Genetic Patterns in Forest Populations: Implications for the Conservation of Key Species in the Udzungwa Mountains,

Submitted by Andrew Edward Bowkett, to the University of Exeter as a thesis for the degree of Doctor of Philosophy in Biological Sciences In September 2012

This thesis is available for Library use on the understanding that it is copyright material and that no quotation from the thesis may be published without proper acknowledgement.

I certify that all material in this thesis which is not my own work has been identified and that no material has previously been submitted and approved for the award of a degree by this or any other University.

Signature: ………………………………………………………….. ABSTRACT

The field of conservation genetics, in combination with non-invasive sampling, provides a powerful set of tools for investigating the conservation status and natural history of rare species that are otherwise difficult to study. A systematic literature review demonstrated that this is certainly the case for many forest- associated antelope species, which are poorly studied and yet constitute some of the most heavily hunted wildlife in Africa. The aim of the present study was to use non-invasive sampling to investigate genetic patterns in forest antelope populations in the high-biodiversity Udzungwa Mountains, Tanzania, within the context of the conservation of these species and the wider ecosystem.

Genetic information was derived from faecal samples collected across the Udzungwa landscape and assigned to five antelope species (N = 618, collected 2006-09). Faecal pellet length was measured for a subset of samples but statistical assignment to species by this method proved unreliable. Phylogenetic analysis using mitochondrial control region sequences unexpectedly revealed that Harvey’s within the Udzungwas are paraphyletic with respect to sequences from a putative sister species from . However, there was no corresponding pattern in the microsatellite dataset suggesting that these mitochondrial lineages do not represent contemporary genetic isolation. Instead, Harvey’s duiker nuclear variation is shaped both by isolation by distance, due to positive spatial autocorrelation at short distances, and clustering of distinct genotypes from western outlying forests. These forests also harbour the endangered Abbott’s duiker and therefore require effective conservation management. Despite being detected throughout the Udzungwas, genetic diversity in Abbott’s duiker was very low in comparison to other species. These results suggest several promising research directions but also have significant conservation implications that will be disseminated to the Tanzanian wildlife authorities and the wider conservation community.

2 ACKNOWLEDGEMENTS

This study would not have been possible without the multiple networks of people that have provided genetic samples, technical guidance, financial and logistical assistance, motivation and support over the course of the last six years. These networks span three continents, five research institutions and numerous partnerships and agreements. I cannot hope to thank every individual who contributed but I am extremely grateful to everyone who helped me learn so much during this project, including many not mentioned below.

I would first like to thank my supervisors Dr Jamie Stevens, University of Exeter, and Dr Amy Plowman, Whitley Wildlife Conservation Trust. Both have shown great faith in my ability to achieve new tasks, Amy for over ten years. I am also grateful to Drs Nicola Anthony and Bettine Jansen van Vuuren, who were both crucial to the success of this study, particularly at the early stages when Jamie didn’t know what a duiker was! Bettine generously hosted me at the University of Stellenbosch for six months and personally watched over my first ever PCRs. Drs Francesco Rovero and Andrew Marshall first encouraged me to join them in Tanzania to research forest antelope.

The following individuals deserve special acknowledgement for their much- needed assistance in various aspects of my research, whether in the office, field or laboratory. In Exeter: Emma Kennedy, Patrick Hamilton, Andrew Griffiths, Angela Pountney, Sarah-Louise Counter, Lyndsey Holland, Laura McDonagh, Jon Ellis, Andy King, Charles Ikediashi and all other members of the Molecular Ecology and Evolution Group past and present. In Paignton: Natasha de Vere, Kirsten Pullen, Vicky Melfi, Dave Ellacott, Michelle Youd and my contemporary PhD students: Tracey Hamston, Kathy Baker, Joanna Bishop and Holly Farmer. In New Orleans: Anne Johnston and Stephan Ntie. In Stellenbosch: Sandi Willows-Munro, Anne Ropiquet, Woody Cotterill, Gayle Pedersen, Thomas Lado, Prince Kaleme and Nico Solomons. In Tanzania: Richard Laizzer, Arafat Mtui, Yahaya Sama, Ali Chitata, Mwajuto Matola, Amani Kitegile, Adinani Seki, Athmani Mndeme, Martin Mlewa, Silvia Ricci, Tim Davenport, Sophy Machaga, Ruben Mwakisoma, Kuruthumu A. Mwamende, Paul and Victoria Banga, Ponjoli Joram Kabepele and Baraka de Graf. I

3 would also like to thank many friends who provided equally important support in a social rather than technical context.

Particular thanks go to those individuals named as co-authors in the following research chapters, some but not all of whom are named above. Trevor Jones played a vital role in retrieving samples from the more remote Udzungwa forests.

The final acknowledgements must go to those people who have had to put up with the most. To my parents and their partners, whose support is probably far more appreciated than they realise, and especially to Alexis – thank you.

This thesis is dedicated to all those people who have shown me kindness and hospitality during the times I have spent working overseas, in particular the late Leonard Mpofu (~1960-2008) and Amani Mahundu (1978-2012).

