SMALL MAMMAL HERBIVORY AND PLANT RECRUITMENT IN GRASSLAND

by

Philip Eric Hulme

A thesis submitted for the degree of Doctor of Philosophy of the University of London and for the Diploma of Imperial College.

Department of Pure and Applied Biology, Imperial College at Silwood Park, Ascot. Berkshire. SL5 7PY March 1990 ABSTRACT

A monthly live-trapping study of small mammals in two contrasting grassland sites was used as a baseline for field experiments aimed at elucidating the role of herbivorous rodents in grassland plant recruitment. Additionally, invertebrate herbivory was studied to gauge the impact of rodent herbivory relative to other sources of plant mortality. The field experiments, using herbivore exclosures, incorporated both short term manipulative studies examining seed and seedling predation and longer term perturbations investigating plant survivorship. Twenty one species of grassland plants were investigated.

Small mammals, in particularApodemus sylvaticus, were the major seed predators in both sites. Rodent seed predation was density dependent, though the degree of density dependence was a function of seed size. Seed burial reduced predation and provided more effective protection to small rather than large seeds. Variations in rodent seed removal between sites and seasons were associated with differences in the abundance of alternative foods. Rodents and molluscs were the major predators of seedlings in both sites. Rodents selected seedlings with respect to their size. The damage inflicted by small mammals on seedlings was severe, often leading to mortality, whereas molluscs more frequently lightly grazed seedlings. Different plant species preferences exhibited by molluscs and rodents implied that their effects on plant recruitment in grasslands would be additive. Rodents significantly reduced the survivorship of a wide range of plant species. Plant survivorship patterns were consistent with the findings of seed and seedling predation studies. No evidence suggested that plants had the potential to compensate for rodent herbivory through increased growth of the remaining plants. Rodents may not only have proximate but also ultimate influences on patterns of plant recruitment, in particular regarding seed germination strategies. Both experimental and theoretical evidence suggest small mammals may play a significant role in grassland vegetational diversity.

2 ACKNOWLEDGEMENTS

I would like to thank my supervisor, Dr M. J Crawley, not only for his help and encouragement during the course of this study, but also for his infectious enthusiasm regarding ecological problems. I am indebted to Professor M. P. Hassell for allowing me the use of the facilities at Silwood Park and for providing me the opportunity to rise to the challenges of both research and teaching. Although many staff were involved in the execution of the work described herein, two are worthy of particular mention. Ted Green is thanked for his resourcefulness, wit and down to earth advice that placed my work in a practical perspective. Ed Woloszyn is acknowledged for his unparalleled ability to access the most obscure publications and for persevering in improving my lay-up shots. My years at Silwood were made memorable by the many good friends found there. They all directly or indirectly helped in the production of this thesis. To name them all would require a list of biblical proportions and to name only a few, an injustice to those omitted. Nevertheless, the friendship I found during those halcyon days at the Farm, in particular will never be forgotten. Finally, I acknowledge the person that has meant most to me these last few years, Elena. Her support, faith and good humour have been invaluable to me and much less would have been achieved without her. This thesis is most affectionately dedicated to Elena. jSolo tu sabes por qu£!

3 TABLE OF CONTENTS PAGE Abstract 2 Acknowledgements 3 List of Tables 10 List of Figures 12 Chapter I: Introduction 14 1.1 Ecological Theory and the regulation of plant populations 14 1.2 The diversity of herbivores and the range of species studied 15 1.3 Phytocentricity, zoocentricity and the intergrated approach 19 1.4 Problems in the analysis of vertebrate herbivory 20 1.4.1 Monitoring natural levels of herbivory 20 1.4.2 Dietary studies 21 1.4.3 Artificial clipping 21 1.4.4 Exclosures 22 1.4.5 Enclosures 25 1.5 Methodology and aims of study 26

Chapter II: The estimation of small mammal abundance 30 2.1 Introduction 30 2.1.1 Herbivore demography and plant-animal interactions 30 2.1.2 Problems in the study of small mammals 31 2.1.3 Small mammal populations at Silwood Park 32 2.1.4 Estimating small mammal abundance 33 2.2 Methods and materials 35

4 2.2.1 Description of experimental sites 35 2.2.2 Design of trapping study 38 2.2.3 Trapping programme 40 2.3 Results 41 2.3.1 Small mammal species composition within the grassland and meadow sites 41

2.3.2 Determination of bias in CMR estimates of A.sylvaticus abundance 43 2.3.3 Fitting the most suitable population model 58 2.4 Discussion 60

Chapter III Temporal dynamics of grassland small mammals 62 3.1 Introduction 62 3.2 Methods and materials 62

3.2.1 Estimation of Asylvaticus density 63 3.2.2 Calculation of population parameters 68 3.3 Results 68 3.3.1 Population density 68 3.3.2 Temporal fluctuations 68

3.3.3 Demography ofA. sylvaticus in grassland 69 3.3.4 Comparison with other habitats 76 3.4 Discussion 78 3.4.1 Between habitat variation in population density 78

3.4.2 Demography of A.sylvaticus in grassland 80

3.4.3 The population dynamics of A.sylvaticus in different habitats 86 3.4.4 The demography of other small mammals in grassland 88

5 Chapter IV: Spatial dynamics of Apodemus sylvaticus 90 4.1 Introduction 90 4.2 Methods and materials 91 4.2.1 Measurement of spatial dispersion 91 4.2.2 Assessment of the influence of habitat structural heterogeneity 92 4.2.3 Assessment of the influence of food supply 93 4.2.4 Assessment of the role of small mammal interspecific association 93 4.2.5 A comparative measure of behavioural and environmental influences 93 4.3 Results 94 4.3.1 Dispersion 94 4.3.2 Habitat structure 99 4.3.3 Food supply 105 4.3.4 Interspecific associations 106 4.3.5 Behaviour and environment 106 4.4 Discussion 106

4.4.1 Patterns ofA. sylvaticus dispersion 106 4.4.2 The role of habitat structure 110 4.4.3 The role of food 115 4.4.4 The influence of other small mammal species 116 4.4.5 The relative roles of behaviour and environment 117

Chapter V: Small mammal seed predation in grassland 123 5.1 Introduction 123 5.2 Methods and materials 129 5.2.1 Choice of seed species 129

6 5.2.2 Experimental design 132 5.2.3 Experimental procedure 139 5.3 Results 142 5.3.1 The impact of different seed predator classes on surface seed numbers 143 5.3.2 Factors influencing rodent surface seed predation 145 5.3.3 Spatial variation in surface seed removal by small mammals 147 5.3.4 Response of small mammal seed predators to changes in seed density 150 5.3.5 The influence of season on small mammal seed predation 151 5.3.6 Plant species variation in small mammal surface seed predation 152 5.3.7 The effect of seed burial on seed removed by small mammals 158 5.4 Discussion 161 5.4.1 Factors influencing small mammal seed foraging in grassland 161 5.4.2 The fate of seed removed by small mammals: caching or consumption? 168 5.4.3 An assessment of the impact of small mammals on grassland seed populations 170 5.4.4 Small mammal seed predation and plant population dynamics 179

Chapter VI: Small mammal seedling predation in grassland 184 6.1 Introduction 184 6.2 Methods and materials 187 6.2.1 Experimental procedure 188 6.3. Results 190 6.3.1 The frequency of seedling damage by different seedling predator classes 190 6.3.2 Spatial variation in frequency of seedling damage by mollusc and small 192 mammals

7 6.3.3 The influence of season on frequency of seedling damage by molluscs and 192 small mammals 6.3.4 Plant species variation in mollusc and small mammal seedling predation 195 6.3.5 The severity of damage to seedlings caused by molluscs and small mammals 197 6.4 Discussion 200 6.4.1 A comparison of mollusc and small mammal seedling predation 200 6.4.2 The impact of small mammals on grassland seedling populations 205 6.4.3 A comparison of small mammal seed and seedling predation: implications for 208 plant recruitment in grassland

Chapter VII: Plant growth and survivorship in relation to small mammal herbivory 214 7.1 Introduction 214 7.2 Methods and materials 216 7.2.1 Experimental design 217 7.2.2 Experimental procedure 217 7.2.3 Controlling for cage effects 218 7.3 Results 219 7.3.1 Between treatment variation in plant species survivorship 220 7.3.2 Between treatment variation in plant species biomass 225 7.3.3 Within population variation in plant size 225 7.3.4 The influence of cages on plant survivorship 227 7.4 Discussion 228 7.4.1 Small mammal herbivory and plant survivorship 228 7.4.2 Predicting patterns of plant survivorship from seed and seedling predation 230 studies

8 Chapter VIII: Small mammal herbivory and plant recruitment in grassland 233 8.1 The impact of small mammal seed and seedling predators 233 8.2 The role of small mammal seed and seedling predators in grassland communities 237

Chapter IX: General conclusions 242

References 245

9 LIST OF TABLES PAGE Table 1.1 Frequency of citation of various mammalian orders in reviews of herbivory 16 Table 1.2 Roles of small mammals in structuring natural communities 18 Table 2.1 Small mammals previously found at Silwood Park 32 Table 2.2 Plant species composition of experimental sites 37

Table 2.3 Recapture data forA. sylvaticus 44

Table 3.1 Population density and dispersion statistics for A.sylvaticus 66

Table 3.2 Densities of A. sylvaticus previously recorded in grasslands 67

Table 3.3 Population data forA. sylvaticus over six seasonal periods 70

Table 4.1 Taylor’s Power Law statistics for whole populationsA. of sylvaticus 98

Table 4.2 Taylor’s Power Law statistics for sex subgroupsA. of sylvaticus 98 Table 4.3 Comparison of vegetation cover parameters 100 Table 4.4 Regression statistics of rodent dispersion against habitat variables 103 Table 4.5 Seed rain and rodent dispersion statistics 104 Table 4.6 Small mammal interspecific spatial association 105 Table 5.1 Review of previous rodent seed predation studies 124 Table 5.2 List of seed species used in predation experiments 130 Table 5.3 Results of analysis of deviance 146 Table 5.4 Correlations between rodent abundance and seed removal 150 Table 5.5 Plant species patterns of rodent seed removal 154 Table 5.6 Correlations between seed weight and rodent seed removal 155 Table 5.7 Correlations in rodent seed species preference in different treatments 155 Table 5.8 Correlations between seed weight and buried seed removal by rodents 161 Table 6.1 Review of previous rodent seedling predation studies 185

10 Table 6.2 Plant species variation in seedling loss due to molluscs and small mammals 194 Table 6.3 Relationship between seed and seedling loss due to rodents 209 Table 6.4 Mechanisms of plant escape from seed and seedling predation 212 Table 7.1 Review of previous studies of rodent influences on plant survivorship 215 Table 7.2 Treatment differences in mean number of plants 221 Table 7.3 Treatment differences in mean plant biomass of plants 224 Table 7.4 Treatment differences in size variability of single plant populations 225 Table 7.5 Treatment differences in mean number of tillers produced by grasses 226 Table 7.6 Patterns of survivorship and biomass in cage controls 227

11 LIST OF FIGURES PAGE Figure 2.1 Structural profile of vegetation in the experimental sites 36 Figure 2.2 Temporal pattern of small mammal captures 42

Figure 2.3 Length of absence ofA. sylvaticus from trapping grids 45

Figure 2.4 Distances moved by A. sylvaticus between successive captures 47

Figure 2.5 Heterogeneity inA. sylvaticus capture frequency 49

Figure 2.6 Known length of life ofA. sylvaticus 51

Figure 2.7 Seasonal patterns in A.sylvaticus trappability 53

Figure 2.8 Within sample capture characteristics A.of sylvaticus 55

Figure 2.9 Between sample capture characteristics A.of sylvaticus 57

Figure 2.10 Comparison of numbersA. of sylvaticus caught and model estimates 59

Figure 3.1 Estimated population size A.of sylvaticus 68

Figure 3.2 Monthly variation inA. sylvaticus demographic parameters 72

Figure 3.3 Residency ofA. sylvaticus in crash and non crash years 75

Figure 3.4 Annual dynamics ofA. sylvaticus in grassland habitats 77

Figure 4.1 Seasonal patterns ofA. sylvaticus dispersion 95

Figure 4.2 Taylor’s Power plots forA. sylvaticus 97

Figure 4.3 Relation between vegetation cover andA. sylvaticus abundance 101 Figure 4.4 Relation between mean aggregation size and population density 107 Figure 5.1 Experimental design 136 Figure 5.2 Experimental procedure 139 Figure 5.3 Seed removal attributable to various seed predator classes 144 Figure 5.4 Spatial variation in rodent seed removal 148 Figure 5.5 Temporal variation in rodent seed removal 149

12 Figure 5.6 Rodent predation at different seed densities 151 Figure 5.7 Seasonal variation in rodent seed predation 152 Figure 5.8 Seed size and rodent seed predation 156 Figure 5.9 Rodent predation of buried seed 159 Figure 5.10 Seed size and rodent exploitation of buried seed 160 Figure 5.11 Seed detection and selection by rodents 161 Figure 5.12 Seed bank type and impact of rodent seed predation 178 Figure 6.1 Seedling losses attributable to various guilds of seedling predators 189 Figure 6.2 Spatial variation in seedling loss 191 Figure 6.3 Seedling weight and rodent seedling predation 196 Figure 6.4 Damage characteristics of mollusc and rodent seedling predation 198 Figure 6.5 Frequency of seedling attack and damage by molluscs 199 Figure 6.6 Germination strategy and impact of rodent seedling predation 211 Figure 7.1 Impact of molluscs and small mammals on plant species survivorship 223 Figure 7.2 Germination strategy and impact of rodents on plant survivorship 231

13 CHAPTER I INTRODUCTION 1.1 Ecological Theory and the regulation of plant populations The determination of factors which regulate plant populations and are responsible for the patterns, abundances and distributions of species is central to our understanding of botanical communities. Hairston, Smith and Slobodkin (1960) and Slobodkin, Smith and Hairston (1967), who compared the regulation of various trophic levels, maintained that abiotic conditions exercise the main limitations upon plant numbers and biomass. These abiotic conditions were said to operate either as density independent factors determining the carrying capacity or as density dependent factors influencing intraspecific competition for limiting resources. In addition, it was proposed that populations of herbivores would be limited by predators and unfavourable abiotic circumstances rather than by food (Andrewartha & Birch, 1954; Hairston et al„ 1960). Hairston’s view received much criticism with regard to its assumptions, consistency, methodology and its lack of evidence from natural situations (Murdoch, 1966; Ehrlich & Birch, 1967). Harper (1977) appeared to resolve this controversy by discussing it in the light of confusion between ultimate and proximate factors. While not denying the possibility of resource limitation influencing plant populations he accepted that their "character may be determined by subtle co-adapted interactions in which predators and parasites play a major part", a conclusion confirmed by Crawley (1983). However, Schoener (1983) and Connell (1983) surveyed existing field studies on competition and felt able to conclude that these supported Hairston’s hypothesis. Fretwell (1987) re-affirmed the view of Hairstonet al. (1960) and further developed the ideas, restricting the influence of herbivores on plant biomass to communities of simple trophic structure. Such a viewpoint may be criticised due to the simplistic view of plant population regulation and its emphasis on gross biomass rather than numbers, species diversity or distribution.

14 In the studies by both Schoener (1983) and Connell (1983), a variety of plant species and habitats were examined, but for the most part they dealt with plants in the vegetative and reproductive phases. Little attention was paid to the processes of plant recruitment, in particular the seedling and seed bank phases. Recently the universal importance of competitive ability and competitive exclusion in plant communities has been criticised (Verkaar, 1987; Mahdi, Law & Willis, 1989), in their place other interspecific relations which operate in a density-dependent manner, especially plant-animal relationships are receiving increased attention (Verkaar, 1987). The aims of the present study were to examine the influence of herbivory on plant populations with particular reference to the processes of plant recruitment. 1.2 The diversity of herbivores and the range of species studied Primary consumers, which numerically dominate the world’s fauna, have been the subject of much study (Crawley, 1983), yet most research has focused on invertebrates, particularly insects (e.g. Strong, Lawton, Southwood, 1984). This pattern undoubtedly reflects the overwhelming abundance of world invertebrate species though does not imply that insects play a greater role in plant population regulation than, for instance, mammals (Crawley, 1989) A comparison between the distribution of the phytophagous habit within the major mammalian orders (Bourltere, 1975) and their frequency of citation in the major pre-1985 publications on herbivory and plant populations (Harper, 1977; Crawley, 1983; Dirzo, 1984; 1985) is presented in Table 1.1. It is evident that the distribution of citations contrasts with the distribution of the phytophagous habit among the orders with an over-emphasis on artiodactyles and an underemphasis on rodents. The reasons for this discrepancy are, first, geographical, those mammal orders which principally fall outside the Northern temperate centres of ecological research have received little attention concerning their herbivorous influences on their respective plant communities. Second, the weight of studies has concerned agricultural herbivorous mammals. Over% 77 of studies on Artiodactyla which includes goat, sheep and oxen are of an agricultural nature ( over 40 % of all mammal citations). Third, large and visible diurnal mammals have received greater attention that small, secretive, nocturnal mammals since the former’s feeding habits are easier to observe in the

15 Frequency of phytophagy Frequency of citation Mammalian order N° of primary Percent of all Harper Crawley Dirzo Total consumer mammalian (1983) species primary (1977) (1984) consumers & (1985)

Marsupalia 117 4.6 0 1.3 0 1.0 Chiroptera 28 11.1 0 0 0 0

Primates 166 6.6 0 0 12.5 1.0 Lagomorpha 63 2.5 3.7 13.3 12.5 11.0 Rodentia 1685 66.8 29.6 22.7 25.0 24.5 Perissodactyla 16 0.6 14.8 6.7 0 8.0 Artiodactyla 170 6.7 51.9 56.0 50.0 54.5

Others 25 1.0 0 0 0 0 Total N° of 27 75 8 110 references Table 1.1 The relation between distribution of the phytophagous nature among Mammalian orders and frequency of citation in current herbivory reviews. field. Rodents, all species of which are phytophagous, appear to show the largest discrepancy in relation to citation frequency and this is particularly true if the sub-order Myom orpha (sm all mammals

(Southern, 1979)) are examined separately. Myom orphic rodents account for over 70 % of ail rodent

species, are distributed over all continents in habitats ranging from freezing arctic tundra through

deserts to humid tropical rain forests, and represent almost half of known phytophagous mammal

species, yet only represent 15 % of the literature of mammalian herbivorous interactions. Harper

16 (1977) states "Most of what we know about the effect of grazing animals on vegetation comes from studies of the larger mammals. It may be a serious mistake to underestimate the role of smaller grazers in the same community." Finally numerous ecological studies purporting to examine plant population regulation have been designed to minimize the "confounding" rodent effects two quotes will serve as examples:

" An unexpected event complicated the experiment on seed population dynamics ... it was found that some

seeds at a number of sites had suffered predation. Numerous droppings and a characteristic "tunnel" through

the long grass suggested that voles might have been responsible for such attack... Evidently it had been a mistake

temporarily to have protected the grass from being grazed by covering it with netting since this allowed voles

to forage the area. After the netting was removed grazing reduced the sward to its original length and there

was no further predation from voles. A s enough seed had been left it was decided to continue the experiment." Sarukhan (1974). pg 156.

" The husk and pulp ... were scraped by hand from the nut to a level approximating the degree of cleanliness

achieved when a rodent eats off the pulp. ( In a pilot experiment, where more than 1,500 fruits were put out

without first removing the pulp, all were removed by rodents in 3 days..). The cleaned nuts were then placed

u n der... c a g e s..." Wilson & Janzen (1972). pg 954. Both quotes share a similar approach of an experiment being unexpectedly influenced by rodents and then needing to be redesigned, not to take into account of the influence of rodents, but to exclude it. In both cases the authors agree tacitly to the importance of rodent herbivory but in neither case was it further examined. Whereas Table 1.1 reviewed the attention exercised by plant population biologists on rodent herbivores, Table 1.2 describes the extent to which small mammal ecologists have examined rodent herbivory. Table 1.2 is derived from a study of three major reviews (Golley, Ryszkowski & Sokur, 1975; Chew, 1978; Hayward & Phillipson, 1979) on the influence of small mammals on community structure and reveals the frequency of citations among major functional roles. It is clear from Table 1.2 that the herbivorous nature of small mammals, in contrast to plant ecology studies, has received much attention from small mammal ecologists.

17 Com m unity G olley Chew Hayward & Total

Function et a l. (1975) Phillipson (1979)

(1978)

Grazers 14.3 14.3 27.4 20.9

Seed predators 28.5 22.9 16.0 20.9

Nutrient Cycling 25.7 20.0 27.5 18.7

S o il Disturbance 28.6 17.0 14.5 25.1

Prey items 2.9 8.6 8.8 7.2

Insect predators 0 8.6 5.8 5.0

Interspecific 0 8.6 0.0 2.2 competition

Total herbivory 42.8 37.1 43.4 41.8

Total structural 54.3 37.0 42.0 43.8

Total N° of 35 35 69 139

references

Table 1.2 The relation between the roles small mammals have in structuring communities and frequency of citation in current reviews of small mammal ecology. Nevertheless, these studies have tended to focus principally on herbivory as a behaviour, describing diets, food preferences and quantities of food consumed, rather than its ecological impact. This zoocentric view of herbivory has led to the understanding that since small mammals only consume a small percentage of available primary production, usually less than 10 %, that their herbivorous impact on plant communities would be small (Hayward & Phillipson, 1979). Zlotin (after Hayward & Phillipson, 1979) estimated that the impact of small mammals in grassland due to direct consumption would not exceed 5 % of the total rodent impact and that wastage (material removed but not consumed, (Petrusewicz & Macfayden, 1970)) and structural effects (soil disturbance and

18 nutrient cycling, Table 1.2, (Golleyet ah 1975)) would be more important. This simplistic view of plant population regulation, echoed by Fretwell (1987) has led small mammal ecologists to focus their attention more on wastage effects (hard to quantify) through studies of grazing, and less on consumptive effects of seed and seedling predation (Table 1.2). The approaches adopted by plant ecologists, who have underestimated the extent of rodent herbivory, and rodent ecologists who have misunderstood the subtleties of plant populations, have led to a gap in our knowledge of herbivory. The present study was developed with this problem in mind and was designed to bridge this gap between plant and rodent ecology. 1.3 Phytocentricity, zoocentricity and the integrated approach In his review of herbivory Crawley (1983) split his study into two sections, those studies which focused on the influence of herbivores on plant populations and those which focused on the influence of plants on herbivore populations. This dichotomy was a product of the either phytocentric or zoocentric nature of previous studies, indeed Crawley (1983) when discussing the interactions between these two components had to rely on theoretical population models in order to draw conclusions. These models themselves highlighted the danger of only examining one half of the plant-herbivore interaction if the patterns observed in nature were to be understood. Plant factors, such as the degree of resource limitation, the ability of plants to compensate for herbivory and the presence of a seedbank may equally affect herbivore numbers as herbivore functional responses, numerical responses and patterns of herbivore attack may affect plant numbers (Crawley, 1983). Small mammals have complex reproductive strategies (Clarke, 1985), behaviour (Montgomery & Gumell, 1985) and food preferences (Hansson, 1985) and their abundance varies both in space (Wolton & Flowerdew, 1985) and in time (Flowerdew, 1985). Detailed knowledge of these factors will be needed to understand the consequences of any interactions with plants. Similarly, plants which often exist in complex plant communities (Crawley, 1986a) within which plant populations vary both in structure (Hutchings, 1986) and life-history (Crawley, 1986b) will need to be examined in detail.

19 The present study has avoided an either phytocentric or zoocentric study of the rodent-plant interaction, investigating in detail the complex nature of small mammal populations, including both temporal (chapter III) and spatial variation (chapter IV) and examining its influence on plant life-history (chapters V, VI) and structure of populations (chapter VII). It is hoped that such an integrated approach will enable the study to be precise and realistic while maintaining generality (Harper, 1982). 1.4 Problems in the analysis of vertebrate herbivory The study of small mammal herbivory is problematic since the secretive, nocturnal behaviour of many small mammals makes field observations of herbivory particularly difficult, and their complex behaviour makes laboratory studies unrealistic. A number of methods have been developed to study the influence of vertebrate herbivores on grassland plants (Risser, 1985) and their suitability to the study of small mammals is discussed below. 1.4.1 Monitoring natural levels of herbivory This method requires that natural levels of herbivore damage to plants are estimated or measured and then correlated with growth and or reproductive plant characteristics (Coley, 1980; Leeuwen, 1983). While this method avoids the manipulative problems of alternative methods outlined below, the results must be interpreted with care since many aspects of the analysis are uncontrolled. Differences in growth and reproductive performance of individuals may reflect important but subtle microhabitat differences rather than, or in addition to, differences in levels of herbivory. Also herbivores may preferentially attack larger or more vigourous individuals (Harper, 1969) which could, in turn, affect the result if control plants are not of equal size and physiological condition. It is also difficult to factor out the quantitative effects of different numbers of herbivores whose damage may appear similar but consequences differ (Leeuwen, 1983). This method is practical in cases where the damage is conspicuous and can be readily assessed (e.g. ring-barking of trees or consumption of flowerheads) but is no use in cases where plants are completely consumed and no evidence remains as to the cause of disappearance (e.g. seedling or

20 seed predation). Nor can it be fruitfully used when natural levels of the plant or phenological stage being investigated are at such a low density (possibly due to herbivory) that a sufficiently large sample size cannot be taken. 1.4.2 Dietary studies Studies of small mammal diets via gut or faecal analysis have a substantial literature (see Hansson, 1970,1985 for reviews), but extrapolation from these diets to effects on plants is difficult since it is never as simple as those plant which are eaten disappear and those avoided expand (P. Oosterveld, personal communication). Food preference and selectivity can only be stated for a certain area at a certain time. A general feature of all herbivores is that plants or plant parts which are most nutritious at that time are taken in preference, and differences occur due to variation in availability in space (Stenseth, Hansson & Myllymaki, 1977). Much information is lost from faecal pellet rather than gut analysis (Phillipson, Sorrazin-Comans & Stomatopoulos, 1983; P. Oosterveld, unpublished data), but the latter method can only be applied if the herbivore population is abundant and can be sampled destructively. 1.4.3 Artificial clipping While artificial defoliation has the advantage of permitting randomization of treatments and the removal of exact amounts of tissue, it rarely is carried out in a maimer which adequately simulates natural defoliation. With respect to grazing mammals, clipping often removes more tissue than the herbivore would, and this exaggerates the severity of the effects (Jameson, 1963), it does not mimic the different ways mammals bite vegetation (Harper, 1977), nor does it take into account the potential influence of animal saliva on plant growth (Reardon, Liewebe & Merril, 1972). Furthermore, tissues may be removed indiscriminately when clipped, whereas grazing herbivores are often selective (Batzli, 1985). Because different types and ages of tissues contribute differentially to plant growth and reproduction, the effect of clipping can differ from those of natural herbivory. For example, clipping may remove apices where regrowth could occur whereas herbivore damage may be confined to leaves and allow axillary buds to develop.

21 Considering the problems associated with artificial herbivory, the method must be applied with care. Natural levels of herbivory on individual plants in their normal habitat should be determined first and then the temporal and spatial patterns found should be mimicked (Elmvquist, Danell,Ericson & Salomanson, 1987; but see 1.4.1). As aconsequence, inmost studies the treatments and resulting effects on reproduction are often extreme and rarely reflect what actually occurs in nature. However they are useful in demonstrating the extreme effects of herbivory and potential response characteristics of host plants. Although there are exceptions, defoliation usually results ina number of predictable responses; a) Many grasses respond to defoliation by increasing the assimilates allocated to young leaves or regrowing tillers (Briske & Stith, 1982). b) When shoots are clipped frequently or severely, root mortality increases along with a decrease in root extension and branching (Detling, Dyer & Winn, 1979). c) The detrimental effects on plant growth increase when the frequency and/or degree of defoliation increases (Westoby, 1980). d) Not only may defoliation reduce root growth, but frequently there is a pronounced diversion of carbohydrates and nitrogen from the roots (Bahrani, Beaty & Tan, 1983). This diversion continues until the shoots develop sufficient photosynthetically active leaf area (May, 1960). e) Defoliation may lead to an increase in the photosynthetic rate of surviving green tillers (Painter & Detling, 1981). f) The above patterns are mediated by the timing of defoliation (Owensby, Smith & Rains, 1977) and soil moisture (Sosebee & Wiebe, 1971). 1.4.4 Exclosures Fencing exclosures are used to protect areas of a plant community from mammalian herbivores and have been used by many authors to study small mammal herbivory (Summerhayes, 1941; Thompson, 1955; Schultz, 1964,1969; Batzli & Pitelka, 1970; Morton, 1974; Cockbum & Lidicker, 1983; Andersson & Jonasson, 1986; Brown, Davidson, Munger & Inouye, 1986). These methods

22 avoid the problems of artificial herbivory although care must be taken in the experimental design to ensure adequate replication for statistical analysis (Hurlbert, 1984) and the provision of "cage controls". Such methods do not provide an easy means of understanding processes and are beset with problems. a) Small mammal exclosures are costly and difficult to maintain. b) "Minimum size of any exclosure that remains in place for more than one year should be 0.1 hectares. A smaller area is subject to undue edge effects and is all but useless for studies of natural succession of plants and animals" (Heady, 1968). The cost of such large vole-proof exclosures is prohibitive. The largest rodent exclosure built had an area of 85 m2 ( Brownet al., 1986), whereas others are much smaller, ranging from 30 m2 (Summerhayes, 1941; Morton, 1974) to as small as 6 m2 (Batzli & Pitelka, 1970; Cockbum & Lidicker, 1983) and even only 1 m2 (Thompson, 1955; Schultz, 1964, 1969; Andersson & Jonasson, 1986). These small sizes of exclosures imply that results derived from these studies should be interpreted with care and the variation of exclosure size will hinder between study comparisons. c) The minimum exclosure size necessary may be reduced if cage controls are constructed to account for fencing edge effects. Physical enclosure of plants reduces wind velocity, increases humidity and may lead to an increase in dry matter production (Cowlinshaw, 1955). The fine mesh sizes needed to exclude small mammals will heighten this effect. Controlling for this effect will mean building additional cages which enable rodent access, further increasing costs. d) Careful replication and randomization of treatments may reduce errors and the minimum exclosure size necessary. However, by comparing different sites which not only may contain different plant communities but also different abundances and diversities of small mammals it will be difficult to determine what part of treatment differences is due to local grazing and what part to local site conditions.

e) Results are not always derived rapidly. Brown et al. (1986) showed that it was not until three years after setting up exclosures that a statistical difference could be seen in desert annual plant species composition between controls and exclosures. Thompson (1955) found no statistical

23 difference in yields of grazed and ungrazed plots in tundra over five years of study, though Batzli (1975) examining the same plots twenty years later did. However, these patterns may reflect variation in habitat response times since Summerhayes (1941) working in temperate grasslands found significant changes within the first growing season. f) With few exceptions, exclosure studies have not been monitored for periods longer than ten years and this is particularly true of small mammal studies. One of the longest exclosure studies on mammals involved rabbits (Watt, 1981), lasting almost 40 years. This study revealed the temporal extent of vegetation change after exclosure and emphasises that even when significant changes occur rapidly there is a danger in extrapolation derived from short term exclosures. In the present example, studies of the patterns of species composition change over the first ten years would not have predicted the changes in the following ten years (Watt, 1962). g) Previous exclosure studies have focused on changes in plant biomass (Thompson, 1955; Schultz, 1964, 1969), plant species composition (Summerhayes, 1941; Cockbum & Lidicker, 1983; Andersson & Jonasson, 1986), seed production and survivorship (Brownet al., 1986), the spatial pattern of plant species (Morton, 1974) or a combination of the above factors (Batzli & Pitelka, 1970). It is clear that the more detailed the analysis, the greater the probability of detecting subtle changes in the plant communities. For instance, Morton (1974) found no significant difference between exclosures and controls for dry weight of standing crop but did for plant spatial pattern. A conflict therefore exists, because more detailed studies will require more frequent destructive sampling (e.g. if seedling recruitment and plant population structure are to be examined). In long term studies, care must be to ensure that long term results are the products of the experimental treatments and not the sampling regime. h) The exclusion of herbivores from sites to which they previously had access may result in differences in vegetation development between exclosures and controls. However, the pattern of change will not necessarily be the same as would occur in vegetation that had never been previously grazed. For example, the long lived grassNardus stricta , persists as the dominant plant for many years after fencing has excluded sheep (Rawes, 1981) and it might be argued that the population

24 processes of this grass are not affected by grazing management. However in ungrazed or less severely grazed sites Nardus is unable to reach dominance (Welch, 1986). This implies that some changes brought about by vertebrate grazing are irreversible at least in the medium term and conflicts with previous ideas of grazing management (Oosterveld, 1985). i) Results of fencing experiments are composed of both density-dependent and density-independent effects. Plant survivorship may be density-dependent through rodent consumption, species selection and functional responses or as a result of interspecific competition or pathogen attack facilitated by herbivory. It may also be density-independent through rodent food wastage or structural effects from faeces production or soil disturbance. It would be difficult to separate out the density-dependent from the density-independent processes let alone separate the relative impact of herbivory from that of other factors. The complexity of experimental designs needed to elucidate these factors may reduce the practicality of exclosure studies. In summary, fencing exclosures provide information on the total impact of a herbivore (or herbivores) on a particular area of vegetation. If, over the long tetm, no changes are observed in the vegetation it may be hypothesized that the herbivore is not exerting a strong influence on the site. If a change is observed after fencing the problem then lies in defining the cause of these changes. 1.4.5 Enclosures Rather than excluding a particular herbivore or herbivores, a herbivore population may be enclosed in a particular area. This is a less common technique than exclosure but has been used to study the influence of small mammal herbivores on vegetation (Fitch & Bentley, 1949; Spitz, 1968; De Vos, 1969). Such experimental techniques suffer similar problems as exclosure studies but there are some particular problems associated with enclosures. It is harder to keep wild animals inside enclosures than outside exclosures due to a natural escape behaviour. This requires increases in costly fencing, and/or larger enclosures. Fencing-in of small mammal populations leads to "frustrated dispersal" and without precautions abnormally high rodent densities build up (Gipps & Jewell, 1979). At these high densities rodents may exhaust their food resources (over-emphasizing their natural impact) and die (Taitt & Krebs, 1985). Culling of rodent populations is often necessary

25 to maintain natural rodent densities, but this interference in the population regulation of small mammals may lead to changes in behaviour, and so to unrealistic results. The advantage of enclosures is that they enable partitioning of the effects of sympatric herbivore species. Fitch & Bentley (1949) separately enclosed gophers, ground squirrels and kangaroo rats and compared their relative impacts. Usually, however, this advantage is outweighed by the disadvantages. It is clear, therefore, that the examination of small mammal herbivory is fraught with difficulty, and none of the above techniques is sufficient to elucidate the processes of the plant-animal interaction. Combinations of these methods may enable a more detailed examination to be made, but this approach has never been adopted to date. Recently the use of large exclosures to examine vertebrate herbivory has been modified through the use of smaller exclosures, "cages", to examine the impact of small mammals on particular points of a plant’s life-history. These have been shown to be successful in examining rodent seed predation (Mittlebach & Gross, 1984), seedling predation (Mills, 1983) and the effect of rodents on plant survivorship (Pyke, 1986; Rice, 1987). Such small cages enable treatments to be replicated and spatial variation of herbivore feeding to be examined. Within cages plants may be manipulated, varying plant species composition, phenological stage, density and period of presentation and then compared to similar uncaged controls. Although this technique is by no means flawless, its great flexibility enables a precise investigation of the influence of small mammals on particular components of a plant’s life-history. This technique was chosen for this study and design and construction of cages with respect to their different functions is described in chapter V. 1.5 Methodology and aims of study

Golley etal. (1975) conclude their review of the role of small mammals in temperate ecosystems with a call for further research if an adequate description is to be made . They define three areas where a lack of information prevents an accurate understanding of small mammal herbivory: a) "the frequency, distribution, abundance and time pattern of seeds and foliage available to the mammals"

26 b) "the feeding behaviour of the mammals, the size and shape of food particles that can be consumed and the time and pattern of searching and feeding" c) "detailed study of the consequences of feeding and activity over time in the context of the plasticity of response of the plants." Chew (1978) confirmed these directives but added that studies should concern themselves with natural ecosystems, "emphasize the effects on vegetation" and "include observations of these actions by which mammals may be regulators of processes important to plants". However a decade after these initial directives, Rose & Bimey (1985), concerning the most widely studied rodent genus, could only conclude "Experiments to evaluate critically the roleMicrotus of in altering the habitat have not been done at even a single location to our knowledge.".

Whereas Golley et al. (1975) and Chew (1978) give guidance to the study of the mammal component of the plant-animal interaction (which is described in Chapter II, III and IV), their description of vegetation study is vague, focusing on "regulation" and "plasticity of plant response". A number of factors are involved in the decision of how best to investigate the influence of small mammals on plants, but all are an attempt to find an optimal solution to the trilemma of precision, generality and reality (Harper, 1982). a) Comparatively little is known about the ecology of grassland rodents, and even such basic information as the range of food plants they utilise is lacking. Details of plant vulnerability in relation to plant phenology are largely unknown. This prevents an informed decision being made as to which plant species are to be investigated. Precision is then compromised by generality since instead of examining a few species in detail, a wider range of species must chosen. This enables the relative importance of rodent herbivory to be gauged over a number of plant species and plant vulnerability to small mammals correlated with a variety of plant attributes. The choice of which plant species to study is discussed in chapter V. b) Crawley (1988) reviews the potential native herbivores have to affect the plants upon which they feed, through changes in plant growth, death rate of mature plants, fecundity, seed production, germination and seedling establishment. The majority of herbivore species only directly affect a few

27 of these processes, some species only one, and this greatly simplifies any study. The feeding behaviour of small mammals, however is extensive, they feed on roots, shoots, leaves, stems, flowers, seedheads, surface seed, buried seed and seedlings (Batzli, 1985; Hansson, 1985). They are therefore likely to influence all the plant processes described by Crawley (1988). To focus on the effects of rodent herbivory via all these feeding habits, taking into account any possible interaction and compensation, finally weighing each feeding habit in relative importance, even for a single plant species is beyond the scope of this thesis. The number of species chosen in this study, necessitates an examination of only a few of the processes in the rodent-plant interaction and although this approach loses some reality, I hope this is compensated for by increased precision. c) The aim of the present study was to examine in detail the influence of small mammals on plant recruitment (chapter V & VI). This does not imply that other herbivore influenced plant processes such as growth or adult mortality are not important, and indeed they may be more important. The influence of small mammals on these latter processes and the dangers of relying on only a plant recruitment study are examined in Chapter VII. d) Some of the reasons for focusing on plant recruitment have been mentioned already, but others include: i) Harper (1977) claims that manipulation of both prey and predator population levels is the key to understanding herbivory. The seed and seedling stages of plants, which represent the critical stages of plant recruitment are the most easily manipulated components in a plant’s life-history. ii) The predation by herbivores on seeds and seedlings as a relation to a plant’s overall fitness is more straightforward than is grazing of a perennial adult since the end result is most often plant death. iii) There is evidence that rodent post-dispersal seed predation is an important source of plant mortality in grassland plants but few of these studies report the effect of seed mortality on plant recruitment (Crawley, 1988). This is further examined in chapter V.

28 iv) The dynamics of seedling populations in grassland are poorly understood, few demographic studies quantify the loss due to predation but it is probable that predation is important (Fenner, 1985). Golleyet al. (1975) report that at least in woodlands, small mammals are major sources of seedling mortality of some tree species. The influence of rodents on grassland seedlings is examined further in chapter VI. e) The factors involved in plant recruitment need to be viewed in the context of plant survivorship. It is conceivable that while rodents may influence seed and seedling survival, abiotic factors such as frost heaving, drought or waterlogging or other biotic factors such as density-dependent seed germination (Inouye, 1980) or seedling survival (Watkinson, 1986) may reduce the significance of herbivory on plant recruitment. Experiments designed to examine this possibility are described in Chapter VII. f) The aim of this study is not to obtain an absolute value of the impact of rodents on plant populations but to examine their role in comparison with other mortality factors. For this reason, I have also examined (in less detail) the influence of invertebrates on plant recruitment. While rodents may inflict mortality on plants it may be of little importance relative to losses to other herbivores, and removal of small mammals from a particular area may only result in a compensatory increase in herbivory by invertebrate herbivores. This partitioning of different sources of mortality is central to our understanding of plant population regulation (Verkaar, 1987). The following chapters describe experiments that examine plant recruitment in detail and for the first time attempt to elucidate the influence of small mammals on plant populations in grassland.

29 CHAPTER II THE ESTIMATION OF SMALL MAMMAL ABUNDANCE 2.1 Introduction 2.1.1 Herbivore demography and plant-animal interactions The extent to which a particular herbivore is food limited will determine the degree to which an examination of its population ecology will be necessary in order to understand its influence on plant populations. The regulation of herbivorous small mammal populations, and the role of food in such regulation is still unclear after many decades of study (Finerty, 1980; Taitt & Krebs, 1985; Cockbum, 1988 for reviews). The following evidence however reveals that the role of vegetation (of which food factors are a component) in herbivorous small mammal population regulation is unequivocal. i) With rare exceptions, small mammal breeding seasons are tied to the vegetation growing period (Krebs, 1966; Lidicker, 1973,1976). ii) The quantity of available food affects litter size, survival rate, length of residency and adult sex ratios (Krone, 1980; Cockbum & Lidicker, 1983; Heske, 1987a; Ostfeld, Lidicker & Heske, 1985; Ostfeld & Klosterman, 1986). iii) The floral composition of the vegetation affects its quality for small mammals through the presence of toxins (Berger, Sanders, Gardner & Negus, 1977) and plant oestrogens (Berger, Negus, Sanders & Gardner, 1981). Both the timing and quantity of reproduction are affected (Cockbum & Lidicker, 1983; Ostfeld & Klosterman, 1986). iv) The degree of vegetation cover influences the predation pressure on small mammals (Bimey, Grant & Baird, 1976). v) The spatial arrangement of vegetation, and the relative quality of these habitat patches not only influences rodent territoriality but also the availability of a dispersal sink (Lidicker, 1988).

30 These studies have principally been on grassland small mammals, however there is evidence of similar patterns in woodland rodents (Alibhai & Gipps, 1985; Flowerdew, 1985). There therefore exists sufficient evidence regarding the influence of vegetation (in particular as a source of food) on small mammal demography to warrant a detailed study of their population ecology. 2.1,2 Problems in the study of small mammals Difficulties in the study of the effect of small mammals on the environment have already been discussed (chapter I), however problems in the examination of rodent demography also exist. The highly developed sensory physiology of small mammals (Stoddart & Sales, 1985) implies a wide range of complex behaviours (Manning, 1981). In comparison to lower vertebrates and invertebrates this increased complexity of behaviour augments, by an order of magnitude, the number of variables required to explain and describe in detail observed demographic patterns. Unfortunately, the necessity of a detailed study is associated with great difficulty in its undertaking. The small size and secretive nature of small mammals hinder natural observation. Static observations have been carried out on small mammals but required either the use of bait points (Kikkawa, 1964) or the complete enclosure of a population (Gipps & Jewell, 1979). Additionally the crepuscular nature of some small mammals require night observation, which although possible under both red (Southern, 1955) and white (Warner, 1978) light necessitates expensive equipment if the range of observations made is to be flexible (Flowerdew, 1976). In general, direct observation of rodent behaviour in the field is possible but is limited by the degree to which the habitat can be modified - cutting down of the obstructing vegetation and by the expense of observation equipment. Quantification of the behaviours observed is difficult since rarely can more than one individual be observed at any one time and collection of sufficient data for analysis is labour intensive. Direct observation was therefore not a suitable method for examining small mammal demography. Their relatively large home ranges (Wolton & Flowerdew, 1985), the social organization of populations (Cockbum, 1988) and their perception of the complex environment in which they live

31 (Stoddart & Sales, 1985) place stringent demands when designing laboratory experiments. Simple rat cages are unable to reflect the habitat sufficiently that faith can be placed in the results being true representations of behaviour and not anomalous laboratory effects. Since direct observations in the field and the laboratory have limited success in the majority of small mammal studies, reliance is placed on indirect methods of study such as trapping. This is the method adopted in the present study. The flaws in this technique and difficulties in inferring population processes from such data are described below (section 2.2.3). 2.1.3 Small mammal populations at Silwood Park

S m a ll m am m al species Brown (1954) McNeill (unpublished) Churchfield & Brow n (1987) rough grassland beech deciduous woodland conifer grassland wood succession 1 2 with no woods understory understory Criceridae

Clethrionomys -- + + + - - - glareolus

M icrotus agrestis + + - + M u rid a e

Apodemus syhaticus + + + + + + +

Apodemus flavicollis - * * Micromys minutus --

Mus musculus

Rattus rattus

Rattus norvegicus G lirid a e

Muscardinus avellanarius + frequently caught - occasionally caught at low density * occasionally caught at high density

Table 2.1 A comparison between the phytophagous small mammal species known to be extant in southern England and those species previously recorded at Silwood Park. Silwood Park, Berkshire, the Field Centre of Imperial College, University of London occupies approximately 100 hectares of rough grassland and woodland with small areas of heathland and

32 some cultivated regions. Previous detailed small mammal live-trapping studies at Silwood Park have examined populations in a variety of sites and habitats. Brown (1954) studied three sites, two rough grasslands and a beech woodland from 1949 to 1951. McNeill (unpublished data) examined two deciduous and one coniferous woodland habitats from 1968 to 1985 and Churchfield & Brown (1987) have investigated a number of successional sites since 1985. Their data are summarised in Table 2.1 and compared to a list of phytophagous small mammals extant in southern England (Corbett & Southern, 1977).

Whereas Apodemus sylvaticus has been trapped in all studies, in all habitats, Microtus agrestis has principally been found in grasslands and Clethrionomys glareolus in woodlands, as found in other small mammal studies (Gumell, 1985).Apodemus flavicollis and Micromys minutus have only rarely been trapped and appear to be more habitat specific (woodland and grassland respectively). Although the three remaining murids are capable of inhabiting all habitats studied (Southern, 1964) none have been previously trapped. These rodents are associated with man, particularly with buildings and outhouses, indeed this is exactly where at leastRattus norvegicus and Mus musculus are found at Silwood (Hulme, personal observation).Muscardinus avellanarius is a rare small mammal species which has declined steadily in numbers over the last decade, it has once been found at Silwood (S. McNeill, personal communication) but is no longer thought to be present. 2.1.4 Estimating small mammal abundance The initial step in any examination of small mammal demography is an estimation of population size. Small mammals are elusive creatines and present sampling problems by virtue of their mobility. Estimation of population size is seldom if ever amenable to direct counts (Montgomery, 1987). Capture-Mark-Recapture (CMR) methods permit such estimation. Nevertheless past studies have either avoided CMR techniques or abused the methods with estimates lacking any indication of error, being based on insufficient data and showing a disregard to the assumptions underlying such methods (Begon, 1983).

