant species are

affected in the same way by the preparation methods.

• That all plant species are distributed randomly on the microscope slide.

• That all plant species are equally identif

The validity of these assumptions will be discussed below.

4.3.4 Diet and vegetation quality

Overall diet quality was e

matter) of samples collected from individually tagged sheep. Faecal nitrogen content is

a reliable indicator of diet quality (O’Donovan, Barnes et al. 1963) and has been used

extensively in the study of wild ungulates (e.g. O’Donovan, Barnes et al. 1963, Leslie

and Starkey 1985, Festa-Bianchet 1988, Nunezhernandez, Holechek et al. 1992,

Branch, Villarreal et al. 1994, Ruthven, Hellgren et al. 1994, Becerra, Winder et al.

1998).

The quality of the available vegetation was also assessed by its nitrogen content.

Samples that were collected in March and August in 1988, 1992 and 1

available for analysis. The samples had been sorted into live herb and live grass

fractions.

Both faecal and vegetation sa

Macaulay Institute, Scotland, by an automated Dumas combustion procedure (Pella and

Colombo 1973) using a Carlo Erba NA1500 Elemental Analyser (Carlo Erba

Instruments, Milan, Italy).

68


4.3.5 Grazing pressure

Grazing pressure, as indicated by sheep population density, was treated as a two level

fac ation greater than 7.1

(=1212 sheep) whereas a low grazing pressure was defined as a log10 population smaller

tha .

Crawley 2002). Population density was assessed in August of each year (see Chapter 3).

Sin t

spring

of year

4.3

To a etation in relation to sward type

(inbye or outbye) and season, linear mixed effects (LME) models were used in order to

samples were taken from

vegetation types within a particular season, and from seasons within a given year. Thus

account would result in pseudoreplication.

e season. The fixed effects were vegetation

were season, sex and body weight.

tor. A high grazing pressure was defined as a log10 popul

n 7 1. The threshold population size of 7.1 was estimated using tree regression (see

ce he population crashes occur at the end of winter, in March, the population in the

of year t+1 was taken to be the same as the population as assessed in the summer

t.

.6 Statistical methods

an lyse the productivity and offtake of the veg

account for the nested sampling design where repeated

an analysis without taking the nesting into

The response variables were primary production, offtake, biomass increment and

measurement error standardised to units of grams of dry matter per square metre per

month (gDM/m2/month). The fixed effects were vegetation type and season while the

nested random effects were year and season within year.

LME models were also used to analyse the biomass of sward components in relation to

sward type (inbye or outbye) and season. The response variables were the biomass of

the sward components, the sward type and th

type and season while the nested random effects were year and season in year.

Diet quality and composition, were evaluated using standard ANCOVA. The response

variable was nitrogen content or the arcsine transformed percentage species composition

while the explanatory variables

All models were simplified according to the principle of parsimony so that non-

significant terms were eliminated (see Crawley 2002 for details). The analyses were

carried out using S-Plus 6.0 release 2 (Insightful Corp.). An α-value of 0.05 as used for

69


assess ng the significance i of main effects while an α-value of 0.025 was used for

interactions (to allow for the greater number of tests).

unt by the nested error structure of the models. Furthermore, the low

replication of different sward types necessitated the collapsing of these factor levels into

” outbye” (FE, WH, MO and CA) in order to increase the

degrees of freedom and, therefore, the power of the analysis. For the purposes of the

f the variation was between

tussock types (i.e. gap or tussock).

s, in the

inbye areas, offtake peaked during the RGP, and then declined into late-summer and

e from differences

differences. The biomass increment tended to peak in late-summer in the outbye but in

4.4 Results

4.4.1 Primary production and offtake

Production and offtake did not differ between tussock and gap samples and were thus

treated as a single factor, although the lack of independence of the samples was taken

into acco

“inbye (HA and AF) and “

analyses, the results were standardised to give estimates with units of gDM/m2/4 weeks.

Above-ground net primary production (ANPP) was significantly higher in the inbye

than in the outbye during every season (Figure 4.3 and Table 4.2). Within the inbye

areas, ANPP tended to peak during the rapid growth phase (RGP) and then decrease

during the late-summer and winter (see Figure 4.2). There was no similar trend for the

outbye areas, where production was uniformly low. The magnitude of the random

effects shows that there was little variation between years, between season-within-years

or between locations within-season-within-years. Most o

Offtake (the “pluck down”) was significantly higher in the inbye areas than in the

outbye areas during the RGP and late-summer but not during the winter. Furthermore,

although there were no seasonal differences in offtake rates in the outbye area

over the winter (Figure 4.4 and Table 4.3). Again, the random effects indicate that most

of the variation was between tussock types rather than between years or seasons-within-

years. However, in this case a greater proportion of the variation cam

between the inbye and outbye locations than for the model for ANPP.

During the RGP, the biomass increment was significantly higher in the inbye than in the

outbye (Figure 4.5 and Table 4.4). However, at other times of year there were no such

70


the RGP for the inbye swards. The random effects show a similar trend to those for

ANPP with most variation being from between-tussock differences rather than from

between-season, between-year or between the inbye/outbye areas.

The estimates of measurement error (Figure 4.6 and Table 4.5) showed that the errors

ctivity and offtake estimates. However, there

were no consistent measurement errors for either inbye areas or outbye areas during any

Annual above-ground net primary productivity (ANPP) of the ungrazed vegetation was

s method tends to underestimate production because

were not small in comparison to the produ

season (i.e. the error statistic was not significantly different from zero). However, there

were differences between the inbye and outbye during the RGP so that, during this

period, ANPP would tend to be overestimated in the inbye areas and underestimated in

the outbye areas. Again, the random effects show that most variation came from

between tussocks rather than from between-years, between-seasons or between sward

types.

considered by summing the production estimate from each sampling plot. Annual

ANPP was significantly higher in the inbye than in the outbye (681±126gDM/m2 vs. –

105±126gDM/m2; Figure 4.7 and Table 4.6) and the outbye annual ANPP was not

significantly greater than zero. The random effects show that there was not much

variation in annual productivity between years but, comparatively, a large amount of

variation between sampling locations. Many other studies have used peak biomass

measurements from permanent exclosures to estimate primary production (Milchunas

and Lauenroth 1993). However, thi

it does not account for leaf turnover and self-shading. Furthermore, despite the fact that

compensatory growth (of above-ground parts) due to herbivory is also likely to occur

most authors only give qualitative measures of grazing pressure (Milchunas and

Lauenroth 1993).

71


Outbye Inbye

RGP Aug.-Oct. Winter RGP Aug.-Oct. Winter

0

50

100

Abo

ve-g

roun

d ne

t prim

ary

prod

uctiv

ity (g

DM

/m^2

/mon

th)

Vegetation type and season (see title)

Figure 4.3: Above ground net primary production, estimated using grazing exclosures, of the inbye and outbye areas on Hirta. The inbye is formerly cultivated grassland and the outbye is mainly heathland. Estimates were made during the rapid growth phase (RGP: March-August), in late-summer (between August and October) and over the winter (October-March). Error bars represent ±1s.e.m. Table 4.2: A summary of the mixed effects model for above-ground net primary production (gDM/m2/month) of the inbye and outbye areas on Hirta. The inbye (Ib) is formerly cultivated grassland and the outbye (Ob) is mainly heathland. Estimates were made during the rapid growth phase (RGP: March-August), in late-summer (between August and October) and over the winter (October-March).

Random effects Std. Dev.

Year 0.142 Season within Year 0.115 VegType within Season within Year 0.132 TussockType within VegType within Season within Year 23.287 Residual 70.525

Fixed effects Term Coefficient Std. Error d.f. t-value p-value

(Intercept) (Ob, RGP) -19.025 17.056 170 -1.115 0.266 Veg. Type (Ib) 121.376 22.186 6 5.471 0.002 Season (late-summer) 25.831 24.015 4 1.076 0.343 Season (winter) 3.950 23.900 4 0.165 0.877 Veg. Type (Ib) :Season (late-summer) -80.262 31.671 6 -2.534 0.044 Veg. Type (Ib) :Season (winter) -90.761 31.963 6 -2.840 0.030

72


0

20

40

60

80


Offt

ake

(gD

M/m

^2/m

onth

)

Outbye Inbye

RGP Aug.-Oct. Winter RGP Aug.-Oct. Winter

Figure 4.4: Offtake from the inbye and outbye areas on Hirta. The inbye is formerly cultivated grasslan

ye and outbye

d and the outbye is mainly heathland. Estimates were made during the rapid growth phase (RGP: March-August), in late-summer (between August and October) and over the winter (October-March). Error bars represent ±1s.e.m. Table 4.3: A summary of the mixed effects model for offtake (gDM/m2/month) from the inbareas on Hirta. The inbye (Ib) is formerly cultivated grassland and the outbye (Ob) is mainly heathland. Estimates were made during the rapid growth phase (RGP: March-August), in late-summer (between August and October) and over the winter (October-March).


Year 0.079 Season within Year Ve within Year Tu Se 1 Residual 30.727

Fixed effects

0.114 gType within Season 7.868 ssockType within VegType withinason within Year 2.366

Term Coefficient Std. Error d t-val p-value

(Intercept) (Ob, RGP) 9

.f. ue

17.132 9.345 170 1.833 0.06Ve 2 Se er) 2 Se 3 Ve son (late-summer) -1 3 Ve 9

g. Type (Ib) 63.671 12.448 6 5.115 0.00ason (late-summ -5.597 13.137 4 -0.426 0.69ason (winter) -7.844 13.132 4 -0.597 0.58g. Type (Ib) :Sea 5.898 17.660 6 -0.900 0.40g. Type (Ib) :Season (winter) -41.712 17.884 6 -2.332 0.05

73


-40

-20

0

20

40


Bio

mas

s in

crem

ent (

gDM

/m^2

/mon

th)

Outbye InbyeRGP Aug.-Oct. Winter RGP Aug.-Oct. Winter

and the outbye is mainly heathland. Estimates were made during the rapid growth hase (RGP: March-August), in late-summer (between August and October) and over the winter

rassland and the outbye (Ob) is mainly heathl ere made during the ra th phase (RGP: March-August), in late-summer (betw ober) and over the win er-March).

Random effects Std.

Figure 4.5: Estimated biomass increment on the inbye and outbye areas on Hirta. The inbye is formerly cultivated grasslandp(October-March). Error bars represent ±1s.e.m. Table 4.4: A summary of the mixed effects model for biomass increment (g/m2/month) from the inbye and outbye areas on Hirta. The inbye (Ib) is formerly cultivated g

and. Estimates ween August and Oct

pid growter (Octob

Dev.

Year 0.087 Season within V T pe within VegType within Season within Year Residual 52.981

Fixed effects

Year egType within Season within Year

0.284 2.975

ussockTy25.688

T Coef Std. p

(Intercept) -38.392 170

erm ficient Error d.f. t-value -value

(Ob, RGP) 15.321 -2.506 0.013 Veg. Ty 60.240 Season (late-suSeason (winter) Veg. TyVeg. Type (Ib) :Season (winter) -51.029 29.304 6 -1.741 0.132

pe (Ib) 20.282 6 2.970 0.025 mmer) 34.483 21.510 4 1.603 0.184

13.531 21.610 28.779

4 6

0.626 -2.353

0.565 0.057 pe (Ib) :Season (late-summer) -67.717

74


-50

0

50


Mea

sure

men

t erro

r (gD

M/m

^2) (

see

title

)

Outbye InbyeRGP Aug.-Oct. Winter RGP Aug.-Oct. Winter

Figure 4.6: Estimated measurement error for the inbye and outbye areas on Hirta (see section 4.3.1 for details). The inbye is formerly cultivated grassland and the outbye is mainly heathland. Estimates were

is formerly cultivated grassland and the outbye (Ob) is

made during the rapid growth phase (RGP: March-August), in late-summer (between August and October) and over the winter (October-March). Error bars represent ±1s.e.m. Table 4.5: A summary of the mixed effects model for the estimated measurement error (g/m2) from the

bye and outbye areas on Hirta. The inbye (Ib)inmainly heathland. Estimates were made during the rapid growth phase (RGP: March-August), in late-summer (between August and October) and over the winter (October-March).


Year 0.143 Season within Year

pe within Season within Year n VegType within

2139.999

0.060 VegTy 0.462 TussockType withiSeason within Year 0.515 Residual

Fixed effects Term Coe Std. Error d.f. t-value p-value

, RGP) -42.793 28.158 170 -1.520 0.130

fficient

(Intercept) (Ob Veg. Type (Ib)

(late-summer) -122.909 eason (winter)

100.224 35.870 6 2.794 0.031 Season (late-summer) 60.083 39.996 4 1.502 0.208 Season (winter) 9.482 39.412 4 0.241 0.822 Veg. Type (Ib) :Season 51.900 6 -2.368 0.056 Veg. Type (Ib) :S -66.167 51.757 6 -1.278 0.248

75


-200

0

200

400

600

800

Ann

ual u

ngra

zed

AN

PP

(gD

M/m

^2/y

r)

Outbye Inbye

Figure 4.7: Mean annual net primary productivity of ungrazed vegetation in the outbye and inbye reas

ye is formerly cultivated rassland and the outbye is mainly heathland.

aof the study area on Hirta. Error bars represent ±1s.e.m. Table 4.6: A summary of the mixed effects model for annual net primary productivity of ungrazed vegetation (gDM/m2) from the inbye and outbye areas on Hirta. The inbg


Year 0.016 Location within Year 350.078 Residual 201.564

Fixed effects Term Value Std. Error d.f. t-value p-value

-10 125.993 18 -0.830 0.418

(Intercept) (Outbye) 4.513 Inbye 785.723 178.181 14 4.410 0.001

4.4. composition and biomass

The he sward types dif om each other and be sea but

in manner. The models f y of war com nts ed

inte ating that the relative va iffere een rin sum In

con produ nd of abov , i cas ing

refers to a single sam id-Ma ef

oint in mid-August rather than to an extended period of time. However, spring

measurements fall within the RGP and summer measurements fall within the late-

summer period.

2 Sward

composition of t fered fr tween sons

a complex or man the s d pone show

ractions indic lues d d betw sp g and mer.

trast to the measurements of ctivity a ftake ( e) n this e, spr

pling point in m rch while summer r ers to a single sampling

p

76


Total biomass was not significantly higher in summer than it was in spring for any of

the vegetation types (Figure 4.8 and Table 4.7). It was highest for the C. vulgaris

dominated Calluna heath and wet heath in both spring and summer. The random effects

indicate that most of the variation came from season-within-year differences rather than

from differences between years.

However, if only high quality items (grass, herbs, and new growth C. vulgaris) were

considered and woody C. vulgaris, bryophytes and dead organic matter (DOM) were

omitted, then the mean biomass was always higher in the summer than in the spring

(Figure 4.9 and Table 4.8). However, the difference was only statistically significant for

the Calluna heath. Again, the random effects indicated that most of the variation came

from season-within-year differences rather than from between-year differences, and as

before, there was considerable variation between vegetation types.

icant between-

erence. For herbs, there were relatively large

stan or the fixed effects there wer no atistically significant

diff seasons for any of etation typ , alth ugh the mean values

were all higher in summer than in the spring. The random effects again showed that

most of tion came from within-yea er th ear

diff herbs and grasses. ses, pr rt the ion

from between-years than f erbs ich st

sourced from seasonal differences.

The pattern is reinforced by the results of the analyses of the biomass of low quality

Molinia swards. The biomass of bryophytes tended to be higher in the

Much of this biomass was made up of grass (Figure 4.10 and Table 4.9) and herbs

e only statistically signif(Figure 4.11 and Table 4.10). For grasses th

season difference was for the Holcus-Agrostis sward, although the dry heath had a p-

value of 0.076 suggesting some diff

dard errors f and thus e st

erences between the veg es o

the varia r differences rath an from between-y

erences for both For gras a greater opo ion of variat

was sourced or the h , for wh mo of the variation is

items including dead organic matter (DOM) (Figure 4.12 and Table 4.11), bryophytes

(Figure 4.13 and Table 4.12) and woody C. vulgaris (Figure 4.14 and Table 4.13).

These all showed that most of the variation came from between-year differences rather

than within-year differences. Of these low quality items, DOM and bryophytes tended

to have a higher mean biomass in spring than in summer, although this difference was

only statistically significant for DOM in the Holcus-Agrostis sward. In spring, the

biomass of DOM was highest in the most favoured Holcus-Agrostis, closely followed

by dry heath and

77


less favoured habitats of the outbye including Calluna and wet heaths and was lowest in

the Festuca sward. There were no between-season differences in biomass of woody C.

vulgaris for any of the vegetation types.

The analysis of the data for young-growth C. vulgaris (Figure 4.15 and Table 4.14)

reveal that there were seasonal differences in the biomass for the Calluna and wet

heaths but not for dry heath. The random effects showed that most of the variation was

sourced from within-year differences rather than from between-year differences,

although the between-year differences were still relatively important.

78


AF CA DH FE HA MO WH

0

200

400

600

Tota

l veg

etat

ion

biom

ass

(gD

M/s

q.

800

Vegetation Type

m)

1000

Spring


0

200

400

600

Tota

l veg

etat

ion

biom

ass

(gD

M/s

q.

800

Vegetation Type

m)

1000

Summer

Figure 4.8: Total standing crop biomass of vegetation (gDM/m2) during spring (March) and summer (August) for each vegetation type represented in the study area on Hirta. See Table 4.1 for the species codes. Estimates were obtained from the weighted means of biomass estimates from tussock and gap plots, based on the “tussockiness” of the sward (see methods). Error bars represent ±1s.e.m. Table 4.7: Summary of the linear mixed effects model for total standing crop biomass of vegetation in relation to vegetation type and season. See Table 4.1 for the species codes. Spring samples were collected in March while summer samples were collected in August.


Year 24.723 Season within Year 85.814 Veg Type within Season within Year 49.285 Residual 319.111

Fixed effects

Term Coefficient Std. Error d.f. t-value p-value

(Intercept Veg. Type=AH, Season=Spring) 144.671 118.252 396 1.223 0.222 Season (Summer) 5.329 162.230 8 0.033 0.975 Veg. Type (CA) 777.774 122.693 93 6.339 <.0001 Veg. Type (DH) 140.419 131.903 93 1.065 0.290 Veg. Type (FE) -20.609 140.359 93 -0.147 0.884 Veg. Type (HA) 162.057 119.181 93 1.360 0.177 Veg. Type (MO) 136.344 161.447 93 0.845 0.401 Veg. Type (WH) 332.980 138.992 93 2.396 0.019 Summer:Veg. Type (CA) -162.091 168.892 93 -0.960 0.340 Summer:Veg. Type (DH) 32.443 181.726 93 0.179 0.859 Summer:Veg. Type (FE) -6.641 192.905 93 -0.034 0.973 Summer:Veg. Type (HA) -23.327 163.911 93 -0.142 0.887 Summer:Veg. Type (MO) -30.649 221.888 93 -0.138 0.890 Summer:Veg. Type (WH) -30.646 191.913 93 -0.160 0.874

79



0

100

200

300

400

500

Vegetation Type

Bio

mas

s of

hig

h qu

ality

item

s (g

DM

/sq.

m)

Spring


0

100

200

300

400

500

Vegetation Type

Bio

mas

s of

hig

h qu

ality

item

s (g

DM

/sq.

m)

Summer

Figure 4.9: Standing biomass of “quality” vegetation (gDM/m2) during spring (March) and summer (August) for each vegetation type represented in the study area on Hirta. See Table 4.1 for the species codes. Estimates were obtained from the weighted means of biomass estimates from tussock and gap plots, based on the “tussockiness” of the sward (see methods). Error bars represent ±1s.e.m. Table 4.8: Summary of the linear mixed effects model for total standing crop biomass of “quality” vegetation in relation to vegetation type and season. See Table 4.1 for the species codes. Spring samples were collected in March while summer samples were collected in August.



Fixed effects


(Intercept Veg. Type=AH, Season=Spring) 35.907 67.806 396 0.530 0.597 Season (Summer) 99.788 93.749 8 1.064 0.318 Veg. Type (CA) 87.598 67.086 93 1.306 0.195 Veg. Type (DH) 16.912 70.174 93 0.241 0.810 Veg. Type (FE) -2.641 73.479 93 -0.036 0.971 Veg. Type (HA) 40.671 65.977 93 0.616 0.539 Veg. Type (MO) 12.844 80.836 93 0.159 0.874 Veg. Type (WH) 39.092 72.514 93 0.539 0.591 Summer:Veg. Type (CA) 245.311 92.623 93 2.649 0.010 Summer:Veg. Type (DH) 112.810 96.888 93 1.164 0.247 Summer:Veg. Type (FE) -4.943 100.987 93 -0.049 0.961 Summer:Veg. Type (HA) 89.345 91.035 93 0.981 0.329 Summer:Veg. Type (MO) 95.962 111.098 93 0.864 0.390 Summer:Veg. Type (WH) 165.089 100.287 93 1.646 0.103

80



0

50

100

150

200

Bio

mas

s of

gra

ss (g

DM

/sq.

m)

Spring


0

50

100

150

200

Bio

mas

s of

gra

ss (g

DM

/sq.

m)

Vegetation Type Vegetation Type

Summer

Estimates d on the

to veget ason. See Table 4.1 for t ies codes. . Spring samples were collected in Marc samples were collected in A

m effects Std.

Figure 4.10: Standing biomass of grass (gDM/m2) during spring (March) and summer (August) for each vegetation type represented in the study area on Hirta. See Table 4.1 for the species codes.

ere obtained from the weighted means of biomass estimates from tussock and gap plots, basew“tussockiness” of the sward (see methods). Error bars represent ±1s.e.m. Table 4.9: Summary of the linear mixed effects model for the standing crop biomass of grass in relation

ation type and seh while summer

he specugust.

Rando Dev.


Fixed effects

Term Coe Std t pfficient . Error d.f. -value -value

(Intercept Veg. Type=AH, Season=Spring) 10.224 396 18.851 0.542 0.588 Season (Summer) 25.748 25.487 8 1.010 0.342 Veg. Type (CA) -0.729 18.726 93 -0.039 0.969 Veg. Type (DH) 15.754 20.006 93 0.787 0.433 Veg. Type (FE) 4.328 21.209 93 0.204 0.839 Veg. Type (HA) 55.814 18.244 93 3.059 0.003 Veg. Type (MO) 23.375 24.173 93 0.967 0.336 Veg. Type (WH) 6.122 20.993 93 0.292 0.771 Sumemr:Veg. Type (CA) 25.737 25.790 93 0.998 0.321 Summer:Veg. Type (DH) 49.551 27.573 93 1.797 0.076 Summer:Veg. Type (FE) 21.672 29.150 93 0.743 0.459 Summer:Veg. Type (HA) 89.695 25.104 93 3.573 0.001 Summer:Veg. Type (MO) 50.125 33.223 93 1.509 0.135 Summer:Veg. Type (WH) 28.992 93 0.069 0.945 1.989

81



0

50

100

150

200

250

Vegetation Type

Bio

mas

s of

her

bs (g

DM

/sq.

m)

Spring


0

50

100

150

200

250

Vegetation Type

Bio

mas

s of

her

bs (g

DM

/sq.

m)

Summer

2Figure 4.11: Standing biomass of herbs (gDM/m ) during spring (March) and summer (August) for each

vegetation type represented in the study area on Hirta. See Table 4.1 for the species codes. Estimates

vegetation type and season See Table 4.1 for the species codes. Spring samples were collected in

were obtained from the weighted means of biomass estimates from tussock and gap plots, based on the “tussockiness” of the sward (see methods). Error bars represent ±1s.e.m. Table 4.10: Summary of the linear mixed effects model for the standing crop biomass of herbs in relation toMarch while summer samples were collected in August.



Fixed effects

Term Coe Std. Error d.f. t-value p-valuefficient

(Intercept Veg. Type=AH, Season=Spring) 22.160 56.300 396 0.394 0.694 Season (Summer) 76.784 78.011 8 0.984 0.354 Veg. Type (CA) -18.375 53.316 93 -0.345 0.731 Veg. Type (DH) -13.107 55.394 93 -0.237 0.814 Veg. Type (FE) -6.875 57.777 93 -0.119 0.906 Veg. Type (HA) -12.727 52.579 93 -0.242 0.809 Veg. Type (MO) -10.938 62.778 93 -0.174 0.862 Veg. Type (WH) -18.708 56.962 93 -0.328 0.743 Summer:Veg. Type (CA) 130.590 73.668 93 1.773 0.080 Summer:Veg. Type (DH) 32.135 76.532 93 0.420 0.676 Summer:Veg. Type (FE) -25.972 79.407 93 -0.327 0.744 Summer:Veg. Type (HA) -1.979 72.611 93 -0.027 0.978 Summer:Veg. Type (MO) 44.521 86.280 93 0.516 0.607 Summer:Veg. Type (WH) 66.666 78.816 93 0.846 0.400

82



0

50

100

150

Bio

mas

s of

DO

M (g

DM

/sq.

m)

Spring

0

50

100

150

Bio

mas

s of

DO

M (g

DM

/sq.

m)


Vegetation Type Vegetation Type

Summer

ere obtained from the weighted means of biomass estimates from tussock and gap plots, based on the

to veget nd season. Spring samples w ected in March while summer samples were colle

m effects Std.