4 TABLE OF CONTENTS

Abstract 2 Acknowledgments 3 Chapter 1 Introduction 18 1.1. Introduction 19 1.2. Methods 21 1.2.1. Literature search 21 1.2.2. Bibliometric analysis 22 1.3. Results 22 1.3.1. Literature search 22 1.3.2. Bibliometric analysis 24 1.3.3. Literature review 26 1.4. Discussion 37 1.4.1. State of scientific knowledge on forest antelope 37 1.4.2. Non-invasive genetics and forest antelope 37 conservation research 1.5. Structure and aims of the present study 40 1.5.1. Chapter aims 41

Chapter 2 Study Site and Methods 44 2.1. Introduction to methods 45 2.2. Study site: Udzungwa Mountains, Tanzania 45 2.2.1. Biogeography 45 2.2.2. Conservation 46 2.2.3. The Udzungwa Mountains as a research site 49 2.3. Sample collection 50 2.3.1. Personnel 53 2.3.2. Site selection and timing 53 2.3.3. Recce and line transect sampling 53 2.4. Sample storage 55 2.5. Laboratory methods 57 2.5.1. DNA extraction 57

5 2.5.2. Molecular marker selection 57 2.5.3. Mitochondrial markers 58 2.5.4. Microsatellites 60 2.5.5. Other potential markers 64 2.6. Methods summary 64

Chapter 3 Can molecular data validate morphometric 66 identification of faecal pellets in Tanzanian forest antelope species? Abstract 67 3.1. Introduction 68 3.2. Materials and methods 69 3.3. Results 70 3.4. Discussion 75 3.5. Acknowledgments 76

Chapter 4 Inferring evolutionary species, lineages and 78 population subdivision from non-invasive genetic sampling of forest antelope across a tropical mountain range Abstract 80 4.1. Introduction 82 4.1.1. Antelope and evolutionary species 83 distribution 4.1.2. Intra-specific lineage diversity in antelope 84 4.1.3. Conservation management units and population 84 subdivision 4.1.4. Study aims and limitations 85 4.2. Methods 85 4.2.1. Study site and subjects 85 4.2.2. Sample collection and processing 86 4.2.3. Genetic analysis 86 4.2.4. Data analysis 87 4.3. Results 89

6 4.3.1. Revised species designations 90 4.3.2. Distribution 91 4.3.3. Phylogenetic analysis 100 4.3.4. Genetic diversity and differentiation 102 4.4. Discussion 107 4.4.5. Antelope taxonomy and evolutionary species 107 distribution 4.4.6. Intra-specific lineage diversity in antelope 110 4.4.7. Genetic diversity and differentiation 111 4.4.8. Conservation and management implications 111 4.4.9. Conclusion 113

Chapter 5 Fragmented forests, fragmented populations? 116 Fine-scale genetic structure in Harvey’s duiker Cephalophus harveyi across a heterogeneous landscape Abstract 117 5.1. Introduction 118 5.2. Methods 119 5.2.1. Study site and sample collection 119 5.2.2. Microsatellite genotyping 120 5.2.3. Spatial autocorrelation and isolation by distance 121 5.2.4. Genetic clusters 122 5.3. Results 124 5.3.1. Spatial autocorrelation and isolation by distance 125 5.3.2. Genetic clusters 129 5.4. Discussion 137 5.4.1. Patterns and processes in Harvey’s duiker genetic 137 structure 5.4.2. Habitat fragmentation and conservation 138

Chapter 6 Distribution and genetic diversity of the 144 endangered Abbott’s duiker Cephalophus spadix in the Udzungwa Mountains, Tanzania

7 Abstract 146 6.1. Introduction 147 6.2. Materials and methods 148 6.3. Results 152 6.4. Discussion 157 6.5. Acknowledgements 159

Chapter 7 General discussion 162 7.1. Preamble 163 7.2. Main findings 163 7.2.1. Morphological identification of forest antelope 163 dung 7.2.2. Genetic patterns as revealed by non-invasive 164 sampling 7.2.3. Spatial genetic structure in Harvey’s duiker 164 7.2.4. Abbott’s duiker in the Udzungwa Mountains 165 7.3. Implications for conservation 166 7.3.1. Conservation status of newly recognised 166 evolutionary species 7.3.2. Uzungwa Scarp Forest Reserve and New Dabaga- 166 Ulang’ambi Forest Reserve 7.3.3. Connectivity and wildlife corridors 167 7.3.4. Feasibility of genetic monitoring of forest antelope 168 populations 7.4. Future research directions 169 7.4.1. Novel genomic resources for forest antelope 169 conservation 7.4.2. Landscape genetics in the Udzungwa Mountains 170 and east African region 7.4.3. Charting the ‘duiker-sphere’ – towards an 171 evolutionary map of the Cephalophini 7.5. Conclusion 172 Bibliography 175 Appendix A. Associated publications 197

8 LIST OF FIGURES

Chapter 1 Introduction Figure 1.1 Numbers of peer-reviewed literature on forest 25 antelope published per year between 1970 and 2012 (ISI Web of Science 2012). Figure 1.2 Figure 1.2. Parsimony tree for the (38 27 taxa) based on the complete mtDNA cytochrome b gene (1140 bp). Figure 1.3 Example of a gap in our scientific knowledge of 29 forest antelope distribution: Cephalophus natalensis and harveyi in sub-Saharan Africa.

Chapter 2 Study Site and Methods Figure 2.1 Map of the Udzungwa Mountains, Tanzania, 51 showing sample collection sites (stars). Figure 2.2 Box-and-whisker plots showing median 56 differences in field estimated age-class and days stored at different temperatures between antelope faecal samples which were successfully genotyped at 8 microsatellite loci.

