Changes in diet resource use by elephants, Loxodonta africana, due to changes in resource availability in the Addo Elephant National Park.

by

Jana du Toit

Submitted in fulfilment

of the requirements

for the degree of

Magister Scientiae

in the Faculty of Science

at the

Nelson Mandela Metropolitan University.

2015

Supervisor: Prof G. I. H. Kerley Co-supervisor: Dr. M. Landman

DECLARATION

I, Jana du Toit (student number: 214359328), hereby declare that the dissertation for the qualification of Magister Scientiae (Zoology), is my own work and that it has not previously been submitted for assessment or completion of any postgraduate qualification to another University or for another qualification. Faecal samples and forage availability estimates were collected by Dr. M. Landman and her team. Diet quality analysis was done by CEDARA Feed Laboratory, and DNA metabarcoding was done by Dr. P. Taberlet and his team at the Labortoire d’Ecologie Alpine.

J. du Toit

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ACKNOWLEDGEMENTS

I would like to express my deepest gratitude and appreciation to the following people, without whom the completion of this dissertation would not have been possible:

This study was funded by a bursary through Prof. Graham Kerley, for which I am deeply thankful. I’d also like to thank SANParks for the opportunity to work in the Addo Elephant National Park, as well as the Mazda Wildlife Fund for providing transport.

To my supervisors, Prof. Graham Kerley and Dr. Marietjie Landman, thank you for the opportunity to work on this project, your assistance, support and sharing your knowledge with me. Your strive for excellence motivated me throughout this study.

My sincerest thanks to Dr. Marietjie Landman, for allowing me to work on the collected faecal samples and forage availability estimates, the CEDARA Feed Laboratory for the diet quality analysis, and Dr. Pierre Taberlet and his team at the Labortoire d’Ecologie Alpine, for analysing the diet using DNA metabarcoding.

To my friends, especially Tiffany Bell, who supported and assisted me throughout this study – you truly made the tough times better.

To my parents, Deon and Marica, thank you for all the sacrifices, love and support, and for allowing me to pursue my passion, albeit far from home. To my sisters, Jandi and Babet, thank you for your unwavering emotional support, and constant encouragement. Without my family this would have been a futile attempt, I truly thank each one of you.

“If you seek knowledge of the creatures of the earth, come close to Him

who created all things, and He will give you enlightenment” anon.

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CONTENTS Declaration i Acknowledgements ii Contents iii Abstract v Chapter 1 1 General introduction 1.1 Introduction 1 1.2 How to choose what to consume? 1 1.3 Theory that predicts diet choice 2 1.4 How can diets shift? 3 1.5 How do we measure diet shifts? 4 1.6 Foraging strategies of herbivores 6 1.7 Elephants as model herbivore 7 1.8 Problem statement, aims and objectives 8 Chapter 2 11 General description of study sites 2.1 The Main Camp and Colchester sections 12 2.1.1 Site description, topography and geology 12 2.1.2 Climate 13 2.1.3 Vegetation 13 2.1.4 Elephant population history and elephants impact case study 15 Chapter 3 17 How does the diet breadth and preference of elephants change with an increase in resource availability? 3.1 Introduction 17 3.1.1 Elephants diet flexibility 17 3.1.2 Elephants impacts on vegetation 19 3.1.3 Hypotheses and aims 19 3.2 Methods 22 3.2.1 Sampling approach 22 3.2.2 Sample collection 23 3.2.3 Sample digestion 23 3.2.4 Microhistological analysis of diet 23 3.2.5 Forage availability estimates 24 3.2.6 Statistical analysis 24 3.3 Results 27 3.3.1 Sampling efficiency 27 3.3.2 Relative forage availability 27 3.3.3 Diet composition 28 3.3.4 Diet preference 31 3.3.5 Plant species vulnerable to elephants herbivory 37 3.4 Discussion 38 3.4.1 Does the diet breadth and preference of elephants change with 38 an increase in resource availability? 3.4.2 Elephants learning foraging behaviour 39 3.4.3 Implications of elephants herbivory 39 3.4.4 Contextualising the study 42 3.4.5 The way forward 44

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Chapter 4 45 How does the quality of diet change with an increase in resource availability? 4.1 Introduction 45 4.1.1 Determinants of diet quality 45 4.1.2 Hypotheses and aims 47 4.2 Methods 48 4.2.1 Techniques in determining diet quality 48 4.2.2 Diet quality composition 49 4.2.3 Sampling approach and procedure 50 4.2.4 Statistical analysis 51 4.3 Results 51 4.4 Discussion 53 4.4.1 Diet quality requirements of elephants 53 4.4.2 Maintaining diet quality in the Main Camp section 55 4.4.3 Drivers of change in the diet of elephants 55 4.4.4 Consequences and implications of elephants being able to 57 maintain diet quality 4.4.5 Contextualising the study 57 Chapter 5 58 Comparing microhistological analysis to DNA metabarcoding of faeces to describe the diet of elephants. 5.1 Introduction 58 5.1.1 Techniques to determine diets of herbivores 58 5.1.2 Hypotheses and aims 60 5.2 Methods 60 5.2.1 Sampling approach 60 5.2.2. Microhistological analysis 60 5.2.3 DNA metabarcoding 60 5.2.4 Statistical analysis 61 5.3 Results 62 5.4 Discussion 64 5.4.1 Comparison of the two techniques 64 5.4.2 The way forward 66 Chapter 6 68 Summary and concluding remarks 6.1 Synthesis of results 68 6.1.1 How the diet, preference and diet quality of elephants changes, 68 with an increase in resource availability. 6.1.2 Comparing the diet of elephants using microhistological analysis 71 to DNA metabarcoding of faeces 6.2 Contextualizing the study 72 6.3 The way forward 73 6.4 Conclusion 74 References 76 Appendix 1 The proportion (± SD) of elephants diet for the Principal Diet Items 96 indicating significant preference or avoidance for the Main Camp and the Colchester sections. Appendix 2 Rainfall (mm) recorded in the Addo Elephant National Park during 100 January 2007 to April 2008 for the study period (June 2007 to April 2008) of de Klerk (2009). Appendix 3 The method used to in DNA metabarcoding for identification of the 101 diet of elephants in the Addo Elephant National Park during February 2014. Appendix 4 The proportion (± SD) of plant families identified in the diet of 102 elephants using Microhistological analysis and DNA metabarcoding.

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ABSTRACT

Animals are restricted in their diets by several factors, most notably the availability and quality of resources. Variation in resource availability causes herbivores to shift their diets seasonally and spatially. Elephants (Loxodonta africana), are known to have extensive impacts on plant communities, altering ecosystem functioning and causing a decline in biodiversity. In enclosed areas, these impacts are increased leading to a decline in resource availability and presumably resource quality. In the Addo Elephant National Park, the Main Camp section has a history of high elephant impacts and therefore reduced resource availability. Whereas, the recently added Colchester section has greater resource availability, due to the absence of elephants in this section since the fencing of the Park. This study investigated the changes in diet (diet breadth, preference and diet quality) of elephants due to an increase in resource availability. Three alternative hypotheses were contrasted: 1) elephants as generalist foragers, 2) elephants as optimal foragers, or 3) elephants learning foraging behaviour. Using microhistological analysis, the diets of elephants were described over five sampling periods (August 2010 – February 2014) in both sections. Forage availability was estimated using a modified line-intercept method, and was used to determine changes in preference by relating forage availability to use. In the Colchester section the diet breadth of elephants increased, and was coupled with a high initial variation between the diets of elephants, which decreased in subsequent sampling periods. This supported the elephants learning foraging behaviour hypothesis. However, there was no increase in diet preference by elephants in the Colchester section, which supported the elephants as generalist foragers hypothesis. There was also no difference in the diet quality of elephants in the Main Camp and Colchester sections, which did not support any of the three hypotheses. The elephants learning foraging behaviour hypothesis is proposed to be the link between the alternate two hypotheses, and given enough time, either of the two could be supported. The lack of difference in preference and diet quality between elephants in the Main Camp and Colchester sections is hypothesised to be due to the population level (not measured for individuals) at which these were measured. Microhistological analysis of faeces was used to describe the diet of elephants, which was compared to the diet described by DNA metabarcoding. Microhistological analysis is a traditional, favoured technique used in describing the diet of wild herbivores, whereas DNA metabarcoding is a relatively new and untested technique. These two techniques have not yet been compared in the diet of megaherbivores. Results indicated that microhistological analysis identified significantly more grass in the diet of elephants, than DNA metabarcoding did, which was expected as previous studies also found overestimation of grasses. Microhistological analysis identified more plant families in the diet of elephants, than DNA metabarcoding. Most of the differences between the two techniques can be attributed to the difference in taxonomic resolution, which was due to the lack of a complete reference collection for DNA metabarcoding. Although either of the two techniques can be used to describe the diet of elephants, the most reliable results would be obtained when using both techniques. The findings of this study suggest that due to the high initial variation between the diets of elephants, with an increase in resource availability, the impacts will also initially be highly varied. This suggests that identifying plant species to monitor elephants impacts initially will be difficult. However, important plant species, or those known to be vulnerable to elephants impacts should be carefully monitored initially and monitoring should not only occur annually, but also seasonally.

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Keywords: African elephant (Loxodonta africana), diet, diet preference, diet quality, diet shift, DNA metabarcoding, microhistological analysis, resource availability, Subtropical Thickets.

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CHAPTER 1 GENERAL INTRODUCTION

1.1 Introduction Herbivores are restricted in their diets by several factors (Stephens & Krebs 1986), most notably the availability and quality of resources. Optimal foraging theory predicts that herbivores feed to maximise profitability, which would result in increased fitness (MacArthur & Pianka 1966; Pyke et al. 1977). Herbivores employ a large variety of foraging strategies depending on their body size, digestive physiology and morphology (Owen-Smith 1988).

Seasonal variation in resource availability causes herbivores to shift their diets, due to changes in availability and quality, presumably, to maximise profitability. By quantifying the diet and diet preference, these diet shifts can be determined. Elephant herbivory is known to play an important part in structuring plant communities (e.g. Stuart-Hill 1992; Conybeare 2004; Kerley & Landman 2006; Kerley et al. 2008; Landman et al. 2008). It is therefore important to understand how their diet responds to a change in resource availability and if a diet shift occurs. Furthermore, it is not known whether a diet shift leads to an increase in diet quality. This dissertation aims to address this gap in the current knowledge.

1.2 How to choose what to consume? The diets of animals and herbivores in particular, are constrained by several factors: resource availability and quality, toxins within the forage, the nutrients required, ingestion, feeding morphology, digestion and excretion rates and time available to forage (Stephens & Krebs 1986). The choice by herbivores to consume forage, therefore, depends upon the costs and benefits associated with the forage (Krebs 1978; Stephens & Krebs 1986), and are based on the following (Stephens & Krebs 1986): 1. “Choices: what is available? 2. Currency: how are these choices evaluated? (costs versus benefits) 3. Constraints: what limits these choices?”

Herbivores have to make diet choices at several hierarchical levels; i.e. landscape, habitat, patches, plant species and plant parts (Krebs 1978). These choices that are assumed to be made by animals are not inferred to be conscious, but rather to reflect what has been driven for by natural selection (Stephens & Krebs 1986). Herbivores must therefore choose diet items on several levels depending on

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availability of resources, the costs and benefits and what limits their choices of diet resources (Krebs 1978; Stephens & Krebs 1986).

For example: at the plant species level, herbivores have to choose between various available plant species, based on the cost of finding and handling and the benefit of consuming forage (Krebs 1978; Stephens & Krebs 1986). This choice may further be limited by the distribution of forage and risks, e.g. predation, associated with obtaining a certain plant species (Krebs 1978; Stephens & Krebs 1986). Herbivores are assumed to evaluate these choices of plant species by their nutrient composition, as well as physical and chemical defences (Krebs 1978). By selecting or preferring a specific plant species, herbivores stand to gain an increase in diet quality, but may have to deal with secondary compounds or toxins (Stephens & Krebs 1986).

1.3 Theory that predicts diet choice To predict the foraging behaviour and choice of forage, the Optimal Foraging Theory (MarArthur & Pianka 1966) predicts that herbivores aim to maximise the profitability of the items consumed (Pyke et al. 1977). The currency used may differ between species, but is usually considered to be either energy (Schoener 1971), or nutrients (Westoby 1974) per unit time. Efficient foraging, therefore, increases profitability in the form of energy or nutrient uptake (Pyke et al. 1977). It is assumed that natural selection has resulted in animals maximising foraging efficiency as this would increase fitness (Pyke et al. 1977).

Optimality Theory predicts that animals are able to distinguish between items of different profitability, and would choose the most profitable option (Krebs 1978). The net gain (benefit) by an animal is dependent on the cost associated with obtaining forage, i.e. the time it takes to find food items, and the handling time associated with consuming the items (Krebs 1978). Most foraging animals fall within one of two categories within the optimal foraging theory framework: time minimisers or profit maximisers (Schoener 1971). Time minimising foragers aim to obtain a fixed amount of resources within the least possible time, whereas profit maximisers have a fixed amount of time to obtain the maximum nutrients possible (Schoener 1971). To determine which category an animal falls into is dependent on the factors influencing the foraging strategy of the animal, e.g. nutrient requirements, predation, mate finding or guarding (Pyke et al. 1977). This may therefore vary continuously due to an animals’ biology and the time scale at which it is measured and changes in the factors influencing foraging efficiency (Pyke et al. 1977).

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The optimal diet of an animal has three properties: animals should specialise in preferred food types (i.e. the choice to consume a food type is irrespective of its abundance); secondly, as less preferred items are included in the diet, diet breadth should increase (i.e. an increase in resource availability should lead to a decrease in diet breadth). And lastly, a food type should either be completely included in the diet and should be eaten whenever it is encountered, or completely excluded and avoided whenever it is encountered (Pyke et al. 1977). However, due to changes in resource availability and quality this is not always possible, and diet shifts may therefore occur. A diet shift is defined as a deviation from the initial diet, with a change in diet breadth and preference either shifting up or down.

1.4 How can diets shift? Within the Optimal Foraging Theory framework it is predicted that animals will aim to maximise profitability (Pyke et al. 1977). Herbivores are commonly divided into three feeding categories, namely grazers, browsers or mixed feeders, they may forage selectively at plant-species, or plant-part levels in order to achieve an adequate diet (Farnsworth & Illius 1998). The presence of secondary compounds (e.g. tannins and terpenoids (Milton 1979; Robbins 1983; Owen-Smith 2002)), and changes in resource quality and availability (e.g. seasonal changes), cause difficulty for herbivores in maximising profitability and obtaining the optimal diet. Megaherbivores, especially, face the problem of maximising profitability as they are adapted to bulk feed on relatively low quality resources (Owen-Smith 1988).

Variations in resource availability, quality and secondary compounds are dependent on seasonality, rainfall and soil quality of the area (Sinclair 1974; Coe et al. 1976; Owen-Smith 1990; Seydack et al. 2000; Owen-Smith 2002), as well as the type of plant species or growth form. Nutrient rich soils, high rainfall and early growing seasons are generally associated with high forage quality and increased resource availability (Sinclair 1974; Grant et al. 1995; Owen-Smith 2002). This variation in resource availability and quality causes animals to shift their diets: mixed-feeders, such as elephants (Loxodonta africana) , tend to graze more when an open grassy habitat are available or during the wet season as fresh grass is available (Williamson 1975; Guy 1976; Skinner & Smithers 1990; De Boer et al. 2000; Codron et al. 2006). Whereas elephants shift to using browse in more woody areas such as woodlands, forests and savannas, or in dry seasons when the grass availability and quality is reduced (Williamson 1975; De Boer et al. 2000). Some herbivores, such as Black rhinoceros (Diceros bicornis), also shift their diets in the presence of competitors (Landman et al. 2013). By shifting between grass and browse when

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either does not offer sufficient nutrients or is not available, e.g. due to seasonal variations in vegetation availability and quality (Guy 1976; Owen-Smith 1988; Codron et al. 2006), phenological influences (Olivier 1978; Langer 1984), or in the presence of competition (Landman et al. 2013), herbivores are mostly able to attain a diet that fulfils their nutritional requirements (Guy 1976; De Boer et al. 2000; Codron et al. 2006; Codron et al. 2011) which may maximise profitability (McNaughton & Georgiadis 1986).

1.5 How do we measure diet and diet shifts? Many procedures exist for the purpose of quantifying the composition of animal diets: direct observation, utilization techniques, fistula techniques, n-alkenes, stomach content examination, and faecal analysis (Sparks & Malechek 1968; Bjugstad et al. 1970; Holechek et al. 1982), as well as isotope analysis (Codron et al. 2011) and DNA metabarcoding (Taberlet et al. 2012a). Each of these approaches has limitations and biases, but also holds advantages (Holechek et al. 1982), and these are briefly covered below.

Direct observation techniques entail observing animals as they feed on plants (Bjugstad et al. 1970). This technique is relatively simple and requires minimal equipment, but plant species identification and quantification poses a problem (Bjugstad et al. 1970). It is also unsuitable for nocturnal, dangerous, rare or shy animals or observing animals in dense vegetation (Bjugstad et al. 1970), due to the difficulty of observing them.

Utilization techniques involve inspection of plant use after an animal has fed. This technique is quick in providing information on plant species and frequency of use. However, the weathering of plants (e.g. breaking, or weather damage), trampling, use by other animals and regrowth of forage presents problems for accurate estimations through this technique (Holechek et al. 1982).

Fistula techniques involve the fistulation of animals, inserted into either the oesophagus or rumen and are generally considered unsuitable for wild animals, as considerable care thereafter is required, and as materials need to be sampled. This technique is also costly, requires a considerable amount of time, labour and expertise and a large number of animals are required as precision in determining the diet is low (Holechek et al. 1982).

Stomach content examination (macro- or microscopic) is generally restricted to large populations of animals or species that are routinely culled, as this technique

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requires animal sacrifice (Vavra & Holechek 1980). Another limitation of this technique is its accuracy, due to the differential digestion of growth forms and identification of partially digested items has proven difficult (Vavra & Holechek 1980).

Faecal analysis (macro- or microscopic) involves the examination of faeces in order to estimate the diet (Holechek & Gross 1982). Faeces are examined either macro- or microscopically and fragments are identified based on a reference collection. This technique holds considerable advantages above other approaches, including that this technique it is useful in comparing diets (Holechek et al. 1982). This technique does however also have disadvantages: it cannot be determined where the foods were consumed and preference indices regarding habitats can therefore not be accurately assigned (Owen 1975; Vavra & Holechek 1980; Holechek & Gross 1982; Holechek et al. 1982). Faecal analysis tends to overestimate the abundance of grasses and to underestimate the abundance of less fibrous growth form, e.g. forbs, in the diet (Vavra et al. 1978; Holechek et al. 1982). Several techniques exist that allows for the reduction of this source of error, e.g. the use of regression equations to correct for the overestimation of fibrous growth forms, subjecting the samples to microdigestion, and in-vitro digestion of collected samples (Vavra & Holechek 1980; Holechek et al. 1982).

The use of stable carbon isotopes allows the pattern of grass:browse in diets of herbivores to be tracked across time (Codron et al. 2011). This is due to the difference in photosynthetic pathways used: most browse and some grasses use C3

(Calvin cycle) photosynthesis, whilst most other grasses use C4 photosynthesis. The ratio of carbon isotopes differ between C3 and C4 plants and can be used to investigate changes in the diet of herbivores over time (Codron et al. 2006; Codron et al. 2011). Carbon isotope analysis is limited to the broader-scale detail of the diet (i.e. grass:browse proportions), when compared to the finer scale detail of traditional techniques used in identifying the diets of herbivores (Codron et al. 2011).

DNA metabarcoding is a relatively new technique used in identifying species from environmental samples (e.g. Stoeckle 2003; Valentini et al. 2009a; Hajibabaei et al. 2011; Taberlet et al. 2012a). DNA metabarcoding uses standard DNA barcodes to identify species, and aims to provide speedy, yet accurate results (Hebert & Gregory 2005). However, DNA metabarcoding is limited by its dependence on PCR (Polymerase Chain Reaction), and a taxonomic reference databases (Taberlet et al. 2012b), as well as the high costs involved.

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Many studies assume that a diet shift occurs without quantifying the diet preference or how it changes, but this may not be the case as animals may simply be responding to changes in resource availability, not necessarily showing a change in preference. Diet preference is defined as the likelihood that a resource will be selected if offered on an equal basis with other resources (Johnson 1980). Diet preference is not static in nature, but is rather a dynamic process which is influenced by forage quality, availability and the availability of alternative resources (Newman et al. 1995; Provenza 1995). In its simplest form, preference or avoidance is assessed by relating the relative availability of potential forage items to the use of these items (Petrides 1975). Forage items that are used in greater proportions than their relative estimated availability are considered preferred, whereas forage items used in lower proportion than their estimated availability are considered to be avoided (Petrides 1975).

Various preference indices (e.g. forage ratio, electivity index, Jacob’s index) have been developed to estimate preference or avoidance of resources (Jacobs 1974; Johnson 1980; Krebs 1989). These indices usually range from -1 to +1; where -1 indicates maximum avoidance and +1 indicates maximum preference (Van Dyne & Heady 1965; Jacobs 1974; Petrides 1975; Johnson 1980). These indices provide an index value that suggests a food item is preferred over another, due to a greater index value, but cannot be statistically tested for significance (Hobbs & Bowden 1982). Confidence intervals may alternatively be calculated for the percentage use relative to the percentage availability of resources (Hobbs & Bowden 1982). Thus, preference is shown for a resource if the percent use is greater than the percent availability, and the confidence interval does not overlap zero. Alternatively, avoidance is shown if percent use is less than the percent availability, and the confidence interval does not overlap zero (Hobbs & Bowden 1982).