Figure 4.12: Standing biomass of DOM (gDM/m2) during spring (March) and summer (August) for each vegetation type represented in the study area on Hirta. See Table 4.1 for the species codes. Estimates w“tussockiness” of the sward (see methods). Error bars represent ±1s.e.m. Table 4.11: Summary of the linear mixed effects model for the standing crop biomass of DOM in relation

ation type acted in August.

ere coll

Rando Dev.


Fixed effects

Term Coe Std t- pfficient . Error d.f. value -value

(Intercept Veg. Type=AH, Season=Spring) 320.021 22.190 96 0.902 0.368 Season (Summer) -7.159 29.178 8 -0.245 0.812 Veg. Type (CA) 15.625 23.578 93 0.663 0.509 Veg. Type (DH) 63.877 25.080 93 2.547 0.013 Veg. Type (FE) 4.078 26.524 93 0.154 0.878 Veg. Type (HA) 142.257 <.0001 23.027 93 6.178 Veg. Type (MO) 89.688 29.998 93 2.990 0.004 Veg. Type (WH) 8.570 26.217 93 0.327 0.745 Summer:Veg. Type (CA) 7.923 32.506 93 0.244 0.808 Summer:Veg. Type (DH) -15.053 34.586 93 -0.435 0.664 Summer:Veg. Type (FE) -7.078 36.454 93 -0.194 0.847 Summer:Veg. Type (HA) -93.104 -2.935 31.718 93 0.004 Summer:Veg. Type (MO) -38.938 41.228 93 -0.944 0.347 Summer:Veg. Type (WH) 3.708 36.231 93 0.102 0.919

83



0

50

100

150

Vegetation Type

Bio

mas

s of

bry

ophy

tes

(gD

M/s

q.m

)

Spring


0

50

100

150

Vegetation Type

Bio

mas

s of

bry

ophy

tes

(gD

M/s

q.m

)

Summer

Figure 4.13: Standing biomass of bryophytes (gDM/m2) during spring (March) and summer (August) for each vegetation type represented in the study area on Hirta. See Table 4.1 for the species codes. Estimates were obtained from the weighted means of biomass estimates from tussock and gap plots, based

the “tussockiness” of the sward (see methods). Error bars represent ±1on s.e.m.

r samples were collected in August. Std.

Table 4.12: Summary of the linear mixed effects model for the standing crop biomass of bryophytes in relation to vegetation type and season. See Table 4.1 for the species codes. Spring samples were collected in March while summe

Random effects Dev.


Fixed effects

Term Coefficient S t-valu ptd. Error d.f. e -value

(Intercept Veg. Type=AH, Season=Spring) 77.529 22.035 396 0.001 3.518 Season (Summer) -24.445 29.792 -0.821 0.436 8 Veg. Type (CA) 52.899 23.103 93 2.290 0.024 Veg. Type (DH) 24.903 93 -0.055 0.956 -1.364 Veg. Type (FE) -23.250 26.542 93 -0.876 0.383 Veg. Type (HA) -14.965 22.415 93 -0.668 0.506 Veg. Type (MO) 34.938 30.642 93 1.140 0.257 Veg. Type (WH) 72.392 26.284 93 2.754 0.007 Summer:Veg. Type (CA) -16.843 31.798 93 -0.530 0.598 Summer:Veg. Type (DH) -1.145 34.306 93 -0.033 0.973 Summer:Veg. Type (FE) -18.042 36.479 93 -0.495 0.622 Summer:Veg. Type (HA) -6.998 30.822 93 -0.227 0.821 Summer:Veg. Type (MO) -34.965 42.113 93 -0.830 0.409 Summer:Veg. Type (WH) -30.364 36.291 93 -0.837 0.405

84



0

200

400

600

Vegetation Type

Bio

mas

s of

old

, woo

dy _

Cal

luna

vul

garis

_ (g

DM

/sq.

m)

Spring


0

200

400

600

Vegetation Type

Bio

mas

s of

old

, woo

dy _

Cal

luna

vul

garis

_ (g

DM

/sq.

m)

Summer

Figure 4.14: Standing biomass of woody C. vulgaris (gDM/m2) during spring (March) and summer (August) for each vegetation type represented in the study area on Hirta. See Table 4.1 for the specicodes. Estimates were obtained from the weighted means of biomass estimates from tussock and gap plots, based on the “tussockiness” of the sward (see methods). Error bars represe

es

nt ±1s.e.m.

ted in August.

Table 4.13: Summary of the linear mixed effects model for the standing crop biomass of woody C. vulgaris in relation to vegetation type and season. See Table 4.1 for the species codes. Spring samples were collected in March while summer samples were collec



Fixed effects


(Intercept Veg. Type=AH, Season=Spring) 3.795 97.096 396 0.039 0.969 Season (Summer) -2.962 132.878 8 -0.022 0.983 Veg. Type (CA) 606.772 103.016 93 5.890 <.0001 Veg. Type (DH) 46.474 111.062 93 0.418 0.677 Veg. Type (FE) -1.313 118.388 93 -0.011 0.991 Veg. Type (HA) -2.083 99.945 93 -0.021 0.983 Veg. Type (MO) -0.688 136.703 93 -0.005 0.996 Veg. Type (WH) 183.755 117.230 93 1.567 0.120 Summer:Veg. Type (CA) -152.502 141.790 93 -1.076 0.285 Summer:Veg. Type (DH) -15.863 153.000 93 -0.104 0.918 Summer:Veg. Type (FE) 0.479 162.709 93 0.003 0.998 Summer:Veg. Type (HA) 1.252 137.433 93 0.009 0.993 Summer:Veg. Type (MO) 0.465 187.880 93 0.002 0.998 Summer:Veg. Type (WH) -12.894 161.869 93 -0.080 0.937

85



0

50

100

150

200

Vegetation Type

Bio

mas

s of

new

gro

wth

_C

allu

na v

ulga

ris_

(gD

M/s

q.m

)

Spring


0

50

100

150

200

Vegetation Type

Bio

mas

s of

new

gro

wth

_C

allu

na v

ulga

ris_

(gD

M/s

q.m

)

Summer

Figure 4.15: Standing biomass of new-growth, young, C. vulgaris (gDM/m ) during spring March) and summer (August) for each vegetation type represented in the study area on Hirta. See Table 4.1 for the species codes. Estimates were obtained from the weighted means of biomass estimates from tussock and gap plots, based on the “tussockiness” of the sward (see methods). Error bars represent ±1s.e.m.

2

able 4.14: Summary of the linear mixed effects model for the standing crop biomass of new-growth,

(

Tyoung, C. vulgaris in relation to vegetation type and season. See Table 4.1 for the species codes. Spring samples were collected in March while summer samples were collected in August.



Fixed effects

Term Coefficien Std. Error d.f. t-value p-valuet

(Intercept Veg. Type=AH, Season=Spring) 0.743 25.138 396 0.030 0.976 Season (Summer) 0.118 34.404 8 0.003 0.997 Veg. Type (CA) 1 9 <05.642 26.483 3 3.989 .0001 Veg. Type (DH) 15.547 28.543 93 0.545 0.587 Veg. Type (FE) -0.016 30.419 93 -0.001 1.000 Veg. Type (HA) -0.257 25.695 93 -0.010 0.992 Veg. Type (MO) 0.500 35.111 93 0.014 0.989 Veg. Type (WH) 53.315 30.124 93 1.770 0.080 Summer:Veg. Type (CA) 89.977 36.450 93 2.469 0.015 Summer:Veg. Type (DH) 29.731 39.320 93 0.756 0.452 Summer:Veg. Type (FE) -0.846 41.807 93 -0.020 0.984 Summer:Veg. Type (HA) -0.604 35.333 93 -0.017 0.986 Summer:Veg. Type (MO) 1.167 48.256 93 0.024 0.981 Summer:Veg. Type (WH) 94.754 41.593 93 2.278 0.025

86


4.4.3 Diet botanical composition

p>0.05) but it did

differ between spring (March) and summer (August). Poa ere

more abundant in the samples collected in spring than in the summer, and Calluna

vulg undant in s r samples than in spring samples. The

percen entation of the other species ed did not differ significantly

between spring and summer (p>0.05) (Table 4.15 and Figure 4.16).

A c ility of plant ies w the ead e a eir

representation in the diet gives an indication of selection and avoidance of the different

species in both spring and summer (Figur

show species tend to be more ntly d a , w the

mor ecies tend to be more fre avo an lec al ws

that ndent on the area for es ent of forage species

availability. For exam , if the inbye is used, the data show that Callun

selected for in both seasons. However, if the outbye area is included then Calluna is

apparently avoided. Also, both methods sh t som the ore preferred grasses

cluding Agrostis, Anthoxanthum and Holcus are avoided, while the less preferred

s voured. Furthermore, both methods indicate that

bryophytes, which have negligible nutritional value, are selected for in the summer.

The botanical composition of the diet was not influenced by sex (

spp. and bryophytes w

aris fragments were more ab umme

tage repres identifi

omparison of the availab spec ithin H Dyk nd th

e 4.17 and Table 4.16). Generally, the data

s that rarer freque selecte than voided hile

e abundant sp quently ided th se ted. It so sho

the outcome is depe chosen the ass sm

ple a was strongly

ow tha e of m

in

Nardu and Carex are relatively fa

87


Fe Ca Ag Po Ho Lo Na An Mo De Cx Bry Unk

0

5

10

15

20

25

e

Spring

Diet component (see title)

% fr

agm

ents

in fa

ecal

sam

pl

Fe Ca Ag Po Ho Lo Na An Mo De Cx Bry Unk

0

5

10

15

Diet component (see title)%

frag

men

ts in

faec

al s

ampl

20

25

e

Summer

Figure 4.16: The botanical composition of the diets of Soay sheep in spring and summer as estimated using the plant faecal plant cuticle analysis technique. There were significant seasonal differences for Calluna vulgaris, Poa and bryophytes (see Table 4.15). Error bars represent ±1s.e.m. The components were Festuca spp. (FE), Calluna vulgaris (CA), Agrostis spp. (Ag), Poa spp. (Po), Holcus spp. (Ho), Lolium spp. (Lo), Nardus spp. (Na), Anthoxanthum spp. (An), Molinia spp. (Mo), Deschampsia spp. (De), Carex spp. (Cx), bryophytes (Bry) and unidentified grasses (Unk).

Table 4.15: The proportion of plant fragments in faecal samples from Soay sheep on Hirta in spring and summer. See also Figure 4.16 which shows the data graphically. Note that although the mean± s.e.m. values are given, the data were counts and were thus poisson distributed. Component Spring Summer Significance

Calluna vulgaris 8.649±1.330 20.714±1.934 F1,98=17.700,p<0.001 Poa spp. 9.389±1.164 4.098±0.627 F1,98=12.410,p<0.001

ryophytes 15.746±1.175 9.941±1.355 F1,98=17.410,p<0.001

ith no seasonal differences (p>0.05)

B

Components w

Component Overall Mean

Holcus spp. 7.982±1.306 Festuca spp. 16.575±1.708 Agrostis spp. 7.571±1.298 Lolium spp. 2.753±0.629 Nardus spp. 5.127±0.828 Anthoxanthum spp. 5.559±1.244 Molinia spp. 6.412±1.335 Deschampsia spp. 7.123±1.644 Carex spp. 4.016±0.774 Unknown Grass 2.613±1.170

88


(a) (b)

Rank availability within Head Dyke (by gDM)

Med

ian

of ra

nk p

ropo

rtion

in d

iet (

by n

o. fr

agm

ents

)

0 2 4 6 8 10 12

0

2

4

6

8

10

12

Fe

Ca

Po

Lo

Na

An

Mo

De

Cx

Bry

AgHo

Rank availability within study area (by gDM)

Med

ian

of ra

nk p

ropo

rtion

in d

iet (

by n

o. fr

agm

ents

)

0 2 4 6 8 10 12

0

2

4

6

8

10

12

Fe

Ca

Ag

Po

Ho

Lo

Na

An

Mo

De

Cx

F

Ca

Po

Lo

Na

An

Mo

De

Cx

e

Ag Ho

Bry BryFe

Ca

Ag

Po

Ho

Lo

Na

An

Mo

De

Cx

Bry

Figure 4.17: The relationship between ranked availability of plant species within (a) the Head Dyke and (b) the study area and their ranked proportion representation in the diet. Availability was estimated from dry biomass in vegetation samples and diet was estimated by faecal plant cuticle analysis of faecal samples collected in spring (blue circles) and summer (red squares). The dashed line represents the line of no selection; points above this line indicate selection while points below the line indicate avoidance. Those species where there was a significant difference between availability and dietary abundance are indicated by a heavy lined symbol where those with no significant difference are plotted with a fine lined symbol. Significance was tested using a Wilcoxon test with α=0.05. The components were Festuca spp. (FE), Calluna vulgaris (CA), Agrostis spp. (Ag), Poa spp. (Po), Holcus spp. (Ho), Lolium spp. (Lo), Nardus spp. (Na), Anthoxanthum spp. (An), Molinia spp. (Mo), Deschampsia spp. (De), Carex spp. (Cx), and bryophytes (Bry).

89


Table 4.16: Summary of the relationships between proportional representation of plant species in the diet of Soay sheep and their ranked availability (by dry biomass per unit area) within the Head Dyke. Median ranked proportion in diet along with the lower (LQ) and upper (UQ) quartiles are given alongside the ranked availability of plant species. The significance of the differences were tested using Wilcoxon tests, the results of which are also presented. Spring Summer

Median rank in

diet

LQ UQ Rank avail.

Wilcoxon tests

Median rank in

diet

LQ UQ Rank avail.

Wilcoxon tests

Festuca 9.75 5.625 12.000 9 Z=-0.616, p=0.538

10 8.625 11.000 10 Z=-0.394, p=0.693

Calluna 7 4.000 10.000 4 Z=4.862, p<0.001

11 8.625 12.000 2 Z=6.179, p<0.001

Agrostis 5.25 3.000 8.875 10 Z=-5.470, p<0.001

7.75 3.125 10.000 12 Z=-6.108, p<0.001

Poa 8 5.125 10.000 8 Z=-0.843, p=0.399

6 3.500 7.375 8 Z=-4.741, p<0.001

Holcus 5.25 3.000 8.500 11 Z=-5.802, p<0.001

7 3.625 10.375 11 Z=-5.667, p<0.001

Lolium 4 3.000 6.000 1.5 Z=6.159, p<0.001

3.5 2.500 7.375 5 Z=-7.07, p=0.478

Nardus 6 3.000 8.375 6 Z=-0.412, p=0.680

6.75 3.000 8.375 2 Z=6.142, p<0.001

Anthoxanthum 4 3.000 9.000 7 Z=-1.492, p=0.136

5.25 3.125 7.500 9 Z=-6.034, p<0.001

Molinia 5.75 3.500 10.750 1.5 Z=6.156, p<0.001

4 3.000 7.875 6 Z=-1.990, p=0.047

Deschampsia 4 2.625 8.375 3 Z=3.882, p<0.001

3.5 3.000 8.625 2 Z=5.939, p<0.001

Carex 5 3.500 7.000 5 Z=0.581, p=0.561

4 3.000 7.375 4 Z=2.039, p=0.042

Bryophyte 10 8.000 11.000 12 Z=-6.095, p<0.001

8 3.625 10.000 7 Z=0.426, p=0.670

4.4.4 Diet and forage quality

Forage quality, as assessed by percentage total nitrogen content, was significantly

higher in the summer for both herbs and grasses (Table 4.17).

Table 4.17: The percentage total nitrogen content of herbs and grasses in March and August. Data were obtained from samples taken in 1988, 1991 and 1992. % total nitrogen content March August Significance of the

seasonal difference

Herbs 2.339±0.168 4.015±0.099 t33 = 8.466, p<0.001 Grasses 1.823±0.041 3.457±0.042 t187 = 26.775, p<0.001

Diet quality, assessed by percentage faecal nitrogen (%FN) content, was best described,

over the range of dates examined, by a quadratic function (%FN=-

0.001jd2+0.029jd+0.596, where jd=julian date; Table 4.18 and Figure 4.18). There were

90


no significant effects of sex or age and no significant interactions. %FN tended to

increase with julian date, rising from ~1.5% in February and reaches a plateau of ~2.5-

3% in the summer.

Table 4.18: Summary of the linear model for faecal nitrogen content of Soay sheep on Hirta throughout the year. Estimates and standard errors are given. P-values were derived by deletion of the term from the model and examination of the resulting change in residual deviance. Residual deviance = 55.576 on 293 d.f., r2-value = 0.482.

Term Estimate Std. Error Change in deviance p-value

(Intercept) 0.596 0.125 - - Julian day 0.0294 0.002 -41.222 <.0001 I(Julian day2) -0.001 0.000 -31.276 <.0001

Excluded terms

Sex p>0.05 Age p>0.05 All interactions p>0.025

91


Date

% fa

ecal

nitr

ogen

con

tent

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

0

1

2

3

4

M

MMMM

MMMMMM

MMMM

MMMMMMM

MM M M

M

MMMMMMMMMMM

M

MMMMMMMMMMMMMMM

MM

M

M

M

MMMMMMM

M

MMMM

M

M

MMM

FFF

FFFFFFFF

F

FF

F

F

FF

F

FF

F

FFFFF

FFFFFF

FFFFFFFFFFF

FFF

FF

F

F

F

F

F

F

FF

FFF

F

FFFF

FF

F

F

F

F

F

F

FF

F

FFFFFFFFFFF

F

F

FF

F

MM

MMM

MMM M

MM

M

M

MMM

M

M

M

M

M

M

MMMM

M

M

MMMMMMMM

M

M

MMMMMMM

MM

MMMMMM

MMM

MM

MM

MMM

MMM

M

MMMMMM

M

MMM

MMMMMMM

M

MMMMMM

M

MMM

MMMMMMMMMM

M

MM

M

MMMMMMMMMMMM

M

MM

MMM

M

M

Figure 4.18: Percentage faecal nitrogen content for sheep on Hirta throughout the year. “F” and “M” denote measurements from females and males respectively. The line represents the prediction from the model summarised in Table 4.18 and has the formula %FN=-0.001jd2+0.029jd+0.596, where jd=julian day and %FN=% faecal nitrogen content. The r2-value of the model is 0.482.

4.5 Discussion

Primary production and offtake

As expected, the estimates of both net primary productivity and the biomass increment

tended to be highest during the rapid growth phase within the Head Dyke where the

sward is dominated by grasses. Furthermore, throughout the year, above-ground net

primary productivity (ANPP) tended to be higher in the inbye than in the outbye,

although the magnitude of the difference decreased as the winter approached. Although

there were no statistically significant seasonal trends for the outbye productivity, the

mean value for ANPP was highest in late-summer, which perhaps indicates that the

Calluna dominated swards do not exhibit the characteristic RGP of the grassy inbye

swards. Common et al. (1991) measured annual primary productivity on hill pasture

swards in Roxburghshire, Scotland, that were comparable to Hirta’s inbye. They

estimated productivities ranging from 386-517gDM/m2. Thus, the measurements

presented here (681±126gDM/m2) are slightly higher, but if the standard errors are

taken into account, they are comparable. Previous workers have noted that the

92


estimation of primary production is notoriously unreliable with some methods

overestimating production by 700% (see Chapter 4 in Tuke 2001 for a review).

However, the methods used here are based on the one that Tuke (2001) found to be

most reliable and the results are reliable in so far as they allow a comparison to be made

between productivity in the inbye and outbye areas.

The estimated measurement errors were not insignificant (Figure 4.6). A combination of

the low productivity and high heterogeneity (especially for the outbye swards) means

that for future studies a considerably larger sample size would be recommended.

Alternatively a different method, such as percentage of shoots browsed or the amount of

each shoot removed, could be used to assess heather utilisation rates (Armstrong and

MacDonald 1992).

The mean offtake rates were consistently higher in the inbye than in the outbye,

reflecting the distribution patterns of the sheep, which tended to favour formerly

cultivated swards of the inbye (Chapter 5). Offtake rates tended to decrease between the

RGP and winter in both the inbye and the outbye swards. This is despite the fact that the

intake rate per sheep is likely to increase during the winter (Iason, Sim et al. 2000).

Sward composition/biomass

There were differences in composition and biomass between the seven vegetation types

and between seasons. The inbye swards (HA and AF) were dominated by grasses, while

the outbye swards (CA, DH, MO and WH) were dominated by Calluna. The mean

biomass of high quality items (grass, herbs and new-growth Calluna) was consistently

higher in the summer than in the spring while the biomass of low quality items such as

dead organic matter (DOM) and bryophytes tended to be higher in the spring in several

of the sward types. Although the Holcus-Agrostis community of the inbye had the

highest abundances of grasses in both spring and summer, it also had the highest

abundance of DOM in the spring. This represents a “dilution” of the high quality forage

and is likely to be an important factor in the foraging decisions of the sheep at this time

of year.

Although this study used the weighted means from gap and tussock samples to give an

accurate representation of the vegetation available to the sheep, previous work by

Milner (1999) on Hirta has shown that they tend to select preferentially from gap rather

93


than tussock vegetation, especially at the end of winter. Furthermore, Milner (1999)

found that the gap selection indices were greater for survivors of the population crash

than for non-survivors, and thus suggested that there was a fitness advantage to grazing

gap over tussock vegetation. Although tussock samples have a higher biomass than gap

samples, they tend to be dominated by dead organic matter and have comparatively

fewer palatable species than gap vegetation (Crawley, unpublished data). Therefore, the

quality of food that the sheep could obtain from the gap vegetation is likely to be higher

than that from tussock vegetation.

Diet composition

The botanical composition of the diet of Soay sheep was first assessed in the 1970s by

Milner et al. (1974). Their results were broadly similar to those presented here despite

the differing methodologies. The two major components were Festuca spp. and

bryophytes in the spring and Festuca spp. and C. vulgaris in the summer. Despite the

fact that Holcus spp. and Agrostis spp. make up a large proportion of sward biomass

(Crawley, unpublished data) and are relatively preferred in Scottish grasslands (King

and Nicholson 1964), they are under-represented in the diet. The bryophytes have

negligible nutritional value and are probably not intentionally ingested.

Many other workers have demonstrated seasonality in the diet composition of free-

ranging herbivores (Rosati and Bucher 1992, Wansi, Pieper et al. 1992, Branch,

Villarreal et al. 1994, Forchhammer and Boomsma 1995, Mohammad, Ferrando et al.

1996, Chen, Ma et al. 1998, Smith, Valdez et al. 1998, Bontti, Boo et al. 1999) and it is

no surprise that Soay sheep also exhibit seasonality in their diets. It was unexpected,

however, that only three of the twelve components assessed showed any seasonal

difference. This may be related to the fact that the sward on Hirta is relatively simple

and species poor so that there may be less opportunity for the sheep to switch to other

food items.

To some extent the seasonal differences in diet composition reflect the seasonal changes

in sward composition. The main differences were in the abundance of bryophytes,

which are more abundant in spring than in summer for both the diet and in all of the

sward types. Furthermore, the increased abundance of new-growth Calluna in the

summer is also reflected in the increased proportion of Calluna in the diet.

94


The changes in composition may be in part due to the changing relative nutritional

quality of the vegetation rather than changes in species abundance. The results of

chemical analysis of vegetation samples showed a significant increase in total nitrogen

content between March and August. This increase is comparable to the seasonal

differences found by other workers including Gonzalez-Hernandez et al. (1999),

Dorgeloh et al. (1998), Chen et al. (1998) and Jiang et al. (1996).

The results of the comparison of species-availability with the proportion estimated to be

in the diet was interesting. It showed that the interpretation of such date is sensitive to

the area chosen from which to assess the availability of the vegetation (i.e. inbye only or

study area). In both analyses it was surprising, given its low nutritional quality, that

bryophytes appeared to be selected for in the summer and only weakly avoided in the

spring. The fact that many of the supposedly palatable grasses including Holcus and

Anthoxanthum were avoided was also unforeseen.

As mentioned in the methods section, the accuracy of the FPCA technique relies on

several assumptions. The two major problems are those of the unequal digestibility and

unequal fragmentation of the plant species during consumption, digestion and sample

preparation. For example, the fact that bryophytes seem to be present in the diet in

relatively high proportion is probably because bryophytes are relatively indigestible and

easily fragmented in comparison with other dietary components. To some extent, these

issues can be addressed by applying correction factors to the measurements, but the

determination of correction factors can be problematical in itself (Leslie, Vavra et al.