33 The most appropriate CMR model for estimating population size is that to which the biological data most closely fits the underlying assumptions. Selection of the CMR model therefore requires not only an understanding of the underlying assumptions but also their statistical validation. Sources of bias in CMR based estimates and statistical validation techniques are described in Blower etal. (1981) and the particular examination of biases relating to small mammals in Montgomery (1985, 1987). The more complex the CMR model, the greater the number of underlying assumptions. In the present case six major sources of bias were examined. i) Marking should not affect animals. ii) Marks should not be lost. iii) Marked individuals must mix randomly with the unmarked population. iv) Samples must be taken at random: an animal’s mark, sex or age should not influence its chance of capture. v) Patterns in captures and recaptures in sequential samples should be consistent with random sampling. vi) Sampling must be performed at discrete intervals and the time spent sampling must be small in relation to the intersampling periods. vii) The population understudy must be effectively closed: gains and losses during sampling must be negligible. While a variety of CMR population models exist (Seber, 1982) only two, the Fisher-Ford (Fisher & Ford, 1947) and the Jolly-Seber (Jolly, 1965; Seber, 1965) methods have had their robustness thoroughly tested when their underlying assumptions are violated (Bloweret al., 1981). Details of the calculation and application of these models are discussed elsewhere (Southwood, 1978; Blower et al., 1981; Seber, 1982), however a number of salient points regarding each model will highlight their particular usefulness in the present study. Both methods assume that rates of gain into the population vary throughout the sample period and both give an indication of the extent of this gain. Both methods also calculate a survival rate over the sampling period but whilst the deterministic Fisher-Ford model assumes a constant survival

34 rate, the stochastic model of Jolly-Seber assumes more realistically that an animal has only a probability of surviving over this period. The Fisher-Ford method is robust for small samples and provided the survival rate remains constant, gives population estimates not dissimilar to those of the Jolly-Seber method. The Jolly-Seber method is usually reliable when 9% or more of the population is sampled and the survival rate is greater than 50% (Bishop & Sheppard, 1973). It is also less sensitive to age-dependent variations in the mortality rate than the method of Fisher-Ford. Another advantage of the Jolly-Seber method is that it provides estimates of the variance associated with the population estimates. If the survival rate (Bloweretal.y 1981) or the probability of capture (Seber, 1982) is low then the variances of the population estimates are unreliable. If both survival and probability of capture are low then Fisher-Ford estimates are more accurate than those of Jolly-Seber, but such low capture probability is unlikely in studies of small mammals (Montgomery, 1987). 2.2 Methods and materials The methodology of small mammal studies has recently been reviewed (Flowerdew, 1976; Gumell & Flowerdew, 1982). A number of salient points are discussed with reference to these reviews in the light of the aims of this study. 2.2.1 Description of experimental sites The two sites, each 30m X 30m, an east facing grassland slope and a low-lying meadow, both lay on acid Bagshot sands at Imperial College, Silwood Park (GR41/944691). The grassland site lay 30m north of the sites studied by Churchfield & Brown (1987) in an area of relatively homogeneous perennial grassland. The meadow site lay approximately 75m north-east of the grassland site, and its edges were bounded by four different habitats. On its southernmost edge lay a heavily rabbit grazed perennial grass "lawn", while rough grassland bounded its northernmost edge, a narrow (<10m) deciduous copse also bordered by grassland and a rush dominated marsh were found to the west and east respectively.

35 Grassland Meadow

Figure2.1 Structural profiles, as determined by point quadrat analysis, ofa)live, b) dead and c) total vegetation in the grassland and meadow sites.

36 GRASSLAND MEADOW plant species % cover plant species % cover Arrhenatherum elatius 32.1 Agrostis capillaris 24.6 Holcus lanatus 17.5 Chamaenerion angustifolium 12.8 Dactylis glomerata 12.2 Juncus effusus 12.2 Agrostis capillaris 12.0 Holcus lanatus 10.5 Festuca rubra 6.2 Holcus mollis 7.0 Rumex acetosa 3.9 Ranunculus repens 5.5 Plantago lanceolata 3.1 Urtica dioica 5.0 Urtica dioica 1.8 Alopecurus pratensis 4.0 Bromus mollis 1.6 Stellaria graminea 2.8 Cirsium arvense 1.6 Poa annua 2.7 Vicia hirsuta 1.3 Poa trivialis 2.2 Achillea millefolium 1.0 Luzula campestris 1.3 Veronica persica 1.0 Cirsium palustre 1.2 Agropyron repens 0.9 Carex muricata 1.1 Galium aparine 0.9 Anthoxanthum odoratum 0.9 Stellaria graminea 0.9 Lotus corniculatus 0.9 Trifolium repens 0.4 Rubus fruticosus agg. 0.9 Juncus effusus 0.3 Trifolium repens 0.7 Acer campestre 0.2 Veronica persicaria 0.6 Holcus mollis 0.2 Dactylis glomerata 0.6 Ranunculus repens 0.2 Rumex acetosa 0.5 Hypochaeris radicata 0.2 Galium aparine 0.6 Rumex crispus 0.1 Salix cinerea 0.3 Rubus fruticosus agg. 0.1 Senecio jacobea 0.2 Cerastium fontanum 0.1 Festuca rubra 0.2 Taraxacum officinale 0.1 Veronica serpyllifolia 0.1 Cerastium fontanum 0.1 Cardamine pratensis 0.1 Lycopus europeus 0.1 Achillea millefolium 0.1 Quercus petrea 0.1 Table 2.2 Species composition and percentage total cover of plant species occurring in the grassland and meadow sites. Nomenclature follows that of Clapham, Tutin & Moore (1987).

37 The botanical species composition and structural profile of the vegetation of each site was assessed following the methods of Churchfield & Brown (1987). Forty point-quadrat samples were taken in each of twenty five 5m X 5m quadrats per site during the 1987 and 1988 growing season. The two sites were botanically different (Table 2.2), of the forty plant species found in the sites only forty percent were common to both. Whilst the grassland was dominated by perennial grasses (87.7% total cover) in particularArrhenatherum elatius, herbs were a greater component of the meadow (46.0% total cover) especially Chamaenerion angustifolium which occurred in several tall growing patches within the sites. Plant species diversity as described by the Shannon H’ Index was significantly higher in the meadow (2.536 compared to 2.191; t test H’; t=85.53, d.f. 55, p<0.001). In order to elucidate the influence of vegetation cover on the spatial distribution of small mammals (chapter IV) the structural profile of the vegetation was examined (Figure 2.1). Similar patterns were found in both sites for the structure of live vegetation cover (Figure 2.1a) but more dead vegetation cover was found at both high and low levels in the meadow than in the grassland (Figure 2.1b), though this difference was reduced when total cover was studied. The influence of food resources on rodent spatial distribution was also undertaken (chapter IV). The seed rain was sampled throughout May-September 1987, in twenty five 0.5m X 0.5m quadrats in both sites. All full seedheads produced by each species of plant within a quadrat were counted and the seed production estimated by multiplying this figure by the mean seed number per seedhead for each species (from a sample of 30 seedheads). 2.2.2 Design of trapping study a) Efficiency of small mammal capture and trap choice Since the population dynamics of these herbivorous rodents was of interest, only live trapping methods were available. Although a variety of trap types exist (Gumell & Flowerdew, 1982) each varying in efficiency (Sealander & James, 1958), availability limited the choice to Longworth traps. b) Probability of encounter and grid design

38 The spacing between trap stations may influence the analysis of small mammal movements (Hayne, 1950) and depends on the type of habitat, the small mammal species caught and their density. Microtus agrestis was known to occur at Silwood Park, often at high densities (Brown, 1954) and this species requires a small trap spacing relative to other small mammals such Apodemusas sylvaticus (5m and 10m respectively, Flowerdew, 1976). This smaller trap spacing was chosen since it was unlikely to influenceA. sylvaticus population estimates, though a larger trap spacing would underestimateM. agrestis population density. Since the spatial distribution of the rodents was to be investigated, a trap grid rather than a trap line design was chosen. The size of the grid was determined by the size of the sites, the trap spacing and the availability of traps. A 5 X 5 trap point grid was chosen, making the size of the grid 25m X 25m, which is small relative to other small mammal studies. The size of the trapping grid will influence estimates of density unless this is corrected for (chapter III) and may influence distances moved by small mammals between successive captures (Faust, Smith & Wray, 1971). However, since the study was to be primarily comparative between sites and the sites had the same design, problems of absolute measurement of movement and density may be less important. At any trap point, traps were set to ensure a high number of captures (in areas of high vegetation cover, along rodent runways etc.) rather than placed at random. While moving of traps between successive captures has been proposed as a means of reducing trap-happiness in rodents (Taitt & Krebs, 1985), Tanton (1969) found that better estimation of population size was obtained from a fixed trapping grid rather than a changed and random array of traps. This latter technique of a non-random fixed array of traps was chosen in the present study. Flowerdew (1976) advised that at least two traps should be placed at each trap point. Due to limited trap availability only one trap was available at each point. This may lead to the underestimation of rodent numbers (Andrzejewski, Bujalska, Ryszkowski & Ustyniuk, 1966) but will be less serious if at least 20% of traps remain empty at each trapping session. This potential source of bias was investigated further in section 2.3.2.4. c) Rodent trap response and bait choice

39 The decision as to whether or not to pre-bait traps before trapping and for how long will depend on the small mammal species studied and the length of the trapping period. Whereas A. sylvaticus appears little influenced by pre-baiting (Gumell 1980),Microtus species often require pre-baiting if appreciable numbers are to be caught (Taitt & Krebs, 1985). However the longer the pre-baiting period the greater the risk of attracting small mammals into the trapping area from outside the study site. Gumell (1980) showed that the effect on captures of a one day pre-baiting period was no different from that of a two day pre-baiting period and both were lost by the second or third night of trapping for mice and voles respectively. This limits the use of short pre-baiting periods to short trapping periods. Since field voles were likely to be caught (section 2.1.3) and trapping periods were to be short (section 2.2.3) a one day pre-baiting period was chosen. 2.2.3 Trapping programme Since small mammals are known to exhibit marked seasonal fluctuations in both abundance and behaviour (Alibhai & Gipps, 1985; Taitt & Krebs, 1985) trapping was undertaken simultaneously at both sites at one month intervals. Montgomery (1987) advised that "the sampling period should be short (25% or less) relative to the interval between samples", with a three week interval between monthly sampling periods this limited the sampling period to five days. Since traps were pre-baited for one day (section 2.2.2) four days of trapping were available in each sampling period. Clean traps, containing straw bedding and rolled oats as bait were used throughout the sampling period. The traps were checked regularly during the trapping periods (three to four times a day depending on weather conditions and rodent abundance). Traps were then removed from the site during intersample periods before being cleaned and replaced at the start of the following sample. Rodents caught were individually marked so that population estimates based on Capture-Mark-Recapture (CMR) techniques and studies of individual movement patterns could be carried out. Three common methods of marking are practised, ear-tagging, toe-clipping and fur-clipping. Ear-tagging is unsuitable for studies of voles, since their small ears rapidly tear after tagging and tag loss is high. Toe-clipping is known to cause short term depression in small mammal trappability (section 2.3.3.1) but is the most frequently used technique due to its low rate of mark

40 loss. Although toe-clipping would have been undertaken, the local use of toe-clipping of small mammals in another study (Churchfield & Brown, 1987) meant that in order to reduce confusion between studies this technique could not be used. Fur-clipping (for method see Gumell & Flowerdew, 1982) is the least intrusive of all marking methods but suffers in that marks may be lost through fur regrowth and moulting. Preliminary observations in the laboratory revealed fur-clip marks to remain identifiable for up to four months. This period is short relative to small mammal life-span, therefore while increasing labour, rodent fur-clips were maintained by re-clipping at each sampling period. The effectiveness of this method is examined in section 2.3.2. 2.3 Results 2.3.1 Small mammal species composition within the grassland and meadow Although trapping of rodents within the sites began in July 1986 a continuous data set was only available from December 1986 to June 1988 since the building of rodent exclosures throughout October and November 1986 in both sites (see chapters V & VH for details) was assumed to be sufficient disturbance to cast doubt on the reliability of trapping over this period. Five species of small mammal were caught in both sites (Figure 2.2), three primarily phytophagous speciesApodemus sylvaticus , Microtus agrestis and Micromys minutus and two insectivorous speciesSorex araneus and Sorex minutus. The species composition of the sites was similar to past studies at Silwood Park (Table 2.1) though the winter peak inM. minutus in the grassland in 1987 (Figure 2.2a) was unexpected as were the low numbers M.of agrestis found throughout the study. Both the grassland and meadow (Figures 2.2a, b respectively) were numerically dominated by A. sylvaticus. Although a greater number ofA. sylvaticus tended to be caught in the meadow this pattern was reversed with respect to M. agrestis and M. minutus. Since insectivores were not marked (section 2.2.3), total number of individuals trapped (TNIT) could not be estimated and the data in Figure 2.2 is total captures and could not therefore be directly compared with the other

41 Figure 2.2 Temporal pattern of captures of small mammals in a) the grassland and b) the meadow sites at Silwood Park.

42 species. In general, S o re x species were uncommon (data forS. a ra n e us and S. m inutus were lumped together in Figure 2.2) none being caught in either site throughout 1988. The individual species patterns are discussed in further detail in chapter III. 2.3.2 Determination of bias in CMR estimates ofApodemus sylvaticus population size

Only Apodemus sylvaticus was caught in sufficient numbers over a long enough series of monthly samples to enable CMR estimation of its abundance in both sites. Assessment of the abundance of other small mammals caught in the sites will therefore be limited to enumeration.

Below, the suitability of the A.sylvaticus data to CMR analysis, in relation to the necessary underlying assumptions was tested. i) Marking should not affect animals. The criterion that marking does not affect the chances of recapture is central to all CMR methods of population estimation. After December 1986, two classes of mice were captured on day 1 of each sampling period in each site: first, those that had been marked before the current month in a previous trapping session, and second, those previously uncaptured and unmarked individuals newly caught, which were marked for the first time before release. The effect of marking, i.e. initial restraint, handling and fur-clipping, was assessed using data for recapture of these two groups. The effect of marking male and female A. sylvaticus was investigated in relation to recapture over 24 hours, 4 days (one sampling period) and at any time after release, using %za test of homogeneity

(Blowere t a l., 1981). Initial examination of seasonal variation in effect of marking using a Fisher Exact Test showed no significant effect of season and data were lumped over all 19 trapping periods. There were no differences between males or females nor between sites (Table 2.3) in rates of recapture of recently marked animals. This test has only rarely been applied to CMR data for small mammals despite the frequent use of leg-ringing, toe-clipping and ear-punching (Twigg, 1976) and the present data

43 W ithin 24 h Within 3 days At any time

rec. not % rec. rec not % rec. rec. not % rec.

G ra ssla n d newly marked 18 7 72 20 5 80 22 3 88 marked previously 21 5 81 21 4 84 21 4 84

x 2 0.17 n.s. 0.0 n.s. 0.0 n.s

M e a d o w newly marked 20 9 69 25 4 86 27 2 93 marked previously 42 23 65 60 5 92 61 4 94

x 2 0.03 n.s. 0.0 n.s. 0.0 n.s.

T a b le 2 3 Recapture data for A. sylvaticus released on day 1 of a four day trapping period in the grassland and meadow sites during December 1986 to June 1988. When released mice were classed as newly marked, by fur clipping or marked during an earlier occasion. Recapture rate (% rec.) was determined within 24 hrs, at any time during the same trapping session and at any time subsequent to the initial release. are the first to be examined with respect to fur-clipping. Whilst toe-clipping (Fairley, 1982) and ear-punching and toe-clipping (Montgomery, 1985) are known to influence the probability of recapture ofA. sylvaticus, the less intrusive method of fur-clipping revealed no such effect. ii) Marks should not be lost. The disadvantage of fur-clipping as a marking technique is that in time the marks will inevitably be lost. Even if marks are maintained by reclipping at the next sampling period, it is conceivable that some marked individuals will leave the sample area for sufficient time for the clip to be lost before returning to the sampling area as unmarked individuals. The extent of this source of bias will depend on the probability that mice left the sites for periods longer than the duration of a fur-clip (approximately 4 months, section 2.2.4). The lengths of absence of mice from both sites between successive intersample captures are displayed in Figure 2.3. Insufficient data prevented the testing of the theoretical frequency distribution for these patterns, though there appeared to be

44 an exponential decline in the frequency of site absence. Approximately 90% of intersample recaptures occurred within one month, over 99% within two months. Only on one occasion was an individual absent from the site for three or more months. This implies that the probability of an individual leaving the site for more than four months before returning to be recaptured was less than 1%. Biases due to the recording of the re-entry of previously marked individuals as the appearance of new individuals was therefore unlikely.

grassland meadow a)

1 2 3 4 1 2 3 4 3 O b )

1 2 3 4 1 2 3 4

length of absence (months)

Figure 2.3 Frequency histogram of length of absence (months) between initial capture sample and first recapture sample for a) male and b) female A. sylvaticus in the grassland and meadow sites. Most recaptures occurred within one month of initial capture indicating that prolonged temporary emigration from sites was rare.

45 iii) The marked individuals must mix randomly with the unmarked population Sufficient movement of the small mammals over the sites must occur to allow random mixing of marks to take place. This is in part catered for in the experimental design, where intertrap distance was chosen with respect to the power of movement of the species concerned in order to obtain a random sample (section 2.2.2). This assumption may be checked by examining small mammal movement between successive captures within a single sampling period and the extent of mixing estimated by the range of distances moved. Grid size and trap spacing are known to influence estimates of movement (Flowerdew, 1976). Small grids are prone to underestimate movement since they suffer a greater edge effect, having a smaller probability of picking up large movements, and so biasing results downward. Wide trap spacing will tend to overestimate movement since they miss many movements smaller than the trap spacing. Due to the biases of a small grid size and the unknown underlying distribution of the distance data, a Kruskall-Wallis one-way ANOVA was used in comparisons. There were no significant differences in the pattern of intrasample movements for season (H=0.991, d.f. 3, n.s.), site (H=2.972, d.f. 1, n.s.) or sex (H=1.139, d.f. 1, n.s.) and neither were any of the interactions significant. Mean intercapture movements were of the order of 7m, though ranged from 0 to almost 30m (Figure 2.4). Two sources of error may have led to biases in this estimate. First, the short trap spacing may have hindered movements by trapping rodents before they had moved a representative distance and second, that distances larger than the site dimensions could never be recorded. A comparison of sources of bias can be made by comparing distances moved between successive captures in the same sampling period (when trap spacing is most likely to influence movements) with distances moved between successive captures between two successive sampling periods (where distances moved will not be related to any trap spacing). There was no significant difference in the patterns of movement between intra and intersample periods (H=0.0052, d.f. 1, n.s., Figure 2.4) nor were patterns different between sites (H=2.4756, d.f. 1, n.s) or sex (H=2.281, d.f. 1, n.s.). No interaction effects were significant. It appeared that the 5m trap spacing did not significantly hinder rodent movements in the two sites (Figure 2.4).

4 6 grassland meadow

Figure 2.4 Frequency histogram comparing distances moved between consecutive trapping days (closed bars) and consecutive trapping sessions (open bars) for both a) male and b) female A. sylvaticus in the grassland and meadow sites. The maximum distance a rodent might register over the trapping period was 28m though the rodents in the grassland site were known to range over greater distances, occasionally being caught in the adjacent study sites of Churchfield & Brown (1987) up to 40m away (S J. Churchfield, personal communication). This observation implied the distance distributions portrayed in Figure 2.4 were sharply truncated to the right. Tanton (1965) estimated the mean distance moved between successive

47 captures for a woodland A.sylvaticus population to be 39m, though due to the wide trap spacing this value is probably an overestimate. Movements by grassland A sylvaticus may be greater than those of woodland (Attuquayefio, Gorman & Wolton, 1986). A mean movement of 7m between successive captures is probably insufficient to permit random mixing of marks, but the above discussion implied that this was probably an underestimate of true movement. The variation associated with this mean and knowledge of the natural mobility of A. sylvaticus was indicative that random mixing of marks in both sites was likely. iv) Samples must be taken at random: an anim al’s mark, sex or age should not influence its chance o f capture. It is often difficult to verify the assumption that sampling is completely random (De Lury, 1954), but as pointed out by Jolly (1965), the important feature of CMR analysis is that the probability of capturing a marked animal (its trappability) is the same as that of capturing any member of the population. Here, the number of captures during the 4 day trapping period, was used to examine the relative trappability between sites, sexes and between those animals making their first appearance and those caught in an earlier sampling period. Frequencies of capture are illustrated in Figure 2.5. Data were collated in each site over the 19 sampling periods and subjected to a three factor analysis of deviance using GLIM with Poisson errors. In contrast to the analysis of marking, new individuals were significantly less trappable than established ones (x2=13.55, d.f. 1, pcO.OOl). This was evident in both the grassland and meadow sites (Figure 2.5). There was no overall difference in the trappability of males and females (%2=2.58, d.f. 1, n.s.) nor between sites (%2=1.39, d.f. 1, n.s.). None of the interactions were significant. The difference in trappability between first and subsequent capture samples was not attributable to trap avoidance by A. sylvaticus (see i) above) but did imply that some animals caught for the first time behaved differently towards traps than animals recaptured. It is possible that the newly caught sample was composed of both resident and transient animals which had different trappabilities, while the recapture sample was composed of resident animals only. To test this hypothesis the analysis was repeated with those animals only ever caught once (transient individuals)

4 8 grassland meadow

number of days captured

Figure 2.5 Proportion of A. sylvaticus caught one, two, three or four times in a single sampling period in the grassland and meadow sites. Data collated for all sampling periods December 1986 to June 1988. a) and b) male and female mice during the first month of capture. Unshaded portion represents proportion of transient individuals, c) and d) male and female mice during their second and subsequent months of capture. Triangles indicate mean number of captures. Heterogeneity in capture frequency within each capture subgroup in each site was tested using a G-test (Sokal & Rohlf, 1981) for the goodness of fit to a zero truncated Poisson distribution (David & Johnson, 1952). Asterisks denote capture subgroups in which their was significant heterogeneity in captures within trapping samples (* p<0.05; ** p<0.01).

49 omitted. No significant difference now existed between newly and subsequently caught individuals (%2=1.01, d.f. 1, n.s., Figure 2.5). Thus, while trappability of newly and subsequently caught individuals did differ, this difference was a product of the residency of the mice and not an artefact of marking. Neither sex nor mark influenced the chance of captureA. of sylvaticus in the present study. The majority of CMR models require that all individuals regardless of their age have an equal chance of surviving from one sample period to the next. Failure to comply with this assumption of age-independent mortality may be one of the most common violations of CMR models in studies of small mammals (Montgomery, 1987). Detailed study of the age structure of the small mammal populations was not undertaken. However if survival was age-independent, loss of individuals should decline exponentially so that the relationship between the logarithm of the frequency remaining in a class and time should be linear (Bloweret al., 1981). The assumption of age-independent mortality is examined in Figure 2.6, where the frequency distribution of the "known" length of life of mice (the number of months over which an individual was known to have been resident in a site) is displayed for each site. Linear regression statistics are provided as a measure of the goodness of fit of a straight line. There was no significant non linearity as determined by polynomial regression analysis in any of the "survivorship" curves. The patterns in both sites were similar (detailed between site comparisons of longevity are dealt with in chapter HI). If sexes are taken together (Figure 2.6a) there was good agreement with a linear relationship in both sites. If sexes are examined separately the relationship weakened for males but remained strong (Figure 2.6b) but disappeared for females (Figure 2.6c). This possible violation of the age-independence assumption for females may have been a product of the small sample sizes and of the crude technique of assessing survival. However, this finding questioned the validity of population estimates derived from examining the size of each sex subpopulation separately. Significant heterogeneity in capture frequency within trapping samples was detected in three subgroups, each showing fewer animals than expected being captured only once (Figure 2.5).

50 grassland meadow

- "" . a) • ■ ■ ■ ■ 100 ' ■

■ ■ ■ ■ ■

■ r2**0.8440 r2=0.9181

2 8-

known length of life (months) Figure 2.6 Relationship between known length of life (months) and the number surviving (scaled as out of 1000 for ease of comparison) for a) male and b) femaleA. sylvaticus in the grassland and meadow sites. Values of r2 are provided as a guide to the degree of confidence that may be placed in the assumption of linear age-independent mortality.

51 Similar heterogeneity was found for woodlandA. sylvaticus by Montgomery (1985). These results suggest that sources of variation other than site, sex, age and experience were important in the trappability of A. sylvaticus. Heterogeneity in capture frequency may be expected because of the collation of data over a large number of trapping periods (Montgomery, 1987). Kikkawa (1964) showed that at least for woodland populations ofA. sylvaticus heterogeneity in capture frequency varied between trapping periods. The size of the data set prevented examination of heterogeneity in capture probability for each monthly sample, however the data set was broken down to enable an examination of the seasonal variation in heterogeneity. In order to maintain a reasonable sample size neither sex nor mark subgroups were separated and this permitted direct comparison with the data of Kikkawa (1964). Seasonal periods were lumped together if total captures fell below 10, and lumped across capture frequencies if any expected frequencies were less than 5 before a G-test of goodness of fit was undertaken (Sokal & Rohlf, 1981). Breaking down the data set into seasonal periods reduced the observed heterogeneity in capture frequency (Figure 2.7). No seasonal sample in the grassland site displayed significant heterogeneity, and in the meadow significant heterogeneity was only evident in spring. Increased numbers of rodents in a site may lead to heterogeneity in capture frequency through a rise in the mean number of captures per trap. As the mean capture per trap rises towards unity the traps sample a decreasing proportion of the true population. Flowerdew (1976) recommended that to avoid such biases the mean capture per trap should never rise above 0.80 on any sampling occasion. On no occasion throughout the study were mean captures per trap greater than 0.80 (highest values were 0.50 and 0.76 in the grassland and meadow site respectively) and the seasonal means reflect this (Figure 2.7). Suchresults mightimply that this potential source of heterogeneity was unimportant in this study, but Flowerdew (1976) worked on the assumption that rodents were randomly distributed in space. This was clearly not the case in the meadow (Figure 4.1). Spatial aggregation of rodents with even a moderate mean capture per trap, implies that in certain areas of a site, the mean capture per trap will exceed 0.80. In such areas those mice whose centres of activity are found nearest traps

52 grassland meadow

a)

i t t 4

b)

c)

1114

d)

®)

12 14

number of days captured

Figure 2.7 Proportion of A. sylvaticus caught one, two, three or four times in a single sam pling period in the grassland and meadow sites. Data collated for both sex and mark subgroups and examined seasonally. For grassland a) W inter 1986-Spring 1987, b) Sum m er-Autum n 1987, c) W inter 1987 d) Spring 1988. For the meadow a) Winter 1986-Spring 1987, b) Summer 1987, c) Autumn 1987, d) Winter 1987, e) Spring 1988. Triangles indicate mean number of captures. T f the mean percentage of traps occupied on any one sampling occasion. A sterisks as in Figure 2.5.

53 will tend to be caught more frequently. v ) Patterns in captures and recaptures in sequential samples should be consistent with random sampling. Sophisticated CMR models require several successive samples from the population understudy. Estimates may be influenced by the incidence of new individuals and the rate of recapture in the series of samples. As sampling progresses, the former might be expected to decline while the later increases; provided a significant proportion of the population is captured (Montgomery, 1985). Also the sample size should remain fairly constant if there is no aversion to traps after initial capture and the population is sampled randomly. These propositions were examined with respect to between site differences. The data consist of the number handled, the number of new individuals - both as a proportion of the total number of individuals handled in the sampling period - and the proportion of recaptures in daily samples, over the four days of each sampling period in each site. These proportions were subjected to a two-way analysis of deviance using GLIM with binomial errors. Between 40% and 70% of individuals known to be alive at the end of a trapping period were caught each day (Figure 2.8a). There was no significant variation in the capture rate between sites (%2=2.34, d.f. 1, n.s.) though mean capture rate was in general higher in the meadow. The level of capture increased after day 1 such that variation over the 4 day trapping period was significant (%2=22.99, d.f. 3, p<0.001). This implied that an initial delay existed in the sampling period before the small mammal population was effectively sampled. The appearance of new individuals (either by birth or immigration) over the four days differed between sites (Figure 2.8b). A significantly greater proportion of new individuals were caught in the grassland site (%2=4.775, d.f. 1, p<0.05) than in the meadow implying the latter population to be more closed. However examination of the mean numbers of new captures per trapping session revealed no difference between sites (x2=0.014, d.f. 1, n.s.) with a mean of 3.72 and 3.83 new captures per session in the grassland and meadow site respectively. The difference between sites

54 grassland meadow

Figure 2.8 Capture characteristics ofA. sylvaticus over a four day trapping period in the grassland and meadow sites, a) proportion of total catch initially caught on any day. b) proportion of total catch initially caught on any day. c) recaptures as a proportion of the catch on days 2 to 4. Bars indicate one standard error of the mean.

55 in proportions of new captures arose because of significantly higher total captures per session in the meadow (%2=4.0, d.f. 1, p<0.05,10.7 animals caught per session in the meadow compared to 6.8 in the grassland). The proportion of mice caught for the first time during the trapping session differed significantly between days (%2=10.4, d.f. 3, p<0.05, Figure 2.8b) with a decline between days 1 and 3 and then levelling off after day 3. This rapidly declining catch of new individuals suggested that a high proportion of mice present in the sampling area were captured over the 4 days of trapping. This is also indicated by the increase in the proportion of recaptures in daily samples (Figure 2.8c), which rose significantly from over 50% on day 2 to over 80% on day 4 (%2=23.22, d.f. 2, p<0.01) and showed no difference between sites (%2=2.33, d.f. 1, n.s.). Both the pattern of the proportion of new mice caught and of the proportion recaptured were consistent with random sampling. v i) Sampling must be performed at discrete intervals and the time spent sampling must be small in relation to the intersampling periods. vii) The population understudy must be effectively closed: gains and losses during sampling must be negligible. The distinction between open and closed populations is fundamental to the construction of statistical models of CMR analysis. Small mammal populations, however, are rarely completely closed and the biologist requires some method of determining whether CMR methods for the estimation of closed populations might be applied to this data. It has generally been accepted that the best evidence for population closure is biological rather than statistical (Montgomery, 1987). It is certain that losses and gains occurred during sampling sessions in each site, and on two separate occasions one A.sylvaticus caught in the grassland was recaptured in the same sampling period in the meadow. There were no readily apparent spatial limits, physical or biological, to the populations under study and the areas sampled were small in relation to the mobility of the rodents. The degree to which movement of small mammals beyond the trapping area might have lead to biases in population estimation can be seen in the observed patterns of capture and recapture (Figure 2.8b, c). The high rates of recapture (>80%) and the rapid decline in the entry of new

56 grassland meadow

Figure 2.9 Capture characteristics of A. sylvaticus over four consecutive monthly trapping samples in the grassland and meadow sites, a) proportion of total catch over four months initially caught on any day. b) proportion of total catch over fourmonths initially caught in any sampling period, c) recaptures as a proportion of the catch in sampling periods 2 to 4. Bars indicate one standard error of the mean.

57 individuals imply that the population in both sites could be regarded as effectively closed during the 4 day sampling periods. A similar conclusion could not be drawn concerning population closure between successive sampling periods. The data set was broken down into a series of four consecutive monthly samples for each site and analysed as in v) (Figure 2.9). Comparison of Figures 2.8 and 2.9 revealed that the erratic temporal patterns of recaptures and the pattern of entry of new individuals into the population did not conform to the expectations of population closure (in none of the analyses of the data presented in Figure 2.9 were significant differences found either between sites or with time). 2.3.3 Fitting the most suitable population model The above discussion highlighted two important potential sources of bias: heterogeneity in capture probability and age-dependent survival rates. The solutions to these biases appear to conflict, the former requiring the data set to be broken down into subgroups before analysis, the latter requiring the lumping together of sexes. However, these sources of bias have little influence on the population estimate. In the case of the Jolly-Seber model, Carothers (1979) found that bias due to heterogeneity in capture probability was remarkably small (less than 1 % of the actual value) and estimates are not greatly affected by age-dependent mortality (Seber, 1982). It is clear from an examination of the validity of model assumptions presented in sections 2.1.4 and 2.3.2 that both the methods of Jolly-Seber and Fisher-Ford may be applicable to the present study. Southwood (1978) proposed that the use and comparison of more than one method of population estimate could be instructive and both these methods are compared below. Monthly population estimates determined by both Jolly-Seber and Fisher-Ford methods were calculated over the period December 1986 to June 1988 using Bailey’s correction for small sample sizes (Bailey, 1951) when sample sizes fell below 20 (Montgomery, 1987). The two CMR methods of population estimation provided similar estimates to each other and to the total number of individuals trapped (TNIT) and all three estimates are shown in Figure 2.10. The Jolly-Seber estimates tended on average to be below (89%, 91%) and Fisher-Ford estimates slightly above (113%,101%) the TNIT values (grassland and meadow respectively. This difference between the

58 1086 1987 1988 Figure 2.10 Total Number of Individuals Trapped (histogram) and population size estimated after the methods of Fisher-Ford (solid line) and Jolly-Seber (broken line) for A. sylvaticus in a) the grassland and b) the meadow sites.

59 two series of estimates is a likely result of the deterministic method of Fisher-Ford overestimating the survival rate between successive samples. There was strong correlation of TNIT values with both Jolly-Seber and Fisher-Ford estimates (respectively Spearman rs=0.941,0.907, d.f. 16, for the grassland and rs=0.978,0.943, d.f. 18 for the meadow, all p<0.01). These findings suggest that most of the A. sylvaticus population in each site was caught, and that both CMR methods provided a good measure of the numerical changes in field populations. This confirms the high probability of capture depicted in Figure 2.8. Mean survival rate over each of the 4 day sampling periods as calculated by the Jolly-Seber method was high for the total population and for male and female subgroups (91%, 90%, 90% respectively in the grassland and 97%, 97%, 92% respectively in the meadow). There was no sampling period over which the survival rate of either of the sexes fell below 50% and total population survival rate never fell below 70%. 2.4 Discussion

In general the patterns of captures of grasslandA. sylvaticus were similar to those of woodland populations studied by Montgomery (1985). The high trappability of this species enables accurate description of its demography to be undertaken even with the crude techniques of live trapping. The presence of significant heterogeneity in capture frequency has frequently been found in trapping studies ofA. sylvaticus (Kikkawa, 1964; Tanton, 1965; Montgomery, 1985). Previously, this heterogeneity was thought to prevent an accurate assessment of rodent population size from being undertaken (Tanton, 1965). However population models developed since then have enabled errors due to such heterogeneity to be minimised (Jolly, 1965; Seber, 1965). This heterogeneity in capture frequency has been thought to be a result of either positive (trap-happy) or negative (trap-shy) rodent behaviour towards traps (Tanton, 1965; Montgomery, 1985, 1987), whereas Kikkawa (1964) concluded that heterogeneity in capture frequency was a product of increased population size and intraspecific competition. While competition, in particular territoriality within a rodent population will lead to increased heterogeneity in capture frequency, as in Mus musculus (Crowcroft & Jeffers, 1961), this in itself is not an explicit explanation of the source

60 of heterogeneity. In the present study heterogeneity was more likely to be a result of the sampling programme. If captures per trap are very high, even if only in subareas of a study site, which is likely if the rodent distribution is aggregated, then bias will occur in the data gathered. Such bias may be reduced by increasing trap numbers per point. In the present study, use of only one trap per trap point inevitably led to heterogeneity in capture frequency occurring in the meadow when mean captures per trap were highest. Nevertheless this source of bias will only influence the population estimates in spring 1988. The patterns of capture in both the grassland and meadow sites were similar, although there was an indication that populations in the grassland were more open than those in the meadow. This will not affect population estimates directly, but by reducing the number of marks liable to recapture may detract from the precision of Lincoln Index based estimates. The high probability of capture and high intrasample survival rates indicated that the Jolly-Seber estimates of population size were the most appropriate to the data sets, though the low monthly sample sizes (less than 10 animals were caught in 84% and 53% of the grassland and meadow samples respectively) implied that the variances associated with such estimates will tend to be unreliable. Having determined the most suitable model of population estimation, it is now possible to examine A sylvaticus demography in relation to previously studied populations. In particular three main demographic features were examined: the level of population density, temporal variability in numbers and heterogeneity in spatial distribution of mice.

61 CHAPTER III TEMPORAL DYNAMICS OF GRASSLAND SMALL MAMMALS 3.1 Introduction

Comparatively few studies have examined the demography ofApodemus sylvaticus in habitats other than deciduous woodland. Whereas the population dynamicsA. of sylvaticus have been extensively studied (Flowerdew, 1985; Montgomery 1989a), confusion is evident in the literature concerning whether the demography of populations in other habitats is similar to that in woodlands. Flowerdew (1985) stated that ''in arable land and hedgerows A. sylvaticus follows the same pattern of change in density as in woodland" while in contrast Montgomery (1989a) claimed "the annual dynamics ofA . sylvaticus in non woodland habitats, principally arable fields, grassland and young plantations, and woodland adjacent to arable fields, differed from those in mixed or deciduous woodland".

The aim of the present study was to compare the population dynamicsA. of sylvaticus in two contrasting grassland habitats with previous studies in similar habitats (Table 3.1) and in particular with demographic patterns in woodland. Three questions were to be answered:

i) To what extent are densities ofA. sylvaticus similar in different natural habitats?

ii) Do the annual dynamics ofA. sylvaticus differ depending on habitat?

iii) Does the demographic composition and population structureA. sylvaticusof vary between habitats? 3.2 Methods and materials The following analyses were all undertaken on data gathered from the live trapping study described in chapter n. 3.2.1 Estimation ofApodemus sylvaticus density While the index of abundance provided by the Jolly-Seber estimates may have enabled between site comparisons, an estimate of density is required if population estimates were to be

62 compared with published studies. Although the area encompassed by the traps was 20m X 20m in each site, the true area sampled was likely to be greater due to an edge effect. Flowerdew (1976) described a number of methods by which this bias may be accounted for, though no guide-lines were presented as to the suitability of each method. Due to the small size of the trapping grids, edge effects were assumed to be large and the method of Smith, Gentry & Golley (1969) was used. This entailed the addition of a boundary strip to the edge of the grid, its width equal to the distance from the edge of the grid to the first trap line with a probability of capture similar to that of the inner trap lines. The small grid size used meant that the strip width was equal to the distance between the central trap and the outer traps (10 m). This increased the trapping area from 0.04 hectares to 0.16 hectares. 3.2.2 Calculation of population parameters In order to successfully describe fluctuations in population density, knowledge of the various population processes underlying the fluctuations is necessary. Birth, death, immigration and emigration are the four fundamental demographic parameters in any study of population dynamics (Begon & Mortimer, 1981). The Jolly-Seber model provides estimates of loss (mortality and emigration) and dilution (immigration and birth) and previous studies have been criticised for not exploiting these parameters fully (Begon, 1983). However when sample size is small, as in the present study, the Jolly-Seber model may seriously underestimate loss (Bishop & Sheppard, 1973) and therefore extrapolation of intrasample to intersample loss estimates will lead to significant errors. Montgomery (1987) suggested that survival rates should be calculated between sampling periods using all captures over a sampling period as a single sample. The Jolly-Seber model requires at least three samples (Blower et al., 1981) and it is clear from Figure 2.9 that violations of CMR assumptions over such long intersample periods will lead to biases in the parameters estimated. Additionally, estimates of dilution require even further samples. It is apparent that while the Jolly-Seber model is a powerful tool, it cannot be used to its fullest extent in the present study. Alternative methods of estimation of loss and dilution were therefore employed.

63 Loss Following Chitty & Phipps (1966) minimum survival rate was based on the subsequent recapture of animals released and is calculated from the formula:

Pmin_ . = number------known------to be alive at t+x number of animals released at t where x is the interval between trapping occasions. Although described as a survival factor, "mortality" (1 - Pmin) includes both death and emigration and is more accurately described as loss. Although methods exist which enable separation of death from emigration, they require the breaking down of sample sizes and in the present study final sample sizes were too small to enable successful analysis. Additionally, these methods do not separate between permanent and temporary emigration, which will lead to an overestimation of emigration. The inability to separate death from emigration unfortunately reduces the power of analysis, however this is a common limitation of small mammal studies. Previous studies have estimated Pmin over a range of time periods ranging from fourteen days (Tanton, 1969) to forty days (Smal & Fairley, 1982), while other studies have used variable time periods making comparison of estimates impossible (Leigh Brown, 1977). In the present study, since intervals between sampling periods were irregular Pmin was standardised to a 30 day period after Smal & Fairley (1982) using the expression: logrPmin30 log30 Pmin, = T where T= the time interval between sessions t and t+x. A standardised period of 30 days was chosen since this enabled comparison between the present study and previous studies in woodland (Watts, 1969; Montgomery, 1980a) and grassland (Jamon, 1986). This standardised procedure assumed constancy of survival between trapping periods. In addition the period of residence a mouse was on a site was calculated as an estimate of rates of loss from the population. Although values tend to differ from direct estimates of mortality (Gibson & Delany, 1984), period of residence has often been used to gauge

64 survivorship of woodmice (Kikkawa, 1964; Tanton, 1965; Smal & Fairley, 1982; Gibson & Delany, 1984; Jamon, 1986). It has the advantage over estimates of Pmin in that it enables the extent of temporary emigration occurring during a sample to be estimated. Gain Two measures of gain were used in the analysis. The first, mistakenly described as recruitment by Jamon (1986) and more correctly described as influx I is the ratio of mice entering the population from session t to t+1 to the population present at t. This value was standardised as above such that: Xt = Pmin, +/, where \ = the rate of population increase (Kikkawa, 1964). The second measure was dilution D, which is the ratio of newly caught mice to previously caught mice within each sample period. This value enabled the structure of the population at each sample to be assessed. In recapture data there is no established way of distinguishing immigration into an area from births which occur there. The direct determination of small mammal birth rates from field studies is notoriously difficult (Krebs & Myers, 1974) and similarly to previous studies this was not attempted. Even distinguishing immigrants from recently bom residents is prone to error. For example immigrant bank voles are often young juveniles and tend to be lighter animals which reduces the effectiveness of weight derived age criteria (Morris, 1972). Juveniles were distinguished in the present study by pelage colour and size. Since these methods may also suffer from the similar biases of weight derived age criteria, a change in the population sex ratio brought about by breeding related changes in social organisation (Flowerdew, 1985) and presence of females in reproductive condition, were also used to gauge the extent of reproduction occurring within a site.

65 YEAR 1986 1987 1988 MONTH D J FM A M JJ A SOND J FM A M J grassland density 43.75 12.50 6.25 12.50 0.00 18.75 6.25 18.75 50.00 50.00 6.25 25.00 31.25 25.00 25.00 62.50 68.75 50.00 75.00 Is 1.87 2.50 8.33 0.00 6.67 0.00 7.14 1.63 1.36 8.33 1.43 0.98 1.25 1.92 1.19 1.75 1.58 0.90 x2 37.10 30.00 38.67 21.00 52.30 21.00 60.90 34.70 27.60 38.67 30.00 23.70 27.70 35.10 28.10 42.00 38.00 20.80 P ** ** ** * * * meadow density 43.75 0.00 18.75 0.00 25.00 6.25 25.00 37.50 43.75 50.00 75.00 81.25 131.25 112.50 75.00 106.25 100.00 75.00 56.25 Is 1.41 3.33 4.86 1.67 4.86 2.29 2.12 1.62 1.73 1.39 1.23 1.03 1.65 1.03 1.00 1.35 1.35 x 2 32.50 35.80 54.90 27.30 54.90 43.40 43.10 37.10 39.33 38.00 35.20 25.60 41.60 25.60 24.00 36.20 34.50 P ** ** ** ** * * ** Table 3.1 Population density estimates derived from the Jolly-Seber model and Morisita’s Index of Dispersion I5, %2 and probability of deviation from a random spatial distribution (I6= 1.00) for A.sylvaticus in the grassland and meadow sites over the period December 1986 to June 1988. Asterisks denote significant departure from a random distribution (* p<0.05; ** p<0.01). AUTHOR HABITAT STUDY DENSITY (N7Ha) SPACING AREA % PEAK CATCH TIME (m) (m2) months max min mean mic herb tot r, woodland associated grassland Leslie, Chi tty & Chitty, spruce plantation 22 12 0 4 9 1.62 90 93 93 0.48 (1953) Hansson (1971a) spruce plantation 24 14 0 5 15 4.40 83 85 89 - Flowerdew, Hall & fenland 15 71 0 22 5 0.53 7 79 79 0.07 Clevedon-Brown (1977) fenland 9 43 3 17 5 0.53 54 85 85 - Gallagher & Fairley (1979) limestone grassland 8 16 12 14 25 4.00 0 0 low - Ferns (1979) larch plantation 12 87 3 35 8 0.63 47 73 77 0.30 Richards (1981) limestone grassland 26 77 1 29 10 0.90 45 66 68 0.42 limestone grassland 26 76 0 44 10 1.40 23 41 41 -0.3 rough grassland 26 122 0 61 10 0.85 26 60 62 0.49 open grassland Brown (1954) rough grassland 24 25 0 8 9 1.00 63 63 65 0.35 rough grassland 24 31 0 9 9 1.00 66 70 71 0.95 Fairley (1967a) sand dune grassland 12 13 1 7 27 1.20 0 0 low 0.52 Hansson (1968) abandoned field 41 6 0 1 25 4.00 91 91 92 0.45 pasture 19 4 0 1 25 4.00 73 73 79 0.46 Jamon (1986) Cam argue grassland 22 91 14 33 10 1.30 low low low 0.84* Cam argue grassland 22 55 6 26 10 0.80 low low low 0.90* Churchfield & Brown (1987) successional grassland 15 72 2 31 8 0.25 22 22 53 -0.27 Moreno & Kufrier (1988) Coto Donana grassland 12 20 2 10 ? 0.64 0 low low 0.81 present study grassland 24 75 0 31 5 0.16 0 0 14 -0.25 meadow 24 131 0 51 5 0.16 8 8 8 0.36 Table 3.2 Comparison ofA. sylvaticus densities previously recorded in grasslands with those of the present study, in relation to variation in the habitats studied, their respective small mammal communities and the experimental designs used. The value of r, denotes the Spearman rank correlation between the annual population dynamics of the mice and idealised woodland population dynamics (Taken from Montgomery (1989a). Asterisks denote significant correlation (* p<0.05). 3.3 Results 3.3.1 Population density The density range 0-75 individuals per hectare in the grassland and 0-131 per hectare (Figure 3.1, Table 3.1) in the meadow were comparable to the range of densities found in previous studies of grassland A. sylvaticus, 0-122 per hectare though variation in mean density was large (Table 3.2). 3.3.2 Temporal fluctuations

1986 1987 1988

Figure 3.1 Estimated population size ofsylvaticus A. in the grassland (solid line) and meadow (broken line) sites over the period December 1986 to July 1988.