1.6 Foraging strategies of herbivores The diet selection of animals is largely based on body size, physiology of the digestive tract, metabolic requirements and functional anatomy: large herbivores are more likely to have a generalist approach in their foraging strategy, as opposed to small herbivores that feed selectively (Demment & van Soest 1985; Owen-Smith 1988). The body size of an animal determines its absolute and relative energy requirements, as well as its ability to extract nutrients from the environment (McNaughton & Georgiadis 1986). Small herbivores must therefore feed selectively due to their high metabolic rates, small gut capacity and relatively fast throughput rates (Demment & van Soest 1985; Kerley et al. 2010). Large herbivores have high

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absolute energy requirements, and must therefore obtain higher quantities of forage (Van Soest et al. 1995). However, they have low mass-specific metabolic rates (Owen-Smith 1998), which requires lower relative energy (per kg body mass) (Demment & van Soest 1985; Owen-Smith 1992). Gut capacity is also influenced by body size, and therefore large herbivores have a greater gut capacity that enables them to ingest larger quantities of forage. The increase in gut capacity also increases the mean retention time and digestive efficiency, which allows lower quality forage to be used (Owen-Smith 1992). This relationship between an increase in body size and tolerance towards lower quality forage is known as the Jarman-Bell principle (Bell 1971; Jarman 1974). Megaherbivores, due to their extremely large size, therefore, show the upper limit of these allometric body scaling relationships (Owen-Smith 1998).

Herbivores use food quality as a guide to gauge digestibility: the cell contents (e.g. sugars, proteins and soluble carbohydrates) indicates the quality of the food item, but must be extracted by breaking down the cell wall (Demment & van Soest 1985). The cell wall requires microbial or mechanical action to be broken down (Van Soest et al. 1995). Small-bodied herbivores (< 20 kg) are not often ruminants, as they cannot afford to delay digestion, due to their high mass-specific metabolic rates and small gut size (Owen-Smith 1992). In contrast, medium-size herbivores maximise digestive efficiency by using fermentation as they are large enough to allow the reticulum to process food (Owen-Smith 1992). Large herbivores, e.g. black rhinoceros and elephants, however, are unlikely to be ruminants, as they have higher absolute energy requirements and must therefore process large quantities of forage (Owen-Smith 1992).Small herbivores feed selectively and may consume only a few plant species, which is high in quality and easy to digest (McNaughton & Georgiadis 1986; Kerley et al. 2009), whereas large herbivores forage on a large variety of plant species that are generally lower in quality and highly fibrous (Owen- Smith 1988). Although elephants feed on and influence a wide variety of plant species, the bulk of their diet is made up of a few species (Kerley et al. 2009).

1.7 Elephants as model herbivores Megaherbivores are defined by Owen-Smith (1988) as plant-feeding terrestrial mammals that in adulthood attain a body mass in excess of one ton. The African elephant (Loxodonta africana) is the largest extant mammalian herbivore, attaining a maximum body mass of more than six tons in males and three tons in females (Owen-Smith 1988; Hall-Martin 1992). Due to their large body size and digestive

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capacity, elephants have very low mass-specific metabolic rates (Owen-Smith 1998), and therefore, show the upper-limit of tolerance towards low quality foods, provided that the quantity is not limited (Owen-Smith 1988; Codron et al. 2011).

With specialised adaptations such as their mobile trunks, tusks and sheer size, elephants are able to access and utilize resources that would otherwise have been inaccessible to them (Owen-Smith 1988). With the help of these specialized adaptations, bark is stripped off trees, trees pushed over to access their roots and the tops of trees are also easily reached (Croze 1972; Barnes 1982). Their tusks and trunks allow for simultaneous foraging, handling and chewing (Croze 1972), which allow elephants to obtain intake rates of up to 2 kg/min in Thicket vegetation (Lessing 2007). Elephants are mixed-feeders, including a wide variety of plant species and plant parts, e.g. bark, roots, twigs and leaves, in their diet (Barnes 1982; Lessing 2007).

Elephants, as hindgut fermenters, have simple stomachs, a cecum, which is not exceptionally large relative to their size, and an uncompartmentalized colon (Owen- Smith 1988). The digestive rate of elephants is relatively fast, but digestive efficiency is low (Owen-Smith 1988; Kerley et al. 2008). Elephants require a low daily food intake relative to their size: approximately 1 - 1.5% dry mass of food daily, per body mass, dependant on age class, sex and season (Owen-Smith 1988). This is equal to over 60 kg dry mass per day or 180 kg wet mass for a fully grown bull (Owen-Smith 1988).

1.8 Problem statement, aims and objectives Elephants influence a large number of ecological processes, e.g. through herbivory, wallowing, path formation, litter production and behaviourally (e.g. by breaking trees) (Boshoff et al. 2001; Kerley & Landman 2006). In addition due to their large size, food requirements and feeding behaviour, elephant herbivory is assumed to be the major mechanism in structuring plant communities (e.g. Laws 1974; Stuart-Hill 1992; Conybeare 2004; Kerley & Landman 2006; Landman et al. 2008). It is therefore essential to investigate and understand the mechanisms influencing elephant herbivory i.e. how elephants use resources, as this may aid in predicting impacts on plant communities (Kerley et al. 2008).

Several studies identify and describe the diet of elephants in a variety of different habitats (e.g. Jarman 1971; Stuart-Hill 1992; Paley & Kerley 1998; Conybeare 2004; Greyling 2004; Guldemond & Van Aarde 2007). However, many studies describe

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the diet of elephants in broad terms only; i.e. growth forms or the proportions of grass to browse (e.g. Koch et al. 1995; Cerling et al. 1999; Codron et al. 2006; Codron et al. 2011). Alternatively, elephant diets are extrapolated from plant-based studies (e.g. Penzhorn et al. 1974; Barratt & Hall-Martin 1991; Midgley & Joubert 1991; Stuart-Hill 1992; Moolman & Cowling 1994; Lombard et al. 2001; Guldemond & Van Aarde 2007), that compare elephant enclosures to areas were elephants have been excluded from. This approach postulates that the differences in plant communities between these areas are due to elephants impacts, in particular elephant herbivory (Kerley et al. 2008; Landman et al. 2008). However, it has been shown by Landman et al. (2008) that several of the impacted plant species were not included in the diet of elephants in the areas where elephants were previously excluded from and, therefore, the differences in plant communities in these areas could not be attributed to elephant herbivory specifically. This highlights the need for studies that quantify elephant diets, instead of inferring their diet from plant-based studies.

Elephant diets vary spatially and seasonally (Codron et al. 2011), but it is not clear how this relates to changes in preference. Optimal Foraging Theory predicts that an increase in diet resources it should lead to a decrease in diet breadth. However, this has not been tested in elephants. Thus, there exists a gap in the knowledge regarding elephants’ diets and preference and how this changes with an increase in resource availability. The need for studies specifically focused on quantifying diets and diet preference, with an increase in diet resources, is thus clearly evident. This is particularly important in South Africa as elephant populations, that continue to expand, are being established in enclosed areas, therefore necessitating the expansion of these areas. These expansions, may lead to increased resource availability, and the exposure of plant communities to elephant impacts. Given the known impacts of elephants on plant communities, it is necessary to predict the impacts following the increase in resource availability, which can assist with limiting and managing the effects of elephants herbivory, where necessary.

The overall study aim is to determine how the diet, and preference of elephants change in response to an increase in diet resources and to test how diet quality responds to this as a consequence, both spatially (between sites) and temporally (over five sampling periods). This study sets out to address the following aims: 1) to quantify the diet of elephants, in order to determine how this changes with an increase in diet resources, 2) to determine how diet preference of elephants responds with an increase in diet resources, 3) to determine how diet quality, as a

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consequence of the change in diet, responds to an increase in diet resources, and 4) to contrast the identified diet of elephants using microhistological analysis and DNA metabarcoding.

This dissertation contains three data chapters, written as individual papers and some repetition should therefore be expected.

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CHAPTER 2 GENERAL DESCRIPTION OF STUDY SITE

This study was conducted in the Main Camp and Colchester sections of the Addo Elephant National Park (AENP), during the period of August 2010 to February 2014. The sites and their features are described in this Chapter.

The Addo Elephant National Park is located at 33°31’S, 25°45’E, within the Eastern Cape Province of South Africa. The Main Camp section is c. 72 km North-East of Port Elizabeth (Fig 2.1). The AENP covers 180 000 ha, stretching from the semi-arid karoo in the north to the coast between the Sundays and Bushmans river mouths, including a marine section. The Main Camp section has been fenced since 1954 and has been expanded extensively (Whitehouse & Hall-Martin 2000; Gough & Kerley 2006; Landman et al. 2012), with the most recent expansion occurring in August 2010. The vegetation occurring in the Main Camp and Colchester sections falls under the Subtropical Thicket of the Albany Centre, which is endemic to the region (Van Wyk & Smith 2001; Vlok et al. 2003). The AENP falls within a semi-arid region of South Africa, receiving < 450 mm of annual rainfall (Stuart-Hill 1992).

Main Camp

Colchester

Port Elizabeth

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Figure 2.1 Map showing the location of the Addo Elephant National Park within South Africa, and the adjacent sampling sites: the Main Camp and Colchester sections.

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2.1 The Main Camp and Colchester sections 2.1.1 Site description, topography and geology The Main Camp section of the AENP spans an area of c. 12100 ha. The adjacent Colchester section is located to the south of the Main Camp section and compromises of c. 12700 ha (Fig 2.1). These sites are very similar, (in terms of the occurring vegetation, topography, geology, and climate) except for the long-term effects, on the occurring vegetation, that elephants have had in the Main Camp section. Extensive habitat modification has been recorded in the Main Camp section (e.g. Penzhorn et al. 1974; Barratt & Hall-Martin 1991; Stuart-Hill 1992; Lombard et al. 2001; Landman et al. 2014), with some areas having been impacted for up to c. 60 years (Whitehouse & Hall-Martin 2000; Landman et al. 2012). The Colchester section presumably has high resource availability, relative to the Main Camp section, as it had not been used by elephants following the fencing of the AENP (Whitehouse & Hall-Martin 2000; Landman et al. 2012); i.e. for at least 56 years.

These sections’ topography is characterised by a series of low undulating hills, rising between c. 40 m.a.s.l. and 360 m.a.s.l. (Paley & Kerley 1998; Mucina & Rutherford 2006). Geologically, the sections are dominated by sandstone and mudstone substrate from the Uitenhage series (Archibald 1955). The soil is typically red-brown granular clay loam soil and is neutral, fine-grained, relatively fertile and rich in humus (Archibald 1955; Hoffman 1989). The Zuurberg limestone plateau dominates the centre of the Main Camp section and is covered by grey calcrete and red-brown aeolian sands (Toerien 1972; Barratt & Hall-Martin 1991). Several natural small pans and waterholes, which are reliant on rainfall, (Paley & Kerley 1998), are scattered throughout these sections. There is no natural permanent surface water in the Main Camp section (Landman & Kerley 2014), but water is provided by 11 artificial water points, which are fed by boreholes (Landman et al. 2012). In the Colchester section, there are four artificial permanent water sources scattered throughout the section, as well as a natural spring (Le Gouvello 2013).

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2.1.2 Climate A mean annual rainfall of 396 mm was recorded between 1961 and 2009 (Fig. 2.2) (at the Citrus Research Station, c. 3 km south-west of the Main Camp section), occurring throughout the year. Rainfall peaks in autumn (March - April) and spring (October – November), but prolonged droughts occur regularly (Hoffman 1989; Barratt & Hall-Martin 1991; De Klerk 2009).

800

600

400

200

Rainfall Rainfall (mm) 0 2010 2011 2012 2013 2014

Years

Figure 2.2 The total rainfall (solid bars) recorded at the Citrus Research Station for the years corresponding to sampling periods (SA Weather Service 2014). Solid line indicates mean annual rainfall recorded between 1961 and 2009.

During the sampling periods of this study (2010 – 2014), most rainfall occurred during March and October, corresponding to the expected peaks in rainfall (Hoffman 1989). Mean daily maximum temperatures range from c. 22°C in the winter to c. 29°C in summer, but temperatures exceeding 40°C are often recorded in summer (Stuart-Hill 1992).

2.1.3 Vegetation The vegetation within the Main Camp and Colchester sections is classified as Subtropical Thicket (Mucina & Rutherford 2006). If undisturbed, it is structurally heterogeneous and diverse and is characterised by dense vegetation consisting of succulent and spinescent shrubs, lianas and woody shrubs reaching 2–4 m (Penzhorn et al. 1974; Vlok et al. 2003; Mucina & Rutherford 2006).

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Main Camp section

Removed fence line

Colchester section

Albany Alluvial vegetation Albany Coastal Belt Algoa Dune Strandveld Coega Bontveld N Kowie Thicket Sundays Thicket 2.5km

Figure 2.3 The six main vegetation types occurring in the Main Camp and Colchester sections of the Addo Elephant National Park (Mucina & Rutherford 2006; Biodiversity GIS 2007). Removed fenceline (August 2010) indicated between sections.

Extensive alteration of the vegetation in the Main Camp section has taken place through the activity and feeding of elephants, leading to a reduction in the biomass and density of the thicket and the disappearance of many plant species (e.g. Aloe africana and Viscum spp.) (Penzhorn et al. 1974; Barratt & Hall-Martin 1991; Stuart- Hill 1992; Lombard et al. 2001; Magobiyane 2006; Landman et al. 2008). The Main Camp section is dominated by Sundays Thicket: the thicket is dense, thorny, evergreen and constitutes a diverse range of plant species dominated by the tree- succulent Portulacaria afra (Spekboom) (Vlok et al. 2003; Mucina & Rutherford 2006). The remaining area is made up of Coega Bontveld, consisting of clumps of low thickets with secondary grassland: Albany Coastal Belt, consisting of grasslands with scattered bush clumps: Albany Alluvial vegetation, consisting of riverine thicket and thornveld, and Kowie Thicket, that is characterised by tall spinescent thicket (Mucina & Rutherford 2006) (Fig 2.3). Dominant plant species include Azima tetracantha, Capparis sepiaria, Gymnosporia spp., Searsia spp., Euclea undulata and Schotia afra. Within both the Main Camp and Colchester sections, old

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agricultural fields (consisting mostly of grasses, dominated by Cynodon dactylon) contribute to the grassland areas (Landman et al. 2008, Landman et al. 2013). The remaining vegetation occurring in the Colchester section is largely intact, due to the absence of elephants since the fencing of the Main Camp section in 1954 (Whitehouse & Hall-Martin 2000; Landman et al. 2008). Sundays Thicket covers more than two-thirds of the Colchester section (Fig 2.3). Coega Bontveld, Albany Coastal Belt and Algoa Dune Strandveld, consisting of dense thickets with stunted trees and shrubs, is also present (Mucina & Rutherford 2006) (Fig 2.3).

2.1.4 Elephant population history and elephants impact case study In 1931, the AENP was proclaimed in order to protect the dwindling population of the last 12 elephants in the Eastern Cape (Penzhorn et al. 1974; Whitehouse & Hall-Martin 2000). The original area of 22.7 km2 was set aside for the conservation of these elephants, but due to conflicts with farmers and lack of fencing, the population increased at a very slow pace (Whitehouse & Hall-Martin 2000). In 1954 the elephant-proof Armstrong-fence was erected, and thereafter the population grew exponentially (Whitehouse & Hall-Martin 2000). There are currently more than 550 elephants in the Park (South African National Parks Unpubl. data). Throughout most of the history of the Park, the elephants density (1.0 – 4.1 elephants/km2) (Penzhorn et al. 1974; Boshoff & Kerley 1999) has been above the recommended carrying capacity of between 0.25 to 0.52 elephants/km2 (Boshoff et al. 2002) for Subtropical Thicket (Boshoff & Kerley 1999; Boshoff et al. 2002; Kerley & Landman 2006; Landman et al. 2013). Prior to the opening of the Colchester section, the elephant density in the Main Camp section was 3.97 elephants/km2 (c. 480 elephants) and is currently 2.21 elephants/km2 (South African National Parks Unpubl. data) in the Main Camp and Colchester sections combined.

The high density of elephants within the Park has led to areas, especially around permanent water sources, being heavily impacted by elephant (Landman et al. 2012). The impacts that elephant have on a habitat, affects the quantity and quality of forage (e.g. van Wyk & Fairall 1969; Penzhorn et al. 1974; Barratt & Hall-Martin 1991; Stuart-Hill 1992; Lombard et al. 2001; Lechmere-Oertel et al. 2005a; Landman et al. 2008). In the Main Camp section, this has led to significant declines in soil nutrients (Kerley et al. 1999; Lechmere-Oertel et al. 2005b), plant species richness, biomass and canopy height, volume and density (summarised in Table 2.1) (Archibald 1955; Penzhorn et al. 1974; Barratt & Hall-Martin 1991; Stuart-Hill 1992; Moolman & Cowling 1994; Johnson et al. 1999; Lombard et al. 2001; Magobiyane 2006; Landman et al. 2008). The plant species richness and

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abundance have also been reduced in this section: Lombard et al. (2001) determined that the species richness and abundance of 75 endemic-rich geophytes and low succulents and two indicator species (Viscum rotundifolium and Viscum crassulae) declined exponentially over time as they were exposed to elephants. It has further been established that 168 plant species, are potentially vulnerable to elephants-driven extinction (Johnson et al. 1999; Lombard et al. 2001). This is especially concerning as the succulent thicket vegetation type occurring in the AENP has a low resilience and a slow recovery following impacts (Lechmere-Oertel et al. 2005a), and many of these plant species are only formally conserved within the Park (Johnson et al. 1999; Lombard et al. 2001).

Table 2.1 The documented impacts of elephants on plants and soil in the Addo Elephant National Park. Impacted component Response to elephants Reference Soil Decline in soil nutrients Kerley et al. (1999) Plant biomass Decreased Penzhorn et al. (1974) Plant canopy height and Decreased Barratt & Hall-Martin (1991) cover Stuart-Hill (1992) Plant volume Decreased Barratt & Hall-Martin (1991) Stuart-Hill (1992). Plant species richness Decreased Archibald (1955) Penzhorn et al. (1974) Moolman & Cowling (1994) Johnson et al. (1999) Lombard et al. (2001) Aloe spp. Extirpated Penzhorn et al. (1974) Barratt & Hall-Martin (1991) Johnson et al. (1999) Lombard et al. (2001). Mistletoes (Moquinella Extirpated Penzhorn et al. (1974) rubra; Viscum spp.) Midgley & Joubert (1991) Lombard et al. (2001) Magobiyane (2006) Geophytes and low Decline in species richness, Moolman & Cowling (1994) succulents density and cover Johnson et al. (1999) Lombard et al. (2001)

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CHAPTER 3 HOW DOES THE DIET BREADTH AND PREFERENCE OF ELEPHANT CHANGE, WITH AN INCREASE IN RESOURCE AVAILABILITY?

3.1 Introduction In order to predict the impacts of elephant, it is important to understand the mechanisms behind this and how they play a role in impacting ecosystems (Kerley et al. 2008; Landman et al. 2008). Elephant herbivory plays an important role in structuring plant communities (Laws 1970; Penzhorn et al. 1974; Lombard et al. 2001; Kerley & Landman 2006), and is therefore assumed to be the primary mechanism responsible for impacts on vegetation (Kerley & Landman 2006; Landman et al. 2008). The effect on the diet of elephants, with an increase in resource availability has not yet been tested. The need therefore exists to investigate this, as it will enable us to predict how vegetation will be impacted. The opportunity arose in the AENP to test this: the Main Camp section has an extensive history of elephant impacts (Chapter 2), whereas the Colchester section has not been used by elephants for over 50 years, following the fencing of the Main Camp section in 1954, and therefore has hypothesised higher resource availability, relative to the Main Camp section.

3.1.1 Elephant diet flexibility Due to their large body size, large daily food requirements and robust feeding style, elephants have a broad diet, feeding on a variety of plant growth forms and species (Owen-Smith 1988; Paley & Kerley 1998; Cerling et al. 1999; De Boer et al. 2000; Codron et al. 2006; Kerley et al. 2008; Landman et al. 2008). Elephants are therefore able to influence a large number of plant species directly through herbivory (Kerley & Landman 2006; Kerley et al. 2008; Landman et al. 2008). Elephants are adapted to bulk feeding on plants that are relatively low in quality, but high in abundance, from which the required nutrients have to be obtained (Williamson 1975; Field & Ross 1976; Owen-Smith 1988; Sukumar 2003). Being a non-ruminant poses a challenge, as elephants have to acquire specialised amino acids from their diet and therefore a large variety of plants have to be included in their diet (Sukumar 2003).

Elephants are versatile foragers exhibiting large variations in grass:browse proportions depending on the region, vegetation cover, water availability, soil nutrients, rainfall and season (Williamson 1975; Field & Ross 1976; Owen-Smith 1988; Koch et al. 1995; Cerling et al. 1999; Codron et al. 2006). Grasses may

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contribute 40 – 70% of the diet in the wet season, when leaves and inflorescence are favoured, whereas in the dry season, when the leaf bases and roots are favoured, grasses may contribute 2 – 40% of the diet (Laws et al. 1975; Williamson 1975; De Boer et al. 2000; Codron et al. 2006; Codron et al. 2011). Browse contributes to the bulk of the diet during dry periods, but poses a challenge because of the presence of secondary chemicals (e.g. resins, tannins, terpenoids) (Emlen 1966; Lindsay 1994). It has been hypothesised that elephants as hindgut fermenters are less able, than ruminants, to metabolise secondary compounds, e.g. tannins (Olivier 1978; Langer 1984), which may restrict nutrient availability in consumed forage (Lindsay 1994), or may act as a deterrent causing elephants to reject a specific plant species (Malpas 1977).