1983). Given these assumptions, which are unlikely to be met, it is clear that

improvements in the methodology are required in order to gain a more quantitative

understanding of this matter.

The increase in total nitrogen content was apparent for both herbs and grasses.

Furthermore, C. vulgaris shoots also have a higher nutritional value in summer than in

spring (Salt, Mayes et al. 1994). The increase in the quality of the available vegetation

was reflected by a comparable increase in total faecal nitrogen content, which is a

reliable indicator of diet quality.

These results give an impression of the characteristics of the vegetation communities

that are available to the Soay sheep on Hirta. The communities show evidence of

95


seasonality in both their productivity and botanical composition, and these changes are

reflected by concurrent changes in the diet composition and quality of the sheep. The

following chapter (Chapter 5) will build on the information presented here by

examining the large-scale selectivity of the sheep in terms of their distribution among

the community types.

96

Chapter 5 - Seasonality, spatial scale and distribution

Chapter 5 : The influence of seasonality and spatial scale

on the distribution patterns and habitat use of Soay sheep

97


The influence of seasonality and spatial scale on the distribution patterns and habitat use of Soay sheep

5.1 Abstract

Selectivity for particular vegetation types is often measured for free-ranging herbivores.

However, the area that is included in the analysis tend to be chosen arbitrarily (e.g.

boundaries of the study area) rather than for any biological reason. Using location and

habitat quality data collected between 1985 and 2002, this chapter assessed habitat

selectivity and habitat matching for the sheep using the study area in spring and winter.

The assessment of forage availability was carried out at several spatial scales. The aim

was to determine whether the spatial scale over which the forage availability is

assessed has an impact on the outcome. The largest scale was the arbitrary size of the

study area. Then, three smaller scales were defined using hierarchical cluster analysis

(HCA) of animal locations to identify stable groupings (hefts). Minimum area convex

polygons (MCP) were drawn to define the area to include in the analysis. Selectivity

patterns were analysed using mixed effects models to allow for the nested design of the

sampling procedure. The main findings were that: (i) the apparent selectivity of the

sheep for different plant communities was influenced by the scale at which the

assessment of forage availability was made; (ii) the formerly cultivated swards of the

inbye had the highest selectivity no matter what scale was used; (iii) the distribution

conformed more closely (but still not well) with the predictions of the ideal free

distribution model when the area under consideration was chosen using the HCA/MCP

method than when it was arbitrarily determined. This result has implications for studies

of foraging theory, in particular for those investigating habitat matching and the ideal

free distribution.

5.2 Introduction

The spatial distribution of animals is often regarded as being driven by a need to

maximise fitness (e.g. Fretwell and Lucas 1970). Animals are, therefore, expected to

aggregate within the most favourable habitat patches (Bailey, Gross et al. 1996, Cezilly

and Benhamou 1996).

98


Habitat quality can be regarded as having both positive and negative elements. For

grazing ungulates, positive elements include the potential nutrient intake rate and the

availability of water, while negative elements include the presence of poisonous or well-

defended species and the presence of predators or competitors (Crawley 1983).

Competition for enemy-free space may be an issue in certain situations (Holt 1977,

1984, Jeffries and Lawton 1984). In simple terms, if two prey species (e.g. grazing

herbivores) are being preyed upon by a single predator species (e.g. large carnivore)

then the predator benefits from the relationship with both prey species. However, the

more the predator species benefits from preying on prey species one then the more prey

species two will suffer (because the predator population will increase). Indirectly,

therefore, prey species one adversely affects prey species two and vice versa. Thus the

two prey species may look like they are competing for a limiting resource (exploitation

competition) when they are competing for the non-limiting resource of enemy-free

space. Mathematical models have shown that the coexistence of prey species under

predation pressure is facilitated by their niche differentiation (Holt 1977, 1984, Jeffries

and Lawton 1984). However, enemy-free space is not likely to be an important issue for

the Soay sheep on Hirta because they do not experience significant predation except for

the actions of gastro-intestinal parasites.

Potential nutrient intake rate of grazing ruminants is influenced by sward species

composition, biomass and sward height (Gordon and Illius 1988a, b, Gordon, Illius et

al. 1996). Seasonal and inter-year changes occur in the characteristics of the vegetation

and thus may affect spatial distribution over these time scales (Festa-Bianchet 1988,

Forchhammer and Boomsma 1995, Hamback 1998, Illius and O'Connor 2000). The

seasonal changes are mainly caused by climatic effects on plant primary production

whilst, although climatic differences play an important role in the inter-year changes,

these may be caused by changes in grazing pressure as a result of changing population

density (Crawley 1983, Milner, Albon et al. 1999, Crawley, Albon et al. 2003).

To some extent, herbivores can compensate for poor food quality and quantity by

increasing their selectivity and/or time spent feeding (Iason, Mantecon et al. 1999).

Nevertheless, seasonal changes in the availability and quality of the forage are often

reflected in the fluctuation of animal condition throughout the year (Bruinderink,

Hazebroek et al. 1994, Hewison, Angibault et al. 1996).

99


Furthermore, intra-specific differences in requirements, for example due to sex, age or

size, may manifest themselves. This is most commonly exhibited by sexual segregation,

which has been hypothesised to be caused by behavioural or physiological differences

between the sexes. For example, Ruckstuhl (1998) documented sexual segregation in

Bighorn sheep (Ovis canadensis) and suggested that it was caused by the differences in

foraging strategy employed by each sex. Another theory is that size difference between

the sexes drives their segregation (the indirect-competition hypothesis). Males, due to

their larger body size and higher forage requirements, may be inferior in indirect

competition to females and may thus be forced into marginal habitats by female grazing

pressure. However, in a large scale manipulation of male and female red deer (Cervus

elaphus) numbers on the island of Rum, Conradt et al. (1999) found no evidence to

support this hypothesis. The issue of sexual and social segregation in Soay sheep has

been dealt with by Conradt et al. (1999) and Ruckstuhl et al. (unpublished manuscript)

and as such will not be considered in this chapter.

Fretwell and Lucas’s (1970) original ideal free distribution (IFD) model assumes that

organisms have an “ideal” (i.e. omniscient) knowledge of the quality of the forage

within the area available to them and that they are “free” to move, with negligible cost,

throughout this environment. Furthermore it also assumes that organisms have equal

foraging abilities, and that they gain equally from the food items that they consume. The

model has been tested on numerous occasions (for reviews see Cezilly and Boy 1991,

Weber 1998, Collins, Houston et al. 2002) and has been adapted to relax both of these

major assumptions (Cezilly and Boy 1991, Spencer, Kennedy et al. 1996, Tregenza,

Parker et al. 1996, Collins, Houston et al. 2002).

The “free” assumption may not hold true because the animals are constrained by

whatever processes control the size and shape of their home range. These processes may

include competition from other species or conspecifics (i.e. enemy avoidance), or may

be due to physical barriers such as rivers or mountain ranges.

Furthermore, the “ideal” knowledge assumption is also unlikely in many situations

(Hakoyama and Iguchi 1997, Rowcliffe, Sutherland et al. 1999, Berec 2000, Collins,

Houston et al. 2002). In order to have an ideal knowledge of foraging conditions, an

animal is required to have had experience of the resources under consideration. It is

obvious that this assumption, in animals with restricted home range territories, is likely

100


to be highly scale dependent. It is likely that if the areas within the animal’s home range

are considered, for which the animal has an intimate knowledge, then the assumption

will hold. However, when the scale is increased to include areas outside the home range

then the assumption is less likely to hold true (the organism’s knowledge is less than

ideal) and the IFD is, therefore, less likely to be met. The spatial memory of sheep is

known to be good (Bailey, Gross et al. 1996, Edwards, Newman et al. 1996b, Edwards,

Newman et al. 1997, Dumont and Petit 1998) and they are likely to be able to easily,

and efficiently, exploit the spatial heterogeneity of their environment.

In this study, I investigate the relationship between the distribution and habitat quality

of Soay sheep on Hirta. It is expected that the sheep will tend to distribute themselves so

that higher quality plant community types will be favoured over poor quality habitats.

Any seasonal changes in habitat quality should, therefore, be reflected in changed

selection patterns by the sheep. However, it is also expected that the sheep will not

conform fully to the ideal free distribution because of the constraints imposed by their

non-omniscient knowledge of their environment and by home range limitations. It is

expected, therefore, that as the spatial scale at which the system is studied is decreased,

the conformation to the IFD will become more apparent.

Furthermore, if the sheep distribution conforms to the IFD, then selection patterns are

expected to be influenced by population density. Thus, at high population densities the

sheep will exploit even the lowest quality plant communities such as the Calluna and

wet heaths, but at low population densities they will tend to occupy only the highest

quality patches such as the Holcus-Agrostis grasslands.

5.3 Methods

Data were collected from a free-ranging and feral population of Soay sheep (Ovis aries)

on Hirta, part of Scotland’s St. Kilda archipelago (57º49’N 08º34’W) situated

approximately 70km west of the Outer Hebrides. The population and the study site are

presented in greater detail in Chapter 2.

5.3.1 Location and habitat choice data

Censuses were carried out during the lambing period (April-May), in mid-summer

(August), and during the mating season (rut) (November-December), between 1985 and

101


2002. Over this period total of 476 censuses were carried out (mean 28.0yr-1, Std.

Dev.=4.40) giving a total of 141,684 observations. The average number of censuses

carried out by season was: Lambing (9.29 yr-1), Summer (8.41 yr-1), Rut (9.76 yr-1).

Fewer than 2% of the animals observed within the study area were untagged, and these

were excluded from the analysis. Because of the disruptions to distribution patterns that

occur during the mating season (rut), data from this period were excluded from the

analysis, this left 84,453 observations in the analysed dataset.

For each census three observers traversed different routes within the study area

simultaneously and located individual marked sheep within plant communities within

1ha blocks on a grid referenced map. The three routes were fixed throughout the study

period and between them covered the whole study area.

5.3.2 Assignment to heft and vegetation availability

The definition of heft used in this analysis follows Coulson, Albon et al. (1999) who

defined it as “a group of individuals using the same resources in space”. These

individuals may compete for resources and will frequently consist of smaller cohesive

sub-groups such as mother-offspring pairs and ram-ram coalitions (Coulson, Albon et

al. 1999).

For each season (summer and spring), within every year (1985-2002) the mean position

of each foraging animal was calculated. Then, a distance matrix was calculated to give

the distance between all pairs of animals for each season, in each year. Animals had to

have been seen in at least 3 of the censuses within the season-year in order to be

included in the analysis so that sheep that only occasionally use the area were excluded

from the analysis.

Hierarchical cluster analysis (HCA) with compact linkage (Gordon 1981) was used on

the distance matrix in order to group individuals together into hefts. This method

hierarchically clusters the population at scales between n clusters of one individual and

one cluster of n individuals (Gordon 1981).

The mean locations of the sheep were plotted onto a vegetation map derived from the

Nature Conservancy map in Jewell, Boyd et al. (1974), and minimum area convex

102


polygons were drawn over the groups. This was repeated for each season-year for

between 1 and 3 clusters.

The area of each heft was calculated, and then, the areas of the seven major plant

community types found on the island were calculated (by cutting out and weighing the

outline of the vegetation area) within the heft areas. This gave an index of vegetation

availability at 4 different spatial scales, for each season within each year (the study area,

and clusters 1-3).

5.3.3 Vegetation

In order to obtain data on vegetation parameters within each of the plant communities

within the study area over the study area (Table 5.1), M.J. Crawley carried out an

assessment of sward characteristics in March and August of each year between 1993

and 2002. Five transects, each with six sampling locations were assessed using sorted

biomass on each occasion.

Table 5.1: The plant community types represented within the study area on Hirta and used in this study. Vegetation Type Code

Agrostis-Festuca grassland AF Holcus-Agrostis grassland HA Festuca-Plantago sward FE Calluna heath CA Dry heath DH Wet heath WH Molinia grassland MO

5.3.4 Population density

Population density was treated as a two level factor. A high grazing pressure was

defined as a log10 population greater than 7.1 (=1212 sheep) whereas a low grazing

pressure was defined as a log10 population smaller than 7.1. The threshold population

size of 7.1 was estimated using tree regression (see Crawley 2002). Population density

was assessed in August of each year (see Chapter 3). The declines in population density

occur between the rut and spring censuses, thus the population in spring of year t is

equal to the population as measured in August of year t-1.

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5.3.5 Selectivity

In each census, the foraging habitat choice of the population was calculated as a

proportion. The relationship between habitat availability and habitat choice was

considered at 4 spatial scales, (1, 2 and 3 clusters determined by HCA, and the

arbitrarily defined 175ha study area which is henceforth labelled scale “A”).

A selectivity index (SI) was then calculated for each plant community for each census

using Equation 5.1: -

⎟⎠

⎜⎝ jA10

Where S

⎟⎜=SI log (5.1)

surrounding the mean positions of the sheep in each of

d by HCA.

j j

was defined arbitrarily (by the study area) or by HCA/MCP (see

ing where there are fewer animals than would be expected given

the quantity of food.

⎞⎛ +jS 01.0

j is the proportion of sheep occupying a specific plant community (j) and Aj is

the proportion of the area of a specific plant community (j). A value of 0.01 was added

to the equation before logging in order to avoid problems caused when there were no

sheep on a particular plant community. The area under consideration was defined by a

minimum area convex polygon

the groups identifie

5.3.6 Matching

If the population follows the ideal free distribution, the proportion of the organism’s

population that occupy an area should be equal to the proportion of the available food

items that also occupy that area (Earn and Johnstone 1997). This is termed matching. To

obtain an index of “matching” (mj) for a particular plant community (j), the proportion

of food available within that plant community (Fj) is subtracted from the proportion of

sheep occupying the community (Sj) (Equation 5.2). The amount of food in a given area

of a particular plant community (Fj) was estimated by multiplying the weighted biomass

per m2 of the quality items (wB ) by the area of vegetation under consideration (A )

(Equation 5.3), which

section 5.3.2 above).

Thus, a positive value indicates over-matching where there are a greater proportion of

animals than would be expected given the proportion of food, and a negative value

indicates under-match

104


The sum of the absolute values of mj (for each of the seven community types, mj)) gives

the overall matching index (M) for the census (Equation 5.4).

jjj FSm −= (5.2)

AwBF ×= (5.3)

∑=

=j

jjmM

7 (5.4)

5.3.7 Statistical methods

All analyses were carried out using S-Plus 6.0 Release 2 (Insightful Corp.).

To analyse differences in selectivity between seasons, years and at different spatial

scales, a linear mixed effects (LME) model was used with a nested random effects error

structure. The explanatory variables (season of year, community type and spatial scale)

were included in the model as fixed effects while the random effects were year, season,

community type, and spatial scale, nested in that order (from largest to smallest).

LME models were also used to analyse the effects of spatial scale on the matching index

(see above). In this case, the explanatory variables were season and spatial scale while

the random effects were year, season and spatial scale, nested in that order (largest

first).

Fixed effects are those explanatory variables associated with an entire population or

with certain repeatable experimental treatments whereas random effects are associated

with individual experimental units drawn at random from a population (Pinheiro and

Bates 2000). The mean values of each random effect are seldom of interest, merely the

distribution of those effects. LME models enable the modelling of correlations that

often exist within grouped data, such as those found in ecological studies where data are

grouped by individual or experimental unit (repeated measures on the same unit over

time), by quadrat and various other levels of, often nested, spatial groupings. LME

models thus allow fixed and random effects to be analysed together and can be used to

model repeated measures without succumbing to the problems of non-independence of

data points.

105


To further assess the importance of spatial scale, season and population density to the

matching of the ideal free distribution, permutation tests were employed. The aim was

to test whether differences in the matching coefficient between different spatial scales,

and between seasons and population densities within spatial scales, were really caused

by the spatial scale treatment, or whether they were simply caused by random variation.

To carry out these tests, the treatment codes for spatial scale were repeatedly shuffled

(10,000 times) and the differences between means reassessed. If there were truly no

differences between the means of treatments (spatial scale in this case) then the

particular treatment label associated with a particular data point would not be important.

On the other hand, if there were differences between the treatment means, then the

treatment labels associated with particular data points would be important (Crawley

2002). The significance of the differences were tested by comparing the observed

differences with the random distribution of differences generated by the permutation so

that, if the observed differences fell outside the 99 percentiles then they were considered

to be significant and if they fell inside then they were not significant.

5.4 Results

5.4.1 Population counts

The population density fluctuated between 694 and 1968 individuals between 1985 and

2002 (see Chapter 2). Between spring 1985 and summer 2002 there were 7 low

population springs, 8 low population summers, 10 high population springs and 9 high

population summers (where a low population was defined as a whole-island population

of <1212 sheep; see above).

5.4.2 Vegetation composition and quality

The results of analyses of vegetation composition and quality are presented in Chapter 4.

5.4.3 Selectivity

The sheep were distributed in a highly non-random manner, both during spring and in

summer. For example, although Holcus-Agrostis makes up only 16% of the available

grazing area (at the arbitrary scale of the study area) it was regularly occupied by >70%

106


of the sheep within the study area (Table 5.2). Figure 5.1 shows the data in graphical

form illustrating the skewed nature of the data.

Table 5.2: The proportions of sheep occupying the seven plant community types (see Table 4.1) in spring and summer. The data are skewed so the median and upper/lower quartiles are presented. Spring Summer

Veg. Type Median Lower Quartile

Upper Quartile

Median Lower Quartile

Upper Quartile

AF 0.112 0.080 0.146 0.051 0.026 0.097 CA 0.014 0.003 0.034 0.006 0.000 0.044 DH 0.028 0.000 0.050 0.046 0.000 0.089 FE 0.000 0.000 0.027 0.040 0.000 0.090 HA 0.738 0.664 0.776 0.687 0.584 0.769 MO 0.035 0.010 0.061 0.014 0.000 0.055 WH 0.013 0.000 0.031 0.038 0.000 0.076

0.0

0.2

0.4

0.6

0.8

1.0

Prop

ortio

n of

she

ep


Vegetation type

Spring

0.0

0.2

0.4

0.6

0.8

1.0

Prop

ortio

n of

she

ep


Vegetation type

Summer

Figure 5.1: Boxplot showing the proportional distribution of Soay sheep during the spring and summer time amongst the seven plant community types present within the study area on Hirta (Table 4.1). For selectivity see Figure 5.2.

The LME model for selectivity indicated that the sheep showed different degrees of

selectivity for the different plant communities, and that the selectivity differed between

seasons (Figure 5.2). The random effects indicated that there was little variation

107


between years, and most of the variation was accounted for by differences in plant

community. Furthermore, the apparent selectivity was dependent upon the spatial scale

at which the analysis was considered (the interaction between spatial scale, community

type and season; Figure 5.2). The ranked habitat selectivities show this more clearly

(Table 5.3). The inclusion of population density did not improve the fit of the model (L.

ratio=63.933, p=0.218). If the population conformed to the IFD then population density

would be likely to have a significant effect on distribution patterns, thus there was no

were not favoured (Table 5.3).

The effect of spatial scale was particularly apparent in the selectivity for CA, FE and

HA in both seasons. Analysis at the arbitrary scale of the study area (A) estimated a low

selectivity for CA. However, as the spatial scale under consideration was diminished

from the arbitrary scale of the study area (A) and then from 1 to 3 clusters, the

selectivity for CA increased. At the smallest spatial scale (3 clusters) CA was selected

more than FE. In spring, the selectivity for FE tended to increase as spatial scale was

decreased, while the selectivity for HA decreased dramatically between scales A and 1

cluster, and continued to decrease, albeit less dramatically, between the scale of 1

cluster and 3 clusters.

The main seasonal difference was that FE (and marginally WH) was more strongly

selected for in summer than in spring (Figure 5.2). These differences were most

apparent at large spatial scales for FE. Selection did not change significantly between

seasons for the other swards types.

evidence that the sheep distribution conformed to the IFD.

The broad pattern that is scale independent is that HA was the favoured plant

community, while MO, DH and WH

108


Spring Summer

-1.0

-0.5

0.0


A1 2 3 A 1 2 3 A1 2 3 A 1 2 3 A 1 2 3 A 1 2 3 A 1 2 3

0.5

Sele

ctiv

ity

Spatial scale and vegetation type

-1.0

.5

Sele

ctiv

ity


A 1 2 3 A 1 2 3 A 1 2 3 A1 2 3 A 1 2 3 A 1 2 3 A 1 2 3

-0

0.0

0.5

Spatial scale and vegetation type

5 and summer for each of the seven plant communities (Table 4.1) and at four spatial scales ranging from the large, arbitrary scale of the study area (A), and the three Figure .2: Selectivity in spring

progressively smaller scales (1-3) as defined by minimum area convex polygons surrounding 1,2 and 3 clusters identified using hierarchical cluster analysis. Note the large effect of spatial scale on apparent selectivity, especially for CA, FE and HA. Error bars represent ±1s.e.m.. Random effects were: year = 0.003, season within year = 0.003, veg. type within season within year = 0.284, scale within veg. type within season within year = 0.140, residual = 0.310.

109


Table 5.3: Ranked habitat selectivity of Soay sheep on Hirta in spring and summer at four spatial scales (1=least favoured, 7=most favoured): ‘A’ (the arbitrary scale of the study area), and 1-3 (the scale as defined by minimum area convex polygons surrounding 1,2 and 3 clusters identified using hierarchical cluster analysis). Spatial scale

Veg. Type Season A 1 2 3

AF Spring 6 4 4 4 CA Spring 2 6 5 7 DH Spring 3 2 2 3 FE Spring 5 5 6 6 HA Spring 7 7 7 5 MO Spring 4 3 3 2 WH Spring 1 1 1 1

AF Summer 5 4 4 4 CA Summer 2 5 5 5 DH Summer 3 2 2 2 FE Summer 6 6 7 7 HA Summer 7 7 6 6 MO Summer 4 1 1 1 WH Summer 1 3 3 3

5.4.4 Matching

The mean group size was 106±12 and the mean stable number of hefts was either 2 or 3

depending on the year. The most stable number of hefts (i.e. clustering) was defined as

the number of hefts that remained unchanged over the largest range of values of the

scalar value generated by the HCA (see methods).

The sheep did not come close to matching the theoretical prediction of the ideal free

distribution (IFD) (a matching index of zero) at any of the spatial scales considered

(Figure 5.3). However, when the scale was defined by a biologically meaningful method

(HCA), the sheep distributions came closer to the predictions of the IFD than they did

when the area was arbitrarily defined at the scale of the study area.

There were no consistent differences in the overall matching index (M) between scales

of 1 and 3 clusters. In spring, the index increased between scales of 1 and 2 clusters and

then decreased between scales of 2 and 3 clusters. In summer there were no differences

between scales (Figure 5.3). The random effects again indicate that there was little

variation in matching between years in comparison to the variation between seasons and

spatial scale.

110


There was no clear trend in differences between seasons. There were only differences at

the two smallest spatial scales (2 and 3). However, the directions of the difference were

not consistent with matching being higher in spring at scale 2 and lower in spring at

scale 3.

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4M

atch

ing

inde

x

A 1 2 3Sp Su Sp Su Sp Su Sp Su

Season and spatial scale Figure 5.3: The matching index (M) comparing the distribution of Soay sheep amongst the available plant communities during spring (Sp) and summer (Su) at four spatial scales. Perfect matching would result in a matching index of zero and the greater the value the worse the match. The spatial scales were ‘A’ (the arbitrary scale of the study area), and 1-3 (the scale as defined by minimum area convex polygons surrounding 1,2 and 3 clusters identified using hierarchical cluster analysis). Error bars represent ±1s.e.m. predicted from the LME model. The random effects were: year = 0.001, season within year = 0.107, scale within season within year = 0.181, residual = 0.160. There were significant

ifferences between all spatial scales in the spring but in the summer there were only significant ifferences between A and each of the scales defined by the MCPs. There were significant differences

between seasons at scales 2 and 3 only. Differences were assessed for significance using permutation tests as described in the methods.

The community specific matching index (mj) differed between plant communities and

there was an interaction with season (Table 5.4 and Figure 5.4). The matching index for

HA was always positive, and did not differ between seasons. The index for AF was

positive in spring, and negative in summer. The index for CA was always low in both

spring and summer but was significantly lower in the spring than it was during the

summer. The index for MO tended to be lower in the summer than during the spring

while the index for WH tended to be lowest in the spring. The indices for DH and FE

remained fairly constant over the range of conditions that were considered.

dd

111


AF CA DH FE HA MO WH CA DH HA MAF FE O WH

-0.1

0.0

0.1

Spring S rSpring S ration type and season

hing index (m) f plant co ty type (Ta .1), dur ing (Sp) patial scales. Perf ching w ult in a matching in d t

lute value the worse t . The scales were ‘A’ (the ar scale o 1-3 (the scale as defined by minim a convex r g 1,2 and using hierarchical analysis t commun pes wer stis-Fest

alluna heath (CA), h (DH) grassla FE), Hol rostis (Hd (MO) and wet heat . Error nt ±1 . predic om the L.