Chapter 3 Can molecular data validate morphometric identification of faecal pellets in Tanzanian forest antelope species? Figure 3.1 Control region neighbour-joining bootstrap 72 consensus phylogeny for faecal DNA sequences from the Udzungwa Mountains, Tanzania (c.530 bp). Figure 3.2 Box-and-whisker plot of faecal pellet length for 73 five species of forest antelope from the Udzungwa Mountains, Tanzania.

9 Chapter 4 Inferring evolutionary species, lineages and population subdivision from non-invasive genetic sampling of forest antelope across a tropical mountain range Figure 4.1a Forest antelope non-invasive sampling locations 92 in the Udzungwa Mountains, Tanzania. Charts represent the proportion of samples assigned to each species. Figure 4.1b Forest antelope non-invasive sampling locations 93 in the Udzungwa Mountains, Tanzania. Charts represent the proportion of major mtDNA control region phylogenetic clades in C. harveyi samples Figure 4.2a Bayesian consensus phylogeny for Cephalophus 96 harveyi CR haplotypes from the Udzungwa Mountains, with representative sequences from other regions and species. Figure 4.2b Bayesian consensus phylogeny for Cephalophus 97 spadix haplotypes from the Udzungwa Mountains, with representative sequences from other regions and species. Figure 4.2c Bayesian consensus phylogeny for putative 98 kirchenpauri CR haplotypes from the Udzungwa Mountains, with representative sequences from other regions and species. Figure 4.2d Bayesian consensus phylogeny for putative 99 Philantomba lugens CR haplotypes from the Udzungwa Mountains, with representative sequences from other regions and species. Figure 4.3 Two dimensional PCA plot of C. harveyi / 102 natalensis microsatellite genotypes labelled by phylogenetic clade (Fig 2a). Figure 4.4a + b Control region median-joining network for C. 103 harveyi and natalensis (a) and C. spadix (b).

10 Figure 4.4c + d Control region median-joining network for N. 104 moschatus including kirchenpaueri (c) and P. monticola including lugens (d).

Chapter 5 Fragmented forests, fragmented populations? Fine-scale genetic structure in Harvey’s duiker Cephalophus harveyi across a heterogeneous landscape Figure 5.1 Global relationship between genetic and 127 geographic distance for C. harveyi in the Udzungwa Mountains, Tanzania. A. Correlogram showing the autocorrelation coefficient (r) for different distance class sizes. B. The autocorrelation coefficient at increasing cumulative distance size classes. Figure 5.2 Local relationships between genetic and 128 geographic distance for C. harveyi within sampling locations in the Udzungwa Mountains, Tanzania. Correlogram showing the autocorrelation coefficient (r) for different distance class sizes in A. Mwanihana, B. Ruipa, C. Lumemo, and D. Uzungwa Scarp. Figure 5.3 STRUCTURE analysis with standard model for C. 131 harveyi genotypes from the Udzungwa Mountains (N = 148). 5.3A: Proportion of each individual’s genotype allocated to each cluster at K = 4 for comparison between models. 5.3B: Mean log likelihood of the data for 5 runs showing that K = 1. Figure 5.4 STRUCTURE analysis with location prior model 132 for C. harveyi genotypes from the Udzungwa Mountains (N = 148). A: Proportion of each individual’s genotype allocated to each cluster at K = 4 for comparison between models. B: Mean

11 log likelihood of the data for 5 runs showing that K = 1. C: Delta K-values for 5 runs showing K = 2. Figure 5.5 Geneland analysis with uncorrelated alleles 133 spatial model (K = 2) for C. harveyi microsatellite genotypes from the Udzungwa Mountains (N = 141). Map shows the posterior mode of population membership for the highest probability model run (black dots are sample spatial coordinates). Figure 5.6 Geneland analysis with uncorrelated correlated 134 alleles spatial model (K = 5) for C. harveyi microsatellite genotypes from the Udzungwa Mountains (N = 141). Map shows the posterior mode of population membership for the highest probability model run (black dots are sample spatial coordinates). Figure 5.7 Map of sampling locations in the Udzungwa 135 Mountains, south-central Tanzania. Colour- coded locations represent C. harveyi microsatellite genotypes allocated to genetic clusters by the Geneland correlated alleles model. Supp. Figure 5.1 Geneland analysis with correlated spatial model 142 + 5.2 for C. harveyi microsatellite genotypes from the Udzungwa Mountains (N = 141). Maps show the posterior mode of population membership for the model run indicated (black dots are sample spatial coordinates). Supp. Figure 5.3 Geneland analysis with correlated spatial model 143 + 5.4 for C. harveyi microsatellite genotypes from the Udzungwa Mountains (N = 141). Maps show the posterior mode of population membership for the model run indicated (black dots are sample spatial coordinates).