By switching between grass and browse in response to seasonal variations in vegetation (Guy 1976; Owen-Smith 1988; Codron et al. 2006), phenological influences (Olivier 1978; Langer 1984) or when either does not offer sufficient nutrients (Owen-Smith 1988; Codron et al. 2006), elephants are able to achieve a diet that fulfils their nutritional requirements (Guy 1976; De Boer et al. 2000; Codron et al. 2006; Codron et al. 2011). This is hypothesised to increase profitability in the form of energy or nutrients (MacArthur & Pianka 1966).

The time constraints associated with the need for elephants to bulk feed implies that elephants should utilize resources in proportion to their respective abundances, thereby adopting a ‘frequency-dependent strategy’ (Laws 1970; Owen-Smith 1988; Codron et al. 2011). Alternatively, elephants may aim to maximise nutrient uptake by showing preference for certain forage items (Laws 1970; Codron et al. 2006; Codron et al. 2011).

Although elephants rely on bulk feeding and a large diet breadth to obtain their nutritional requirements (Laws et al. 1974; Owen-Smith 1988; Kerley & Landman 2006), they are selective feeders at the plant species level (Kerley & Landman 2006; Owen-Smith & Chafota 2012), showing preference towards specific plant species (e.g. the genera Acacia, Azima, Cynodon, Grewia, Portulacaria and Schotia) and rejecting others (e.g. the genera Burkea, Capparis, Euclea and Scolopia) (Barnes 1982; Stokke & du Toit 2000; Styles & Skinner 2000; Kerley & Landman 2006; Owen-Smith & Chafota 2012). This is presumably based on nutritional content or lack of plant defence mechanisms (e.g. secondary compounds) (Barnes 1982; Styles & Skinner 2000; Codron et al. 2006; Codron et al. 2011).

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There has been no prior study done in order to investigate these dietary changes of elephants with increased resource availability. Our lack of understanding regarding the potential changes in the diet of elephant with changes in resource availability is therefore evident. We have no prior knowledge on the potential changes in their diets, or why this may occur i.e. in order to increase diet quality, or as there is more potential food items available. This study is therefore vital in order to better our understanding on this matter.

3.1.2 Elephant impacts on vegetation Elephants are known to influence a large variety of ecological processes (Owen- Smith 1988; Van Rensburg et al. 1999; Matthews et al. 2001; Botes et al. 2006; Kerley & Landman 2006) through several mechanisms (e.g. herbivory, tree pushing behaviour, path formation, seed dispersal, wallowing and several others) (Owen- Smith 1988; Ruess & Halter 1990; Chafota & Owen-Smith 1996; Johnson et al. 1999; Kerley & Landman 2006). The impacts that elephants have on a habitat, affects the quantity (e.g. van Wyk & Fairall 1969; Penzhorn et al. 1974; Barratt & Hall-Martin 1991; Stuart-Hill 1992; Lombard et al. 2001; Landman et al. 2008) and quality of forage. Over-utilization by herbivores leads to a decrease in resources and a loss of plant cover, biomass and abundance (van Wyk & Fairall 1969; Penzhorn et al. 1974; Lombard et al. 2001; Lechmere-Oertle et al. 2005a), which in turn leads to soil erosion, desertification and a decrease in forage quality (Lechmere-Oertle et al. 2005b). This process is accelerated by the occurrence of herbivores at high densities in enclosed areas (Penzhorn et al. 1974; Sinclair 1974; Van der Waal et al. 2003). The diet of elephants are considered to be one of the major shaping forces for vegetation, therefore the investigation of their diets are crucially important. This study may increase our understanding as to how their diet changes and in turn, how this influences vegetation in areas previously protected from elephants.

3.1.3 Hypotheses and aims Previous studies (e.g. Codron et al. 2006; Landman et al. 2008) define a diet shift as a deviation from the initial diet, but do not take into consideration the change in diet preference that may accompany the change in diet. Therefore a diet shift is defined, for the purpose of this study, as a deviation from the initial diet, with a combined change in diet breadth and preference. The main aim of this chapter is to determine how the diet and preference of elephants change with an increase in resource availability. Specifically: 1) to quantify the diet of elephants with an increase in diet resources, and 2) to relate the forage availability estimates to

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resource use of elephants, in order to estimate preference and how this may change, with increased resource availability.

The opportunity arose to test this, in the AENP. The Main Camp section, has a history of high impacts (and therefore reduced food availability (Table 2.1) (e.g. Penzhorn et al. 1974; Barratt & Hall-Martin 1991; Stuart-Hill 1992; Moolman & Cowling 1994; Johnson et al. 1999; Lombard et al. 2001; Kerley & Landman 2006; Landman et al. 2013; Landman et al. 2014)), and the Colchester section, has low impacts and therefore assumed high food availability, due to the absence of elephants for an extended period. Because there is no prior study that has investigated this matter, we have no understanding of how elephants may respond to an increase in resource availability. Therefore, three hypotheses for changes in diet breadth and preference with an increase in diet resource availability are contrasted (Fig 3.1):

H1 Elephants as generalist foragers: will broaden their diet breadth (by including novel resources) with an increase in resource availability, but preference will remain constant as they maximise uptake by foraging on available items. As generalists, elephants would not show preference towards high quality foods as generalist foraging would allow adequate nutrition to be obtained. Although generalist feeders, the presumed ‘higher quality’ resources that are available within the Colchester section (due to the absence of elephants for more than 50 years), would increase the diet quality of elephants. (Diet quality will be investigated in Chapter 4).

H2 Elephants as optimal foragers: Optimality Theory (MacArthur & Pianka 1966) predicts that animals will select for the best quality food items. It is therefore expected that the dietary breadth of the elephants should decrease sharply as they increase the proportion of preferred resources in their diet, which are assumed to be abundant in the Colchester section. This is expected to be coupled with a sharp increase in diet quality (Chapter 4).

H3 Elephants learning foraging behaviour: they may employ a strategy which is a combination of the elephants as generalist foragers hypothesis and optimal foraging theory (MacArthur & Pianka 1966). Elephants are known to have a learning culture (Sukumar 1990; Lee & Moss 1999), which may allow them as generalists to learn about novel resources and as to whether or not they should be consumed, preferred or avoided. It is therefore hypothesised that they will increase their diet breadth gradually over time, while learning behaviour occurs, and would decrease gradually.

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Preference and diet quality increases, but is due to learning events, and would therefore show a gradual increase.

The prediction is made that elephants will show an increase in diet breadth to include novel resources as well as an increase in preference and diet quality (Chapter 4).

From this Chapter it is hoped to gain an understanding of how elephants change their diet breadth and preference, with an increase in diet resources. In habitats with increased resource availability, the effects that elephants have on vegetation may be increased as ‘high-quality’ preferred resources may be available. This makes these plant species vulnerable to the impacts of elephants. By identifying the diet of elephants, it may aid in predicting the impacts that elephants will have on plant communities, as well as on vulnerable and important plant species (Johnson et al. 1999; Lombard et al. 2001; Landman et al. 2008) and how to manage these effects.

breadth

Diet

Preference

Diet quality Diet

Time Increase in resource availability Figure 3.1 Illustration of three contrasting hypotheses to explain changes in diet breadth, preference and diet quality with an increase in diet resource availability: elephants as generalists (red), as optimal foragers (blue) or as learning behaviour occurs (green).

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3.2 Methods

3.2.1 Sampling approach Sampling occurred from August 2010 through to February 2014 (Table 3.1) (Samples prior to February 2014 were collected by Dr. M. Landman. During February 2014, samples were collected by Dr. M. Landman and myself). A time lapse approach was adopted across sites, but seasons were also distinguishable. Seasons were distinguished based on temperature, rainfall and frost and was taken into account due to the seasonal availability of some plant species, mostly forbs, geophytes and grasses (Rutherford 1984; Hoffman & Cowling 1990). Sampling was undertaken on five occasions across sites (Table 3.1).

Anyone interested in using the plant species availability data, should please contact Dr. M. Landman or Prof. G. I. H. Kerley, with whom the data are lodged.

Table 3.1 The sampling approach for faecal collection and forage availability estimates indicating seasons and sites. Forage availability estimates were conducted at both sites. Faecal sampling and forage availability estimates were done by Dr. M. Landman for August 2010, December 2010, April 2011, July 2011 and September 2012. Faecal sampling for February 2014 was done by Dr. M. Landman and myself. Sampling period Time Season Faecal sampling Forage availability period Main Camp Colchester estimates August 2010 0 Winter X

Fence dropped between the Main Camp and Colchester sections

December 2010 1 Summer X X

April 2011 2 Autumn X X X

July 2011 3 Winter X X

September 2012 4 Spring X X X

February 2014 5 Summer X X

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3.2.2 Sample collection To ensure that results were not skewed by elephants foraging in one section and defecating in the other, the presence of the elephants in a particular section were confirmed using GPS collars (M. Landman pers. comm.). Distinguishable herds were also identified and their residency within a section was confirmed, prior to the collection of faecal samples.

Faecal samples were collected within each site, across all five sampling periods. Faecal samples prior to February 2014, were collected by Dr. M. Landman, and for the February 2014 sampling period by Dr. M. Landman and myself. Samples were collected in labelled brown paper bags and oven-dried at 50 °C for a week. For microhistological analysis of faecal samples, samples were ground through a 2 mm mesh and stored until further processing.

3.2.3 Sample digestion for microhistological analysis Five grams of each faecal sample was boiled in 20 ml 55% nitric acid for two minutes, after which the sample was diluted with 100 ml of water and boiled for a further five minutes (Landman et al. 2008). After the completion of the digestion process, samples were rinsed with water through a 250 µm sieve (Macleod et al. 1996) and stored in formalin acetic acid (25% distilled water, 60% alcohol, 10% formalin and 5% glacial acetic acid) until microscopic analysis.

3.2.4 Microhistological analysis of diet Microhistological analysis of faecal samples to identify the diet of animals is a widely used technique. This technique allows for the identification of plant fragments based on their epidermal characteristics (Sparks & Malechek 1968; Holechek et al. 1982). Positive identification of plant fragments are made from a reference collection, based on distinguishing features.

The Centre for African Conservation Ecology’s plant epidermal reference collection, which contains more than 420 potential food items (plant species), was used to identify plant epidermal fragments in faeces. Where appropriate, additional plant material was incorporated into the existing collection to include all food items potentially available to elephants at the study sites, which was collected by Dr. M. Landman. Given the comparative approach used in this study any bias was considered to be consistent across the data set, and therefore the observed difference in fragments between sites and sampling periods, were indicators of diet differences.

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A small amount (<1 ml) of each prepared digested faecal sample was placed on a gridded microscope slide and viewed under a compound microscope at 400x magnification. One hundred epidermal fragments were identified per sample to species-level where possible. Twenty faecal samples were analysed per site and sampling period, for a total of 100 samples per site. All identification of plant epidermal fragments was done by the same observer. Plant species nomenclature follows the most recent South African National Biodiversity Institute (SANBI 2013) list.

3.2.5 Forage availability estimates Relative forage availability estimates were conducted during August 2010, April 2011 and September 2012 in both the Main Camp and Colchester sections (Table 3.1), by Dr. M. Landman. Fifteen transects per site and selected sampling period, each 50 m in length and located at least 5 m from roads, were randomly assigned to be representative of vegetation types and topographical variations at each site. A modified canopy line–intercept method was used (Barbour et al. 1987) to estimate forage availability. Because elephants have a maximum foraging height of roughly 8 m (Croze 1972), only plant material below this height was recorded as potentially available forage. The height, cover and shape of all plants encountered along the transects were recorded. Relative forage availability was calculated as the linear area for each plant species recorded along the transect lines. Total linear area was calculated per transect and combined for total linear area per section and sampling period. Representative samples of all unidentified encountered plants were collected, labelled, dried and positively identified, by Dr. M. Landman.

3.2.6 Statistical analysis The diets of elephants within the Main Camp and Colchester sections were contrasted at several levels: all diet items, principal diet items (PDI) and by grouping plant species into broad growth form categories (i.e. grasses, woody shrubs, succulents, forbs, lianas, geophytes and epiphytes).

The same statistical analysis method used by Landman et al. (2008) was followed: The number of faecal samples required per site per season to adequately describe the diet of elephants was determined through sampling efficiency curves (50 random iterations). This was done by plotting the accumulation of the number of new plant species identified per faecal sample (Landman et al. 2008). Because the accumulation curves for April 2011, July 2011, September 2012 and February 2014 did not reach a steady asymptote (Fig. 3.2), the non-parametric Incidence-based

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Coverage Estimator (ICE) (Foggo et al. 2003), was used to estimate total species richness for these sampling seasons. ICE estimates total species richness based on the relative proportions of common, infrequent and unique species (Foggo et al. 2003). Analysis was performed in EstiMateS version 7.5.

Dietary breadth was investigated for each sampling period and site, using the total number of plant species identified per faecal sample. The Coefficient of Variation (CV) was calculated for the number of plant species identified per faecal sample for each site and sampling season.

Petrides (1975) defined principal diet items (PDI) as the foods consumed in the greatest proportions. These plant species collectively contributes to the bulk of the diet. The method for determining PDI follows Landman et al. (2013): for each site and sampling period, the contribution of consumed plant species were ranked in decreasing order of abundance and cumulative contributions were plotted. If the slope of the curve was at least 10% in relation to the most dominant plant species, the item was considered a PDI. Thus, items below 10% in relation to the most dominant plant species, was considered to be infrequently consumed and contribute relatively little to the bulk of the diet.

Non-Metric Multidimensional Scaling (n-MDS) ordinations, based on Bray-Curtis resemblance matrices of frequency-of-occurrence data (Clarke 1993; Clarke & Gorley 2006), were used to visualize patterns in the diets between and within sampling periods and sites. The n-MDS plots were constructed using PDI as it eliminates unnecessary ‘noise’ in the data in the form of infrequently consumed species. The high-dimensional relationships among samples were represented in two-dimensions. An indication of the ‘goodness-of-fit’ is given by the Stress value (Clarke 1993; Clarke & Gorley 2006). A Stress value of <0.20 indicates a good fit, whereas a Stress value of >0.20 indicates a high level of tension in the plotted data and was corroborated using hierarchical agglomerative cluster analyses (Clarke 1993).

To test if there were significant differences in diets between sites and sampling periods, a non-parametric Analysis of Similarity (ANOSIM; 5000 Monte Carlo permutations) (Clarke & Gorley 2006) was used. To indicate the diet separation across sampling periods and between sites, the R-values were used (Landman et al. 2013). The null hypothesis was tested that the diet of elephants within and between sites are the same. If the test parameter R is approximately 0, the null hypothesis is accepted. Whereas if the test parameter R is approximately 1, the

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replicates within a site are more similar to one another than to the replicates of the other site and the null hypothesis is rejected (Clarke & Gorley 2006). Primer version 6 was used to perform multivariate analyses (Clarke & Gorley 2006).

To test for differences in the use of growth forms between sites and over sampling seasons, Analysis of Variance (ANOVA) procedures were used. All percentage data were arcsine transformed for normality and heteroscedasticity of variances (Quinn & Keough 2002). A Dunnett post-hoc test (Zar 1999) was performed to determine where the significant use of specific growth forms between sites lie (Quinn & Keough 2002). Analyses were performed in Statistica version 12.

Preferences were calculated by comparing the forage availability data (August 2010, April 2011 and September 2012) against the corresponding utilization data (December 2010, April 2010 and September 2012), collected by Dr. M. Landman. Availability data for August 2010 was compared to utilization data for December 2010, as forage availability estimates were conducted before the reintroduction of elephants into the Colchester section. This year (2010) was also marked by a severe drought (SA Weather Service 2014) (Fig. 2.2) and relative availability was therefore considered to remain reasonably constant for this period. To test for a shift in preference by elephants between sites, diet preference was estimated by calculating the abundance and proportion of preferred plant species in the diet per site and sampling period.

Preference was assessed by calculating the confidence intervals (CI) of percentage use of plant species and growth forms identified in the diet. Thus, preference was shown for plant species or growth forms if the percent use was greater than the percent availability recorded along the transects. Alternatively, avoidance was shown if percent use was less than the percent availability recorded (Hobbs & Bowden 1982). Preference or avoidance was considered to be significant if the diet resource use minus the diet resource availability was greater than zero and the confidence interval overlapped zero. All percentage data were arcsine transformed for normality and heteroscedasticity of variances (Quinn & Keough 2002). A χ2 goodness of fit tested the null hypothesis that there was no difference in the abundance or proportion of preferred (or proportion of avoided) foods in the diet of elephants within and between sites (Quinn & Keough 2002).

Species known to be vulnerable to elephants impacts (e.g. Viscum spp. (Penzhorn et al. 1974; Midgley & Joubert 1991; Lombard et al. 2001), Aloe spp. (Penzhorn et

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al. 1974; Barratt & Hall-Martin 1991) and low succulents and geophytes (Johnson et al. 1999; Lombard et al. 2001), were used to test if elephants used and preferred these food items (Table 2.1) as predicted This was done by calculating the preference of elephants for these species.

3.3 Results 3.3.1 Sampling efficiency Sampling efficiency curves reached an asymptote (62 and 72 plant species identified in the diet of elephants in the Main Camp and Colchester sections, respectively) for December 2010, but not for the other sampling seasons (Fig. 3.2). Based on the estimated totals from ICE, 88.3 – 97.5% of the diet could be described from the identified number of samples across sites and sampling periods, following the analysis of 20 faecal samples. This confirms that the sampling size was adequate to describe, and contrast the diets. The diet items not encountered during analyses were potentially missed due to their particularly rare occurrence in the diet or were lost during the preparation for analysis (Landman et al. 2008).

3.3.2 Relative forage availability During forage availability estimates in the Main Camp section, a total of 239 plant species were identified across sampling seasons. The forage availability estimates of plant species within the Main Camp section was dominated by woody shrubs (57.8% (April 2011) - 81.8% (September 2012)), succulents (10.9% (September 2012) - 19.7% (April 2011)) and grasses (1.4% (August 2010) - 7.6% (April 2011)). Dominant plant species included: Euclea undulata (13.9%), Portulacaria afra (12.7%), Capparis sepiaria (8.8%) and Schotia afra (8.2%).

Within the Colchester section, a total of 330 plant species were identified across sampling seasons. The forage availability estimates of plant species within the Colchester section was dominated by woody shrubs (67.3% (August 2010) - 78.5% (September 2012)), succulents (10.8% (September 2012) - 15.6% (April 2011)) and grasses (2.4% (September 2012) - 12.6% (April 2011)). Euclea undulata (11.6%), Portulacaria afra (9.0%), Schotia afra (7.3%) and Capparis sepiaria (5.9%) were the dominant plant species across sampling periods within the Colchester section. A total of 456 plant species were identified across sites, with 103 (22.6%) species shared between sites. Similar forage availability estimates were established across sites: this was due to the method used to establish forage availability estimates, as well as the representation of vegetation types sampled in the transects.

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3.3.3 Diet composition A total of 144 plant species were identified in the diet of elephants across sites, with 93 plant species being identified in the Main Camp section and 128 plant species identified in Colchester. Seventy-seven (53.5%) of the total identified dietary plant species were shared between sites.

The total number of plant species identified in the diet per site for each sampling period was consistently higher in the Colchester section than in the Main Camp section (Fig 3.3). However, the differences in diet richness between sites were not the same across sampling periods for the mean number of plant species identified per faecal sample (F = 5.61; DF = 1, 4; P < 0.001). Specifically, for December 2010 the mean number of plant species identified in the diet of elephants in the Colchester section was significantly higher than in the diet of elephants in the Main Camp section (Fig. 3.3). No difference (P > 0.05) was found in the mean number of plant species identified in the diet between sites for any of the other sampling seasons.

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90 90 December 2010 80 April 2011 80 70 70 60 60 50 50 40 40 30 30 20 20

10 10 0 0 0 5 10 15 20 0 5 10 15 20 90 July 2011 90 September 2012 80 80 70 70 60 60 50 50 40 40 30 30 20 20 10 10 0 0 Total number of plant species 0 5 10 15 20 0 5 10 15 20 February 2014 90 Figure 3.2 Accumulation curves (50 random iterations) of 80 plant species recorded per faecal sample for the Main 70 Camp (black) and Colchester (grey) sections over the sampling periods. 60 50 40 30 20 10 0 0 5 10 15 20 29

Total number of faecal samples

90 80 * 70 60 50 40 30

20 Numberof plant species 10 0

December 2010 April 2011 July 2011 September 2012 February Sampling period Figure 3.3 The total number plant species (grey) and the number of PDI (black) identified per site and sampling period. Asterisks (*) indicate significant differences between sites at α = 0.05.

Similarly, the Coefficient of Variation (CV) showed an exponential decline for the diet of elephants in the Colchester section (F = 90.94; DF = 1, 3; P = 0.002), approaching that of the Main Camp section in July 2011 (Fig. 3.4). Whereas, in the Main Camp section, the CV did not show any significant change over time (F = 1.03; DF 1, 3; P > 0.05).

0.2 y = 0.1737x-0.52 R² = 0.97 0.15

0.1

0.05 y = -0.0012x + 0.09

Coefficient Coefficient ofVariation R² = 0.02 0 0 Dec 20101 Apr 20112 Jul 20113 Sept4 2012 5 Feb 2014 6

Sampling period Figure 3.4 The Coefficient of Variation for the total number of plant species identified per faecal sample for the Main Camp (black) and Colchester (grey) sections.