0.2

Mat

chin

g in

dex

ummeummeVeget

Figure 5.4: The matc four s

or each mmuni ble 4 ing sprde

and summer (Su), atgreater the abso

ect mathe match

ould resspatial

x of zero anbitrary

he f the

study area), and um are polygons sur oundin d 3 clusters identifie cluster ). Plan ity ty e Agro uca grassland (AF), C dry heat , Festuca nd ( cus-Ag A), Molinia grasslanmodel (Table 5.4)

h (WH) bars represe s.e.m ted fr ME

112


Table 5.4: Summary of the LME model for the matching index (m) for individual plant communities for Soay sheep on Hirta at low and high population densities during spring and summer and at four spatial scales. The spatial scales were ‘A’ (the arbitrary scale of the study area), and 1-3 (the scale as defined by minimum area convex polygons surrounding 1,2 and 3 clusters identified using hierarchical cluster analysis). Plant community types were Agrostis-Festuca grassland (AF), Calluna heath (CA), dry heath (DH), Festuca grassland (FE), Holcus-Agrostis (HA), Molinia grassland (MO) and wet heath (WH). Random effects Std. Dev.

Year 0.000 Year/Season 0.000 Year/Season/Veg 0.058 Year/Season/Veg/Scale 0.134 Residuals 0.069

Fixed effects

Term Value Std. Error d.f. t-value p-value

Intercept (Spring, AF) 0.075 0.022 7196 3.473 0.001 VegCA -0.217 0.031 186 -7.060 <.0001 VegDH -0.111 0.031 186 -3.612 0.000 VegFE -0.069 0.031 186 -2.254 0.025 VegHA 0.123 0.031 186 3.995 0.000 VegMO -0.110 0.031 186 -3.572 0.001 VegWH -0.197 0.031 186 -6.423 <.0001 Season (Summer, AF) -0.128 0.031 15 -4.093 0.001 VegCA: Summer 0.252 0.044 186 5.714 <.0001 VegDH: Summer 0.096 0.044 186 2.182 0.030 VegFE: Summer 0.159 0.044 186 3.610 0.000 VegHA: Summer 0.146 0.044 186 3.297 0.001 VegMO: Summer 0.046 0.044 186 1.042 0.299 VegWH: Summer 0.236 0.044 186 5.356 <.0001

g the distribution of some animals

Agrostis swards that dominate the area within the Head Dyke, the maritime Festuca-

5.5 Discussion

Selectivity

The highly non-random distribution of the sheep reflects differential selection for the

different plant communities that is caused by differences in quality between the swards.

Although the role of shelter is important in definin

(Cransac and Hewison 1997, Apollonio, Festa-Bianchet et al. 1998, Bailey, Dumont et

al. 1998), its role in defining distribution patterns for Soay sheep on Hirta is likely to be

minimal because of the wide availability of cleits and stone walls on the island,

especially within the study area (see map in Chapter 2 and Stevenson (1994))

Overall the selection is greatest for the previously cultivated and high quality Holcus-

113


Plantago swards and Agrostis-Festuca swards. The four least favoured swards were

consistently wet heath, Calluna heath, dry heath and Molinia dominated grassland.

However, it is apparent that the spatial scale can be important in making more precise

on in terms of nutritive value, primary production and preferences of the species

present. Where communities are dominated by preferred, nutritious species they will

that have complex diets made up of many species (and, therefore, currencies). Perhaps a

currency of total nitrogen could be used, but the effects of other variables that are likely

qualitative judgements about the relative selection for particular plant communities.

These patterns are consistent with the results of earlier qualitative work on Soay sheep

distribution by Milner and Gwynne (1974), and are similar to Hunter’s (1962)

observations of domesticated sheep in south-east Scotland. These showed that sheep

tended to favour communities dominated by Agrostis and Festuca on brown earth type

soils. The reasons for these preferences are related to the relative values of the

vegetati

tend to be preferred by the foragers whereas communities that are dominated by

unpalatable or poisonous species, for example, would be less preferred. Social factors

may also play a role. For example, social and sexual segregation can exist when

differences in behaviour make living together difficult (Miquelle, Peek et al. 1992,

Conradt 1998, Ruckstuhl 1998, Ruckstuhl and Neuhaus 2000). These differences are

not necessarily related to between-sex differences in habitat use (Conradt 1999).

Matching

Matching is defined as the difference between the proportion of food available in a

patch with proportion of organisms occupying the patch (see section 5.3.6). Thus

perfect matching occurs if the difference is equal to zero. The distribution did not come

close to satisfying the predictions of the IFD at any of the spatial scales that were

considered. However, the distribution was significantly closer to the predictions of the

IFD when the area under consideration was defined using a biologically meaningful

method rather than when it was defined arbitrarily.

One of the main problems with the IFD model, as applied to grazing herbivores, is that

of currency. The IFD is a carnivore-centric model and deals with a currency of discrete

prey items. Therefore, it is not entirely appropriate for the analysis of grazing herbivores

114


to be important such as rate of primary production, sward height/structure, and the

interactions between species would also apply.

support themselves on lower quality forage (Illius and Gordon 1987, Illius

1989).

These problems are reflected in the discrepancies in matching indices (mj) between

plant communities. Although, in the analyses, the currency of gDM of “quality items”

was used, these weights may not be comparable across communities due to the differing

species composition of the communities. For example, in the HA community, the

quality items are mainly the grasses, Festuca and Poa, whereas in the WH, DH and CA

communities the quality items is primarily composed of new growth Calluna vulgaris.

This may cause problems because the different species have different digestibilities and

preferences.

Nevertheless, the apparent overmatching for the Soay sheep is consistent with work

carried out on other ungulates such as goats (Illius 1999). Using mathematical models

Hakoyama (2003) predicted that the degree of over/under-matching would be dependent

on the variability of food quality within patches. Specifically he predicted that when the

resource variance was higher in the good patch than it was in the poor patch then

undermatching would occur. Conversely when the variance of the poor patch was

higher than (or equal to) that of the good patch then overmatching would occur. Since

the heterogeneity of the poor quality swards on Hirta (e.g. the wet and dry heath and the

Calluna heath) is known to be relatively high in comparison to that of the inbye

grasslands (Crawley, unpublished data and see Chapter 4), this may go some way to

explaining the overmatching that was apparent in this study.

Another important factor is that all individuals are not equal in their foraging ability –

one of the assumptions of the IFD (Humphries, Ruxton et al. 2001). For example bite

size (Gordon and Illius 1988a), intake rate (see Chapter 7) and diet

composition/selective ability (Gordon and Illius 1988a, b), and thus competitive ability,

all vary between individuals (and within individuals temporally). Differences in

digestive efficiency may also be an important factor, with larger animals being better

able to

115


Overall conclusions

remains clear that the spatial scale over which measurements of resource availability

are made can have a significant impact on the outcome and interpretations that are made

f the analyses. However, on St. Kilda, general qualitative patterns are apparent that are

These are that the most favoured plant community is the

Holcus-Agrostis grassland, and that the least favoured communities are the wet and dry

eaths and the Molinia grassland. Furthermore, seasonal patterns are apparent and show

that the Festuca swards are significantly more favoured in the summer than in the

pring.

It

o

independent of spatial scale.

h

s

116

Chapter 6 - Maternal and environmental effects on birth weight and survival

Chapter 6 : Maternal and environmental effects on

offspring birth weight and early survival

117


Maternal and environmental effects on offspring birth weight and early survival.

6.1 Abstract

Juvenile survival is a critical component in the regulation of wild animal populations. It

is influenced by characteristics inherited from the parents, by the amount of maternal

provisioning and by environmental factors. This study concentrated on the latter two

factors. Maternal resource provisioning occurs during gestation and during suckling

and may have long term effects upon survival and future breeding success.

Environmental influences operate through forage availability to mother and lamb, and

via weather severity, which affects both thermoregulation and time available for

troduction

ber of individuals in

foraging.

In this study 14-years of life-history, vegetation and weather data from a population of

Soay sheep (Ovis aries L.) from the Scottish island of St. Kilda are used to explore these

factors. Lamb birth weight was influenced by maternal condition and forage

availability. Furthermore, survival was strongly influenced by early life-history

parameters such as birth weight, maternal condition and forage availability and some

weather parameters during late gestation/weaning. The effect of forage availability was

independent of population density. Sexual differences, driven by differing growth

strategies, were also important.

6.2 In

Juvenile survivorship is often a critical density-dependent process regulating wild

animal populations (Dobson and Oli 2001, Oli and Dobson 2003). Therefore, an

appreciation of the factors that influence survivorship is crucial if the population

dynamics of wild animals are to be understood.

A variety of factors may influence the survivorship of juvenile mammals. These are (1)

offspring-specific characters such as birth weight, sex and the num

the litter (Morris 1996, Keech, Bowyer et al. 2000). (2) Maternal characteristics such as

the age and condition of the mother (Keech, Bowyer et al. 2000) and (3) environmental

factors such as weather severity and food availability (Forchhammer, Clutton-Brock et

al. 2001).

118


Many studies have found that birth weight is of prime importance to the juvenile

survivorship of many mammal species, with heavier offspring being more likely to

survive both to nutritional independence (weaning), and to sexual maturity, than light

offspring (e.g. feral Soay sheep (Ovis aries: Clutton-Brock, Price et al. 1992), housed

domesticated sheep (O. aries: Malik, Razzaque et al. 1998, Mukasa-Mugerwa, Lahlou-

Kassi et al. 2000), domesticated goats (Capra hircus: Perez-Razo, Sanchez et al. 1998,

Awemu, Nwakalor et al. 1999), pigs (Sus scrofa: Roehe and Kalm 2000), red deer

(Cervus elaphus: Loison, Langvatn et al. 1999), and Columbian ground squirrels

(Spermophilus columbianus: Neuhaus 2000)). However, contrary to these studies Côté

and Festa-Bianchet (2001) found that birth weight of mountain goat (Oreamnos

americanus) kids did not affect survival to weaning.

In many mammal populations, heavier females give birth to heavier offspring than

lighter females. For example, mountain goats (Oreamnos americanus: Côté and Festa-

(Birgersson and Ekvall 1997, Kojola 1997).

mates (Clutton-Brock, Albon et al. 1980, Coltman, Bancroft et al. 1999, Kruuk,

Bianchet 2001), fur seals (Arctocephalus tropicalis: Georges and Guinet 2000), and

grey seals (Halichoerus grypus: Pomeroy, Fedak et al. 1999). In the latter case,

maternal condition was also measured (by comparing body weight with skeletal size)

and also had a positive association with birth weight. In fact, it is probable that it is

condition that is the primary explanatory variable rather than weight, and the reason that

weight appears to be important is because it is highly correlated with condition.

However, the direct measurement of condition is problematical, especially in field

conditions. As such, it is thus rarely considered per se, and weight is often used as a

proxy.

Maternal malnutrition could be a major predisposing factor influencing juvenile

mortality, initially via its negative effect on birth weight, which is due to the allocation

of resources to the foetus during gestation, and then via its effect on lactational quality.

Since the availability of food for grazing ungulates is directly influenced by grazing

pressure, such effects are likely to be exacerbated at high population densities

Sexual differences in the factors limiting reproductive success generally favour large

size and rapid growth rate in males, whose reproductive success is usually correlated

with strength and antler/horn size, which influence their ability to acquire and defend

119


Clutton-Brock et al. 1999). The reproductive success of females on the other hand is

usually not as dependent on body size as that of males (Kruuk, Clutton-Brock et al.

1999). Therefore, these differences result in contrasting growth strategies between the

sexes, with females being more conservative than males, allocating a greater proportion

of their resources to insulation and fat reserves rather than to growth. As a consequence,

offspring resource requirements, and thus survival rates, can differ between the sexes,

with the survival of the faster growing males being reduced in comparison to the slower

by the foetus (Robinson, Sinclair et al. 1999). It is, therefore, expected that an

alteration in nutrition via forage availability or quality would influence birth weight and

re time interacting rather than foraging (Begon, Harper et

growing females. For example, Perez-Razo (1998) found that the survival of male kids

was significantly lower than that of female kids for five breeds of goat. In addition,

Loison et al. (1999) reported that for red deer calves, the survival of males was lower

than that of females. However, Côté et al. (2001) made the opposite finding; the

survival-to-weaning of mountain goat kids was higher for males than for females,

however, there were no sex differences in survival to one-year of age.

Throughout pregnancy, the maternal diet influences foetal growth both directly, by

supplying essential nutrients, and indirectly, by altering the expression of the maternal

and foetal endocrine mechanisms that regulate the uptake and utilization of these

nutrients

consequently survival probability. Although Higginbottom (2000) demonstrated that

spatial variation in food quality can lead to individual variation in reproductive success

of red-necked wallabies (Macropus rufogriseus), most studies that have considered food

availability have used population density as a proxy for forage quality, with the

assumption that increasing population density reduces food availability (i.e. pure

indirect intra-specific resource exploitation competition). However, population density

may also result in increased direct interference competition by increasing the amount of

interaction that occurs between individuals. This may result in a reduced foraging

ability as individuals spend mo

al. 1990). There may also be competition for other resources such as shelter.

The timing of parturition, with respect to plant phenology, may also reflect the

importance of nutrition in juvenile survival. There is an obvious survival advantage to

ensuring that the energetic demands of lactation coincide with the onset of the rapid

growth phase of the vegetation and, therefore, the greatest availability and quality of

120


forage. Also, early born individuals have the advantage of a longer period of access to

high-quality vegetation thus enabling faster growth rates and, therefore, allowing a

larger body size to be achieved before the winter when body size influences survival via

the effect of surface area to volume ratio on thermoregulation (Schmidt-Nielsen 1983).

However, an early birth may also have disadvantages, for example, if birth occurs

before the rapid growth phase starts or before weather conditions improve.

t foraging (e.g. high temperatures: Owen-Smith

(1998)).

mother to feed, nutritional stress is placed

on the growing foetus or suckling lamb. Clearly, the newborn lamb will also be directly

t se weather conditions and, because of its small size, the threshold

adults.

f are buffered by the availability of fat reserves that can

be used during times when the costs of foraging outweigh the benefits, which means

uld have to endure because it covers the

vegetation with a layer that the animal may not be able to penetrate whilst foraging.

Empirical evidence for this is again scarce, but Sarno et al. (1999) reported that survival

The influence of weather conditions on herbivore forage availability and quality are

well established and operate mainly via the accumulated effects of temperature and

irradiance on the photosynthetic process, thus affecting plant growth and phenology

(Fitter and Hay 1987). However, weather conditions may also have an important

influence over animal foraging behaviour. The negative effects of severe weather

conditions such as high wind speeds, rain and low temperatures can limit the time

available for foraging; when weather conditions are especially severe animals tend to

shelter rather than forage. There is, therefore, a trade-off between the need to obtain

nutrients to maintain condition and the increased rate of reduction in condition caused

by exposure to the severe weather whils

In this case, the effects on juvenile survival and birth weight will be via the maternal

effect; by reducing the time available for the

affec ed by adver

weather severity is likely to be lower and, therefore, the effects greater than on

The ef ects of inclement weather

that animals that are already in poor condition are likely to be more severely affected

than those in good condition.

Empirical evidence for the effects of weather severity on juvenile ungulate survival are

scarce but Adams (1995) found that survival of neonate caribou (Rangifer tarandus)

was reduced following a severe winter. Heavy snowfall is probably one of the most

extreme weather conditions that ungulates wo

121


of juvenile guanaco (Lama guanicoe) in the Torres del Paine national park was reduced

by 6% for every 1cm increase in winter snowfall. It should be noted, however that

snowfall is relatively scarce on Hirta (see Chapter 3). Rainfall, which is not scarce on

St. Kilda, can also be importan ause e wetting of the coat of an animal can

dramatically increase the heat flux ulso Catch le et al. 2001).

The importance of these environ l p meters in early life cannot be overlooked.

Although reproductive success will be influenced by current environmental conditions,

the effects of environmental cond s in onths of life and even earlier,

via the maternal effect, may als si effects have been

demonstrated by Kruuk et al. (1999) for red deer and by Festa-Bianchet (2000) for

bighorn sheep (O. canadensis). These effects are often density-dependent and, for some

time, time-lagged density-dependence has been appreciated to be associated with

complex population dynamics (Leslie 1959, Turchin 1990). The effects of parasites,

1998), predators and prey (Stenseth, Falck et al.

1998) have typically been invoked but trans-generational maternal effects may also be

) situated

approximately 70km west of the Outer Hebrides. In this study, a sub-sample of 842

mothers born between 1989 and 2002 were

studied (Table 6.1). However, in 2001, an outbreak of foot and mouth disease

t bec th

(Co n, po

menta ara

ition the first few m

o be gnificant. Such long-term

pathogens (Hudson, Dobson et al.

important (Benton, Ranta et al. 2001).

In this chapter the effects of maternal condition, nutrition and parasite burden and

environmental factors on the birth weight and early survival of Soay lambs on Hirta, St.

Kilda are investigated (see also Chapter 2).

6.3 Methods

Some of the methods described here are presented in greater detail in Chapter 2.

6.3.1 Study area and species

The study subject was the free-ranging population of Soay sheep (Ovis aries L.) of

Hirta, part of Scotland’s St. Kilda archipelago (57º49’N 08º34’W

individuals of both sexes with known

(Ferguson, Donnelly et al. 2001b, a) restricted data collection to dead individuals, as

such, these data were of limited use.

122


Table 6.1: Number of animals of each sex available for this analysis. To be included, the lamb’s mother must have been caught the previous summer (for data collection purposes) and tcaught and weighed within 7 days of birth. Data from 2001 was not useab

he lamb had to have been le because of restrictions to

data collection during the foot and mouth disease epidemic. Year Male Female

1989 5 0 1990 9 8 1991 19 19 1992 28 30 1993 47 40 1994 42 53 1995 28 30 1996 34 35 1997 61 42 1998 55 48 1999 29 28 2000 44 51 2001 4 2 2002 28 23

Total 433 409

6.3.2 Birth date and survival

Birth ates were determid ned by direct observation of the population on a twice-daily

o

concerning survival was inconclusive in the case of the

other five and these were thus excluded from the analysis.

6.3.3 Morphometric measurements

basis. The lambs were considered to have been successfully weaned if they survived to

6 weeks after birth. The life-histories of tagged animals on Hirta have been monitored

since 1985 with regular censuses being carried out in spring, summer and autumn, and

daily mortality searches being undertaken between February and April of each year. For

many of the animals (n=625), survival could be determined from known date of death.

For others (n=204), census data was examined to determine whether the animal

survived the particular cut-off points. Seven animals were not seen after their initial

capture shortly after birth so their age at death is unknown (i.e. they are censored).

However, two of these had paternities attributed to them, and were thus considered t

have survived to weaning. Data

The mothers of all of the animals used in the analysis were caught in August of the year

prior to parturition allowing body measurements to be taken. Body mass was measured

to the nearest 0.1 kg with a carry-net and drop-scales. Hind leg length, measured with

123


callipers to the nearest millimetre from the tubercalcis of the fibular tarsal bone to the

distal end of the metatarsus, provided an index of skeletal size.

no significant interactions. The most important

variable was the age at first capture (detailed results are presented in Section 6.4.2).

Most lambs were caught soon after birth (92.8% within 5 days, median at 2 days, mean

at 2.23 days) to allow ear tagging and weighing. The first few weeks of a lamb’s life are

a period of rapid growth. Therefore, in an attempt to control for the effects of catch date,

body mass was standardised to that expected on day 1 (day of birth) using a generalised

linear model with a gaussian link function. Explanatory variables that remained in the

minimum adequate model were age at capture, twin status, population density, birth

date and maternal weight. There were

Age at first capture (days)

Wei

ght a

t firs

t cap

ture

(kg)

0 2 4 6

1

2

3

4

Figure 6.1: The relationship between age at first capture and weight at first capture. Although the line illustrates the prediction of a linear model of these two variables, the minimum adequate model also included twin status, population density, birth date and maternal weight as main effects. Detailed results are presented in Section 6.4.2 (Table 6.7).

6.3.4 Parasite burden

Maternal parasite burden was assessed using a modified version of the McMaster faecal

egg count (FEC) technique (MAFF 1971). This provides a measure of the number of

parasite eggs per gram of fresh faeces (epg). Although several taxa were considered,

124


only strongyles, which have been implicated as the main pathogenic parasite to affect

the Soay sheep on St. Kilda (Gulland 1991), were considered in this study. These

Teladorsagia spp., include Trichostrongylus spp., Chabertia ovina, Bunostomum

ephalum, and e a

ate of worm burdens

topsy is r 1 ).

lation den

d populati e carrie n

August of each year. Because most lambs are born in April (in year t+1), a count from

ear t thus provided an estimate of population density, covering the period between

tra-specific competition.

6.3.6 Weather variables

To provide an index of weather severity, seven weather variab

The weather data were derived from e o B sp Data Centre

(www.badc.nerc.ac.uk). Most of th a er o o f Benbecula

(50km SE of St. Kilda: 57º 46’, -7º 47’). However, this station ceased recording most of

e variables in 1996 and, from this date, records from the Isle of Rum (57º 01’, -6º 28’)

were used to predict the values that would be expected from the Isle of Benbecula

weather station using linear regression. The correlations between the two weather

stations are summarised in Table 6.3.

The North Atlantic Oscillation (NAO) index is a measure based on the pressure gradient

between the North Atlantic and southern Europe. It provides an encapsulation of a

number of variables including temperature, wind-speed, wind direction and

precipitation, such that a low index signifies dry, cold and still weather, whereas a high

index signifies wet, windy and warm weather (Wilby, O'Hare et al. 1997). The values

used in this study were obtained from J.W. Hurrell (Unites States National Centre for

Atmospheric Research) (http://www.cgd.ucar.edu/~jhurrell/nao.stat.winter.html) and

consisted of the difference in normalised sea level pressure between Lisbon, Portugal

and Stykkisholmur, Iceland between December and March (Hurrell 1995). The data are

trigonoc Strongyloides papillosis. These egg counts are believed to b

reliable estim burden and the correlation of FEC with worm

assessed by au high (Grenfell, Wilson et al. 1995, Boyd 1999, Braishe 999

6.3.5 Popu sity

Whole-islan on counts of adults and lambs of both sexes wer d out i

y

April in year t to April in year t+1. The population density estimates provide an index

of grazing pressure and of the intensity of in

les were used (Table 6.2).

the r cords f the ritish Atmo heric

ese d ta w e rec rded n the Isle o

th

125


normalised to avoid the domination of the series by the greater variability of the

northern station.

Correlations between the over-winter NAO-index m e and the other weather

variables are given in Table 6.4.

Table 6.2: The d units of the weather les th y. All o univariate data were collected he standard metho pl by UK Office (see badc.nerc.ac.uk/ he NAO w obta fro .W. Hurrell (www.cgd.ucar. t.winter.html). Term Definition Units

easur

efinitions and variab used in is stud f the using t ds em oyed the Met data/surface/). T

edu/~jhurrell/nao.stadata ere ined m J

Gale days number of days in a me period wher peed ded ots for at least 10 min

days Total34 kn

given tiutes that d

e wind s s exceeay.

Max. temperature The mean of the maximum daily air temperatures in a given time period. ºC Min. tem erature The meanp of the minimum daily air temperatures in a given time period. ºC Absolute min. temperature The lowest recorded air temperature recorded in a given time period ºC Rainfall The amount of precipitation falling into a standard 5 inch rain gauge in a

given period mm

Snow/sleet days Number of days in a given time period where snow, sleet or hail was recorded.

days

Growing days Number of days where the temperature exceeded 5 º C days NAO index The atmospheric pressure gradient between Iceland and Portugal unitless

Table 6.3: The correlation coefficients of the measurements from Benbecula and Rum between January and May. Correlations are based on yearly data from 1957-2002. Significance to p<0.05 is indicated by an asterisk. See Table 6.2 for definitions and units.