68 The variation in population density over the period December 1986 to July 1988 in both sites is illustrated in Figure 3.1. Two important points are evident. First, that in both sites densities over the first six months of 1988 were between two to five times higher than for the first six months of 1987. This was most likely a reflection of both the severity of the winter/spring of 1986-87, and the relative mildness of the similar period the following year. Second, in 1988 the grassland population lacked the characteristic over-winter peak and spring decline observed in woodland populations (Watts, 1969; Southern, 1979; Montgomery, 1989a) which was evident in the meadow site. Initially, temporal patterns were similar in each site, an autumn high in 1986 crashed in the following winter followed by a summer of low numbers which ended in the autumnal increase in September. At this point the patterns in the two sites diverged. The meadow continued the autumnal increase leading to an overwinter high followed by a spring decline, whilst in the grassland numbers dropped in October 1987 and slowly recovered over the following six months producing a late spring peak which began to decline in July 1988. 3.3.3 Demography Aof. sylvaticus in grassland The capture rate and therefore estimated population size of small mammals is prone to environmental noise. Weather conditions and moonlight (Kikkawa, 1964) may affect capture rate. Catches are increased if the weather is warm and overcast (Flowerdew, 1976). Evans (1942) found that greatest numbers ofA. sylvaticus were caught in abnormally wet weather and least in periods of drought while Gumell (1978) found that cold wet weather reduced catches. These sources of variation which are often not related to the underlying population fluctuations may lead to errors in population estimates if the trapping period is short. To overcome such biases discussion of the temporal fluctuations was restricted to seasonal periods rather than monthly sampling periods. Below, the seasonal patterns in density fluctuations depicted in Figure 3.1 are described in relation to the population parameters described above (Table 3.3).

69 SEASON winter spring summer autumn winter spring grassland density 20.63 10.42 25.00 27.08 27.08 60.42 X 0.8837 0.1539 2.1180 1.9950 1.7840 0.8003 Pmin 0.3918 0.1539 0.3025 0.7011 0.6569 0.6801 Influx 0.4919 0.0000 1.8150 1.2940 1.1270 0.1418 Dilution 0.2500 0.7500 0.5833 0.3571 0.3619 0.3465 sex ratio 0.5000 0.5000 0.5000 0.7000 0.6900 0.6250 h 2.7200 2.2200 2.4000 0.8600 0.9500 1.3800 x 2 63.50 35.00 63.00 24.14 21.62 50.00 P ♦♦ n.s. ** n.s. n.s. ** meadow density 20.83 10.42 35.42 68.75 106.30 93.75 X 0.2275 1.3410 1.5670 1.2420 1.0700 0.7739 Pmin 0.2275 0.8409 0.8265 0.6562 0.7383 0.5886 Influx 0.0000 0.5000 0.7374 0.5859 0.3222 0.1854 Dilution 1.0000 0.3000 0.3393 0.4276 0.2536 0.2170 sex ratio 0.5000 0.6000 0.8000 0.8260 0.7300 0.7730 I 8 1.5900 3.5300 2.5300 1.5000 1.1900 1.1700 x 2 39.85 54.31 91.11 64.37 48.85 48.49 P * ** ** *♦ ** ** Table 3 3 Population data for six seasonal periods over which fluctuations in abundancesylvaticus of A. were monitored. The following values (apart from the dispersion data) were calculated as seasonal monthly means: density, mean estimated population density per hectare determined by Jolly-Seber model; X, mean rate of population increase; Pm in, mean minimum survival; Influx, mean proportion of new individuals entering population; Dilution, mean proportion of population composed of new individuals; sex ratio, mean proportion of population composed of males. The values of Morisita’s Index were calculated for the whole seasonal period (legend is the same as Table 3.1). Winter ia8fi=lS8Z The populations in both sites crashed over the harsh winter of 1986-87, a result of the very low values of Pmin over this period, in particular between December and January (Figure 3.2a). The populations in both sites declined simultaneously between these months implying a similar cause. Both food supply and temperature are believed to be important in determining the rate of

70 decline in woodmice (Flowerdew, 1985). Heavy snow falls and very low ground temperatures occurred over the crash period, and deep (>30cm) snow cover was present in both sites for the greater part of January, which corresponded with the intersample period. Spring 1987 By spring, adult immigrants had moved into both sites, though numbers remained low (Figure 3.2b). The lack of any evidence of breeding behaviour in either of the sites in spring implied that variations in Pmin were not associated with changes in "strife" at the start of a breeding season (Flowerdew, 1985) but most likely reflected differences in population structure. Whereas 20% of woodmice caught in winter were still present in spring in the meadow none were recaught in the grassland. These residents comprised 40% of all woodmice caught in the meadow in spring, reflecting the low dilution (Table 3.3), while in the grassland numbers were maintained principally by immigration (Figure 3.2b). Summer 1987 By summer both sites supported a higher density of mice, reflecting the increasing plant productivity and food supply in the habitats. In the grassland, this increase in numbers was principally due to the entry of adult immigrants in August (Figure 3.2b), most of which were in reproductive condition. Such movements into grassland by adult woodmice are not uncommon, especially as seed abundance increases and are thought to be associated with woodmice moving from woodlands to adjacent fields to breed and avoid competition (Corke, 1977; Leigh Brown, 1977). While this is a possibility, in the present case it appeared most likely that these immigrants arrived as a result of local land management practice since the surrounding grasslands were being cut at this time and their resident rodents may have moved to areas of more consistent cover, such as the grassland site. In the meadow the resident adults reached reproductive condition in early summer. Breeding in the site was confirmed by changes in social organisation (reflected in the strong male bias in population sex ratio, Table 3.3), followed by captures of pregnant females and later, by the appearance of young in the population. The high population Influx at this time (Figure 3.2b),

71 1986 1987 1988

Figure 3.2 Monthly variation in a) minimum survival (Pmin) and b) Influx sytvaticusof A. in the grassland (solid line) and meadow (broken line) sites. Periods when no mice were captured prevented a continuous description.

72 reflected this recruitment into the population with over 50% of all individuals caught in summer being young juveniles. Therefore while a peak in Influx occurred in both sites in summer, in the grassland it was a result of immigration while in the meadow principally recruitment. Throughout the summer, Pmin was lower in the grassland than in the meadow (Figure 3.2a). Autumn 1987 In early autumn the grassland population comprised a few adult males and non-reproductive sub-adults which were the sole survivors of the August immigrants and a number of additional non-reproductive sub-adult immigrants. Although the survival of the autumn cohort was high (Table 3.3), the population crashed in October, a reflection of the low survival of the summer immigrants (Flowerdew, 1974) and lower rates of immigration into the site in autumn (Figure 3.2b). At this time one of the sub-adult females of the August immigrants reached reproductive condition (vagina perforate in September, presence of nipples in October). In November the first juveniles were caught in the site and by the spatial aggregation of their captures relative to the reproductive female, were assumed to be her litter (Tanton, 1965). The high survival of sub-adults and juveniles were initial conditions for an increase in population size. In the meadow 58% of the autumn population was composed of newly caught juveniles and 21% of juveniles first captured in the previous season. The presence of reproductive adults and the clumped distribution of juvenile captures was indicative that the newly caught juveniles were recruits rather than immigrants. Winter 1987 Although the population structure of the grassland over autumn appeared to herald the onset of a period of rapid population increase, numbers over winter remained relatively constant and low. Values of Pmin were particularly variable over this period (Figure 3.2b). The population in the meadow reached its peak abundance over winter. Loss from the meadow population fell over this period (Figure 3.2a, Table 3.3), a common observation in woodland

73 populations (Flowerdew, 1985). While juveniles still entered the meadow in December (Figure 3.2b), these were the product of the tail end of the breeding season in autumn and throughout the rest of the winter there was no evidence of any reproduction, nor immigration in the meadow. Spring 1988 Numbers in the grassland rose sharply between February and March and were a reflection of the entry of sub-adults into the population. The lack of any reproductive adults in the grassland at this time and the strongly male biased sex ratio (86%) of these animals suggested that these were immigrants rather than recruits. Survival of these spring immigrants and the resident woodmice remained below 70% and the fall in immigration over the following months (Figure 3.2b) led to the gradual decline in numbers as described by the rate of population increase falling below unity (Table 3.3). Spring survival was much higher than the previous spring reflecting the resident nature of the population in 1988. There was evidence of a typical spring population decline in the meadow. Loss from the population rose over this period and was higher than in the previous spring (Table 3.3). The upswing in the sex ratio at this time was indicative of changes in social organisation related to the onset of the breeding season. The uncertainty in attributing the source of loss from the rodent populations throughout the above discussion of seasonal patterns makes for difficult between site comparisons of minimum survival. In general, values of Pmin over the whole experimental period were significantly higher in the meadow than in the grassland (means 0.6610 and 0.5003 respectively, Kruskall-Wallis H=4.7772, d.f. 1, p<0.05). However, such variation in loss might have been the result of temporary emigration (related to rodent mobility), permanent emigration (dispersal) or in situ mortality. Since Pmin showed marked annual variation (Figure 3.2a), the data was broken down into two years, a crash year from December 1986 to November 1987 and a non crash year from June 1987 to May 1988 in order to examine the generality of the finding of higher Pmin in the meadow. Significant between site differences existed in the crash year (H=4.9558, d.f. 1, p<0.05) but not the non crash year (H=2.0058, d.f. 1, n.s.). This between year difference

7 4 grassland meadow a)

o •■e eCLo

123456789 10 11 123456789 10 11

residency (months)

Figure 3 Proportion of A.sylvaticus population residing for known periods of time (months) in a) a crash year from December 1986 to November 1987 and b) a non crash year from June 1987 to May 1988 in the grassland and meadow sites. Open bars reflect proportion of transient individuals. appeared to be associated with a jump in minimum survival in the grassland from 0.4086 in the crash year to 0.5852 in the non crash year while minimum survival in the meadow remained relatively constant 0.6594 and 0.7031 in the two years respectively. While between site seasonal

75 variations in Pmin have been discussed above, the rise in Pmin in the grassland was indicative of a between year change in Pmin over and above that due to seasonal variations. This sudden increase is clearly visible in Figure 3.2a. The distribution of period of residence in the grassland in the crash and non crash years were significantly different (H=3.8847, d.f. 1, p<0.05, Figure 3.3). Two patterns were discernible. First, the length of residence was greater in the non crash year with 17% of the animals remaining on the site for or more months compared to only 7% in the crash year. Second, the proportion of ephemeral captures (those animals caught in only one sampling period) was greater in the crash year with 70% of all captures being ephemeral compared to only 47% in the non crash year. These patterns reflected a lower rate of loss in the non crash year which may have resulted from increased survival or reduced emigration. 3.3.4 Comparison with other habitats In order to examine the dichotomy in views concerning the population dynamics of non woodland A. sylvaticus in comparison with woodland populations, Spearman rank correlations (Sokal & Rohlf, 1981) were carried out between data collected over one complete annual cycle gathered from the previous studies presented in Table 3.2 and characteristic woodland annual cycle data presented in Montgomery (1989a). The results are shown in the final column of Table 3.2 and it was apparent that both Flowerdew (1985) and Montgomery (1989a) were correct in so far as some non woodland populations do show characteristic woodland population fluctuations though most don’t. However, it should be noted that those woodmice in non woodland habitats that had similar population fluctuations as woodland populations were almost exclusively restricted to arid Mediterranean grasslands (Jamon, 1986; Moreno & Kufner, 1988) and were probably uncharacteristic of temperate non woodland habitats. Comparisons between the single study dynamics presented in Table 3.2 and the smoothed mean dynamics of the data in Montgomery (1989a) were prone to accentuate differences due to environmental noise. In order to reduce this bias, comparisons were made using the mean density values of either previous open grassland or woodland associated grassland data using

76 7 0

m on th

Figure 3.4 Mean population density ofsylvaticus A. extracted from published studies of populations studied over at least one year in woodland associated grasslands (n=6, broken line) and open grasslands (n=6, solid line). Sample sizes for monthly points are portrayed above figure. Only those points based on a sample size greater or equal to three are plotted in order to avoid small sample bias. only those studies that had used live trapping techniques with a small (< 10 m) trap spacing throughout at least one complete annual cycle of numbers. Since sample sizes were inevitably small, only those mean values based on sample sizes greater than or equal to three were used, so that individual study biases could be reduced. The mean seasonal patterns for both open grassland and woodland associated grassland habitats are shown in Figure 3.4. Spearman rank correlation analysis revealed that both open grassland (rs=0.800, d.f. 8, p<0.01) and woodland associated grassland (rs=0.7045, d.f. 10, p<0.05) population fluctuations were significantly correlated with the characteristic pattern of woodland population fluctuations presented in Montgomery (1989a). This would seem to imply

77 that environmental noise was a major source of discrepancy in the correlations shown in Table 3.2. However care should be taken in the interpretation of results based on small sample sizes, since the patterns observed may have been biased by certain studies. This potential source of error existed in the open grassland analysis which was strongly biased by the Mediterranean studies of Jamon (1986). If this study bias was removed from the open grassland data the correlation between the open grassland and woodland woodmouse population fluctuations disappeared (rs=0.125, d.f. 3, n.s.). In order to examine whether the fluctuations in the grassland and meadow sites were representative of particular habitats, Spearman rank correlations were undertaken between mean monthly densities in each site of the present study drawn over the whole experimental period and those mean monthly values drawn from Figure 3.4 and Montgomery (1989a). This analysis revealed that the meadow site while not exhibiting any significant correlation with previous temperate open grassland studies (rs=0.75, d.f. 2, n.s.) did with both woodland associated grassland (rs=0.7159, d.f. 9, p<0.05) and woodland (rs=0.7675, d.f. 10, p<0.01) studies. The annual fluctuations in the grassland site were not significantly correlated with any of these data sets (r,=-0.600, d.f. 3, n.s.; rs=0.1477, d.f. 9, n.s.; rs=-0.2378, d.f. 10, n.s.) nor with those of the meadow site (rs=-0.1608, d.f. 10, n.s.). 3.4 Discussion 3.4.1 Between habitat variation in population density In order to explain the variation in density among previous studies, a number of explanatory variables were collected. Both variations in experimental methodology and site characteristics may have influenced density estimates. Both trap spacing and study area varied between studies, the two apparently inversely related as a function of labour. While large study areas may present a more general assessment of the resident small mammal community, density estimates differ little from those derived from smaller study areas (Hansson, 1975) as long as edge effects are accounted for (Flowerdew, 1976). Trap spacing will influence estimates, a smaller trap spacing will catch a greater proportion of the resident population (Tanaka, 1966). In order to enable comparison of

78 previous density estimates, estimates were corrected to account for edge effects and those undertaken with a trap spacing greater than 1 Om were excluded. Clearly, methodological differences did account for some of the between study variation in density estimates, in particular very low density estimates, though variation still existed (Table 3.2).

The presence of other small mammals which might compete with A.sylvaticus and so reduce density estimates is displayed in Table 3.2, as the proportion of total captures which were other species of small mammal at the peakA. sylvaticus catch. These data were broken down to examine the relative importance of Microtus agrestis, all herbivorous small mammals (including Clethrionomys glareolus and Apodemus flavicollis) and all small mammals (including Sorex species). To avoid the confounding effects of habitat variation only those studies undertaken in Silwood Park grasslands (Brown, 1954; Churchfield & Brown, 1987; and the present study) were examined. There did appear to be an influence of the small mammal community composition on A. sylvaticus density, in particular the density of M. agrestis. Where M. agrestis densities were high, A. sylvaticus densities were low, a point discussed by Brown (1954). The extent of any competition between the two species is discussed in chapter HI.

While methodology and small mammal community composition may influencesylvaticus A. abundance, variations in the habitats examined such as levels of productivity and site management (grazing, cutting etc.) may also play a role. No data on these various aspects of previous studies were available, but the data set may be broadly divided into two groups: open grasslands and woodland associated grasslands such as forest edges and clearings. Woodland associated grasslands were differentiated from open grasslands primarily on their small mammal species composition, the former containing woodland specialists suchC. as glareolus and A. flavicollis implying a strong woodland edge effect. Mean population densities of A.sylvaticus were higher in the woodland associated grasslands than in the open grasslands (36.31 ha'1 and 23.78 ha'1 respectively). The few studies which have simultaneously examined both grassland and woodland habitats have always found higher A. sylvaticus densities in the latter (Brown, 1954;

79 Newson, 1960; Gallagher & Fairley, 1979) which might imply woodland associated grasslands should support a higher density ofA. sylvaticus than open grasslands. The data trend supports this hypothesis though its statistical significance is limited by the small sample sizes (Table 3.2).

While little data was available, it appeared that the densities of A . sylvaticus on the two experimental sites are comparable to previous grassland estimates of population size. The grassland site was representative of open grasslands studies, while the meadow site with its higher densities was more representative of a woodland associated grassland. This is not surprising since this latter site was bordered by a deciduous copse and the vegetation structure was more akin to woodland than grassland habitats (section 2.2.2). Whether this conclusion is supported by patterns of temporal variations in density is discussed below. 3.4.2 Apodemus sylvaticus demography in grassland That the population crash occurred at the onset of winter 1986-87 rather than spring 1987 was indicative of a reduction in food supply (Leigh Brown, 1977) being the cause, in concert with low temperatures, rather than antagonistic behavioural interactions (Gumell, 1978). The diet of grassland woodmice is composed of (in order of relative frequency), seeds, invertebrates and plant material (Churchfield & Brown, 1987). In grassland the abundance of invertebrates and the dietary quality of vegetation reach an annual low in winter (Stronget al., 1984) and seeds are scarce (Thompson & Grime, 1979), indicating that habitat quality in terms of food, is low for woodmice. While evidence for starvation occurring in woodmouse populations is circumstantial (Flowerdew, 1985), where it has been found it was associated with winter crashes in numbers (Leigh Brown, 1977; Green, 1979; Pelz, 1979). Although the exact source of loss in the present studies could not be stated, it was most probable that the seasonal low in food abundance, exaggerated by a poor acorn crop over Silwood the previous autumn, combined with sudden and prolonged freezing temperatures resulted in the death of the majority of woodmice in the sites. In spring, while both sites contained immigrant individuals, only in the meadow were a number of residents still present. It is possible that the relatively higher mouse survival in the meadow in spring 1987 was a reflection of higher resident survival, while the low values of Pmin

80 in the grassland reflected poor disperser survival. Flowerdew (1974) found that adult immigrant woodmice survived poorly in new sites even in the absence of resident individuals. Dispersing individuals are known to suffer high mortality (Gaines & McClenaghan, 1980), particularly through increased risk of predation (Metzgar, 1967; Ambrose, 1972). The data set was too small to examine this possibility statistically, however, the observation that in the meadow all residents first caught in winter survived throughout spring to be recaught in summer, while none of the immigrants first caught in spring in either site were ever caught in a future season, supported such a hypothesis. The question that then arises is why woodmice were able to establish themselves in the meadow and not in the grassland? Throughout the summer, Pmin was lower in the grassland than in the meadow (Figure 3.2a). The density of overwintered males is thought to determine the summer density of woodmice (Flowerdew, 1985), however, since the meadow was the only site where overwintered males were still present this could not be the cause of the discrepancy in Pmin between sites. The abundance of food at this time indicated that neither were variations in resources responsible. None of the woodland studies which observed emigration into neighbouring grasslands (Kikkawa, 1964; Bergstedt, 1965) examined the subsequent survival of emigrants. The immigrants into the grassland differed from the norm of breeding season dispersers (Fairbaim, 1978; Gaines & McClenaghan, 1980) in that they were primarily females in reproductive condition, suggesting that the cause of the dispersal was an external agent such as local grass cutting. The fate of these individuals was unknown. Only 20% of the females and 30% of the males were recaught one month later, possibly their loss from the population was a result of poor immigrant survival, exaggerated by the additional costs of reproduction. In the meadow Pmin was high (Fig 2.12a, Table 3.3) indicating that juvenile survival was high possibly related to the scarcity of overwintered adult males, suggesting that the population was entering the increase phase of its annual dynamics.

81 The high survival of woodmice in the grassland over autumn reflected the different population age-structure from previous seasons. The population was primarily composed of sub-adults some of which had reached reproductive condition and juveniles (some of which may have been immigrants). Flowerdew (1974) found that sub-adult immigrants benefited in terms of greater survival when emigrating from breeding populations while adults did not. No data exists on the relative survival of immigrants in relation to age though Gumell (1982) found poor adult survival over summer and autumn. Since most inputs into the grassland were through immigration, the dominantly sub-adult population in the grassland would be expected to survive longer than the previously adult immigrant population. Additionally, the lack of overwintered males in the autumn population would result in high juvenile survival, raising Pmin for the whole population in autumn (Figure 3.2a, Table 3.3). In the meadow in autumn the pattern of the disappearance of the previous adult population and its replacement by young individuals was consistent with the findings of woodland studies (Flowerdew, 1978). While loss from the population was low for this period (Table 3.3) it rose with respect to the previous spring and summer. The majority of the loss was among the younger cohorts. Though the source of the loss was unknown, Flowerdew (1978) pointed out that emigration, particularly of juveniles is highest during the annual phase of increase. The appearance of juvenile immigrants in the grassland at this time, suggested that emigration of juveniles was the main source of loss from the meadow population. The high rates of recruitment into the meadow population led the population size to increase over autumn. The increased overwinter (1987-88) loss of grassland mice, unlike that in the meadow, appeared to be associated with the adult rather than juvenile component of the population and was probably linked more to mortality rather than to dispersal. The seasonal reduction of food supply in the grassland may have been a cause of this mortality. Resource reduction may additionally have led to the curtailment of reproduction in the site, since no reproductive animals were caught over this period. Comparison with the previous winter revealed that although food

82 supply was similar in both seasons, survival in 1987-88 was higher. This confirmed the ideas of Flowerdew (1985) that while food supply is important in overwinter survival so are climatic conditions. A small number of juveniles entered the grassland in winter and the lack of any reproduction in the site and their random spatial distribution of captures suggested these were immigrants rather than recruits. Local grassland populations were high at this time (Churchfield, unpublished data) and these immigrants were probably the result of dispersal from these areas. In the meadow* the reduction in loss from the population reflected that at peak densities dispersal rates fall (Krebs & Myers, 1974; Fairbaim, 1977). Woodland studies have revealed that in spring, adult males show increased aggressive behaviour (Gumell, 1978) and females take up breeding territories (Wolton, 1985). These changes may be detrimental to the survival of subordinate individuals (Flowerdew, 1974; Gumell, 1978). Indirect evidence was available for increased aggression at this time in the meadow by the number of injured mice caught, particularly suffering from cut tails, an injury associated with interspecific aggression (Gumell, 1978). Whether the losses from the meadow population were through emigration or mortality is uncertain though two factors suggested the former cause. First, most losses were in the sub-adult male component of the population, a group often associated with spring dispersal (Watts, 1969; Flowerdew, 1978). Second, the spring immigration into the grassland at this time, must have been the result of emigration occurring elsewhere. While no individuals caught in the meadow were recaught in the grassland at this time, other local grassland populations also exhibited a sharp decline between February and May (Churchfield, unpublished data), similar to that of the meadow. If these local sites were the source of the grassland immigrants, then the similarity of the decline in the meadow was suggestive that this decline was also associated with emigration. It is improbable that variation in temporary emigration could account for the between year difference in Pmin between the crash and non crash years in the grassland. Grassland woodmice may be more mobile than their woodland counterparts (Attuquayefioet al., 1986) but the present study revealed no difference between sites in movement (Figure 2.4) nor in absence from the site

83 between successive captures (Figure 2.3) which would highlight possible biases due to temporary emigration. A rise in the value of minimum survival was therefore most likely to have resulted from an increase in survival or a reduction in permanent emigration or both.

Due to the high trappability ofA. sylvaticus (chapter II), it was possible to gauge the extent to which losses of the ephemeral individuals were due to emigration or mortality. If ephemeral individuals were transient dispersers moving through the site then since the grid size was small they would tend to be caught only once before moving off the site. Individuals which attempted to reside and remained on the site over the trapping period would have a high probability of recapture. Examination of Figure 3.3 revealed that approximately half of ephemeral captures in the grassland were due to transient individuals which passed through the site and that this proportion remained constant between the crash and non crash years (52% and 47% respectively). Therefore it is unlikely that the changes in Pmin in the grassland between crash and non crash years were due to more individuals becoming resident on the site but rather, individuals becoming resident on the site for longer periods due to higher survival. No data were available regarding the exact source of mortality of the rodents in the grassland. External factors such as predation or parasitism most probably influenced small mammal demography in both sites but no evidence exists to suggest that these factors were likely to vary between both sites and years. With this in mind differences in minimum survival were examined in the light of changes in population structure.

The initial grassland A. sylvaticus population differed from the crash year in that it was composed of sub-adult rather than adult immigrants. If survival is for the most part age-independent (Figure 2.6) then juveniles and subadults will possess greater expectancy of future life and will, all other factors being equal, tend to remain on the site for longer periods. The expected period of residence for an individual may be calculated from the difference between its present age and its mean life expectancy. For A. sylvaticus mean life expectancy from birth ranges from five (Gibson & Delany, 1984) to seven months (Southern, 1964) under natural field conditions. Non destructive assessment of rodent age is difficult and reliance has

84 tended to be placed on age/weight curves (Morris, 1972), though variation in such curves exist forA. sylvaticus both for different habitats (Flowerdew, 1972; Gibson & Delany, 1984) and seasons (Green, 1979). In order to conform with previous studies adults were taken as those individuals weighing 21 g or more (Flowerdew, 1972. 1974; Green, 1979; Montgomery, 1980a) and juveniles as weighing less than 15 g (Gumell, 1978) with sub-adult weights falling between these two values. Age criteria determined from the previously described growth curves indicated that the age of adulthood ranged from 3 to 5 months, sub-adulthood from 2 to 3 months and juveniles anywhere beneath this last set of figures. This implied an expected mean residence for adults of between 1-4 months and sub-adults 2-5 months. After the winter crash of 1986-87 the grassland population became effectively extinct and entries throughout the following year were through immigration of adults which had a low expectation of residency. Mean residency for this period was 1.57 months (Figure 3.3) a figure comparable to that of mean adult residency. Only in November 1987 did juveniles first appear in the site and the population was composed primarily of sub-adult immigrants. These individuals possessed a higher mean life expectancy as reflected in the rise in mean residence to 2.25 months in the non crash year (Figure 3.3) a figure comparable to sub-adult mean life expectancy. In the meadow, mean residence during the crash year was equal to that of the non crash year in the grassland (2.25 months, Figure 3.3). This reflected the younger population structure in the meadow due to breeding commencing in May, five months earlier than in the grassland. The eight month outlier (Figure 3.3) represented the survival of the only juvenile caught in either site before the winter crash, confirming that age at first capture was an important correlate of residency. Mean residency increased in the meadow to over three months in the non crash year (Figure 3.3), though the change was not statistically significant (Kruskall-Wallis H=2.3918, d.f. 1, n.s.). Neither was mean residency significantly different between sites in the non crash year (H=3.5206, d.f. 1, n.s.). However these statistical conclusions masked important differences in the patterns of residency. In the meadow the range of residency rose to 11 months (Figure 3.3)

85 though mean residency was prevented from reflecting this change by a related rise in transiency in the site. The long tail in the residency distribution reflected the presence of long lived individuals in the meadow. These individuals are believed to be important in determining the demography of A. sylvaticus, especially through their antagonistic interactions with juveniles (Flowerdew, 1985). The rise in transiency in the site reflected this antagonism in the form of juvenile emigration, in particular in the phase of population increase in the autumn (see above). It is therefore apparent that not only is the age structure of a population important in the determination of residency patterns but also the social structure, the two often being inter-related. Mean values of Pmin over 30 days for an annual period in previous studies ranged from 0.65 (Montgomery, 1980a) to 0.75 (Smal & Fairley, 1982) in deciduous woodland and 0.65 (Jamon, 1986) in grassland. It was evident that in the meadow site mean values of Pmin fell within this range in both the crash (0.659) and non crash (0.703) years (Figure 3.2a). The values of mean Pmin in the grassland in the crash (0.409) and non crash (0.585) years were consistently below the mean values of previous studies, and this again may be explained by differences in population age structure. Watts (1969) estimated minimum survival over 30 days for adult woodmice only and found a mean of 0.421, lower than the average for the total population studies quoted above. The mainly adult population in the grassland in the crash year had a remarkably similar mean value of minimum survival, reflecting this lower adult survival. In the non crash year the mean value of Pmin rose, but it was not until late Autumn 1987 that Pmin values reached a level comparable to previous studies (Figure 3.2a), and over the following six months mean Pmin rose to 0.679. This period of change in the survival of the grassland population corresponds with the transition between an adult-immigrant to a sub-adult resident population. 3.4.3 The population dynamics ofsylvaticus A. in different habitats Density fluctuations in the meadow appeared to be representative of density fluctuations occurring in other woodland associated grasslands and woodlands though not those of temperate open grasslands while those fluctuations in the grassland site were not representative of any

86 previously studied habitats. The lack of correlation between the present grassland study and previously studied grasslands may have been a result of the small sample sizes but may also have been a reflection that no characteristic pattern exists for grasslandssylvaticus population fluctuations. Montgomery (1989a) explained the lack of correlation between woodland and non woodland woodmouse population fluctuations, as the latter’s dynamics being more variable. Since the annual dynamics ofA. sylvaticus are characterised by seasonal variations in density, it is more correct to describe non woodland woodmouse population dynamics as being more erratic than those of woodland populations and that the characteristic seasonal variations are less discernible. The erratic fluctuations in non woodland populations may have been the result of increased environmental noise due to less temporally stable habitats and/or the sampling of low population densities or reflected inherent differences in the mechanism of population regulation.

This latter conclusion is in part supported by the present study. In the meadow,A. sylvaticus density fluctuations were characteristic of woodland populations and the population density within the site was principally determined by rates of birth, death and emigration with the influence of immigration being small in comparison. In the grassland, immigration appeared to play a more important part in the determination of population density. It therefore appears that densities of A. sylvaticus in woodland associated grasslands are determined by factors such as birth, death and emigration, which may be density-dependent (Montgomery, 1989a) and therefore potentially regulatory while those in open grassland are influenced more by immigration, which tends to be independent of the density of the resident population (Krebs & Myers, 1974). This explains why grassland population dynamics are so difficult to characterise. Although care must be taken when making generalisations from only one study, the grassland site shared a number of characteristics common to other grassland studies that may lead to similarities in their determination of population dynamics. Grasslands differ from woodland and woodland associated grasslands in that they tend to support lower densities ofA. sylvaticus (Table 3.2). If habitat suitability is temporally variable then local population extinctions will

87 tend to occur leading the population dynamics to be tied to recolonisation events such as immigration. Woodlands and woodland associated grasslands with their higher woodmouse population densities are less likely to suffer local extinctions and therefore immigration into such sites may play a secondary role with respect to recruitment in determining population density. In cases where woodland population dynamics have been examined at low densities (Montgomery, 1989b) little consistency in the temporal abundance was found. Perhaps it is not surprising that the studies of Mediterranean grasslands which supported high densities of A.sylvaticus (Jamon, 1986) reflected patterns similar to those of temperate woodlands. It is clear that further research into the demography ofA. sylvaticus in non woodland habitats is necessary if the population dynamics of this species are to be fully understood. 3.4.4 The demography of other small mammals in grassland

Apodemus sylvaticus numerically dominated the small mammal captures in both sites. The other species caught,Sorex araneus, Sorex minutus, Microtus agrestis and Micromys minutus, were either caught at low densities or for short seasonal periods preventing detailed analysis of their population dynamics (Figure 2.2). Shrews were caught throughout the first year of study and though CMR studies were not undertaken, the low total numbers of (re)captures per trapping session (<10) for both species combined was suggestive of low population densities. Shrews appeared more abundant in the grassland than in the meadow. In both sites patterns of captures and recaptures, peaking in summer, followed the seasonal patterns of abundance characteristic of shrews (Churchfield, 1986). Short-tailed voles were uncommon captures in both sites. Numbers were low throughout 1987, a phenomenon general to the Silwood grasslands (Churchfield & Brown, 1987). In spring 1988, numbers picked up in both sites and Microtus was a common component of summer captures. The sporadic nature of captures and the low densities recorded for the most part of the study did not permit more detailed examination of the population dynamics.

88 Harvest mice have previously been uncommon components of Silwood small mammal studies and the sudden appearance of relatively large numbers in the grasslands in autumn 1987 was unforeseen.Micromys minutus was caught in all samples in the grassland from September 1987 to May 1988 whereas in contrast in the meadowM. minutus was only caught twice, once in September 1987 and again in May 1988. The seasonal pattern of numbers in the grassland was consistent with the pattern characteristic of the species (Trout, 1978a). In general, peak numbers occurred in late autumn, and declined during winter and early spring leading to almost complete absent over summer. This pattern appeared to be due to harvest mice living above ground level among tall grass stems where they built their nests in summer. This "disappearance" of harvest mice was thus a product of the inadequate method of sampling the arboreal populations with Longworth traps. Grassland habitats provided less aerial cover as winter progressed and food resources above ground level declined after most seed had been shed. Harvest mice are known to move to ground level at this time where the make nests at the base of grass tussocks (Trout, 1978a). It was while they remained as ground dwelling animals that they were prone to capture in Longworth traps. It is unknown to what extent the decline in numbers over winter and spring was due to mortality or dispersal. Certainly by May breeding commences (Trout, 1978a) and the majority of the population had dispersed into the vegetation by this time. However it is difficult to understand any benefit to the harvest mice in returning to the arboreal habitat in winter so soon after leaving it in autumn since both food and cover above ground level were poor at this time. It is possible that the initial decline was linked to mortality while the latter part of the decline was due to dispersal up into the vegetation. The role of interspecific competition in these patterns remains unclear. The ability ofM. minutus to use arboreal habitats is undoubtedly the key to its coexistence with other grassland rodents. In winter, when both the availability of suitable arboreal microhabitats and food supply were low then interactions between species are likely to be highest. Interspecific interactions will tend to be restricted to this brief seasonal period and by spring M. minutus will use increasingly the arboreal habitat. The exact form of any interspecific interactions that might occur and their outcome await further study.

89 CHAPTER IV SPATIAL DYNAMICS OF A. SYLVATICUS ABUNDANCE 4.1 Introduction The spatial distribution of a herbivore with respect to its prey will determine the extent to which prey may escape from attack in space. The existence of spatial refuges from herbivory has important implications for the stability of herbivore-plant interactions (Crawley, 1983). This chapter reports the spatial relationship ofApodemus sylvaticus inhabiting two grassland sites. The study has four aims: i) To establish whether individuals are distributed at random, and if not what form the dispersion takes. ii) To investigate whether associations exist between the rodent distribution and physical or biological aspects of the habitat. iii) To examine the influence of other small mammal species sharing the same habitat in the use of space byA. sylvaticus. iv) To assess the relative influence of intraspecific behavioural spacing mechanisms and environmental factors in determining the spatial distribution ofA. sylvaticus.

The habitat requirements ofA. sylvaticus populations have been insufficiently studied to draw conclusions regarding the importance of particular environmental variables in determining rodent distribution. However, forMicrotus species, correlations have been found between spatial distribution and a variety of environmental factors, most notably soil (Hardy, 1945), moisture (Watson, 1956; Getz, 1961), food (Batzli, 1968) and vegetation cover (Bimey, Baird & Grant, 1976). In the present study only the latter two factors were examined with respect toA. sylvaticus.

The evidence for the role of vegetation cover in the spatial distribution ofA. sylvaticus is contradictory. While a number of studies have revealed correlations between vegetation cover and abundance in deciduous woodland (Fairley, 1967b; Corke, 1970; Fairley & Comerton, 1972; Gallagher & Fairley, 1979; Montgomery, 1980b) and grassland (Fairley, 1967a) a similar number

90 have not revealed such conclusions either in deciduous woodland (Evans, 1942; Miller, 1958; Kikkawa, 1964; Ashby, 1967; Southern & Lowe, 1968; Fairley & Jones, 1976; Smal & Fairley, 1982) or in grassland (Gallagher & Fairley, 1979). Montgomery (1980b) suggested that this variation may have been attributable to the extent of interspecific competition within the small mammal communities studied. However, there appeared to be no trend in this more extensive literature study, relating habitat preference of A. sylvaticus to numbers of eitherA . flavicollis nor C. glare olus which were the major potential competitors in the habitats studied above (Gumell, 1985). For temperate small mammal communities, Grant (1972) and Schoener (1983) have shown that species actively compete with each other at certain times, in certain habitats and this affects their local density and distribution. The role of competitors on A.sylvaticus density was examined in chapter HI and one of the aims of this chapter was to examine to what extentsylvaticus A. was spatially associated with the four other species of small mammal,Microtus agrestis, Micromys minutus. Sorex araneus and Sorex minutus which were caught in the two sites. 4.2 Methods and materials 4.2.1 Measurement of spatial dispersion Dispersion, that is the pattern of the distribution of a population in space was examined following Montgomery (1980b) using Morisita’s Index (Morisita, 1962; Elliot, 1977) and Taylor’s Power Law (Taylor, 1961; Elliot, 1977). While many indices of dispersion exist, Morisita’s Index (I5) is recommended when a detailed analysis of the pattern of dispersion is required, it is independent of the sample mean and total numbers in the sample but is a strong function of the number of sampling units (Elliot, 1977). Therefore it serves as a comparative index of dispersion when each sample contains the same number of sampling units. It has been widely used in studies of small mammals (Batzli, 1968; Grant & Morris, 1971; Montgomery, 1980b) and following Montgomery (1980b) has been used in the present study in the analysis of seasonal variation in dispersion since it is well suited to the marked fluctuations of rodent populations.

91 While correlations between I5have previously been used to examine the density-dispersion relationship (Grant & Morris, 1971) such methods are not suitable for between study comparisons due to the sensitivity of I5to the number of sampling units. A more useful technique is that of Taylor’s Power Law which is independent of the sample mean, total numbers in a sample and the number of sampling units (Elliot, 1977). Taylor’s Power Law requires a number of different sets of samples such that there are a number of estimates of variance and mean and is therefore unsuitable for the analysis of seasonal patterns. Following Montgomery (1980b) who first applied Taylor’s Power Law to small mammal populations, the mean number ofA. sylvaticus caught per sampling unit (trap set) for each of 19 monthly samples and its associated variance were calculated. This data differed from that of point counts often used in Taylor’s Power Law analyses (Southwood, 1978) in that by using data gathered from successive captures of individual animals in a single sample the data reflects the spatial distribution of movement. These values were logarithmically transformed and the variance plotted against the mean for each site. The slopes(b) and intercepts (a)of the curves are characteristics of the population and were calculated by regression analysis. Since log mean is not error free normal regression methods are prone to error (Bliss, 1971), though Southwood (1978) argued that normal regression should be sufficiently accurate in most studies. In the present analysis normal linear regressions of log Y against log X were used to estimate the parametersa and b and their statistical significance checked by regression of log X against log Y. 4.2.2 Assessment of the influence of habitat structural heterogeneity Vegetation samples were taken in late spring/early summer in 1987 and 1988. The point quadrat vegetation data (section 2.2.2) were broken down into live and dead vegetation components and subdivided into low vegetation (0-20 cm in height) and high vegetation (20-40cm in height). Due to the seasonal changes in vegetation structure, correlations were only made with small mammals caught over this period, rather than the whole experimental period as previous studies have undertaken. Since data were counts, regression analysis was carried out using the GLIM procedure with Poisson errors.

92 4.2.3 Assessment of the influence of food supply

Apodemus sylvaticus has a varied diet (Hansson, 1985), though in summer the dominant item is plant seeds (Holisova, 1975) and particularly in grassland, grass seeds (Hunter, Johnson & Thompson, 1987). As an index of the local food supply the seed rain over the period July-August 1987, was estimated in each site (chapter III). This data was broken down into large grass seed rain, small grass seed rain and forb seed rain and compared to the spatial distribution of A. sylvaticus over this sampling period. 4.2.4 Assessment of the role of small mammal interspecific association Southwood (1978) has stressed that, wherever possible, measurement of interspecific association should be based on presence-absence data. In the present study this was done for two species comparisons by examining capture data per session. At any single trap point there may have been no captures, captures of one species only or captures of two species. Comparisons between species were then made by compiling a 2 X 2 contingency table of presence-absence. However, since the proportion of traps which contained woodmice in any session was often low, the presence-absence data were lumped into seasonal classes following Montgomery (1980b). In the case of Sorex araneus and S. minutus, captures were so infrequent that the species were lumped together. 4.2.5 A comparative measure of behavioural and environmental influences Southwood (1966, page 34) described a method whereby using the negative binomial distribution, the relative roles of behaviour and environment in the determination of spatial distribution may be separated. Using the formula of Arbous & Kerrich (1951) for mean aggregation size (A,) then any value of A less than 2 indicates a distribution determined by environmental rather than behavioural means. Values of A. of two or more may be caused by behavioural means or by both factors. Mean aggregation size is calculated by:

93 where x is the mean number of captures per trap, v is a function with a %2 distribution at p = 0.5 and k is the negative binomial exponent describing the spatial distribution of captures. 4.3 Results 4.3.1 Dispersion Morisita’s Index (I5), whose significance from random (I5=1.0) was tested by a chi-square test (Elliot, 1977), indicated that A sylvaticus was significantly clumped in 38.9 % of grassland and 47.1 % of meadow samples (T able 3.1). These contrast with a value of 7 0% for woodlandA . sylvaticus (Montgomery, 1980b). However, in both sites I5was greater than 1.5 in over 50% of samples, indicating that the lack of statistical significance may have been the result of small sample sizes. To overcome this problem and to examine seasonal patterns of dispersion, monthly samples were lumped within seasons and seasonal values of I5 calculated. It was clear from the seasonal values of I5(Table 3.3) and the seasonal spatial patterns represented in Figure 4.1 that whiled sylvaticus was significantly clumped in all seasons in the meadow, this was only true in half the seasons in the grassland. As in woodland populations seasonal variations in dispersion were not well defined (Montgomery, 1980b). Montgomery (1980b) suggested that peak contagion may be due to the association of males, females and juveniles during the breeding season but no discernible patterns were evident in the present study regarding seasons in which reproduction took place and the pattern of dispersion. Seasonal variation in dispersion may be related to the seasonal fluctuations in density characteristic ofA. sylvaticus discussed above. The relationship between monthly I5 and density was negative in both sites but only significantly so in the meadow (Spearman rs=- 0.3308, d.f. 16, n.s.; r,=-0.8266, d.f. 15, p<0.01 respectively) and this pattern was maintained for seasonal I5as well (rs=- 0.5286, d.f. 4, n.s.; rs=-0.886, d.f. 4, p<0.05 for the grassland and meadow respectively). These results implied that at least in the meadow the degree of spatial clumping within theA. sylvaticus population was inversely related to its density.

94 i) iv)

ii) v)

iii) v>)

a) Figure 4.1 Seasonal changes in the pattern of abundance in the a) the grassland and b) the meadow sites. The large outer square schematically represents each site and the smaller inner squares the total captures of A. sylvaticus at each of 25 trap points distributed over each site. Seasonal periods are i) Winter 1986, ii) Spring 1987, iii) Summer 1987, iv) Autumn 1987, v) Winter 1987, vi) Spring 1988.

95 0 1-3 4-6 7-9 10-12

number of captures at each trap point Figure 4.1 continued.

96 Figure 4.2 Log X log plots of variance, on mean numberA. ofsylvaticus captures per trap for data over the period December 1986 to June 1988 in a) the grassland and b) the meadow sites. Broken line indicates log variance = log mean, points lying above this line tend towards aggregation.

97 HABITAT b S. E.b tb-l P a S.E.. L-o P grassland 0.96 0.10 0.04 n.s. 0.11 0.05 2.30 * meadow 0.84 0.07 2.19 * 0.17 0.02 7.01 * low mouse 0.97 0.10 0.36 n.s. 0.21 0.03 6.92 *** density high mouse 0.47 0.19 2.78 * 0.23 0.05 5.01 *** density woodland 0.81 0.22 1.91 n.s. 0.11 0.08 2.96 * Table 4.1 Statistics and their standard errors (S.E.) of regression lines fitted to log X log plots of variance on mean captures per trap, for the grassland and meadow sites, additional data from Montgomery (1980b) on woodland populations is provided for comparison. The deviations of the slopes (b), from 1.0, and the intercepts (a), from 0 are tested in t-tests. Asterisks denoting statistical significance follow the legend used in previous tables. Data was taken for a complete experimental period in all cases but was broken down in the meadow site to examine the existence of curvature in the plots.

HABITAT b S.E.b tb-l P a S.E.„ L-i P grassland males 0.91 0.16 0.56 n.s. 0.07 0.10 0.87 n.s. females 1.12 0.15 0.88 n.s. 0.23 0.09 2.48 * meadow males 0.97 0.07 0.47 n.s. 0.18 0.03 6.24 ** females 1.22 0.11 1.98 n.s. 0.24 0.08 2.94 * woodland male 0.99 0.10 0.20 n.s. 0.11 0.07 3.19 * female 0.92 0.05 3.25 * -0.03 0.05 1.22 n.s. Table 4.2 Statistics and their standared errors (S.E.) of regression lines fitted to log X log plots of variance on mean capture per trap, for the grassland and meadow sites broken down into sex subgroups. Additional data from Montgomery (1980b) on woodland populations is provided for comparison. Legend is the same as Table 4.1.