12

Chapter 6 Distribution and genetic diversity of the endangered Abbott’s duiker Cephalophus spadix in the Udzungwa Mountains, Tanzania Figure 6.1 Global distribution of the endangered Abbott’s 149 duiker (red hatching). Highland areas referred to in the text are italicized. Modified from IUCN (2012). Figure 6.2 Map of the Udzungwa Mountains, Tanzania, 150 showing recorded presence of Abbott’s duiker. Figure 6.3 mtDNA control region Bayesian consensus 154 phylogeny for C. spadix from the Udzungwa Mountains (~600 bp). Figure 6.4 Neighbour-joining dendrogram based on DAS 155 distances (Chakraborty & Jin 1993) using genotype data from seven microsatellite loci for Abbott’s duiker. Figure 6.5 Two-dimensional PCA plot of Abbott’s duiker 156 microsatellite genotypes labelled by geographical regions in Tanzania. Supp. Figure 6.1 Abbott’s duiker photographed in Mwanihana, 161 Udzungwa Mountains National Park, Tanzania. Supp. Figure 6.2 Abbott’s duiker photographed in Nyumbanitu, 161 Kilombero Nature Reserve, Tanzania.

13 LIST OF TABLES

Chapter 1 Introduction Table 1.1 Search terms and results for forest antelope 23 and comparative ungulate taxa using ISI Web of Science and additional articles from combined World Wide Web searches Table 1.2 Forest antelope found in Tanzania. 35

Chapter 2 Study Site and Methods Table 2.1 Site characteristics, sampling effort and 52 number of collected samples for nine forest blocks in the Udzungwa Mountains, Tanzania. Table 2.2 Primers used to amplify the mitochondrial 59 control region from antelope faecal samples. Table 2.3 PCR conditions for control region (CR) and 61 microsatellite markers (MPLX) with antelope faecal DNA. Table 2.4 Microsatellite primers and multiplex design for 63 use with antelope faecal DNA. Table 2.5 Preparation of 10x primer mix for use with the 64 QIAGEN Multiplex PCR Kit as recommended by the manufacturer (each primer at 2 µM) and relative primer concentrations used with antelope faecal DNA.

Chapter 3 Can molecular data validate morphometric identification of faecal pellets in Tanzanian forest antelope species? Table 3.1 Species identity of antelope dung piles from the 74 Udzungwa Mountains, Tanzania, as established by mitochondrial control region sequencing compared to predicted species from a linear

14 discriminant analysis of faecal pellet length.

Chapter 4 Inferring evolutionary species, lineages and population subdivision from non-invasive genetic sampling of forest antelope across a tropical mountain range Table 4.1 Best-fit evolutionary substitution models 88 selected for Maximum-Likelihood analysis of forest antelope mitochondrial control region haplotypes from the Udzungwa Mountains. Table 4.2 Sample size (N), number of mitochondrial 94 control region haplotypes, haplotypic richness and genetic diversity values for four species of antelope sampled in twelve locations in the Udzungwa Mountains, Tanzania. Table 4.3 Sample size, polymorphism, unbiased expected 95

heterozygosity (HE) and allelic richness for two species of antelope sampled in nine locations in the Udzungwa Mountains, Tanzania. Table 4.4 Pair-wise mitochondrial control region 105 uncorrected genetic distances between red duiker clades and species.

Table 4.5 Pair-wise FST values between sampling 106 locations for two species of antelope in the Udzungwa Mountains, Tanzania. Table 4.6 Analyses of molecular variance among and 107 within sampling locations for three species of forest antelope in the Udzungwa Mountains, Tanzania. Supp. Table 4.1 Details of mitochondrial control sequences 114 included in this study excluding those derived from faecal samples.

Chapter 5 Fragmented forests, fragmented populations? Fine-scale genetic structure in

15 Harvey’s duiker Cephalophus harveyi across a heterogeneous landscape Table 5.1 Number of C. harveyi microsatellite genotypes 124 (N) from nine sampling locations in the Udzungwa Mountains together with mean genetic distance (Peakall and Smouse 2006), kinship coefficient (Loiselle et al. 1995), mean and maximum Euclidean distance (km). Table 5.2 Microsatellite locus details for 141 C. harveyi 125 genotypes from the Udzungwa Mountains, Tanzania. Table 5.3 Isolation by distance in C. harveyi in the 126 Udzungwa Mountains, Tanzania. Table 5.4 Genetic diversity and equilibrium values for C. 136 harveyi inferred spatial genetic clusters in the Udzungwa Mountains, Tanzania.

Table 5.5 Pair-wise FST (below diagonal) and Jost’s D 136 values (above diagonal) between inferred spatial genetic clusters (K = 4) for C. harveyi in the Udzungwa Mountains, Tanzania.

Chapter 6 Distribution and genetic diversity of the endangered Abbott’s duiker Cephalophus spadix in the Udzungwa Mountains, Tanzania Table 6.1 Forest characteristics and Abbott’s duiker 153 survey results for the ten forest blocks studied in the Udzungwa Mountains, Tanzania. Table 6.2 Frequency of Abbott’s duiker mtDNA control 156 region haplotypes recovered from the Udzungwa Mountains, together with available data from other regions.

Table 6.3 Number of alleles (Na), observed (HO) and 157

unbiased expected (uHE) heterozygosities for

16 seven microsatellite loci in Abbott’s duiker (n = 19) and Harvey’s duiker (n = 149) within the Udzungwa Mountains. Table 6.4 Mitochondrial control region diversity in four 157 species of forest antelope in the Udzungwa Mountains, Tanzania.

17 CHAPTER 1.

INTRODUCTION

18 1.1. Introduction

The importance of genetic factors in conservation was first recognised in the early 1970s (Frankel 1970, 1974) and shortly thereafter became integral to the birth of modern conservation biology, a multidisciplinary approach to studying the loss of biodiversity and identifying the most appropriate preventative actions (Soulé 1985). The role of genetics in population decline and recovery was outlined in key chapters of the seminal publication in conservation biology (Soulé and Wilcox 1980) and had a major influence on conservation policy, particularly the design of nature reserves (Soulé and Simberloff 1986) and captive breeding programmes (Soulé et al. 1986).