Variable Jan.-Mar. Jan. Feb. Mar. Apr. May

Gale days *0.52 *0.79 0.33 *0.78 *0.59 NA Growing days *0.94 *0.92 *0.94 *0.87 NA NA Max. temp. *0.92 *0.98 *0.98 *0.95 *0.96 *0.95 Absolute min. temp. *0.83 *0.86 *0.89 *0.52 *0.74 *0.76 Min. temp. *0.92 *0.95 *0.97 *0.90 *0.92 *0.90 Rainfall *0.57 *0.88 *0.90 *0.92 *0.92 *0.94 Snow/sleet days *0.55 *0.72 *0.53 *0.70 0.41 *0.55

126


Table 6.4: Correlation coefficienles record

ts between the overwinter NAO index and the univariate weather variab ed on Benbecula and Rum. Correlations are based on yearly data from 1957-2002. Significance to p<0.05 is indicated by an asterisk. See Table 6.2 for definitions and units

Correlations overwinter NAO index Month

Variable Jan. Feb. Mar. Jan-Mar

Gale days *0.42 *0.43 0.04 *0.50 Growing days -0.16 *0.43 *0.44 0.20 Max. temp. *0.47 *0.45 0.28 *0.55 Absolute min. temp. *0.57 *0.44 0.19 *0.56 Min. temp. *0.41 *0.45 *0.37 *0.59 Rainfall *0.54 *0.50 *0.48 *0.73 Sleet/snow days *0.42 *0.43 0.04 *0.50

6.3.7 Vegetation variables

In order to obtain estimates of plant species composition by biomass over the study area

ley assessed the sward characteristics between 1993 and 2002. Five transects,

ter-tussock) at random in each sampling location.

s of vegetation (Table 6.5) were then calculated for the whole study area (Ov),

and for both the inbye (Ib) and outbye (Ob) areas alone.

Table 6.5: Vegetation terms used in this study and their codes as used in this study. The areas for which the measurements were meaned, were inbye, (Ib) outbye (Ob) and overall (Ov).

Term Code Units

M.J. Craw

each with six sampling locations were assessed in March and August of each year

(although only the March data are used in this study). Ten of the locations were outside

the Head Dyke (the outbye area) and twenty were within the Head Dyke (the inbye

area).

Dry biomass estimates were estimated at each sampling point by harvesting two 0.2 x

0.2m quadrats (one tussock and one in

Although each quadrat was sorted to species level, cruder categories of “grass”, “herb”,

new-growth Calluna vulgaris and old-growth, woody C. vulgaris are used here. Each

sample was oven-dried at 80ºC, and weighed. The mean dry biomass of the seven

categorie

Mean of total dry biomass T g/20cm2

Mean of total dry biomass minus dry biomass of woody Calluna vulgaris TmCV g/20cm2

Mean of grass dry biomass G g/20cm2

Mean of dry biomass of grass + herb + new growth (green) C. vulgaris (High quality items) Q g/20cm2

Mean of dry biomass of dead organic matter (DOM) DOM g/20cm2

Mean of dry biomass of bryophyte BRYO g/20cm2

Mean of dry biomass of new growth (green) C. vulgaris CVN g/20cm2

Density of quality items (Q/T) HQD unitless

127



Birth weight

Linear regression models were fitted and us re the effect of maternal and

environmental characteristics on birth weight. Models were fitted with main effects and

first-order interactions and then simplified, using an automated step procedure, which

uses an exact measure of Akaike Inform n C rion (AIC) (Venables and Ripley

1999). AIC is expressed by the f la x lo ikelihood + 2 x n where n is the

number of parameters in the mod co arisons of fitted models, the smaller the

AIC is, the better the fit. Backwards elimin n o rther non-significant terms and the

collapsing of factor levels were carried out rawley 2002 for details).

Survival

Although 39.8% of the ewes only appear once in the dataset, many had more than one

ed to explo

atio rite

ormu –2 g-l

el. In mp

atio f fu

as required (see C

offspring (mean =2.61, median=2, min=1, max=10) and the survival probabilities of

successive offspring from the same mother may be not be independent. Ideally, a

logistic mixed effects model (i.e. with a binomial error structure) and maternal identity

fitted as a random effect would be used. However, S-plus (the package used here) does

not allow the fitting of such a model.

Instead, to ensure independence of each data point, a single offspring from each mother

was chosen at random to be used in the analysis (Table 6.6). After excluding individuals

for whom survival was not discernible with certainty, this resulted in a dataset of 322

lambs.

128


Table 6.6: The number of male and female lambs available for analysis by conventional logistic GLM. To be included, the lamb’s mother must have been caught the previous summer (for data collection purposes) and the lamb had to have been caught and weighed within 7 days of birth. Data from 2001 was not available because of restrictions to data collection during the foot and mouth diseasavoid non-independence of survival probabilities, a ewe could only contribute one, rando

e epidemic. To mly chosen,

lamb to the dataset. Year Female Male

1990 5 5 1991 9 4 1992 12 12 1993 17 16 1994 17 12 1995 8 11 1996 15 4 1997 15 16 1998 18 22 1999 14 11 2000 22 33 2001 0 0 2002 13 13 Total 165 159

A logistic GLM with a binomial error structure and logit link function was used to

analyse the survival data. The binary response variable was survival to weaning, and the

explanatory variables were birth weight, sex, twin status, maternal age class, maternal

weight and maternal strongyle parasite egg count. A high grazing pressure was defined

as a log10 population greater than 7.1 (i.e. 1212 sheep) whereas a low grazing pressure

was defined as a log10 population smaller than 7.1. The threshold population size of 7.1

was estimated using tree regression (see Crawley 2002). All main effects and first-order

interactions were included in the initial model. This was simplified by stepwise deletion

of non-significant terms to produce a minimum adequate model (MAM).

Subsequently, weather (Table 6.3) and vegetation terms (Table 6.5) were added to the

MAM in turn, and the reduction in residual deviance was assessed in order to determine

how much variation that the term explained. The resulting model was then checked to

ensure that the inclusion of the weather or vegetation terms did not cause existing terms

to fall below significance. Possible density dependence of the weather and vegetation

terms were checked by including the interaction between the environmental variable

and population density and checking whether the fit of the model was improved. All

terms were assessed for non-linearity using graphical model checking methods

(Venables and Ripley 1999).

129


All statistics were carried out using S-Plus 6.0 release 2 (Insightful Corp.). An α-value

of 0.05 as used for main effects while a smaller α-value of 0.025 was used for

interactions, in order to account for the greater number of tests carried out.

6.4 Results

6.4.1 Birth date

Julian birth date (days after January 1st) varied between 89 and 223 (Figure 6.2) but the

median was conservative, varying only between 98.5 and 116 between 1989 and 2002.

Individuals with outlying birthdates (julian birth date>200) were excluded from the

analyses. There was no significant relationship between population density and median

birth date when population density was considered as a continuous variable (F

1,12=0.205, p=0.659), nor when it was considered as a two-level factor (t = 1.413,

d.f.=320, p = 0.158). Twins did not tend to be born earlier/later than singletons (t = -

1,215=3.394, p<0.001) there was no effect of population density (F1,227=1.718,

1.206, d.f. = 320, p = 0.229) and the timing of birth was similar for female and male

lambs (t = 1.209, d.f. = 320, p = 0.227). Although birth date differed between cohorts

(F

p=0.191).

130


80 100 120 140 160 180 200 220

0

10

20

30

40

50

60

Julian day

Freq

uenc

y of

birt

hs o

f tag

ged

lam

bs (1

989-

2002

)

Figure 6.2: Frequency of births by julian birth date for tagged lambs between 1989 and 2002. The

itive

association), twin status (twins were born lighter then singletons), population density

(lambs were lighter at higher density) and julian birth date (a small positive

association). Excluded terms were sex, maternal hind-leg length, maternal parasite

burden and maternal age class. The residual deviance of the model was 67.291 on 316

d.f. and the r2-value was 0.55.

Attempts to fit maternal age class were made in three ways; (1) as a four level factor

(1=lamb, 2=yearling, 3=sub-adult and 4=adult), (2) as a 2 level factor (1=lambs,

2=yearlings-adults) and (3) as another 2 level factor (1=lambs and yearlings, 2=sub-

adult and adult). The most significant result came with method 2, but this term still

dropped out of the model (Deviance= 3.314, p=0.069). In other words, if weight is

controlled for then maternal age is not significant.

outliers with birth dates >200 were excluded from the analyses.

6.4.2 Birth weight

Multiple linear regression revealed that weight at first capture was influenced by a range

of factors (Table 6.7), the most influential of which was maternal weight which had a

positive association with birth weight. This was followed by age at capture (pos

131


Table 6.7: Summary of the linear model for weight at first capture of Soay lambs born between 1989 and 2002 giving estimates, their standard errors and t-values. P-values were derived by deletion of the term from the model and examination of the resulting change in residual deviance. Residual deviance = 67.291 on 316 d.f., r2-value = 0.551

Term Estimate Std. Error Change in deviance p-value

(Intercept) -0.952 0.302 - - Age at capture 0 5 0 1 .138 0.018 -12.9 1 <0.0 0Twin status (Twin) -0.747 0.072 14 0 1 Population -0.386 0.056

-4.0 <0.0 0 density (High) -9.951 0.0016

Julian b 0.011 0.002 irth day -4.014 0.0451 M 0.090 0.007 14 0 1 aternal weight -4.0 <0.0 0

E xcluded terms

M ngth p>0.05 aternal hind-leg leM rden p>0.05 aternal parasite buM p>0 aternal age class .05 S p>0.05 ex A ctions p>0.025 ll first order intera

Addition of vegetation and environm ntal p rameters

None of the weather variables mad a sig icant improvem th l (p>0.05,

Table 6.8) nsity pend ce was detected 5)

In addition, eight of the twenty vegetation parameters were significant to the p<0.05

level as m s linea function and the es itive

(Table 6.9). Population density rema n , i.e.

the model orsene if the opulation density oved.

e a

e nif ent to e mode

. Furthermore, no de de en (p>0.0 .

ain effects when fitted a a r ir slop were all pos

ined i the model alongside the vegetation term

was significantly w d p term was rem

132


Table 6.8: The effect of inclusion of weather terms as main effects on the minimum adequate model (MAM) for birth weight. The MAM had a residual deviance of 69.073. The ∆ in deviance and p-values from comparisons of the MAM (without a weather term) and a new model with the weather term fitted as a main effect. The slope of the effects (±1s.e.m.) are also given. None of the interactions between the weather term and population density were significant (p>0.025). P-values of <0.1 are indicated with a “.”. a = some data were unavailable for testing this term, thus the change in degrees of freedom was –26 rather than –1.

Term Slope ±Std. Error ∆ Dev. p-value

NAO index -1.549 0.213 Max. temp. (Jan.) -0.946 0.331 Max. temp. (Feb.) -0.360 0.549 Max. temp. (Mar.) -0.073 0.787 Max. te p. (Ja m n.-Mar.) -0.511 0.475 Min. te n 5 5mp (Ja .) -0.05 0.81 Min. temp (Feb.) -1.386 0.239 Min. te (Mar.) 0 .00 6 6mp .098 0 7 -2.95 0.08 . Min. temp (Jan.-Mar.) 3 -1.74 0.187 Gale da Jan 9 ys ( .) -0.27 0.598 Gale da Feb.) 1 ys ( -0.25 0.616 Gale da Mar 5.32 .)ays ( .)a - 3(-26d.f 0.999 Gale da Jan.-Mar. 8 ys ( ) -0.35 0.550 Rainfall (Jan.) 5 -0.07 0.784 Rainfal b.) 2 l (Fe -0.12 0.726 Rainfall (Mar.) 6 -0.01 0.899 Rainfall (Jan.-M 6 ar.) -0.10 0.745 Sleet an ow a 2 6d sn days (J n.) -0.44 0.50 Sleet an ow 3 d sn days (Feb.) -0.78 0.376 Sleet an ow days (Mar.) 9 5d sn -0.04 0.82 Sleet an ow days (Jan.-Mar.) 5 d sn -0.70 0.401 Abs. Min. temp. (Jan.) -0 .01 7 .051 0 2 -3.60 0.058 . Abs. Min. temp. (Feb.) 7 -0.26 0.605 Abs. Min. temp. (Mar.) 7 -0.35 0.550 Abs. M mp M 7 in. te . (Jan.- ar.) -0.99 0.318 Growing days (Jan.) 0 .00 3 .027 0 8 -2.71 0.100 . Growing days 9 (Feb.) -0.02 0.864 Growing days (Mar.) 0.028 0.007 -3.500 0.061 . Growing days (Jan.-Mar.) -2.610 0.106

133


Table 6.9: The effect of inclusion of vegetation terms on the minimum adequate model (MAM) for birth weight. Terms were added as main effects but because no vegetation data was collected until summer 1993, the model was refitted using a subset of data from 1993-2002 first. Data from 1995 and 2001 were

d took place in April instead of March. The resulting model had a devi fect of the inclusion of vegetation terms on the MAM was assessed

by adding the term to the MAM and examining the change in residual deviance. Slope ±1s.e.m. are given

val of umed

to have dropped out of the model if p>0.05. Density dependence was not checked because the GLM

exclude because the assessment residual ance of 45.807. (A) The ef

where appropriate. Significance codes: p<0.025 = **, p<0.05 = *. (B) The results of the assessment of the effects of the inclusion of the vegetation term on the population density term, assessed by remothe population density term from the new model and checking the residual deviance. The term is ass

algorithm in S-plus would not iterate to a solution. Location: Ib=Inbye, Ob=outbye, Ov=Overall. DOM=dead organic matter. HQ=high quality items (live grass, live herbs and new growth Calluna vulgaris), CVW = old-growth, woody C. vulgaris. (A) Main effect (B) Effect on density term

Term Loc. Slope ±Std. Error ∆ Dev. p-value ∆ Dev. p-value Drop

out?

C. vulgaris (new) Ib -0.092 0.761 -2.201 0.333 Yes DOM Ib -2.578 0.108 -4.687 0.096 Yes Grass Ib 0.155 ±0.034 -4.252 0.039 * -6.360 0.042 No Density of HQ Ib -1.561 0.212 -3.670 0.160 Yes High quality Ib 0.135 ±0.031 -3.930 0.047 * -6.039 0.049 No Total Ib -3.356 0.067 -5.465 0.065 Yes Total – CVW Ib -3.837 0.050 -5.945 0.051 Yes

C. vulg w) Ob aris (ne -3.161 0.075 -5.270 0.072 Yes DOM Ob 0.663 -2.298 0.317 Yes -0.189 Grass Ob 0.289 ±0.060 - 0.033 * -6.664 0 No 4.556 .036 Density Q Ob 5.124 ±0.918 0.015 ** -8.051 No of H -5.943 0.018 High quality 084 ±0.018 0.042 * -6.247 Ob 0. -4.139 0.044 No Total 0.632 -2.338 0 Yes Ob -0.229 .311 Total – Ob 0.059 -5.663 CVW -3.554 0.059 Yes

DOM -2.143 0.143 -4.252 0 Yes Ov .119 Grass Ov 0.222 ±0.043 -5.234 0.022 ** -7.343 0.025 No Density 0.080 -5.171 Ye of HQ Ov -3.062 0.075 s High q 0.015 ** -7.984 uality Ov 0.164 ±0.03 -5.875 0.018 No Total Ov -2.671 0.102 -4.780 0.092 Yes Total – 0.063 ±0.014 -3.968 0.046 * -6.077 0.048 No CVW Ov

6.4.3 eight

here were distinct differences in the growth rate of males and females as indicated by

weight gain between 4 months and 14 months of age. Males were approximately 14%

heavier than females at 4 months of age and this difference had increased to 25% at 14

months of age (Figure 6.3).

Sexual differences in w gain

T

134


Months since birth

Mea

n w

eigh

t (kg

)

0 2 4 6 8 10 12 14

0

5

10

15

20

Figure 6.3: Weights of male (open symbols/dashed line) and female (closed symbols/solid line) Soay sheep at birth, at four months and at twelve months of age. Error bars represent ±1 s.e.m.

6.4.4 Survival to weaning

While 67 of the 322 sheep died prior to weaning (20.8%), only a further 7 (2.1%) had

died by the beginning of September when they were approximately six months old.

Six of the initial nine terms included in the analysis of the survival to weaning data

remained in the minimum adequate model (Table 6.10). The remaining main effects

terms were birth weight, sex, log10 strongyle egg count, maternal weight, twin status,

and population density (as a two level factor). The two interactions were population

density x birth weight and population density x maternal weight. Excluded terms were,

maternal age class, maternal hind leg length and all of the other first-order interactions.

135


Table 6.10: Summary of the minimum adequate generalised linear model for survival to weaning of Soay lambs born between 1989 and 2002. Coefficients are given along with their standard errors and t-values. P-values were derived by deletion of the term from the model and examination of the resulting change in residual deviance.

Term Coefficient Std. Error Change in deviance p-value

(Intercept) -5.867 2.761 - -

Birth weight 7.539 5.616 NA NA Sex -0.823 0.366 -5.236 0.022 Log10 Strongyle count -0.248 0.078 -10.923 <0.0001 Maternal Weight -0.332 0.491 NA NA Twin status 14.527 2.719 -41.882 <0.0001 Population density 5.520 3.050 NA NA

Interactions

Population density: Birth Weight 14.658 6.685 -7.055 0.008 Population density: Maternal Weight

-1.457 0.585 -15.166 <0.0001

Excluded terms

Maternal hind-leg length p>0.05 Maternal age class p>0.05 Julian bAll oth

irth date p>0.05 er first order interactions p>0.025

136


Birth Weight (kg)

Pro

babi

lity

of s

urvi

val t

o w

eani

ng

1.5 2.0 2.5 3.0 3.5

0.0

0.2

0.4

0.6

0.8

1.0

(a)

Maternal Weight (kg)

Pro

babi

lity

of s

urvi

val t

o w

eani

ng

10 20 2515 30

0.0

0.2

0.4

0.6

0.8

1.0

Low DensityHigh Density

(b)

log Strongyle egg count (log(eggs/g))

Pro

babi

lity

of s

urvi

val t

o w

eani

ng

0 2 4 6

0.0

0.2

0.4

0.6

0.8

1.0

(c)

Figure 6.4 birth weight and population density, (b) m l w n population density an te burden (log o p bability of survival to oay lambs born between 1989 and 2002. Lines ent t n from the GLM, poin ta, error bars represent ±1 backtran rmed s.e.m

: The influence of (a) parasi

aterna eight a dd (c) maternal weaning for S

10 eggs per gram of fresh faeces) n the predic

roiorepres

ts represent da sfo ..

Singleton Twin

0.0

0.2

0

0.6

0

1

Twin Status

Pro

babi

lity

of s

urvi

val t

o w

eani

ng

.4

.8

.0

(a)

Female Male

0.0

0.2

0.4

0.6

0.8

1.0

Sex

Pro

babi

lity

of s

urvi

val t

o w

eani

ng

(b)

Figure 6.5: The influence of (a) twin status and (b) sex on the probability of survival to weaning for Soay lambs born between 1989 and 2002. Error bars represent ± 1 backtransformed s.e.m.

The final model revealed that both birth weight and maternal weight interacted with

population density to influence the probability of survival to weaning. The most

137


significant interaction was the latter (Table 6.10). The probability of survival increased

with birth weight at both high and low population densities but the slope of the increase

was slightly lower at higher population density (Figure 6.4a). The effect size for this

was small although the difference was significant due to the large sample size used.

The probability of survival also increased with maternal weight. Again this interacted

with population density with the slope being lower at higher densities than for low

densities (Figure 6.4b).

Of the main effects, twin status was the most important variable, with singletons being

significantly more likely to survive than twins (Figure 6.5a). This was followed, in

order of decreasing significance, by maternal parasite burden with increasing log10

strongyle loads having a negative effect on survival probability (Figure 6.4c). Lastly,

sex was influential with females being more likely to survive than males (Figure 6.5b).

Addition of weather and vegetation parameters

ppears to

The addition of weather parameters to the minimum adequate model of survival to

weaning described above revealed that only two of the twenty-nine variables considered

had a significant effect on survival (Table 6.11). The most influential of these a

be over-winter NAO for the winter immediately before birth. However, an examination

of the plot suggested that this is likely to be a statistical artefact caused by an outlier in

1996 (Figure 6.6a).

The only univariate weather variable that was significant (p<0.05) was the number of

sleet/snow days in March with a negative association. An examination of the plot

suggested that, although the relationship was statistically significant, the effect size was

small and there the variation was large (Figure 6.6b). All of the terms fitted best as

linear functions and there was no hint of density dependence for any of the terms

(p>0.05).

138


Table 6.11: The effect of inclusion of weather terms as main effects on the minimum adequate model (MAM) for survival to weaning which had a residual deviance of 193.430. (A) The effect of the addition of weather terms to the MAM, assessed by adding the term to the MAM and checking the change in residual deviance. Slopes ±1s.e.m. are given where appropriate. (B) The results of the assessment for density dependence, checked by adding the interaction between the weather term and population density to the new model and checking the change in residual deviance. a = term best fitted as a quadratic function y=–2.323(±0.663)+ 1.337(±0.498)2. Significance codes: p<0.05 = *, p<0.10= .. a = some data were unavailable for testing this term, thus the change in degrees of freedom was –26 rather than –1.

(A)

Term Slope ±Std. Error ∆ Dev. p-value

NAO -0.456 0.138 -12.446 <0.001 * Max. temp. (Jan.) -0.305 0.581 Max. temp. (Feb.) -2.482 0.115 Max. temp. (Mar.) 0.405 0.238 -2.954 0.086 . Max. temp. (Jan. -2. 0.-Mar.) 230 135 Min. temp (Jan.) -0.038 0.846 Min. te (Feb.) -1. 0.18mp 722 9 Min. temp (Mar.) -0.015 0.902 Min. temp (Jan.-Mar.) -0. 0.47 522 0 Gale d .) -0. 0.35ays (Jan 847 8 Gale da -0.937 0.333 ys (Feb.) Gale d ar.)a . (-26d 0.94ays (M -15 544 .f.)a 6 Gale da .-Mar.) -2.014 0.156 ys (Jan Rainfa n.) 0 0.94ll (Ja .004 8 Rainfall (Feb.) -1.775 0.183 Rainfall (Mar.) -0.001 0.001 -2.966 0.085 . Rainfall (Jan.-Ma 0 0.64r.) .211 6 Sleet and snow days (Jan.) -1.05 0.305 Sleet a ow d -0. 0.49nd sn ays (Feb.) 476 0 Sleet a ow d r -0.116 0.05 -4. 0.02nd sn ays (Ma .) 3 786 9 * Sleet a ow days (Jan.- ar.) -0.038 0.02 -2. 0.088 . nd sn M 3 911 Abs. Min. temp. (Jan.) -1. 0.30 050 6 Abs. M emp. (Feb.) -0. 0.87in. t 023 7 Abs. M emp. -2. 0.10in. t (Mar.) 598 7 Abs. M emp. a -0. 0.95in. t (Jan.-M r.) 003 8 Growing days (Ja -1. 0.29n.) 118 0 Growing days (Feb.) -0.305 0.581 Growing days (Mar.) -2.862 0.091 Growing days (Jan.-Mar.) -2.134 0.144

139


Overwinter NAO

Prob

abilit

y of

sur

viva

l to

wea

ning

-4 -2 0 2 4

0.0

0.2

0.4

0.6

0.8

1.0

90

91

92

93

94

95

96

97

9899

00

02

(a)

Prob

abilit

y of

sur

viva

l to

wea

ning

Sleet/snow days (Mar.)

0 2 4 6 8 10

0.0

0.2

0.4

0.6

0.8

1.0

90

91

92

93

94

95

96

97

9899

00

02

(b)

mes (e.g. 99=1999, 00=2000 etc.) from and represent the raw data. The error bars represent ±1 s.e.m.

The addition of vegetation parameters to the minimum adequate model revealed that 12

ere significant as linear terms (non-linearity was checked by fitting

quadratic terms) (Table 6.12). However, for five of these, population density dropped

Figure 6.6: The effect on survival to weaning of (a) over-winter NAO index (p=0.029) and (b) snow/sleet days in March (p<0.001). The lines represent the predictions of the models to which the terms have been added as a linear main effect. The numbers within the points indicate which year the data co

of the 20 variables w

out of the model when the vegetation term was fitted (apparently, population was acting

purely as a surrogate for food availability). Of the remaining seven, six were “outbye

terms” and one was an “overall” term. The best fit was for the outbye biomass of

Calluna vulgaris, which caused a reduction in deviance of 14.420 from the original

model that had a residual deviance of 113.318. Density dependence could not be

checked because the GLM fitting algorithm could not reach a solution if the interaction

between population density and the weather variable was included.

140


Table 6.12: The effect of inclusion of vegetation terms on the minimum adequate model (MAM) for survival to weaning. Terms were added as main effects but because no vegetation data collected until summer 1993, the model was refitted using a subset of data from 1993-2002 first. Data from 1995 and 2001 were excluded because the assessment took place in April instead of March. The resuhad a residual deviance of 113.318. (A) The effect of the inclusion of vegetation terms on the

lting model MAM was

assessed by adding the term to the MAM and examining the change in residual deviance. Slopes ±1s.e.m. ate. Significance codes: p<0.01 = ***, p<0.025 = **, p<0.05 = *. (B) The

o of the effects of the inclusion of the vegetation term on the population density term, assessed by removal of the population density term from the new model and checking the residual

are given where appropriresults f the assessment

deviance. The term is assumed to have dropped out of the model if p>0.05. Density dependence was not checked because the GLM could not iterate to a solution. Location (Loc.): Ib=Inbye, Ob=outbye, Ov=Overall. DOM=dead organic matter. HQ=high quality items (live grass, live herbs and new growth Calluna vulgaris), CVW = old-growth, woody C. vulgaris. (A) (B)

Term Loc. Slope Std. Error ∆ dev. p-value ∆ dev. p-value

Popn. density drops out?