98 Variance was greater than the mean in all but one month in the meadow and all but four in the grassland such that in both sites most points lay to the left of the b=l line, which indicates conformity to a Poisson distribution (Figure 4.2). The level of contagion as indicated by the variance mean ratio, tended to change disproportionately with the population density; dispersion ofA. sylvaticus was almost random at high densities though at low densities aggregation of individuals was marked. In the meadow b was significantly less than 1.0, indicating that aggregation of woodmice was a function of population density (Table 4.1). In the grassland and woodland (data from Montgomery (1980b)) b did not deviate significantly from 1.0. These results appeared to confirm the results of the Morisita’s Index correlations above. There was no significant difference between the grassland and meadow slopes (t=0.7591, d.f. 31, n.s.) and for all three studies values of a and b were remarkably similar (Table 4.1). Examination ofTaylor’s Power Law regression statistics for the sex subgroups in the grassland and meadow as well as data from the woodland study of Montgomery (1980b) revealed no clear pattern regarding spatial distribution and density (Table 4.2). In no habitat were the slopes of males and females significantly different. 4.3.2 Habitat structure In the grassland none of the variables examined explained any significant variation whereas most did in the meadow (Table 4.3). Care must be taken in analysing the meadow results, since multiple regression techniques revealed that none of the variables explained any significant independent variation. For this reason, only live vegetation (20-40cm) was accepted as an explanatory variable since it was the single variable that explained most variation. In the meadow a maximum of 32.4% of total variance was explained by vegetation cover characteristics, which while significant was still low, and the maximum full model explained only 42.3%. The reason for this low value was twofold. First, small mammals were unlikely to respond to variations in vegetation cover in such a fine grained manner as was revealed by point quadrat samples. This is borne out to a certain extent by Figure 4.3a which shows small mammal abundance to jump upwards at around 2 touches/pin for live vegetation (20-40cm) with little evidence of a smooth

99 * P * n.s. n.s. n.s. meadow meadow 3.8 19.6 12.1 1988 - outlier - 1988 21.9 variation explained P n.s. <0.1 n.s. n.s. n.s. 17.1 n.s. n.s. 16.1 n.s. n.s. n.s. 35.8 n.s. 1988 meadow 3.5 n.s. 6.6 n.s. 9.5 n.s. 4.5 3.8 n.s. 10.1 10.5 n.s. 16.15 variation explained P n.s. 6.2 n.s. 1988 grassland 1.0 n.s. 2.0 n.s. 0.1 0.1 2.0 n.s. 13.4 n.s. <0.1 n.s. <0.1 variation explained * P * * * * ** n.s. 11.6 n.s. <0.1 n.s. 1987 meadow 32.4 26.9 variation explained in the grassland and meadow sites in early summer of 1987 and 1988. Final column refers to the re-analysis re-analysis the to refers column Final 1988. and 1987 of summer early in sites meadow and grassland the in P n.s. 1.0 n.s. 5.7 n.s. n.s. n.s. 23.5 n.s. 27.1 n.s. 19.3 n.s. n.s. 42.3 sylvaticus 1987 grassland 2.7 0.3 n.s. 3.0 0.4 0.1 n.s. 11.7 n.s. 1.7 n.s. 8.2 0.8 4.7 <0.1 n.s. 27.5 variation explained Proportion of variation explained by linear multiple regression (r2 X 100) in models relating vegetation cover parameters as determined by point point by determined as parameters cover vegetation relating models in 100) X (r2 regression multiple linear by explained variation of Proportion

live total dead per pin per (0«20cm) 0.2 43 (0-40cm) (0-20cm) (0-40cm) 0.3 (0-20cm) (0-40cm) (20-40cm) vegetation N° touches touches N° (20-40cm) vegetation vegetation (20-40cm) 2.1 whole model whole Table Table of the meadow data omitting an outlier (see text for details). Asterisks as in previous tables. previous in as Asterisks details). for text (see outlier an omitting data meadow the of quadrats to spatial distribution of A. A. of distribution spatial to quadrats

100 grassland meadow a)

f o

■g b) 3 C 7 ■ 1 ■ ------!------101 I 1 a 9- I 1 I 1 8- • 1 ■ a l 1 7- a 1 I 1 ■ * 6 a i □ □ 1 i 1 5 a □ 1 1 4- o 1 1 ■ a ■ 1 aa a a aa a 3 a a. 1 i I 2 a a a i 1 1 ■ a j a a i ------r------1------1------1------1------1------o-» i < • » i i i e-r r " i i » i rM" v‘"i r"'

live vegetation cover (20-40cm)

Chamaenerion angustifollum D Rubus fruticosus •

Figure 43 Graphical representation of the relationship between live vegetation cover (20-40cm) andA. sylvaticus abundance in the grassland and meadow sites in early summer of a) 1987 and b) 1988. Broken line represents possible threshold cover value above which mice are less sensitive to changes in vegetation cover (see text for details). gradient. Second, it was in part due to the vegetation sampling quadrats being placed at random with respect to the small mammal traps and therefore vegetation samples may not have corresponded exactly with the small mammal microhabitat. This discrepancy between vegetation and rodent samples was amplified by the fact that small mammal traps, unlike other samples were not placed at random but were placed to maximise captures (Flowerdew, 1976; chapter II). This meant that

101 they were placed in areas where vegetation cover tended to be greatest. In the grassland, where vegetation cover was abundant, this made little difference but in the meadow, where vegetation cover was variable, it meant that vegetation samples tended to undersample the microhabitat where rodents were captured. This sampling discrepancy was highlighted and the importance of cover confirmed by capture of woodmice in the stands ofChamaenerion angustifolium, which provided the densest cover, being significantly higher than expected (%2=66.31, d.f. 1, p<0.001) in comparison to other vegetation types (Figure 4.3a). Of all the vegetation parameters examined, only live vegetation (20-40cm) significantly differed between experimental sites being almost twice as extensive in the grassland than in the meadow (Kruskall-W allis H=16.39, d.f. 1, pcO.OOl, Table 4.4). The coefficient of variation provided a means of examining the variability of the parameters independently of their means, which for live vegetation (20-40cm) did differ between sites. Comparison of the coefficient of variation using a test of equality (Sokal & Rohlf, 1981) revealed that live vegetation (20-40cm) was significantly more variable in the meadow site (Table 4.4). In the 1988 sample, although the spatial pattern of the vegetation and its structure had changed relatively little in comparison to the previous year, woodmouse spatial distribution was no longer significantly correlated in either site with any of the measured vegetation variables (Table 4.3). Examination of the number of woodmouse captures in relation to live vegetation (20-40cm) revealed that some of the discrepancy between years and correlations in the meadow site was due to an outlying point (Figure 4.3b). This capture point was in denseRubus fruticosus agg. growth, surrounded by short, rabbit grazed grass; the cover values for the sample 5m X 5m quadrat greatly underestimated the cover of the microhabitat where rodents were caught. When this point was left out of the regressions, vegetation cover (20-40cm) was found to explain significant variation in relation to rodent abundance in the meadow, though less than it had in 1987 (Table 4.3).

1 0 2 N° touches mean N° of touches comparison between variance to mean distribution coefficient of comparison per pin site means ratio variation between site coefficients of variation grassland meadow H P grassland meadow grassland meadow grassland meadow F(24.24) P live vegetation (0-20cm) 7.60 8.60 3.77 n.s. 0.47 0.36 regular regular 24.6 20.0 1.37 n.s. (20-40cm) 2.99 1.60 16.39 *** 0.37 0.52 regular random 35.4 57.0 2.80 ** (0-40cm) 10.59 10.20 0.17 n.s. 0.50 0.43 regular regular 15.3 20.4 2.08 * dead vegetation (0-20cm) 9.73 9.38 0.52 n.s. 0.84 1.02 random random 29.4 32.9 1.05 n.s. (20-40cm) 0.45 0.67 2.37 n.s. 0.56 0.48 random regular 111.8 84.2 1.20 n.s. (0-40cm) 10.18 10.05 0.25 n.s. 0.91 1.10 random random 29.9 33.0 1.01 n.s. total vegetation (0-20cm) 17.33 17.98 0.62 n.s. 0.54 0.55 random random 17.8 17.5 1.03 n.s. (20-40cm) 3.44 2.28 8.36 *** 0.50 0.82 regular random 37.9 60.1 2.37 * (0-40cm) 20.77 20.26 0.44 n.s. 0.56 0.79 random random 16.4 19.7 1.35 n.s. Table 4.4 Comparative data on vegetation cover parameters used in the examination ofsyivaticus A. spatial distribution in Table 2.8. Data are mean number of touches per point quadrat compared by a Kruskall-Wallis non parametric one way ANOVA (H); variance to mean ratio enabling comparison of the distribution of parameters and the coefficient of variation as an index of the variability of the parameters. Asterisks as in previous tables. seed mean seed density per m2X 103 distribution variance variance categories explained P explained P grassland meadow grassland meadow grassland meadow large grass 4.61 0.87 aggregated aggregated 1.6 n.s. 19.4 * small grass 8.02 7.16 aggregated aggregated 3.7 n.s. <0.1 n.s. total grass 12.63 8.03 aggregated aggregated 5.5 n.s. 7.6 n.s. total forb 1.63 5.72 aggregated aggregated 3.3 n.s. 3.2 n.s. total seed 14.27 13.75 aggregated aggregated 10.3 n.s. <0.1 n.s. whole model 24.6 n.s. 24.8 n.s. Table 4.5 Comparison of seed density parameters, their means, distributions and the proportion of variance they explained in linear multiple regression relating to A. sylvaticus spatial distribution in the grassland and meadow sites in summer 1988. Interspecific Association number of samplesspatial association P grassland x2 Microtus agrestis 2 4.53 *

Sorex species 3 0.49 n.s.

Micromys minutus autumn 3 0.26 n.s. winter 3 4.83 * spring 3 0.95 n.s. total 9 6.22 * meadow

Sorex species 3 0.02 n.s.

Table 4.6 %2 values from contigency analyses using presence-absence data examining the significance of negative spatial association between A. sylvaticus and other small mammal species co-occurring in the grassland and meadow habitats. 4.3.3 Food supply The results of the regression analysis revealed that seed rain played little part in the determination of the observed rodent spatial distributions (Table 4.5). The only significant association was negative between rodent spatial distribution and large grass seed rain in the meadow. Closer examination of this relationship found it to be artifactual and simply reflected the strong negative spatial relationship between areas of high vegetation cover due toChamaenerion angustifolium where rodent abundance was highest (see above) and areas of lower vegetation cover due to the large seed producing grass Alopecurus pratensis from which rodents were absent. Therefore rather than explaining variation in rodent spatial distribution due to seed production, it reflected the pattern of vegetation cover.

105 4.3.4 Interspecific associations

In the grassland associations between small mammal species existed (Table 4.6). A. sylvaticus was spatially segregated from M. agrestis over the summer 1986 and fromM. minutus in winter 1987, but there was no spatial association betweenSorex species in either site. 4.3.5 Behaviour and environment In both sites mean aggregation size was a function of population density (Spearman rank correlation rs=0.8762, d.f. 16 and rs=0.9118, d.f 15, both p<0.01, for the grassland and meadow respectively, Figure 4.4). Apart from at the highest densities in the meadow, the spatial distribution of A. sylvaticus in both sites was determined principally by environmental factors. The relationship between mean aggregation size (X,) and population density was similar in both sites though the slopes were just significantly different from each other (t=2.043, d.f. 31, p=0.05). 4.4 Discussion 4.4.1 Patterns ofA. sylvaticus dispersion The agreement in the aggregative response to overall population levels between both sites and the study of Montgomery (1980b) was surprising since the studies were drawn from three structurally different habitats, and though species consistency was suggested by Taylor & Taylor (1977) this was the first experimental support for this assumption. The observation that as densities increase small mammal dispersion tends to randomness has also beenfound forPeromyscus leucopus (Bendell, 1959) and Microtus pennsylvanicus (Grant & Morris, 1971) though in neither case was the exact relationship analysed. However this cannot be taken as a general small mammal attribute since Apodemus flavicollis showed the opposite relationship (Montgomery, 1980b). This interspecific difference in density-dependent dispersion has been widely acknowledged for invertebrates and microorganisms (Taylor, 1961; Taylor & Taylor, 1977). Following Taylor (1961) this disparity may be explained as those populations aggregated at high density tend to become regular when density diminishes (A. flavicollis) and those aggregated at low density to become regular when density increases (A. sylvaticus). While this statement is successful in describing the

106 population danslty (N/Ha)

Figure 4.4 Graphical representation of the relationship between mean aggregation size A.of sylvaticus populations and population density in a) the grassland and b) the meadow sites. Broken line represents mean aggregation size above which behavioural processes are important in the determination of spatial distribution.

107 patterns it is not an explicit explanation of the mechanisms underlying them. The variety of density-dispersion relationships are thought to reflect interspecific differences in the behaviour of an organism which governs its spacing relative to its neighbours and that the fact it is density dependent reflects a fundamental life process (Taylor, Woiwod & Perry, 1978). Grant & Morris (1971) put forward a hypothesis that "at low population density only the most suitable habitat is occupied, but as the density increase recently recruited animals are induced, by aggressive interactions with older residents to leave the most suitable and enter less suitable habitats". This hypothesis is suitable only for the case of patterns similar to that A.of sylvaticus. There is supporting evidence that a behavioural mechanism, such as that described by Grant & Morris (1971), exists for A.sylvaticus. Montgomery (1989a) provided evidence that density-dependent regulation of population size occurs during the phase of population increase a period where changes in the spatial distribution of individuals might be expected. Montgomery & Gumell (1985) presented data that this increase in numbers may be associated with increased aggression and this aggression may be directed against newly recruited individuals (Flowerdew, 1985). It is therefore likely that density-dependent changes in the level of aggression in a population, related to spacing behaviour may be the cause of the density-dependent changes in the spatial distribution of individuals, in particular in the meadow site. The model of Grant & Morris (1971) referred to the importance of reproduction in the changes in population spatial distribution with density in so far as reproduction was directly linked to population increase. How thenA is. sylvaticus reproduction related to population density and habitat variability? These two aspects of female demography are poorly documented. Krebs & Myers (1974) found evidence for density dependent breeding in small mammals, though the only specific studies onA. sylvaticus have examined the influence of the density of the female subpopulation on breeding rather than the density of the whole population (Montgomery, 1989b). Both these studies, however, indicated that as density increases the proportion of females reproducing declines. Studies on the influence of habitat on the spatial distribution of reproducing femalesylvaticus A. have never

108 been undertaken, though studies on other similar small mammal species suggest that breeding females space themselves in relation to food supply (Ims, 1987), and that competition for suitable breeding sites occurs. If female reproduction is density dependent and possesses a spatial component related to habitat variability which may lead to interspecific competition, then the patterns of dispersion of breeding females may be viewed as a special case of the Grant & Morris (1971) model. The onset of the breeding season with the additional demands of reproduction may be viewed as reducing the density threshold at which behavioural interactions come into operation in the determination of spatial distribution. If this were true, Taylor Power plots of mean female density should be more shallow than those of males over a similar density range. Consistency existed in the patterns in the grassland and meadow with neither of the subgroups in either site deviating significantly from the b=l line. The lack of a difference between sexes in all studies is probably a reflection of both the limited period over which female spacing behaviour occurs (Montgomery, 1989b). In addition the patterns of dispersion will be influenced both by within and between sex behaviour. This will reduce the extent to which sex differences may be detected. However in the woodland, while no significant sex differences existed, female slopes did deviate significantly from the b=l line. Indeed Montgomery (1989b) found that although the dispersion of adult male and female woodmice followed both the pattern and absolute values for the population as a whole, adult females were less aggregated than males during the middle and latter periods of reproductive activity. The absence of aggregation among adult females at this time was taken to suggest that females spread out during the breeding season due to theirs maintenance of exclusive ranges, while males do not (Wolton, 1985). This between study difference may be explained by the more consistent breeding in the woodland study. The data of Montgomery (1980b) spanned three full breeding seasons while the present study examined only one, in 1987, and this was limited by the population crash the preceding winter. This implied that the grassland and meadow studies were less likely to reveal significant changes in spatial distribution related to

109 breeding. Nevertheless, even when full breeding seasons were studied the influence of changes in spatial distribution of females on the patterns of dispersion of the whole population was slight (Montgomery, 1980b, Table 4.2). Female spacing behaviour related to breeding may influence the pattern of population dispersion. However, its density dependent nature and its relation to environmental variability imply that its influence will parallel that of the population as a whole. Where female density is high and resources limited, females will tend to be less clumped than males, however the duration of such periods of spacing behaviour is short in relation to the annual cycle ofsylvaticus A. and the influence on the pattern of dispersion of the whole population will be limited.

If density was the sole parameter determining A. sylvaticus dispersion the pattern of dispersion of woodmice in the grassland and the meadow at the same population density should be similar. Examination of Figure 4.2 reveals this was not the case, particularly at low population density where the meadow population was consistently more aggregated than that of the grassland. The hypothesis of Grant & Morris (1971) stipulates that habitat heterogeneity will also determine the spatial distribution of rodents. In the following section the influence of the habitat on the dispersion of A. sylvaticus in relation to small mammal population density was examined with the particular aim of explaining these between site differences. 4.4.2 The role of habitat structure The model of Grant & Morris (1971) assumes two facts about the relationship between small mammals and their habitat. First, that correlations exist between habitat characteristics and small mammal spatial distribution which are based on habitat preference (as suggested by their use of the word suitability) rather than an external agent imposing a differential distribution of individuals among patch types upon the population (Wiens, 1976). Second, that preferred habitats are patchily distributed. It was evident that in the meadow, vegetation cover may play an important role, in concert with population density, in determining woodmouse spatial distribution. The question now asked is why was this not so in the grassland? It is unlikely that the grassland and meadow woodmouse

110 populations differed in their habitat requirements, especially vegetation cover, rather it is more probable that between site differences reflect differences in the type of vegetation cover available in each site. Live vegetation (20-40cm), which was a determinant of woodmouse distribution in the meadow, may have been as important in the grassland but was so extensive that clear correlations with woodmouse density could not be made. Few woodmouse captures were made in either site where live vegetation (20-40cm) was less than two touches per pin (Figure 4.3a). Two pieces of evidence suggested that a threshold existed around this level of two touches per pin of live vegetation (20-40cm) above which woodmice prefer to reside. First, most woodmice captures were made where cover was above this value, indeed apart from one individual all mice were captured above this value in the grassland, while in the meadow a significantly greater number (over six times as many) were captured in vegetation cover above this threshold (x2=8.044, d.f. 1, pcO.OOl). Second, while woodmouse abundance was related to whether vegetation cover was above or below this value, there was no consistent relationship between vegetation cover and abundance once this threshold had been crossed (Figure 4.3a). This may have indicated that once the basic requirements of vegetation cover had been satisfied, other factors such as food, nest sites, moisture or social structure may have determined the spatial distribution. This conclusion as to why between site variation existed regarding correlations between vegetation cover and rodent spatial distribution differed from previous hypotheses. Grant & Morris (1971) claimed that correlations would tend only to be found in "patchy environments (i.e. whose habitat structure varies from place to place)". It is clear from Table 4.4 that in neither sites where any of the vegetation parameters, in particular live vegetation (20-40cm), distributed in an aggregated manner. Smal & Fairley (1982) stated that a lack of variability in vegetation cover was the source of poor correlations with rodent spatial distribution. Examination of the coefficient of variation (Sokal & Rohlf, 1981) associated with the vegetation parameters did reveal that variability in the abundance of cover may be important in determining the extent small mammals respond to vegetation cover.

Ill Vegetation structural variability has been recorded in twoA. sylvaticus studies (Evans, 1942; Southern & Lowe, 1968) though neither found significant correlations between vegetation cover and rodent spatial distribution. This implied that the response ofA. sylvaticus to vegetation cover was either weak, non linear or both. While no absolute measure of the strength of the small mammal response to vegetation cover exists,A. sylvaticus has been found to be less dependent on vegetation cover than other British small mammals, in particularM. agrestis and C. glareolus. For example in all the studies mentioned at the beginning of this chapter which examined vegetation cover and rodent spatial distribution all found a positive relationship for glareolusC. while studies differed regarding A. sylvaticus. In addition, A. sylvaticus has been known to have resided in agricultural habitats in spring where vegetation cover was minimal (Pelz, 1979). Nevertheless, even a weak response, if linear, should enable a trend to be detected over the extensive variation in vegetation cover found in the deciduous woodland habitats studied by Evans (1942) and Southern & Lowe (1968). These authors used broad vegetation categories and the lack of any trend in rodent abundance related to these categories was suggestive of a non linear relationship. If a non linear relationship, with a low threshold, does exist then only those studies with sufficient variation in the occurrence of the below and above threshold vegetation cover would tend to pick up its association with rodent distribution. While previous studies have used a variety of subjective cover categories which prevented direct comparison, most described a "no cover" category which enabled an examination of the extent of below threshold cover. It may expected that habitats in which cover was sparse would be most likely to reveal associations of rodent distribution with vegetation cover, while those with abundant vegetation may not. Of the few studies that did provide a description of the cover categories there was a consistent trend with those habitats with a high proportion of the habitat bare showing a positive association of woodmouse spatial distribution with vegetation cover (Fairley, 1967b, 62% habitat bare; Corke, 1970, 52%; Fairley & Comerton, 1972, 58%; Montgomery, 1980b, 56%) while those with a low proportion of the habitat bare did not (Southern & Lowe, 1968, 23%). It is interesting to note that three other studies (Evans, 1942; Miller, 1958; Kikkawa, 1964) which did not find a relationship

112 with vegetation cover were all undertaken in the same habitat as Southern & Lowe (1968) in Wy tham wood, Berkshire and all three described ground cover as being abundant. These patterns were reflected by those of the present study with the meadow, with 76% of cover below the threshold, showing a correlation of rodent spatial distribution with vegetation cover while the grassland with only 28% did not. Therefore, while the presence of variation in vegetation cover is often necessary for the detec tion of rodent-cover relationships this variation can only be examined from a rodent perspective. Studies which have failed to do this and used very broad vegetation groupings (Evans, 1942; Southern & Lowe, 1968; Fairley & Jones, 1976) also failed to find rodent cover associations. The influence of habitat variation may be summarised with reference to the work of Ashby (1967). He compared rodent abundance in two halves of three transects in three different habitats with the available vegetation cover. In two transects (1 & 2) where vegetation was either equally abundant (1) or equally absent (2) no difference between the rodent abundance in the half transects was found and he claimed this as evidence that woodmice did not distribute themselves relative to vegetation cover. However if his data is re-analysed for transect three where the upper half had no ground cover while the lower half did, then a significant difference is found. Over two different experimental periods woodmice were more abundant in the lower half than would have been expected by chance (x2=16.86, d.f. 1, %2=12.71, d.f. 1, both p<0.01). Further support for the existence of a vegetation cover threshold was found in Figure 4.3b. An assumption of the threshold hypothesis is that rodents should be more sensitive to variations in vegetation cover beneath the threshold than above. While no correlations with live vegetation cover (20-40cm) and rodent abundance were significant above or below the value of two touches per pin, correlations were consistently higher below than above the threshold (Spearman rs for grassland, below threshold- rs=0.900, d.f. 3, n.s., above rs=0.106, d.f. 18, n.s.; meadow below- rs=0.2676, d.f. 14, n.s., above rs=0.2619, d.f. 6, n.s.). The lack of a significant correlation beneath the threshold was in part due to in the grassland, the short range of cover values (1.1 -2.0 touches/pin) and the few sample points (5) and in the meadow, the confounding sampling discrepancy (Figure 4.3b). Indeed

113 when those points, known to have been situated in areas of above threshold cover were removed from the below threshold correlations, a significant result was found (rs=0.6294, d.f. 10, p<0.05, Figure 4.3b). The reduction in correlation in the meadow between rodent spatial distribution and vegetation cover between 1987 and 1988 occurred over a period ofA. sylvaticus population increase (Figure 3.1) and was consistent with the hypothesis of Grant & Morris (1971) that the association between animal distribution and the distribution of habitat structural features tends to weaken as animal density increases. Increases in small mammal density in both sites were associated with greater rodent abundance in areas where previously rodents had been scarce or absent (Figures 4.3a,b). This was borne out by the rodent distributions while being significantly clumped in the 1987 sample (I5=4.60, x2=81.5, p<0.001 in the grassland and I5= 3.06, %2=85.8, p<0.001 in the meadow) either became random (I5=1.09, %2=30.39, n.s. in the grassland) or less aggregated (Is=1.57, %2=53.6, p<0.001 in the meadow) in 1988. In order for consistency with the hypothesis of Grant & Morris (1971), those habitats occupied in the 1987 sample should have been the "most suitable habitats". This statement implies preference for habitat areas, which should be consistent between years, therefore those capture points where rodents were most abundant in 1987 should have been the same in 1988. Rank correlations between rodent spatial distribution in 1987 and 1988 revealed this to be a correct assumption in the meadow (rs=0.521, d.f. 23, p<0.01) where A. sylvaticus abundance was consistently higher in areas of Chamaenerion angustifolium. In the grassland the assumption of preferred habitat was weaker (rg=0.399, d.f. 23, p=0.05) as this correlation was due to one capture point only, located beneath a small shrub (Acer campestre). If vegetation cover above the normal level of small mammal activity (20-40cm), rather than at the same level (0-20cm), is important in determining their spatial distribution, it is indicative that avian predators may be influential in determining habitat preference. A number of small mammal specialist birds of prey were seen near the sites including both diurnal -Falco tinnunculus (kestrel), Asio flammeus (short-eared owl) and nocturnal Stryx- aluco (Tawny owl) species. Apodemus

114 sylvaticus is a major component of the diet of these birds (Southern, 1954; Korpimaki, 1985) and since all use visual cues in locating prey, vegetation cover is important to predator success, tall growing vegetation reducing hunting efficiency. Mammalian predators, known to have hunted in the sites - Mustela nivalis (weasel), Vulpes vulpes (red fox), and Felis catus (domestic cat) utilise aural and olfactory cues as well as visual cues (King, 1985) and in this respect vegetation cover, though still important, may be less of a defense against these predators than against birds of prey. It is possible that the relative impacts of mammalian and avian predators may determine the degree to which small mammals are associated with particular cover types. Unfortunately, this interesting idea could not be further examined since no estimates of predation were available nor was there any data which related vegetation cover to mammalian predator success. 4.4.3 The role of food Food addition experiments (Hansson, 1971b; Flowerdew, 1972) have shown that food supply may influence the abundance ofA. sylvaticus and correlations between rodent spatial distribution with food distribution might have been expected. At least three reasons for not detecting a relationship existed. First, it may have been due to the crude method of assessing food supply. Seed production in a habitat may not reflect seed availability to a ground dwelling herbivore, since this will depend on the proportion of seed that becomes available at ground level, its quality and its suitability as food. These factors will depend upon the abundance of other seed predators, of alternative foods and on rodent seed preference and foraging behaviour, few of which were known. Second, it is likely that trade-offs exist between food supply and the abundance of vegetation cover in the determination of rodent spatial distribution (Pelz, 1979). Although the present data were not amenable to this analysis due to the different periods of vegetation and seed production samples, examination of patterns of both vegetation cover and seed production revealed no explanatory trends in either site. In the meadow site, the spatial separation of food resource in the form of large grass seeds and vegetation cover implied that trade-offs in the site between the two variables were unlikely and that vegetation cover was a more significant explanatory variable.

115 Third, that at the low rodent population densities in the summer of 1987 per capita food abundance was high and would not have been a sufficiently limiting factor to affect rodent spatial distribution. This aspect of rodent seed predator satiation is examined in chapter V and while pre-empting those conclusions it was clear that at the low population densities and the high seed production in the grassland that food was unlikely to be limiting in this site. 4.4.4 The influence of other small mammal species Although spatial segregation conforms to one prediction of the Principle of Competitive Exclusion the division of space betweenA. sylvaticus and other small mammals, may not have resulted from interspecific competition but rather, differential exploitation of resources which were separated spatially. The lack of consistency between spatial distributions in successive samples for either A. sylvaticus, M. agrestis orM. minutus in the grassland and the observation of the extensive overlap in the spatial distributions of these species did not support the existence of spatially separated resources. Competition can only be inferred from these negative associations. Detailed species removal experiments (Grant, 1972) and studies of interspecific behavioural interactions (Gumell, 1985) would be needed to confirm any suggestion of competition. There was however, some evidence to suggest that the negative association betweenM. agrestis and A. sylvaticus was due to competitive interactions sinceM. agrestis has been found to exclude other small mammal species as its density increases (Brown, 1954; Myllymaki, 1977). The lack of any significant association between A. sylvaticus and Sorex species was consistent with the findings of Gumell (1985).

Withrespect toM. minutus, there are apparently no published studies concerning its interaction with other small mammal species. Using the criteria suggested by Reynoldson & Bellamy (1971) it was clear that there was potential for competition between A.sylvaticus and M. minutus. i) The comparative distribution and abundance of two potential competitors should be amenable to an explanation based on competition (see above discussion, Table 4.6). ii) Potential competitors should exploit a common resource which actually or potentially is limited thus forming a basis for competition. Both species are omnivorous and their diets in

116 grasslands are very similar (Trout, 1978a; Dickman, 1986; Churchfield & Brown, 1987). The spatial segregation of the two species was greatest in winter when not only were densities of each species high but food resources were low. iii) Conspecifics should have the facility for intraspecific competition. This facility may be employed in interspecific relationships. Intraspecific competition and aggression is well known for bothA. sylvaticus (Gumell, 1978) and M. minutus (Trout, 1978b). Studies of dyadic encounters would be needed to examine whether this facility for intraspecific aggression would be translated to interspecific aggression. Grant (1972) and Schoener (1983) have pointed out that where aggression is involved in competition the larger species (A sylvaticus, has a body mass four times that ofM. minutus (Southern, 1954)) tends to be competitively superior.

This evidence suggested that there was potential for competition between A.sylvaticus and M. minutus. Further evidence was provided by the significant negative correlation (Spearman rs = -0.6917, d.f. 7 p < 0.05) in the temporal variation of A. sylvaticus density and the captures of M. minutus (Figure 2.2, Table 4.6). While such negative correlations may suggest competition and are often used as evidence for competition (Gumell, 1985) care must be taken in examining such data (Grant, 1972). The temporal pattern ofM. minutus captures followed that typical of the species (Trout, 1978b, chapter III). Previous studies M.on minutus failed to present data on the abundance of other co-occurring small mammals and it was therefore impossible to say whether the temporal patterns ofM. minutus were related to competition or not. The only conclusion that may be drawn was that a negative association existed between the two species and this was particularly so in winter. The causes of such negative association and the extent to which each species influenced the other await further study. 4.4.5 The relative influence of behaviour and environment

The spatial distribution ofA. sylvaticus was influenced by its population density, the habitat structural heterogeneity and interspecific associations. Using the classification of Usher (1969, 1971) the significant correlation of both (rn) the number of aggregations (Spearman rs=0.8762, d.f.

117 16, pcO.Ol; rs=0.9222, d.f. 15, p<0.01) and (rs) the number of individuals forming the aggregations (Spearman rs=0.8638, d.f. 16, p<0.01; rs=0.9118, d.f. 15, p<0.01) with woodmouse population density in the grassland and in the meadow respectively implied type III aggregations. These aggregations are understood to be a product of both a fixed attribute of the species and movement of individuals in relation to selection of suitable microhabitats. While these conclusions were straightforward they gave little idea of the relative influences of behavioural and environmental factors on the spatial distribution ofA. sylvaticus. This analysis suggested that at least two different processes influence rodent spatial distribution and that density dependent behavioural regulation of spatial distribution should only occur at relatively high population densities. If this is the case then the Taylor’s Power plots (Figure 4.2) should have been composed of two components, a density independent component (b=l) at low densities and a density dependent component at high densities (b o l). If Taylor’s Power plots for the meadow were only carried out over a similar density range as found in the grassland then the pattern of spatial distribution with density was found to be density independent (Table 4.1). Infact the slope of the relationship was very similar to that of the grassland (b=0.9663, SEb=0.095, against b=0.9582, SE^O.096, Table 4.1). Unfortunately, insufficient data points were present at the higher densities in the meadow to be certain of the relationship with density, though if an arbitrarily chosen lower mean capture per trap of 1 is taken then those points above this value did reveal significant density dependence in changes in spatial distribution (b=0.47, SE^O.19, tb=]=2.767, p<0.05, Table 4.1) This finding was indicative of the existence of curvature in the Taylor Power Law plots in the meadow. Indeed Taylor et al. (1978) found significant curvature in Taylor Power plots in 10% of the species they studied. A polynomial regression of the meadow data revealed that a quadratic model fitted the data better than a linear model (r2 rising from 0.893 to 0.936) though the difference was not statistically significant. This lack of significance was most likely due to the few samples recorded at high populations densities.

118 The influence of habitat variability on the Taylor Power Law relationship had not previously been explored. While increased habitat variability over the same density range influenced little the slope of the relationship it did appeared to shift the curve upwards, raising the intercept a (from 0.11 in the grassland to 0.206 in the meadow). Previously a had been regarded largely as a scaling factor related to sample size (Taylor, 1961), though more recently it has been shown that both environment and sample size affect a (Taylor & Woiwod, 1982). In the present study where sample size was the same in both sites, a may be a useful parameter describing the extent of habitat variability, independently of habitat productivity. The slope b had previously been thought not to be influenced by habitat variability (Taylor & Woiwod, 1982) though it is clear that if the Power law is curved then habitat variability and productivity may influence the shape of the relationship. Attempts to fit a linear model to curved data will lead to the slope being influenced by the extent of curvature present in the data and therefore by environmental variables. This conclusion disagrees with that of Tayloret al. (1978) that b is generally characteristic and constant for the species. Examination of the data provided by Taylor etal. (1978) showed that for the few species that had been previously examined in different sites, values of b varied by up to 20% depending on the habitat. Variation in the value of b in this study and that of Montgomery (1980b) onA. sylvaticus was consistent with these findings. It is therefore clear that while b may be characteristic of a particular species it cannot be assumed constant in different habitats. The above discussion assumed that the influence of environmental factors are constant in time. This may be true of some factors such as vegetation cover but it is unlikely to be the case with food resources or interspecific competitors. These latter dynamic environmental factors will tend to increase the amount of scatter of the points in the Taylor Power Law plots. An estimate of this scatter is the standard error of the appropriate regression line, and a comparative index is the percent of b which this standard error represented. In both sites this percentage was small (10% and 8.9% in the grassland and the meadow respectively) indicating a reasonably constant environmental influence. These values was surprisingly low considering that the scatter was also a function of temporal changes in sampling efficiency (chapter A). This conclusion supported the hypothesis that vegetation cover was the single most important environmental determinant of spatial

119 distribution in the two sites. This was substantiated further by the lack of any seasonal trends in the pattern of points which might have indicated changes in food supply. Moreover, the similarity of the scatter within the two sites implied that the sudden appearance of the possible "competitor" Micromys minutus in the grassland influenced little the distribution ofA. sylvaticus. The greater scatter (27.2%) recorded by Montgomery (1980b) for A. sylvaticus may have been a reflexion of the influence of the competingA. flavicollis population on its spatial distribution. In order to understand the impact of spatial density dependence on the population dynamics of A. sylvaticus it would be necessary to have detailed information on the spatial variation in reproduction, mortality and dispersal, associated with this spatial density dependence. Montgomery (1989b) stated that density dependent natality and mortality have the capacity to lead to overall changes in abundance and hence the potential for temporal density dependence, while dispersal only results in a redistribution of the population without directly influencing population size. As yet no study has examined in detail the spatial aspect of mortality or dispersal though evidence does exist for spatial density dependent reproduction (Montgomery, 1989b). Montgomery (1989b) further stated that regulatory spatial density dependence, in the form of density dependent reproduction occurred during population increase and that non regulatory spatial density dependence, in the form of dispersal, occurred during the population decline, while density dependent mortality (in particular predation) played little role in the regulation of the population. Although the present study did not examine in detail spatial variation in population parameters, there is sufficient evidence to suggest that dispersing individuals may have lower survival rates (chapter III) either through predation, interspecific aggression or starvation and that dispersal may indirectly be regulatory. What role this dispersal plays in the population decline needs to be further investigated. Evidently, an understanding of the spatial aspect of density dependence is critical to the analysis of A. sylvaticus population dynamics. The present study has highlighted the relative importance of habitat and population density effects. Increased habitat variability is proposed to increase the regulatory influence of spatial density dependence, whether through reproduction or mortality linked to dispersal. The influence of this increased regulatory power on population

120 dynamics can only be hypothesized, however it is interesting to note that where habitats are spatially variable - ice-polygons in tundra (Brown, Everett, Webber, MacLean & Murray, 1980) or in habitat mosaics of traditional small farm systems (Myllymaki, 1979) small mammal species show marked multiannual population cycles whilst the same species in less spatially variable habitats do not. If regulatory spatial density dependence increases in strength as the habitat becomes more variable it may become overcompensating. Overcompensating density dependence has been shown from discrete generation models to lead to multiannual population cycles (Maynard-Smith, 1968). To what extent such models may reflect the demography of small mammals will depend how well a discrete model can describe the populations, the strength of the spatial density dependence (the degree of interspecific aggression evident in the population) and the responsiveness of the species to variations in habitat variability. These ideas are the subject of further study beyond the scope of this thesis. However it is interesting to note regarding the importance of the species responsiveness to habitat variability that habitat specific species such as Clethrionomys glareolus and Microtus agrestis, which have strong habitat requirements and would therefore be more sensitive to variations in habitat quality have a tendency to show multiannual population cycles in heterogeneous habitats while habitat generalists such asA. sylvaticus and some Peromyscus species do not.

It was clear that spatial distribution ofA. sylvaticus was a product of the interaction of both density and environmental variables. The influence of one cannot be described without reference to the other. In general, the conclusion of Grant & Morris (1971) that at low population density environmental factors influence the spatial distribution more than behavioural factors while at high population density the reverse is true, was supported. It was probable that a number of environmental variables influenced the spatial distribution ofA. sylvaticus. The present study only examined two of the most commonly studied variables, vegetation cover and food. Results from the study showed that positive correlations between an environmental variable and rodent spatial distribution will only tend to occur when the environmental variable is limiting. Taylor’s Power Law plots were found to be useful tools in the examination of the relative influences of behavioural and environmental factors on spatial distribution. The intercept (a) of these plots reflected the habitat variability and

121 in studies using the same samples sizes may be used as a comparative index. The slope (b) and evidence of curvature in the slope reflected the degree to which spatial distribution was density dependent over the range of densities studied. The degree of scatter associated with the regression was not only reflection of sampling errors but also an index of the influence of dynamic extrinsic variables such as food supply and competitors in the determination of spatial distribution. The ideas of Grant & Morris (1971) were further developed to incorporate mechanisms by which habitat variability and productivity might influence the relationship of spatial distribution with population density.

122 CHAPTER V SMALL MAMMAL SEED PREDATION INTjRASSLAND 5.1 Introduction The diets of the majority of phytophagous small mammals are composed, to varying degrees, of plant seed and the rodents caught in the present study are no exception (Hansson, 1985). In order to understand the implications of this small mammal seed predation in natural plant communities both an estimation of the extent of seed losses attributable to small mammals and of the impact of such seed losses on the population dynamics of mature plants is needed. The aim of the present chapter was principally to examine the role small mammals have as post-dispersal seed predators in grassland plant communities. A vast literature exists regarding rodent post-dispersal seed predation and the few reviews available restrict themselves to particular ecosystems (Price & Jenkins, 1986). An attempt to draw together the numerous field studies is presented in Table 5.1. The table is restricted to field studies since the difficulties in laboratory designs of providing a realistic background food abundance and of presenting realistic seed densities reduce their ability to predict patterns in natural systems. Additionally, only those studies which examined removal of naturally occurring seeds are presented. This latter requirement led to the omission of the majority of the desert rodent coexistence studies which used commercial seed species such as millet or barley (e.g. Brown, Grover, Davidson & Lieberman, 1975; Mares & Rosenzweig, 1978; Reichman, 1979; Abramsky, 1983) since such alien seed species may have been harvested at unnaturally high or low rates. A variety of methods has been used to estimate the impact rodent populations have on seed populations. The most indirect method is to infer impacts by calculating the amount of seed necessary to maintain a small mammal population from metabolic rates, population densities, diets and energy contents of available seeds. Consumption estimates can then be divided by seed production estimates to determine the proportion of seeds consumed. In general small mammal impacts on the seed component of primary production are an order of magnitude greater than small mammal impacts on

123 + temporal seed burial seed density size seed seed species + + + habitat between SOURCES OF VARIATION IN SEED LOSS SEED IN VARIATION OF SOURCES + + within habitat 9 8 % 12 93 87 33 90 3-6 0.2 loss rate 0-65 0-53 LOSS 25-75 moth studied predators predators other seed seed other (days) duration METHOD 1-7 0-67 (mg) range weight 1 1 28 2 3 No species G G G G D G G G G G 1 D DW 1 10 HABITAT AUTHOR BIOENERGETICS OBSERVATIONAL Odum, Connell & & Connell Odum, (1962) Davenport Pearson (1964) Pearson Batzli & Pitelka (1970) Pitelka & Batzli Chew & Chew (1970) Chew & Chew Poulet (1972) Poulet Soholt (1973) Soholt D Pulliam & Brand (1975) Brand & Pulliam French, Grant, Grodzinski & & Grodzinski Grant, French, (1976) Swift Marshall & Jain (1970) Jain & Marshall Platt (1976) Platt Gardner (1977) Gardner DW Heithaus (1981)Heithaus DW Boyd & Brum (1983) Brum & Boyd (1987) Wastljung & Nilsson + variable had known effect on seed removal by rodents - variable had no known effect on seed removal by rodents. by removal seed on effect known no had variable - rodents by removal seed on effect known had variable + Table 5.1 A review of previous studies which examined rodent seed predation in plant communities using bioenergetic, observational, seed dish and cage exclosure exclosure cage and dish seed variables to Key grassland. observational, G bioenergetic, woodland,and using tropical TW communities plant woodland, in deciduous predation DW seed rodent woodland, examined coniferous which CW studies semi-desert, previous SD of desert, D review 5.1 A : Table habitats to Key techniques.

124 AUTHOR HABITAT METHOD LOSS SOURCES OF VARIATION IN SEED LOSS No weight duration other seed loss within between seed seed seed seed temporal species range (days) predators rate habitat habitat species size density burial (mg) studied % SEED DISH Jarvis (1964) DW 1 14 75-100 + + Ashby(1967) DW 3 15 1-100 + + + + Radvanyi (1970) CW 1 90-365 4-42 + Samkhan (1974) G 3 14 20-50 O’Dowd & Hay (1980) D 1 1 ants 25-43 + - Hay & Fuller (1981) D 7 1 birds 0-86 + + Webb & Wilson (1985) DW 2 1 7-100 + + - . Casper (1988) SD 1 21 50-80 - + i Schupp (1988) TW 1 84-196 50-90 + + van Tooren (1988) G 5 0.2-3 6-11 20-60 + - -+ Table 5.1 cont. + + temporal + + + + + seed burial - - + + seed density - + + size seed + + + + + seed species + + + + + + + + habitat between + + + + + + + + + within habitat % 60 100 loss rate 1-45 5-90 10-80 4-100 22-52 0-100 LOSS LOSS SEED IN VARIATION OF SOURCES 15-80 24- 24- 57 55-68 25- 25- 100 ants 0-86 ants birds birds birds rabbits studied as aboveas 89-100 predators predators other seed seed other chipmunks 2 1 7 7 birds 90 37-75 60 80 45 120 6-9 2-30 11-30 2-12 (days) 30-180 ants 19-97 duration METHOD 1-7 (mg) range weight 1-1231 1 1 1 3 4 1-16 3 1.6-2.5 3-5 ants 1 1 3 6 5-1449 8 6 0.2-27 3 ants No species G G 1 90 83-90 G G 6 0.1-7 G G 1 2-71 CW SD DW DW DW 1 TW DW 1 3-7 ants TW DW DW AUTHOR HABITAT Watt (1919) (1919) Watt (1923) Watt Shaw (1968) Shaw Jensen (1985) Jensen Gardner (1977) Gardner DW 1 Heithaus (1981) Heithaus DW Gashwiler (1970) Gashwiler Huumink (1986) Huumink Kelrick, MacMahon, MacMahon, Kelrick, Borchert & Jain (1978) Jain & Borchert Perry & Fleming (1980) Fleming & Perry van der Meijden (1988) Meijden der van CAGE EXCLOSURES CAGE Culver & Beattie (1978) Beattie & Culver Vericaar, Schenkeveld & & Schenkeveld Vericaar, Klinkhammer, de Jong & & Jong de Klinkhammer, Harvey & Meredith (1981) Meredith & Harvey Mittlebach & Gross (1984) Gross & Mittlebach Parmenter & Sisson (1986) Sisson & Parmenter Greig-Smith & Sagar (1981) Sagar & Greig-Smith Abbott & van Heurck (1985) Heurck van & Abbott Table 5.1 cont. 5.1 Table

126 total primary production (Table 5.1), as suggested in chapter I. The extensive variation in estimates has been attributed to between habitat variations (Ryszkowski & French, 1982). Although impacts will be a function of habitat, seed species diversity and granivore diet specificity, additional variation is attributable to methodological differences between studies (in particular the extent rodent diets were studied). For example, the high removal values in deserts are the result of the removal values being specific for the single seed species which dominated the diet of the rodent species studied (Chew & Chew, 1970; Soholt, 1973) while the low values of removal (Frenchet al ., 1976) are the result of a mean diet estimate for the whole rodent community compared against all seed production in the habitat. It is clear that the published bioenergetic impact estimates fail to provide a detailed picture of the influence small mammal seed predation might have on plant communities. Only when impact estimates are determined following detailed, species specific, diet analyses and compared to similarly detailed, species specific, plant seed production may bioenergetic impact estimates be of use and then only as a gauge of the potential influence of the rodents on the plant communities. In some cases a more direct estimate of seed consumption can be obtained from the direct field observation of the remains of seeds (Table 5.1). This method suffers the disadvantage that it is likely to underestimate seed removal, since rodents often carry seeds away from the site of foraging before consuming them (Price & Jenkins, 1986) and can only be used in cases where seed consumption leaves readily identifiable remains. In cases where both observation of remains and caged seed manipulation methods have been used the former consistently underestimated seed removal in the latter (Gardner, 1977; Heithaus, 1981). The two methods described above both suffer a further restriction in their utility in that they do not permit manipulation of environmental variables which might influence seed predation. The following two methods, termed for convenience seed dish and cage exclosure, share several similarities, the most important being that they enable the manipulation of a variety of seed variables. The basic principle underlying seed dish and cage exclosure techniques is that a known number of seeds is placed in the habitat and their loss from the habitat is monitored over a period of time.

127 The difference between the techniques is that in the former seeds are simply put out into the habitat open to all predators, while in the latter a series of exclosures (cages in the case of small mammals) placed over the seeds enables the determination of the source of loss. Seed dish techniques are only suitable in communities where small mammals are the only seed predators otherwise loss cannot be attributed solely to this guild of seed predators. In habitats where a range of different seed predator guilds exist or where comparisons of seed removal between a variety of seed predator guilds is necessary exclosure techniques are the only satisfactory solution (Table 5.1). Although many rodent seed predation studies exist, comparison between studies is hindered by methodological differences in particular the densities of seeds (from 2 to over 4000) used in the studies and the duration of seed exposure to predators (from 1 to 200 days). Furthermore, the opportunity for generalisations derived from these studies is reduced by the small number of plant species that have been studied (less than 20% of studies examined more than 5 seed species simultaneously). Finally, while a range of environmental variables has been found to influence rates of seed loss (Table 5.1) no study has placed these variables in a comparative perspective. Indeed, as yet the influence of seed size, seed density and seed burial on rates of seed removal remain unclear (Table 5.1). Nevertheless, if only the manipulative experiments are examined then it is clear that in the majority of studies (>85%) some plant species suffer extensive (>50%) seed predation. This conclusion is all the more important since the studies examined cover a wide range of habitats including tropical rain forest, deciduous woodland, temperate grassland, semi-desert and desert. The aims of the present seed predation study were as follows: i) To assess the relative importance of grassland small mammals as post dispersal seed predators compared to ground dwelling invertebrates (in particular carabid beetles and ants). ii) To examine the influence of the following environmental variables (Table 5.1) on the rate of seed loss: a) How do rates of loss vary spatially within a site?