Despite this early role in the theoretical development of conservation biology, the significance of genetic factors in the decline and management of threatened wild populations has long been the subject of debate (Hedrick et al. 1996; Asquith 2001; Caro and Laurenson 1994). Some authors argued that environmental and demographic factors would drive populations to extinction before any negative genetic impact, such as inbreeding depression, could take effect (Lande 1988) and that there was very little evidence for deleterious effects on wild populations (Caughley 1994). This argument, together with the artificial distinction between small versus declining population paradigms (Caughley 1994), has largely been superseded by theoretical frameworks that incorporate both ecological and genetic agents of decline (Soulé and Mills 1998) and, crucially, by direct evidence from wild populations (Saccheri et al. 1998; Madsen et al. 1999).

The field of conservation genetics can be defined as the theory and practice of genetics in the preservation of species as dynamic entities capable of coping with environmental change (Frankham et al. 2002). This broadly encompasses the genetic management of small populations, resolution of taxonomic and management units, and the use of genetic analyses in forensics and understanding species biology (Frankham et al. 2002), but could also include any application where quantitative or molecular genetic information is used in the conservation of biodiversity.

19 These applications of conservation genetics were limited in the past by the technical and logistical difficulty of obtaining molecular data on rare and threatened species in the wild. However, the widespread adoption of efficient polymerase chain reaction (PCR) methods to amplify nucleic acids and the development of suitable molecular markers has allowed the recovery of genetic information from an ever-increasing array of source material. In particular, non- invasive sampling of faeces (Kohn and Wayne 1997), hair (Sloane et al. 2000) and shed feathers (Rudnick et al. 2005) is now commonly employed in research into threatened species (Goossens et al. 2005; Zhan et al. 2006). Such sampling involves minimal disturbance to the population of interest but provides a suite of genetic analyses previously only possible with invasive sampling methods (Beja-Pereira et al. 2009).

African forest antelope are typically shy, crepuscular or nocturnal, and found in habitats where visibility is low, making them difficult to study in the field. These species thus provide a good example of circumstances where non-invasive genetics could provide a wealth of information that would otherwise be very difficult or impossible to obtain. Such information is crucial to the scientific management of species that are highly threatened by habitat loss and exploitation and yet remain poorly studied compared to their savannah counterparts (Bowkett 2003). A pertinent case-study may be that of the Udzungwa Mountains in south- central Tanzania, where the forest antelope community has only recently been described (Dinesen et al. 2001; Rovero and Marshall 2004; Bowkett et al. 2008) despite the presence of at least one globally endangered species, Abbott’s duiker Cephalophus spadix (Rovero et al. 2005; Jones and Bowkett 2012), and the documented threat of illegal hunting for (Rovero et al. 2010).

This chapter provides an introduction to forest antelope, quantifies the state of scientific knowledge regarding their natural history and conservation by way of a systematic literature search, and assesses the potential role of conservation genetics in improving this knowledge using examples from . It also sets out the structure and aims of the following chapters that investigate genetic patterns in forest antelope populations across the Udzungwa Mountains and

20 highlight the implications for conservation management of this important landscape.

1.2. Methods

1.2.1. Literature search

A comprehensive and repeatable strategy for searching scientific literature is a critical part of the systematic review process (Roberts et al. 2006). Systematic reviews of interventions, in order to establish evidence-based practice, are common in medicine (Higgins and Green 2011), and have recently been introduced for conservation and environmental management (Centre for Evidence-Based Conservation 2010). We followed the literature search guidelines of Pullin and Stewart (2006) in order to reduce bias and allow this review to be repeated in the future.

For the purposes of our literature review we defined forest antelope as any member of the bovid tribes Cephalophini (Blyth, 1863) and (Scalter and Thomas, 1894) plus the two forest-dwelling members of (Blyth, 1863): bushbuck scriptus (Pallas, 1776) and T. euryceros (Ogilby, 1837). This definition is a broad one as it includes the savannah-dwelling bush or grey duiker Sylvicapra grimmia (Linneaus, 1758) and the bushbuck species complex (Groves and Grubb 2011), whose members are found in many different wooded habitats. It does not include the savannah or desert inhabiting genera and Madoqua, which were traditionally included in Neotragini but are now generally accepted to be members of the (Groves and Grubb 2011).

We used ISI Web of Science (WOS) as our primary electronic database. After several trial runs to gauge the likely volume and precision of searches, we combined terms within each antelope tribe using Boolean operators and wildcard terms and searched between 1899-2012 (Table 1.1). We also ran search terms in Google Scholar but the large number of spurious hits precluded analysis due to time constraints (e.g. for the duiker search terms there were 9,430 total ‘hits’ but

21 checking the first 50 results only returned 12 relevant articles, 18th September 2012). We also conducted World Wide Web searches for articles and other publications by reviewing the first 50 hits from www.google.com and www.dogpile.com (a meta-search engine that combines results from multiple algorithms). The “any of these words” option was selected under advanced search options, although we were unable to include the alternative spelling of T. euryceros (Table 1.1) in dogpile.com searches due to character limits. Time constraints limited our search to documents in English and we only included book chapters and grey literature if cited by peer-reviewed articles (or found through web searches).