The PDI contributed 70.4 – 86.3% of the diet of elephants in the Main Camp and Colchester sections (Appendix 1). Between 22.5% and 46.3% of the PDI were shared between sites across sampling periods. During winter (July 2011) the shared number of PDI between sites was the lowest (22.5%), whereas in spring (40.6%

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September 2012) and summer (46.3% February 2014 and 41.5% December 2010) the shared number of PDI between sites were at their highest. There was an increase in the similarity of the use of PDI between sites over time, however, this was not significant.

The relatively low percentage of shared PDI across sites was confirmed by n-MDS ordinations with a high degree of dissimilarity in diet composition between sites within sampling periods (range: 63.0% - 70.1%; Fig. 3.5), which was statistically significant in all cases (P < 0.001). Average similarity between samples within sites increased significantly (F = 3.56; DF = 4; P = 0.04) between December 2010 and February 2014 (44.9% to 50.8%) in the Main Camp section. Similarly, in the Colchester section, average similarity between samples increased significantly (F = 5.61; DF = 4; P = 0.03) from 39.9% in December 2010 to 48.1% in February 2014 (Fig. 3.5). This indicates an increase in diet similarity within sites over time.

The test parameter R, which was used to indicate the degree of diet separation across sampling periods and between sites, did not approximate 0 during any sampling period. Thus, the null hypotheses (i.e. replicates within and between sites are the same), was rejected (F = 0.39; DF =1, 4; P = 0.00032).

Two-way ANOVA analyses revealed that there were significant interactions in the consumption of growth forms between sites for sampling periods (Fig. 3.6). Post- hoc analyses revealed that there was a significant difference between sites in the use of forbs for April 2011 and July 2011; epiphytes showed significant differences in use for September 2012 and succulents showed significant differences in use for February 2014 (Fig. 3.6).

3.3.4 Diet Preference Significant preference was shown towards grasses (P < 0.05) and significant avoidance for woody shrubs (P < 0.05) across both sites in the diet of elephants over all three sampling periods, for which forage availability data are available (Fig 3.7). In addition, in the Colchester section, forbs were significantly preferred (P < 0.05) in December 2010, whereas forbs and lianas were significantly preferred here (P < 0.05) in September 2012. Succulents were significantly avoided in the Main Camp section in April 2011 (Fig. 3.7).

2 In the Main Camp section, there was no significant difference (χ = 0.97; DF = 2; P > 0.05) in the proportion of preferred foods over sampling periods (range: 0.53 – 0.61; Fig. 3.8). Similar trends were observed for avoided foods in the Main Camp section

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2 (range: 0.32 – 0.36; χ = 0.10; DF = 2; P > 0.05). In the Colchester section, there

2 was no significant difference (χ = 0.83; DF = 2; P > 0.05) in the proportion of preferred foods over sampling periods (range: 0.51 – 0.60); and there was also no significant difference observed for avoided foods over sampling periods (range: 0.33

2 – 0.38; χ = 0.08; DF = 2; P > 0.05; Fig. 3.8).

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December 2010 April 2011 July 2011 2D stress: 0.25; 2D stress: 0.20; ANOSIM R: 0.75; 2D stress: 0.20; ANOSIM R: 0.73; P < 0.001 ANOSIM R: 0.69; P < P < 0.001 0.001

December 2010 April 2011 July 2011 2D stress: 0.25; ANOSIM R: 0.69; 2D stress: 0.20; ANOSIM R: 0.73; 2D stress: 0.20; ANOSIM R: 0.75; P < 0.001 P < 0.001 P < 0.001

Axis 2 September 2012 Figure 3.5 Non-metric Multidimensional February 2014 2D stress: 0.24; ANOSIM R: 0.65; Scaling ordinations of principal diet items in 2D stress: 0.24; ANOSIM R: 0.64; the diet of elephants in the Main Camp (black) P < 0.001 P < 0.001 and Colchester (clear) sections over sampling periods.

2

Axis September 2012 2D stress: 0.24; ANOSIM R: 0.65; P < 0.001 33

Axis 1

70 70 Site x Growth form parameter: December 2010 Site x Growth form parameter: April 2011 60 F = 2.28 60 F = 4.80

50 DF = 1, 6 50 DF = 1, 6 P = 0.04 P < 0.001 40 40

30 30

20 20

10 10 *

0 0 Grasses Woody Succulents Forbs Lianas Geophytes Epiphytes Grasses Woody Succulents Forbs Lianas Geophytes Epiphytes 70 shrubs 70 shrubs Site x Growth form parameter: September 2012 60 Site x Growth form parameter: July 2011 60 F = 3.41 F = 4.71 50 DF = 1, 6 50 DF = 1, 6 P < 0.001 40 P = 0.03 40

30 30 20 * 20

% Diet 10 10 *

0 0 Grasses Woody Succulents Forbs Lianas Geophytes Epiphytes Grasses Woody Succulents Forbs Lianas Geophytes Epiphytes shrubs 70 shrubs Site x Growth form parameter: February 2014 Figure 3.6 Percentage contributions ( SD) of broad growth forms to the 60 F = 3.48 diet of elephants in the Main Camp (black) and Colchester (grey) sections 50 DF = 1, 6 over sampling periods. Asterisks (*) indicate a significant difference P = 0.02 between sites at α = 0.05. Statistics indicate ANOVA outputs for site x 40 growth form parameters. 30 20 * 10 34 0 Grasses Woody Succulents Forbs Lianas Geophytes Epiphytes Growth forms shrubs

December 2010 April 2011 0.4 0.4 * 0.3 * 0.3

0.2 0.2 * 0.1 Woody 0.1 Woody shrubs shrubs Succulents 0 0 Grasses Succulents Forbs Lianas Geophytes Epiphytes Grasses Forbs Lianas Geophytes Epiphytes -0.1 -0.1 -0.2 -0.2 * * -0.3 -0.3 * -0.4 -0.4

Availability ± CI

– Figure 3.7 Normalised utilisation of growth forms (mean ± 95% CI) September 2012 0.4 identified in the diet of elephants, in the Main Camp (black) and * Colchester (grey) sections over the sampling periods. If use = 0.3 availability and the confidence interval overlaps 0, it is used in proportion to availability. Preference was significant (indicated by 0.2 * * asterisk (*)) if use > availability and the confidence interval did not overlap 0. Whereas avoidance was significant (indicated by 0.1 Woody asterisk (*)) if use < availability and the confidence interval did not shrubs 0 overlap 0. Preference was only estimated for the three sampling

Preference = Use Grasses Succulents Forbs Lianas Geophytes Epiphytes periods for which forage availability estimates were established. -0.1

-0.2

-0.3 35 -0.4 * Growth forms

1.00 0.90 0.80 0.70 0.60 0.50 0.40 0.30 0.20 0.10

Proportionspeciesofplant consumed 0.00 Main Camp Colchester Main Camp Colchester Main Camp Colchester Dec 2010 Apr 2011 Sept 2012 Sampling period Figure 3.8 The proportion of plant species identified in the diet of elephants which were avoided (black), preferred (grey) or used in proportion to their availability (clear) across sites for the sampling periods for which forage availability data are available.

2 In the Main Camp section, there was no significant difference (χ 2 = 0.95; P > 0.05) in the percentage contribution to the overall diet of preferred foods of elephants, over sampling periods (range: 45 – 48%; Fig. 3.9). Similar trends were observed for

2 the percentage contribution of avoided foods at the same site (range: 35 – 40%; χ 2 = 0.38; P > 0.05). In the Colchester section, similar trends were observed as there

2 was no significant difference (χ 2 = 0.57; P > 0.05) in the percentage contribution of preferred foods over sampling periods (range: 58 – 66%; Fig. 3.9), and neither was there a significant difference for the percentage contribution of avoided foods

2 (range: 27 – 30%; χ 2 = 0.21; P > 0.05).

2 Across sampling periods, there was no significant difference (χ 5 = 6.70; P > 0.05) in the percentage contribution of preferred foods between the Main Camp (46.7%) and Colchester (61.3%) sections. A similar trend was observed for the percentage contribution of avoided foods to the diet of elephants across sites, as there was no

2 significant difference (χ 5 = 6.90; P > 0.05) between the Main Camp (40.3%) and Colchester (29.0%) sections (Fig. 3.9).

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100 90 80 70 60 50 40 30 20

Percentcontribution diet to 10 0 Main Camp Colchester Main Camp Colchester Main Camp Colchester Dec 2010 Apr 2011 Sept 2012 Sampling period Figure 3.9 The percentage contribution of plant species identified to the diet of elephants which were avoided (black), preferred (grey) or used in proportion to their availability (clear) across sites for the sampling periods for which forage availability data were available.

3.3.5 Plant species vulnerable to elephant herbivory Plant species that have been identified as being vulnerable to elephants herbivory Table 3.2(Johnson et al. 1999; Lombard et al. 2001; Landman et al. 2008), (e.g. Viscum spp., Aloe africana, Aloe ferox, Cussonia spicata, Mesembryanthemum spp., Bulbine spp., and Dietes iridioides) were eaten during the sampling periods, in the Colchester section. With the exception of Viscum spp., all of these other plant species significantly preferred during all the sampling seasons, for which forage availability estimates were available. Viscum spp. were significantly preferred only during September 2012 (Appendix 1).

Table 3.2 The documented plant species that is vulnerable to elephant herbivory in the Addo Elephant National Park. Vulnerable plant species Reference

Penzhorn et al. (1974) Viscum spp. Midgley & Joubert (1991) Lombard et al. (2001) Magobiyane (2006)

Penzhorn et al. (1974) Aloe spp. Barratt & Hall-Martin (1991) Johnson et al. (1999) Lombard et al. (2001)

Cussonia spicata Lombard et al. (2001) Davis (2004) Mesembryanthemum sp. Davis (2004) Davis (2004) Bulbine sp. Landman et al. (2008) Dietes iridiodes Johnson et al. (1999)

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3.4 Discussion

3.4.1 Does the diet breadth and preference of elephants change with an increase in resource availability? The overall aim of this chapter was to determine how the diet and preference of elephants change with an increase in resource availability. This was done in order to address the gap in the current knowledge, in this regard.

The impacts of elephants in the Main Camp section have been well documented in the literature and include a significant decline in plant species richness, biomass and canopy height, volume and density (Penzhorn et al. 1974; Barratt & Hall-Martin 1991; Stuart-Hill 1992; Moolman & Cowling 1994; Lombard et al. 2001; Landman et al. 2013; Landman et al. 2014) (See Table 2.1). Whereas, elephants have been excluded from the Colchester section for at least 56 years (Whitehouse & Hall- Martin 2000), and therefore the vegetation was relatively intact. Thus, it is hypothesised that there was an increase in resource availability in the Colchester section, compared to the Main Camp section, which can be seen by the increase in plant species numbers

In the present study, elephants in the Colchester section increased their diet breadth: although this was not significant. The total number of plant species identified per sampling period was consistently higher in the Colchester section than in the Main Camp section (albeit not significant), where many plant species (e.g. Viscum spp., Cussonia spicata, and the alien Opuntia ficus-indica (Johnson et al. 1999; Lombard et al. 2001; Kerley & Landman 2006; Landman et al. 2008)) have been shown to have been reduced in abundance or have disappeared presumably due to elephants impacts (Kerley & Landman 2006; Landman et al. 2008). In contrast with the Optimal Foraging Theory, the diet breadth of elephants increased in the Colchester section. This increase in diet breadth is hypothesised to be due to the increase in the resource availability in the Colchester section. In addition, the Coefficient of Variance indicated that the initial diet (December 2010 and April 2011) of elephants in the Colchester section was highly varied, in terms of species richness, but decreased in subsequent sampling periods (Fig 3.4).

The results show support for the H3 hypothesis: elephants learning forage behaviour (Fig 3.1). This was however not coupled with an increase in preference, which supported the H1 Elephants as generalist foragers hypothesis (as preference did not change). Elephants are known to be generalist foragers (Owen-Smith 1988), but learning behaviour has been observed in elephant calves, with regards to foraging

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(Sukumar 1990; Lee & Moss 1999). In this Chapter, support was shown for two of the three hypotheses, and therefore it is needed to investigate changes in diet quality for further support of the hypotheses.

3.4.2 Elephants learning foraging behaviour Learning foraging behaviour may occur over time, due to previous experience whether a potential food item should be used or discarded (Sukumar 2003). Learning foraging behaviour is a process, associated with the physical properties of the food item as well as the physiological consequences of consumption (Thorhallsdottir et al. 1990; Sukumar 2003). Learning behaviour, therefore, occurred in elephants following their introduction into the Colchester section. With an increase in resource availability, elephants sampled a wide variety of food items initially (December 2010) causing their diets to be highly divergent between individuals (Fig. 3.4; Fig. 3.5).

Learning behaviour associated with changes in diet have been seen in many animals such as fish (Croy & Hughes 1991; Warburton 2003), birds (Brown et al. 1991; Avery 1994) and mammals such as rats (Galef 1977; Galef et al. 1988), rabbits (Altbacker et al. 1995; Hudson et al. 1999), monkeys and apes (Boesch & Boesch 1990; Byrne 1993; Jaeggi et al. 2010), small ruminants (Thorhallsdottir et al. 1990; Baumont et al. 2000) and in the Asian elephant (Elephas maximus) (Sukumar 1990). Provenza & Balph (1987) and Provenza (1995) showed that previous experience with diet items causes animals to revise their diet selection: experienced and inexperienced animals have different foraging strategies in the same environment, with the inexperienced animals eating less in volume, even though they spend more time searching and handling forage.It is therefore not surprising that elephants may have strongly associated learning behaviour occurring, when exposed to novel diet resources.

3.4.3 Implications of elephants herbivory Elephants have a large diet breadth and 144 plant species were identified in their diet in the Main Camp and Colchester sections of the AENP in this study. Previous studies (Paley & Kerley 1998; Davis 2004; Landman et al. 2008) in the AENP collectively identified 146 plant species in the diet of elephants. The diet breadth of elephants varies greatly, with the diet of elephants in other areas (e.g. Namibia) comprising of only 33 plant species (identified by direct observation) (Viljoen 1989), compared to 200 plant species in the diet of elephants in Uganda (Blake 2002; Campos-Arceiz & Blake 2011). This, however, means that many plant species are

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influenced through elephants herbivory (Kerley & Landman 2006). In the Subtropical Thickets of the AENP, this is of concern as many plant species are endemic to the area (Johnson et al. 1999; Van Wyk & Smith 2001; Vlok et al. 2003), making these species vulnerable to extirpation due to elephant herbivory (Moolman & Cowling 1994; Lombard et al. 2001; Kerley & Landman 2006;Landman et al. 2008).

The role of elephants herbivory in causing a decline in plant species richness, biomass and canopy height, volume and density have been well document by previous studies in Thicket vegetation (Table 2.1) (e.g. Penzhorn et al. 1974; Barratt & Hall-Martin 1991; Stuart-Hill 1992; Moolman & Cowling 1994; Paley & Kerley 1998; Lombard et al. 2001; Kerley & Landman 2006; Landman et al. 2008), as well as in a variety of other vegetation types (e.g. Laws 1970; Leuthold 1977; Barnes et al. 1994; Whyte et al. 1996; Barnes 2001; Jacobs & Biggs 2002; Skarpe et al. 2004). This study found that preferred resources in the Colchester section had already declined in availability post-August 2010. Although, this is difficult to assess, since only relative forage availability estimates were done with small sample sizes and no seasonal replications, there was a decrease in the total linear area of lianas and epiphytes, as well as a decline in the linear area of Aloe africana, Cussonia spicata, Viscum spp. and several other species following the first sampling season (August 2010). This may be due to elephant herbivory, but may be due to other factors such as knock-on effects associated with elephants (Kerley & Landman 2006). However, the results of the microhistological analysis of faecal samples confirmed that these growth forms and species were present in the diet of elephants in the Colchester section, and therefore at least some of the decline in specific plant availability post-August 2010 can be attributed to herbivory.

In novel habitats, or with an increase in resource availability, elephants are likely to impact ‘high quality’ novel resources initially, but sampling a wide variety of available plant species. Plant species vulnerable to elephant herbivory (Penzhorn et al. 1974; Midgley & Joubert 1991; Johnson et al. 1999; Lombard et al. 2001) were all (except Viscum spp.) significantly preferred during all seasons (Appendix 1), which is similar to the findings in terms of diet composition and preference to that of Landman et al. (2008). The effects of elephant on vulnerable and important plant species, especially when these plant species are preferred, is predicted to be severe in novel habitats as elephants target these plant species. This highlights the importance of diet and preference studies, to identify plant species that elephants will impact initially, as elephants are able to feed down the preference scale when a preferred plant species is extirpated.

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In some cases, periods associated with an increase in nutrients, e.g. fruiting periods (Short 1981; Blake 2002), is associated with an increase in the use and preference of plant species by herbivores. This is presumably linked to an increase in diet quality (Short 1981; Blake 2002). For example; during September 2012 the preference for Viscum spp. in the diet of elephants in the Colchester section was significant. This is attributed to its fruiting during this time (spring – summer) (Van Wyk & Smith 2001; Mucina & Rutherford 2006) and high availability in this section as opposed to the Main Camp section (where the availability of Viscum spp. has declined considerably (Midgley & Joubert 1991; Johnson et al. 1999; Lombard et al. 2001; Magobiyane 2006)). Similarly, it is expected that during late summer (January – February) the alien invasive succulent Opuntia ficus-indica fruits and would therefore also be significantly preferred by elephants. Although further investigation is necessary to confirm this, elephants were seen eating the fruit during this period (pers. obs.) and a similar trend is therefore expected. It is hypothesised that the increase in use and preference for plant species during fruiting periods is associated with an increase in diet quality.The increase in use and preference of specific plant species during fruiting has been recorded elsewhere for forest elephants (Loxodonta cyclotis) (e.g. in the Bia National Park (Ghana)) during fruiting the Cherry Mahogany (Tieghemella hecklii )and Guinea Plum (Parinari excelsa) is preferred, and in the Lopé Reserve (Gabon) the Bitterbark Tree (Sacoglottis gabonensis) is preferred during its fruiting period (Short 1983; White 1994). This may be advantageous for plant species reliant on elephants for seed dispersal (Feer 1995; Campos-Arceiz & Blake 2011), but this may also increase the susceptibility of plants vulnerable to the impacts of elephants herbivory. This shows that preference may not only differ due to availability annually, but also seasonally during periods of increased nutrients. Monitoring of elephants herbivory should, therefore, not only be considered annually, but also seasonally. The increase in seasonal preference by elephants may not necessarily always be a drawback: the invasive succulent Opuntia ficus-indica (Morgan & Zimmerman 1991), which was previously present in high abundance historically in the Main Camp section, has nearly been extirpated in this section. In this case, elephants may aid in controlling this alien invasive species.

The results provide evidence that the elephant herbivory in the Colchester section likely reflect the past herbivory by elephants in the Main Camp section. This implies that elephant herbivory will initially have a strong focus on preferred novel resources, but as learning behaviour occurs that elephants will focus their diet to a narrower range of diet resources. It is predicted that this change in their diet will

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reflect their preference for certain plant species and growth forms. As these preferred resources are removed, it is hypothesised that elephants will shift their diet down the preference scale thus showing a cascading series of impacts (Landman et al. 2008). It is predicted that eventually diets will converge between sites as resources will become homogenised. In the Colchester section this may lead to a decrease in plant species abundance, volume, and biomass and may eventually lead to the extirpation of plant species as seen in the Main Camp section (Penzhorn et al. 1974; Barratt & Hall-Martin 1991; Stuart-Hill 1992; Moolman & Cowling 1994; Johnson et al. 1999; Lombard et al. 2001; Kerley & Landman 2006; Landman et al. 2008).

3.4.4 Contextualising the study A limitation of this study was due to the restricted replication of sites (e.g. Hurlbert 1984); i.e. the Main Camp and Colchester sections were sampled multiple times (sampling was replicated), but treatments were not. Therefore, in order to make this study more robust, it would have been required to sample multiple Main Camp and Colchester sections. Unfortunately as this was not possible, it restricts this study in being generalised beyond the current study sites. The temporal scale at which this study was conducted allowed for the three hypotheses to be contrasted, and support being shown for two of the hypotheses. However, with an increase in the time frame of this study, the support for the hypotheses may change. This may be due to the learning behaviour occurring and given enough time, the diet of elephants may approach the optimal foragers hypothesis, or may revert to generalist foragers. The scale at which the diet preference of elephants was measured; i.e. at population level, individual diet preference of elephants may have been insufficient to determine if learning behaviour was occurring. This may be due to the learning behaviour occurring at different rates in individuals, which the mean of the population may not reflect.

Previous studies in Sub-tropical Thicket have estimated the total number of PDI between 14 (using direct observation (Paley & Kerley 1998)) and 13 species (Davis 2004). In this study, the total number of PDI identified in the diet of elephants was between 22 and 43 plant species across sites. This increase in the number of PDI identified may be due to a number of analytical factors. Davis (2004) identified 90 plant species (of which 13 were PDI) in the diet of elephants in the Main Camp section using microhistological analysis, which represents 86.5% of the potential diet based on ICE estimates. The present study identified 93 and 128 plant species of in the diet of elephants in the Main Camp and Colchester sections respectively,

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which represents 88.3% - 97.5% of the potential diet across sites and sampling seasons, based on ICE estimates. This increase in the number of species identified in the diet of elephants may have been a factor contributing to the high number of PDI species identified in this study, as well as due to the estimated high percentage of the diet that could potentially be described. Alternatively, the high number of PDI species in the diet may be due to observer error (Slater & Jones 1977; Vavra et al. 1978; Vavra & Holechek 1980; Holechek et al. 1982).