Calluna vulgaris Ib -5.652 ±2.835 -4.058 0.044 * -4.059 0.131 Yes Density of HQ Ib -0.769 0.381 -0.770 0.681 Yes DOM Ib 0.468 ±0.206 -5.538 0.019 * -5.539 0.063 Yes Grass Ib -0.947 0.330 -0.948 0.622 Yes High quality Ib -0.476 0.490 -0.477 0.788 Yes Total Ib -2.337 0.126 -2.338 0.311 Yes Total -CVW Ib 0.248 ±0.122 -4.654 0.031 * -4.655 0.098 Yes

Calluna vulgaris Ob 0.917 ±0.285 -14.420 0.000 *** -14.421 0.001 No Density of HQ Ob 20.066 ±7.642 -7.316 0.007 *** -7.317 0.026 No DOM Ob -1.123 0.289 -1.124 0.570 Yes Grass Ob 1.305 ±0.481 -7.934 0.005 *** -7.935 0.019 No High quality Ob 0.609 ±0.191 -12.979 0.000 *** -12.980 0.002 No Total Ob 0.134 ±0.054 -6.849 0.009 *** -6.850 0.033 No Total -CVW Ob 0.291 ±0.120 -6.382 0.012 ** -6.383 0.041 No

Density of HQ Ov 0.000 0.989 -0.001 0.999 Yes DOM Ov -2.397 0.122 -2.398 0.301 Yes Grass Ov -2.304 0.129 -2.305 0.316 Yes High quality Ov 0.584 ±0.264 -5.446 0.020 ** -5.447 0.066 Yes Total Ov 0.372 ±0.130 -9.711 0.002 *** -9.712 0.008 No Total -CVW Ov 0.275 ±0.124 -5.457 0.019 ** -5.458 0.065 Yes

141


Biomass CV.Ob (g/m^2)

Pro

babi

lity

of s

urvi

val t

o w

eani

ng

60 80 100 120

0.0

0.2

0.4

0.6

0.8

1.0

94

96

97

9899

00

02

(a)

Pro

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of s

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o w

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ng

HQD.Ob (unitless)

3.5 4.0 4.5 5.0 5.5 6.0 6.5

0.0

0.2

0.4

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0.8

1.0

94

96

97

9899

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02

(b)

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o w

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ng

Biomass G.Ob (g/m^2)

30 40 50 60

0.0

0.2

0.4

0.6

0.8

1.0

94

96

97

9899

00

02

(c)

sur

viva

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Biomass Q.Ob (g/m^2)

Pro

babi

lity

of

80 100 120 140 160 180 200

0.0

0.2

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1.0

94

96

97

9899

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02

(d)

(e) (f) (g)

Biomass T.Ob (g/m^2)

Prob

abilit

y of

sur

viva

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ning

600 700 800 900 1000

0.0

0.2

0.4

0.6

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1.0

94

96

97

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02

Biomass TmCV.Ob (g/m^2)

Prob

abilit

y of

sur

viva

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ning

10 12 14 16

0.0

0.2

0.4

0.6

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94

96

97

9899

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02

T.Ov (g/m^2)

Prob

abilit

y of

sur

viva

l to

wea

ning

0.22 0.26 0.30 0.34

0.0

0.2

0.4

0.6

96

0.8

1.0

97

9899

00

94

02

Figure 6.7: The effect on survival to weaning of mean biomass of vegetation terms which were significant after controlling for population density. (a) biomass Calluna vulgaris (new) in the outbye, (b) density of high quality items in the outbye, (c) biomass of grass in the outbye, (d) biomass of high quality items in the outbye, (e) total biomass in the outbye, (f) total biomass minus the old-growth, woody C. vulgaris in the outbye and (g) overall total biomass. See Table 6.12 for codes. The lines represent the predictions of the models to which the terms have been added as main effects. The numbers within the points indicate which year the data comes from and represent the raw data. The error bars represent ±1 s.e.m.

142


6.5 Discussion

The scope of this study included ewes of varying ages (1-14 years old) and under

different weather conditions, vegetation abundance and population densities spanning a

period of 14 years. This allowed the effects of the interaction between these variables

and early life history traits of their offspring to be explored in relation to birth weight

and early-survival. Furthermore, unlike many previous studies, which used population

y ion quality with the assumption that it is synonymous with

parasite burden (Gulland 1991).

Therefore, a measure of the quality and quantity of available food is essential.

of resources for transfer to the developing foetus and there is good evidence

but also by influencing the body composition of neonate lambs. They found that lambs

densit as a proxy for vegetat

grazing pressure (e.g. Clutton-Brock, Stevenson et al. 1996, Clutton-Brock, Illius et al.

1997, Portier, Festa-Bianchet et al. 1998, Schmidt, Stien et al. 2001), the analyses

presented here include empirical estimates of vegetation quality parameters as

covariates. This is important because, whereas predation is a major cause of juvenile

mortality for many wild ungulate populations (e.g. Smith and Anderson 1996, Sarno,

Clark et al. 1999, Keech, Bowyer et al. 2000), it is not a significant mortality source in

Hirta’s Soay sheep population. For these animals, the main cause of mortality for sheep

of all age classes is malnutrition, often exacerbated by

6.5.1 Birth weight

As expected, ewe condition (weight) had a positive association with offspring birth

weight. Similar effects have been found in several mammalian taxa including pinnipeds

(Pomeroy, Fedak et al. 1999, Ellis, Bowen et al. 2000, Georges and Guinet 2000) and

ruminants (Clutton-Brock, Price et al. 1992, Robertson, Hiraiwahasegawa et al. 1992,

Clarke, Yakubu et al. 1997, Clutton-Brock, Wilson et al. 1997, Andersen, Gaillard et al.

2000, Keech, Bowyer et al. 2000). It is likely that maternal condition affects the

availability

that nutrition during early foetal development can affect foetal growth trajectories and

size at birth (Robinson 1996, Robinson, Sinclair et al. 1999, Robinson, McEvoy et al.

2000). Thus, ewes in poor condition would have fewer resources available for the

developing foetus, which would, as a result, grow more slowly and attain a lower birth

weight than would be the case if the ewe was in good condition. In fact, Clarke et al.

(1997) showed that maternal condition was crucial, not only in influencing birth weight,

143


born to light ewes had proportionally less perirenal adipose tissue and smaller liver,

heart, kidneys, brain, adrenals and thyroid than lambs born to heavy ewes.

The negative effect of population density in the year prior to birth on birth weight is

also indicative of a nutrition effect via the effect of grazing pressure on forage quality

and quantity. This supposition is supported by the fact that the population density term

drops out of the model when many of the terms for food availability are added. This sort

of resource-based density-dependent effect on birth weight is common in many wild

animal populations and has previously been reported for the Soay sheep on St. Kilda

(Clutton-Brock, Price et al. 1992, Forchhammer, Clutton-Brock et al. 2001).

The timing of birth may also be important with birth weight tending to increase with

julian day. Furthermore, since birth weight was shown to be an important factor in

survival, the timing of birth may have important effects later on. This small effect may

either be related to the increase in plant productivity, and, therefore, food

ingletons. This effect is well established for many species that

can have multiple offspring and it is well known that individual birth weight declines

quality/quantity, that accompanies the coming of warmer and sunnier weather as the

winter ends and the spring progresses. Work examining the association between phases

of rapid plant growth and parturition have shown increased juvenile survivorship at

these times (Rubin, Boyce et al. 2000) and several have hypothesised that the rapid

growth phase is an influential driving force behind the evolution of birth synchrony

(Linnell and Andersen 1998, Sinclair, Mduma et al. 2000). It is likely that the increased

birth weight associated with a later date of parturition results from the elevated forage

quality that becomes available during late gestation.

Twins, which must share maternal resources during pre-natal development, have a

lower birth weight than s

with the number of offspring in the litter (e.g. Greeff, Hofmeyr et al. 1992, Schwartz

and Hundertmark 1993). It is interesting that there was no sex difference in birth

weight; this is a further indication that the sexual size dimorphism apparent in adults is

mainly due to higher growth rates of male offspring. Despite this, there is some

evidence that, in utero, males can be more effective at exploiting the increased levels of

resources associated with maternal phenotypic superiority than females (Kojola 1997).

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Although there is some evidence that climate during foetal development (as measured

by NAO) can influence birth weight (Post, Forchhammer et al. 1999) no effects were

found in this analysis. Although little empirical work has been carried out on the effects

of weather conditions on foraging behaviour, some evidence exists to show that adverse

teration of nutrient allocation patterns so that the ewe

The fact that most of the lambs that died did so during weaning underlines the

conditions can limit the amount of time spent foraging (Owen-Smith 1998). It was

expected that, because of the increased thermoregulatory costs associated with it, harsh

weather in late-Winter/early-Spring would limit maternal foraging time during the

gestation period and would thus have an influence on the amount of resources available

for allocation to the growing foetus and thus influence birth weight. This result indicates

that although harsh weather can indeed limit the time spent foraging (J. Pilkington,

unpublished observations) this is probably compensated for, either by (1) increasing the

time spent foraging when the weather improves, (2) by adopting a strategy of energy

conservation or (3) by the al

allocates fewer resources to self-maintenance rather than fewer resources to the foetus.

There is some evidence for (1) and (2) in response to poor forage for musk ox (Ovibos

moschatus) in Greenland (Forchhammer 1995, Forchhammer and Boomsma 1995).

However, there is no evidence for (3). On the contrary, Festa-Bianchet and Jorgenson

(1998) have shown that bighorn sheep adopt a “selfish” maternal care strategy when

resources are scarce – reducing care to favour their own weight gain over the

development of their lambs.

6.5.2 Survival to weaning

importance of this life-history stage. Birth weight was one of the most important factors

in determining early survival. This compares well with other studies which also found

birth weight to be of prime importance (Clutton-Brock, Price et al. 1992, Perez-Razo,

Sanchez et al. 1998, Neuhaus 2000). However, it was found that birth weight interacted

with population density so that its effects were more pronounced at high densities than

at low densities. In other words, the effect of birth weight was more important when

resources were scarce than when they were abundant.

A direct link between maternal condition and survival to weaning was demonstrated,

thus supporting the initial hypothesis. However, this again interacted with population

145


density so that maternal condition was more important when resources were scarce.

This effect was probably due to the effect of maternal condition upon lactation. Milk

production is correlated with female condition, with females in poor condition

producing less, and possibly poorer quality milk than those in superior condition

(Bencini and Pulina 1997). The interaction indicates that at high population densities,

when resources are scarce, the quality of maternal provisioning becomes even more

important.

Interestingly, maternal age did not significantly affect survival after controlling for

weight and size. Initially it was expected that the offspring of older, more experienced,

l rate decreases as litter size increases (Greeff, Hofmeyr et al. 1992). In effect,

twins compete for resources from the mother and if these resources are scarce, then one

or both of the twins may perish. The higher growth rate of males that results in higher

nutritional demands during the fast growth period of weaning also seems to affect

survival. Although males were less likely to survive to weaning than females, there was

no interaction with maternal weight. Therefore, the hypothesis that males are less likely

to survive because their mothers cannot meet their higher demand for milk was not

supported. If this were the case, it would be expected that there would be no sex

difference in the survival of offspring of heavy ewes. The mechanism causing the lower

early survival rates of males thus remains unclear.

There was a strong effect of maternal parasite burden on offspring survival, with the

offspring of ewes with a heavy burden less likely to survive. Immune system function

is, to some extent, an inherited trait (Iraqi, Behnke et al. 2003), and ewes with poor

immune function (and, therefore, heavy parasite burdens) are likely to produce offspring

that also have weak immune function and that are, therefore, less likely to survive. This

idea is supported by recent molecular genetics work on the St. Kilda Soay sheep by

ewes would have been greater than those of young, inexperienced, ewes of the same

weight. A similar effect could also be induced if older ewes were genetically superior to

their younger counterparts: an effect that might be expected of a population that

experiences frequent fluctuations where the unfit are weeded out. However, these

findings may indicate that learned experience and genetic superiority do not play a

significant role in survival to weaning.

The effect of twin status was as expected and supported initial expectations e.g. that

surviva

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Coltman et al. (1999) and Paterson et al. (1998) who demonstrated that increased

parasite burden is associated both with increased homozygosity of the major

istocompatibility complex (MHC) and decreased overwinter survival probability. This

t because it shows that heterozygosity in the MHC is positively associated

with immune function.

An alternative explanation could be related to patterns of maternal resource allocation.

parasite effect would also arise if heavily parasitised ewes adopted the selfish strategy

of allocating a greater proportion of their finite resources to resisting infection rather

an to the care of their offspring (lactation) than non-parasitised ewes. Evidence from

(LeB and Festa-Bianchet and Jorgenson (1998) have

presented empirical evidence suggesting that ewes adopt a selfish strategy when

sources are scarce and the lamb is in utero. Therefore, it seems likely that they would

adopt a similar strategy when under attack from parasites. However, this view is at odds

with that of Coop and Kyriazakis (1999b) who state that “the function of growth,

pregnancy and lactation are prioritised over the expression of immunity”.

Several authors have demonstrated an association between high over-winter NAO

indices and reduced over-winter survival in north-European ungulates including red

deer and Soay sheep (Forchhammer, Stenseth et al. 1998, Milner, Elston et al. 1999,

Catchpole, Morgan et al. 2000, Forchhammer, Clutton-Brock et al. 2001). In this study,

which examined survival between birth and weaning for newborn lambs rather than

overwinter survival, there was a similar, but weak and unconvincing, negative effect. It

seems likely that the effect of NAO is diluted to some extent, because it is brought

about via its effect on the mother during gestation rather than a direct effect on the lamb

itself.

The effect of the only significant univariate weather variable, the number of snow/sleet

days in March was also small. However, care must be taken with the interpretation

because the occurrence of snow is a relatively rare event on St. Kilda and the spatial

correlation of this weather event was poor. Analyses are thus prone to the influential

effects of outliers in the data. Furthermore, some of the data used here were predicted

for Benbecula from the Rum data, despite the correlation between the two sites being

poor for some variables (Table 6.3). For these reasons, obtaining reliable weather data

h

is significan

A

th

Bighorn sheep indicates that ewes are flexible in their resource allocation patterns

lanc, Festa-Bianchet et al. 2001)

re

147


from St. Kilda itself is a priority. Automatic weather stations were installed in late 1

and 2000 but several years of t

attempted.

999

his data are required before further analyses can be

r getation variables that were considered had a significant effect on

eir inbye equivalents. The most

l. The fact that

e or for shelter) and interference

6.6 Conclusions

These results show that the birth weight and survival probabilities of Soay sheep on St.

ilability has an effect on survival while

Seve al of the ve

survival to weaning over and above the effects of population density. To my knowledge

this is the first time this has been demonstrated in a long-term dataset. It was initially

expected that, because most of the animals forage in the high-quality, inbye, areas, the

inbye measures would be the most important. However, this was not the case, and

outbye measures were much more important than th

important parameter was the outbye biomass of the new growth of C. vulgaris, a slow

growing plant that is sensitive to grazing pressure. It is, therefore, likely that most of the

effects of the vegetation parameters are due to the high correlation with grazing pressure

rather than a direct effect of vegetation biomass on juvenile surviva

population density remains in the model alongside the forage parameters indicates that

forage availability may operate while controlling for population density. This indicates

that both resource competition (for available forag

competition may be operating. The latter may take the form of increased

aggressive/sexual behaviour or increased vigilance for competitors that accompany high

population densities. Further behavioural work on the frequency and type of interactions

between animals are required to investigate this point.

Kilda are influenced by forage availability and population density as well as maternal

characteristics. The fact that vegetation parameters remained in the model alongside

population density indicates that vegetation ava

controlling for population density. The effects of weather variables on survivorship

were unconvincing. More weather data collected from St. Kilda itself are required to

resolve this issue. These findings have significance for explaining the observed

fluctuations in population size that are characteristic of the sheep population on St.

Kilda.

148

Chapter 7 - Foraging strategy and parasite burden Chapter 7 - Foraging strategy and parasite burden

149

Chapter 7 : Foraging strategy and parasite burden of

Soay sheep on St. Kilda

149

Chapter 7 - Foraging strategy and parasite burden

Foraging strategy and parasite burden of Soay sheep on St. Kilda

7.1 Abstract

Parasites have a range of both physiological and behavioural effects on their hosts. A

common behavioural effect is parasite-induced anorexia (PIA). Theoretical work has

shown that gastrointestinal (GI) parasites can potentially influence herbivore

nge: 443-1063 gDM/day). There was neither any evidence of PIA nor

of any parasite-induced changes in diet selectivity.

, Urquhart, Armour et al. 1996).

population dynamics by increasing mortality rates. Additionally, mathematical models

indicate that PIA could also have a significant effect.

This chapter reports on an experiment carried out in August-September 2001 to

determine whether the feral Soay sheep (Ovis aries L.) population on Hirta exhibit PIA

and, if they do, to ascertain whether diet selectivity is also altered. The n-alkane

technique was used to estimate food intake rates and diet composition.

Overall, intake rate increased with body weight and the mean over both sexes was

689gDM/day (ra

However, this work was carried out when the sheep were approaching peak physical

condition, and parasites are known to have a greater effect on immuno-compromised

hosts that are in poor condition. Thus, if the experiment were to be repeated in the

winter, when the sheep are in poor condition, the effect of parasites is likely to be larger

and thus easier to detect.

7.2 Introduction

Parasites can have a range of both physiological and behavioural effects on their hosts

dependent on the biology of the host and of the specific parasite and its life-cycle stage

(Soulsby 1968

For the gastrointestinal (GI) parasites of ungulates, the main immediate effects are

increased endogenous protein loss, increased mucoprotein secretion and damage to the

gut tissue (Soulsby 1968, Urquhart, Armour et al. 1996). Abomasal parasites such as

Teladorsagia circumcincta and Haemonchus contortus cause damage to the parietal

cells of the abomasum, impairing secretion and elevating abomasal pH from 2-3 to 6-7

(Sykes and Coop 1979). This affects digestive enzyme efficiency and, therefore, impairs

150


the breakdown of food (Sykes and Coop 1979). It also reduces the lysis of anaerobic

bacteria, and the resulting survival of substantial numbers of rumen bacteria in the

abomasum may significantly lower the bacterial protein available to the sheep

(Simcock, Joblin et al. 1999). This is especially important considering that >50% of the

Meagher and O’Connor (2001) have recently reported that although infection of deer

One of the major effects of infection is a reduction in voluntary food intake (anorexia).

anorexia has been demonstrated in a number of vertebrate taxa

including rats (Rattus norvegicus) (Horbury, Mercer et al. 1995, Roberts, Hardie et al.

effects of undernutrition on the immune system are considered. The traditional

a pathological response to parasitism and has no

functional basis. Nevertheless, with the above paradox in mind Kyriazakis et al. (1998)

protein absorbed by the ungulate host is sourced from these bacteria (Simcock, Joblin et

al. 1999).

Intestinal parasites such as Trichostrongylus colubriformis and Nematodirus battus

cause mucosal thickening and the stunting of micro-villi possibly reducing the

absorption of amino acids, fat and minerals (Coop and Holmes 1996, Coop and

Kyriazakis 1999a). Calcium and phosphorus retention is often reduced in infected

animals (Sykes and Coop 1976) and can result in reduced skeletal mineral deposition

and, therefore, reduced growth rate .

mice (Peromyscus maniculatus gracilis) with the nematode Capillaria hepatica does

not reduce basal metabolic rate, it does reduce cold-stress maximum oxygen

consumption and thermogenic endurance and is, therefore, likely to reduce survival

through cold periods.

Parasite-induced

1999, Mercer, Mitchell et al. 2000), toads (Bufo bufo) (Goater and Ward 1992), mice

(Mus musculus), reindeer (Rangifer tarandus) (Arneberg, Folstad et al. 1996) and sheep

(Ovis aries) (Niezen, Waghorn et al. 1995). In sheep intake is commonly reduced by

30-60% (Poppi, Sykes et al. 1990).

From the host’s point of view anorexia seems paradoxical because the parasites impose

extra metabolic and nutritional demands on their host and thus one might expect an

increase in intake to compensate. This is especially true when the effects deleterious

explanation is that anorexia is

take the view that parasite induced anorexia is a behavioural adaptation which serves a

151


function and results in specific advantages for the host. Their review (Kyriazakis,

Tolkamp et al. 1998) proposed five functional hypotheses that would account for the

observed anorexia. These were (1) the parasite induces anorexia for its own benefit; (2)

intake decreases to starve the parasites; (3) a reduction in energetic efficiency leads to

reduced intake; (4) the anorexia promotes an effective immune response; (5) anorexia

leads to increased diet selectivity. After careful analysis, they found that only

hypotheses (4) and (5) remained valid in a functional and general way.

r, protein is not known to produce as severe

une system (Chandra 1993). However, for

herbivores, where the concentrations of these nutrients are lo hese issues are unlikely

to be important.

The main physio chanism allowing sheep to overcome the effects of

parasitism is the immune response, which is costly and time-consuming to put into

ction and acquire (Svenson, Raberg et al. 1998). Therefore, it is conceivable that

y have evolved to carry out the same role. For example,

potential hosts could feed selectively to (1) avoid food items that are common sources

that alter their internal environment and make it less

herbivore hosts have developed the foraging skills needed to take advantage of plant

s use behaviour as a weapon in the host-parasite

“arms race”.

The fact that immune response is affected by macro/micro-nutrient intake is well

documented (Bundy and Golden 1987, Chandra 1993). Excess protein acquisition can

impair the rate at which the immune response is acquired, as well as its effectiveness

(van Houtert and Sykes 1996). Howeve

immunotoxic effects as excesses of certain nutrients such as zinc, selenium and vitamins

A and E, which can also impair the imm

w, t

logical me

a

behavioural mechanisms ma

of parasites, (2) consume items

hospitable or (3) select foods with anti-parasitic compounds. Evidence for the latter is

equivocal but Hutchings et al. (2003) show that there is strong evidence suggesting that

properties to combat parasites and thu

If the nutrient intake falls below the animals’ requirements then metabolic reserves will

be used up, thereby leaving the animal vulnerable to periods of food shortage. However,

a change in selection strategy may compensate for the induced anorexia. For example,

Cosgrove and Niezen (2000) found that lambs actively compensated for the metabolic

protein deficiency caused by parasites by selecting a higher proportion of protein rich

white clover (Trifolium repens) in their diets. They suggested that grazing animals could

152


detect the metabolic signals associated with parasitism and make a behavioural response

to mitigate the harmful nutritional effects and evidence for this ability certainly exists

(Hutchings, Athanasiadou et al. 2003).

and, therefore, reducing the population (and hence grazing

pressure) and; (2) by causing anorexia and hence reducing grazing pressure without

ade into the effects of parasitism on the food

intake parameters of the sheep. The aims were to determine whether, and to what degree

lant-herbivore community is then considered.

ome to a free-ranging and feral population of Soay sheep (Ovis

aries L.), which are the only important vertebrate herbivores on the island. The

ales of the 1999 cohort) were captured in August

2001. There were three treatments within each sex. Treatment one involved two

These properties mean that parasites may have important indirect effects on the plant

communities. Pathogens have the ability to act as functional predators by causing a

reduction in grazing pressure by the host animals. They can do this in two ways; (1) by

causing increased mortality

necessarily reducing herbivore population size. The effect of the Myxoma virus on

rabbits in England and the consequential effects on grassland communities is an

excellent example of the dramatic effects that pathogens can have (Dobson and Crawley

1994). Although GI parasites are unlikely to have such a dramatic effect they can

theoretically reduce grazing pressure through anorexia.

In this chapter a detailed investigation is m

GI parasites reduce intake rate, and to determine whether the sheep alter any aspect of

the foraging patterns in order to compensate for the effects of parasitism. The relevance

of the results in relation to the p

7.3 Methods

Data were collected from Village Bay area of the island of Hirta, part of Scotland’s St.

Kilda archipelago (57º49’N 08º34’W) situated approximately 70km west of the Outer

Hebrides. The island is h

dominant and most pathogenic parasite of the Soay sheep is the strongyle Teladorsagia

circumcincta (Gulland and Fox 1992), which is associated with reduced forage intake

rates (Poppi, Sykes et al. 1990, van Houtert and Sykes 1996). Descriptions of the sheep

population, their parasites and the study site are presented in greater detail in Chapter 2.