1.2.2. Bibliometric analysis

As a course measure of the current scientific knowledge of forest antelope species we compared the raw unedited search results in WOS for forest antelope genera, between 1970 and 2011, with those for other ungulate taxa (Table 1.1). We then processed the forest antelope results to remove spurious records and duplicates, and to check for consistency with Price’s law of scientific literature growth (Price 1951). Price’s Law states that productivity in a specific scientific field will be exponential unless a saturation point has been reached, after which growth changes to linear and eventually declines to zero. Alternatively, artificial boundaries to research, such as a lack of resources, may prevent exponential growth (Price 1951). We measured productivity as the number of relevant articles published per year with equal weighting given to all articles (Prieto et al. 2011). It is important to note that articles did not need to be primarily focused on forest antelope taxa to be included as relevant, only to refer specifically to those taxa in the title, abstract or key words.

1.3. Results

1.3.1. Literature search

The WOS searches generated 475 relevant hits using the forest antelope search terms (Table 1.1). Although the earliest article was published in 1903 the vast

22 majority of articles (96.5%) were published after 1970. This is probably because much of the older natural history literature has not been electronically archived. Search results were exported and stored in EndNote X5 (Thomson Reuters, New York, NY, USA).

The World Wide Web searches were variable between taxa in terms of relevancy (Google: 32-82%; dogpile: 42-84%) but didn’t reveal any publications not already found in WOS. Much highly relevant information on forest antelope was contained in publications other than journal articles (Kingdon 1982, 1997; East 1999; Plowman 2003; Wilson and Mittermeier 2011; Groves and Grubb 2011).

Table 1.1. Search terms and results for forest antelope (in bold) and comparative ungulate taxa using ISI Web of Science and additional articles from combined World Wide Web searches (all searches executed on 19th September 2012). Number of species based on Wilson and Reeder (2005).

Search term Number WOS WOS WWW of total relevant extra species articles articles articles duiker* OR "Cephalophus" OR "Philantomba" 18 330 262 0 OR "Sylvicapra" OR "Cephalophinae" OR "Cephalophini"

Neotragus OR "" OR "" OR 3 96 40 0 "" OR "dwarf antelope" OR "pygmy antelope" OR "Neotragini"

"Tragelaphus scriptus" OR "Tragelaphus 2 175 173 0 sylvaticus" OR "bushbuck" OR "Tragelaphus euryceros" OR "Tragelaphus eurycerus" OR (bongo AND antelope)

Gazella 10 1,029 - -

Connochaetes OR * OR (gnu and 2 438 - - antelope)

Capreolus OR "roe " OR "chevreuil" 2 2,355 - -

Odocoileus OR "white-tailed deer" OR "" 2 6,762 - - OR "black-tailed deer" OR "Virginia deer"

Loxodonta africana OR "savannah elephant" OR 1* 1,227 - - "savanna elephant" OR "African elephant"

* Includes L. cyclotis (considered a subspecies until recently).

23 While not a precise comparison, due to differences in the number of species included in search terms, the raw search results for other ungulates indicate that forest antelope are under-studied at least compared to temperate or charismatic species (Table 1.1).

1.3.2. Bibliometric analysis

Analysis of scientific productivity was restricted to 1970 and 2011 to avoid the large number of years prior to this period that contained no relevant publications and the potential effect of conducting searches on different dates during 2012.

Scientific knowledge of duiker and forest tragelaphines is clearly growing as measured by peer-reviewed articles (Fig. 1.1A and C). For both these groups, growth was significantly exponential as predicted by Price (1951) although linear relationships explained more of the variation. In contrast, research into Neotragus species appears stagnant (Fig. 1.1B).

24

Figure 1.1. Numbers of peer-reviewed literature on forest antelope published per year between 1970 and 2011 (ISI Web of Science 2012). Exponential (narrow line) and linear (broad line) models were fitted to the data to check for compliance with Price’s Law of scientific productivity. A. Duiker species, exponential: y = 1E- 187e0.2163x (r2 = 0.458; P < 0.001), linear: y = 0.384x – 759.22 (r2 = 0.725; P < 0.001); B. Neotragus species, exponential: y = 3E-97e0.1097x (r2 = 0.077; P = 0.121), linear: y = 0.027x - 52.833 (r2 = 0.084; P = 0.129); C. Forest Tragelaphus species, exponential: y =4E-119e0.137x (r2 = 0.281; P < 0.001), linear: y = 0.268x - 530.25 (r2 = 0.588; P < 0.001).

25 1.3.3. Literature review

1.3.3.1. Forest antelope natural history

Natural history is the scientific endeavour of discovering, describing and classifying facts concerning landscapes and biodiversity, in this case forest . Such idiographic fact gathering is crucial, if often unappreciated, to the subsequent formation of nomothetic (law-like) generalizations in science (Cotterill and Foissner 2010). Here we use the term natural history to encompass what is known about forest antelope in terms of their evolution, taxonomy, distribution, ecology and behaviour. How this knowledge relates to conservation, monitoring and management is dealt with in the following section.