128 b) How do rates of loss vary in different habitats? c) To what extent do different seed species vary in rates of loss? d) Is seed size a reliable parameter for predicting rates of loss? e) Does seed removal vary in relation to seed density? f) Does the burial of seeds influence these patterns in rates of loss? g) Are these patterns in rates of seed loss constant in time? iii) To ascertain how rodent demography and behaviour acting through these environmental variables influence the rate of seed removal. iv) To predict the ecological circumstances under which post dispersal seed predation by rodents might influence plant population dynamics. 5.2 Methods and materials The seed predation experiments were undertaken in the grassland and meadow sites described in detail in section 2.2.2. 5.2.1 Choice of seed species A common finding of previous rodent seed predation experiments is that rates of seed loss vary between seed species (Table 5.1). Thus, the choice of seed species may influence the assessment of the importance small mammal seed predators play in the plant community. Many of the manipulative studies have used seed species known to be components of small mammal diets (Ashby, 1967; Gardner, 1977; Hay & Fuller, 1981; Jensen, 1985) or suspected of suffering high rates of seed predation (Watt, 1919,1923; Gashwiler, 1970; Radvanyi, 1970; Culver & Beattie, 1978; O’Dowd & Hay, 1980; Heithaus, 1981; Abbot & van Heurck, 1985; Casper 1988). This bias in seed species choice limits the extent to which the influence of small mammal seed predation can be extrapolated to whole plant communities. Little information exists concerning rodent seed preferences or for the fate of post dispersed seeds in grassland and seed species choice in the present experiment was made independently of any such information. Seed species choice was based on the following criteria:

129 SEED SPECIES SEED WT GRASSLAND MEADOW BOTH NEITHER (mg) ONLY ONLY SITES SITE Agrostis capillaris 0.1092 * Alopecurus pratensis 1.2054 * Dactylis glomerata 1.0816 * Deschampsia flexuosa 0.6632 * Festuca ovina 0.8918 * Festuca pratensis 2.5250 * Festuca rubra 1.1970 ★ commutata Festuca rubra litoralis 0.8926 * Festuca rubra rubra 1.0788 * Holcus lanatus 0.4348 * Lolium perenne 2.2180 * Phleum pratense 0.4490 * Poa annua 0.3622 * Poa pratensis 0.3736 * TOTAL GRASS 2 1 6 5 Lotus corniculatus 1.1260 ♦ Medicago lupulina 1.8782 * Medicago sativa 1.7002 * Plantago lanceolata 1.2748 * Trifolium dubium 0.5946 * Trifolium pratense 1.9212 * Trifolium repens 0.6252 * TOTAL FORB 1 1 3 2 TOTAL SPECIES 3 2 9 7 Table 5.2 List of the seed species used in the seed predation experiment classified by their occurrence in each of the habitats studied. a) All seed should be from species of grasses and forbs which naturally occur in the British Isles. b) Seed species should be representative of the weight and size ranges commonly found in grassland plant seed. c) Seed shape should be similar between species and seeds should not possess highly specialised dispersal structures. Grassland seeds exhibit a wide range of shapes (Harper, Lovell & Moore, 1970) often associated with the presence of dispersal structures (e.g. plumes, wings, hooks and oil bodies).

130 The presence of additional structures may confound relationships between seed removal and seed size, since they may influence rodent foraging independently of caryopsis size. While it is recognized that these dispersal structures may be important influences in rates of seed removal by small mammals, the examination of this aspect of seed predation was beyond the scope of the present study. d) Both species common and alien to each habitat (see section 2.2.2) should be used in order to compare the effect of seed familiarity on seed removal rate. e) Seed choice should encompass several grassland plant families, a range of species in each plant family, several species of the same genus and if possible a number of varieties of the same species. This multi-levelled approach was designed to examine at what level rodents are able to discriminate seeds and whether particular removal patterns exist within plant families and seed species of the same genus. f) Seed supply should be reliable and quality of seed should be high. Although a number of seed species were abundant in the habitats studied reliance had to be placed in wildflower seed suppliers for alien species. In order that all seeds used should come from similar environments it was necessary that all seed species chosen were available from wildflower seed suppliers. The following organic seed suppliers were used - W.W. Johnson & Son Ltd., Boston, Lincolnshire, and Emorsgate Seeds, King’s Lynn, Norfolk, since together they provided the widest range of grassland seed species. Nevertheless the reliance on seed suppliers constrained seed choice through their seed availability and cost. The seed species used in the present study are listed in Table 5.2. The above conditions regarding species choice limited selection almost exclusively to two plant families, the Gramineae and the Leguminosae. The study was therefore designed to focus more on variations in seed removal within rather than between plant families. Restriction of non-graminaceous plant species solely to the Leguminosae may limit the extent to which generalisations can be made regarding the findings of any experiments since seed predation of the Leguminosae may differ from that of other grassland plant families with similar seed morphology, in particular the Plantaginaceae, Polygonaceae, Ranunculaceae and Scrophulariaceae. In order to gauge to what extent focusing on the Leguminosae may have led to bias in the results an additional

131 species Plantago lanceolata (Plantaginaceae) was also studied. This species was chosen since it occurred in both habitats and possessed a similar seed weight (1.275mg) as those of the legumes (mean legume seed weight 1.307mg). Comparisons between seed removal patterns of the

Leguminosae andP.la n c e o la ta could therefore be used as indicators of the generality of the legume results. In total, twenty one seed species (and varieties) were chosen (Table 5.2), an order of magnitude greater than previous studies (Table 5.1). All species chosen (apart fromFestuca rubra var. lit or alls) were common in grasslands of southern England, nevertheless approximately half of all species used were alien to the specific habitats studied (Table 5.2). In each of the two major plant families chosen, species sharing the same genus were examined; in the Gramineae F e s tu c a spp. (3) and P o a spp. (2) and in the LeguminosaeT rifo liu m spp. (3) and M e d ic a g o spp. (2). In addition three varieties ofFestuca rubra were studied including var. litoralis which is generally restricted to tidal flats though is now more frequently used as an agricultural grass (Hubbard, 1984). The seed weight range (0.1 to 2.5mg) was representative of the seed weights found in open habitats, short grass and meadows (Salisbury, 1942) and was similar to previous grassland rodent seed predation studies (Table 5.1). 5.2.2 Experimental design Since an aim of this study was to quantify the relative impacts of invertebrates and small mammals as post dispersal seed predators it was necessary to rely on exclosure rather than simple seed dish techniques. The following methods of seed predator exclosure were used:

Small mammal exclosure. Small mammals were excluded from seed experiments by means of a wire mesh cage. A range of mesh sizes from 0.5cm (Verkaaret a l., 1986) to 1.2cm (Abbot & van Heurck, 1985) have been used to exclude rodents. Preliminary studies revealed that while a mesh size of 1.0cm prevented entry of adultApodemus sylvaticus, a mesh size of approximately 0.64cm was necessary to additionally exclude juvenile mice.

132 Rodent exclosures, each covering an area of 25cm X 25cm, were composed of two meshes. The first consisted of lm high sheets of 1cm mesh with the lowest 20cm bent outwards and buried to a depth of 10cm (to inhibit rabbits from digging around cages). The top 20cm was also bent outwards to form a lip preventing rodents gaining access by climbing up the mesh and fine plastic netting was strung over the top of the cage preventing bird access. Although this cage design was a sufficient barrier to most rodents its purpose was to prevent rabbits and birds access to the cages. In order that the cages were small mammal proof the 1cm mesh cage possessed a further mesh "skirt" which consisted of 40cm high sheets of 0.64cm lathing (diamond mesh, Wood’s Yard Building Supplies, Twyford, Berkshire) with the lowest 5cm bent outwards and buried to a depth of 10cm (to inhibit small mammal burrowing at the edge of exclosures). Cages were initially tested by placing Longworth traps within cages to examine whether small mammals could enter and further tested by placing individualA. sylvaticus inside the cages and observing their ability to escape. These experiments confirmed the small mammal proof nature of the cages. The mesh sizes used in the rodent exclosures, while preventing small mammal access were sufficiently large as to allow entry of ground dwelling arthropod seed predators.

Ground arthropod exclosure. Physical barrier methods were inappropriate in the case of arthropod exclosures and therefore chemical means were utilised. Previous studies have shown that in grasslands both carabid beetles (Kjellsson, 1985) and ants (Mittlebach & Gross, 1984) may be important post dispersal seed predators. The choice of pesticide was based on its effectiveness at controlling these arthropod seed predators and its lack of influence on small mammal populations and/or foraging. The pesticide chosen was the granular insecticide "Dursban 5G" (5% w/w active ingredient - chlorpyrifos, Dowco 179, Dow Chemicals) and was sprinkled at a dose rate of 15kg/ha (each 25cm X 25cm received approximately O.lg of pesticide). Chlorpyrifos has a broad range of insecticidal activity through contact, ingestion or vapour action while being non phytotoxic and is persistent for 60 to 120 days (Worthing & Walker, 1987). It is particularly effective in controlling ground coleoptera and ants (Kenaga, Whitney, Hardy and Doty, 1965; Hulme, personal observation). No

133 studies of its hazard to wild small mammals have been undertaken though it is rapidly detoxified in rats, dogs and a range of other species (Worthing & Walker, 1987). With an oral acute LD50 for rats of 135-163mg/kg mice would have to at least consume all the pesticide in a 25cm X 25cm exclosure to reach the LD50 for rats. Laboratory studies revealed that "Dursban" appeared not to influence the feeding activity of A. sylvaticus nor were rodents observed consuming the pesticide (Hulme, personal observation). Since "Dursban" is volatile and possesses an odour and olfactory cues may be important in rodent seed foraging (Howard, Marsh & Cole, 1968), seed experiments were undertaken at least 24hrs after pesticide application by which time the odour was considerably reduced.

Mollusc exclosure. Although molluscs are not commonly thought of as seed predators they do consume seeds (Godan, 1983) and are capable of passing through a mesh size of 0.64cm. The following exclosure was designed principally with regard to mollusc seedling predation (see chapter VI) though it was used in the present study to maintain the comparability of herbivore influences. As with the arthropod exclosure, a chemical rather than physical barrier method was selected. Two commonly used molluscicides are Methiocarb and Metaldehyde, the latter was chosen since although less potent it is specific to molluscs, with reasonable persistence (Godan, 1983) and would not affect other seed feeding invertebrates (Worthing & Walker, 1987). The preparation used in the present study was slug pellets "Mifoslug" (6% w/w active ingredient - metaldehyde, BBO Farmers, Twyford, Berkshire) similarly to "Dursban" the molluscicide was applied at a dose rate of 15kg/ha at least 24hrs before the start of an experiment in order to destroy any resident molluscs. Evaluation of the hazard to wild woodmouse populations has not been undertaken, though similar studies on methiocarb revealed toxicity under both laboratory (Tarrant & Westlake, 1988) and field conditions (Hare, 1984). This toxicity appears due to small mammals consuming the bran base of the slug pellets.

Laboratory studies on A.sylvaticus revealed that although metaldehyde slug pellets appeared not to influence rodent feeding activity, mice did consume a small amount of pesticide (Hulme, personal observation). Implications of this finding for the impact on wild populations are hard to

134 predict, though since toxicity of metaldehyde to rats is approximately 16% that of methiocarb it might be expected that the impact of metaldehyde on wild populations would be less severe. This is supported by the finding that woodmouse death in laboratory studies occurred within 12hrs of consumption of methiocarb (Tarrant & Westlake, 1988), while mice were still alive after several days of exposure to metaldehyde (Hulme, personal observation). Similarly, small mammals would have to consume all slug pellets placed in an exclosure to reach the LD50 for rats. Experimental treatments were created by combining the above exclosure methods as follows: a) Exclosure control. As a measure of the success of the exclosures in preventing seed predation from the various guilds of seed predators, a treatment combining all exclosure methods was used. Additionally, by combining both pesticide applications within a small mammal proof cage it was possible to assess background losses due to environmental conditions such as wind and rain. b) Arthropod exclosure. This treatment consisted of a small mammal exclosure within which "Dursban" was applied. Losses from this treatment were attributed to molluscs. c) Mollusc exclosure. This treatment consisted of a small mammal exclosure within which "Mifoslug" was applied. Losses from this treatment were attributed to ground arthropods. d) Invertebrate exclosure. This treatment consisted of both pesticide applications and was designed to gauge small mammal seed removal. A small mammal access cage was constructed using a mesh size of 3.0cm. The design was similar to the 1.0cm cage in the small mammal exclosure but the larger mesh size permitted small mammal access while preventing damage to the experiments from rabbits or stray dogs. Treatments were replicated five times in each of the two sites. Since within site spatial variation in rates of seed removal were believed to be important in the small mammal-seed interaction (Table 5.1), a 5 X 4 grid of cages spaced at 5m intervals was chosen in each site rather than a random spatial distribution. The predominant gradients in seed removal variation in each site were unknown and therefore a modified Latin square design was used to designate treatment position (Snedecor & Cochran, 1978). Since the grid was in fact rectangular, the design consisted of a Latin square to which was added an additional column in which treatments were designated at random (Figure 5.1).

135 This design could not be analysed as a true Latin square since the rows and columns did not contain the same number of treatment replicates, but the design was used since it provided an estimate of the extent of spatial variation in seed removal.

KEY

A - ARTHROPOD ACCESS ONLY C - CONTROL NO ACCESS

B - MOLLUSC ACCESS ONLY D - SMALL MAMMAL ACCESS ONLY Figure 5.1 Plan of the experimental design showing the grid of exclosures (small squares) and the spatial distribution of the treatments (A to D) ascribed to them. Subscripts (1-5) related to the small mammal access treatment (D) correspond to the replicate notation used in Figure 5.4. In each site only five cages were accessible to rodents and this limited their exposure to any harmful effects of pesticide application. Throughout the trapping regime (chapter II) care was taken to inspect rodents caught for any signs of pesticide poisoning and variations in population density at the time of pesticide application were also examined. Preliminary studies showed that most rodent seed removal from open cages occurred within 2 to 3 days (Hulme, unpublished data) and was in agreement with previous findings regarding patterns of rodent seed removal in relation to duration of seed exposure (Perry & Fleming, 1980; Verkaar etal., 1986). A period of seed exposure of 3 days was chosen since this permitted rodents

136 to encounter seed dishes and was comparable to periods of previous studies (Table 5.1). This period was chosen with respect principally to rodents and it is possible that if the invertebrate seed predators have lower mobility dependent seed encounter rates then this 3 day period may underestimate their seed removal (Kelrick et al., 1986). Evidence from the frequency of carcasses of invertebrates in pesticide treated exclosures over the 3 day periods indicated that while mobility dependent seed encounter rates will be important in determining rates of seed removal, between seed predator guild variations in seed encounter rates were catered for within this 3 day period. Laboratory studies (Hulme, unpublished) revealed that seeds in dishes were accessible to the majority of post dispersal seed feeders. Additionally, maximum exposure time was bounded by the finding that a number of seed species would begin to germinate within 5 to 8 days of being moistened either by dew or rain. The changes in seed characteristics, both physical (opening of seed coat, softening of endosperm) and chemical (breaking down of carbohydrates, secondary compounds etc.) associated with germination would undoubtedly influence seed predation and become a source of error. The 3 day period of exposure was short in relation to the germination time scale of the species used implying that even if seeds were moistened on the first day of exposure changes in seed characteristics over three days were minimal. Exposing each of 21 seed species individually for three days would entail a duration for each seed experiment of over two months (21 spp X 3 days). This time scale was far too long to be compatible with the aims of the study. Seed species were therefore presented in randomly chosen groups of three reducing the duration of each seed experiment to 3 weeks (7 dishes X 3 days). Seeds were presented in the field in 9cm plastic petri dishes split into three separate compartments of equal size, each compartment was filled with silver sand upon which seeds of only one species were placed. Protection from rain took the form of a plastic sheet 20cm X 20cm supported on wooden pegs 15cm above the dishes. After three days of exposure seed dishes were collected, remaining seeds were counted and signs of predators (faeces, seed remains, disturbance of sand etc.) recorded. Dishes were then cleaned and replaced in the field. Each replicate of each treatment received

137 sequentially seven single seed dishes with different seed species combinations, each for 3 days. The temporal order of seed dish presentation for each replicate was randomised such that in any one exposure period the same fifteen seed species (5 reps X 3 spp) were presented in all treatments though replicates of each treatment had different species combinations. Presentation of more than one species at any one time may lead to seed species neighbours influencing rates of loss. The low seed densities used in the present study (see below) were unlikely to influence removal of neighbouring species by attracting seed predators to particular seed dishes. Furthermore, at these densities any variation in seed predation due to seed neighbours would be small in relation to the influence of food resources in the local environment since seed predator food consumption was not restricted solely to the seed species presented in dishes. The use of seed dish and exclosure methods to examine seed predation in the field has recently been criticised (Kjellson, 1985; Kelrick et al., 1986). However, these criticisms, described below, were taken into account in the experimental design. a) Each source of seed loss should be quantified separately in order that it is not mistaken for other sources (Kjellson, 1985). This criticism was levelled primarily at experimental designs which did not take into account the range of post dispersal seed predators present in the communities studied and assumed all losses to be attributable to a particular seed predator. In the present study although it was not feasible to design experiments accounting for each single seed predator species seed losses were broken down into a number of seed feeding classes; ground arthropods, molluscs and small mammals and checked with a control. The use of the specific exclosure methods enabled accurate quantification of these sources of seed loss. b) Seed loss must strictly be a consequence of seed use by appropriate granivores and can be accurately measured (Kelricket al., 1986). This criticism was aimed at studies which used large amounts of seed in trials and relied on seed remaining as a guide to seed loss. Causes of erroneous estimates of consumption from seed dishes include changes in moisture content (weight) of seeds and biases due to composition of residual material (whether full seeds, broken seeds or seed coats). These sources of error may be overcome buy careful examination of oven dried seed remains but

138 can be neglected altogether if results are based on seed counts, as in the present study, rather than seed weights. c) All granivore classes should have an equal or known probability of finding dishes (Kelricket al., 1986). Seed dishes should be physically accessible to all seed predators within a particular class and all granivore classes should have an equal probability of finding dishes. The combination of laboratory studies to test the former and field observations to check the latter revealed these assumptions to be correct. d) The influence of changes in the quantity, species composition and dispersion of the natural resource background on rates of seed loss should be taken into account in analyses (Kelricket al., 1986). See below. 5.2.3 Experimental procedure

DENSITY 10 1

SEASON WINTER SPRING WINTER

BURIAL

Figure 5.2 Schematic representation of the experimental procedure indicating the major interactions between the experimental manipulations of seed density, season and seed burial. The following seed manipulations were undertaken (Figure 5.2):

139 Seed density. The behaviour of seed predators with respect to seed density will determine their ability to regulate seed dynamics (Hassell, 1978). Previous rodent seed predation studies (Table 5.1) have used a range of seed densities often without any ecological basis. Of those studies that have investigated the influence of seed density on rates of seed removal most have used a range of two seed densities (O’Dowd & Hay, 1980; Heithaus, 1981; Casper 1988) though some have used three (Mittlebach & Gross, 1984; Jensen, 1985) and even five (Webb & Willson, 1985). The scale of the present study limited the seed range to two seed densities. As one of the aims of the present study was to investigate how effective seed predators were at depleting seed resources, the lowest seed density possible, one seed per species per dish, was chosen as the lower seed density. The upper seed density of 10 seeds per species per dish was chosen since it not only reflected natural seed densities of a range of grassland species (Thompson & Grime, 1979) but also because this density had been frequently used in previous studies (O’Dowd & Hay, 1980; Heithaus, 1981; Mittlebach & Gross, 1984; Klinkhammeret al., 1988; van Tooren, 1988) therefore facilitating between study comparisons. It was at this seed density that seasonal comparisons of rates of seed loss were made. Seeds were placed evenly over the silver sand in each compartment, this aided finding of seeds after each trial and prevented any "clumped seed" effects influencing predation.

Season. Experiments using the above two seed densities were begun in the winter of 1987-88 (from 7 December 1987 to 14 January 1988 and from 15 February 1988 to 10 March 1988 for the 10 seed and single seed experiments respectively). Winter was chosen since it represented a period sufficiently late that all seed fall in both sites had ceased and autumn germination had finished. Winter represents a period of relative stasis in the annual dynamics of grassland seed banks (Thompson & Grime, 1979) and this was important since it prevented local variations (temporal and spatial) in seed abundance related to seed dispersal or germination from altering background food supplies and so influencing rates of seed removal. Presentation of seeds in winter rather than during the period of seed fall as has been the norm in previous studies of grassland seed predation (Table 5.1) may not truly reflect natural conditions.

1 4 0 The majority of seeds chosen in the present study possess either permanent or winter transient seed banks (Thompson & Grime, 1979) and would be present naturally throughout winter and the seed experiments would reflect these conditions. However, winter predation studies on large grass seeds which are normally only present in the summer and autumn may lead to erroneous conclusions. In order to overcome this possible source of error the winter experiments were compared to a preliminary seed predation experiment undertaken in both sites during the period of seed fall in summer/autumn 1986. This experiments used the same experimental design described above but with 0.75g of each seed species in each seed dish compartment rather than silver sand and seeds. The aim of this preliminary study was to assess the ability of seed predators to exploit large seed resources, such as those that occur over the period of seed fall. The density of seeds used had little ecological basis since such high density clumps of seed rarely occur in grassland and use of weight of seed remaining as an index of seed removal is prone to criticism (see above). Since this experiment confounded both season and density effects it could not directly be compared to the winter studies and discussion of this preliminary study was therefore restricted to estimates of predator impact. Additionally, although predation studies undertaken during periods of natural seed dissemination (Table 5.1) have confirmed that the seeds found in small mammal diets over summer-autumn are the results of post dispersal seed predation (Holisova, 1975; Butet, 1985; Churchfield & Brown, 1987; Hunteret al., 1987), it is unknown whether seeds found in grassland small mammal diets in winter are the direct result of foraging at this time or the result of exploitation of previously collected seed caches (Hansson, 1985). A number of previous studies (Table 5.1) highlighted that rates of woodland seed removal varied in time, principally between years. This variation may have resulted from changes in predator density, seed density and or abundance of alternative foods. In spring, germination reduces the seed pool (Thompson & Grime, 1979) and the abundance of alternative food for small mammals increases (Gumell, 1985) while small mammal abundance tends to decline (Flowerdew, 1985; Montgomery, 1989a). Experiments were therefore undertaken in the spring (from 28 March 1988 to 18 April 1988) since this period represented a period of high alternative food abundance and low small

141 mammal population density while winter represented a period of low alternative food abundance and high small mammal population density. Comparisons at these two environmental extremes would enable the extent of seasonal variation in rates of seed loss to be gauged.

Seed Burial. Most natural seed is not present on the soil surface but is usually buried beneath soil. The influence of seed burial on rates of seed predation is therefore an integral component of any study and it is surprising how infrequently it has been examined (only 27% of experimental studies in Table 5.1). Although viable seeds may be found as deep as 30cm in grassland soils (Chippindale & Milton, 1934) most are found in the top 2cm (Harper, 1977) of which only those in the top 1cm are likely to germinate and produce successful seedlings (Naylor, 1985). In the present study, for each seed density, seeds were placed beneath 1cm of silver sand over which was placed the surface seed treatments. Seed density of surface and buried seed were always equal and care was taken with buried seed in order that they did not form small seed clumps beneath the sand. The combination of both buried and surface seed treatments in the same dish enable loss rates to be directly comparable but may suffer from the two treatments interacting, in particular the presence of surface seed enhancing buried seed detection as was found forDipodomys deserti (Lockard & Lockard, 1971). This source of error was tested for by comparing rates of loss of buried seed only and buried seed over which was placed surface seed for four seed species. No significant differences in rates of loss of buried seed in the two treatments was found (Hulme, unpublished data). This implied that this method of combining treatments maintained experimental realism and was probably more realistic since in the field both buried and surface seed are likely to occur together. 5.3 Results

Results of two seed species were not analysed; the small size ofAgrostis capillaris seed made recovery of buried seed unreliable, and the hydroscopic awn ofDeschampsia flexuosa led to seed dispersing from plates when moistened and being lost. The following results are described for the remaining 19 species.

142 5.3.1 The impact of different seed predator classes on surface seed numbers Small mammals were the major post dispersal seed predators in both sites in autumn and winter (Figure 5.3). A separate two way analysis of deviance was undertaken for each experiment on the proportion of seeds removed using GLIM with binomial errors. Seed loss was consistently highest in the treatment to which small mammals had access and apart from one occasion this was the only treatment in which seed loss was significantly higher than that of the controls (10 to 70 times higher). The small losses from the controls (<10%) was indicative that most sources of seed loss had been catered for by the experimental design. Only one other source of seed loss was significantly higher than the controls. This was attributable to molluscs in the grassland in winter (Figure 3.2). Most ecological studies of molluscs have focused solely on their grazing of plants (Harper, 1977; Dirzo, 1984,1985), however, reference to continental agricultural literature (reviewed in Godan, 1983) revealed that a wide range of mollusc species consume seeds. In the winter study the most common mollusc seen in the grassland was Deroceras reticulatum (MUller) and in the meadow Arioti ater (L.). The former species is a serious agricultural pest throughout Europe and is known to subsist on plant seeds while the latter is of less agricultural importance (Godan, 1983). This difference between these slug species may explain why seed removal by molluscs in the grassland was six times that of the meadow.

Deroceras reticulatum is known to consume a range of seed species though with a preference for herbaceous rather than graminaceous seeds (Godan, 1983). A 24hr laboratory seed preference trial usingD. reticulatum and the species used in the seed predation study (Table 5.2) in the same 3 species combinations revealed consumption of onlyMedicago sativa, Lotus corniculatus, Medicago lupulina and Trifolium dubium (Hulme, unpublished data). Although this confirmed previous findings regarding mollusc seed feeding it did not explain the losses of grass seed from the mollusc access treatment. A number of possibilities exist,D. reticulatum may consume grass seeds after all legume seeds are consumed, other species of mollusc present in the grassland may

143 70

a) 60

50

20

CONTROL ARTHROPOD MOLLUSC RODENT

70 GRASSLAND

60

50

40

30

20

10

CONTROL ARTHROPOD MOLLUSC RODENT

F igu re 5 3 Seed removal attributable to various seed predator guilds in the grassland and meadow sites, a) High seed density experiment (0.75 g of seed) in Autumn 1986. b) Ten seed experiment in Winter 1987. Error bars represent one standard error of the mean.

144 consume grass seeds and/or losses of seed are through mollusc mediated seed dispersal. Some seed species (particularly small grass seeds) frequently became attached to the slimy exterior of slugs for sufficient time to be carried away from the seed dishes and hence to be recorded as lost. It was not possible to estimate to what extent seed losses from molluscs were due to dispersal or consumption, however it appears that even if all losses were from direct consumption the impact of molluscs on post dispersed seed populations was small in comparison to that of the small mammals. Pitfall trapping in the summer/autumn 1986 for ground dwelling invertebrates in concert with 24hr seed preference trials revealed the following potentially important arthropod post dispersal seed predators, Pterostichus niger (Schaller), P. madidus (Fabricius), P.cupreus (L.),P. versicolor (Sturm) and Amara aulica (Panzer) (Hulme, unpublished data). Nevertheless, seed losses attributable to this class of seed predator were low in the seed predation experiment undertaken at the same time as the ground arthropod census (3%, Figure 5.3) and in no case did losses ever approach the level attributable to small mammals. The finding that small mammals were the major post dispersal seed predators in both sites led to the examination of the influences of seed density and season being undertaken with only the small mammal access treatment and controls. 5.3.2 Factors influencing rodent surface seed predation In order to assess the relative importance of the main sources of variation in surface seed removal found in previous studies (Table 5.1, section 5.1) two four way analyses of deviance using GLIM with binomial errors were undertaken examining the separate effects of season and density (since these treatments were mutually exclusive, Figure 5.1) on spatial and species variation in the proportion of surface seed removed (Table 5.3). The size of the model prevented analysis of second or higher order interactions and these were examined by breaking the model into two, one for each experimental site. Seed removal by rodents is a function of the probability of a seed dish being located and the preferences exhibited by rodents for particular seed species. The former depends on the mobility of the seed predator and its population density, while the latter is related to seed species characteristics

145 SEASON DENSITY TREATMENT deviance F value df P TREATMENT deviance F value df P replicate 124.39 31.10 4 *** replicate 125.78 31.45 4 *** species 44.63 2.48 18 *** species 32.81 1.82 18 * site 3.51 3.51 1 site 0.20 0.20 1 season 1.78 1.78 1 density 11.60 11.60 1 *** re.sp 89.19 1.24 72 re.sp 161.56 2.24 72 *** re.si 124.85 31.21 4 *** re.si 169.05 42.26 4 *** re.se 19.28 4.82 4 *** re.de 40.24 10.06 4 ♦ ** sp.si 18.02 1.00 18 sp.si 47.29 2.63 18 ** sp.se 31.59 1.76 18 * sp.de 14.68 0.82 18 si.se 9.02 9.02 1 ** si.de 4.59 4.59 1 * residual 238.00 238 residual 238.00 238 total 704.70 379 total 845.80 379 GRASSLAND replicate 43.46 10.87 4 *** replicate 67.85 16.96 4 *** species 50.90 2.83 18 *** species 151.17 8.89 18 season 0.11 0.11 1 density 42.27 42.27 1 *** re.sp 104.14 1.45 72 re.sp 729.36 10.13 72 *** re.se 27.08 6.77 4 *** re.de 49.86 12.47 4 sp.se 39.50 2.19 18 * sp.de 82.57 4.59 18 *** residual 72.00 72 residual 72.00 72 total 337.19 189 total 1195.08 189 MEADOW replicate 470.34 117.59 4 *** replicate 2639.07 629.77 4 *** species 42.20 2.34 18 ** species 319.57 17.75 18 *** season 21.28 21.28 1 *** density 27.79 27.79 1 *** re.sp 218.44 3.03 72 *** re.sp 1203.49 16.72 72 **% re.se 54.46 13.62 4 *** re.de 767.91 192.00 4 *** sp.se 82.31 4.57 18 *** sp.de 222.03 12.34 18 *** residual 72.00 72 residual 72.00 72 total 961.03 189 total 5251.85 189 Table 5.3 Results of analysis of deviance examining the following effects replicate (re), site (si), season (se), species (sp) and density (de) and their interactions for both seasonal and density comparisons. Larger models were re-analysed to examine within site patterns of the parameters. Statistical significance * p<0.05, ** p<0.01, *** p<0.001

146 and local food abundance. This implies that the probability of a seed being removed has a stochastic component and the correct error distribution will approach a beta-binomial rather than binomial distribution (Aitkin, Anderson, Francis & Hinde, 1989). Such overdispersion in the data may lead to errors in analysis and were corrected for by scaling deviances by the residual deviance divided by its degrees of freedom (Aitkin et al., 1989). The resulting scaled deviances for terms in the models were compared with percentage points of the F-distribution (Aitkinet al., 1989). 5.3.3 Spatial variation in surface seed removal by small mammals The present analysis refers to variation in seed loss within a particular habitat and seed predator assemblage. Between habitat variation will necessarily share these within habitat findings but will be influenced more by variations between seed predator assemblages. Discussion of between site variations in patterns of seed loss will be made where appropriate. It is evident from Table 5.3 that highly significant within site spatial variation in seed removal existed in both sites. Spatial patterns in seed removal differed between sites, though this is not surprising since in both sites treatment replicates were placed independently of any habitat characteristics. Additionally the spatial patterns of seed loss varied with both the seed density and the season of presentation in both sites (Figure 5.4). The spatial variation in seed removal was highly correlated with spatial variations in small mammal abundance in both sites in all surface seed manipulations (Table 5.4). This implies that spatial variations in seed removal due to manipulations of seed density or season were the result of changes in the spatial distribution of small mammals dining these experiments rather than the influence of either seed density or season on rodent seed foraging behaviour. Greater spatial variation in seed removal existed in the meadow than in the grassland (Table 5.3, Figure 5.4) reflecting the more aggregated small mammal distributions in the meadow (chapter IV). There was significant spatial variation in the seed species removed (Table 5.3). Seeds were put out in the two sites for a total of seven, 3 day periods, each period corresponding to a particular seed species combination. Some replicates were more frequently visited by small mammals than

147 100

REPLICATES

F ig u re 5.4 Spatial variation in seed loss from the sm all m ammal access treatment, as determined by between replicate variation (1 to 5, see Figure 5.1), in the grassland and meadow sites, a) Ten seed experiment in winter, b) Ten seed density experiment in spring, c) Single seed density experiment in winter. Error bars represent one standard error of the mean.

148 1 2 3 4 5 6 7

TIME PERIOD

Figure 5.5Temporal variation in the number of small mammal access treatment replicates encountered by sm all mammals over the seven, three day periods o f seed exposure in the grassland and meadow sites, a) Ten seed density experiment in winter, b) Ten seed density experiment in spring, c) Single seed density experiment in winter. Error bars represent one standard error of the mean.

149 others leading to a wider range of species being removed in these replicates i.e. meadow site. This explains why spatial variation in seed species removal was always greater in the meadow than in the grassland (Table 5.3).

GRASSLAND MEADOW surface seed surface seed buried seed

SEASON r s d f P d f P r, P W IN T E R 0.3477 93 ** 0.5107 93 ** 0.4547 ** SPRING 0.3439 93 ** 0.5670 93 ** 0.5789 ** TOTAL 0.3230 188 ** 0.5767 188 ** 0.5158 ** LOW 0.5778 93 ** 0.4678 93 ** 0.4961 ** Table 5.4 Spearman rank correlations (r.) between local rodent abundance and seed removal from rodent access treatments for the 10 seed (winter, spring and total) and single seed (low) experiments. Sufficient data was only available in the meadow for the examination of buried seed correlations. Statistical significance as in T ab le S 3 . Additionally, any temporal component in the spatial variation in seed removal would lead to different species groups suffering varying predation pressures. The temporal variation in number of cages visited during each of the seven, 3 day periods is shown in Figure 5.5. In most cases there was little temporal variation in rates of seed removal in the experiments (coefficients of variation (CV) all < 50%, Figure 5.5). However during the low seed density experiment in the grassland temporal variation in cage visits was marked (CV = 81.6%, Figure 5.5). Since during any one period each replicate contained different seed species this would lead to between replicate variation in seed species removed. This explains why in the grassland significant spatial variation in seed species removed existed only when density and not seasonal comparisons were made (Table 5.3). 5.3.4 Response of small mammal seed predators to changes in seed density In both sites small mammals removed fewer single seeds than seeds placed in groups of 10 (Figure 5.6), though only in the grassland was the reduction significant. The effect of reduced seed density was significantly greater in the grassland with seed removal being reduced by over 65% compared to only 20% in the meadow. This density dependence in rodent seed removal was not an artefact produced by changes in rodent density or spatial distribution between experiments but

1 5 0 50

SEED DENSITY Figure 5.6 The effect of seed density on small mammal attributed seed loss in the grassland and meadow sites. Error bars represent one standard error of the mean. appeared to be the result of changes in rodent foraging. For example, in the grassland, on 40% of the occasions low seed density replicates were definitely visited by rodents (as determined by presence of fresh faeces) not one seed was removed. 5.3.5 The influence of season on small mammal seed predation Seasonal patterns in seed removal significantly differed between the two sites (Table 5.3, Figure 5.7). In winter seed removal in both sites was similar (approximately 42%), however in spring whereas seed removal remained at this level in the grassland it fell, though not significantly, in the meadow to 32.1%. This lower removal in the meadow in spring appeared to be due to one particular seed group not being encountered by small mammals (see Section 5.3.6, Table 5.5) and this seasonal drop may have simply been an artefact of the experimental design. This would imply that total surface seed removal varied little between winter and spring in either site.

151 50

COo4 0 Q LU UJ CO 3 0

OUL. L U 2 0 O< I—Z OLU 1 0 cc111 1 0

WINTER SPRING SEASON

Figure 5.7 The effect of the season of seed exposure on small mammal attributed seed loss in the grassland and meadow sites. Error bars represent one standard error of the mean.

5.3.6 Plant species variation in small mammal surface seed predation The plant species to which a particular seed belonged significantly influenced its probability of being removed by small mammals (Tables 5.3,5.5). Attempts at explaining such species variation have provided a number of potential explanatory seed characteristics including calorific content (Reichman, 1977), nutritional content (Kelricketal., 1986), seed toxicity (Sherbrooke, 1976), seed size (Abramsky, 1983) and seed handling time (Rosenzweig & Sterner, 1970). The large number of seed species used prevented a detailed examination of how these variables influenced the observed seed species variation in seed removal. Instead, the study focused on three parameters, seed weight (size), monocot/dicot differences, and seed familiarity (Table 5.2). These three parameters were checked for independence using one way ANOVAs. There was no significant dependence of seed

152 weight on monocot/dicot differences (F(117)=0.62, p>0.05) nor on seed familiarity in either the grassland (F(117)=2.98, p>0.05) or meadow (F(117)=2.17, p>0.05) and neither was seed familiarity a function of monocot/dicot differences (Table 5.2). The analyses presented in Table 5.3 were repeated with the seed species parameter replaced by the three parameters described above. The variable seed weight was converted to a factor (size) by labelling all species with a seed weight less than the median seed weight (approximately lmg) as "small" and greater than lmg as "large". The main results are provided in Table 5.5 and the following discussion deals with significant trends found in the analyses. The full model (composed of the three parameters described above) combined over both sites, accounted for little of the variation attributable solely to species main effects (40.79% and 29.72% in the season and density comparisons respectively). None of the parameters explained any significant variation attributable to species in the density comparisons. In the seasonal comparisons, of the three parameters, only seed familiarity was a significant main effect (F(1 ,34«)“4.58, p<0.05). Seed species found in the local habitat of the seed predators had a higher probability of being removed than alien species. This difference in seed removal related to familiarity was more evident in the grassland site than in the meadow (Table 5.5). Care must be taken in interpretating this result since familiarity may have, by chance, selected a preferred subset of the seed species unrelated to their presence in the habitat. The seed species familiarity classification of the grassland was used in the meadow to test whether the familiar species in the grassland were inherently preferred to the unfamiliar species independently of their presence in the local habitat. There was no consistent pattern in loss of familiar or unfamiliar seed in the meadow when the grassland seed classification was used. This result supported the finding that seed familiarity may influence the probability of a seed being removed. A significant interaction occurred between the three parameters (F(134S)=8.02, p<0.01). Only two "small" dicotyledon species were used and no conclusions regarding their interaction with seed familiarity can be made. The following trend in removal in the remaining species was found: familiar grasses (either size) > large forbs (either familiarity) > unfamiliar grasses (either size).

153 SEED SPECIES GRASSLAND MEADOW winter spring low winter spring low buried Dactylis glomerata 58 76 20 40 - 68 20 18 Agrostis capillaris Poa annua 46 56 20 36 36 20 14 Poa pratensis 56 20 20 36 38 40 22 Phleum pratense 54 10 0 38 38 40 34 Festuca rubra litoralis 60 26 0 38 40 40 38 Festuca pratensis 24 44 40 40 38 40 38 Lolium perenne 24 40 40 40 36 40 38 Trifolium ora tense 32 56 40 40 34 40 32 Alopecurus pratensis 48 14 20 58 2 40 52 Festuca rubra 46 24 20 46 6 60 commutata 26 Tri folium dubium 28 18 20 46 6 40 8 Deschampsia flexuosa Festuca ovina 68 80 0 40 40 20 38 Medicaso luoulina 60 70 0 42 42 20 40 Medicago sativa 56 70 20 40 36 40 18 Holcus lanatus 56 72 0 48 42 20 0 Tri folium repens 56 72 20 40 28 40 10 Festuca rubra rubra 24 44 0 66 20 20 34 Lotus corniculatus 8 36 0 54 36 40 24 Plantago lanceolata 8 12 0 18 24 20 0 Total mean 42.74 44.21 14.74 42.42 32.11 33.68 25.47 S.E. 38.97-46.59 40.42-48.07 11.63-18.51 38.66-46.2728.62-35.81 31.38-36.07 21.86-29.43 Grass mean 47.00 42.17 15.00 43.83 33.67 33.33 29.33 S.E. 42.71-51.89 37.43-47.05 11.15-19.8740.91-46.80 30.92-36.53 30.45-36.35 26.09-32.81 Forb mean 35.43 47.71 14.29 40.00 29.43 34.29 18.86 S.E. 29.58-41.75 41.39-54.11 9.56-20.81 36.26-43.86 26.01-33.09 30.51-38.27 15.37-22.94 P Large seed mean 35.27 44.18 18.20 44.00 31.09 34.55 29.09 S.E. 30.56-40.29 39.19-49.30 13.78-23.60 40.95-47.10 28.30-34.03 31.51-37.72 25.72-32.71 Small seed mean 53.00 44.25 10.00 40.25 33.50 32.50 20.50 S.E. 47.00-58.90 38.41-50.25 6.33-15.45 36.74-43.86 30.16-37.01 29.02-36.19 17.09-24.40 P * Familiar seed mean 47.09 51.64 10.91 43.60 32.80 30.00 20.60 S.E. 42.04-52.20 46.54-56.70 7.53-15.55 40.40-46.85 29.82-35.92 26.95-33.23 17.52-24.07 Unfamiliar seed mean 36.75 34.00 20.00 41.11 31.33 37.78 30.89 S.E. 31.19-42.69 28.58-39.88 14.70-26.62 37.78-44.52 28.25-34.59 34.33-41.34 27.10-34.96 P * T ab le 5.5 Mean percentage seed removal by rodent in each site for 10 seed (winter and spring) and single seed (low) surface seed experiments and for buried 10 seed removal in winter in the meadow. Order of species reflects the three species presentation groups. Figures beneath means represent the one standard error each side of the mean. Statistical significance as in previous tables.

154 GRASSLAND MEADOW SEASON species r. df P r, df P WINTER total -0.1131 93 0.1058 93 grass -0.0971 58 0.1613 58 forb 0.1336 33 0.0720 33 SPRING total 0.1116 93 0.0710 93 grass 0.0349 58 0.0150 58 forb 0.2714 33 0.225 33 LOW total 0.4192 93 ** 0.2061 93 * grass 0.4579 58 ** 0.2616 58 * forb 0.3750 33 * 0.1225 33 Table 5.6 Spearman rank correlations (r„) between seed weight and rodent seed removal in each site for the 10 seed (winter and spring) and the single seed (low) experiments. Correlations were also separately undertaken on grass and forb subgroups. Statistical significance as in previous table.

GRASSLAND MEADOW WINTER SPRING LOW WINTER SPRING GRASSLAND SPRING 0.4982* LOW -0.1390 0.1175 MEADOW WINTER -0.1193 0.1482 0.0162 SPRING 0.6180** 0.5118* -0.1675 -0.2465 LOW -0.0452 -0.2825 0.5781** 0.2022 -0.2346 Table 5.7 Spearman rank correlations of species preferences exhibited by rodents between and within sites in the 10 seed (winter and spring) and single seed (low) surface seed experiments. Statistical significance as in previous tables.

155 10 6 0 b) cir

5 0

4 0

3 0

20

10

0 LARGE SMALL SEED SIZE

Figure 5.8 The influence of seed size on the effect of seed density on small mammal attributed seed loss in a) the grassland and b) meadow sites. Error bars represent one standard error of the mean. Although the factor size, used in the above analyses of deviance, would have highlighted any major influence of seed size on seed removal, in particular any interactions with the other seed parameters, finer examination of the influence of seed size on seed removal can be achieved by using the variable, seed weight. Spearman rank correlations of seed removed with respect to seed weight for both seasons and densities only revealed significant differences in the low density comparisons (Table 5.6). Additionally, the correlation of seed removal with seed weight was greater

156 in the grassland than in the meadow and greater for grasses than for forbs (Table 5.6). However, although significant correlations existed between removal and seed weight, regression analysis revealed that seed weight, similarly to seed size, explained little of the deviance (4.92% and 0.12% in the grassland and meadow respectively). Significant species interactions with both season and density existed (Table 5.3). Since different seed groups were presented sequentially in each site care was taken not to confuse species differences with those attributable to temporal variations in rodent seed foraging activity (Figure 5.5). This was undertaken by examining the original data and checking if changes in seed removal were a dish effect (due to variation in the number of rodent encounters per seed group) or due to a species effect (due to variation in the number of seeds removed per dish encountered). As regards seasonal patterns of seed species removal, although there was consistency in the overall species removed in both sites, the patterns within sites differed significantly (Table 5.3). In the grassland, although increased removal of all large forb seed species in spring (Table 5.5) led to significant seasonal variation in seed species removal (Table 5.3), significant correlation existed between seasons in the general pattern of species removed (Table 5.7). However in the meadow, the highly significant seasonal variation in seed species removal (Table 5.3) showed little trend with respect to the species removed (Table 5.5), and unlike the grassland no significant correlation existed between seasons in the general pattern of species removed (Table 5.7). This lack of correlation between seasons in the meadow was in part due to one particular seed group(A. pratensis, F. rubra var commutata & T. dubium) not being encountered at all by rodents during spring (Table 5.5). However, significant correlation between seasons in the general pattern of species removal was still not found if this group was omitted from the analysis (rs=0.1132, d.f. 14, n.s.). This may imply that rodents in the meadow were foraging differently in winter than in spring. This hypothesis is supported by the general pattern of species removal in both seasons in the grassland and in spring in the meadow being significantly correlated with each other while none were correlated with winter patterns in the meadow (Table 5.7).

157 In both sites significant variation existed in species removal with respect to seed density (Table 5.3). Although not significant, there was a trend in both sites for large, unfamiliar species such as M. sativa, L. perenne, F. pratensis to be removed proportionally more at low seed densities than at higher (Table 5.5). These species comprise three of the four largest seed species used confirming the previous seed weight correlations (Table 5.6). In neither site did significant between density correlations exist as regards the general pattern of species removed though at low seed density the patterns in both sites were significantly correlated (Table 5.7). This indicates that whereas in winter at the higher seed density rodents may have foraged differently between sites at low seed density they foraged similarly. The influence of seed size on removal at different densities is shown in Figure 5.8. Removal of both seed sizes in each site was reduced at the lower seed density. In the grassland, where density dependent seed removal was strongest (Figure 5.6), removal of small seeds was more responsive to a lowering of seed density than removal of large seeds (81.0% reduction compared to 48.4%). No such pattern was evident in the meadow (Figure 5.8).