A limitation in estimating preference may be due to the modified canopy line– intercept method used to establishing relative forage availability estimates. This method may underestimate the availability of certain plant species with patchy distributions. This may have caused some plant species to be calculated as preferred due to their low relative availability recorded, even though they may not be truly preferred by elephants. The nature of the study sites also added an inherent limitation in this study, which is due to the fact that the two sections were open to elephants to roam freely between the sections. Elephants have a relatively fast gut through-put time of 24 hours (Owen-Smith 1988), but may have moved between sections within this time, thereby skewing results; i.e. having foraged in one section and moved into the other section and defecated there. This however was overcome by the use of radio tracking collars and identifying distinguishable herds, which confirmed the resident presence of the elephants in a particular area of the sections (M. Landman pers. comm.).

Despite these limitations, there were also several strengths in this study. By adopting a time lapse approach, with sampling occurring on five occasions within both the Main Camp and Colchester section, it was possible to track changes in diet and preference. This approach was novel, since most studies focus on a single sampling occasion or seasonal sampling (e.g. Codron et al. 2006; De Klerk 2009; Landman et al. 2013). Moreover, several studies assume a diet shift, without determining the change in preference (e.g. Codron et al. 2006), and focussing on describing the diet in broad terms (e.g. proportion of grass:browse in the diet). This study was also novel in its ability to identify diet use and preference by elephants at plant species level over several sampling seasons. The estimation of the proportion of preferred plant species over three sampling periods was another novel application in the present study, and allowed for the changes in preference over time to be explored between sites. By estimating both the changes in diet and preference, it was able to distinguish between three contrasting hypotheses.

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3.4.5 The way forward Following the present study, the gaps in knowledge includes the identification of drivers responsible for a change in diet breadth, but not preference. This will be investigated in Chapter 4, where diet quality will be tested as driver of changes in diet. The increase in use and preference of specific plant species during fruiting (e.g. Viscum spp. and Opuntia ficus-indica) also requires further investigation, as this can be useful in predicting periods of increased impacts, or identifying elephants as a possible biological control. With several elephant populations being established in small reserves, it would be useful in furthering this study by applying this research approach across multiple sites and increased time period, during times when these reserves are expanded and resource availability increases.

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CHAPTER 4 HOW DOES THE QUALITY OF DIET CHANGE WITH AN INCREASE IN RESOURCE AVAILABILITY?

4.1 Introduction Westoby (1974) proposed that large generalist herbivores do not feed to optimise energy intake per unit time (the classical model of optimal diet (Emlen 1966; Schoener 1971)), but rather aim to maximise nutrients consumed from bulk foraging. It is therefore deduced that an animal’s choice of diet resources is constrained by its minimum required nutrients, the amount of forage available and the nutrients available within the forage it consumes (Westoby 1974). Elephants, being food quantity rather than quality limited (Owen-Smith 1988), are able to use relatively low-quality forage due to their fast throughput rate, large gut size and being hindgut fermenters (Owen-Smith 1988; Van Soest et al. 1995; Clauss et al. 2007). However, some constraints in diet quality are applicable despite these adaptations: these constraints are the minimum quality of forage needed to maintain body condition and to allow for growth and reproduction (Meissner 1982; Owen- Smith 1988; Woolley et al. 2009).

The reduction in diet quality negatively affects the health, body condition, growth and reproduction rate of animals (Robbins 1983; Sukumar 2003). If the diet quality needed for maintenance or growth is inadequate, a decrease in body condition is expected (Sinclair 1974; Erasmus et al. 1978), leading to eventual decreased reproduction rates; i.e. fitness and increased mortality rates (Sukumar 2003). De Klerk (2009) found evidence of decreased body condition of elephants in the Main Camp section of the AENP: a reduction in body condition was observed as a consequence of the low diet quality and reduced food availability, due to overutilization. However, the de Klerk (2009) study was conducted during a drier period. De Klerk (2009) furthermore, found that diet quality in the AENP varied between seasons, with faecal protein being the highest during summer.

In nutritionally stressed elephants of the Murchison Falls Park in Uganda, a decrease in body condition was associated with a decreased rate of reproduction and increased intercalving intervals (Laws & Parker 1968). As diet quality is ultimately linked to the maintenance of body condition and fitness (Robbins 1983; Sukumar 2003), it is important to investigate how diet quality responds as a consequence of a change in diet due to an increase in resource availability (Chapter 3).

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4.1.1 Determinants of diet quality The quality of forage is generally determined by composition, seasonality, rainfall and soil quality of the area (Sinclair 1974; Coe et al. 1976; Owen-Smith 1990; Seydack et al. 2000; Owen-Smith 2002). Nutrient rich soils and high rainfall is generally associated with high forage quality (Sinclair 1974; Owen-Smith 2002). Although differences in resource quality are predominantly driven by large-scale environmental factors (Owen-Smith 1990; Grant et al. 1995; Seydack et al. 2000), other factors such as growth form, plant species, plant growth stage, overgrazing and exposure to herbivores also play an important part (Robbins 1983; Penzhorn et al. 1974; Lombard et al. 2001; Sukumar 2003).

Specific growth forms, plant species and plant parts have different forage quality: fibrous plants and plant parts, such as the bark of trees and dry leaves, are generally associated with low protein and low digestability, whereas less fibrous plants and plant parts such as young leaves and shoots are associated with higher protein content (Robbins 1983; Sukumar 2003). Browse usually provides high quantities of crude protein (Sukumar 1989; Koch et al. 1995), whilst grass, although high in silica and highly fibrous during the dry season, contributes to the protein uptake and provides suitable fibre:protein intake ratio as secondary metabolites are largely absent (Lindsay 1994). The impacts of herbivores on the quantity of forage (e.g. Penzhorn et al. 1974; Barratt & Hall-Martin 1991; Landman et al. 2008), in turn also affects the quality of forage: the loss of plant cover, biomass and abundance (Penzhorn et al. 1974; Lombard et al. 2001; Lechmere-Oertle et al. 2005a), leads to increased erosion, a decrease in soil nutrients and ultimately desertification (Lechmere-Oertle et al. 2005b).

Diet quality in the AENP was found by De Klerk (2009) to be fairly poor compared to Asante Sana, Shamwari and Blaauwbosch; faecal protein was the lowest at the AENP compared to the other sites for both summer and winter. Hall-Martin (1992) and Seydack (2000) found that in the AENP there is a high N (nitrogen) to C (carbon) faecal constituent ratio, compared to that of elephants in Knysna.

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4.1.2 Hypotheses and aims It was predicted that with an increase in resource availability, elephants would increase diet breadth, preference and diet quality. Three hypotheses were contrasted (Fig. 3.1):

 H1 hypothesis: elephants as generalist foragers

 H2 hypothesis: elephants as optimal foragers

 H3 hypothesis: elephants learning forage behaviour.

In Chapter 3, the response of elephants in terms of diet breadth and preference to an increase in resource availability was determined. With an increase in resource availability, the diet breadth of elephants increased (albeit not significant) and varied significantly during the first sampling period (December 2010), but variance between the diets of elephants decreased in subsequent sampling periods. This supported the H3 hypothesis: elephants learning forage behaviour. However, there was no difference between the preference of elephants in the Main Camp and Colchester sections, which supported the H1 hypothesis: elephants as generalist foragers. As a consequence of a change in diet, diet quality is expected to change as well.

High diet quality is linked to a low cell wall to cell content ratio (Robbins 1983). In the Colchester section, with an increase in resource availability, it is presumed that higher quality resources would be available due to the exclusion of elephants from this site for almost 60 years (Whitehouse & Hall-Martin 2000; Landman et al. 2012). An increase in diet quality in the Colchester section is expected to be associated with a decrease in Neutral Detergent Fibre (NDF) and Acid Detergent Lignin (ADL), and an increase in crude protein and phosphorous, compared to the Main Camp section.

In order to determine how diet quality changes, it is necessary to investigate how diet quality parameters responds as a consequence of a change in the diet of elephants, with an increase in resource availability. It is predicted that elephants will show an increase in diet quality in the Colchester section, as compared to the diet quality of elephants in the Main Camp section.

Three hypotheses were contrasted for changes in diet quality with an increase in diet resource availability (Fig 4.1):

H1 elephants as generalist foragers: will show a slight increase in diet quality, as presumed ‘higher quality’ resources are available in the Colchester section, which can be utilised.

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H2 elephants as optimal foragers: will show a sharp increase in diet quality, presumably by increasing preference towards high quality food resources in the Colchester section. Because this hypothesis was not supported by the diet changes (Chapter 3), and will therefore not be considered in this Chapter.

H3 elephants learning forage behaviour: changes in diet quality will occur gradually as learning behaviour occurs and thus will gradually increase over time in the Colchester section.

Diet quality Diet

Time Increase in resource availability Increase in resources Figure 4.1 Illustration of the three contrasting hypotheses for changes in diet quality with an increase in diet resources: elephants as generalists (red), as optimal foragers (blue) or as forage learning behaviour occurs (green).

4.2 Methods

4.2.1 Techniques in determining diet quality Increase in diet resources Several methods exist for the determination of diet quality in wild herbivores: stomach or rumen content diet quality analysis requires the sacrifice of animals and considerable variation occurs in crude protein concentrations depending on the technique used and whether or not rumen liquid and microbes were washed out before analysis (McCullagh 1969a; Malpas 1977; Owen-Smith 1988). The use of oesophageal fistulas for the determination of diet quality is deemed fit for only large populations of animals as precision with this technique is low (e.g. crude protein concentrations are on average 2.7% higher when estimated with this technique) and therefore a large study sample is needed (Engels et al. 1971; Owen-Smith 1988). Fistulated animals also require considerable care, which is time and cost intensive and requires animals to be tamed (Engels et al. 1971; Holechek et al. 1982), which makes this technique unsuitable for use in elephants. The use of faecal samples for diet quality analysis has proven to be practical for wild animals since fresh collected faecal samples represent the foods consumed within the last 24 – 48 hours (Rees 1982). Also, the use of faecal samples provides unlimited sampling opportunity and

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there is no interference with the normal feeding habits of animals, thus the true diet is represented (Leslie & Starkey 1987; Ulrey et al. 1997).

For the purpose of this study faecal analysis was selected, as it is the most practical method of studying the diet of wild herbivores (Erasmus et al. 1978) and is useful as it allows comparisons to be made seasonally as well as between different populations (Leslie & Stark 1987). Although there has been some debate on the use and accuracy of estimating diet quality using faecal samples (Hobbs 1987), faecal nitrogen, fibre and phosphorus have been found to be accurately correlated to dietary intake (Leslie & Stark 1987; Sinclair 1974; Wrench et al. 1997; Smith et al. 2005).

4.2.2 Diet quality composition The nutritional quality of foods is determined by the specific proportions of proteins, fibre, minerals, vitamins, lignin, as well as secondary metabolites (Robbins 1983; Owen-Smith 2002; O’Connor et al. 2007). The presence of fibre, lignin and secondary compounds, such as tannins or terpenes, reduces the nutrient availability of dietary resources, and may even be toxic (Milton 1979; Robbins 1983; Owen- Smith 2002). The determination of dietary quality in animals includes that of crude fibre analysis (NDF), acid detergent lignin (ADL), crude protein content and elements such as phosphorus (P) and sodium (Na).

Crude protein, measured in terms of dietary nitrogen (%N x 6.25), is regarded as a limiting component in wildlife diets and is essential for the maintenance and growth of animal tissue (Robbins 1983). The quality of the protein differs between plant species, seasons, growth stage and growth forms categories and is dependent on its amino acid composition (Robbins 1983). Generally nitrogen is high in early growth of plants and is thus elevated during spring and summer, whereas in mature plants and during winter the protein content decreases drastically (Robbins 1983). Protein concentrations are dependent on the ratio of cell wall to cell content; it therefore decreases as fibre content increases (Erasmus et al. 1978; Owen-Smith 2002), and may also be limited by digestion inhibitors and toxins (Robbins 1983).

Dietary fibre has proven useful in determining dietary quality (Van Soest et al. 1991; Sponheimer et al. 2003; Smith et al. 2005) and refers to the plant cell wall, which is composed of cellulose and hemicellulose (Robbins 1983). High fibre content is associated with low quality food (Erasmus et al. 1978; Owen-Smith 1988), as an increase in fibre limits the digestibility of the food and restricts the extraction of cell

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nutrients (Keys et al. 1969; Bell 1971; Janis 1976; Robbins 1983; Owen-Smith 2002). Lignin (an indigestible form of fibre) is one of the main components that limits digestibility and extraction of nutrients from forage, as it is indigestible due to its resistance to enzymatic digestion, fermentation by gastrointestinal flora and acid hydrolysis (Robbins 1983). Secondary compounds are usually bitter in taste acting as feeding deterrents, and may also be toxic (e.g. terpenoids) to gastrointestinal flora (Robbins 1983). Secondary compounds therefore limit the digestibility of consumed vegetation, thereby effectively lowering the available protein (Milton 1979; Robbins 1983; Owen-Smith 2002).

Elements and minerals play an important role in the maintenance of cellular activity and body functions (Robbins 1983), and are crucial in determining animal condition, fertility, production and mortality (Underwood 1977). Essentially two minerals, phosphorous (P) and sodium (Na) are distinguished as being the minerals primarily important in animals’ diets (Weir 1972; Robbins 1983; Ulrey et al. 1997). Phosphorus is essential for metabolism (e.g. energy, nerve tissue and amino acid metabolism), muscle contraction, and along with Calcium (Ca), is largely associated with skeletal formation (Robbins 1983). Phosphorus is therefore essential for growth and reproduction in animals (Robbins 1983). As there is a high ratio of Ca:P (Koen et al. 1988) in the AENP, calcium was not expected to be limited and was therefore not measured.

4.2.3 Sampling approach and procedure The same faecal samples used for microhistological analysis (Chapter 3), were used to estimate diet quality for both sites across sampling periods as well. Sampling occurred over five sampling periods across both sites, from December 2010 to February 2014 (Table 3.1).

Faecal samples were collected in brown paper bags by Dr. M. Landman, marked and oven-dried at 50 °C for a week. Five samples were randomly chosen for each site during each sampling period for determining diet quality. Samples were homogenised, ground through a 1 mm mesh, and then analysed for neutral detergent fibre (NDF), acid detergent fibre (ADF), nitrogen (N) and phosphorous (P). Diet quality analysis was done at the KwaZulu-Natal Department of Agriculture, CEDARA Feed Laboratory. NDF and ADF were determined using the Van Soest method (Van Soest 1981). Nitrogen was determined using the Dumas method (CEDARA Feed Laboratory unpubl. data) and converted to crude protein (%N x 6.25) as is standard practice (Robbins 1983). Phosphorous was determined using

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the dry ashing Hunter’s method (CEDARA Feed Laboratory unpubl. data). All values are expressed as percentage dry matter (% DM).

4.2.4 Statistical analysis To test for differences in diet quality between sites and over sampling periods Analysis of Variance (ANOVA) procedures were used. Percentage data were arcsine transformed for normality and heteroscedasticity of variance (Quinn & Keough 2002). Analyses were performed in Statistica version 12.

4.3 Results Despite the observed changes in diet of elephants in both sections (Chapter 3), there was no difference in diet quality over time (Fig 4.1). All dietary components tested (NDF, ADL, crude protein and P) showed no difference between sites for all sampling periods.

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Site x parameter: December 2010 April 2011 F = 1.84 F = 1.03 DF = 1, 3 DF = 1, 3 P = 0.16 P = 0.39

100 Site x Quality parameter: December 2010 100 Site x Quality parameter: April 2011 90 F = 1.84 90 F = 1.03 80 DF = 1, 3 80 DF = 1, 3 70 P = 0.16 70 P = 0.39 60 July 2011 60 50 F = 0.61 50 40 DF = 1, 3 40 30 P = 0.61 30 20 20 10 10 0 0 NDF ADL Crude protein P NDF ADL Protein P 100 100

SD 90 Site x Quality parameter: July 2011 90 Site x Quality parameter: September 2012

±

F = 0.61 80 80 F = 2.58 70 DF = 1, 3 70 DF = 1, 3 P = 0.61 60 atter 60 P = 0.05 50 m 50 40 40 30 30 20 20

% Dry % Dry 10 10 0 0 NDF ADL Protein P 100 NDF ADL Protein P 90 Site x Quality parameter: February 2014 Figure 4.1 The mean diet quality ( SD) of elephants in the Main 80 F = 1.54 Camp (black) and Colchester (grey) sections over sampling 70 DF = 1, 3 60 periods. Statistics indicate ANOVA outputs for site x diet quality. P = 0.22 50 NDF: Neutral detergent fibre. ADL: Acid detergent lignin. Protein 40 refers to crude protein (Nitrogen x 6.25). P: Phosphorus. 30 20 52 10 0 Diet quality parameter NDF ADL Protein P

4.4 Discussion In this chapter, change in diet quality was tested as a consequence of the change in diet seen in Chapter 3. However, diet quality showed no difference between sites for all the sampling periods, which does not support any of the three hypotheses.

The findings may be due to number of factors: because of the variance in the quality of available resources, elephants in the Colchester section may not be able to increase their diet quality; i.e. the high quality of some resources may therefore be offset by the low quality of other diet resources. This may have resulted in the diet quality of elephants not in increasing in the Colchester section, leading to the subsequent rejection of the H1 hypothesis: elephants as generalist foragers, as well as the H2 hypothesis: elephants as optimal foragers. . There was no support for the

H3 hypothesis: elephants learning forage behaviour. However, in Chapter 3, the lack of change in preference was attributed to be due to the population level at which it was investigated (i.e. the diets of elephant were not described and quantified multiple times for individuals, but rather several individuals from the population were sampled multiple times) . Subsequently, the lack of change in diet quality may be due to this as well; individual elephants may be in different stages of learning foraging behaviour i.e. individuals may have different diet qualities, but the diet quality of the population may not reflect this.

Similar to this study de Klerk (2009) had a small sample size (n=5) for determining diet quality, but found significant differences in the diet quality of elephants seasonally. Seydack et al. (2000) also had a small sample size (n =12) and found significant differences in the diet quality of two elephant populations, one in Knysna and one in the AENP.

Elephants have a strong preference towards certain plant species and growth forms (Chapter 3), but this, however, did not increase their diet quality of elephants in the Colchester section. Preference towards certain plant species and growth forms may therefore not be linked to quality, but other drivers may be responsible for the change in diet.

4.4.1 Diet quality requirements of elephant There is little consensus in the literature as to the diet requirements for elephants (e.g. McCullagh 1969b; Malpas 1977; Meissner et al. 1990; Dierenfeld 1994; Lindsay 1994; Ulrey et al. 1997; Pretorius et al. 2012). Crude protein concentration estimates range from 6% (Malpas 1977) to 14% (Ulrey et al. 1997) dry matter in the diet of elephants. Young growing elephants (<1000 kg) were estimated to require c.

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0.3 kg (10%) of digestible protein daily (McCullagh 1969b; Lindsay 1994), but extrapolations of nutrient requirements of horses (Equus ferus) (Ulrey et al. 1997) estimated that 4-12 year old elephants would require 12% crude protein, whereas adult elephants would require 8% crude protein to maintain body condition, which increases during reproduction to 10-14% (Ulrey et al. 1997; Pretorius et al. 2012). Phosphorous requirements have been estimated at between 0.15 and 0.3% dry matter (Dierenfeld 1994; Ulrey et al. 1997; Wrench et al. 1997; Pretorius et al. 2012) for maintenance in adult elephants which translates to 14 g daily (Sukumar 1989), with young growing elephants and pregnant or lactating females requiring up to 22 g daily (0.3% dry matter) (Sukumar 1989; Ulrey et al. 1997). Acid detergent lignin (ADL) was estimated at 17.4 – 26.5% dry matter for elephants in the South Cape region (Seydack et al. 2000). The natural diet of free-ranging elephants has a high fibre content, and the neutral detergent fibre (NDF) is estimated at 50-70% dry matter (Malpas 1977; Owen-Smith 1988; Dierenfeld 1994) with an average of 62% NDF in diet of Kruger National Park’s elephants (Meissner et al. 1990) The diet quality of elephants in the Main Camp and Colchester sections were considerably higher during the present study, than during a previous study by De Klerk (2009), which employed the same methods. De Klerk (2009) found that the body condition of elephants in the Main Camp section was significantly lower, compared to the body condition of elephants in other reserves. This was due to poor diet quality in the Main Camp section of the AENP and very low rainfall during the study period (De Klerk 2009) (Appendix 2). Faecal phosphorus during the De Klerk (2009) study ranged from 0.12% in spring to 0.25% in summer, whereas in the present study it up to more than three times as high during the sampling periods (0.36%), across sites. Faecal NDF during the de Klerk (2009) study (70.82% - 79.49%) was comparable to the faecal NDF found in this study (74.73% - 82.56%), whereas faecal protein was almost twice as high during certain sampling periods of this study (5.14% - 14.16%) compared to the De Klerk (2009) study (4.29% - 8.55%). The increase in diet quality (increase in faecal phosphorus and faecal protein) during the present study is attributed to the increased rainfall during this study (Fig 2.2), compared to rainfall during the sampling period of de Klerk (2009) (Appendix 2).

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4.4.2 Maintaining diet quality in the Main Camp section The ability of elephants to use relatively low quality resources and still be able to maintain adequate diet quality, makes them successful in a variety of habitats (Owen-Smith 1988; Sukumar 2003). This is attributed to their fast digestive throughput rate and large gut volume which enables them to obtain adequate diet resources by bulk feeding (Owen-Smith 1988). This ability allows elephants to exist at high densities without being density dependant regulated, as was found by Gough & Kerley (2006) in the Main Camp section of the AENP.