7.3.1 Selection and treatment

Study animals (25 females and 26 m

153


boluses. The first was an intrarumenal controlled release capsule (CRC) (Captec Ltd.,

Manurewa, New Zealand) releasing two man-made alkanes into the rumen at 50mg/day

ent two was an alkane CRC only and treatment

ale animal from the MP- group was only treated with an

in order to measure intake rate (see below). The second was an intrarumenal CRC

releasing 2.1g of the anthelmintic drug albendazole over 90 days thus reducing parasite

burden to zero for this period. Treatm

three was a control with no boluses.

The treatment groups were thus male with anthelmintic drench (MP-), male without

anthelmintic drench (MP+), male control (MC), female with anthelmintic drench (FP-),

female without anthelmintic drench (FP+) and female control (FC). Due to a problem

with one of the boluses, one m

anthelmintic and no alkane (Table 7.1).

Table 7.1: Treatment groups and numbers in the experiment investigating foraging behaviour and parasitism of Soay sheep on Hirta in August 2001.

Treatment Female (F)

Male (M)

1) Alkane + Anthelmintic (P-) 9 9 2) Alkane only (P+) 8 9 3) Control (C) 8 7 Anthelmintic only 0 1

7.3.2 Intake parameters

Botanical compositionof the diet

Botanical composition of the diet was estimated using two methods; microscopic faecal

plant cuticle analysis (FPCA) and plant wax component profile analysis.

Faecal plant cuticle analysis (FPCA)

Data concerning the botanical composition of the diets were collected using the faecal

plant cuticle analysis (FPCA) method. The cuticles of plants are much less digestible

than other parts of the plant and, as such, fragments can remain identifiable after passing

e, the cell wall type (thick, thin, smooth,

corrugated, pitted), stomata and guard cell characteristics, the shape of silica bodies, the

through the digestive tract of an animal thus allowing the quantification of diet

composition. The identification of the cuticular fragments is based on epidermal

characteristics including cell size and shap

154


type and distribution of hairs, the presence of hooks and papillae as well as other

features such as striations and surface markings (Baumgartner and Martin 1939, Milner

and Gwynne 1974).

The use of these techniques is well suited to studies of free-ranging animals since it

causes minimal disturbance, and has proved to be useful over a wide range of taxa

(Putman 1984). The technique I used was a modification of Sparks and Malechek’s

(1968) method as follows.

Faecal samples were freeze-dried, ground to a powder using a coffee grinder, and

washed through a 1mm screen to remove large fragments and then through a 0.2mm

e

pe at a

ination). Successive systematic traverses

of the slides were made until at least 100 epidermal fragments were identified. The

Dove, Mayes et al.

mesh screen to remove small unidentifiable particles. This standardised fragment size to

between 0.2mm and 1mm. The sample was then soaked for 5 minutes in 5ml of

concentrated HNO3 in a test tube, in order to remove pigment from the fragm nts and

aid identification. The sample was made up to 100ml with distilled water and boiled for

2-3mins to complete the clearing process.

The mixture was placed in a round-bottomed bowl and, while stirring, a sample was

taken using a plastic pipette. Three drops of the substance was then placed on a slide

and a coverslip placed on top.

The slides were examined under a phase-contrast binocular microsco

magnification of 100x (or 200x for closer exam

relative abundance of each category gave an estimate of botanical composition.

Wax component method

The particular patterns of concentrations of cuticular wax components (alkanes, alkenes,

alcohols and sterols) are specific to individual plant species (Dove and Mayes 1991,

Dove, Mayes et al. 1996, Chen, Scott et al. 1999) or parts of plants (

1996). By comparing the profiles occurring in faecal samples with the wax component

profiles of species represented in the diet it is possible to deduce diet composition

assuming that the components are fully recovered or that the relative recoveries are

known (Dove and Mayes 1991).

155


Extraction

To measure the wax component concentration of each sample the components were first

extracted from the sample. The extraction methods for alkanes/alkenes differ slightly

g gas chromatography

according to the following methods (Mayes, Lamb et al. 1986) with the modifications

alt et al. (1994).

was then added and

the tube was resealed and vigorously shaken. After separation into two liquid layers the

top (non-aqueous) layer was tr

extracts were then redissolved in 0.3ml heptane, with warming, and applied to

a small column containing silica gel (Kieselgel 60, 70-230 mesh) with a bed volume of

ptane and ethyl acetate (80:20 v/v). After evaporation to

were redissolved and, after warming, transferred to an auto

sampler vial for analysis by gas chromatography.

from those of alcohols and sterols.

Plant samples were oven-dried at 80˚C for 48 hours and faecal samples were freeze-

dried for 48 hours. Samples were ground to a powder with a household coffee mill. The

ground samples were analysed for wax component content usin

reported in S

Duplicate 0.1g samples of dried, ground faeces were weighed into glass GC vials fitted

with screw caps with PTFE-lined inserts. A solution of C22 and C34 in decane (0.3mg/g

of each alkane) was added by weight (0.11g) to each tube as an internal standard,

followed by 1.5ml ethanolic KOH (1M). The tubes were then capped and heated for 16

hours at 90˚C in a dry-block heater. After partial cooling (to 50-60˚C) 1.8ml n-heptane

was added and the tube was capped and shaken gently. 0.5ml water

ansferred to a second 4ml GC vial with a polyethylene

Pasteur pipette. Another 1.8ml heptane was added and the extraction was repeated,

adding the non-aqueous layer to the same vial. The solution in the vial was evaporated

to dryness on a dry-block heater fitted with a sample concentrator blowing air into the

vial. The

1ml. The hydrocarbons were eluted into a third 4ml GC vial by the addition of 2.5ml n-

heptane to the column. At this point the alcohols (see the next section) were eluted off

by the addition of 3ml he

dryness the hydrocarbons

The herbage was treated in a similar way to the faeces with the exceptions that larger

(0.2g) samples were used with greater quantities of liquid reagents (2ml ethanolic KOH,

0.6ml water and 2 x 2ml n-heptane).

156


The extracted and purified extracts were analysed on a Pye Unicam PU4500 gas

chromatograph (GC) fitted with a flame-ionisation detector. The column was a 30x0.75

mm (i.d.) bonded-phase wide-bore capillary, type SBP1 (Supelco, Bellafonte, Pa., USA)

med from 225-290˚C at 6˚C per minute. The carrier gas

en C22 and C35 were determined along with the

s extracted immediately before the C30 and C32 alkanes

(referred to C30-ene and C32-ene respectively). Then the alcohols between 1-C26-ol and

erol that fractioned after 1-C28-ol (henceforth referred to as 1-C29-ol)

ated using the EATWHAT software package (Dove

996). The package uses a non-negative least squares (NNLS) algorithm

which iteratively compares the faecal and vegetation wax component concentrations.

affected by, the species and wax

components included in the calculations. I included all species that occurred at

and was temperature program

was helium, and tetratriacontane (C34 n-alkane) was used as an internal standard. The

injector and detector were maintained at 340˚C. Each sample was extracted in duplicate

and each duplicate was injected twice into the GC column. A mixture of n-alkane

standards was injected after every tenth sample for calibration of the detector response.

Peak areas were measured using a Spectra Physics SP4400 computing integrator and

concentrations were calculated using Spectra Physics “Winner” software.

The concentrations of alkanes betwe

concentrations of the alkene

1-C28-ol and the st

were determined.

The concentration of each alkane was corrected for its recovery in the faeces using

recovery data obtained from domestic sheep (Mayes, RW, unpublished data). The

concentrations of alkenes were corrected for recovery by interpolation using the same

data. The concentrations of other components were not corrected for recovery.

Diet composition

The diet compositions were estim

and Moore 1

The aim of the iterations is to find the proportions of diet components that minimise the

squared deviations between observed and fitted wax component concentrations, while

obeying the constraint that all the concentrations be positive or zero.

These calculation methods depend on, and are

abundances of >5% in either the main grazing area (within the head dyke) or within the

entire study area. These were Calluna vulgaris, Agrostis spp., Holcus spp., Festuca

spp., Trifolium repens, Plantago lanceolata, Anthoxanthum odoratum and Bryophytes. I

157


included the alkanes C24 to C31, C33 and C35, the alkenes C30-ene and C32-ene, and the

alcohols 1-C26-ol and 1-C28-ol, and the sterol 1-C29-ol. C32 and C34 were not included

because they were the dosed alkane and the internal standard respectively.

Because the concentrations of the different kinds of components differ by orders of

agnitude, the concentrations of the alkenes, alcohols and sterols needed to be

p ore being used in the calculations. The concentrations were multiplied

by the result of the mean alkane concentration divided by the mean non-alkane

as 21.72 for alkenes, and 0.20 for alcohols.

+. The

intrarumenal CRC released two even chain-length alkanes, n-dotriacontane (C32) and n-

m

mani ulated bef

concentration. This w

Intake rate

Intake rate was estimated for sheep in treatment groups MP-, FP-, MP+ and FP

hexatriacontane (C36), into the rumen at a constant rate (~50mg per day) for a period of

approximately 20 days. An estimate of organic matter intake (OMI) could then made by

comparing the concentration of specified even and odd-chain length alkanes in the

faeces and diet (Mayes, Lamb et al. 1986, Dove and Mayes 1991);

ji

ii H

FF

HDF

FOMI ×−

×=

jjj

(7.1)

ane content was predicted by summing the product of the alkane

Total faecal nitrogen (%FN) content of samples was determined by an automated

Dumas combustion procedure (Pella and Colombo 1973) using a Carlo Erba NA1500

Elemental Analyser (Carlo Erba Instruments, Milan, Italy). This is a reliable indicator of

forage quality (O’Donovan, Barnes et al. 1963) and has been used extensively in the

where OMI is the organic matter intake in kgDM day-1, Fi and Fj are the faecal

concentrations of the specified odd-chain and even-chain alkanes, Hi and Hj are the

herbage concentrations of the same alkanes and Dj is the daily dose of the even-chain

alkane.

Dietary alk

concentration within each species represented in the diet and the proportion of the diet

composed of each species.

Chemical characteristics

158


study of wild ungulates (e.g. O’Donovan, Barnes et al. 1963, Leslie and Starkey 1985,

Festa-Bianchet 1988, Nune ndez, H t al. 1992 h, Villarre l.

thven, Hellgren et a 4, Becerra, r et al. 199

Hour-long focal watches of infected and uninfected animals were carried out using

Psion 5 handheld computers running “Voyeur” software (Sunadal Data Solutions,

Edinburgh, UK) which allowed the recording of the time spent feeding, ruminating,

interacting with other sheep and other behaviours. Bite rate was assessed with 5-minute

watches carried out during the focal watch. The number of bites taken over this period

was recorded using a tally counter and at least 4 bite rate measurements were taken over

a period of 3 weeks from each sheep.


Diet Composition

An ANCOVA model was fitted for each plant species with arcsine transformed

percentage composition (again measured from the last 2 measurements per sheep to

remove the pseudo-replication) as the response. Initial parasite burden, treatment,

starting point by

-significant terms, starting with the highest order

interactions (see Crawley 2002).

Intake Rate

elmintic.

A maximal analysis of covariance model (ANCOVA) was created using these data. The

response variable, intake rate, was non-gaussian, and as such it was logged (base 10) in

zherna olechek e , Branc al et a

1994, Ru l. 199 Winde 8).

Time allocation and bite rate

weight, hind leg length and sex were fitted as explanatory variables. Interaction depth

was limited to three-way interactions and quadratic terms were included to test for non-

linearity. A minimum adequate model was produced from this

backwards elimination of non

The effects of the repeated-measures pseudo-replication were removed by taking the

average of the intake rate measurements from the last 2 (taking the last one if fewer

measurements were made) intake rate measurements for each sheep. This is where

greatest differences in intake rate would be expected because more time would have

elapsed since being treated with the anth

159


order to normalise the errors. The explanatory variables were treatment, weight, hind

leg length, sex and initial parasite burden (see Chapter 3 for the methodologies).

esults

intake rate over both sexes was

689gDM/day (range: 443-1063gDM/day). This compares well with estimates from

or the sex. This allowed the examination of the effect of sex

because it removed the confounding effect that existed between weight and sex (the

all four terms dropped out of the model to leave just the intercept

a 32=152.800, p<0.001). The fact that sex dropped out of these

models suggested that it is not likely to be a major factor in determining intake rate.

Interaction depth was limited to three-way interactions and quadratic terms were

included to test for non-linearity. The models were simplified in the same way as for

botanical composition.

7.4 R

7.4.1 Intake rate

Over the range of weights considered, the mean

domestic sheep breeds such as Scottish Blackface (Iason, Mantecon et al. 1999).

The first method of examining the intake rate data used a condition measure instead of

weight and hind leg length (model 1). The condition was assessed as the actual weight

minus the mean weight f

lightest male was 22.2kg while the heaviest female was only slightly heavier at 22.7kg).

Other explanatory variables were treatment and log10 strongyle burden.

Upon simplification,

(estim te=6.505±0.043; t

Therefore, it ceased to be a problem that sex and weight are confounded, and the

analysis could be carried out including weight and hind leg length but omitting sex

(model 2).

For model 2, the explanatory variables were treatment, weight, hind leg length and log10

strongyle burden. After simplification the minimum adequate model included only

weight (Table 7.2 and Figure 7.1).

160


Table 7.2: Summary of the minimum adequate model for log intake rate of Soay sheep on Hirta in summer 2000. The r2-value was 0.174. Term Va ue -value lue Std. Error F-val p

(Intercept) 5.954 1 0.000 0.220 27.12Weight 0.023 52 0.016 0.009 2.5

Weight (kg)

log

inta

ke ra

te (g

DM

/day

)

15 20 25 30

6.2

6.4

6.6

6.8

7.0

Figure 7.1: The relationship between intake rate and body weight for Soay sheep on Hirta in August 2000. Intake rate was estimated using the n-alkane method. Females are represented with squares and males are represented with circles. Treated animals are represented with filled symbols while untrea

nimals are represented with open symbols. The line (formula = y=5.94 + 0.02x) represents the

.4.2 Botanical composition

d faecal plant cuticle analysis

and 14% respectively). Anthoxanthum odoratum (4%), Poa spp.

spp. (7%) were the other minor grasses. No treatment, sex or

detected (p>0.05).

tedapredictions of the linear model (Table 7.2) for which the r2-value was 0.174.

7

Metho 1 – Microscopic

Overall the diet composition was estimated to be made up of 77% grasses, 8% herb, 2%

moss and 14% Calluna vulgaris. Within the grasses Festuca spp. was the most abundant

(25%) and Nardus stricta least abundant (3%). Holcus spp. and Agrostis spp. had

similar abundances (13

(4%) and Deschampsia

weight effects were

161


Method 2 – wax component analysis

The alkane/alkene and alcohol concentrations differed markedly between plant species

ep, none of the

included explanatory variables (weight, sex, hind leg length, initial parasite burden and

mained in the final ANCOVA models.

and thus facilitated the use of the NNLS-technique to estimate diet composition. The

wax component contents are given in the Appendix. The alkenes in particular allowed

Agrostis to be distinguished from the other plant species because it differed in the

concentration of alkene that was extracted between the C29 and C30 alkanes (labelled

C30b).

For all six of the species considered important in the diet of Soay she

treatment) re

The proportion in the diet was highest for Festuca spp. (0.53±0.016) and lowest for

Holcus spp. (0.01±0.001). In order of declining proportion the other proportions of the

other intermediate species were: Agrostis spp. (0.16±0.024), Anthoxanthum odoratum.

(0.12±0.023), Calluna vulgaris 0.12±0.015 and Plantago lanceolata (0.06±0.010)

(Figure 7.2).

162


Ag An Ho Fe Pl Ca

0.0

0.1

0.2

0.3

0.4

0.5

Species

Pro

porti

on c

ompo

sitio

n

osition (by %gDM) of the diets of Soay sheep in the study estimated using in faeces and vegetation samples with the use of a non-negative least

squares (NNLS) algorithm (detailed in Dove and Moore 1996). Ag=Agrostis spp., An=Anthoxanthum

0.86±0.01 bites/second and

Figure 7.2: Botanical compalkane/alkene concentrations

odoratum, Ho=Holcus spp., Fe=Festuca spp., Pl=Plantago lanceolata, Ca=Calluna vulgaris. The error bars represent ±1s.e.m.

7.4.3 Bite rate

The final ANCOVA model for bite rate revealed one significant first order interaction

(sex x hind leg length) and two main effects (sex and hind leg length). Bite rates of

females increased with hind leg length whereas the bite rates of males decreased

slightly. Furthermore, the intercepts for each sex were also different (Table 7.3and

Figure 7.3). Over the range of hind leg lengths included in the study females tended to

have a higher bite rate than males (means

0.70±0.01bites/second respectively.

163


Table 7.3: Summary of the ANCOVA model for the effects of sex and hind leg length (mm) on the bite rate (bites/second) of Soay sheep of both sexes. Terms Value Std Error t-Value P-value

Intercept 0.303 0.303 -0.308 0.760 Sex (Male) 0.055 0.055 1.906 0.064 Hind leg 0.086 0.086 1.564 0.126 Sex x Hind leg 0.824 0.020 -2.074 0.044

Hindleg length (mm)

Bite

rate

(bite

s/se

c)

0.6

0.8

1.0MaleFemale

165 170 175 180 185 190 195

Figure 7.3: The influence of hind leg length and sex on bite rate for Soay sheep on Hirta in summer 2000. The lines represent predictions from the ANCOVA model (Table 7.3).

7.4.4 Time allocation

There were no significant differences between treatment groups in the percentage time

allocation to feeding activities (ruminating and feeding: 86%) or other activities (14%).

Furthermore there was no correlation between body mass or skeletal size and these

measurements.

7.5 Discussion

The aim of this experiment was to determine the effects of parasitism on the foraging

behaviour of mammalian herbivores. This is important because, in theory, parasitism

164


has the potential to influence plant community composition and biomass, which, in turn,

will affect carrying capacity for the host animal and other grazers.

The first aim was to discover whether GI parasites reduced voluntary feed intake under

free-ranging conditions. The secondary aim was to determine what mechanisms were

involved in this – i.e. is bite rate/bite size reduced or is less time allocated to feeding?

The final aim was to establish if the observations from indoor (Coop and Holmes 1996)

and simple, highly controlled, outdoor trials (Cosgrove and Niezen 2000), that animals

compensate for infection by selecting higher quality (i.e. higher nitrogen content) food

stuffs occur in a more complex, non-agricultural situation.

Intake rate

The observed intake rates (mean 689gDM/day, range 443-1063gDM/day) were roughly

what would be expected from an animal of the size of the Soay sheep used in this study

(mean 24.02 kg, range 13.6-31.7 kg). For example, Scottish Blackface sheep, which are

heavier than Soay sheep consume about 1.2kgDM/day (Iason, Mantecon et al. 1999).

As might be expected, intake rate was weakly but positively correlated with body

weight. However, there was no significant effect of parasite burden on intake rate.

In the literature, there are frequent observations that voluntary intake rate is decreased

under GI parasitism, and reductions of 30-60% are common (Poppi, Sykes et al. 1990)

so the measurements made in this experiment are not consistent with these reports.

One possible explanation for the discrepancy between my results and the previous work

is related to animal condition. Previous work has shown a link between condition and

immune response (Halvorsen, Stien et al. 1999) and it seems likely that animals with a

poor resistance and resilience to GI nematode infection would suffer more than those

with a good resistance. Furthermore, several studies have shown that anorexia is dose

dependent, with higher parasite burdens causing a greater decrease in food intake (e.g.

with reindeer (Arneberg, Folstad et al. 1996), and sheep (Kyriazakis, Anderson et al.

1996)).

This experiment was carried out in the summer, when animals were approaching peak

physical condition. In the winter, when animals are in poorer condition, their immune

responses are likely to be weaker and, therefore, a similar parasite challenge is likely to

165


have a greater effect. It is, therefore, hypothesised that if the experiment were to be

repeated in the winter, with animals in poor condition, the reduction in intake would be

een in animals of all sizes and weights. Furthermore, it would be suspected that smaller

als would still be more affected than larger ones. However, for reasons of animal

welfare, the handling of the Soay sheep on Hirta is kept to a minimum over the winter,

nd a large-scale study would be difficult to justify.

aily intake is a function of bite rate, bite mass and time allocation, and bite mass is

made up of bite size (incisor arcade breadth), bite depth and the density of the grazed

tratum (Gordon and Illius 1988a, 1992). In this study, there was no effect of parasite

ce mouth size and sward conditions

presumably remained unchanged throughout the short duration study, it is likely that the

echanism for reduction in intake with decreasing body weight was a reduction in bite

depth.

Botanical composition

Previous studies have shown that animals may be able to detect the metabolic signals

associated with parasitism and respond by selecting higher quality food items to

mitigate the harmful nutritional effects caused by GI parasitism. For example, Cosgrove

and Niezen (2000) found that moderately parasitised lambs offered a Trifolium-Agrostis

sward selected more for the high protein Trifolium than lightly parasitised lambs (31%

vs. 24%). This, and other, studies that have shown this kind of response have all been

studied using simple sward mixtures in tightly controlled circumstances (e.g. Cosgrove

and Niezen 2000). Theoretically this alteration in selectivity could have implications for

plant community structure (Dobson and Crawley 1994).

However, in contrast with the simple mixtures offered in the studies mentioned above,

the pattern of plant availability for the Soay sheep in this study was much more

complex. The animals were free-ranging over a number of habitats ranging from high

quality Holcus-Agrostis sward to low quality wet heath/sphagnum bog (Crawley, Albon

et al. 2003). Even within the Holcus-Agrostis sward, which was favoured by the study

animals, there was a complex mix of species including 7 grass genera, 14 herb genera

and several bryophyte species, as well as a contrast between tussocks and gaps

(Crawley, Albon et al. 2003).

s

anim

a

D

s

treatment on bite rate or time allocation and, sin

m

166


With such a complex mixture it is perhaps not surprising that changes in selectivity

were not detected with the sample size employed. This result indicates that in such a

omponent analysis method to

determine intake rate and diet composition for a free-ranging ungulate. To my

ites reduce intake in a complex non-agricultural

environment and, therefore, fails to corroborate Grenfell’s (1988, 1992a) theoretical

. This is especially

problematical with wild herbivores or protected herbivores because the use of fistulae or

udy has shown that, with the use of

complex pasture any parasite-induced changes in selectivity are not likely to be large

enough to cause long term (or indeed detectible) changes in composition away from the

“equilibrium”-state, especially considering (1) the ability of plants already living in

grazed areas to recover from heavy grazing during the rapid-growth period in the spring

(Crawley, Albon et al. 2003) and (2) the decline in grazing pressure that occurs during

the population crashes that are caused by the reduction in available forage and heavy

parasite burdens.

This is, one of only a few uses of the plant cuticle wax c

knowledge the only other example is work by Bugalho and co-workers who studied the

foraging ecology of red deer in Portugal (Bugalho, Milne et al. 2001, Bugalho, Mayes

et al. 2002). However, some work has also been done on free-ranging hares (Lepus

timidus) (Hulbert, Iason et al. 2001) and rabbits (Oryctolagus cuniculus) (Martins,

Milne et al. 2002).

Conclusion

Theoretical models have shown that directly transmitted parasites have the potential to

influence plant abundance by reducing the food intake of their hosts This field

experiment failed to show that paras

work with empirical data.

The capture, dosing, and sample collection involved are highly labour intensive and

require a team of skilled field workers. One of the main problems with using this

technique has been the determination of diet composition

rumen sampling is inappropriate. However, this st

wax components, the accuracy of diet composition estimates using the NNLS-method

can produce satisfactory results.

The idea that sward composition may also be influenced by the parasite-induced

alteration of the feeding behaviour of their herbivore host is an intriguing one. However,

167


this sort of behavioural change has only been demonstrated for ungulate-GI parasite

systems in simple cases (e.g. Cosgrove and Niezen (2000)). The current study did not

find any evidence for a significant change in selectivity for any of the species consumed

and it, therefore, provides no experimental evidence that GI parasites could cause any

significant change in botanical composition of a complex sward in this manner.

168

Chapter 8 - General discussion

Chapter 8 : General discussion

169


General discussion The Soay sheep project, which has its roots in early work by Jewell and co-workers in

the 1960s (Jewell, Boyd et al. 1974), has provided many insights into the workings of

animal populations, using Soay sheep (Ovis aries L.) on Hirta (St. Kilda) as a model.

Research has included studies of the population dynamics of the sheep (e.g. Grenfell,

Wilson et al. 1998), of the relationship between the sheep and their parasites (e.g.

Gulland 1992, Gulland and Fox 1992), the behaviour of the sheep during the mating

vegetation

lly, Chapter 7 reported on a field

Dyke was both the most productive sward, and had the highest standing-crop biomass

season (e.g. Coltman, Bancroft et al. 1999, Preston, Stevenson et al. 2001, Preston,

Stevenson et al. 2003) and the molecular genetics of the population (e.g. Bancroft,

Pemberton et al. 1995, Pemberton, Coltman et al. 1999).