There was significant variation in removal of species sharing the same genus e.g.Trifolium , Festuca. However, different varieties ofFestuca rubra did not significantly differ in patterns of loss. 5.3.7 The effect of seed burial on seed removal by small mammals Examination of the impact of different predator classes on buried seed removal in the first winter experiment (see section 5.5.1) revealed small mammals to be the only seed predators of those studied in the two sites removing buried seed. Therefore the influence of seed burial on seed removal was compared with that for surface seeds for small mammals only using an analysis of deviance using GLIM with binomial errors. Comparisons of spatial variation in seed removal were not undertaken since they were presumed similar due to simultaneous presentation of both buried and surface seed. This assumption was supported by the significant correlations of buried seed loss with small mammal abundance (Table 5.4).

158 WNTER SPRING LOW EXPERIMENT

Figure 5.9 The effect of seed burial on small mammal attributed seed loss in a) the grassland and b) meadow sites in each of the three experiments - Ten seed density in spring and winter and single seed density (low) in winter. Error bars represent one standard error of the mean. Burial significantly reduced seed removal in both sites for both density (F(1608)=98.75, pcO.OOl) and season (F(1608)=168.6, pcO.OOl) comparisons. Buried seed removal significantly differed between sites in both density (F(lt60g)=51.83, pcO.OOl) and season (F(lt60g)=31.19, pcO.OOl) comparisons. In winter the influence of seed burial on seed removal was significantly greater in the grassland reducing loss of the higher seed density by 98.5% and of the lower by 100% whilst

159 in the meadow these values were 40% and 87.5% respectively (Figure 5.9). Although in both sites less seed loss occurred at the low seed density, similarly to surface seed removal, the difference was only significant in the grassland. Even when single seeds were buried rodents in the meadow removed over 4% of seed.

co Q LUo LU CO

o LU oc 1X1 o 1X1cc a.

S E E D S I Z E

Figure 5.10 The influence of seed size on the degree to which small mammals were able to exploit the ten seed density buried seed resource in the meadow site in winter. The Y-axis represents the percentage of seeds removed when buried seed was dug up. Error bars represent one standard error of the mean. Values within histograms represent the number of seed species in each weight category. Buried seed removal by small mammals varied significantly with season (F(160g)=18.58, p<0.001) being significantly less in spring than in winter though seasonal patterns of buried seed removal significantly differed between sites (F(lt60B)=10.11, p<0.001). Whereas in the grassland no significant difference existed between seasons in the removal of buried seed, in the meadow, loss was significantly reduced in spring to only 5% of its winter value (Figure 5.9).

160 There were no significant species effects of seed burial and an examination of the influence of the three seed parameters on rates of buried seed removal for the higher seed density in the meadow revealed no significant patterns. However, significant correlations did exist between seed removal and seed weight (Table 5.8) though sufficient data were only available for the winter experiments in the meadow. Significant positive correlations existed between seed weight and buried seed removal at both seed densities, being substantially stronger at low than higher seed densities (Table 5.8). The three species most frequently removed by rodents in both sites, seasons and densities wereF. pratensis, L. perenne and T. pratense which represented the three largest seed species used (all approx. 2mg). Nevertheless, at the higher density in the winter experiment in the meadow even small seeds (1.5mg) seeds tended to be removed at each encounter (F(3 2l)=4.852, p<0.05, Figure 5.10). HIGH DENSITY LOW DENSITY r, df P r, df P GRASS 0.3031 58 * FORB 0.3480 33 * TOTAL 0.2708 93 ** 0.5505 93 ** Table 5.8 Spearman rank correlations (rs) between seed weight and seed removal by rodents of buried seed during the 10 seed (high density) and single seed (low density) experiments in the meadow site in winter. Statistical significance as in previous tables.

5.4 Discussion 5.4.1 Factors influencing small mammal seed foraging in grassland The above results, in comparison with previous studies .(Table 5.1), enable an examination of the mechanisms by which seed variables influence the rate of seed removal by small mammals. Small mammal seed removal is a function of the probability that a small mammal encounters a particular seed and of its seed preference. The first component will involve the spatial distribution

161 of the seed relative to that of the predator, the olfactory and visual stimulus of the seed and acuity of the rodent. The second component will involve local food abundance (as perceived by the rodent through hunger), the physical and chemical nature of the seed and rodent preference for seed type and distribution. The commonest observation regarding seed removal by rodents is its spatial variation. Although previous studies have noted that this variation was a result of spatial variation in small mammal density, this study represents the first occasion in which direct correlations of spatial variation in seed removal with local rodent abundance have been made. Small mammal abundance was associated with levels of vegetative cover (Chapter IV) leading to seed loss being lower in open areas. Therefore the spatial heterogeneity of the vegetation cover in a particular habitat will be a major determinant of the spatial variation in seed loss. For this reason the greater spatial heterogeneity of vegetation in the meadow site led to greater variability in seed removal. However, there appears little consistency in previous findings as to the role of vegetation cover. While in old-fields rodent attributed seed losses were lower in open areas (Mittlebach & Gross, 1984) the reverse was true in sand-dune grasslands (Klinkhammeret al., 1988). Neither of these studies monitored rodent abundance in relation to habitat characteristics. It appeared that rodent abundance in the latter study was unlikely to be limited by vegetation abundance since open areas were dominated by tall growing herbaceous vegetation (e.gRubus caesius, Urtica dioica) and that patterns of seed predation were more related to local seed abundance than vegetation cover. The influence of trade-offs in vegetation cover and seed density are highlighted by those studies examining the distance-density responsiveness of rodent seed predators to highly localised seed production (Janzen, 1970; Connell, 1971). Distance responsiveness has been recorded in desert communities (O’Dowd & Hay, 1980; Hay &Fuller, 1981), temperate woodlands (Webb & Willson, 1985) and moist tropical forests (Schupp, 1988) but not in semi-deserts (Casper, 1988) nor in dry tropical forests (Perry & Fleming, 1980). This variation may be the result of variation in vegetation cover around point sources of seed production, but may also reflect different distance responsiveness

162 of seed predator species and/or local seed abundance. This lack of certainty emphasises the importance of detailed demographic studies of the seed predators in the elucidation of seed predation patterns. The present study was not designed to test for distance responsiveness in seed predation. This was in part a result of the grassland habitats studied. The interaction between localised seed production and heterogeneity of vegetation cover necessary for the exhibition of distance responsive seed predation was not evident in either site. This implied that distance responsiveness for herbaceous seeds was unlikely to be important within these grassland communities. An extension of local spatial variation in seed removal is the variation that exists between habitats. The two sites while showing similar patterns of seed removal in spring, differed considerably in winter. In winter, in comparison to the grassland site the seed predators in the meadow: i) Exploited low seed densities more effectively. ii) Were less responsive to variations in seed size. iii) Exhibited less preference for a variety of seed attributes, including seed familiarity and monocot/dicot differences. iv) Exploited buried seed more proficiently. These differences may have been due to between site differences in small mammal seed predator species composition and/or local resource availability. Rodent species composition did vary between sites during the seed experiments. Although Micromys minutus did consume some seed in the grassland most losses in both sites were attributable (as determined by seed remains and size of faeces) to Apodemus sylvaticus. While M. minutus foraging may have influenced such seed variables as seed species preference, it cannot explain the lack of buried seed removal in the grassland. Only on 9% of the occasions when A.sylvaticus were known to have foraged on the higher density seed dishes in the grassland were buried seeds removed compared to 80% in the meadow. Additionally, seasonal patterns of seed removal in the grassland

163 were not compatible with seasonal changes in rodent species composition. These findings tend to imply that between site differences in seed removal were related to differences in A. sylvaticus foraging. The fact that between site differences in seed removal patterns possessed a seasonal component is suggestive of an environmental influence on rodent seed foraging. Spring is generally compared to winter as a period of increased food abundance for small mammals (Hansson, 1985). The similarity of seed removal patterns in both sites in spring is indicative that between site differences in winter may have been due to between site differences in food availability. It was impossible to monitor rodent resource availability in both sites and therefore no direct evidence exists to support this hypothesis. However, the differences between the two sites in removal patterns described above can all be explained by rodents in the meadow exploiting the local resources more effectively, indicating indirectly that the resources available to the rodents may have been more limited in the meadow. Although no absolute measure of site resource availability was made the relative resource availability (per rodent) was likely to be lower in the meadow since winter A. sylvaticus densities were 3 to 5 times those of the grassland. Whereas all previous studies which examined rodent predation on a number of seed species found interspecific differences in removal (Table 5.1) few examined the bases for these differences. In order to understand seed preference related to seed characteristics it is first necessary to understand the mechanism of rodent seed selection. A representation of the steps involved by rodents in selecting seeds, developed from Lawhon & Hafner (1981), is shown in Figure 5.11. No study has simultaneously examined the influence of all the potential seed characteristics shown in Figure 5.11 on seed removal. Most have focused on the most easily measurable character: seed weight (size). Seed weight is clearly an important seed characteristic being involved in four out of the five selection steps (Figure 5.11), yet confusion exists as to its role in seed removal (Table 5.1). This may be understood by examining the range of seed weights used in those studies which have compared more than two species and provided seed weight data. Those studies using a wide range of seed weights have tended to find seed weight to be important (Mittlebach & Gross, 1984; seed

164 SEED AUTHOR

ATTRIBUTE z o OLFACTION o d o u r J e n n in g s oLU ( 1 9 7 6 ) tu o f size Lawhon & o VISI ON LU c o lo u r H a f n e r CO v o lu m e ( 1 9 8 1 )

f w e ig h t L a w h o n & TACT ILE O s h a p e H a f n e r COO ( 1 9 8 1 ) LU r handling- Kaufman Q tim e O HULLING & C o llie r oz te x tu r e ( 1 9 8 1 ) s 1r to x ic ity K e lric k o LL. TASTE energy- et al. v a lu e ( 1 9 8 6 )

Figure 5.11 Schematic representation of the hypothesized process of seed detection and selection exhibited by granivorous rodents. weight range=100; Jensen, 1985, swr=1000; Kelrick etal., 1986, swr=100) while those using small seed weight ranges (van Tooren, 1988, swr=10; this study, swr<10) have not. Although an important component of seed selection, no single step of the selection process is governed solely by seed weight, and it is likely that at each step trade-offs exist between the various seed characteristics involved. Where little variation in weight exists between seed species these other seed characteristics (odour, shape, colour, texture etc.) will influence seed choice to a greater extent and therefore correlations with seed weight are less likely. The seed weight range of groups of species from various habitats in Britain vary from 10-fold for woodland ground flora to 10000-fold for woodland trees (Salisbury, 1942). It is therefore likely that seed weight will be an important determinant of seed predation patterns in these plant communities. However, patterns for particular habitats will not only be determined by the seed weight range but by the abundance of seeds with different weights. For instance in the grassland

165 the seed weight range was 100-fold though the range of the most abundant seed species was only 10-fold. Variations in seed weight will be still be important in determining relative risk of seed predation by small mammals in this habitat, but since rodents are likely to encounter common seeds more often, seed weight will be less important in determining rodent seed diet. The only seed character found to influence seed removal significantly in the present study was that of seed familiarity. Familiar seeds tended to have a higher probability of removal than unfamiliar seeds, especially in the grassland. Preference for familiar seeds by A. sylvaticus and three other species of small mammal has been found under laboratory conditions (Partridge, 1981). This has been attributed to rodents maintaining digestive efficiency through limiting variations in diet. The present experimental design was not suitable for testing such a theory although the results were consistent with it. Nutritional cues are important in determining the pattern of preference (Partridge & Maclean, 1981), therefore both background resource levels and nutritional characteristics of the seeds will influence removal. Seed familiarity will only play an important role in seed selection while the choices are of similar nutritive value (of which no evidence was available in the present study). This is highlighted by the finding by Kelrick et al., 1986) that rodents preferred a highly nutritive alien seed species (millet) over commonly occurring, less nutritive, native seed species. No agreement exists in past studies (Table 5.1) as to the role of seed density in determining seed removal. The present study showed that the degree of density dependence exhibited by rodents regarding the seed resource depended on the local abundance of alternative foods. This may explain the confusion regarding its role in seed predation, but this cannot be tested since none of the previous studies presented data on local food abundance. Density dependence was stronger for small than for large seeds. Therefore the size of seeds used in experiments may determine the extent to which density dependence may be detected. Although similar ranges of density were used, there is a clear trend that those studies examining predation on very large seeds (>10mg) failed to detect density dependent seed predation (O’Dowd & Hay, 1980; Heithaus, 1981; Jensen, 1985; Webb & Willson, 1985) while those using smaller

1 6 6 seeds (<5mg) did (Casper, 1988; this study). The validity of the density-size interaction is supported by the finding that at low seed densities seed weight became more important in determining seed loss. This implies that seed weight is a more important characteristic for seeds placed individually than when found in groups. Individual seed characteristics, even when seeds are found in groups, are important in the three final steps of seed selection (e.g. tactile, handling and taste, Figure 5.11). This implies that individual characteristics are less important than group characteristics at either the olfactory and/or visual steps (detection) of seed selection when seeds are presented in groups. Seed weight is a component of both these steps. At low seed density seed detection is the rate limiting step which determines seed removal and therefore seed weight is most influential. At higher seed densities seed detection is no longer the rate limiting step and other seed attributes become increasingly more influential in determining seed loss. The relative importance of visual and olfactory cues in seed detection will be reflected in the importance of seed weight in buried seed removal. Almost without exception seed burial has previously been shown to reduce seed removal (Table 5.1). Only van Tooren (1988) found no effect of seed burial but this may have resulted from seeds being buried under a layer of bryophytes rather than soil or sand which might have offered more protection from seed predators. This study represents the first field evidence for density dependent buried seed predation, a process previously thought to be unlikely (Verkaar, 1987). Seed weight was correlated with removal of buried seed at both the low and higher seed density. No previous field studies have examined the role of seed size on buried seed removal by rodents although laboratory studies have also shown the importance of seed size on buried seed removal (Howardet al. 1968; Jennings, 1976). The similarity in results of laboratory experiments undertaken in complete darkness in order to prevent visual cues, with the present study, suggests that olfactory cues are important in seed detection by small mammals in the field. The finding that seed weight is correlated with seed removal when ten seeds are buried but not when these seeds are present on the surface indicates that burial alters the importance of the various steps in the selection process (Figure 5.11). It appears likely that the influence of seed burial on seed removal by rodents is primarily to reduce

167 seed detection by limiting olfactory cues. A further similarity of this study with laboratory studies of seed burial was that relative to small seeds not only were large seeds more frequently detected but also a greater proportion of large seeds were removed at each encounter. The laboratory studies did not provide an explanation for such patterns. A possible clue stems from the work of Lawhon & Hafner (1981) who found "once an animal has detected an area rich in food odour and positioned itself over the area of greatest odour intensity, the olfactory sensory apparatus may be insufficiently focused to discriminate between tiny particles of food and nearby non food items; to achieve their finer level of discrimination, a more highly focused finely tuned mechanism is required.". If visual and/or olfactory cues play a role in this more highly focused mechanism then it can be understood why a smaller proportion of small items was detected. Although temporal variation in seed removal patterns has been covered in discussion of between site differences, comparison with previous studies may be instructive. Variation in seed removal in different years has been found in previous studies and attributed either to changes in rodent abundance (Gashwiler, 1970; Radvanyi, 1970) or to changes in the abundance of the particular seed studied (Gardner, 1977). The present study highlighted another level of temporal variation: that due to seasonal changes in local food supply. These three sources of temporal variation will undoubtedly interact and this emphasises the importance of studying seed removal on not only a number of spatial scales but also temporal ones as well. It is clear from Table 5.1 that this has rarely been undertaken and represents a significant gap in the understanding of rodent-seed interactions. 5.4.2 The fate of seed removed by small mammals: caching or consumption? Small mammals are known to cache food items (Smith & Reichman, 1984), and this begs the question whether seed loss due to rodents was attributable solely to consumption or whether some seeds were cached. In general, the ultimate fate of cached seeds is death via consumption though in very rare cases seed may survive caching and germinate (West, 1968; Abbott & Quink, 1970). For oaks, it has been suggested that germination from caches may be the major source of recruitment (Jensen & Nielsen, 1986). However, the data on small mammal seed caching in temperate

168 ecosystems stem almost exclusively from woodland studies on predation of large seeds (>0.1g). Relatively little is known regarding the fate of grassland seeds once removed by rodents. Previous field studies have failed to find any evidence of caching (Marshall & Jain, 1978; Mittlebach & Gross, 1984). Rodents may move seeds away from seed dishes, but this is generally so that the seeds may be eaten under cover (Marshall & Jain, 1978). A number of reasons suggest that the grassland small mammals in the present study were unlikely to cache seeds: a) Under laboratory conditions although A.sylvaticus hoarded food pellets (>lg) this behaviour was never seen with regard to seeds (Hulme, personal observation). b) Hoarding will be a function of the food value of the item hoarded and the cost of hoarding (Andersson & Krebs, 1978). Small seeds, such as those used in the present study, are less likely to be hoarded than the larger tree seeds that A. sylvaticus has previously been shown to cache. c) Caching small seeds will only be economic if mice can increase their seed load. Unlike desert heteromyids or geomiids, which do cache small seeds (Smith & Reichman, 1984), A. sylvaticus does not possess specialised cheek pouches which would enable it to be an efficient hoarder of small seeds. While it is very unlikely that any of the seeds in the present study were cached, the possibility of seed caching cannot definitely be excluded. However, the following evidence suggests that even if seeds were cached, the likelihood of them germinating and surviving is so low as to be negligible: a) If caching animals have overlapping, undefended home ranges, and if olfaction is the primary means of locating caches, as in A.sylvaticus , then cache recovery parallels exactly the process of initial seed harvest (Price & Jenkins, 1986). In such cases the death of the cacher will have only a minor effect on the recovery of its caches. b) Smith & Reichman (1984) have shown that animals whichjdo not maintain exclusive territories and are likely to suffer greater intraspecific than interspecific cache robbery, such as A.sylvaticus, will tend to larderhoard. c) Larderhoards made by A. sylvaticus, tend to be found within the burrow system, 10-50cm beneath

169 the soil surface (Montgomery & Gumell, 1985). It is improbable that seeds clOmg would be able to successfully emerge from such depths. d) Where the fate of cached seeds has been studied in detail the survival and establishment of seedlings from scatterhoarded seeds has been shown to be low (Abbott & Quink, 1970, 0.8% for pine seeds; McAdoo, Evans, Roundy, Young & Evans, 1983, 0.02% for desert annual; Jensen & Nielsen, 1986, 2.5% for oak acorns). Larderhoarded seeds are likely to have an even smaller probability of successful establishment. e) Should soil disturbance bring the seeds to the soil surface, the high density of seeds often found in small mammal larderhoards (Howard & Evans, 1961; Gates & Gates, 1980) would result in a severely competitive seedling environment. Not only would this competitive environment reduce the probability of successful establishment but the high density of seedling would be likely to attract both predators and pathogens. 5.4.3 An assessment of the impact of small mammals on grassland seed populations Small mammals were found to be the major post-dispersal seed predators in the grassland sites studied. Studies which have compared predation by invertebrates, birds and larger mammals have found this to be true not only for grasslands (Mittlebach & Gross, 1984; Klinkhammeret al., 1988) but of many ecosystems including deserts (Brown, Reichman & Davidson, 1979), semi-deserts (Kelrickera/., 1986), deciduous (Watt, 1919,1923; Heithaus, 1981), coniferous (Gashwiler, 1970) and tropical (Abbot & van Heurck, 1985) forests. Additionally, of the predators examined only rodents have been shown to predate buried seed (Reichman, 1979; Abramsky, 1983). A number of points should however be taken into account when interpreting these patterns. First, they are based on total consumption values and it is likely that more specialist invertebrate seed predators may have an important impact on particular seed species. This has been found true in grasslands for carabids (Kjellsson, 1985) and ants (Platt, 1976) and also for ants in deserts (Brown et al., 1986) and dry tropical forests (Perry & Fleming, 1980). Second, these data ignore the impact of pre-dispersal seed predators, often invertebrates, which may have an equal or greater impact on

170 seed dynamics than post-dispersal predators (Louda, 1989). Third, although only rodents have been shown to dig up buried seed, this does not imply that they are the only predators of buried seed. The seed dish techniques used prevent an estimation of losses due to soil dwelling seed predators such as earthworms (McRill & Sagar, 1973) or invertebrate larvae. Fourth, small mammal seed predation has only a major impact in communities which support rodent seed predators. Even similar, but geographically distinct habitats may show considerable variation in rodent seed predation (Mares & Rosenzweig, 1978). It is therefore clear that generalisations about the relative impact of small mammals on seed populations, if made without reference to all other possible seed predators, below and above soil, pre- and post-dispersal of seeds, will tend to overestimate their influence. In order to avoid such errors it is assumed that where seed feeding small mammals inhabit a particular habitat at moderate densities they will influence the seed dynamics of the habitat. The following discussion examines the patterns of such influence. There are two major mechanisms by which seeds may escape predation, either through predator satiation and/or the existence of seed refuges from predators. The first mechanism examines to what extent rodents can reduce high densities of seed to significantly lower levels and the approach is primarily energetic. The second mechanism deals with the ability of rodents to reduce low seed densities down to local extinction and is a function of seed predator foraging behaviour. To determine whether grassland rodents may be satiated by the seed rain it is necessary to calculate the energetic demands of the predator and compare it to the energy supply available in the form of seeds. IndividualApodemus sylvaticus, the major seed predator in the two sites studied, have an average daily energy budget (DEB) of 10.29kcal.animar1.day'1 in summer (Grodzinski, 1985). The average energy content of grassland seeds is 5.07kcal.g'1 (Golley, 1961), though this value must be reduced to take into account of rodent feeding inefficiencies e.g. seed digestibility, assimilation (91.1% and 86.6% respectively, Grodzinski, 1985) and proportion of seed not consumed (22.5%, mean proportion of seed coat of 14 species of grass, Hulme, unpublished data). Therefore

171 average seed energy content is S.nkcal.gram'1, which implies A. sylvaticus should consume approximately 3.25g.day'! of seed, this was confirmed by average laboratory seed consumption of S^g.day'1 (Hulme, unpublished data). Estimates of the seed resource were achieved by using the 1987 seed rain data for each dominant species in both habitats and multiplying the seeds.m'2 by species seed weight to reach a value of seed weightin'2. This value was then compared with total rodent seed consumption per m2, which was calculated as the product of daily seed consumption, rodent density per m2, and the duration of seed supply. The duration of seed supply was estimated with respect to the type of seed bank each species possessed (Thompson & Grime, 1979), summer transient seed banks were assumed to be present only during period of seed rain, winter transient seed banks until spring, while permanent seed banks were always present. Predator satiation was most likely to occur during the peak period of seed rain, which was assumed to last from July to September, a period when the diet of grasslandA. sylvaticus is almost exclusively granivorous (Holisova, 1975; Butet, 1985; HunteretaL, 1987). In the grassland, small mammals could potentially consume 7.7% of the available seed. This low value is due to the grassland site being dominated by the large seeded grass Arrhenatherum elatius, which in terms of weight produced over 65 % of the seed resource. If this species is removed from the analysis, potential consumption by small mammals remains low at 22.6%. Of course, these values may hide important species differences due to small mammal seed preference. This appeared unlikely in the grassland, the site was dominated by grasses and if any preferences existed they were likely to be towards A. elatius, the largest seed in the site. If A. sylvaticus focused its feeding onA. elatius a maximum of 12% of its seed would be lost. Similarly in the meadow, small mammals are predicted to consume only a fraction of the total seed resource (6.77%). The dominant seed producer in this site was Chamaenerion angustifolium, producing, in terms of weight, 85% of the seed resource. However, this value is likely to overestimate the contribution of C.angustifolium to the local seed rain, much seed is dispersed by wind out of the site and of that which remains the majority becomes trapped within the tall vegetation stands of

1 7 2 this species. If this species is removed from the analysis, potential consumption of seed rises considerably to 44.8%, almost twice that of the grassland site when the dominant species is removed from the analysis. AlthoughA. sylvaticus is known to consume the seed of all the dominant seed producers, if they are assumed to show greater preference for the larger seeds then, in contrast to the grassland site, they have the potential to consume almost all large seeds (94.8%). The estimates of rodent impact on total seed populations in the two sites were similar and are consistent with previous grassland bioenergetic estimates (Table 5.1). In the grassland site, where the seed rain is dominated by large seeded grasses, rodent seed preference may be less important in determining impact and it is likely that during the peak period of seed rain, seed may escape from small mammal predators through predator satiation, a hypothesis first proposed by Janzen (1978). In the meadow, although small mammals are unlikely to be resource limited, small mammal seed preferences may lead to complete reduction of a number of seed populations. It is therefore clear that bioenergetic estimates can only present an accurate picture of the impact of rodent seed predation if seed preferences are also considered. For example, in both these communities rodents may significantly influence non dominant seed producers, which possess large, preferred seeds. These species, such asRanunculus repens and Trifolium pratense may suffer disproportionately high seed losses, and may have to rely on refuges from seed predators in order to escape seed predation. Although predator satiation may be a viable strategy for large seeded grasses which only possess transient seedbanks, itis unlikely to holdfor those species with permanent seedbanks. Indeed the selection of predator satiation via seed masting is incompatible with the possession of a permanent seedbank (Janzen, 1978).

Apodemus sylvaticus maintains a primarily granivorous diet throughout autumn and winter (Holisova, 1975; Butet, 1985; Hunteret al., 1987). Rodents will therefore rely on the seed resource produced in summer/autumn at least until spring, a period of approximately 5 months (October to February). Seed resources will not only be less over this period due to the germination of all those species with summer transient seedbanks but also the germination of a proportion of seeds of species

173 with permanent seedbanks. This latter source of loss from the seed pool was not determined, but for some species the proportion may be as high as 50% of the initial seed rain (Thompson & Grime, 1979). Estimates of seed removal by small mammals over this period are similar in both sites (30.7% and 28.4% in the grassland and meadow respectively). It is clear that predation pressure on overwintering seeds may be at least 4-5 times that experienced by seeds during the period of seed rain, rising to 10 times if 50% germination is assumed. Nevertheless, sufficient seed exists in both sites to support the small mammal populations. If most small seed has become buried by October, then rodents will exhibit preference for larger seeds and in particular small seeds (<0.1mg) such as Agrostis capillaris, C. angustifolium and Juncus effusus may escape predation by small mammals. If small mammals focus on seeds larger than 0. lmg then in the grassland predation pressure increases to 35.2% while in the meadow this value soars to 708.2%. These between site differences result from the meadow seed bank being dominated by small seeded species (C. angustifolium and J. effusus). This implies that in the meadow seeds larger than O.lmg are likely to suffer over 20 times the predation pressure than similar seeds in the grassland. This prediction is in keeping with the experimental results regarding between site differences in buried seed removal in winter. It is evident that in the meadow small mammals have potential of reducing some seed populations, in particular those with seed weights greater than O.lmg, to local extinction. The bioenergetic estimates presented in Table 5.1 are likely to have underestimated the impact of small mammals in plant communities not only due to the disregard for small mammal seed preferences but also a failure to incorporate in their models temporal changes in seed dynamics. Seed banks have been classified into four categories (Thompson & Grime, 1979), each possessing distinct temporal dynamics. There is overwhelming evidence that these temporal patterns are determined principally by physiological processes within seeds with respect to the environment (Grime, 1989). Below, predictions are made as to how small mammal seed predation may modify these temporal patterns.

Type I Summer transient seed banks, primarily large seeded grasses.

1 7 4 These species tend to produce large seed biomass and this is likely to satiate local seed predators when these species dominate grassland communities. Although rodents will consume a large number of these seeds, total consumption will remain a small proportion (< 10%) of the seed rain. Only where isolated clumps of these species occur, or where small mammals exhibit strong preference for a non dominant grass species, may seed rain fail to satiate local rodent seed predators.

Type II Winter transient seed banks, primarily large seeded herbs. This group contains large seeded legumes shown above to suffer extensive small mammal seed predation. Seed bank data reveals that some leguminous species consumed by small mammals are capable of producing substantial seed banks e.g. Ulex europeus, 130-20,500 seeds.m2 (Roberts, 1981); Medicago lupulina, 600-2000 seeds.m2 (Pavone & Reader, 1982). However these observations tend to stem from plant communities in which these plant species were particularly abundant and in most grassland types the numbers of seeds of large seeded legumes tend to be low (Roberts, 1981). The presence of these seeds throughout winter implies a high probability of predation by small mammals. A possible mechanism by which these seeds may escape predation is through chemical defense. Leguminous seeds are renown for their toxic properties (Bell, 1972, 1978). Although in field experiments rates of loss of large legume seed at low density were high, when rodents were fed a single legume seed speciesad libitum in the laboratory rarely was more than 0.5g of seed consumed in any 24hr period. This may explain how, when large seeded legume seed production is high, large seed banks may be found. Although seed density can be very high, rodents may be unable to fully exploit the seed resource and a significant amount of seed may escape predation to germinate in the spring. However, where local seed density is low, small mammals may have the potential to reduce these seed populations completely. Insufficient data exists concerning small mammal seed preferences of Type n seed bank seeds to make generalisations regarding the role of toxins in the maintenance of winter transient seed banks.

Type III Low density persistent seed banks, small seeded species.

175 These species combine rapid germination (late summer) and small seed size as exemplified by small seeded grasses Agrostis capillaris, Deschampsia caespitosa, Holcus lanatus, Poa annua and P. trivialis. Although rodents consume these seeds, their short residency on the soil surface (either through loss from germination or burial) will limit the impact of rodents. It has been shown that while rodents will detect these seeds when buried, they are inefficient at exploiting them. Rodents may certainly reduce the seed banks of these species but are unlikely to lead to local extinction of populations.

Type IV High density persistent seed banks, small seeded species. Differ from Type HI in that a smaller proportion of seeds germinate after dispersal and species includeTrifolium repens, T. dubium, T. medium and Plantago lanceolata as well as many species with seed «0.1mg. Those species with seed weight less than O.lmg, are likely to escape rodent seed predation almost completely once buried. Seeds larger than O.lmg are likely to suffer a similar rodent impact as Type III seeds, although in the present study there was a tendency for the small Trifolium spp. seeds to be less frequently consumed when buried than Type HI seeds of similar size. If this is a general difference between Type HI and Type IV seeds then lower predation of the latter seeds may enable them to maintain higher density seed banks. However, such differences may exist due to different seed longevities or susceptibilities to earthworm seed predation (McRill & Sagar, 1973). The possible role of small mammal predation in selecting particular seed bank strategies is highlighted by P. lanceolata. Although almost all Type IV seeds are small

176 of other large seeds which maintain persistent seed banks Rumex obtusifolius (l.lmg) and Thlaspi arvense (0.95mg) are also unpalatable toApodemus sylvaticus (Peltz, 1989) supporting the hypothesis that seed predation is involved in ultimate seed bank patterns. The total impact of rodent seed predation on the seed community of a particular habitat will depend not only on the ability of seed to escape in time (through rapid germination) or in vertical space (through burial) but also on the horizontal spatial heterogeneity of rodent foraging. The two site in the present study reflected the importance of spatial heterogeneity in enabling seed to escape predation. Small mammals have the ability to not only influence the density of seeds in a habitat but also their spatial distribution. Seed banks are frequently spatially heterogeneous (Thompson, 1986). Small mammal seed predation could certainly be a source of large scale heterogeneity in seed banks though detection of this effect would be complicated by spatially heterogeneous seed rain patterns. The influence of small mammals on grassland seed banks may be substantial (Figure 5.12). The different seed bank types differed significantly with respect to seed loss (F(3182)=4.766, p<0.05). For both surface and buried seed removal a significant negative correlation existed between probability of small mammal predation and extent of seed bank. However, it is clear that the greatest variation existed when seeds were buried, Type I seed banks suffering almost six times as much predation when buried than Type IV seed banks. This is related to the effectiveness of burial in reducing seed predation. Had this pattern simply been a function of seed size it would have been difficult to gauge the relative importance of seed predation in determining seed bank patterns relative to other selective forces acting on seed size. However, although seed size plays a role in determining buried seed removal, in particular between transient and permanent seed banks, within these seed bank categories the role of seed size is not obvious. The correlation of rodent seed preference (as determined by proportion of seeds removed), rather than seed size, with seed bank type, further supports the hypothesis that rodent seed predation has an ultimate role in seed bank patterns. Nevertheless, the descriptive correlative approach as a guide to understanding causation is manifestly often likely to mislead (Harper, 1969). Further experiments are necessary before the exact role

177 60

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LUQ UJ 4 0 CO —I t=< ^ 3 0 OLL. LLI CD £ 20 os DC UJ ° - 10

II III IV

SEED BANK TYPE Figure 5.12 The relationship between seed bank type and seed removal by small mammals from buried and surface seed in the ten seed density experiment in the meadow in winter. Error bars represent one standard error of the mean. Mean seed weights for each seed bank type were: I-1.39mg, H-1.66mg, ni-0.40mg, IV-0.83mg. Values within histograms represent the number of species in each seed bank class. rodent seed predators play in the natural selection of seed dormancy is known. The risk of seed predation may not only influence seed bank dynamics but also other aspects of reproductive ecology, as shown by the comparative study of threeRanunculus species by Sarukhdn (1974). Small mammals were the major post dispersal seed predators ofRanunculus repens,R. bulbosus and R. acris. All three species possess large seeds (approx. 2mg) and are likely to suffer predation even when buried. This is probably a reason why although the seed is relatively long lived (>2years) they form a relatively small component of grassland seed banks (Sarukhdn, 1974). Ranunculus repens which suffered the highest seed predation (38-54%), rarely reproduced by seed and regeneration was almost exclusively via vegetative propagation. In contrast,R. bulbosus and R. acris which suffered less seed predation (20-35%) relied proportionally more on seed for regeneration.

178 Care must be taken in interpreting natural seed bank patterns in relation to small mammal seed predation. Knowledge not only of the present small mammal population, but also the small mammal history (on a scale similar to that of seed longevity) of a particular habitat would be needed in order to test whether small mammals had influenced seed bank patterns. These data are completely lacking from all major published seed bank studies. Future experiments necessary to test the above hypotheses may take the form of replicated rodent exclosure/enclosure studies (Nelson & Chew, 1977) in combination with both a rigourous seed rain (Rabinowitz & Rapp, 1981) and seed bank sampling programme (Thompson & Grime, 1979). 5.4.4 Small mammal seed predation and plant population dynamics In practice it is exceedingly difficult to determine whether the predation of seeds matters in nature (Harper, 1977). Extrapolation from seed removals from seed dishes to impact on plant demography is likely to lead to erroneous conclusions. In desert ecosystems, where the influence of seed predation on plant populations has been most intensively studied, high rates of seed loss through rodent granivory are often only translated into small changes in the plant community (Soholt, 1973; Nelson & Chew, 1977; Brown et al., 1986). For example, consumption of over 95% of Erodium cicutarium seeds by Dipodomys merriami were predicted to reduce plant density by 30% (Soholt, 1973). Harper (1977) predicted four roles seeds produced by plants may play in their population biology and which may be influenced by seed predation: a) The increase in population size locally b) The replacement of individuals in a population that die. c) The recolonisation of new areas at a distance from the parent population. d) The display of genetic variation. The demographic and distributional consequences of seed losses will vary with: a) The degree of plant dependence on seed regeneration.

179 b) The variation in the probability of seed escape through dispersion, dispersal and predator satiation. c) The opportunity for subsequent compensation for seed loss (see chapter VI). Of the sixteen species used in the present study for which data is available concerning their regenerative strategies (Grime, Hodgson & Hunt, 1988), ten depend almost wholly on seed for regeneration - Alopecurus pratensis, Dactylis glomerata, Holcus lanatus, Lolium perenne, Lotus corniculatus, Medicago lupulina, Poa annua, Phleum pratense, Trifolium dubium and T. pratense. It is of interest that this group contains many of the large seeded species which suffered heaviest seed predation. Five species depend on both seed and vegetative regeneration - Festuca ovina, F. pratensis, Plantago lanceolata which depend principally on seed and Festuca rubra rubra, Trifolium repens which depend primarily on vegetative regeneration. Only one species, Poa pratense, regenerates almost exclusively by vegetative means. However, a general belief held by plant ecologists is that even for plants that depend on seed for regeneration, recruitment from seed is rare in perennial grasslands and that plants are microsite rather than seed limited (Harper, 1977; Grime, 1979; Crawley, 1986; Silvertown 1987; Andersen, 1989). This implies that in comparison with desert plant communities, where suitable microsites are not rare (Ellner & Shmida, 1981) similar rates of seed predation in perennial grasslands are likely to result in even smaller changes in the plant community. The belief that plants in perennial grasslands are microsite limited arises from numerous studies which have examined the establishment of sown seed in open and closed microsites (Cameron, 1935; Foster, 1964; Cavers & Harper, 1967; Putwain, Machin & Harper, 1968; Miles 1973,1974; Thomas & Dale, 1975; Hawthorn & Cavers, 1976; Gross, 1980; Gross & Werner, 1982; Crawley and Nachapong, 1985; Klinkhammer & de Jong, 1988; Kelly, 1989). However, without exception those species which showed improved recruitment in open vegetation were small seeds (

180 This implies small mammal seed predation is likely to be more important for large seeded plants than for small. Indeed, it can be predicted that small mammal seed predation will be unlikely to influence the population dynamics of small seeded species due to their persistent seed bank habit, microsite rather than seed limitation, perennial growth form and low rates of buried seed removal. This conclusion that the population dynamics of large seeded species will be affected by small mammal seed predation more than small seeded species is consistent with findings for deserts (Brown et al., 1986) and sagebrush communities (Halligan, 1974). For large seeded species the influence of predation will depend on predator satiation and plant compensation. Both these aspects are a function of local seed density. Where seed is found at high density it is likely that either predator satiation and/or plant compensation will act to reduce the impact of small mammal seed predation. At low seed density conclusions as to effects on plant populations will reflect those made concerning impact on seed populations. It is therefore predicted that the demography of species with Type II seed banks will be most influenced by small mammal seed predation. This is supported by evidence that populations ofCarlina vulgaris are limited by small mammal seed predation (Greig-Smith & Sagar, 1981). For perennial and particularly clonal plants, death may be a rare event and the responsibility of the seed in maintaining populations may be minor (Harper, 1977). However, for annuals, the seed is the critical link between generations. It is predicted that small mammal seed predation may influence the maintenance of populations of annuals which suffer extensive seed predation e.g. Polygonum persicaria, Raphanus raphanistrum and Medicago lupulina. The role of seed in colonisation will be related to the frequency of disturbance in a particular community. The role of small mammal seed predators in colonisation processes is unclear and will depend on the relationship between frequency of disturbance and rodent abundance (highly disturbed areas may support few rodents) and the foraging of rodents-on disturbances (depend on size of disturbance). If disturbances are small in size and rodents forage on disturbances then rodents may limit colonisation. On the other hand colonisation of larger disturbances such as landslips etc. are less likely to be influenced by rodent seed predation.

181 The influence of rodent seed predation on plant dispersal will depend on the seed shadow produced. Since dispersal distance is inversely related to seed weight (Harper, 1977) large seeded species at risk from small mammal seed predators are likely to be found close to the parent plant. If the seed shadow is of a similar scale as a single small mammal foraging area, and rodents forage in a density dependent manner then, depending on the maximum seed density, seed may escape predation at the tails of the seed shadow (Janzen, 1970). If the seed shadow is of the scale of more than a single rodent foraging area or not all regions of the seed shadow are equally accessible to rodents either due to rodent territorial behaviour or lack of cover, rodents will be unable to respond fully to total seed density and the tails of the seed shadow may suffer seed predation independently of areas of high seed density. If it is primarily through seeds that a variety of recombinants is hazarded to the environments (see Gill (1986) for evidence of somatic mutations) then predators taking seeds may be a vital selective force although irrelevant in determining the size of populations (Harper, 1977). For all practical purposes it is impossible to use a straightforward experimental approach to answer evolutionary questions (Price & Jenkins, 1986). However, predictions may be made regarding the selective pressure rodent seed predation may exert on heritable seed traits. Examination of the seed bank patterns suggests rodent seed predation may be an important influence determining the selection of seed dormancy strategies. Additionally, the population dynamics of a plant species may be influenced by predation, even in situations where rodents do not consume a large proportion of seed of a particular species, through the modification of the outcome of interspecific competition. A number of theoretical models have shown that the outcome of interspecific competition for suitable microsites for plants recruiting from seed may be dependent on levels of seed production (Skellam, 1951; Fagerstrom & Agren, 1979; Crawley & May, 1987). Small mammals may act to reduce the number of seeds in microsites and so influence the outcome of plant interspecific competition (see chapter VIII for more detailed discussion).

182 In summary, rodents are important post dispersal seed predators, in particular of buried seed. Winter rather than summer is the period of most intense seed predation. Large seeded species are most likely to suffer predation and where these seeds are at low density rodents may lead to their local extinction. The strong correlation between predation risk and seed bank type suggests rodents have both a proximate and ultimate influence on seed populations.

183 CHAPTER VI SMALL MAMMAL SEEDLING PREDATION IN GRASSLAND

6.1 Introduction A considerable number of factors may cause seedling mortality (Fenner, 1985; 1987 for reviews). Although many demographic studies have been carried out on a wide range of species (e.g. Burdon, Marshall & Brown, 1983; Dolan & Sharitz, 1984; Waite, 1984; Kachi & Hirose, 1985; Klemow & Raynal, 1985) few have attempted to quantify the causes of seedling mortality. Phytophagous rodents are known to consume seedlings (Hansson, 1985), but in contrast to their role as seed predators, many fewer studies have been undertaken to quantify their influence (compare Table 6.1 with Table 5.1). This is surprising since the evidence suggests that rodents may have a sizeable effect on seedling mortality, with over 75% of studies reporting rodent attributed seedling mortalities of at least 50% (Table 6.1). Three approaches have been used in the quantification of seedling mortality (Fenner, 1987; Table 6.1): a) Seedlings are monitored at frequent intervals and reasonable deductions are made from the available evidence as to the probable cause of death. The accuracy of these observational studies is highly dependent on the frequency with which seedlings are monitored, and in most cases the period is too long, with many seedlings being missed, leading to an underestimation of mortality. Fenner (1987) concludes that in these observational studies 24h observation is the only solution. b) Correlations are established between mortality and possible relevant factors such as seedling density, distance from parent, density of surrounding vegetation, etc. These studies avoid some of the drawbacks of simple observational studies by manipulating known densities of seedlings (or seeds) but are limited by the ability of observers to specify the source of mortality and be aware of all possible relevant factors. c) Sources of mortality are experimentally manipulated by altering the environment of seedlings, e.g.

1 8 4 AUTHOR HABITAT N° species Other predators SEEDLING SEEDLING VARIABLES LOSS percentage within between species temporal habitat habitats OBSERVATION Sviridenko(1940) * DW 8 0-100 + Zhukov (1949)* DW 1 50 Dinesman (1961) * DW 3 arthropods 25-41 + Kanervo & Myllimaki (1970) NW 3 9-50 + MANIPULATION Wood (1938) DW 1 99.5 Pigott (1985) DW 3 32-100 + + + EXCLOSURES Bramble & Goddard (1942) DW 1 deer 4-60 Paulsen (1950) SD 1 47 Hermann & Chilcote (1966) CW 1 fungi 13-57 + Gashwiler (1971) CW 3 arthropods 0-28 + fungi Pyke (1986) G 2 0-60 + + Rice (1987) G 2 8-89 + - Table 6.1 Review of previous studies that have examined small mammal seedling predation, the methodology used, the seedling loss found and sources of variation in that loss. Key to habitats: DW deciduous woodland; CW coniferous woodland; NW woodland nursery; SD semi-desert; G grassland. Values from studies marked with asterisk * are taken from Golley et al. (1975). by the addition of water or nutrients or by the exclusion of predators. However, exclosure studies should be designed to encompass the major guilds of seedling predators, rather than lump predators together. For example a number of studies have highlighted the importance of vertebrate seedling predators (Tinner, Alcorn & Olin, 1969; Halligan, 1971; Mills, 1983; Mack & Pyke, 1984) but have failed to distinguish effects due to lagomorphs and those due to small rodents. Examination of previous studies of small mammal seedling predation (Table 6.1) reveals the following patterns: a) Over 80% of studies have involved woodland species, fewer than 15% of species studied have been in grasslands. b) Although a number of seed predation studies noted that small mammals also consumed seedlings (Watt, 1919,1923; Ashby, 1967), of all the seed predation studies only one, Gashwiler (1970,1971), followed up results of seed predation with a study of the fate of seedlings. c) Not only have fewer studies of small mammal seedling predation been undertaken, but also, in comparison with seed predation studies, the relative influence of variables such as spatial & temporal variation, seedling species and density are poorly understood. d) The impact of rodents on seedling populations relative to the impact of other seedling predator guilds has rarely been attempted. With respect to these patterns, the aims of the present study were to: i) Examine the impact of rodent seedling herbivores in relation to other seedling herbivores. In particular, to compare rodent impact with that of molluscs which are known to be major seedling herbivores (Harper, 1977; Godan, 1983;Dirzo, 1984,1985; Fenner, 1985,1987). ii) Investigate the role of temporal (seasonal), spatial and species variation in determining seedling loss. iii) Determine the relative predation pressure exhibited by rodents on the seed and seedling phases of recruitment. Several studies which have presented evidence of the role rodents may play in plant recruitment (Inouye, Byers & Brown, 1980; Greig-Smith & Sagar,

186 1981; Christy & Mack, 1984) have failed to determine whether impact occurred solely at the seed or seedling phase or throughout both. An understanding regarding the timing of impact may have consequences for the dynamics of the plant-herbivore interaction (Crawley, 1983). iv) Compare and contrast temporal, spatial and species patterns of rodent seed and seedling predation. v) Predict species whose recruitment is most at risk from rodent herbivores and examine the ultimate role rodents may play in germination strategies. 6.2 Methods and materials In practice determining the cause of death of seedling present formidable problems (Fenner, 1987). While the methods described above have gone some way in overcoming some of these problems they are still inadequate if detailed studies of the fate of seedlings are to be made. Major problems arise from manipulative experiments which followed the fate of seedlings from a known number of directly sown seed: a) A large proportion of seedlings may emerge and disappear between periods of monitoring. A series of factors may act to reduce the number of seedlings emerging including seed dormancy, density dependent seed germination, seed predation or use of initially dead seed. Attributing this source of loss as seedling mortality will lead to errors. b) Variations in the timing of seedling emergence between replicates or variation in numbers emerging may inhibit direct comparisons of replicates due to the potential effects of seedling density or age on the interaction. This problem would be increased greatly if more than one species was to be compared. c) Manipulations of temporal patterns of seedling appearance will be dependent on the germination phenology of the seed species used. This may limit to what extent seedlings may be presented to herbivores during different seasonal periods. A means of overcoming all these problems is, rather than manipulate seeds and follow the emergent seedlings, to manipulate seedlings directly. The simplest way of undertaking such an

187 experiment is to place dishes containing the manipulated seedlings out into the field. This was the technique adopted in the present study, and represents the first occasion that this technique, previously only used for seed predation, has been undertaken with seedlings. The seedling predation experiments were designed for comparison with the seed predation studies. All experiments were undertaken in the grassland and meadow sites described in section 2.2.2 and both the seed species chosen and the experimental design, using exclosures, were identical to those described for the seed predation studies and are described in sections 5.2.1 and 5.2.2 respectively. 6.2.1 Experimental procedure Seeds of each species were sown under heated glasshouse conditions, at different times, to account for variations in germination rate and period of field presentation, such that at the time of field presentation all seedlings were of a similar age. All seedlings used were approximately one week old and at the cotyledon stage, no seedlings were presented if they had already produced their first pair of true leaves. Seedlings were carefully pricked out, discarding any abnormal or damaged seedlings, into three way partitioned petri dishes which had been filled with moist potting compost. As in the seed experiment, for each species, seedlings were pricked into one compartment such that the final dishes contained three different species of seedlings. Dishes were randomly distributed amongst the five replicates of each of the four exclosure treatments such that each replicate received a unique (three) species combination, but that the treatments received the same variety of (fifteen) species on each occasion. The seedling dishes were then placed in the field within the exclosures and left for three days under a polythene roof to prevent heavy rain from washing the seedlings away. This period of three days was chosen since it was long enough to allow herbivores to find seedlings and short enough that any herbivore attacks over this period would be directed against seedlings at the cotyledon rather than leaf stage. This latter point was important since it was hypothesized that growth form and age might influence patterns of attack. It also enabled direct comparison with the seed predation

188 studies. Seedlings were checked once each day. As in the seed predation study, each experiment comprised seven of these three day periods in order to encompass the wide range of species used. The temporal order of presentation was randomised.

a)

ARTHROPOD MOLLUSC RODENT

Figure 6.1 Mean percentage seedling loss from the four exclosure treatments in the grassland and meadow sites in a) spring and b) autumn. Error bars represent one standard error of the mean. The density of seedlings used in all experiments was ten seedlings per species per dish. This density was chosen since it was likely to reflect the density of emergent seedlings around a parent plant, and would enable any preferences exhibited by seedling herbivores to be detected more easily.