Despite the relatively low productivity (Stuart-Hill & Aucamp 1993; Henley 2001) of the Sub-tropical Thicket, it has a high accumulated biomass in its intact state (Penzhorn et al. 1974; Stuart-Hill & Aucamp 1993; Henley 2001). The vegetation occurring in the Sub-tropical Thicket is also evergreen and nutritious (Koen et al. 1988; Stuart-Hill & Aucamp 1993) and is able to sustain production through droughts (Koen et al. 1988; Stuart-Hill & Aucamp 1993; Henley 2001). This may have attributed to the maintenance of diet quality in the Main Camp section by elephants during this study, despite the decline in plant volume, biomass and number of plant species (e.g. Penzhorn et al. 1974; Barratt & Hall-Martin 1991; Stuart-Hill 1992; Moolman & Cowling 1994; Paley & Kerley 1998; Lombard et al. 2001; Kerley & Landman 2006; Landman et al. 2008), and implies that elephants in the Main Camp section was not yet quality or quantity limited in terms of diet resources during this study.

4.4.3 Drivers of changes in the diet of elephants Changes in diet of elephants may be driven by several factors such as the quality of forage, palatability, availability and location of resources and sex and life stage requirements of individual elephants (Laws et al. 1974; Howery et al. 1998; Stokke & du Toit 2000; Greyling 2004)

Despite a change in the diet of elephants in the Colchester section, it was not coupled with an increase in diet quality. This implies that diet quality is not a driver of the observed changes in diet by elephants in the Colchester section, but that other drivers may be facilitating this change in diet with an increase in resources availability.

Palatability may be a driver of changes in the diet of elephants, and is linked to morphological characteristics of forage such as the absence of thorns or spikes (Howery et al. 1998), the absence of secondary compounds (Robbins 1983; Howery et al. 1998), and the presence of preferred constituents (e.g. soluble carbohydrates

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and organic acids) (Robbins 1983). Elephants in the Colchester section may therefore have changed their diet in relation to the availability of palatable resources. This, however, is expected to have been coupled with an increase in diet quality since palatability is reliant on the absence of secondary compounds, which limits the digestibility of consumed forage (Robbins 1983). However, an increase in diet quality was not observed, and there is no evidence of difference in palatability of resources between sites, this is therefore an unlikely driver of the diet changes in the present study.

The availability and location of resources may drive elephants to change their diets: elephants may therefore have consumed resources depending on their abundance (Laws et al. 1974) or proximity to water (Fullman & Child 2013) and not necessarily due to their nutritional composition. This may lead to change in diet, not necessarily related to the quality of forage (Pyke et al. 1977). However, this is contradicted by the strong evidence for preference shown by elephants towards certain plant species and growth forms (Chapter 3).

Life stages and sex not only determines the amount of forage required, but also the feeding behaviour and nutritional demands of animals (Stokke & du Toit 2000; Greyling 2004; Shannon et al. 2006) which can lead to differences in nutrient requirements according to the Body Size Hypothesis (Stokke & du Toit 2000). Mature elephants bulls require larger amounts of forage (a 6000 kg bull will require c. 62 kg dry mass per day (Owen-Smith 1988)), when compared to juveniles and females (Owen-Smith 1988)), and are therefore be able to utilize lower quality forage (Laws et al. 1974; Owen-Smith 1988; Greyling 2004). This may have influenced the diet quality obtained in this study as sex and age information was not available, and samples selected for diet quality analysis may therefore have over or under represented the mean diet quality of the elephant population at both sites. However, this is unlikely as faecal samples were collected to be representative of the population.

The specific drivers associated with changes in diet are therefore difficult to identify and is most often combination of multiple factors (e.g. Kimirei et al. 2013). Therefore pin-pointing the exact cause of the change in the diet of elephants in the Colchester section, without a coupled increase in diet quality remains speculation.

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4.4.4 Consequences and implications of elephants being able to maintain diet quality It is clear that elephants are able to maintain diet quality in the Main Camp section, despite the decrease in resource availability, and that elephants in the Colchester section retain the same diet quality as in the Main Camp section, regardless of the increase in diet resources. This means that elephants in the Main Camp and Colchester sections can continue to forage and impact vegetation, whilst maintaining diet quality, despite the change in resource availability. This allows elephants to impact vegetation, which can lead to overgrazing, without negative consequences to their health, body condition or fitness (Gough & Kerley 2006).

Once the vegetation of Sub-tropical Thickets becomes overgrazed, it loses its ability to sustain production (Koen et al. 1988; Stuart-Hill & Aucamp 1993; Henley 2001), and to regenerate (Lechmere-Oertel et al. 2005a). It was hypothesised by Gough & Kerley (2006) that elephants will eventually deplete the accumulated forage resources in the Main Camp section, which will lead to a decreased diet quality coupled with a decrease in body condition, growth and reproduction rate (Laws & Parker 1968; Owen-Smith 1990; Van der Waal et al. 2003; Gough & Kerley 2006; De Klerk 2009).

4.4.5 Contextualizing the study Due to the lack of information associated with possible drivers of changes in diets, it is difficult to attribute the change in diet to specific factors, other than to speculate on this and the implications thereof. Further investigation into the drivers of diet change is therefore needed, taking multiple factors into account (as outlined in 4.4.3)

The present study found an increase in diet quality of the elephants in both the Main Camp and Colchester sections of the AENP, compared to the de Klerk (2009) study. Since resource quality was not tested in this study, as in de Klerk’s (2009) study, it is not possible to compare resource quality in both the Main Camp and Colchester sections, as well how that influenced the retaining of diet quality in the Colchester section, with increased resource availability.

Further studies are needed specifically focused on drivers of diet quality, as well as the changes in resource quality associated with an increase in resource availability.

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CHAPTER 5 COMPARING MICROHISTOLOGICAL ANALYSIS TO DNA METABARCODING OF FAECES TO DESCRIBE THE DIET OF ELEPHANTS. 5.1 Introduction 5.1.1 Techniques to determine diets of wild herbivores Many procedures exist for the purpose of estimating the composition of herbivore diets: direct observation, fistula techniques, stomach content examination, n- alkanes and faecal analysis (Sparks & Malechek 1968; Bjugstad et al. 1970; Theurer et al. 1976; Holechek et al. 1982). Each of these approaches has limitations and biases, but also holds advantages (See Chapter 1; Holechek et al. 1982).

Faecal analysis holds considerable advantages above the other approaches, including that this technique is useful in comparing diets (Holechek et al. 1982). It also does not interfere with the normal habits of the studied animals, permits unlimited sampling and places no restriction on animal movement (Holechek et al. 1982). Faecal analysis’ greatest disadvantage is accuracy due to the disproportional excretion of consumed forage and the differential digestion of growth forms (Owen 1975; Holechek et al. 1982). It also cannot be determined where the foods were consumed and preference indices regarding habitats can therefore not be accurately assigned (Owen 1975; Vavra & Holechek 1980; Holechek et al. 1982). Faecal analysis tends to overestimate the abundance of grasses and underestimate the abundance of forbs in the diet (Vavra et al. 1978; Holechek et al. 1982). Several techniques exist that allows for the reduction of this source of error: the use of regression equations to correct for the overestimation of grasses, subjecting the samples to microdigestion and in-vitro digestion of collected samples (Vavra & Holechek 1980; Holechek et al. 1982).

Microhistological techniques have grown in popularity since the 1970’s and are considered as one of the most favoured techniques to determine the diet composition of herbivores (Vavra & Holechek 1980; Holechek et al. 1982). Microhistological analysis of faeces allows for the identification of plant fragments from a reference collection, based on their epidermal characteristics (Sparks & Malechek 1968; Holechek et al. 1982). Several advantages exist for this technique: precision is high; fewer samples are needed than with alternative methods to obtain the same level of accuracy (Anthony & Smith 1974; Holechek et al. 1982). Disadvantages include that considerable equipment and labour is required for

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analysis, an extensive plant reference collection is needed, considerable observer training is needed in order to accurately identify plant fragments, many plant species are difficult to separate at species or genus level, identification is tedious and time- consuming, destruction of plant species may occur during preparation and observer bias may occur (Vavra & Holechek 1980; Holechek et al. 1982). Microhistological analysis is known to overestimate fibrous growth forms, and to underestimate less fibrous growth forms (Vavra & Holechek 1980; Holechek et al. 1982). This may be due to the in-vivo digestion and mastication techniques used, which may cause less fibrous growth forms to become damaged to the extent of becoming disintegrated, or unidentifiable (Anthony & Smith 1974; Vavra & Holechek 1980; Holechek et al. 1982).

Although DNA metabarcoding has proven to be a powerful tool in identifying species from environmental samples (e.g. Stoeckle 2003; Fazekas et al. 2008; Valentini et al. 2009a; Taberlet et al. 2012a), it has several limitations. DNA metabarcoding is heavily reliant on the Polymerase Chain Reaction (PCR) process and taxonomic reference databases (Taberlet et al. 2012b). The reliance of DNA metabarcoding on PCR is troublesome as PCR in itself has several limitations: the introduction of errors through the substitution, insertion or deletion of nucleotides (Cline et al. 1996), the difficulty in finding a suitable variable primer (Riaz et al. 2011; Taberlet et al. 2012b) and the constraint of having to analyse different kingdoms and classes separately (Taberlet et al. 2012b). In the case of both techniques, namely microhistological analysis and DNA metabarcoding, reference databases are difficult to construct as they are time and resource consuming to build, require a high level of precision and need taxonomic experts to identify reference material (Taberlet et al. 2012b).

The use of DNA metabarcoding in identifying environmental samples is a relatively new technique, having appeared around the 1980’s (e.g. Nanney 1982; Gibson et al. 1988). It aims to achieve speedy, yet accurate, automated species identification using standard DNA barcodes (Hebert & Gregory 2005). The selection of suitable standard DNA barcodes for plants have proven to be a challenge, due to the variety of criteria they have to meet (summarised in Valentini et al. 2009a; Taberlet et al. 2007). Although this had proven problematic (Hebert et al. 2003), it has been largely overcome with the selection of two 500-800 basepair plastid fragments of the large subunit of ribulose 1,5-bisphosphate carboxylase gene (rbcL) and the maturase K gene (matK) being selected as suitable DNA barcodes for plants (Hollingsworth et al. 2009).

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5.1.2 Hypotheses and aims Microhistological analysis can be seen as a traditional technique in estimating herbivore diets from faeces, whereas DNA metabarcoding is a relatively new emerging technique. Both techniques hold several advantages and disadvantages, and may prove to be complimentary in describing the diets of herbivores: microhistological analysis is limited in particular by the over-estimation of fibrous plants and inherent observer bias (Owen 1975; Vavra & Holechek 1980; Holechek et al. 1982), whereas DNA metabarcoding is limited by its dependence on PCR (Taberlet et al. 2012b).

The aim of this chapter was to contrast the outputs of microhistological analysis, with DNA metabarcoding. By contrasting the two techniques, the hypothesis will be tested that DNA metabarcoding will provide increased numbers of identified plant families in the diet of elephants, due to its taxonomic resolution. It is further hypothesised that because microhistological analysis is known to overestimate grasses (Vavra & Holechek 1980; Holechek et al. 1982), DNA metabarcoding will show relatively lower estimates of grasses and higher estimates of browse in the diet of elephants.

5.2 Methods 5.2.1. Sampling approach Forty-eight fresh faecal samples in total (n=24 in each section) were collected during February 2014 in the Main Camp and Colchester sections of the AENP (Chapter 3). Each sample ( 1 – 5kg) was homogenised, a subsample taken for DNA metabarcoding and the rest of the faecal sample used for microhistological analysis, except for the eight samples that were not used for diet identification in Chapter 3. To test the differences between techniques, samples were combined between sites.

5.2.2 Microhistological analysis The same method for the digestion and analysis of faecal samples was followed as set out in Chapter 3.

5.2.3 DNA metabarcoding Sample analysis was conducted at the Laboratoire d'Ecologie Alpine in Grenoble, France, by Dr. P. Taberlet and his team. Sample analysis followed methods for the P6 loop of the trnL intron (Taberlet et al. 2007) as described in De Barba et al. (2014) (see Appendix 3).

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5.2.4 Statistical analysis DNA metabarcoding had identified a total of 11353250 sequences available for identification from the faecal samples, whereas a total of 4800 fragments had been identified with faecal microhistological analysis which was available for identification. Due to Genbank being used for the identification of sequences in DNA metabarcoding, the taxonomic resolution between the two techniques differed vastly: using DNA metabarcoding, 73.9% of the sequences could not be identified to species or genus level, whereas with microhistological analysis 5.0% of the fragments could not be identified to species level. However, at higher taxonomic levels (i.e. family), 0.4% of the sequences could not be identified to family level by DNA metabarcoding and 1.4% of the fragments could not be identified to family level using microhistological analysis. Due to these differences in resolution at lower taxonomic levels (genus and species level), comparisons between the two techniques were made at the family level. Genbank was therefore not a suitable reference collection, as many sequences was not present or could not be identified to species or genus level.

The diets of elephants during February 2014, identified by microhistological analysis and DNA metabarcoding were contrasted at several levels: grouping plant species into broad growth forms; i.e. grasses and browse, all plant families identified in the diet, plant families that correspond with principal diet items (PDI) and by calculating the Coefficient of Variance (CV). The CV was calculated for the mean number of families identified for each of the techniques used. This provides a measure of the relative amount of variability within the diet identified between techniques. To test for differences in the percentage contribution of grass and browse to the diet, as identified from the two techniques, Analysis of Variance (ANOVA) was used. All percentage data were arcsine transformed for normality and heteroscedasticity of variances (Quinn & Keough 2002). A Dunnett post-hoc test was performed to determine where the significant differences identified in the diets between techniques lie (Quinn & Keough 2002). Analyses were performed in Statistica version 12.

Principal diet items (PDI) families are the families corresponding to the sequences or fragments identified as collectively contribute to the bulk of the diet (Petrides 1975). All identified families were summed and PDI families were estimated. Method follows Landman et al. (2013) as set out in Chapter 3.2.6.

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A χ2 goodness-of-fit was used to test the null hypothesis that there was no difference in the total number of plant families and PDI families identified between techniques (Quinn & Keough 2002).

5.3 Results Of the 48 samples used to identify the diet of elephant in both the Main camp and Colchester sections, one of the samples was unsuccessful in being identified with DNA metabarcoding and was therefore excluded from analysis. ANOVA analysis revealed significant interactions between the different techniques used and the percentage contribution of growth forms to the diet of elephants (F = 69.96; DF= 1, 1; P < 0.001). A Dunnett post-hoc test revealed significant differences: the contribution of grasses to the diet of elephants was found to be significantly higher (P < 0.001) when identified by microhistological analysis. Whereas the contribution of browse to the diet of elephants was found to be significantly higher (P < 0.001) when identified by DNA metabarcoding, than microhistological analysis (Fig. 5.1).

120 * 100

80

60 diet 40 *

20 Percent contribution to thePercentcontribution to 0 Grass Growth forms Browse Figure 5.1 The percentage contribution (± SD) of growth forms in the diet of elephants identified using microhistological analysis (black) and DNA metabarcoding (grey). Asterisks (*) indicate a significant difference between techniques at α = 0.05.

A total of 44 and 28 plant families were identified in the diet of elephants using microhistological analysis and DNA metabarcoding, respectively (Fig. 5.2).

2 However, there was no difference (χ 1 = 3.13; P > 0.05) between techniques in the number of families identified. Of the identified families, twenty-three plant families were identified in common by both the microhistological analysis and DNA metabarcoding. Microhistological analysis identified 8 PDI families, whereas DNA metabarcoding identified 18 PDI families in the diet of elephants (Fig. 5.3). There

2 was also no difference (χ 1 = 3.12; P > 0.05) between techniques in the number of PDI families identified.

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50 45 40 35 30 25 20 15 10

5 Number ofplant families identified 0 Microhistological analysis DNA metabarcoding

Technique

Figure 5.2 The total number of plant families (grey) and the number of families identified as PDI (black), identified in the diet of elephants.

The CV revealed that variation between samples in the mean number of plant families identified between techniques was higher when identified using DNA metabarcoding (CV = 29%), compared to microhistological analysis (CV = 14%).

Twenty-five families were identified with microhistological analysis, which were not identified with DNA metabarcoding: Capparaceae (4.7% contribution to the diet), Crassulaceae (2.6%), Euphorbiaceae (2.5%) and Portulacaceae (2.5%), among other families. Five families were identified with DNA metabarcoding that had not been identified with microhistological analysis: Bignoniaceae (0.4% contribution to the diet), Geraniaceae (0.1%), Rubiaceae (0.1%), Salicaceae (0.5%) and Santalaceae (0.2%) (Appendix 4).

Using microhistological analysis, Poaceae was identified as the most dominant plant family, which contributed 26.7% to the total diet, followed by Apocynaceae (7.5%) and Celastraceae (4.9%). DNA metabarcoding identified Fabaceae (16.1% contributed to the total diet) as the dominant plant family in the diet of elephants, followed by Poaceae (12.4%) and Celastraceae (11.7%) (Fig. 5.3).

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40 35 30 25 20 15 10

Percent contribution to diet Percentcontributionto 5

0

Poaceae

Vitaceae

Fabaceae

Aizoaceae

Cactaceae

Araliaceae

Malvaceae

Ebenaceae

Sapotaceae

Sapindaceae

Celastraceae

Acanthaceae

Capparaceae

Crassulaceae

Apocynaceae

Boraginaceae

Asparagaceae

Anacardiaceae Amaranthaceae PDI families Plumbaginaceae

Figure 5.3 The mean percentage contribution (± SD) of the identified PDI families to the diet of elephants, using microhistological analysis (black) and DNA metabarcoding (grey).

5.4 Discussion 5.4.1 Comparison of the two techniques The hypothesis that DNA metabarcoding would provide a lower proportion of grass to the diet of elephants, compared to microhistological analysis, was supported. This reflected the higher proportion of grass identified in the diet of elephants, when identified by microhistological analysis.

The hypothesis that DNA metabarcoding will provide an increased number of identified families, compared to microhistological analysis was rejected. This is due to the higher number of families identified by microhistological analysis. However, the Coefficient of Variance (CV) indicated a greater variance in the mean number of plant families identified per sample when using DNA metabarcoding. Therefore DNA metabarcoding may identify more variance in the samples when identifying sequences i.e. plant species that contribute little to the diet.

Soininen et al. (2009) compared the use of microhistological analysis and DNA metabarcoding in describing the diet of two vole species, Microtus oeconomus and Myodes rufocanus. Results showed that there was agreement in terms of plant groups (e.g. eudicots, monocots, monilophytes, euphyllophytes and bryophytes) identified by the two techniques, but that DNA metabarcoding provided more taxonomic detail on the diet, identifying 13 and 17 plant species, compared to 9 and 11 plant species identified by microhistological analysis, respectively (Soininen et al.

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2009). This however was attributed to the taxonomic scaling differences between the two techniques, as less than 20% of the fragments could be identified to genus level using microhistological analysis (Soininen et al. 2009). In the present study 73.9% of the sequences could not be identified to species or genus level using DNA metabarcoding, whereas with microhistological analysis 5.0% of the fragments could not be identified to species level. This was attributed to the incomplete reference collection, Genbank, used to identify the sequences for DNA metabarcoding. Therefore, in the present study the resolution of microhistological analysis was better than that of the DNA metabarcoding.The study by Soininen et al. (2009) found broad agreement between microhistological analysis and DNA metabarcoding. This contrasts the current study: although there was some agreement between the two techniques, there were also large discrepancies in terms of the percentage contribution of growth forms to diet of elephants, the plant families identified by one technique, but not the other and the PDI families identified by the techniques.

The overestimation of grass through microhistological analysis (Vavra & Holechek 1980; Holechek et al. 1982), was assumed to be compensated for by DNA metabarcoding, as it does not have the inherent limitations (e.g. process of in-vivo digestion, observer bias and training needed in identifying the plant epidermal fragments) of microhistological analysis.

Several plant families identified in the diet of elephants were identified by one of the techniques, but not with the other. The plant families identified in the diet of elephants with microhistological analysis, but not with DNA metabarcoding could be attributed to two possibilities: it could be a fault in the microhistological identification; i.e. that it was a mistake in the identification of the plant epidermal fragment, and that the plant family was not present in the diet of elephants. This was however unlikely due to the confirmation, by faecal microhistological analysis and direct observation, of these plant families in the diet of elephants in the AENP by previous studies: e.g. Portulacaceae and Capparaceae are known to be important food sources for elephants (Paley & Kerley 1998; Davis 2004; Landman et al. 2008; Landman et al. 2012). The alternative possibility is that the DNA sequences needed to identify the taxa are absent from Genbank, or the markers used to identify the sequences do not correspond to the sequence markers on Genbank and therefore identification could not be possible. Sequences for Capparaceae, Euphorbiaceae and Ptaeroxylaceae are present on Genbank for the identification of taxa, however Portualacaceae was not present on Genbank. The use of incorrect markers to

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identify the sequences, is therefore deduced to be the cause of plant families not being identified using DNA metabarcoding. The plant families identified in the diet of elephants with DNA metabarcoding, but not with microhistological analysis, was attributed to their rarity in the diet, as these plant families each contributed less than 1% to the diet, making it difficult to identify these families by microhistological analysis. Alternatively this may be due to observer error, an erogenous identification in the reference collection, misidentification or due to in-vivo digestion which may have caused damage to some fragments, making them unable to be identified.