However, the relationship between the sheep and their food supply, the vegetation, has

been largely neglected, despite the fact that food supply (alongside parasite burden and

weather) has important implications for survival and fecundity. It was, therefore, the

aim of this thesis to address this deficiency with a combination of analysis of data

derived from long-term monitoring of the population and a field experiment.

It was first appropriate to assess the availability of vegetation across the study area

(Chapter 4), then to assess the way in which the sheep population uses the

communities, both in terms of offtake rates and botanical composition of the diet

(Chapter 4) and in terms of large-scale distribution patterns over the available

vegetation communities (Chapter 5).

Subsequently, Chapter 6, used a generalised linear modelling approach to focus on the

relationship between forage availability (and other factors) and juvenile survivorship, a

major determinant of population change. Fina

experiment designed to address the effect of gastrointestinal (GI) parasite burden and

condition on the rates of forage consumption by the Soay sheep.

Forage productivity, composition and availability

As might be expected, the formerly cultivated Holcus-Agrostis sward within the Head

of live grass and herbs throughout the year. In contrast, the heath and mire habitat had a

comparatively low productivity and standing-crop biomass of live grass and herbs. The

170


estimates of inbye annual above-ground net primary productivity (ANPP) were

comparable to other estimates from Scottish hill pasture (Common, Eadie et al. 1991).

Within the Head Dyke, ANPP fluctuated throughout the year, peaking during the rapid

growth phase (RGP), but was still apparent over the winter, albeit at a lower level.

Outside the Head Dyke the ANPP peaked in the summer. The fact that significant

ANPP still occurred in the winter (for the inbye) is interesting because it had previously

been assumed that over-winter production was negligible.

Although the Calluna vulgaris heath had the highest total standing-crop biomass it also

had the lowest productivity and a high proportion of the biomass was composed of

woody old-growth C. vulgaris. The estimation of outbye productivity proved to be

problematic due to the high degree of heterogeneity in the sward which inflated the

standard errors of the estimates. In order to address this problem the sample size should

s such as grasslands. Clearly further

work is required to address this issue.

Seasonality of Soay sheep diet

spring than in the summer, and Calluna vulgaris fragments were more

abundant in the summer than in the spring. These results were consistent with Milner

species tended to be selected for more frequently than they were avoided, while the

either be increased considerably or a different method should be used. Methods that

focus on individual shoots are an option. For example, the percentage of shoots browsed

or the amount of each shoot removed could be used to compare utilisation rates in

different areas of Calluna heath (Armstrong and MacDonald 1992). Unfortunately this

approach would not allow the comparison of offtake or production rates between

Calluna heath and other, non-heath, vegetation type

Chapter 4 also presented an assessment of the seasonality in the species composition of

the diet of Hirta’s Soay sheep. Dietary species composition was assessed using the

faecal plant cuticle analysis (FPCA) method (Sparks and Malechek 1968). The major

components were Festuca spp. and bryophytes in the spring and Festuca spp. and

Calluna vulgaris in the summer. Poa spp. and bryophytes were more abundant in the

diets in the

and Gwynne’s (1974) earlier work on the Soay sheep.

A comparison of the availability of the species with their abundance in the diet gave an

indication of selection/avoidance patterns (Figure 4.17, Page 89). In general, rare

171


opposite pattern was true for common species. Despite the fact that Holcus spp. and

Agrostis spp. make up a large proportion of sward biomass (Crawley, unpublished data)

and are relatively preferred in some Scottish grasslands (King and Nicholson 1964),

t in the diet in relatively high proportion is

probably because bryophytes are relatively indigestible and easily fragmented in

Moore

1996) are a step in the right direction, and were used successfully in this thesis (Chapter

ost favourable habitat patches (Bailey, Gross et al. 1996, Cezilly

and Benhamou 1996). One of the major models that has been used to describe such

als are equal in foraging ability, have an omniscient

knowledge of their habitat and are free to move at negligible cost.

they were under-represented in the diet. The bryophytes have negligible nutritional

value and are probably not intentionally ingested. Furthermore, the sensitivity of the

analysis to the precise area that is chosen from which to assess the availability of

species, highlighted the importance of spatial scale in the assessment of selection

patterns. This issue became more apparent with the analysis of large-scale distribution

patterns in Chapter 5.

The accuracy of the FPCA technique relies on several assumptions (see section 4.3.3).

The two major problems are the unequal digestibility and unequal fragmentation of the

plant species during consumption, digestion and sample preparation. For example, the

fact that bryophytes seem to be presen

comparison with other dietary components.

In future studies, these issues could be addressed by applying correction factors to the

measurements, but the determination of correction factors can be problematical in itself

(Leslie, Vavra et al. 1983). New methods that rely on the differences in the wax

components of plant cuticles (Dove 1992, Dove and Mayes 1996, Dove and

7). However, they have their own limitations, not least the requirement of sophisticated

laboratory equipment.

Large-scale distribution patterns

The spatial distribution of animals is often regarded as being driven by a need to

maximise fitness (e.g. Fretwell and Lucas 1970). Animals are, therefore, expected to

aggregate within the m

distributions is the ideal free distribution (IFD: Fretwell and Lucas 1970) which

assumes that the individu

172


To assess these kinds of models, workers often use indices of selectivity in order to

assess the relative preference for different patches or vegetation types (Crawley 1983).

However, the spatial areas that are included in the analyses are often chosen arbitrarily

rather than for any biological reason. To address these issues, Chapter 5 used location

and habitat quality data to assess habitat selectivity and matching3 for the sheep using

the study area. The impact of the spatial scale (over which the habitat conditions were

assessed) on the outcome of the analyses was also considered.

The on for the

different vegetation types that were caused by differences in quality between the

ore, currencies). These problems are

highly non-random distribution of the sheep reflected differential selecti

swards. However, the degree of selectivity for particular vegetation types was largely

dependent on the spatial scale used. However, certain scale independent, qualitative

patterns were apparent. As might be expected, the selection was greatest for the

previously cultivated and high quality Holcus-Agrostis swards that dominate the area

within the Head Dyke, the maritime Festuca-Plantago swards and Agrostis-Festuca

swards. The four least favoured swards were consistently wet heath, Calluna heath, dry

heath and Molinia dominated grassland. These patterns are consistent with the results of

earlier qualitative work on Soay sheep by Milner and Gwynne (1974), and are similar to

Hunter’s (1962) observations of domesticated sheep in south-east Scotland.

The matching index indicated that the distribution did not come close to satisfying the

predictions of the IFD at any of the spatial scales that were considered. However, the

distribution was significantly closer to the predictions of the IFD when the area under

consideration was defined using a biologically meaningful method, such as hierarchical

cluster analysis followed by the drawing of minimum area convex polygons, rather than

when it was defined arbitrarily by the study area.

The discrepancies between the observed distributions and the predictions of the IFD

could be explained by inadequacies in the IFD model as applied to grazing herbivores.

The IFD is a carnivore-centric model and deals with a currency of discrete prey items.

Therefore, it is not entirely appropriate for the analysis of grazing herbivores that have

complex diets made up of many species (and, theref

zero. (see section 5.3.5 and Earn and Johnstone (1997)).

3 Habitat matching is the difference between the proportion of food available in a patch and the proportion of organisms occupying the patch and is a measure of how well the distribution of the sheep matches the theoretical predictions of the IFD. Thus perfect matching with the IFD occurs if the difference is equal to

173


further reflected in the discrepancies in matching indices between vegetation

communities. Although, in the analyses, the currency of biomass of “quality items” was

used, these weights mean different things in different areas because the communities

differ in species composition. For example, in the Holcus-Agrostis community, the

quality items are mainly the grasses, Festuca and Poa, whereas in the wet-, dry-, and

Calluna heath communities the quality items are primarily new-growth Calluna

vulgaris. A currency of total nitrogen could be used, but the effects of other variables

such as rate of primary production, sward height/structure, and the interactions between

these problems with the IFD model, it remains clear from Chapter 5 that the

spatial scale at which measurements are made can have a significant impact on the

de of analyses of both habitat selectivity and

is is

potentially influenced by characteristics inherited from the parents, by the amount of

species may also be important. Another important factor that violates an assumption of

the IFD is that all individuals are not equal in their foraging ability (Humphries, Ruxton

et al. 2001). For example, bite size (Gordon and Illius 1988a), intake rate (see Chapter

7) and diet composition/selective ability (Gordon and Illius 1988a, b), and thus

competitive ability, all vary between individuals.

Despite

outcome and interpretations that are ma

matching to the IFD. The issue of the selection of appropriate spatial scales in the

analysis of distribution patterns is, therefore, an important one and should not be

overlooked.

Juvenile survival: maternal provisioning and environmental factors

Often, one of the most important regulatory mechanism affecting wild animal

populations is juvenile survival (Dobson and Oli 2001, Oli and Dobson 2003). Th

maternal provisioning (Keech, Bowyer et al. 2000) and by environmental factors such

as weather severity and food availability (Forchhammer, Clutton-Brock et al. 2001).

Chapter 6 used life-history, vegetation and weather data to explore the effect of the

latter two.

Maternal resource provisioning occurs during gestation and during suckling and may

have long term effects upon survival and future breeding success (Festa-Bianchet and

Jorgenson 1998, Reale, Bousses et al. 1999, Andersen, Gaillard et al. 2000).

Environmental influences operate via forage availability and via weather severity,

174


which affects both thermoregulation (e.g. Owen-Smith 1998) and the time available for

foraging (Champion, Rutter et al. 1994).

In agreement with previous work on ungulates (Clutton-Brock, Price et al. 1992,

Robertson, Hiraiwahasegawa et al. 1992, Clarke, Yakubu et al. 1997, Clutton-Brock,

Wilson et al. 1997, Andersen, Gaillard et al. 2000, Keech, Bowyer et al. 2000), Chapter

6 showed that lamb birth weight was influenced by both maternal condition and forage

ost of the lambs that died did so during weaning underlines the

portance of this life-history stage. Birth weight was one of the most important factors

in determining early survival. This compares well with other studies, which also found

to be of prime importance (Clutton-Brock, Price et al. 1992, Perez-Razo,

1) It could indicate the inheritance of an effective immune function from the

mother;

availability. Maternal condition is likely to affect the availability of resources for

transfer to the developing foetus and there is good evidence that nutrition during early

foetal development can affect foetal growth trajectories and size at birth (Robinson

1996, Robinson, Sinclair et al. 1999, Robinson, McEvoy et al. 2000). Thus, ewes in

poor condition would have fewer resources available for the developing foetus, which

would, as a result, grow more slowly and attain a lower birth weight than would be the

case if the ewe were in good condition.

The fact that m

im

birth weight

Sanchez et al. 1998, Neuhaus 2000). However, the effect of birth weight was found to

be more important when resources were scarce than when they were abundant.

Maternal condition also interacted with population density to influence survival to

weaning so that maternal condition was more important when resources were scarce.

This effect was probably due to the effect of maternal condition upon lactation. Milk

production is correlated with female condition, with females in poor condition

producing less, and possibly poorer quality milk than those in superior condition

(Bencini and Pulina 1997). Furthermore, the interaction indicates that at high population

densities, when resources were scarce, the quality of maternal provisioning became

even more important.

The strong negative effect of maternal parasite burden on offspring survival could

indicate one of two things:

175


Immune system function is, to some extent, an inherited trait (Iraqi, Behnke et al. 2003),

and ewes with poor immune function (and, therefore, heavy parasite burdens) are likely

produce offspring that also have weak immune function and that are, therefore, less

pport of this hypothesis, recent work has demonstrated an

association between elevated parasite burden and both the homozygosity of the major

istocompatibility complex (MHC) and decreased over-winter survival probability

(Paterson, Wilson et al. 1998, Coltman, Pilkington et al. 1999). Or;

2) it could be the result of a selfish maternal resource allocation strategy.

In other words, an effect of maternal parasite burden would also be apparent if heavily

selfish strategy of allocating a greater proportion of their

nite resources to resisting infection rather than to the care of their offspring (lactation)

would adopt a similar

strategy when under attack from parasites. However, this view is at odds with that of

Coop and Kyriazakis (1999b) who state that “the function of growth, pregnancy and

lactation are prioritised over the expression of immunity”.

Weather severity also influenced survival. The North Atlantic Oscillation (NAO) index

was more successful in explaining variation in survival than were the univariate weather

parameters. This is puzzling given the correlation that exists between NAO and weather

severity in Northern Europe (Hurrell 1995, Wilby, O'Hare et al. 1997). Previous

workers have shown a large effect of NAO on over-winter survival in northern

ungulates (Forchhammer, Stenseth et al. 1998, Milner, Elston et al. 1999, Catchpole,

Morgan et al. 2000, Forchhammer, Clutton-Brock et al. 2001) so an effect on juvenile

survival was to be expected. Thus, in the analysis presented in Chapter 6, the effect size

appeared to be lower for juvenile survival than for those for over-winter survival

presented in the above papers. This may be explained by the fact that the effects of the

weather are buffered because juveniles can remain relatively sheltered while suckling,

whereas adults must forage in more exposed areas.

The lack of effect of univariate weather parameters on survival (except for the effect of

sleet/snow days) is probably indicative of poor quality weather data rather than a real

to

likely to survive. In su

h

parasitised ewes adopted the

fi

than non-parasitised ewes. Festa-Bianchet and Jorgenson (1998) have presented

empirical evidence suggesting that ewes adopt a selfish strategy when resources are

scarce and the lamb is in utero. It is, therefore, likely that they

176


1

on

77

stra

te that GI parasites

lack of effect. The co les between Benbecula and Rum

( le 6.3) ere poor for many of the variables and thus the weather correlations

between Benbecula and ikely to be poor. This chapter, therefore,

m s it cle ta from St. Kilda itself must be a priority.

Although autom ic weather stations were installed in late-1999 and 2000, several years

of the kind presented in Chapter 6

would be successful.

There was was influenced by forage availability

independently source competition (for

available fo e) and interference competition may be operating on St. Kilda. The latter

m creased vigilance for

com etitors that accom nsities. Further behavioural work on the

frequency and type of interactions between animals are required to investigate this

point.

Parasite burden and foraging behaviour

P ites ha a ran f bot hys and behavioural effects on their hosts. A

common behavioural ef anorexia (PIA) (Kyriazakis, Tolkamp et

al. 998). Th t GI parasites can potentially influence

herbivore population dynam cs b i reasing mortality rates (Grenfell 1988, Grenfell

1992a). Addition indicate that PIA could have also have a

s t effe herbivore dynamics without

necessarily altering herbivore population density (Grenfell 1988, Grenfell 1992a).

Chapter 7 used an experimental approach in an attempt to determine whether the Soay

s hib he selectivity is also altered.

The n-alkan position

( d Ma 996). Overall, intake rate increased with

body weight and was consistent with expectations. There was, however, neither

evidence of PIA nor of any parasite-induced changes in diet selectivity, both of which

have been demonstrated in domesticated sheep (Symons 1985, Coop and Holmes 1996,

Kyriazakis, Tolkam al. 1998, Thamsborg and Agergaard 2002). Thus, Chapter 7

provided no em o d can influence plant-

rrelations of the weather variab

Tab

ake

local data

ay take th

p

aras

1

ignifican

heep ex

Dove an

w

St. Kilda are also l

ar tha

at

t obtaining local weather da

will be required before further analyses of

clear evidence that survival

of population density, thus indicating that both re

rag

e form of increased aggressive/sexual behaviour or in

pany high population de

ve ge o h p iological

fect is parasite induced

eoretical work

i

has s

y

how

nc

n tha

ally, mathematical models

ct on grazing pressure and, therefore, plant-

it PIA and, if t y do, to ascertain whether diet

e technique was used to estimate food intake rates and diet com

yes 1991, Dove and Mayes 1

p

iric

et

al p support t em


178

herbivore dynamics by affecting grazing pressure independent of population density

(sensu Grenfell 1988, Grenfell 1992a). However, this work was carried out when the

sheep were approaching peak physical condition, and parasites are known to have a

greater effect on immuno-compromised hosts that are in poor condition (Coop and

H mes 1996). Therefore, if the experiment were to be repeated in the winter, the

treatment effect is likely to be larger and thus easier to detect.

In hindsight it was perhaps foolhardy to imagine that such effects could be detected

with the approach, and sample sizes, used for this study. A power analysis suggests that

with the sample sizes employed, and the estimated variance, a difference between

t tment groups of ±30% would be the best that could be detected with α=0.05.

Nevertheless, this work is one of the few studies that have used the n-alkane technique

in a free-ranging animal. (see also, red deer (Bugalho, Milne et al. 2001, Bugalho,

Mayes et al. 2002), hares (Hulbert, Iason et al. 2001) and rabbits (Martins, Milne et al.

2002)).

Conclusion

By providing valuable insights into the interactions between the sheep and their food

source on St. Kilda, this thesis has, to some extent, filled the gap that has existed in the

research carried out so far on St. Kilda. It has provided estimates of forage availability,

productivity and offtake within the study area, and has shown that forage availability

plays an important role in influencing birth weight and juvenile survival of Soay lambs

independently of population density. It has also provided descriptions of the distribution

patterns of the sheep in relation to the plant communities represented within the study

area and has highlighted the importance of spatial scale in the assessment of these

patterns. Furthermore, it has provided estimates of the intake rate and species

composition of the diet of adult sheep.

The interaction between Soay sheep and their food supply is, alongside the interaction

with their parasites, integral to understanding the dynamics of this apparently simple,

but actually distinctly subtle model system.

ol

rea

Appendix

179

Appendix: The plant cuticle wax component concentrations of

species available on St. Kilda in August 2001.

Appendix 1: The plant cuticle wax component concentrations of

length of the component. b=an alkene, ba=a sterol.

Species C

180

species available on St. Kilda in August 2001. The numbers refer to the chain

21 C23 C24 C25 C26 C27 C28 C29 C30b C30 C31 C32b C32 C33b C33 C35 C36 C37 C30ba C32ba C33ba Agrostis canina 4.4 9.1 10.3 14.1 8.5 16.3 8.7 60.8 19.5 6.5 107.7 16.4 3.7 1.9 27.6 8.3 2.4 0.0 297.0 249.2 29.6

Anthoxanthum odoratum 2.4 14.8 10.4 16.9 8.6 29.2 9.0 40.0 1.1 5.8 59.6 1.8 4.1 0.0 42.2 21.3 4.1 0.0 17.1 27.0 0.0

Deschampsia flexuosa 4.9 12.5 22.1 23.7 21.4 41.7 30.3 199.2 1.4 16.5 293.7 0.0 8.3 0.0 57.7 3.1 5.2 0.0 21.4 0.0 0.0

Molinia caerulea 1.9 8.6 12.5 24.8 15.2 45.6 24.5 64.7 0.0 7.6 33.7 0.0 2.4 0.0 12.3 2.5 1.8 0.0 0.0 0.0 0.0

Nardus stricta 0.0 6.4 10.6 9.5 7.0 17.0 7.8 100.6 0.0 9.0 184.6 1.2 9.1 5.6 86.6 4.1 0.0 0.0 0.0 18.6 85.8

Holcus lanatus 6.5 20.7 18.7 27.0 17.7 28.8 14.2 23.4 0.9 15.0 19.4 0.9 9.2 0.0 8.0 2.9 3.2 0.0 13.9 13.7 0.0

Poa spp. 3.8 9.0 15.0 15.8 12.7 19.1 10.9 160.0 2.1 9.5 393.2 2.3 6.5 0.0 152.8 15.3 2.3 0.0 32.2 35.1 0.0

Festuca rubra 3.4 9.9 15.0 17.4 13.5 24.6 11.2 224.7 1.5 13.7 577.2 1.3 8.6 0.0 140.6 8.2 0.0 0.0 23.1 19.1 0.0

Lolium perenne 2.2 9.1 14.4 29.8 17.7 42.0 16.3 86.2 1.7 14.1 150.4 0.0 9.7 0.0 114.2 19.2 1.7 0.8 25.7 0.0 0.0

Anagalis tenella 1.7 8.4 15.2 12.8 11.9 17.2 11.7 45.2 1.7 23.8 747.0 1.1 33.9 0.0 85.9 4.0 0.0 0.9 26.3 16.8 0.0

Armeria maritima 4.0 7.9 13.4 12.2 11.3 50.2 29.6 164.6 1.2 24.5 315.7 0.8 18.4 1.6 113.1 14.8 0.0 1.4 18.4 12.7 25.0

Leontodon autumnalis 2.4 11.2 19.1 16.4 16.0 15.7 13.8 25.6 0.9 10.2 51.2 0.8 6.2 0.0 22.8 5.5 0.0 1.5 14.1 12.4 0.0

Thymus spp. 2.6 6.9 9.7 6.5 6.8 19.4 18.0 227.8 5.7 34.4 301.1 13.3 52.4 4.5 249.6 14.5 1.9 1.6 86.8 202.2 68.6

Cerastium fontanum 2.0 6.2 10.2 7.7 8.6 12.4 13.2 56.3 1.1 11.9 73.6 3.6 3.5 2.2 7.9 2.2 2.2 0.0 16.1 54.7 33.1

Viola riviniana 1.8 8.9 13.6 10.3 8.3 10.0 8.5 23.2 0.0 5.6 39.2 0.0 3.2 0.0 9.5 2.1 1.9 0.0 0.0 0.0 0.0

Ranunculus acris 1.8 5.1 9.1 10.7 8.2 12.2 8.6 31.9 0.9 5.4 75.3 0.0 3.9 1.2 47.8 6.3 2.0 1.0 13.8 0.0 18.6

Rumex acetosa 2.5 12.7 20.7 23.2 24.9 24.6 23.3 60.9 2.0 17.3 79.5 0.0 5.8 1.1 8.2 3.3 2.5 0.0 30.0 0.0 17.1

Plantago maritima 6.3 6.7 9.2 12.6 10.2 196.1 109.4 450.8 1.5 36.6 500.2 0.0 27.2 0.9 150.6 15.9 1.9 1.7 22.6 0.0 14.3

Trichophorum caespitosum 3.2 5.1 6.3 7.0 5.4 14.0 5.4 40.5 0.0 2.6 30.6 0.0 2.1 1.6 9.5 1.8 0.0 0.0 0.0 0.0 24.6

Luzula spp. 2.2 12.2 17.6 16.8 13.8 28.9 15.4 122.4 0.0 17.7 482.7 0.0 13.1 0.0 298.5 35.5 2.2 1.4 0.0 0.0 0.0

Sagina procumbens 0.0 8.2 15.6 16.8 17.2 23.1 16.7 48.8 2.3 11.6 71.7 1.5 6.6 1.8 20.2 3.9 2.3 0.0 35.1 22.9 27.3

Potentilla erecta 2.7 64.4 15.5 22.4 14.4 27.1 14.2 50.9 1.0 15.1 138.0 1.9 17.3 5.8 89.1 23.3 2.6 0.9 15.3 29.6 87.7

Plantago coronopus 2.0 6.0 10.7 13.6 12.6 26.5 17.0 144.5 4.7 38.3 327.7 2.2 22.7 2.1 40.4 5.5 1.7 1.2 71.9 33.3 32.1

Narthecium ossifragum 3.1 120.4 21.6 26.5 17.2 30.9 13.8 25.9 1.3 7.7 26.7 0.0 4.8 1.5 15.9 3.4 1.6 0.8 19.4 0.0 23.2

Species C

181

21 C23 C24 C25 C26 C27 C28 C29 C30b C30 C31 C32b C32 C33b C33 C35 C36 C37 C30ba C32ba C33ba Plantago lanceolata 2.7 7.8 8.8 17.5 9.2 22.0 12.5 78.5 0.7 20.9 254.5 1.8 28.4 10.2 164.1 45.8 3.8 21.7 11.2 27.4 154.6

Erica cinerea 4.0 9.4 13.6 15.7 12.3 38.3 14.2 162.4 0.0 34.4 1249.4 1.1 75.9 1.2 681.7 5.6 0.0 0.9 0.0 16.6 18.5

Carex 3.5 19.3 28.1 27.1 16.6 55.3 26.1 291.0 0.8 19.8 347.3 0.0 8.6 0.0 98.3 6.0 1.9 0.0 12.3 0.0 0.0

Bryophyte 1.7 5.3 9.7 10.9 8.9 22.4 9.5 33.9 0.9 6.7 56.3 1.4 5.5 70.4 29.7 4.7 2.6 1.0 13.9 21.5 1071.4

Trifolium repens 0.0 9.6 16.7 22.8 2.7 26.5 20.5 39.6 2.7 12.2 31.9 1.1 6.6 2.1 8.1 2.6 2.3 0.0 40.9 16.1 31.5

Calluna vulgaris 6.4 58.3 12.6 337.9 25.0 924.2 34.5 733.3 0.7 43.8 1614.2 2.6 96.5 0.9 1108.9 35.9 2.3 2.6 10.8 39.1 14.4

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