189 It also enabled comparison with the ten seed predation experiments (chapter V). Both the number of seedlings damaged and the severity of damage were recorded. The experiments were repeated in both sites in both spring (20 March to 28 April, 1987) and autumn (8 October to 8 November, 1987) to elucidate if seedling loss differed between these two seasons of seedling appearance in the field. 6.3 Results Assessment of seedling predation differs from that for seeds in that it possesses both a quantitative component (how many seedlings were attacked) and a qualitative component (how severely each seedling was damaged). In the following analyses both the quantitative component (described as the frequency of attack) and a qualitative component (described as severity of damage) are examined. As a result of low or erratic glasshouse germination of three speciesAlopecurus pratensis, Deschampsiaflexuosa, Festuca rubra var. litoralis and fungal infection of a fourth,Lotus corniculatus, the total number of seedling species used in the experiment was reduced. The following results refer to the remaining seventeen species used. 6.3.1 The frequency of seedling damage by different seedling predator classes The proportion of seedlings attacked by each seedling predator class in each site in spring and in autumn was analysed by a three factor analysis of deviance using GLIM with binomial errors. Highly significant differences existed between treatments (F(31335)=148.06, p<0.001). In general molluscs were the dominant seedling predators followed by small mammals while arthropods had the least impact (Figure 6.1). Patterns significantly differed with both site (F(31335)=80.48, p<0.001) and season (F(31335)=22.31, p<0.001). Additionally, all three factors interacted significantly (F(3,i335)=12.8, p<0.01). Seedling loss from the controls was generally negligible, supporting the hypothesis that the exclosure methods were successful and that the important guilds of seedling predators had been catered for in the experimental design. Since only molluscs and small mammals appeared to exert an impact on the seedlings significantly different from the controls (Figure 6.1) patterns of seedling loss with respect to time and season were examined with these two groups only.

190 Figure Spatial6.2 variation in percentage seedling loss as described by between replicate (1-5) variation for Error bars represent one standard error of the mean. small mammals and molluscs in a) grassland site in autumn b) meadow site in spring c) meadow site in autumn.

PERCENTAGE INT1AL SEEDLINGS ATTACKED PERCENTAGE INTTIAL SEEDUNGS ATTACKED PERCENTAGE IMTIAL SEEDLINGS ATTACKED REPUCATES RODENT 25 30 20 15 10 25 30 20 25 30 20 10 15 10 15 0 5 0 5 5 0 MOLLUSC REPLICATES 6.3.2 Spatial variation in frequency of seedling damage by molluscs and small mammals Since the spatial distribution of different treatments was unrelated and did not reflect the same spatial variation (Figure 5.1), two separate three factor analyses of deviance using GLIM with binomial errors for both the mollusc and small mammal data were undertaken. Significant variation existed between each of the five replicates of both mollusc and small mammal access treatments (F(4 3i4)=40.53, pcO.OOl and F(4317)=25.65, pcO.OOl, respectively, Figure 6.2). Not surprisingly the spatial distribution of attack differed between sites (F(4 314)=34.26, pcO.OOl and F(4 317)=14.14, p<0.01, for molluscs and small mammals respectively, Figure 6.2). However, significant seasonal variation was only apparent for molluscs (F(4 314)=10.79, p<0.05). Where sufficient data were available (autumn samples only), significant correlations existed between small mammal density and number of seedlings damaged (Spearman rs=0.3822 and rs=0.5411, both d.f. 83, p<0.01, for the grassland and meadow site respectively). No data were available regarding the spatial distribution of molluscs, though evidence exists, from both laboratory (Cook, 1981) and field (South, 1965) studies, of spatial aggregation of numbers which might explain the between replicate variation. 6.3.3 The influence of season on frequency of seedling damage by molluscs and small mammals The numbers of seedlings attacked by both molluscs and small mammals were a strong function of the season of presentation (Fa 314)=28.31 and F(1317)=50.09, respectively, both pcO.OOl). Although in both sites seedling loss due to molluscs increased in autumn, temporal patterns of mollusc seedling predation differed between sites (F(1314)=15.68, pcO.OOl). Whereas in the meadow the proportion of seedlings attacked less than doubled in autumn, seedling loss in the grassland in autumn was almost twenty times higher than in spring (Figure 6.1). This between site difference may be explained by their different mollusc composition. Observations revealed that the dominant mollusc in the grassland wasDeroceras reticulatum, a species primarily found active in

192 the autumn, only rarely being found in spring (Barnes & Weil, 1944; Jennings & Barkham, 1975). In contrast, the meadow was dominated by Arion ater and to some extent by Arion hortensis F6r., Both of which are active in spring and autumn (Barnes & Weil, 1944; Jennings & Barkham, 1975). These seasonal patterns of slug activity and the differing slug communities in the two sites, may explain why low loss rates attributable to molluscs were found in the spring in the grassland, while losses were similar in both seasons in the meadow. The autumnal increase in seedlings attacked in the meadow may have been due to changes inmollusc densities, butmay also have been due to changes inclimatic conditions. Autumn minimum temperatures over the 21 days of each experiment were significantly higher than those in spring (one way ANOVA, F(140)=5.89, p<0.05). Slug activity, and therefore feeding, is limited by cold temperatures (Dainton, 1954; Webley, 1964; Crawford-Sidebotham, 1972) and it might therefore be expected that seedling attack might be higher in autumn. Both seasonal changes in mollusc community composition and increased activity of slugs in autumn may be the source of the seasonal variation in the spatial distribution of seedling attack found for molluscs (section 6.3.2). Significant between site differences also existed with regard to the seasonal patterns of small mammal seedling predation (F(1317)=50.49, p<0.001). Detailed information exists concerning the seasonal variations in small mammal abundance in the two sites (Figure 3.1). It appears that in both sites the low proportions of seedlings attacked in spring are a result of the low small mammal spring densities in both sites. In effect small mammals had almost disappeared from both sites at this time and it is not surprising that few seedlings were consumed. The higher seedling losses in the meadow reflect the marginally higher small mammal densities in this site in spring. In autumn small mammal populations increased in both sites (Figure 3.1). In the grassland, where small mammal population density was approximately three times that in spring, the proportion of seedlings attacked by small mammals significantly increased (Figure 6.2). In the meadow small mammal density increased eightfold, yet no significant seasonal difference in seedling loss was observed. While increased attack of seedlings in the grassland was correlated with the increased density suggesting this to be the cause, in the meadow no effect of increased density was apparent.

193 SMALL MAMMAL MOLLUSC *G autumn spring autumn mean autumn spring autumn mean grassland meadow meadow damage grassland meadow meadow damage Dactylis glomerata 8 0 0 4.00 6 0 2 3.75 Agrostis capillaris 2 0 0 3.00 0 0 4 3.50 Poa annua 2 0 0 3.00 10 0 10 3.50 Poa pratensis 6 0 0 4.00 10 16 0 3.62 Phleunt pratense 4 0 0 2.00 8 14 8 3.00 Festuca rubra litoralis Festuca pratensis 20 0 0 3.90 6 0 0 3.93 Lolium perenne 30 6 0 3.61 6 4 10 3.25 Trifolium vratense 44 4 4 3.39 18 20 8 2.78 Alopecurus pratensis Festuca rubra 8 0 0 4.00 5 0 18 3.00 commutata Trifolium dubium 6 0 4 2.80 0 0 22 2.83 Deschampsia flexuosa Festuca ovina 4 0 0 4.0 12 0 6 3.70 Medicago lupulina 10 12 10 2.69 12 0 14 3.20 Medicago sativa 16 4 4 2.69 2.5 26 20 2.54 Holcus lanatus 6 6 0 4.00 0 0 4 3.50 Trifolium repens 4 2 2 2.75 15 14 20 2.57 Festuca rubra rubra 4 0 2 3.50 20 0 6 3.17 Lotus corniculatus Plantago lanceolata 6 6 2 2.29 16 0 6 3.18 Total mean 10.71 2.35 1.69 3.26 8.88 5.53 9.41 3.06 S.E. 9.56-12.00 1.83-3.02 1.25-2.28 ±0.09 7.79-10.09 4.70-6.50 8.32-10.62 ±0.07 Grass mean 8.70 1.09 0.19 3.64 7.74 3.09 6.30 3.32 S.E 7.45-10.15 0.70-1.71 0.06-0.57 ±0.09 6.14-9.70 2.14-4.44 4.88-8.09 ±0.08 Forb mean 14.33 4.67 4.33 2.92 11.11 10.00 15.00 2.78 S.E. 12.33-16.72 3.49-6.22 3.20-5.83 ±0.13 8.52-14.38 7.65-12.97 12.12-18.41 ±0.10 P * ** ** *** ** ** *** Large seed mean 17.69 4.00 2.90 3.22 11.05 6.25 10.51 2.98 S.E. 15.66-19.94 3.05-5.24 2.08-4.01 ±0.11 8.83-13.76 4.64-8.37 8.37-13.13 ±0.08 Small seed mean 4.67 0.89 0.67 3.39 6.91 4.89 8.44 3.15 S.E. 3.68-5.90 0.51-1.54 0.35-1.26 ±0.19 5.24-9.05 3.55-6.69 6.65-10.67 ±0.10 P *** * * Familiar seed mean 8.33 2.00 1.14 3.25 10.69 5.56 6.82 3.15 S.E. 7.17-9.67 1.39-2.88 0.69-1.86 ±0.12 8.88-12.81 4.12-7.45 5.20-8.89 ±0.10 Unfamiliar seed 2.75 mean 16.67 2.31 3.27 4.09 , 5.50 12.25 2.94 S.E. 14.18-19.51 2.88-3.81 1.60-3.32 ±0.14 2.48-6.68 4.00-7.52 9.96-14.97 ±0.08 P ** Table 6.2 Mean percentage seedling attacks by rodents and molluscs in each site and the mean severity inflicted by each herbivore group on each seedling when attacked. Order of species reflects the three species presentation groups. Statistical significance of comparisons * p<0.05; ** p<0.01; *** p<0.001.

194 To understand these two contrasting patterns a knowledge of the spatial distribution of the rodents in the two sites is required. Although seedling losses in both sites varied significantly with respect to the replicate position, the variability in the numbers of replicates visited, as determined by the coefficient of variation, differed between sites. In the grassland in autumn the spatial variability in rodent attack had a CV=44.6, comparable to the spatial variability found for molluscs in both sites at this time (CV=43.9 and 46.4 for the grassland and meadow respectively). However, in the meadow the spatial variability was over twice that of grassland rodents or molluscs (CV=108.3). Indeed, in the meadow, small mammals, due to their aggregated distribution (chapter IV), effectively had access to only two replicates (3 & 5, Figure 6.2), whereas in the grassland all replicates were visited. This limited the amount of experimental seedlings available to meadow rodents in autumn and reduced their total impact. 6.3.4 Plant species variation in mollusc and small mammal seedling predation Significant species variation in proportion of seedlings attacked existed for both molluscs and small mammals (F(16 266)=36.87, p<0.01 and F(16 269)=85.32, pcO.OOl, respectively). Plant species patterns of mollusc seedling predation varied significantly with both site (F(16266)=26.78, p<0.05) and season (F(16266)=32.88, p<0.01). This variation in seedling predation with site and season was not found for rodents and was probably attributable to seasonal and site variations in mollusc community composition. Comparison between small mammal and mollusc seedling species consumption was limited by the paucity of data for rodents in the meadow (Table 6.2). Since rodent seedling species predation patterns did not differ significantly between sites or season, rodent data were combined to facilitate comparison. No significant Spearman rank correlations existed between any of the mollusc-small mammal seedling species comparisons. Seedling species patterns were examined with respect to three parameters, size (as estimated by seed endosperm weight), monocot/dicot differences and seedling familiarity (Table 5.2). The influence of these three factors on seedling attack was analysed (including season and site) in a five factor analysis of deviance using GLIM with binomial errors. Both molluscs and small mammals

195 SEEDLING WEIGHT (mg)

Figure63 Relationship between the percentage initial seedlings attacked by small mammals in the grassland in autumn and seedling weight. Regression line fitted using GLIM with binomial errors: y = 8.22x + 0.27, 1^=0.191, p<0.001. attacked a significantly greater proportion of dicotyledonous than monocotyledonous seedlings

(F(U o4)= 10.64 and F(13a7)=6.49, respectively, both p<0.001). However, whereas molluscs did not appear to attack seedlings with respect to their size, small mammals attacked a significantly higher proportion of large seedlings than small (F(1307)=43.51, p<0.001, Table 6.2). This relation between the proportion of seedlings attacked and their seedling size was examined in further detail by a GLIM regression between the two variables. In the grassland in autumn the proportion of seedlings attacked by rodents was significantly correlated with seed endosperm weight (F(182)=19.32, r2=0.191, p<0.001, Figure 6.3), no such pattern existed for any of the other comparisons. Seedling familiarity played little role in the frequency of attack by either herbivore group on seedlings. The significantly higher consumption of unfamiliar seedlings by small mammals in the grassland in autumn simply reflected that some of the largest seedlings were also unfamiliar.

196 6.3.5 The severity of damage to seedlings caused by molluscs and small mammals Seedling consumption differs from seed consumption in that not all seedlings attacked are destroyed and a gradient exists between seedling grazing, where seedlings have potential for recovery and predation where the seedling is killed. Five categories of damage were recorded in the field. Categories 1-4 represent progressively greater damage. These represented for dicotyledons: 1- Partial removal of cotyledon leaf tissue. 2- Removal of at least 50% of cotyledon leaves. 3- Removal of cotyledon leaves and at least 50% of hypocotyl. 4- Loss of complete seedling. For monocotyledons, categories 1-3 represented increasing loss of leaf tissue. Category 5, for both monocotyledons and dicotyledons, represented removal of basal parts of seedling e.g. meristem, roots etc., but not of cotyledons. Significant differences between molluscs and small mammals existed in the patterns of damaged inflicted on seedlings when attacked when compared for all seedlings (%2=28.42, d.f. 4, p<0.001) and when monocotyledonous (%2=16.47, d.f. 4, p<0.01) or dicotyledonous (%2=16.17, d.f. 4, p<0.01) seedlings were examined separately (Figure 6.4). The modal damage category for molluscs with respect to both grass and forb seedlings was 3, with over 47% of seedlings attacked in this category. However, patterns of grass and forb damage significantly differed (%2=24.09, d.f. 4, p<0.001) with a over four times the proportion of forb seedlings as grass seedlings being only lightly grazed (categories 1 & 2). In contrast the modal damage category for small mammals with respect to both grass and forb seedlings was 4, with over 40% of seedlings attacked in this category. Similarly to molluscs, patterns of grass and forb damage significantly differed (%2=21.64, d.f. 4, p<0.001) with over eight times the proportion of forb seedlings as grass seedlings being only lightly grazed.

197 60

6 0 b) | | GRASS FORB

5 0

4 0

LU O 3 0 Q< IU 20 10 111 CL 0 1 2 3 4 5 DAMAGE CATEGORY

Figure 6.4 Pattern of damage to grass and forb seedlings when attacked by a) small mammals and b) molluscs. Additionally, small mammals exhibited category 5 damage over four times more frequently than molluscs. The style of category 5 damage differed between herbivore groups; rodents tended to uproot seedlings and consume roots and the base of the stem while molluscs grazed the base of the stem level with the soil surface, both leaving the rest of the stem intact. Both rodents and molluscs exhibited this type of feeding behaviour more frequently with grass than forb seedlings (Figure 6.4).

198 These findings indicated that rodent damage to both herb and grass seedlings tended to be more severe than that of molluscs with over 57% of seedlings in categories 4 and 5 (definite seedling mortality) compared to only 34% for molluscs. This finding that rodent seedling attacks were more severe than those of molluscs may imply that conclusions drawn regarding herbivore impact on seedlings based solely on frequency of attack may be erroneous. To investigate patterns of severity, the damage categories were revised and categories (4 & 5) were merged into a single category (4) indicating definite seedling mortality. For each herbivore group data sets were combined over sites and seasons and non-parametric Kruskall-Wallis one way ANOVAs were used to investigate the patterns of damage on each occasion seedlings were attacked.

PERCENTAGE INITIAL SEEDLINGS ATTACK Figure 6.5 Negative correlation between the frequency a seedling is attacked and the severity of damage inflicted by molluscs. r,= -0.7862, d.f. 15, p<0.01. Key: Agrostis capillaris (Ac), Dactylis glomerata (Dg), Festuca ovina (Fo), F . pratensis (F p ), F. rubra rubra (F it), F. rubra commutata (Frc), Holcus lanatus (HI), Lolium perenne (L p ), Medicago lupulina (Ml), M . sativa (Ms), Phleum pratense (Ph), Poa annua (Pa), P. pratensis (Pp), Plantago lanceolata (PI), Trifolium dubium (Td), T. pratense (Tp), T. repens (Tr).

199 In general, rodent damage was significantly more severe than that of molluscs (H=8.13, d.f. 1, p<0.01, Table 6.2), small mammals attacking grass, but not forb seedlings significantly more severely than molluscs (H=13.86, d.f. 1, p<0.001, for grasses and H=1.20, d.f. 1, p>0.1, for forbs). Significant variation in severity of damage existed between seedling species for both molluscs (H=44.71, d.f. 16, p<0.001) and small mammals (H=38.98, d.f. 16, pcO.OOl). However species severity patterns for molluscs and small mammals were significantly correlated (rs=0.6336, d.f. 15, p<0.01), both herbivore classes inflicting significantly greater damage to grass than to forb seedlings (H=20.13 and H=19.76 for molluscs and small mammals respectively, both d.f. 1, pcO.OOl). No significant patterns existed for either herbivore group with respect to seedling size or familiarity (Table 6.2). Correlations between the severity of attack and the frequency of seedling attack revealed that while no significant relationship existed for small mammals (rg=-0.0907, d.f. 15, p>0.05), for molluscs the damage inflicted on seedlings was inversely correlated with the frequency with which they were attacked (rs=-0.7862, d.f. 15, p<0.01, Figure 6.5). 6.4 Discussion 6.4.1 A comparison of mollusc and small mammal seedling predation With respect to plant demography molluscs have been presented as major seedling predators (Harper, 1977; Crawley, 1983; Fenner, 1987). The present study supports this finding, but provides evidence for the hypothesis that small mammals may on occasion have an equal or more severe impact on seedlings. An understanding of two components of seedling predation are necessary in order to compare and make predictions as to the relative roles each herbivore group may have on seedlings. The first is the probability of encounter of a herbivore with a particular seedling, which is a function of both herbivore density and mobility. The second, the fate of the seedling once found, which is a function of the likelihood of it being consumed and the severity of damage inflicted.

200 Direct comparisons of herbivore density are problematic since individual molluscs and small mammals differ in their consumption of seedlings. For example, in laboratory feeding experiments a single mollusc may consume up to 10 one week old seedlings per day (Gillman, 1987) whereas a vole may consume up to 300 in 15 minutes (Pyke, 1987). Mollusc densities range from 8 to 59m'2 in woodland (Jennings & Barkham, 1975) and 9 to 50m'2 in grassland (South , 1965), whilst on a similar scale small mammal density ranges from 0 to 0.012m'2 (chapter HI). It is therefore clear that even accounting for lesser feeding by molluscs, the maximum seedling impact of small mammals in grasslands, on density terms alone, is only likely to equal the impact of molluscs at low to medium densities. However, from a seedling’s perspective, not only the value of absolute herbivore density, but both its spatial and temporal variation (due to the seasonal appearance of seedlings) are important in determining the probability of encounter. Two scales of spatial variation exist, between different habitats and within a single habitat. While a variety of factors influence the density of molluscs (Godan, 1983) and small mammals (chapter IV), both have been shown to exhibit strong associations with the availability of cover. From a consideration of scale, the requirements of cover for molluscs are likely to be less exacting than those for small mammals, and what constitutes minimum cover for molluscs is unlikely to fulfil the criterion of small mammals. It may therefore be postulated that in grazed pastures (such as those studied by John L. Harper’s group), where the height of vegetation is low, molluscs rather than small mammals will be significant seedling predators. However, as cover increases, from grazed land through rough grassland to woodlands the role of small mammals as seedling predators will increase relative to molluscs. In general, the spatial distribution of molluscs within a site is aggregated (Dirzo and Harper, 1982), a product of their poor dispersal (South, 1965) and aggregative behaviour (Cook, 1981). The dispersion of small mammals is dependent on the spatial distribution of cover and population density, and may be either aggregated or random (chapter IV). Additionally, the scales of clumping differ between the two herbivore groups, molluscs aggregate on a scale similar to that of single plants (South, 1965) whereas for rodents the scale is at least an order of magnitude greater encompassing

201 whole plant populations (chapter IV). This implies that where only molluscs are the dominant seedling herbivores, the aggregated nature of this herbivory will present opportunities for seedling refugia to exist where molluscs are locally rare (Crawley, 1983). However, where local distributions of small mammals and molluscs overlap, foraging by small mammals where molluscs are rare will reduce the number of seedling refiigia, altering the dynamics of the seedling-herbivore interaction. Therefore, even when mollusc densities are high and those of rodents low, small mammals may still exert an important influence on seedling populations. This is highlighted by Figure 6.1, where randomly distributed small mammals in the grassland in autumn, due to their greater mobility are able to encounter seedlings at least as frequently as spatially aggregated molluscs in the same site. Both mollusc (Barnes & Weil, 1944) and rodent (chapter III) populations show marked seasonal fluctuations in density which will determine their impact on seedlings. The seasonal patterns of seedling attack in the grassland (Figure 6.1), were particularly amenable to an explanation of seasonal changes in herbivore densities. Correlations between small mammal density and seedling attack have previously been found (Pyke, 1986) but no studies have investigated the implications of this temporal variation on the seedling-herbivore interaction. Although woodland rodent numbers often crash in spring and rise in autumn (Montgomery, 1989a), temporal patterns of rodent density in grassland are less predictable (chapter HI). Therefore, while it might be hypothesized that in woodlands spring losses of seedlings attributable to small mammals may be lower than those in autumn, in grassland, reference to Figure 3.1, highlights the possibility that spring losses can, on occasion, be as high or higher than autumnal losses. Seasonal patterns of mollusc abundance are less well understood than those of small mammals, though evidence suggests peak abundances in spring and autumn (Barnes & Weil, 1944; Jennings & Barkham, 1975). Although temporal variations in mollusc seedling predation will be related to particular species dynamics, for exampleD. reticulatum, additional temporal variation may result from variations in local climatic conditions. Periods of cold and/or dry weather coinciding with seedling emergence may reduce mollusc foraging (Godan, 1983), a restriction not evident for small mammals.

202 Although the plant feeding of molluscs (reviewed in Dirzo, 1980) and small mammals (reviewed in Hansson, 1985) have been frequently studied, most studies have examined consumption of adult plants and not seedlings, which may differ. The few studies undertaken on mollusc seedling consumption support the finding of the present study that molluscs tend to avoid grass seedlings relative to forbs (Dirzo & Harper, 1980; Cottam, 1985). Although a similar, though weaker, pattern was evident for small mammals, they appear to discriminate seedlings more on the basis of size. Importantly, molluscs and small mammals differ in the frequency with which they attack different seedling species. For example, although comparisons of mollusc and small mammal mean frequency of attack for all seedlings combined in the grassland in autumn (Figure 6.1) were not significantly different, small mammals did attack Lolium perenne and Trifolium pratense significantly more frequently than molluscs and molluscsFestuca rubra rubra more frequently than rodents (Table 6.2). Therefore even where rodents and molluscs co-occur and even forage in the same areas, their effects on seedling populations will not altogether be compensatory but may be additive. For instance, while predation on large forb seedlings may be similar for both molluscs and rodents, the latter will attack large grasses more frequently and the former small forbs, with both herbivore groups avoiding small grass seedlings. Finally, even where molluscs and small mammals feed on the same seedling species, consequences of that feeding on seedling survival may differ, depending on the herbivore group concerned. Small mammals handled seedlings in a different manner to molluscs, in general inflicting more severe damage (Figure 6.4, Table 6.2). However, both herbivore groups shared similar feeding behaviours with respect to grass and forb seedlings. Grass seedlings were more frequently cut at the base of the hypocotyl (or in the case of small mammals pulled up and the basal meristem and roots consumed), leaving the felled remains of the seedling intact, than forbs. This type of feeding behaviour has been previously found for molluscs (Dirzo & Harper, 1980) and associated with avoidance of high silica content of grass leaves (Cottam, 1985). Forb seedlings were more frequently lightly damaged than grasses. This also appears to be common mollusc feeding behaviour (Gillman,

203 1987) which may be associated with the animals sampling plant tissues, rejecting those containing secondary compounds. These two distinct feeding patterns lead to both herbivore groups inflicting more severe damage to grass than to forb seedlings. Small mammals inflicted more severe damage to grass seedlings than molluscs, though both herbivore groups inflicted similar damage to forb seedlings. This indicates that not only are rodents more likely to attack large grass seedlings more frequently than molluscs but will also inflict more severe damage on each occasion. However, whereas the severity of damage inflicted by rodents to seedlings may reinforce some of their frequency of attack patterns, the opposite appears true regarding molluscs (Figure 6.5). Those seedlings most frequently attacked were those least damaged. This finding has important implications. If the original frequency of attack analyses are repeated (section 6.3.1), using only data points in damage categories 4 and 5, the initial significant difference between small mammal and mollusc seedling predators disappears. The pivotal question regarding these patterns is to what extent the different damage categories lead to differences in seedling survival. Clearly complete consumption (category 4) led to seedling mortality. However, with regard to basal feeding (category 5), Poa annua seedlings have been known to recover even when grazed down to the soil surface by slugs (Dirzo & Harper, 1980) and between 15 and 30% of one week old Bromus tectorum and Agropyron spicatum seedlings have been known to recover from similar damage caused by small mammals (Pyke, 1987). This implies that some seedlings may recover from mollusc category 5 feeding, though none will survive the root and meristem consumption exhibited by small mammals. As more seedling tissue remains the probability of seedling survival post grazing is likely to increase, the light grazing (categories 1 and 2) most likely being sub-lethal (Fenner, 1987). However, it is the fate of seedlings with category 3 damage that is of most concern, if this severity of damage is lethal then differences between herbivore groups, with respect to the severity of feeding, disappear (81.4% and 79.6% of mollusc and small mammal damage, respectively, was category 3 or more severe). The likelihood of seedling survival after category 3 damage will probably vary with the seedling species concerned. Pyke (1987) found that the ability of grass seedlings to resist grazing

204 was related to rapid root development, however other factors including the accessibility of the basal meristem and the quantity of seed reserves remaining post seedling emergence will be involved (Crawley, 1983). In addition, the environment in which a seedling is found will determine its capacity for recovery. The presence of competitors, either other seedlings (Dirzo & Harper, 1980) or adult plants (Chater, 1931) may lead to seedling death even when damage is slight. The present study supports previous work regarding the importance of molluscs as seedling consumers and predicts this group to be a source of significant seedling mortality in short vegetation. Where both small mammals and molluscs co-occur in a habitat a series of factors will determine their relative influence on seedling populations. Rodents and molluscs forage differently in space and both will act to reduce seedling refugia. Where both herbivore groups feed in the same area, differences in seedling species consumed will tend to make their impacts additive rather than compensatory. Finally, the variation in severity of damage inflicted by molluscs and rodents may imply that even when the same species is consumed, the greater severity of rodent seedling damage may have different consequences for the fate of seedlings. 6.4.2 The impact of small mammals on grassland seedling populations Because of the difficulties involved in determining the exact cause of death of individual seedlings in the field, few demographic studies quantify the loss due to predation (Fenner, 1985). Whereas seedling death due to pathogens, drought or frost heaving is often recognisable, due to both the presence of characteristic signs of mortality and the time scale over which death occurs (several days), seedling predation occurs over a much shorter timescale (minutes) and often leaves no evidence regarding the cause of death. It is likely that many of the seedlings which simply disappear between censuses (Foster, 1964; Miles, 1973,1974; Mack & Pyke, 1984) are the result of seedling predation. Indeed, such unknown sources of loss can account for a substantial proportion of seedling mortality (6-60% in the study of Mack & Pyke, 1984). It is interesting to note that with respect to the two herbivore groups in the present study, seedling predation by small mammals, which most frequently consume the whole seedling, is likely to go unnoticed more frequently than the grazing damage of molluscs.

205 The results of the present study, confirmed previous observations regarding the ability of small mammals to destroy seedlings (Table 6.1). Over a three day period rodents were capable of severely damaging or killing up to 44% of seedlings of some species (Table 6.2). If this predation rate is maintained throughout the window of seedling susceptibility to herbivory, 10 to 14 days after emergence (Fenner, 1987) small mammals could feasibly consume 100% of large seedlings. Nevertheless, a number of authors have suggested that seedling losses due to predation by small mammals may simply compensate for other sources of seedling mortality (Hermann & Chilcote, 1965; Borchert & Jain, 1978; Pyke, 1986). However all three studies share a similar experimental feature in that initial seed densities were unrealistic, leading to intense seedling competition and increased likelihood of "damping off’. However, even when the effects of small mammal seedling predators are additive rather than compensatory they may only account for a small proportion of an overall high seedling mortality. A number of mortality factors act on seedlings, both density dependent (e.g. self thinning, pathogen attack and other predators) and independent (e.g. climatic factors). To understand the impact of small mammals on seedling mortality the influence of these factors relative to rodent induced mortality must be examined. Clearly the influence of neighbours may be strongest when the plant is a seedling (Ross & Harper, 1972), and field evidence of density dependent seedling mortality exists (Symonides, 1977), yet the degree to which a seedling population may compensate for seedling loss will depend on the time of that loss. For example, seed or seedling loss before self thinning has occurred may be compensated for, possibly even reducing overall plant mortality (Borchert & Jain, 1978), whilst seedling loss after thinning may increase overall plant mortality. Field evidence suggests density dependent seedling mortality occurs in the initial period of establishment (Symonides, 1977). Glasshouse studies have also indicated high density dependent mortality at the seedling stage and that it is a function of the initial density of seedlings, the plant species involved and the local micro-environment (Harper, 1977). Most density dependent seedling mortalities were found under field conditions to occur within the first fortnight (Symonides, 1977), a much shorter period than found in glasshouse studies, though this may be explained by the more

206 stringent light conditions present in established vegetation. The timescale found for natural seedling populations indicates that scope exists for seedling predators to influence plant recruitment by killing seedlings after intraspecific competition has occurred. Due to spatial heterogeneity of seed dispersal and microsite distribution, not all seedlings will emerge in a highly competitive environment. Seedlings establishing at low density may still be at risk from small mammal seedling predators and consumption of these seedlings not likely to be compensated for. Density independent losses of seedlings such as rain-wash, drought, frost-heaving and burial may also occur and may account for sizeable loss of seedlings (Sharitz & McCormick, 1973; Silvertown & Dickie, 1980; Mack & Pyke, 1984). Nevertheless, while these processes may act as "key factors", only density dependent processes such as seedling predation or fungal infection (Augspurger, 1983) may regulate plant populations (Crawley, 1986b). No studies have compared the impact of predators and pathogens on plant populations. Evidence suggests that while pathogens may cause high seedling mortalities (Harper, 1977) predation by generalist herbivores may be a more powerful mechanism of population regulation. For example a) Losses to pathogens may be dependent on local climatic conditions and may be restricted to warm seasonal periods (Mack & Pyke, 1984) b) The ability of pathogens to destroy large numbers of seedlings is dependent on the spacing of those seedlings, often restricting severe damage to seedlings at high densities (Watkinson, 1986). c) The host specificity of pathogens and their relatively short dispersal distances implies that their effects are likely to be strongest in dense monocultures, while their ability to regulate patchy populations may be poor (Harper, 1977). In summary, small mammal seedling predators are likely to influence grassland seedling populations, indeed rather than simply being a component of naturally high seedling mortality they

207 are likely to be a significant source. However, it should be borne in mind that their influence relative to other sources of mortality will vary with the species concerned and in both time and space (Mack & Pyke, 1984). Implications of small mammal seedling predation are discussed below. 6.4.3 A comparison of small mammal seed and seedling predation: implications for plant recruitment in grassland Patterns of small mammal seedling predation parallelled a number of those found with respect to seed predation (chapter V). The different spatial distributions of small mammals in both sites revealed the extent to which small mammals may cause local variations in seedling survival. In addition, both seed and seedling predation varied seasonally. However temporal variations in seedling predation appeared to be due to changes in rodent density rather than to local changes in resource availability. The most interesting comparison is that regarding variations in loss between the seed and seedling stages of the same species. Comparisons of absolute losses are hindered by differences in both the distribution and density of small mammals between the seed and seedling experiments. Only examination of seedling losses in the grassland in autumn and seed losses in winter in the grassland were made, since small mammal population differences were lowest in these cases. In general seedling losses for each species were an order of magnitude less than losses of seeds. However for large seeds which produced large seedlings, losses were more similar (Table 5.5,6.2). Comparisons between any of the ten surface seed species patterns of loss (Table 5.5) with any of the three seedling patterns (Table 6.2) failed to reveal any significant correlations. This implies that with respect to small mammal predation foraging differs between these two groups of prey items and that loss rates of seed are not accurate predictors of losses of seedlings. The dominant small mammal predator of both seeds and seedlings wasApodemus sylvaticus. This rodent is omnivorous though seeds form the greater part of its diet (Hansson, 1985), this may explain its greater impact on seeds rather than seedlings. It should be borne in mind that these relative impacts of small mammal seed and seedling predation may have differed considerably had the dominant small

208 mammal been the more folivorousMicrotus agrestis. The patterns stem from both low seedling and seed loss to both high seedling and seed loss and are summarised in Table 6.3. How might these differential losses at the seed and seedling level influence plant recruitment? PERCENTAGE SURFACE SEED LOSS LOSS <40% >40% S Poa pratensis Dactylis glomerata E Phleum pratense Poa annua E Festuca rubra commutata Festuca ovina D <5% Trifolium dubium L Holcus lanatus I Trifolium repens N Festuca rubra rubra G Plantago lanceolata L Festuca pratensis Trifolium pratense O > 5 % Lolium perenne Medicago lupulina S Medicago sativa s Table 63 Relationship between percentage seed and seedling loss due to small mammals for sixteen grassland plants. Plant recruitment is composed of two periods within which small mammal predation of individuals may have different consequences for the demography of the plant population: the fate of seeds up to the period of seedling emergence and the fate of seedlings up to plant establishment. If a plant population is seed limited then predation in either of these two periods will most likely have a similar influence on the plant population. However, if a plant population is microsite limited then predation at the seed stage may be less influential, since it may be only another component of naturally high seed wastage (chapter V). In these cases mortality of seedlings may be a more powerful mechanism of population regulation. The importance of seed in perennial plant recruitment has already been discussed (chapter V). Recruitment of plants via seedlings is also commonly viewed as rare in perennial grasslands, though recent work suggests recruitment from seedlings may be more common than previously thought (Eriksson, 1989). Evidence suggests that the lack of seedlings may be due less to lack of microsites than to other sources of mortality. Glasshouse experiments have frequently shown that seedlings may survive for extended periods (up to 10 months) without growth when emerging in a

209 competitive environment. This resistance to inanition as described by Chippindale (1948) has been found for both grass (Chippindale, 1932) and forb (Fenner, 1978) seedlings, and parallels that of seedling banks in trees. This behaviour has also been noted for herbaceous seedlings transplanted to the field (Cavers & Harper, 1967), though it has not been described occurring naturally. A possible cause of this discrepancy between glasshouse and field observations may be that seedlings during this often lengthy period of resistance have a high probability of being attacked by seedling predators. The above discussion regarding recruitment in perennial grassland indicates that seedling predation may be a more powerful regulatory mechanism than predation of seeds. Therefore in comparing the impacts of seed and seedling predation, the latter source of loss should be weighted accordingly. Unfortunately estimating this degree of weighting is difficult. In general, if both low seed densities and seed burial are accounted for, the likelihood of a seed surviving seed predation by small mammals is a function of its seed size (chapter V). If the seed axis of Table 6.3 is changed from surface seed loss at a density of 10 seeds to the probability of seed survival, it leads to the three species in the upper right quadrant moving to the upper left and the two species in the lower left moving to the lower right. Patterns of seedling predation appear to accentuate the patterns regarding seed mortality. Implications of this finding regarding recruitment relative to the germination strategies related to seed banks are highlighted by Figure 6.6. Although significant differences exist between the seedling predation and seed bank type, these differences primarily reflect seedling size variations.

Type I : Both seeds and seedlings may suffer high mortalities from small mammal predators, and this is particularly true of the larger seeded species. Small mammal seed and seedling predators may be satiated by a large seed output but this seed will need to satiate these predators sufficiently long enough to enable seedlings to emerge, become established and of sufficient size to resist predation. The satiation of rodent seedling predators through the emergence of many seedlings is unlikely. Their lower digestibility and nutritional value with respect to seeds will imply more seedlings than seeds would be required to satiate a similarly sized rodent population. Additionally, these species tend to be grasses which may only have a small component of tissue removed (basal

210 40

Q 35 LU 0 130 COCD Z_I 25 QLU LU _COI 2 0 E< £= 15 LU CD 10 Otr

I II III IV SEED BANK STRATEGY Figure 6.6 Relationship between percentage seedlings attacked by small mammals and the plant species seed bank strategy. Numbers within bars represent the number of species in each category. Mean seedling weights for the categories are: I=1.17mg, 11=1.19mg, 111=0.3 lmg, IV=0.70mg. Error bars represent one standard error of the mean. meristem) by rodents yet are killed.

Type I I : Similarly to species of Type I seed banks, both seeds and seedling suffer high predation from small mammals. Recruitment of Type II species was previously predicted to be the most at risk from the effect of rodent seed predators, the similarly high seedling losses implies that this predation pressure continues after the seed has germinated.

Type III: The seed of this seed bank group tend to escape small mammal predation through burial in the soil. However an additional mechanism of escape from these predators is germination. Rapid germination of these species occurs soon after dispersal from the parent plant, reducing the opportunities for rodents to consume seed before burial. Seedlings were infrequently consumed by rodents and it is likely that they have only a small impact on seedling dynamics.

Type I V : Similar to Type III.

211 LOSS TOTAL SEED LOSS PROBABILITY LOW HIGH S E E RECRUITMENT ESCAPE IN TIME D LOW RARELY PREDATOR VIA RAPID L LIMITED GERMINATION 1T N G L ESCAPE IN SPACE ESCAPE O HIGH VIA DISPERSAL VIA PREDATOR S SATIATION s Table 6.4 Hypothetical relationship between probability of seed and seedling loss due to herbivores and mechanisms of plant recruitment related to predator escape. Clearly the relative impacts of seed and seedling predation pressure may select for particular recruitment strategies. Although a variety of predators and pathogens may influence either seed or seedling mortality, small mammals have potential to significantly influence both. By investigating the relative risks from predation of seeds and seedlings, four predator avoidance strategies may be formulated (Table 6.4). Experimental evidence from the present and previous chapters reveals that a number of germination strategies of grassland plants conform to expectations regarding predator avoidance (in particular Types I, HI and IV). In order to understand the extent to which predation pressure has moulded germination strategies of seeds, the selective influences of seasonal differences in seedling survival must be known. For example many of the germination strategies may simply be related to the seedling survival trade-offs of rapid germination, winter mortality due to climate and spring mortality due to plant competition (Silvertown, 1981). The temporal unpredictability of rodent seedling predation implies that it is unlikely to be a selective force on the season of germination. However the relative intensity of seed and seedling predation may influence the extent seeds delay germination once shed. Insufficient data exists regarding the selective pressures acting on seeds related to germination, nevertheless this study highlights a potential role of predation in these strategies.

212 In summary, small mammals, in particularApodemus sylvaticus, may influence both the seed and seedling steps of plant recruitment. The patterns of seedling predation accentuate those previously found for seeds, implying small mammal influence on plant recruitment may be greatest for large seeded legumes and other forbs growing in small localised populations. In addition, large seeded grasses at densities insufficient to satiate local small mammals may also be affected. The ultimate role predators may play in determining plant germination strategies relative to climatic and competitive interactions is discussed.

213 CHAPTER VII PLANT GROWTH AND SURVIVORSHIP IN RELATION TO SMALL MAMMAL HERBIVORY 7.1 Introduction Although predation studies have revealed that small mammals may significantly influence seed and seedling survival of grassland plants (chapters V and VI), an understanding of the fate of seeds and seedlings surviving from, or in the absence of, small mammal herbivory is necessary to gauge herbivore impact. A variety of plant demographic mechanisms may lead to the over-estimation of the influence of small mammal herbivory as determined by short term predation studies: a) Density dependent seed or seedling mortality (self-thinning) may compensate for rodent predation. Rather than representing an increased pressure on population numbers, it may simply be a component of the high mortality experienced during recruitment. b) The influence of abiotic sources of mortality as determined by the roles of water, soil, disturbance or temperature may be sufficiently severe as to make small mammal herbivore impacts negligible. c) Seedlings may be able to compensate for removal of plant tissue by rodents (sub-lethal attacks) by regrowth. However, small mammal herbivory may not only be restricted to the seed and seedling phases of plant recruitment. Evidence suggests that small mammals, in particular microtines, may have a substantial impact on established plants through selective grazing (Batzli, 1975; Hayward & Phillipson, 1979). Repeated and frequent grazing by small mammals on the same adult plant population may severely retard growth and reduce competitive ability (Pyke, 1987). In order to gauge the impact of small mammals on grassland plant demography, an estimate of this grazing is needed.

214 + + X species variation dry mean weight + dry total weight X + + + N° seed mean + + N° seed total + + spikelets spikelets per tiller per PLANT VARIABLE RESPONSE VARIABLE PLANT N° tiller -- height + + + + N° plant plant examined competition intraspecific intraspecific interspecific intraspecific + + + ants other water factors studied substrate intraspecific cage controls no effect size no gap intraspecific 5 16 23 none interspecific N° of of N° censuses EXPERIMENTAL PROCEDURE EXPERIMENTAL 9 4 1 8 10 16 duration (months) 1 4 5 2 2 N° species G G D CW

et al. et Author Habitat Borchert & & Borchert (1980) Jain (1978) Jain Pyke (1983) Pyke Christy & Christy Inouye Inouye (1984) Mack Rice (1987) Rice G Table 7.1 Review of previous studies examining the influence of small mammals on plant survivorship. Each study is categorised by the experimental procedure procedure experimental the by categorised is study Each survivorship. plant on mammals small of influence the examining studies previous of 7.1 Review Table undertaken and the response of a variety of plant variables to exposure to small mammal herbivory. Key: + variable reduced by small mammal herbivory, - variable variable - herbivory, mammal small by reduced woodland. variable + conifer Key: CW herbivory. desert, D mammal small grassland, to G exposure habitats to to Key variables herbivory. plant of mammal variety small a by of response increased the variable and x undertaken herbivory, mammal small by unaffected

215 The few studies that have examined the survivorship of plants in relation to small mammal herbivory have been exclusively North American (Table 7.1). The small number of plant species examined and the greater proportion of annual over perennial plants studied (over 85%) restrict the extent to which previous findings may be extrapolated to European perennial grasslands. The present study had the following aims: i) To test the predictions regarding the susceptibility of plant species to small mammal herbivory as determined by the extent of seed and seedling predation. ii) To examine the impact of small mammal seed and seedling predation on plant survival relative to grazing damage. iii) To compare predation losses relative to other sources of plant mortality such as frost, drought, plant competition and other herbivores. iv) To examine the extent plants may compensate for herbivory through increased production of biomass and/or reproductive tissue. v) To investigate how small mammal herbivory might influence the variation in size of individuals within a plant population. Both plant death rates (Hutchings, 1986) and fecundities (Watkinson, 1986) are size dependent. However, any non-linearities in these relationships will lead to erroneous predictions of plant performance based solely on mean plant size (Crawley, 1983) and therefore an estimate of the degree of plant size variation is needed. 7.2 Methods and materials The plant survivorship experiments were designed for comparison with both the seed and seedling predation studies and were undertaken in the grassland and meadow sites described in section 2.2.2. All 21 species used in the seed and seedling studies (see section 5.2.1) were used in the survivorship experiments. Four exclosure treatments were used to investigate the influences of arthropod, mollusc and small mammal herbivores and checked against an exclosure control, each replicated five times in each site. For each treatment the cage and chemical exclosure methods used were the same as those previously described in section 5.2.2.