Despite the reduced number of plant families identified with DNA metabarcoding, the Coefficient of Variance (CV) for the mean number of plant families identified in the diet of elephants was significantly higher when identified by DNA metabarcoding. This indicates that DNA metabarcoding gives more varied results in terms of the number of families identified in the diet of elephants, which was expected. This indicates a sampling effect: it may be due to the fact that the DNA metabarcoding is relatively unrestricted in terms of the number of sequences that may be identified, whereas microhistological analysis is specifically restricted to the first 100 identifiable plant epidermal fragments (Landman et al. 2008). This allowed DNA metabarcoding to identify as many plant families as possible per faecal sample, whereas microhistological analysis was restricted to a total possible 100 families i.e. if every plant epidermal fragment identified as belonged to a different plant family, a maximum number of 100 plant families would be identified per faecal sample.

5.4.2 The way forward Very few studies (e.g. Soininen et al. 2009; Ando et al. 2013) could be found that compared microhistological analysis and DNA metabarcoding available for describing and quantifying the diet of herbivores from faecal samples. No other study could be found that compared the diet of megaherbivores using DNA metabarcoding and microhistological analysis, which makes this novel. It is important to investigate the diet of megaherbivores using DNA metabarcoding, due to their large diet breadth, as it may aid in identifying and describing their diets quickly, when compared to microhistological analysis, which requires a considerable amount of time (Holechek et al. 1982). This is also useful as these techniques may be used in conjunction to provide more reliable results.

Several limitations within both techniques have been highlighted, with their dependency on taxonomic reference collections being the most limiting factor.

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Because DNA metabarcoding is still relatively new, public taxonomic reference collections such as Genbank are incomplete, with several DNA sequences being absent, incomplete or inaccurate (Yoccoz 2012; Cristescu 2014). Thus, DNA metabarcoding should be considered as a complimentary technique, instead of a replacement for classic techniques (Yoccoz 2012). Although DNA metabarcoding is considered to be the way forward in the identification of complex environmental samples, e.g. faecal, water or soil samples (Yoccoz 2012; Cristescu 2014), classic techniques such as microhistological analysis should not be discarded (Yoccoz 2012). Microhistological analysis has had more than 30 years of refining the technique, modifying procedures as to minimise bias and to obtain accurate quantification of samples (Vavra & Holechek 1980; Holechek et al. 1982; Yoccoz 2012). Microhistological analysis has been established in the present study to be more useful than DNA metabarcoding in identifying samples to lower taxonomic resolution, due to the lack of reference collection in DNA metabarcoding. DNA metabarcoding is still in the process of being refined, and will require considerable time before this procedure is perfected (Yoccoz 2012). This study highlights the need for further investigation and comparison between the two techniques, especially at higher taxonomic resolutions.

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CHAPTER 6 SUMMARY AND CONCLUDING REMARKS

This study set out to determine how the diet of elephant responds to an increase in resource availability. This has not been tested before and thus this study set out to address this gap in knowledge. The changes in diet by elephants were determined temporally, over five sampling periods, with an increase in resource availability. This was done by addressing four aims:

1) describing the diet of elephants, to determine how this changes with an increase in diet resource availability (Chapter 3)

2) estimating how the diet preference of elephants changes with an increase in resource availability (Chapter 3)

3) determining how diet quality responds as a consequence of a change in diet with an increase in resource availability (Chapter 4)

4) contrasting the identified diet of elephants using microhistological analysis and DNA metabarcoding of faeces (Chapter 5)

This study achieved these four aims set out in Chapter 1, and in doing so improved our understanding of how elephants respond to an increase in resource availability. However, there are some limitations in this study that should be taken into account that may constrain these results in being generalised across elephant populations. The specific limitations, strengths and recommendations have been detailed in each of the three data chapters (Chapters 3, 4 and 5), and overarching themes are discussed in this Chapter.

6.1 Synthesis of results 6.1.1 How the diet, preference and diet quality of elephants changes, with an increase in resource availability. To address the overall aim of this study, it was intended to test how the diet breadth, preference and diet quality of elephants responds to an increase in resource availability. Three alternative hypotheses were contrasted to test this:

H1 Elephants as generalist foragers: would broaden their diet breadth (by including novel resources) with an increase in resource availability, but preference would remain constant as they maximise uptake by foraging on available items. This was expected to be coupled with a slight increase in diet quality in the Colchester

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section, as ‘higher quality’ resources were presumed to be available, due to the absence of elephants for more than 50 years (Whitehouse & Hall-Martin 2000).

H2 Elephants as optimal foragers: Optimal Foraging Theory (MacArthur & Pianka 1966; Pianka 1994), predicts animals will select for the best quality food items. It was therefore expected that the dietary breadth of the elephants should decrease sharply as they increase the proportion of preferred resources in their diet, which were assumed to be abundant in the Colchester section. This was expected to be coupled with a sharp increase in diet quality.

H3 Elephants learning foraging behaviour: may have employed a strategy which was a combination of the elephants as generalist foragers hypothesis and Optimal foraging theory (MacArthur & Pianka 1966). Elephants are known to have a learning culture (Sukumar 1990; Lee & Moss 1999), which may have allowed them as generalists to learn about novel resources and as to whether or not they should be consumed, preferred or avoided. It was therefore hypothesised that they would increase their diet breadth gradually over time, while learning behaviour occurs, which would then narrow off gradually. Preference and diet quality was expected to increase, but would due to subsequent learning events, showing a gradual increase.

Using microhistological analysis of faeces to describe the diet of elephants in the Main Camp and Colchester sections of the AENP, over five sampling periods, it was determined that the diet breadth of elephants increased with an increase in resource availability in the Colchester section. This was linked to a high initial variance between the diets of elephants in the Colchester section, which decreased in subsequent sampling periods. This showed support for the H3 hypothesis: elephants learning foraging behaviour (Chapter 3).

The diet preference of elephants was estimated by relating the forage availability estimates to their diet use. However, the results showed no difference in the preference of elephants between the two sections; i.e. there was no change in preference with an increase in resource availability in the Colchester section. This supported the H1 Hypothesis: elephants as generalist foragers.

As a consequence of a change in diet (increase in diet breadth, but no change in preference) by elephants following the increase in resource availability, it was expected that diet quality would change as well. Using faecal samples, the diet quality of elephants was determined in the Main Camp and Colchester sections. Results showed that there was no difference between the diet quality of elephants in

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the Main Camp and Colchester sections. This did not support any of the three hypotheses. Therefore, a change in the diet due to an increase in resource availability, did not improve diet quality. Diet quality was, therefore, not a driver of a diet change. Elephants in the Main Camp section was able to maintain diet quality, despite the reduction in plant biomass, volume, canopy height and cover as well as plant species richness (e.g. Penzhorn et al. 1974; Barratt & Hall-Martin 1991; Stuart-Hill 1992; Moolman & Cowling 1994; Johnson et al. 1999; Lombard et al. 2001; Kerley & Landman 2006; Landman et al. 2013; Landman et al. 2014)) (see Chapter 2 for details).

Previous studies also found an increase in the diet breadth of elephants, with an increase in diet resources in Subtropical Thickets (e.g. Davis 2004), and in other habitats (e.g. Codron et al. 2006). Most studies related to changes in diet breadth of elephant focus on seasonal changes (e.g. McCullagh 1969a; Laws 1970; Koch et al. 1995; Cerling et al. 2004; Osborn 2004; Codron et al. 2006) in the diet of elephants. Whereas, the present study occurred over five sampling periods, focussing on the changes in the diet of elephants, with an increase in diet resource availability, and this is therefore novel.

The time frame in which this study was conducted may have been the limiting factor in supporting the two alternative hypotheses. The H3 hypothesis: elephants learning foraging behaviour, was hypothesised to be the link between the H1 hypothesis: elephants as generalist foragers, and the H2 hypothesis: elephants as optimal foragers (Fig 3.1). Therefore, given enough time, the Optimal Foraging Theory or elephant as generalist foragers hypothesis may have been supported.

The implications of the H3 hypothesis: elephants learning foraging behaviour, being supported, are that the diets of elephants following an increase in resource availability will be highly varied. This might mean that the elephants herbivory on plant communities will be highly varied initially. This will considerably increase the difficulty of predicting impacts, at least initially, and identifying suitable plant species as indicators of elephants impacts (Landman et al. 2008). However, as learning behaviour occurs the variation between elephant’s diets (in the section with increased resources) decreases. This implies that the initial diet of elephants, following re-introduction into the section with increased resource availability, is a weak indicator of potential long-term impacts, due to the high initial variance between diets. Initially impacts will strongly be focused on preferred novel resources, but that as learning behaviour occurs elephants will focus their diet on a

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narrower range of resources. The change in diet is assumed to be due to the availability of preferred resources and that as they are removed, their preference will shift down the scale. This means that elephants will shift their diet down the preference scale, showing a cascading series of impacts (Kerley & Landman 2006).

6.1.2 Comparing the diet of elephants using microhistological analysis to DNA metabarcoding of faeces To address the fourth aim of this study, the diet of elephants identified using microhistological analysis and DNA metabarcoding of faeces was contrasted during a single sampling period (Chapter 5). It was hypothesised that DNA metabarcoding would provide a lower proportion of grass:browse in the diet of elephants, compared to microhistological analysis (as microhistological analysis is known to overestimate the abundance of grass in the diet (Holechek et al. 1982)). Results indicated that microhistological analysis identified a significantly greater proportion of grass (26.7%) in the diet of elephants, compared to DNA metabarcoding (12.7%). This was consistent with the set hypothesis. It was further hypothesised that DNA metabarcoding would identify greater numbers of plant families than microhistological analysis. This was hypothesised due to the fact that DNA metabarcoding is relatively unrestricted in terms of the number of sequences that can be identified (Taberlet et al. 2012b). However, results showed that microhistological analysis provided more identified plant families. This was due to the incomplete reference collection, Genbank, used in the identification of the diet of elephants using DNA metabarcoding. This highlights the need for an adequate reference collection in the use of both microhistological analysis and DNA metabarcoding.

This overestimation of grasses by microhistological analysis in this study was in accordance with previous studies (see Chapter 1; Vavra et al. 1978; Holechek et al. 1982). However, as the over- or underestimation of growth forms by DNA metabarcoding has not yet been established. The difference in taxonomic resolution highlighted, once again, the importance of reference collections in both techniques. The results obtained from the present study suggest that either of the techniques can be used to describe the diet of elephants, but it is hypothesised that the most reliable results would be obtained when using both techniques.

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6.2 Contextualizing the study This study was characterised by its robust sampling approach, determining diet, preference and diet quality of elephants over multiple sampling periods, with an increase in resource availability in one of the sites. There were, however, limitations to this study.

Although sampling occurred over five sampling periods, it is hypothesised that this may have been inadequate to show support for the two alternative hypotheses (H1 hypothesis: elephants as generalist foragers, and the H2 hypothesis: elephants as optimal foragers). As elephants learn foraging behaviour, their diet breadth, preference and diet quality may approach the Optimal foraging hypothesis, or the generalist foragers hypothesis (Fig.3.1). This may occur given enough time, but the five sampling periods occurring over four years (Table 3.1) might have been inadequate to observe these changes.

Sampling, (Samples prior to February 2014 were collected by Dr. M. Landman. During February 2014, samples were collected by Dr. M. Landman and myself), occurred over multiple sampling periods between the two sections, but treatments (i.e. sections) were not replicated, i.e. pseudoreplication occurred (Hurlbert 1984). This may be a limiting factor that prevents this study from being generalised across elephants populations. The nature of the study sites was an inherent limitation to this study, as elephants could freely move between the sections. This was overcome by the use of radio tracking collars and the identification of distinguishable herds, to monitor their movements between sections and to ensure residency within a section, prior to faecal sample collection (M. Landman pers. comm.). This was done to ensure that the faecal samples collected, represented the diet of elephants within that section.

Despite these limitations, there were also several strengths to this study. By adopting a time lapse approach, with sampling occurring on 5 occasions within both the Main Camp and Colchester sections, it was possible to track diet breadth of elephants with an increase in diet resource availability. This is novel, since most studies (e.g. Codron et al. 2006; De Klerk 2009; Landman et al. 2013) focus on a single sampling occasion or seasonal sampling. While most studies focus on determining broad diet shifts, in terms of grass:browse proportions, without determining preference (e.g. Codron et al. 2006), this study was robust in its ability to identify use and preference by elephant at species level over several sampling seasons. The estimation of preference over three sampling periods was a novel

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aspect of the present study, and allowed for the changes in preference over time to be explored between sites.

6.3 The way forward This study provided preliminary insights into how the diet, preference and diet quality of elephants responds following an increase in resource availability. However, there are several questions that are unanswered.

Firstly, the replication of treatments in a similar study is recommended, as this will provide more robust results in terms of being able to generalise the results between elephants populations. A possible research question may be:

 Does the diet, preference and diet quality of different elephant populations respond similarly to an increase in resource availability in different reserves (with the same vegetation type)?

It is also recommended to increase the temporal scale of the study, as this will allow alternative hypothesis to be considered given enough time. A potential research question may be:

 How does the diet of elephants change in the long-term (10+ years) due to an increase in resource availability?

There also exists a need to further investigate the potential change in diet quality as a consequence of changes in diet, with replication occurring over multiple sites, incorporating the quality of resources across sites. This would allow the changes in diet quality to be linked to the change in resource quality, which may aid in understanding why elephants show diet changes with an increase in resource availability. Potential research questions may include:

 Does the diet quality of individual elephants change, due to changes in the diet and diet preference as a consequence of increased resource availability?  Does resource quality increase with an increase in resource availability?  Does resource quality improve in areas where elephants are excluded in the long-term?  If diet quality does not act as a driver for the change in diet and diet preference of elephants , what drives elephants to change their diet due to an increase in resource availability?

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The present study was a pilot project in comparing DNA metabarcoding to microhistological analysis for megaherbivores, and has highlighted the need for similar studies, as this would allow the differences and limitations of the two techniques to be investigated. However, it is strongly recommended that for both techniques reference collections are constructed or updated. This will assist with accurate identification of plant species to the highest possible taxonomic resolution, and will aid in comparing the two techniques, as well as accurately describing the diets. The need also exists to investigate the accuracy of DNA metabarcoding in identifying diets of herbivores.

Potential research questions may include:

 How accurately does DNA metabarcoding describe the diet of herbivores?  How well does microhistological analysis of faeces describe the diet of megaherbivores, compared to DNA metabarcoding, when using diets of known composition?

6.4 Conclusion In Africa, elephant populations are at two opposite ends of the scale: in some regions elephants vulnerable to extinction, whilst in other regions elephants are overabundant (van Aarde & Jackson 2007). Elephant populations are now commonly being established in small, fragmented or enclosed, protected areas in South Africa (Blanc et al. 2003). Within these areas, population growth rates are often very high (Slotow et al. 2005), which in turn places resources under severe strain, leading to the decline in resource availability, plant biomass and volume (Penzhorn et al. 1974; Barratt & Hall-Martin 1991; Moolman & Cowling 1994; Lombard et al. 2001; Matthews et al. 2001; Cerling et al. 2004; O’Connor et al. 2007), alteration of plant communities (Laws 1970; Corfield 1973; Stuart-Hill 1992; Conybeare 2004; Kerley & Landman 2006; Landman et al. 2008), and transformation of landscapes (Gaylard et al. 2003; Skarpe et al. 2004).

An hypothesised increase in resource availability has been shown in the Colchester section of the AENP, to lead to an increase in diet breadth with highly varied diets between elephants initially. This means that the impacts of elephants will also be highly varied initially, which makes the identification of plant species suitable to monitor these impacts difficult. Over time, elephants learn foraging behaviour, which decreases the variation between the diets of elephants, resulting in more similar diets. It has also shown that elephant impacts should not only be assessed

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annually, but seasonally as the use and preference of elephants changes in response to seasonal variations in resources and periods of increased nutrient availability, i.e.fruiting.

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A ppendix 1 The proportion (± SD) of diet for the Principal Diet Items indicating significant preference (+) or avoidance (-) for the M ain Camp and the Colchester sections. Dashes indicate that the item was absent from the diet, n-PDI indicates that the item was not considered to be a Principal Diet Item. Underlined species indicates species vulnerable to elephant herbivory (Penzhorn et al . 1974; Barratt & Hall-M artin 1991; M idgley & Joubert 1991; Lombard et al . 2001) 10-Dec 11-Apr 11-Jul 12-Sep 14-Feb M ain Camp Colchester M ain Camp Colchester M ain Camp Colchester M ain Camp Colchester M ain Camp Colchester Grasses Poaceae Aristida diffusa - 1.75 ± 2.10 + - - n-PDI n-PDI 1.90 ± 3.11 + n-PDI - n-PDI Poaceae Cymbopogon pospischilii 1.40 ± 2.06 + - - - 1.10 ± 2.31 - 1.20 ± 2.53 + - n-PDI - Poaceae Cynodon dactylon 11.25 ± 3.43 + 11.25 ± 1.83 + ### ± 1.68 + ### ± 5.02 + 11.05 ± 1.36 ### ± 1.47 11.75 ± 1.41 + 11.05 ± 1.54 + ### ± 1.19 11.00 ± 1.17 Poaceae Enneapogon scoparius - - - 1.95 ± 3.85 + ------Poaceae Eragrostis curvula 3.05 ± 2.91 + n-PDI 8.00 ± 5.39 + 3.65 ± 4.78 + 4.25 ± 3.73 8.05 ± 2.65 2.60 ± 3.07 + 2.15 ± 3.13 + 3.25 ± 3.42 2.55 ± 3.27 Poaceae Eragrostis obtusa n-PDI n-PDI n-PDI - 3.45 ± 3.41 - 3.05 ± 3.33 + 1.20 ± 2.55 + 1.95 ± 2.76 n-PDI Poaceae Eustachys paspaloides - - - 5.10 ± 5.49 + - - - - 1.30 ± 2.39 - Poaceae Panicum deustum 3.10 ± 4.41 + 1.55 ± 2.39 - 8.85 ± 6.47 + 2.30 ± 5.07 + 3.20 ± 4.07 5.90 ± 4.68 3.45 ± 4.06 + 3.60 ± 3.83 + 3.20 ± 4.21 2.60 ± 3.68 Poaceae Panicum maximum - 1.10 ± 1.83 + - - 3.35 ± 4.02 n-PDI 2.60 ± 3.68 + - n-PDI n-PDI Poaceae Pennisetum clandestinum 2.15 ± 3.51 + 2.40 ± 2.89 + - 2.50 ± 5.86 _ 4.80 ± 3.94 2.90 ± 4.29 1.35 ± 3.01 + 2.70 ± 3.89 + 3.80 ± 4.07 2.75 ± 3.65 Poaceae Sp. O n-PDI ------Poaceae Sporobolus fimbriatus - 2.45 ± 3.27 + - 3.80 ± 4.95 + - 3.45 ± 3.98 - 1.40 ± 2.58 + - 1.75 ± 2.53 Poaceae Stipa dregeana 1.75 ± 3.19 + 1.55 ± 2.65 + - - - - 1.25 ± 2.57 + - n-PDI - Poaceae Themeda triandra 1.75 ± 2.57 + - - - - 2.05 ± 2.86 1.75 ± 2.90 + 1.50 ± 2.40 + 1.30 ± 2.43 1.70 ± 2.54 Woody shrubs Fabaceae Acacia karroo 1.85 ± 1.98 + 2.50 ± 2.52 - 1.70 ± 2.75 + n-PDI 1.45 ± 2.35 1.35 ± 2.46 1.65 ± 2.35 - n-PDI 1.95 ± 2.33 1.35 ± 2.16 Apocynaceae Asclepias fruticosa 1.35 ± 1.79 + - - 1.50 ± 2.33 + - 1.85 ± 3.05 - n-PDI n-PDI n-PDI Asparagaceae Asparagus africanus n-PDI - n-PDI n-PDI n-PDI n-PDI - 1.30 ± 2.23 + - n-PDI Asparagaceae Asparagus crassicladus n-PDI n-PDI n-PDI 2.45 ± 2.98 + n-PDI n-PDI n-PDI n-PDI n-PDI n-PDI Asparagaceae Asparagus densiflorus - n-PDI - - 1.40 ± 2.09 - n-PDI - - - Asparagaceae Asparagus racemosus - 1.45 ± 1.88 + - 1.55 ± 1.96 + - - - n-PDI n-PDI - Asparagaceae Asparagus sp. ------n-PDI - 1.20 ± 1.96 n-PDI Asparagaceae Asparagus striatus n-PDI - n-PDI - n-PDI n-PDI n-PDI - n-PDI - Asparagaceae Asparagus suaveolens - - - n-PDI - - - - - n-PDI Asparagaceae Asparagus subulatus 1.60 ± 2.14 + 1.50 ± 2.21 + 1.55 ± 2.11 + - - n-PDI n-PDI - n-PDI - Salvadoraceae Azima tetracantha 4.90 ± 2.77 - 3.75 ± 3.42 + 4.55 ± 3.49 - 4.00 ± 3.58 + 3.55 ± 2.76 4.00 ± 2.68 5.25 ± 2.94 + 7.60 ± 2.21 + 6.30 ± 0.98 6.00 ± 2.55 Capparaceae Boscia oleoides - n-PDI n-PDI - 1.25 ± 2.22 n-PDI n-PDI - 1.20 ± 2.17 - Capparaceae Cadaba aphylla n-PDI - n-PDI - n-PDI n-PDI n-PDI - n-PDI n-PDI Capparaceae Capparis sepiaria 1.45 ± 2.11 - 1.55 ± 2.16 - 2.60 ± 2.64 - - 2.45 ± 2.28 n-PDI 1.70 ± 2.47 - n-PDI 1.85 ± 2.37 1.35 ± 2.16 Apocynaceae Carissa bispinosa 2.90 ± 2.43 - 2.15 ± 2.58 - 2.60 ± 2.60 + - 3.05 ± 2.72 n-PDI 4.35 ± 2.64 - n-PDI 5.20 ± 2.07 - Apocynaceae Carissa haematocarpa ------n-PDI Celastraceae Cassine peragua - n-PDI - 2.05 ± 3.02 + - 1.65 ± 2.72 - 1.55 2.19 + - n-PDI Rutaceae Clausena anisata - - - - - n-PDI - - - - Euphorbiaceae Clutia affinis 1.10 ± 1.97 + - n-PDI 1.80 ± 2.59 + 1.45 ± 2.39 n-PDI 2.65 ± 2.83 + 1.15 ± 2.16 + 1.90 ± 2.59 1.95 ± 2.61 Arailaceae Cussonia spicata - 1.60 ± 2.06 + - 1.95 ± 2.80 + - 1.35 ± 2.28 - 3.85 ± 2.91 + - 4.45 ± 3.24 Ebenaceae Diospyros dichrophylla n-PDI 1.50 ± 1.93 + n-PDI n-PDI n-PDI n-PDI n-PDI 1.05 ± 1.99 + n-PDI n-PDI Salicaceae Dovyalis caffra n-PDI - n-PDI - - n-PDI - - - - Boraginaceae Ehretia hottentotica ------Boraginaceae Ehretia rigida n-PDI 1.30 ± 2.00 + n-PDI n-PDI - - 1.35 ± 2.48 + 2.15 ± 3.18 + n-PDI 2.90 ± 2.69 Ebenaceae Euclea undulata n-PDI 1.60 ± 2.39 - 2.05 ± 2.54 - 1.45 ± 2.09 - n-PDI 1.55 ± 2.50 1.50 ± 2.46 - 2.30 ± 2.56 - 2.70 ± 2.62 1.65 ± 2.16 Phyllanthaceae Flueggea verrucosa - - - n-PDI - - - - n-PDI - M alvaceae Grewia occidentalis - n-PDI ------n-PDI M alvaceae Grewia robusta - 1.15 ± 1.90 - - 1.95 ± 2.76 + - n-PDI - 2.15 ± 2.48 + - 2.20 ± 2.63 Celastraceae Gymnosporia capitata n-PDI - n-PDI n-PDI 1.10 ± 2.27 - n-PDI - 1.55 ± 2.48 - 96