Antelope is a generic term referring to horned ungulates from two subfamilies within the Bovidae (Fig. 1.2). These are the , including and spiral- horned antelope (Tragelaphini), and the , including all other antelopes, sheep and (Gentry 1992). Tribes within these subfamilies diverged between 17 and 15 million years ago (Hassanin et al. 2012), coincident with the Middle Miocene Climatic Optimum, a global warm period, the end of which saw a marked transition to a period of global cooling that led to increased aridity in Africa (Senut et al. 2009). This aridity may have resulted in the isolation of forests at a continental scale and the emergence of forest-associated lineages that later became tribes such as Cephalophini () and Neotragini (dwarf antelope). The modern bovid taxa emerged in successive radiations over the last 12 million years (Hassanin et al. 2012) with many duiker species diverging as recently as the Pleistocene (Johnston and Anthony 2012).

26

[This image has been removed by the author of this thesis for copyright reasons]

Figure 1.2. Parsimony tree for the Bovidae (38 taxa) based on the complete mtDNA cytochrome b gene (1140 bp). Bootstrap values are shown above nodes. Forest antelope, as defined in this chapter, are highlighted in yellow. Traditional names for taxonomic tribes are shown on the right. Note that the savannah-dwelling grey duiker was also included in our literature search. Modified from Matthee and Robinson (1999a).

27 Duiker taxonomy traditionally comprises of three genera within the Cephalophini: the smallest species in Philantomba, the species-rich Cephalophus and the savannah-specialist Sylvicapra. Molecular evidence suggests further divisions within Cephalophus constituting three clades: giant duikers (including C. spadix), east African red duikers and west African red duikers (van Vuuren and Robinson 2001). The most comprehensive molecular phylogeny for Cephalophini (Johnston and Anthony 2012) supports the recognition of four major lineages, Philantomba and the three previously described within Cephalophus, but places Sylvicapra as sister to the giant duikers leaving Cephalophus paraphyletic. This relationship was also found in a mitochondrial genome-wide phylogeny of the Cetartiodactyla (Hassanin et al. 2012).

This recent divergence, as supported by the allopatric distribution of various sister species (Johnston and Anthony 2012), may be one reason why the taxonomy of forest antelope, particularly duikers, is so confusing. Much of the difficulty centres on whether certain populations should be considered young species or subspecies (Perrin et al. 2003; Groves and Grubb 2011), or even eco-types (Wronski et al. 2009). In the bushbuck species complex, traditional taxonomy has obscured two highly divergent lineages that are more closely related to other tragelaphine species than they are to each other (Moodley et al. 2009). Several duiker species are often treated as subspecies by different authorities. For example, Harvey’s duiker C. harveyi is recognised by some authorities (Kingdon 1997; East 1999) but treated as a subspecies of C. natalensis by others (Ellerman et al. 1953; Wilson and Reeder 2005). This different treatment of variable populations has been highlighted by the most recent ungulate taxonomy which recognises forty-one species within Cephalophini (Groves and Grubb 2011), see Table 1.2.

Forest antelope taxonomy is further weakened by gaps in our knowledge of these species’ distributions. Harvey’s duiker again provides an example as the species is listed by East (1999) as present in (Fig. 1.3), but these populations are not mentioned by Kingdon (1997) or Groves and Grubb (2011). Grubb et al. (2003) outline further examples from all over Africa.

28

[This image has been removed by the author of this thesis for copyright reasons]

Figure 1.3. Example of a gap in our scientific knowledge of forest antelope distribution: Cephalophus natalensis (red) and harveyi (black) in sub-Saharan Africa (according to the African Antelope Database 1998). Red dots in Malawi and (encircled) are considered C. harveyi by other authorities, e.g. Groves and Grubb (2011). Blue dots represent populations of unknown taxonomic status. Dots: Present but abundance unknown, Blocks: Population estimated between 1,000 and 10,000. Modified from East (1999).

In terms of ecology and behaviour, generally more is known about the forest tragelaphines, particularly bushbuck, than the duikers or neotragines. The ecology of bongo, the only large social forest antelope, has been studied in South (Hillman 1986; Hillman and Gwynne 1987) and Central African Republic (Klaus- Hugi et al. 1999; Klaus-Hugi et al. 2000) and the species has also been studied in captivity (see below). Behavioural observations of bushbuck have been made at many sites (Allsopp 1978; Odendaal and Bigalke 1979; Okiria 1980; Simbotwe and Sichone 1989; MacLeod et al. 1996; Coates and Downs 2005, 2006) including detailed research into ecology and social organisation in Queen Elizabeth National Park, Uganda (Apio et al. 2010; Apio and Wronski 2005; Wronski and Apio 2006; Wronski et al. 2006a; Wronski et al. 2006b). However, much of this research has been undertaken in savannah habitat rather than dense forest (Wronski et al. 2009).

29 What is known of duiker ecology mainly concerns what they eat, and what they are eaten by, although preliminary research has been undertaken on the role of small antelope faecal deposits in local nutrient cycling in savannah woodlands (Lunt 2011). Duikers and neotragines are commonly preyed upon by mammalian carnivores (Hart et al. 1996; Henschel et al. 2005), eagles (Boshoff et al. 1994), and probably pythons (Python spp.). They are also amongst the species most heavily hunted by people in rural areas (see below).