216 7.2.1 Experimental design Each treatment consisted of a lm2 area of grassland from which all surface vegetation had been removed, enclosed by the particular cage treatment. The bare soil was broken up and raked to prepare a suitable surface in which to sow seeds. Within each exclosure, twenty one 15cm x 1cm sowing areas were marked out in a 3x7 grid. Each row and column was at least 10cm from the next in order that interspecific interference might be reduced. A 15cm guard strip surrounding the sowing grid limited interference from both the exclosure cage and the surrounding vegetation. Within each 15cm x 1cm sowing area fifty seeds of a single, randomly assigned, species were sown. The seeds were then lightly covered with soil and firmed down. The top of each exclosure was covered by bird netting. The treatments were randomly assigned in a 4 x 5 grid, spaced at 5m intervals. The location of the grid was the same as the seed and seedling cages though orientated 90° to this 5x4 grid of smaller cages such that neighbouring cages in the two grids were never more than 7m apart. It could therefore be assumed that patterns observed for the seed and seedling predation studies would be representative of those occurring in the larger cages. Laboratory studies revealed substantial species variation in the proportion of seeds germinating. Rather than account for this by sowing more seed of the poorer germinators (Pyke, 1983), the same number of seeds was sown for each species. The sowing density of fifty seeds was used since it would provide a sufficiently large number of plants to enable examination of individual variations in size. In addition, the small sowing area would provide an opportunity for self-thinning to occur which, would highlight the possibility of species compensating for plant loss through herbivory (Borchert & Jain, 1978). 7.2.2 Experimental procedure Once seeds were sown in November 1986, all exclosures were censused monthly, the number of seedlings present of each species was counted, their phenological state recorded and any signs of herbivory, disturbance and other sources of mortality noted. After the majority of plants had bolted in May, accurate censusing of numbers was prevented and only signs of mortality and

217 production of flowers were recorded up to harvest in August-September 1987. At each census any non-sown species appearing in the plots, whether vegetative or a seedling, was removed in order to reduce interspecific interactions. After harvest all plants were oven dried at 70°C for 7 days, and the plant parts were weighed as follows: root, total above ground biomass, total above ground reproductive biomass and total seed mass. The total numbers of individuals, flowers and seeds produced were also recorded. For each treatment, one of each of the twenty one species sowings was randomly chosen from the five replicates for detailed examination. For each individual plant in a particular sowing the above measurements were made and in addition the number of tillers produced was also recorded. Although a number of parameters have been used to described size variation of plants, the most widely used measure is the Gini coefficient (Weiner & Solbrig, 1984). In the present study the coefficient of variation (CV), which is closely related to the Gini coefficient (Hara, 1988), was preferred due to its general availability in statistical packages. However, the dependence of statistics of variation on the sample size (Sokal & Rohlf, 1981) limited examinations of plant size inequalities to samples of at least 30 plants. 7.2.3 Controlling for cage effects In November 1987 an experiment was undertaken to overcome the criticism that long term exclosure studies rarely take into account the influence of the cage itself in vegetational changes (chapter I). The original control and small mammal access cages were maintained and a new treatment—cage effect, was created. This treatment used the previous mollusc access only cages which were modified to allow small mammal access by cutting a 5cm wide hole all along the base of the cage. Pesticides were applied as in the other two treatments. This treatment, when compared to the small mammal access treatment would reflect any differences due to different cage designs. Four plant species, reflecting the range of total species, were used in this study, two grasses, Loliumperenne and Agrostis capillaris and two forbs, Trifolium pratense and Plantago lanceolata. The experimental procedure followed that described for the main experiment with the exception that plants were harvested a month earlier in July-August 1988.

218 7.3 Results A number of factors reduced the amount of data that could be gathered at harvest. a) By August the root systems of most species were sufficiently developed that digging the plants up caused significant loss of root tissue. The suitability of root weights as a variable was therefore put in doubt. b) Many perennial grasses take two years to flower (Wilson & Thompson, 1989) and only 5 of the 14 grasses had produced any flowers by August 1987. Additionally, even within a species, the time of flowering varied between replicates such that by harvest some plants had already produced and shed seed while others were still initiating flowers. These two effects significantly reduced sample sizes of reproduction related parameters. c) A number of species known to have low germination probabilities under laboratory conditions also produced few plants in the field i.eDeschampsiaflexuosa and Festuca rubra var. litoralis. The leguminous species which germinated soon after sowing, in contrast to most grasses which appeared up to two months later, suffered high mortality over the winter due to heavy frosts and prolonged snow cover and produced few plants by the final harvest. d) The exclosures were disturbed by the production of molehills over the winter, particularly in the meadow site where over 50% of exclosures were affected. The production of molehills was believed to be artefactual, reflecting a response of moles to the cages, since production of molehills was restricted to the inside edge of the cages and appeared over-represented inside the cages relative to the vegetation outside. Where this damage was severe and led to complete loss of seeds or seedlings of a particular species through burial the sample was omitted from further analysis. e) Two exclosures in the meadow site were completely flooded in winter and remained submerged until mid-spring. These, too, were omitted from further analyses. These five factors led to analyses being restricted to two parameters: the numbers of individuals and above ground biomass. The unknown error distribution related to these parameters meant that non-parametric analyses were undertaken and following the study of Pyke (1983) Kruskall-Wallis one-way ANOVAs were applied to the ranked data for numbers, biomass and numbers of tillers.

219 While ranking raw data between treatments within a single species and within a single treatment between all species was permissible, ranking raw data over all treatments and all species was not. This resulted from between species variation in the absolute values of certain variables unrelated to treatments i.e. low numbers due to low germination or small biomass due to short growth form, masking between treatment differences. To enable between treatment comparisons, treatment differences were first ranked within single species, and total species variation between treatments analysed on these within single species ranks. This effectively made all species comparable irrespective of absolute variations in parameters. 7.3.1 Between treatment variations in species survivorship Examination of species variation within the controls revealed significant species differences in number of plants harvested in both the grassland and meadow sites (H=66.91 and H=33.22 respectively, both d.f. 20, p<0.001). This is not surprising since it reflects species differences in germination probabilities, self-thinning and resistance to abiotic sources of mortality related to frost, nutrients or drought. The scope of this study prevented a detailed examination of this underlying species variation but instead focused on additional species variation related to the effects of treatments. In the grassland significant variation existed between treatments in the numbers of plants present at final harvest (H=35.04, d.f. 3, p<0.001). However, only the mollusc and small mammal access treatments reduced plant numbers significantly below those of the controls (H=8.70, d.f. 1, p<0.01 and H=26.66, d.f. 1, p<0.001, respectively, Table 7.2). Comparisons between these two treatments revealed small mammals to have reduced plant numbers significantly more than molluscs (H=4.93, d.f. 1, p<0.05). The small number of replicates hindered detailed investigation of between treatment differences for particular species. However, compared to the controls, small mammals did significantly reduce numbers ofAlopecuruspratensis and Festuca pratensis and in addition numbers of three other species (allTrifolium spp.) were also reduced by more than 75%. In contrast, although

220 SPECIES GRASSLAND MEADOW Control Arthropod access Mollusc accessRodent access Control Arthropod access Mollusc accessRodent access Agrostis capillaris 28.40 16.00 18.20 16.80 10.00 24.67 20.40 19.40 Alopecurus pratensis 15.50 7.80 5.60 1.80 * 3.75 1.50 1.00 1.25 Dactylis glomerata 25.80 19.60 17.00 16.20 11.00 9.75 10.60 9.00 Deschampsia flexuosa 4.00 3.60 4.00 2.60 4.75 2.50 2.80 1.40 Festuca ovina 32.00 39.80 34.20 30.00 21.33 14.75 19.75 22.20 Festuca pratensis 29.80 17.80 14.60 6.60* 6.75 3.00 3.20 5.60 Festuca rubra 34.00 30.20 21.50 22.20 17.33 11.25 8.00* 16.80 commutata Festuca rubra litoralis 1.75 0.60 1.60 1.00 0 0.50 0.00 0.00 Festuca rubra rubra 11.20 15.25 13.25 14.60 9.25 6.25 4.00 5.20 Holcus lanatus 8.80 8.20 14.00 10.00 9.00 4.67 4.20* 9.80 Lolium perenne 26.60 31.75 30.80 16.20 9.00 7.75 7.80 7.40 Lotus corniculatus 0.60 1.75 0.60 0.60 0 0.50 0.40 0.20 Medicago lupulina 0.00 0.60 0.25 0.00 0 1.50 0.20 0.75 Medicago sativa 3.75 5.60 2.40 1.60 0.67 2.67 0.20 0.60 Phleum pratense 29.60 17.40 15.00 * 19.20 13.25 10.00 9.75 15.20 Poa annua ' 14.20 10.80 5.80 7.60 7.75 9.00 4.20 7.80 Poa pratensis 7.40 7.20 6.25 5.20 10.75 10.00 5.40 9.50 Plantago lanceolata 21.80 30.80 25.20 23.00 28.00 44.33 18.60 40.00 Trifolium dubium 9.80 5.60 6.80 1.40 4.75 2.25 0.75 3.20 Trifolium pratense 12.80 12.40 7.50 2.80 0.00 2.75 0.80 0.20 Trifolium repens 9.20 4.40 3.60 2.20 0.33 0.00 0.00 1.50 Table 7.2 Mean number of plants harvested for each of twenty one species in each of four exclosure treatments in the grassland and meadow sites. Asterisks next to means indicates treatment was significantly different from the control. * P<0.05. molluscs significantly reduced numbers ofPhleum pratense, no species were reduced by as much as 75% and only four were reduced by more than 50% (Table 7.2). No species to which ground arthropods (seed feeding carabids and ants) had access differed from the controls. Treatment patterns in the meadow contrasted with those of the grassland (Table 7.2). Although treatments differed significantly (H=9.58, d.f. 3, p<0.05) differences were less than those in the grassland. Only molluscs significantly reduced plant numbers with respect to controls (H=4.50, d.f. 1, p<0.05). Species significantly affected by mollusc herbivores were Holcus lanatus and Festuca rubra var. commutata (46.6% and 43.2% respectively), and in addition three species Alopecurus pratensis, Medicago sativa and Trifolium repens were reduced by over 70%, though not significantly. Comparisons of the controls revealed that the number of plants establishing in the meadow were significantly lower than in the grassland (H=22.5, d.f. 1, p<0.01). The source of this between site difference in survivorship is unknown. This between site variation complicated comparisons of the treatments between the sites. To overcome this, between site comparisons were made on the percentage of the mean plant numbers in a particular treatment relative to the mean numbers in the controls. This analysis revealed that while no significant difference existed between the grassland and meadow sites for molluscs (with means of 81.2% and 69.5% the number of control plants, respectively, H=1.62, d.f. 1, p>0.05), for rodents reductions of plant numbers relative to controls in the grassland were significantly greater than in the meadow (63.2% compared to 110.4%, H=7.73, d.f. 1, p<0.01). This between site difference in rodent herbivory appears to be a function of the aggregated distribution of rodents in the meadow (chapter IV). Examination of the position of the survivorship cages relative to areas of small mammal abundance in the meadow revealed that all small mammal access cages were located in relatively open areas where rodent foraging was unlikely.

222 170

160 - HI

150 -

140 -

130 - Frr

120 - Lp PI 110 - O Fo W 1 0 0 - Df

| 90 - Frl Pop 80 -

70 - Td M s 60 - ^ Frc Tp 50 - Fp Php 40 - Ap Tr P a 30 -

20 - ■ i------r------1------r n------r i i i r 0 20 40 60 80 100 120

SMALL MAMMAL Figure 7.1 Relationship between the percentage of control plant survivorship represented by molluscs and small mammals for 19 species in the grassland site. Dotted line represents equality. Key: AcAgrostis capillaris; Ap Alopecurus pratensis; Dg Dactylis glomerata; Df Deschampsia flexuosa; Fo Festuca ovina; Fp F. pratense; Frc F. rubra commutata; Frl F. rubra litoralis; Frr F. rubra rubra; HI Holcus lanatus; Lp Lolium perenne; Ms Medicago sativa; Php Phleum pratense', Pa Poa annua; Pop P. pratensis; PI Plantago lanceolata; Td Trifolium dubium; Tp T. pratense; Tr T. repens. The degree to which the numbers of different plant species were reduced relative to the controls was significantly correlated between small mammals and molluscs in grassland (rs=0.7316, d.f. 17, p<0.01, Figure 7.1). No such relationship was found in the meadow. In the grassland small mammals reduced survivorship of most plant species more than molluscs (16 out of 19 points lie above the line of equality in Figure 7.1). The significant correlation between the influence of these two herbivore classes on plant, species survivorship appears to be due to their lack of consumption of small seeded grasses which produce small seedlings and their preference for forb and large grass seedlings. Between these two extremes lie a number of grass species for which no discernible correlative patterns exist.

223 0.436 0.111 0.109 0.424 0.206 0.509 0.078 0.002 0.001 0.417 0.004 0.056 0.002 0.183 0.516 0.148 0.162 0.248 0.068 0.527 0.178 0.226 0.383 0.458 0.235 0.730 0.007 0.4850.078 0.981 0.342 0.080 Mollusc access Rodent access Table 7.2. MEADOW 0.080 0.563 0.350 0.067 0.173 0.127 0.291 0.277 3.019 2.432 2.173 0.130 0.219 0.083 0.023 0.007 0.830 0.2910.111 0.673 Arthropodaccess 1.337 0.285 0.280 0.179 0.424 0.935 0.068 0.848 0.068 Control 0.497 0.442 0.210 0.127 0.340 0.165 0.319 0.275 0.685 0.622 0.241 0.123 0.639 0.457 0.098 0.075 0.116 2.339 1.971 1.976 0.921 0.729 Molluscaccess Rodent access GRASSLAND 0.060 0.393 0.731 1.277 0.237 0.046 0.208 0.332 0.218 0.224 0.367 0.316 3.526 0.003 0.030 0.406 0.095 0.120 0.650 0.198 0.395 0.622 0.397 0.358 0.158 0.254 0.062 0.113 0.142 0.097 0.128 0.153 0.337 0.472 0.297 0.375 2.742 0.343 0.253 0.558 0.548 0.777 0.366 0.910 0.023 0.048 0.023 0.002* 0.002 0.017 0.471 0.224 0.665 0.393 0.452 0.078 0.097 0.137 0.131 0.086 0.363 0.427 0.352 0.652 0.105 0.106 2.534 0.102 0.077 0.163 0.036 , 0.021 0.030 0.181 Control Arthropod access ‘ ‘ 0.405

Mean plant biomass (grams) for twenty one species in each offour exclosure treatments in the grassland and meadow sites. Asterisks as in SPECIES Dactylisglomerata Festuca ovina Festucapratensis commutata Agrostis capillaris Alopecuruspratensis Deschampsiaflexuosa Festuca rubra Festuca rubra litoralis Holcus lanatus Festuca rubra rubra Loliumperenne Medicago lupulina Lotus corniculatus Trifoliumpratense Medic ago sativaMedic Trifolium repens Phleumpratense Trifolium dubium Poaannua Poapratensis Plantago lanceolata Table 7.3

224 7.3.2 Between treatment variations in species biomass No significant variation between treatments in mean biomass of plant species was found in either the grassland (H=1.40, d.f. 3, p>0.05) or in the meadow (H=3.48, d.f. 3, p>0.05), nor were significant differences found between sites when controls were compared (H=3.72, d.f. 1, p>0.05, Table 7.3). The small sample sizes and large variation in individual plant weights (see below) reduced the strength of tests for particular species differences. Only for one species,Medicago sativa, did small mammals significantly reduce mean biomass relative to the controls (H=4.581, d.f. 1, p<0.05). This implied that while herbivory by small mammals and molluscs influenced plant numbers they had little effect on mean plant biomass. 7.3.3 Within population variations in plant size SPECIES GRASSLAND TREATMENTS Control Arthropod access Mollusc access Rodent access CV % n CV % n CV % n CV % n Agrostis capillaris 153.3 31 129.3 30 148.4 36 Dactylis glomerata 118.4 37 110.2 39 125.3 33 115.7 37 Festuca ovina 87.5 40 80.5 40 76.1 40 103.2 40 Festuca pratensis 173.5 40 124.5 33 135.8 31 Festuca rubra 84.1 37 68.4 41 149.7 34 95.3 40 commutata Lolium perenne 124.8 48 81.7 40 100.0 40 110.4 40 Plantago lanceolata 152.0 32 139.5 36 189.2 40 162.3 31 Table 7.4 Treatment variations in the extent of plant size hierarchies as described by the coefficient of variation (CV) for single populations of seven perennial species in the grassland site. Only those populations with sample sizes (n>30) included. Only one third of the species studied were sufficiently numerous (n>30) to allow examination of individual plant variation (Table 7.4). All species in all treatments had high CVs (>50%), due to the populations being composed of relatively few large individuals and many small ones, (a frequent finding for plant monocultures Harper, 1977). Significant between species differences in individual variation were found (one-way ANOVA on ranks F(619)=9.54, p<0.001), ranging from species with relatively low variation (<100%) i.e. Festuca ovina,F. rubra var. commutata to others

225 with particularly high variation (>150%) i.e.Plantago lanceolata. Significant variation also existed between treatments (H=9.03, d.f. 3, p<0.05), though this was related to the relatively low variability of the arthropod access treatment, and there were no significant differences between the other three treatments (H=0.66, d.f. 2, p>0.05). However, relative to the controls, the arthropod treatment differed by on average only 17% (range 7-35%), and the ecological significance of this small difference is hard to judge. SPECIES GRASSLAND TREATMENTS Control Arthropod access Mollusc access Rodent access Agrostis capillaris 3.194 5.967 5.611 ±0.569 ±0.951 ±0.744 Dactylis glomerata 1.541 1.128 1.242 1.054 ±0.163 ±0.075 ±0.138 ±0.054 Festuca ovina 2.575 3.225 2.525 2.425 ±0.258 ±0.305 ±0.224 ±0.288 Festuca pratensis 1.250 1.121 1.484 ±0.167 ±0.095 ±0.196 Festuca rubra 3.216 3.024 4.353 2.775 commutata ±0.425 ±0.267 ±0.715 ±0.303 Lolium perenne 2.875 2.375 2.725 2.225 ±0.335 ±0.247 ±0.286 ±0.281 Table 7.5 Mean number of tillers produced by six monospecific perennial grass populations in each of four treatments in the grassland site. Sample sizes as in Table 7.4. Values following ± represent one standard error of the mean, and are given only as a guide to the degree of variability associated with the mean. Significant between treatment differences in tillering were only found for Agrostis capillaris and Dactylis glomerata (H=12.63 and 11.43 respectively, both d.f. 3, p<0.01; Table 7.5). Interpretation of these differences is hindered by lack of replication and may be the result of factors acting independently of treatment (i.e. soil quality). Treatment differences were not consistent between species, small mammals increasing tillering in A. capillaris, but causing a reductionD. in glomerata. Although small mammal grazing has been shown both to increase and decrease tillering of grasses, depending on the timing of grazing (Pyke, 1983), the causes of these differences were unknown in the present study. Comparisons of all species revealed no significant between treatment differences (H=6.52, d.f. 3, p>0.05) indicating that in general, there was little evidence of herbivores influencing tillering.

226 7.3.4 The influence of cages on plant survivorship GRASSLAND SPECIES Control Cage Rodent control access number mean plant mean plant mean biomass number biomass number biomass Dactylis glomerata 28.00 0.053 16.25 0.044 14.50 0.011 ±2.83 ±0.018 ±5.32 ±0.026 ±4.21 ±0.005 Lolium perenne 26.50 0.017 12.25 0.021 4.50 0.005 ±7.24 ±0.002 ±6.02 ±0.016 ±2.72 ±0.002 Plantago 14.25 0.007 20.75 0.054 15.25 0.014 lanceolata ±7.37 ±0.004 ±8.72 ±0.023 ±5.95 ±0.006 Trifolium pratense 3.75 0.016 0.25 0.001 0.75 0.003 ±2.50 ±0.026 ±0.25 ±0.75 MEADOW SPECIES Control Cage Rodent control access plant mean plant mean plant mean number biomass number biomass number biomass Dactylis glomerata 23.00 0.050 23.67 0.024 17.25 0.043 ±2.65 ±0.017 ±15.71 ±0.008 ±9.15 ±0.008 Lolium perenne 14.00 0.011 12.00 0.047 17.50 0.049 ±0.58 ±0.005 ±9.16 ±0.032 ±7.90 ±0.023 Plantago 19.33 0.047 19.67 0.013 29.00 0.010 lanceolata ±12.98 ±0.005 ±10.50 ±0.007 ±13.48 ±0.023 Trifolium pratense 3.00 0.001 0.67 0.001 0.50 0.156 ±3.00 ±0.67 ±0.29 ±0.126 Table 7.6 Mean numbers and biomass of four perennial species in the grassland and meadow comparing the influence of cages themselves on these variables with respect to normal controls and small mammal access treatment. Values following ± represent one standard error of the mean, and are given only as a guide to the degree of variability associated with the mean. There was no significant difference in plant survivorship or biomass between normal small mammal access cages and cage controls either in the grassland (H=0.28 and 3.74, for numbers and biomass respectively, both d.f. 1, p>0.05) or in the meadow (H=2.32 and 0.73, both d.f. 1, p>0.05, Table 7.6). The possibility that the above patterns were products of the cages themselves rather than the exclosure of particular herbivore classes can therefore be rejected.

227 7.4 Discussion 7.4.1 Small mammal herbivory and plant survivorship All previous studies on the influence of rodent herbivory on plant survivorship have revealed that small mammals are capable of significantly reducing plant numbers (Table 7.1), and this study is no exception. However, this study is the first to use a sufficiently large number of species that generalisations can be made. Not all grassland plants were equally affected by small mammals, and three distinct classes of plant survivorship existed. The survivorship of some, suchPlantago as lanceolata, Holcus lanatus, Festuca rubra rubra and F. ovina, appeared not to be influenced by small mammal herbivory (Table 7.2). It is interesting to note that these species constitute some of the most abundant grassland plants at Silwood Park. In contrast, other species such asAlopecurus pratensis, Trifolium dubium, T.pratense, T. repens and Festuca pratensis, may have their survival reduced by between 80 and 90% when exposed to small mammals. Again, it is interesting that these species are scarce or absent from Silwood grassland. Most species however, had their survivorship reduced to a lesser degree ranging from 30 to 60%. Studies which focus primarily on plant numbers (Christy & Mack, 1984) may overestimate the impact of rodents by not accounting for the ability of plant populations to compensate for losses of individuals. For example, Borchert & Jain (1978) found that while rodents significantly reduced numbers of four annual grasses, the resulting reduced interference from neighbours enabled the surviving plants to grow larger and be more fecund, compensating for predation losses. In the present study there was no evidence for surviving plants being significantly larger than plants never exposed to small mammal herbivory (Table 7.3). Unfortunately insufficient data were available to compare plant fecundities. Since fecundity is related to size (Hutchings, 1986), and size variation did not significantly differ between treatments (Table 7.4) coupled with the possibility that the relationship between size and fecundity may be linear (Rees & Crawley, 1989), it appears unlikely that plants were able to compensate for herbivory, at least in their first year. The study of survivorship of herbaceous perennials is made difficult by their often being exceptionally long lived (Watkinson, 1986). It is possible that on a time scale similar to perennial plant life-span, populations

228 may eventually compensate for losses due to herbivory early on in their history. While this is possible, demographic evidence suggests it to be unlikely. Most plant mortality is associated with the initial stages of plant establishment (in herbaceous perennials this represents the first year) after which mortality is low and more or less constant, limiting the opportunity for further compensation (Silvertown, 1987). This lack of evidence for subsequent plant compensation for small mammal herbivory is supported by little variation in growth form of plants exposed to herbivory compared to controls (Table 7.5). Grasses appear to respond to small mammal grazing by increased production of tillers (Borchert & Jain, 1978; Pyke, 1983). However increased tiller production appears to be a function of the severity of grazing (Pyke, 1983). The lack of evidence for an influence of small mammals on grass tillering in the present study not only supports the evidence for lack of plant compensation but is suggestive that the mechanism of herbivory differed from these two previous studies.

The most abundant small mammal herbivore in the present study wasApodemus sylvaticus which rarely grazes plants (Hansson, 1985), while in the two North American studies, the most abundant rodent herbivores were Microtus californicus and M. montanus, which subsist primarily by grazing (Batzli, 1985). It should therefore be borne in mind that the results of the present study primarily reflect the influence of rodent seed and seedling predation and not that of grazing and that patterns (in particular those of biomass and growth form) may have differed considerably had Microtus agrestis been the dominant small mammal herbivore. Where both small mammals and molluscs foraged together, species patterns of plant survivorship were similar (Figure 7.1). This may indicate that for many species herbivory by these two herbivore classes is compensatory. However, in most cases small mammals reduced plant survivorship by a greater amount than molluscs. This may have arisen due to: a) Small mammals consuming both seeds and seedlings, while molluscs fed only on seedlings. b) Rodent damage to seedlings may be more severe than that of molluscs preventing seedling recovery. c) The greater mobility of rodents may enable them to consume a greater proportion of plants while

229 they were still susceptible to predation. 7.4.2 Predicting patterns of plant survivorship from seed and seedling predation studies There is a significant bias in the extent to which the influence of small mammals on plant populations has been examined. Most commonly the only role studied is that of seed predation (see chapter V), followed by seedling predation (though rarely in concert with seed predation studies, see chapter VI). Least studied of all is their influence on plant survivorship. Although results of seed and seedling predation studies are often extrapolated to predict impacts on plant populations, the present study is the first to test the accuracy of seed and seedling studies in determining plant survival. In general, results from survivorship studies were consistent with patterns of seed and seedling predation (Figure 7.1). Species with large seeds (>lmg) which produced large seedlings comprised four out of the six species whose survival was most reduced by small mammals, while species with small seeds (<0.5mg) which produced small seedlings, or species whose seeds and seedlings were found to be unpalatable to small mammals comprised all six species least influenced by small mammals. The majority of the remaining species which fell between these two susceptibility extremes also fell between the extremes of survivorship. However, not all species survivorship patterns reflected susceptibilities to rodent seed and seedling predation. In comparison to its high rates of seed and seedling loss, the survivorship ofLolium perenne was relatively unaffected by small mammals. The reason for this is unknown, but the example highlights the fact that errors can be made when extrapolating solely from predation studies. Clearly, to be able to make accurate predictions of plant fate from seed and seedling predation studies, the relative importance of these two phenological stages to the plant population must be known. Greater importance may be placed on the seedling stage for species with low germination percentages i.e. Deschampsiaflexuosa and Festuca rubra litoralis, while those species with almost negligible levels of recruitment even in the absence of small mammals are unlikely to be affected by rodent predation at either stage i.e. Lotus corniculatus and Medicago lupulina.

230 90

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Q_3 LU s

LJU occ Q_LU a

SEED BANK STRATEGY Figure 7.2 Relationship between the mean percentage of plants relative to control reduced by small mammal exposure and the seed bank strategy of 19 plant species in the grassland site. Numbers within bars are the number of species in each seed bank category. Error bars represent one standard error of the mean, and are given only as a guide to the degree of variability associated with the mean. In the present study seedling predation may have been more important than seed predation. Two small seeded forbs Trifolium dubium and T. repens, which had similar susceptibilities to seed predation as small seeded grasses but greater susceptibility to seedling predation were found to suffer marked reductions in survivorship. In addition, if the percentage plant mortality caused by small mammals relative to control losses is compared for each of the four seed bank/germination strategies, then patterns of mortality reflect seedling losses more than seed losses (compare Figure 7.2 with Figure 6.6). This increased importance of seedling predation is not surprising since an artificially high seed density was part of the experimental design. Whether this reflects natural differences between seed and seedling mortalities is unknown (see the discussion of chapters V and VI).

231 The predictability of plant survivorship from seed and seedling predation studies depends on two factors: the demographic importance of seed and seedling loss to plants, and the relative impact the herbivore has on the seed, seedling and adult plant. It is suggested that predation studies will reflect plant survivorship most accurately incases where the herbivore’s feeding activity is restricted to only one of these stages and that this stage is demographically important with little scope for compensation through subsequent reductions in natural mortality. For example in desert communities dominated by annual plants, the survivorship of these annuals reflects the seed preferences of rodents, which are almost exclusively granivorous (Inouyeet al., 1980). However, in California annual grasslands, where the rodent herbivore not only consumes seeds and seedlings but also grazes adult plants, patterns of plant survivorship do not reflect the rodent seed preferences (Borchert & Jain, 1978). It is interesting to note that in this last exampleLolium multiflorum, while suffering rodent seed losses, revealed no reduction in plant survivorship, reflecting the patterns found in the present study forL. perenne. It follows that seed and/or seedling predation studies will be least predictive in perennial plant communities where the rodent herbivore additionally grazes adult plants. In summary, small mammals may significantly reduce some plant populations through seed and seedling predation. Indeed, small mammals may exert a stronger influence than molluscs on grassland plant survivorship. There appeared to be no evidence for plants being able to compensate for losses encurred at the seed and seedling stage. Although the experimental design did not completely reflect recruitment in natural vegetation due to the lack of established vegetation when seeds were sown, it is clear that small mammal herbivory may represent a significant hazard to plant recruitment in grassland. Plant survivorship reflected rodent seed and seedling predation, though the latter was more important in the present study. Results from predation studies used to predict plant population fate should be interpreted with caution. An understanding of the herbivore feeding activity and the natural demography of the plants is essential. Seed predation studies that have not examined seedling fate may have drawn erroneous conclusions about the role of rodents in plant demography.

232 CHAPTER VIII SMALL MAMMAL HERBIVORY AND PLANT RECRUITMENT IN GRASSLAND 8.1 The impact potential of small mammal seed and seedling predators Fundamental to the study of a predator-prey system is the evaluation of the predator potential to alter prey populations. A variety of factors are involved in such an evaluation (Buckner, 1966; Crawley, 1989). a) Food capacity The capacity for destruction of prey by the predator is related to its per capita energy intake, including assimilative efficiency, required for the satisfactory performance of daily activity, growth and reproduction. Due to their small size and homeothermic habit, small mammals have a high energy expenditure and require a high rate of food intake (Bourlidre, 1975). Per unit body mass, small mammals are likely to have greater energy requirements than both invertebrates, due to the latter being poikilotherms, and larger mammals, due to the latter having lower metabolic rates. b) Food specificity Small mammals consume a wide range of food items (Hansson, 1985), even when feeding is primarily granivorous (chapters V and VI). However, although small mammals exhibit broad diets, marked species differences in palatability exist. In general three types of palatability categories are found: a few highly palatable species, many species consumed but not highly preferred and a few species avoided (chapters V and VI). Polyphagous herbivores are likely to reduce the abundance of at least some of their prey species (in particular those most highly preferred) because their numbers are more or less independent of the abundance of these species when they are scarce (Crawley, 1989). However, once these preferred species disappear from the small mammal diet, the impact on individual species in the "consumed" category will be buffered by the presence of similarly palatable species. Only when herbivores are forced to choose between "consumed" and "avoided" species are numbers of the former species likely to be reduced significantly.

233 c) Starvation tolerance Herbivore population growth will cease at higher feeding rates for starvation sensitive than for starvation tolerant species (Crawley, 1983). Therefore, the susceptibility of a herbivore to starvation will determine its ability to reduce prey items to local extinction. Small mammals tend to be buffered from starvation by both behavioural and physiological adaptations. Their polyphagous nature and their ability to store energy both indirectly in the form of fat reserves and directly through caching food items during periods of food abundance, enable them to be independent of short term reductions in food supply. Over the longer term, changes in intestinal length and composition suggest greater tolerance of reduced food quality (Hansson, 1985). Finally, when deprived of food, small mammals may even enter torpor to conserve energy (Walton & Andrews, 1981). Theory predicts that small mammals have potential to cause a great reduction in plant abundance while enabling higher equilibrium densities of their own populations to persist (Crawley, 1983). d) Numerical response The ability of predators to respond to local changes in food abundance will depend on the relative strength of the stimulus, the mobility of the predator and its reproductive rate of increase. Experimental food additions to habitats containing small mammals almost always lead to an increase in local small mammal population density for both voles (reviewed in Taitt & Krebs, 1985) and mice (Fordham, 1971; Flowerdew, 1972; Taitt, 1981). These increases are initially rapid (a few days) primarily due to immigration of neighbouring small mammals into the experimental area. Over the longer term, increased food abundance may lead to increased reproduction. The polyestrous pattern of small mammal reproduction, high rates of conception, large litter sizes and short gestation and growth periods provide small mammals with a production potential unequalled among other homeothermic vertebrates (Bourli6re, 1975). This enables small mammals to respond rapidly to variations in natural food supplies (Flowerdew, 1985). The degree by which small mammal populations are limited by food rather than other factors will determine the opportunities for long term changes in rodent numbers in response to long term changes in food supply. The food addition experiments quoted above, while increasing numbers in

234 the short term were frequently unable to maintain higher population densities in the long term. In general, it is thought that while granivorous small mammal populations, such as thoseA. sylvaticus, of may be food limited, folivorous species are believed to be predator limited (Hansson, 1987). This implies that granivorous species are likely to be able to mount a more effective numerical response to changes in food density. e) Functional response Few data exist as regards the functional response of small mammals to changes in prey numbers, and those that do exist are based on laboratory studies. Studies which have focused on the rodent response to changes in the abundance of a single prey (groats, Muetzelfeldt, 1975; willow shoots, Lundberg, 1988) have revealed Type II functional responses, whereas in two prey systems, Type III functional responses have been found (pupae and dog biscuits, Holling, 1959). In the field, where rodents respond to several prey species, the functional response is most likely to be a sigmoid Type Illresponse. Because of suchclear-cut density dependence, predation of this kind is widely recognized as having the potential to regulate prey populations (Hassell & May, 1986). Most interest in the consequences of different functional responses to predator prey interactions has focused on their influence on the stability of the interaction (Hassell, 1978; Crawley, 1983). In general, sigmoid functional responses of polyphagous predators are likely to be strongly stabilizing though only in systems where the individual predator-prey links are themselves stable (Hassell, 1978). In order to assess the potential impact of particular functional responses on prey populations estimates of three parameters are needed: the minimum prey density exploitable, the predator searching efficiency and the prey handling time. The minimum prey density exploitable determines to what extent a predator can reduce prey abundance, the lower the value, the greater the impact. Estimates from the seed predation study reveal that small mammals may remove considerable numbers of singly placed surface seeds, indicating that they have potential to reduce surface seed numbers completely, whether a similar pattern exists for seedlings is unknown.

235 The prey handling time determines the prey density at the asymptote of Type II and III functional responses. The lower the asymptote (the higher the handling time), the greater the probability that prey may escape predator regulation. Prey handling time depends on the time spent pursuing and subduing individual prey items, the time spent eating each prey and any time spent resting or cleaning as a result of feeding (Hassell, 1978). For small mammals, at high seed or seedling densities, due to the high speed of prey processing, the most important limiting factor to prey exploitation will be satiation. The food limited nature of granivorous rodents, combined with the large numbers of grassland seeds that can be consumed by a single individual, suggest that satiation occurs only occasionally and that for the most part rodents are foraging beneath this asymptote (chapter V). The searching efficiency of a predator determines how rapidly the curve approaches this upper asymptote. The higher the searching efficiency the greater the impact on prey populations (Crawley, 1983). Searching efficiency is a function of the predator’s reactive distance, the speed of movement of the predator and of the prey and the proportion of attacks that are successful. The stationary nature of seeds and seedlings and their frequent death when attacked by highly mobile small mammals, implies that the reactive distance of small mammals may be the best indicator of their searching efficiency. The powers of seed detection exhibited by small mammals has previously been discussed (chapter V) and it is likely that small mammals have a relatively high searching efficiency. This is supported by the steepness of functional response curves found for small mammals (Holling, 1959; Muetzelfeldt, 1975; Lundberg, 1988). In conclusion small mammal seed and seedling predators, due to their high energetic requirements, ability to resist starvation and respond to local variations in prey density are likely to exert a strong influence on their preferred prey items. The high asymptote and steep slope of small mammal Type HI functional response curves indicate that not only will small mammals be able to regulate prey populations but where populations are regulated they may be maintained at low abundances. Whether such opportunities exist in grassland communities is discussed below.

236 8.2 The role of small mammal seed and seedling predators in grassland communities It is widely recognized that a number of factors, both abiotic and biotic, influence the structure and species composition of grassland plant communities (Grubb, Kelly & Mitchley, 1982; Crawley, 1986b; Fowler, 1988). The influence of rodent seed and seedling predation on plant recruitment relative to these other factors has previously been discussed (chapters V, VI and VII). Rather than reiterate these conclusions, the present discussion focuses on the broader aspect of the role of small mammal seed and seedling predation may have in community patterns of grassland plants. The suggestion that separation in niche space could be the key to coexistence of plant species (Grubb, 1977; Harper, 1977; Newman, 1982; Tilman, 1986) has recently been questioned by the finding that large niche overlaps may occur amongst coexisting adult plants in grassland (Mahdi,et al., 1989). However, this latter study, as the authors are aware, omitted to examine niche separation at the phase of regeneration. A number of theoretical models suggest that species niche differentiation at this stage may be sufficient to explain plant species coexistence (Skellam, 1951; Fagerstrdm & Agren, 1979; Newman, 1982). Experimental examination of species differences in regeneration requirements have failed to find sufficient between species variation to explain coexistence (Silvertown & Wilkin, 1983). However this last study failed to examine an important source of species variation - that of susceptibility to predation. Both experimental (chapters V, VI and VII) and theoretical (section 8.1) evidence suggests small mammal herbivores may significantly influence grassland seed and seedling dynamics. The plant species variation in plant susceptibility to rodent predation suggests sufficient differentiation to enable species coexistence. In order to predict the impact of a food limited, polyphagous small mammal herbivore on plant community structure, knowledge of at least two components of the plant-animal interaction is necessary: the degree of frequency dependent feeding by the herbivore and the interspecific competitive dominance of the preferred prey items (Harper, 1969).

237 Both pro-apostatic (Holling, 1959; Soane & Clarke, 1973) and anti-apostatic (Greenwood, Blow & Thomas, 1984; Greenwood, Johnston & Thomas, 1984) selection of prey items has been found in the laboratory for small mammals. This apparent disagreement may be resolved by an examination of the methodological differences in the studies. Holling (1959) used baits which significantly differed in their attractiveness to small mammals, as the relative abundance of the preferred prey item increased so did its rate of consumption by rodents. Greenwood et al. (1984a, b) used baits of similar attractiveness and found that rare prey items were frequently consumed disproportionately more than common prey items. These patterns suggest that small mammals will feed pro-apostatically on preferred prey items, but where differences in prey item attractiveness is slight, rodents may consume more of the rare item because it stands out from the norm (Greenwood, 1985). If predator preferences increase with the relative density of the prey then theoretical models predict greater permissible niche overlap between prey (Roughgarden & Feldman, 1976; Comins & Hassell, 1976), though this is likely only to occur where prey items significantly differ in their relative profitabilities (Greenwood, 1985). Polyphagous small mammals may therefore be capable of maintaining plant species coexistence. Changes in plant species diversity will depend on the whether the preferred plant species is competitively dominant or not. Theory predicts that the former case would lead to greater increases in plant diversity due to greater competitive release (Harper, 1969; Crawley, 1983). Anti-apostatic feeding is strongly destabilising in predator-prey interactions (Caswell, 1978). Any prey item initially relatively rare would be consumed to extinction. Observations of anti-apostatic predation by rodents are likely to be products of the simplified laboratory environments used. Where only two prey "species" are used, the rarer prey will stand out from the uniform environment of alternative prey, however in nature the background environment for small mammals using olfactory cues will be complex, and rare cues, unless particularly preferred or avoided, will not stand out. Additionally, small mammals may show no frequency dependent selection of prey items. This feeding behaviour is often associated with plants that are so attractive that herbivores eat them

238 whenever they are found (Crawley, 1989). Such plant-herbivore interactions are dynamically unstable often leading to complete exclusion of the plant species from the plant community (Crawley, 1983). A small mammal example of such an interaction is that of voles feeding Solidagoon virgaurea in Lapland (Andersson & Jonasson, 1986). While the above discussion has focused on equilibrium models of plant-herbivore interactions, the demography of small mammals enables them to have non-equilibrial influences on plant communities, through their temporal fluctuations in numbers (chapter III) and heterogeneity of their spatial distribution (chapter IV). Caswell (1978) states "it seems entirely possible that a fluctuating predator population could provide a varying environment on precisely the right time scale to keep competition from reaching its equilibrium". Plant coexistence mediated by temporal recruitment fluctuations may be due to a storage effect (Warner & Chesson, 1985). If adult plants survive well, an occasional favourable recruitment can sustain population numbers over long periods. For example, periodic crashes in small mammal numbers (i.e. spring 1987, chapter III) may enable species which normally do not recruit in the presence of small mammal predation to become established. However, this storage effect due to predator release is only likely to work for strongly competitive species which are highly susceptible to predation. Seed and seedling experiments indicated that small mammal predation pressure may be significantly spatially heterogeneous (chapters V and VI). This spatial heterogeneity, if maintained for a relatively long period with respect to plant species longevities, may enable competing species to coexist (Caswell, 1978). The consistency in small mammal spatial distribution between years in the meadow site (chapter IV) is suggestive that rodent spatial heterogeneity may be a source of predator mediated plant coexistence. However, focusing on the role small mammal seed and seedling predators play in maintaining vegetational diversity ignores the influence adult plants may have on plant regeneration. For plant species which rely entirely on seed and seedlings for regeneration, adult longevity will determine the time scale over which variations in recruitment will be translated into changes in the adult plant

239 abundances. In an annual grassland, seed and seedling predator mediated variations in species recruitment will be reflected in the following generation of adults. At the other extreme, in grasslands dominated by long lived herbaceous perennials, the time scale over which predator mediated variations in species recruitment will be observed in changes in the adult population will be determined by the frequency of disturbance (including adult mortality). Assuming predation pressure remains relatively constant, variations in species recruitment to the adult population may only be observed after several years. Although equilibrium and non-equilibrium models of predator mediated plant species coexistence are not mutually exclusive (Caswell, 1978), a storage effect is only likely to lead to coexistence in communities dominated by species with low adult mortality, age dependent fecundity and high reproductive rate (Warner & Chesson, 1985). The role of a storage effect in grassland species diversity is therefore probably limited to grasslands dominated by long lived perennials. In summary, small mammal seed and seedling predators have the capacity to maintain plant species diversity in grasslands by both equilibrial and non-equilibrial mechanisms. However, detecting small mammal effects in grassland communities is likely to be difficult. In perennial grasslands, the long time scale over which changes in plant species composition are likely to occur would restrict the usefulness of simple exclosure experiments, unless combined with perturbations increasing adult mortality. Nevertheless, interpreting results from such perturbations may be particularly hard. For example, if coexistence is due to an equilibrial mechanism, such as frequency dependent consumption of seeds and seedlings of a dominant plant species, artificially increasing the mortality rate of adults may increase plant species diversity. Whereas if coexistence is due to a storage effect increasing adult mortality should lead to a loss of species from the community (Warner & Chesson, 1985). These problems are less serious in plant communities dominated by annuals. Clearly, examining the role of predation in plant species coexistence will not only require long term field perturbations but also an understanding of the predators feeding behaviour and preferences, temporal and spatial demography, as well as the competitive ability of the plants studied. These

240 requirements present a formidable task for plant ecologists. Nevertheless, small mammals are sufficiently ubiquitous and exhibit sufficient traits that, where they do occur, their influence on plant community structure and composition should not be ignored.

"We simply do not know whether it is the direct density- and frequency dependent interactions between higher plants coupled with physical heterogeneity in the environment and mediated by dispersal phenomena that account for the greater part of natural vegetation - in which case all these other phenomena o f.. predation .. are just noise in the system. However it may be that, in the majority of natural communities it is these.. predators .. that are the real determinants of community structure. It may be that it is competitive interactions between higher plants that are the forces that confuse the picture." John L. Harper (1988)

241 CHAPTER IX GENERAL CONCLUSIONS a) A two year live trapping study of small mammal populations in two contrasting grassland sites revealed Apodemus sylvaticus to be the most abundant species b) Populations of A.sylvaticus were temporally variable. Only in grassland associated with woodland margins were temporal fluctuations in abundance predictable and reflected the annual cycle of abundance commonly found for this species in deciduous woodlands. In open grasslands, the lower population density led to erratic temporal dynamics reflecting unpredictable periods of local extinction and recolonisation by immigrants. c) Populations ofA. sylvaticus were spatially variable. The degree of spatial heterogeneity in small mammal abundance in a particular site was a function of the degree to which available cover was limiting and the rodent population density. Where cover was limiting, small mammal distribution became significantly less aggregated at higher densities. This finding suggested that S-model interpretations of small mammal dispersion are probably inappropriate. d) A field based examination of seed predation on 19 grassland species revealed: i) Small mammals were the major source of seed loss in both sites. ii) Predation losses were density-dependent. Higher seed densities suffered proportionally greater seed losses than low seed densities. However, the degree of density dependence was found to be a function of the seed size. iii) Burial of seeds reduced predation. Buried seeds were less often exploited by small mammals than surface seeds, though the effectiveness of burial depended on seed size. At high seed densities, not only were large seeds more frequently found by small mammals but were also exploited more efficiently than clumps of small seeds. At low seed density only large seeds were exploited when buried.

242 iv) Predation of buried seeds by small mammals varied with the abundance of alternative foods. This led to buried seed removal varying with season, being less in spring, and between sites being in general less in the grassland. v) Seed loss was spatially heterogeneous. This appeared to reflect the spatial distribution of small mammals (see c). e) Seed species susceptibility to rodent predation was correlated with the type of seed bank possessed by that species. In general, species with Type II seed banks appeared most susceptible to predation as a result of their large seed size and limited ability to satiate local rodent seed predators. f) A field based examination of seedling predation of 17 grassland species revealed: i) Small mammals and molluscs were the major seedling predators in both sites. ii) Rodents appeared to select seedlings primarily with respect to their size. iii) Between site differences in small mammal attributed seedling loss reflected differences in the spatial distribution of rodents, while seasonal differences in such loss reflected temporal changes in rodent abundance. iv) Where rodents and molluscs had overlapping spatial distributions their effects appeared to be additive due to their different species preferences. In addition, it was hypothesized that rodents would have an overall greater impact due to their greater mobility and more severe damage inflicted on seedlings by their feeding. g) A one year exclosure experiment which followed the survivorship of 21 grassland species found that where rodents had access to plants, they significantly reduced plant survivorship, more so than molluscs or ground arthropods. There was little evidence to suggest that plants were able to fully compensate for predation losses, since none of the herbivore groups examined appeared to influence mean plant biomass, number of tillers or size hierarchies of the plants studied. In general, plant survivorship patterns were consistent with predictions from seed and seedling predation studies, though with more weight on the latter. The study revealed that extrapolation to plant survivorship from seed predation losses may lead to erroneous conclusions, and even when seedling predation is additionally studied, caution should be taken in extrapolating from these results to patterns of plant demography.

243 h) Both experimental and theoretical evidence suggests small mammals may exert a significant impact on seed and seedling numbers of certain species. The frequency dependent nature of rodent feeding, and both the spatial heterogeneity and temporal fluctuations in rodent abundance imply small mammals may play a significant role in grassland vegetational diversity.

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