Celastraceae Gymnosporia heterophylla 2.50 ± 3.32 + n-PDI 2.65 ± 3.53 + n-PDI n-PDI 2.15 ± 3.57 2.30 ± 2.74 + n-PDI - - Celastraceae Gymnosporia polyacanthus 1.95 ± 2.54 + n-PDI 3.35 ± 3.86 + 5.60 ± 4.79 + n-PDI n-PDI n-PDI 1.40 ± 2.58 + 2.05 ± 2.76 2.40 ± 2.84 Apiaceae Heteromorpha arborescens - n-PDI - n-PDI - n-PDI - n-PDI - n-PDI Sapindaceae Hippobromus pauciflorus ------n-PDI Oleaceae Jasminum angulare ------n-PDI - - Euphorbiaceae Jatropha capensis 1.65 ± 2.46 + n-PDI n-PDI - 1.25 ± 2.27 - 1.75 ± 2.34 + - n-PDI - Solanaceae Lycium ferocissimum - - - n-PDI n-PDI - - n-PDI - n-PDI Capparaceae M aerua cafra n-PDI 1.10 ± 1.83 + 1.60 ± 2.14 + - 1.40 ± 2.09 - n-PDI - 1.75 ± 2.47 - Celastraceae M ystroxylon aethiopicum 2.70 ± 2.11 + 1.65 ± 2.25 + n-PDI 1.35 ± 2.62 + n-PDI n-PDI n-PDI 1.05 ± 2.16 + n-PDI n-PDI M eliaceae Nymania capensis ------n-PDI Oleaceae Olea europaea n-PDI - n-PDI - n-PDI n-PDI n-PDI - n-PDI - Sapindaceae Pappea capensis 1.30 ± 2.11 + - n-PDI - 1.15 ± 2.08 n-PDI n-PDI - n-PDI - Plumbaginaceae Plumbago auriculata 2.10 ± 2.47 + 1.15 ± 1.69 + 2.80 ± 3.12 + n-PDI 1.95 ± 2.86 2.65 ± 3.31 2.05 ± 2.74 - 1.65 ± 2.43 + 1.85 ± 2.64 1.50 ± 2.42 Polygalaceae Polygala myrtifolia - - - 2.60 ± 3.05 + - 1.70 ± 2.72 - 1.20 ± 2.17 - - 1.45 ± 2.35 Ptaeroxylaceae Ptaeroxylon obliquum n-PDI 1.40 ± 2.26 - n-PDI n-PDI n-PDI n-PDI 1.45 ± 2.09 + 2.45 ± 2.46 - 1.30 ± 2.34 n-PDI Celastraceae Putterlickia pyracantha 1.30 ± 2.13 + 1.30 ± 2.25 + 2.25 ± 2.79 + n-PDI n-PDI 1.80 ± 2.69 2.45 ± 2.63 + n-PDI 1.70 ± 2.45 n-PDI Rhizocarpaceae Rhizocarpon obscuratum - - n-PDI - n-PDI - n-PDI - n-PDI n-PDI Chenopodiacea Salsola kali - - - n-PDI - n-PDI - - - - Lamiaceae Salvia scabra ------n-PDI Fabaceae Schotia afra 2.30 ± 3.05 - 2.55 ± 2.09 - 3.10 ± 3.34 - 3.90 ± 4.17 - 2.70 ± 2.94 n-PDI 2.65 ± 2.85 - 2.75 ± 2.67 - 2.55 ± 3.19 1.80 ± 2.57 Fabaceae Schotia latifolia - n-PDI - 1.75 ± 2.77 + - 1.25 ± 2.00 - n-PDI n-PDI n-PDI Rhamnaceae Scutia myrtina 1.10 ± 2.02 + 1.60 ± 2.21 + - 1.40 ± 3.23 - 1.50 ± 2.46 n-PDI n-PDI n-PDI 1.65 ± 2.64 1.15 ± 2.06 Anacardiaceae Searsia crenata - 1.10 ± 1.83 - n-PDI n-PDI 1.20 ± 2.40 n-PDI - n-PDI n-PDI n-PDI Anacardiaceae Searsia glauca - - - - n-PDI - - - n-PDI - Anacardiaceae Searsia incisa - - - n-PDI ------Anacardiaceae Searsia longispina 4.15 ± 3.72 + 2.85 ± 3.00 + 5.65 ± 3.70 + 8.55 ± 4.29 + 5.80 ± 2.65 5.55 ± 3.65 4.15 ± 2.62 + 3.05 ± 3.24 + 4.05 ± 2.54 3.00 ± 3.15 Anacardiaceae Searsia pterota 2.65 ± 2.52 - 1.65 ± 1.95 + n-PDI - 1.45 ± 2.21 n-PDI n-PDI n-PDI n-PDI n-PDI Anacardiaceae Searsia refracta - - - n-PDI ------Anacardiaceae Searsia sp. - - - n-PDI - n-PDI - - - - Asteraceae Senecio chrysocoma - 1.25 ± 2.05 + - n-PDI - 1.70 ± 2.36 n-PDI n-PDI - n-PDI Asteraceae Senecio ilicifolius ------n-PDI - - Fabaceae Sesbania punicea - - - n-PDI - n-PDI - n-PDI - - Sapotaceae Sideroxylon inerme 1.55 ± 2.11 - n-PDI n-PDI n-PDI 1.40 ± 2.54 - 1.30 ± 2.34 - - n-PDI n-PDI Sp. C 1.15 ± 1.93 ------Sp. E 1.35 ± 2.23 1.65 ± 2.46 - n-PDI - n-PDI - n-PDI - - Sp. G - n-PDI ------Sp. L - 1.10 ± 2.02 ------Sp. M n-PDI n-PDI n-PDI - - - n-PDI - - - Sp. P - 1.55 ± 2.24 n-PDI - n-PDI n-PDI n-PDI - n-PDI n-PDI Sp. R ------1.95 ± 2.16 - 1.25 ± 2.34 Sp.T ------n-PDI Succulents Asphodelaceae Aloe africana n-PDI n-PDI n-PDI - n-PDI n-PDI n-PDI n-PDI n-PDI n-PDI Asphodelaceae Aloe ferox - n-PDI - n-PDI - - - n-PDI - n-PDI Asphodelaceae Aloe pluridens - - - n-PDI - n-PDI - - - - Asphodelaceae Bulbine sp. - n-PDI - - - - n-PDI - - n-PDI Crassulaceae Cotyledon campanulata ------n-PDI - - Crassulaceae Crassula cordata - 1.20 ± 1.94 + - - - - - n-PDI - - Crassulaceae Crassula ovata - - - - 1.55 ± 2.26 - n-PDI - n-PDI -

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Crassulaceae Crassula perforata 3.20 ± 2.80 + 1.10 ± 1.59 + n-PDI n-PDI 1.20 ± 2.14 n-PDI 1.50 ± 2.21 + 1.05 ± 1.99 + 1.80 ± 2.61 1.85 ± 2.41 Crassulaceae Crassula spathulata - - - - n-PDI n-PDI - - n-PDI - Crassulaceae Crassula tetragona - 1.25 ± 2.10 + - - - n-PDI n-PDI - - - Euphorbiaceae Euphorbia caerulescens - - - n-PDI ------Euphorbiaceae Euphorbia caterviflora - - - - - n-PDI - n-PDI - n-PDI Euphorbiaceae Euphorbia inermis - - - n-PDI ------Euphorbiaceae Euphorbia mauritanica 1.20 ± 2.17 + 1.30 ± 1.95 + 1.90 ± 2.61 + n-PDI 3.20 ± 3.02 2.00 ± 2.99 2.50 ± 2.63 + n-PDI 1.10 ± 2.29 n-PDI Euphorbiaceae Euphorbia rhombifolia ------n-PDI Euphorbiaceae Euphorbia tetragona - - - 2.50 ± 3.24 - - n-PDI - n-PDI - - Euphorbiaceae Euphorbia triangularis 1.40 ± 2.06 + - - - n-PDI - n-PDI - n-PDI - M esembryanthemaceaeM esembryanthemum sp. ------n-PDI - n-PDI Cactaceae Opuntia ficus-indica n-PDI 1.45 ± 2.11 + - 2.15 ± 3.25 + n-PDI n-PDI - 1.70 ± 2.41 + n-PDI 3.90 ± 2.17 Portulacaceae Portulacaria afra 2.25 ± 2.65 - 2.40 ± 2.35 - 3.45 ± 2.35 - n-PDI 2.80 ± 2.26 2.50 ± 2.12 2.95 ± 2.21 - 2.85 ± 2.01 - 2.25 ± 2.38 2.55 ± 2.06 Sp. D - 1.15 ± 2.11 ------Sp. S ------n-PDI - n-PDI M esembryanthemaceaeTrichodiadema intonsum - - - n-PDI n-PDI n-PDI - n-PDI - 1.05 ± 1.96 Forbs M alvaceae Abutilon sonneratianum - - n-PDI - 1.40 ± 2.30 - - - - - Aizoaceae Aizoon rigidum 2.05 ± 2.82 + 1.80 ± 2.12 + 2.05 ± 2.58 + n-PDI 2.00 ± 2.55 2.00 ± 2.66 n-PDI 1.60 ± 2.35 + 1.40 ± 2.06 1.35 ± 1.98 Scrophulariaceae Aptosimum procumbens n-PDI - - - - - n-PDI - n-PDI - Asteraceae Arctotheca calendula ------n-PDI - - Acanthaceae Blepharis capensis n-PDI 1.35 ± 1.98 + n-PDI n-PDI 1.25 ± 2.02 n-PDI n-PDI - n-PDI - Commelinaceae Commelina benghalensis 1.30 ± 2.00 + - n-PDI - n-PDI - - - - - Asteraceae Cuspidia cernua 1.55 ± 2.09 + - 2.35 ± 2.66 + - 1.75 ± 2.38 - n-PDI - 1.10 ± 1.80 - Asteraceae Gazania krebsiana ------n-PDI - - Acanthaceae Hypoestes aristata 1.80 ± 2.12 + n-PDI n-PDI - n-PDI n-PDI n-PDI n-PDI - n-PDI Acanthaceae Hypoestes forskaolii n-PDI n-PDI n-PDI n-PDI n-PDI n-PDI - n-PDI n-PDI n-PDI Fabaceae Indigofera sp. - - - n-PDI - - - n-PDI - n-PDI Lamiaceae Leucas capensis - - n-PDI - n-PDI - n-PDI - 1.05 ± 1.64 n-PDI Scrophulariaceae Phyllopodium cuneifolium - - - n-PDI - n-PDI - - - - Sp. I - 1.25 ± 1.83 ------Sp. N n-PDI n-PDI ------Sp. Q ------1.65 ± 2.39 - n-PDI Lianas Apocynaceae Astephanus marginatus - - - - - n-PDI - - - - Behniaceae Behnia reticulata n-PDI n-PDI n-PDI n-PDI 1.45 ± 2.09 n-PDI 1.50 ± 1.99 + 1.30 ± 2.11 + - n-PDI Ranunculaceae Clematis brachiata - - - n-PDI - - - n-PDI - n-PDI Cucurbitaceae Cucumis africanus - - - n-PDI - n-PDI - n-PDI - n-PDI Apocynaceae Cynanchum sp. ------n-PDI - - - Cucurbitaceae Kedrostis nana 1.15 ± 1.84 + - n-PDI - 1.55 ± 1.90 - 2.00 ± 2.36 + - n-PDI - Geraniaceae Pelargonium peltatum - - 1.45 ± 2.39 + ------Vitaceae Rhoicissus digitata 2.15 ± 1.93 + n-PDI 1.65 ± 2.70 - 1.70 ± 2.45 + 1.45 ± 1.99 n-PDI 3.05 ± 2.70 + 2.10 ± 2.47 + 2.45 ± 2.37 1.20 ± 2.17 Vitaceae Rhoicissus rhomboidea - - - n-PDI - n-PDI - n-PDI n-PDI n-PDI Vitaceae Rhoicissus tridentata - - n-PDI n-PDI ------Apocynaceae Sarcostemma viminale n-PDI - n-PDI - - - n-PDI - n-PDI - Asteraceae Senecio macroglossus - 2.15 ± 2.48 + n-PDI 1.70 ± 2.90 + - n-PDI - n-PDI n-PDI 1.40 ± 2.58 Sp. A - n-PDI ------Cucurbitaceae Zehneria scabra - - - 98 - n-PDI - - - - -

Geophytes Amaryllidaceae Brunsvigia gregaria ------n-PDI - - Iridaceae Dietes iridioides - n-PDI - n-PDI - - - n-PDI - n-PDI Amaryllidaceae Haemanthus albiflos - n-PDI ------1.30 ± 1.72 Iridaceae Hesperantha radiata ------n-PDI Dracaenaceae Sansevieria hyacinthoides - n-PDI ------n-PDI n-PDI Sp. B n-PDI n-PDI - - - n-PDI - - - - Epiphytes Loranthaceae M oquiniella rubra ------n-PDI - Viscaceae Viscum sp. - n-PDI n-PDI n-PDI - n-PDI - 2.20 ± 2.09 + - 1.20 ± 1.70 Total number of PDI 38 43 22 27 36 22 31 32 31 30 Contribution to diet (%) 8 5.4 0 8 0 .4 0 8 0 .4 0 8 3 .8 5 8 6 .3 0 70 .3 5 8 0 .9 5 76 .6 5 77.6 5 72 .55

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Appendix 2: Rainfall (mm) recorded in the Addo Elephant National Park during January 2007 to April 2008 for the study period (June 2007 to April 2008) of de Klerk (2009).

80

70

60

50

40

30 Rainfall Rainfall (mm)

20

10

0

Months

100

Appendix 3: The method used to in DNA metabarcoding for identification of the diet of elephants in the Addo Elephant National Park during February 2014.

Sample analysis was conducted at the Laboratoire d'Ecologie Alpine in Grenoble, France, by Dr. P.Taberlet and his team. Sample analysis followed methods for the P6 loop of the trnL intron (Taberlet et al. 2007) as described in De Barba et al. (2014).

Extracellular DNA from faeces was extracted using a saturated phosphate buffer

(Na2HPO4; 0.12 M; pH 8) and the NucleoSpin© Soil extraction kit (Macherey-Nagel, Düren, Germany), skipping the lysis step. DNA amplifications were carried out on a volume of 20 μL, with the AmpliTaq® Gold 360 DNA Polymerase (Life Technologies, Foster City, CA), 10 mM Tris-HCl, 50 mM KCl, 2 mM of MgCl2, 0.2 mM of each dNTP, 0.2 μM of each primer (forward primer: GGGCAATCCTGAGCCAA; reverse primer: CCATTGAGTCTCTGCACCTATC; Taberlet et al. 2007), and 0.004 mg of bovine serum albumin (BSA, Roche Diagnostic, Basel, Switzerland). Denaturation for 10 minutes at 95°C followed, after which 40 cycles of 30 seconds each at 95°C and 55°C followed by 1 minute at 72°C. Additional specific tags were added on to the 5’ end of the primer, which allowed sequences to be assigned to their respective samples (Valentini et al. 2009b). Three PCR rounds per sample were carried out with different tags on the 5' end of the primers. PCR products were then pooled, and purified using the MinElute PCR purification kit (Qiagen GmbH, Hilden, Germany). The purified PCR products were sequenced on the HiSeq 2500 (Illumina Inc., San Diego, CA 92121 USA) using a paired-end approach (2x100bp).

The output of sequencing was analyzed using obitools (http://metabarcoding.org/obitools). First, the direct and reverse primers corresponding to a single molecule were aligned and merged using the Illuminapairedend program, taking into account data quality during the alignment and the consensus computation (De Barba et al. 2014). Primers and tags were identified using the Ngsfilter program. The amplified regions, excluding primers and tags, were kept for further analysis. Strictly identical sequences were clustered together using the Obiuniq program, keeping the information about their distribution among samples (De Barba et al. 2014). Sequences shorter than 10 bp, or containing ambiguous nucleotides, or with occurrence lower or equal to 100 were excluded using the Obigrep program (De Barba et al. 2014). Taxon assignation was achieved using the Ecotag program (Pegard et al. 2009) and using Genbank as reference collection.

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Appendix 4: The proportion (± SD) of diet identified using Microhistological analysis and DNA metabarcoding of elephants in the Addo Elephant National Park during February 2014. Dashes indicate that the family was absent from the diet. Microhistological DNA metabarcoding analysis

Acanthaceae 1.12 ± 0.45 1.68 ± 0.57

Aizoaceae 1.39 ± 0.64 2.03 ± 1.35

Amaranthaceae 0.72 ± 0.23 2.09 ± 1.34

Anacardiaceae 4.82 ± 1.32 7.46 ± 3.47

Apiacea 0.10 ± 0.21 -

Apocynaceae 7.53 ± 3.48 3.07 ± 1.67

Araliaceae 2.13 ± 1.36 1.66 ± 0.54

Asparagaceae 2.30 ± 1.53 6.51 ± 3.56

Asphodelaceae 1.37 ± 0.87 -

Asteraceae 2.46 ± 1.72 0.45 ± 0.24

Behniaceae 0.33 ± 0.42 -

Bignoniaceae - 0.35 ± 0.13

Boraginaceae 2.13 ± 1.76 2.27 ± 1.00

Cactaceae 2.11 ± 0.94 2.21 ± 1.01

Capparacea 4.70 ± 2.47 -

Celastraceae 4.93 ± 1.35 11.66 ± 4.21

Colchicaceae 0.33 ± 0.22 -

Crassulaceae 2.57 ± 0.96 -

Cucurbitaceae 0.48 ± 0.47 -

Dracaenaceae 0.52 ± 0.32 -

Ebenaceae 3.10 ± 1.45 3.77 ± 1.11

Euphorbiaceae 2.46 ± 1.94 -

Fabaceae 4.53 ± 1.45 16.00 ± 6.36

Geraniaceae - 0.04 ±

102

Iridaceae 0.56 ± 0.23 -

Lamiaceae 1.12 ± 0.69 -

Malvaceae 1.24 ± 0.47 4.15 ± 1.14

Meliaceae 0.17 ± 0.32 -

Mesembryanthemace 0.70 ± 0.87 - ae

Oleaceae 0.43 ± 0.34 -

Phyllanthaceae 0.08 ± 0.23 -

Plumbaginaceae 1.53 ± 0.89 9.72 ± 4.13

Poaceae 26.70 ± 6.90 12.35 ± 7.25

Polygalaceae 0.68 ± 0.24 -

Portulacacea 2.46 ± 1.53 -

Ptaeroxylaceae 0.89 ± 0.24 -

Ranunculaceae 1.97 ± 0.98 -

Rhamnaceae 1.35 ± 0.34 1.12 ± 0.98

Rubiaceae - 0.10 ± 0.03

Rhizocarpaceae 0.48 ± 0.23 -

Rutaceae 0.43 ± 0.52 0.06 ± 0.14

Salvadoraceae 0.58 ± 0.46 -

Salicaceae - 0.47 ± 0.53

Santalaceae - 0.14 ± 0.33

Sapindaceae 0.81 ± 0.34 6.05 ± 2.83

Sapotaceae 1.08 ± 0.56 1.85 ± 0.46

Solanaceae 0.19 ± 0.23 0.04 ± 0.01

Unknown 1.53 ± 0.68 0.04 ± 0.04

Viscaceae 0.58 ± 0.34 0.05 ± 0.03

Vitaceae 2.30 ± 1.25 2.62 ± 0.98

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104