Small antelope have often been assumed to be high-concentrate feeders, foraging for highly dispersed but energy-rich foods such as fallen fruit, e.g. Jarman (1974). However, while duikers preferentially feed on fruit when available (Feer 1995), most feeding observations, and analysis of rumen contents, reveal a significant proportion of dicotyledonous leaves in the diet (Feer 1989a; Hofmann and Roth 2003). This is particularly the case in lower productivity habitats, for example dry coastal forest where Philantomba monticola, Natal red duiker C. natalensis and suni antelope Neotragus moschatus feed primarily on mature fallen leaves (Lawson 1989; Bowland and Perrin 1998). Additional dietary items include fungi, blossoms and root tubers, as well as occasional matter (Hofmann and Roth 2003). Duikers have been known to eat small birds (Wilson 1966) and one of the first ever camera-trap photos of Abbott’s duiker C. spadix appears to show a frog in its mouth (Rovero et al. 2005).

Small forest antelope behaviour is largely a consequence of their feeding strategy. Unlike larger they do not need to spend long periods feeding (Owen- Smith 1988). Instead, they can remain hidden in dense vegetation for much of the time to avoid predators and ruminate between foraging bouts (Bowland and Perrin 1995). The smallest duiker species (genus Philantomba) are able to meet their feeding requirements from a small area and therefore monogamous pairs maintain fixed territories (Dubost 1980; Perrin et al. 2003). Larger species cannot secure adequate resources within a defended territory and therefore their home ranges overlap extensively, at least in Natal red duiker (Bowland and Perrin 1995).

Other species likely vary in their social behaviour and at least one, the white- bellied duiker C. leucogaster, has a skull characteristic of head-butting in other

30 ungulate species, perhaps indicating aggressive territorial defence despite its relatively large size (Snively and Theodor 2011). It should be emphasized that current knowledge of forest duiker behaviour is based largely on a small number of radio-telemetry studies which, according to our literature search, include a total of six species in four different countries (Feer 1989b; Bowland and Perrin 1995; Hart 2000; Mockrin 2010).

1.3.3.2. Forest antelope conservation and management

The majority of forest antelope species are not listed as threatened in the IUCN Red List (IUCN 2012). However, conservation assessment of these species has followed the traditional taxonomy, which often treats morphologically similar but distinct populations as single widespread polymorphic species (Cotterill 2003b). This may obscure the true conservation status of threatened populations. Indeed, several threatened forest antelope taxa, e.g. the endangered Brooke’s duiker C. ogilbiyi crusalbum, are listed as subspecies by the IUCN (2012). Any taxonomic re- assessment accepted by the IUCN, following the publication of Groves and Grubb (2011), is likely to lead to further antelope taxa being listed as threatened (see Table 1.2 for potential examples from Tanzania).

The major threats to forest antelope are habitat loss and over-harvesting. The most threatened species, such as Aders’ duiker C. adersi (Critically endangered) and Abbott’s duiker C. spadix (Endangered), have highly localised distributions due to historical and ongoing habitat loss. However, many species appear to adapt well to disturbed habitats such as secondary forest (Fimbel 1994; Hofmann and Roth 2003). Therefore, the greatest collective threat to forest antelope is overharvesting of populations for wild meat – so called “bushmeat”.

Hunting for bushmeat encompasses a wide range of socio-cultural circumstances from subsistence hunting by resident forest communities to illegal poaching in protected areas. Typically, forest antelope abundance is lower in hunted, compared to non-hunted, areas (Muchaal and Ngandjui 1999; Hart 2000; Van Vliet and Nasi 2008a). However, hunting is only a threat to population persistence if it is not biologically sustainable. Several studies have concluded that duiker hunting is locally unsustainable (Wilkie and Carpenter 1999; Fa et al. 2000), although such

31 analyses are confounded by the difficulty in estimating population densities and species-specific reproductive and demographic parameters (van Vliet and Nasi 2008b). Local extinctions as a result of hunting have rarely been reported in the scientific literature although are not entirely undocumented, e.g. van Vliet et al. (2007).

Wilson (2001) suggested that certain duiker species could be “farmed” to supply meat to rural communities and alleviate the impact of hunting. Generally, forest antelope seem unsuitable for intensive rearing as they have relatively low reproductive rates (typically one young per year) and some species are territorial (Eaves 2000). However, some form of ranching whereby population densities are maintained artificially through supplementary feeding and/or predator exclusion may have potential. Blue duiker P. monticola, one of the most heavily hunted species, are reported to have smaller territories in better quality habitats (Perrin et al. 2003).

The potential impact of hunting on forest antelope populations highlights the necessity of long-term monitoring, particularly for globally endangered species. Similarly, surveys are needed to improve our knowledge of species distribution and conservation status. However, such enterprises are by no means straightforward (Bowkett et al. 2006). Common approaches to estimating forest antelope density include line transect surveys (Struhsaker 1997; Rovero and Marshall 2004), drive counts (Bowland 1990; Noss 1999), territory mapping (Bowland and Perrin 1994), and extrapolation from faecal deposit counts (Schmidt 1983; Plumptre and Harris 1995).

More recently, automatic camera-traps have been used to monitor terrestrial communities, including forest antelope, e.g. Ahumada et al. (2011). Camera-traps have also been the principal method employed in surveys for threatened duiker species (Andanje et al. 2011; Jones and Bowkett 2012). One promising survey tool is the use of fake alarm calls to attract duikers as used by traditional hunters in some area (van Vliet et al. 2009).

32 All these survey methods have limitations for long-term monitoring. Line transect methods require regular sightings for accurate density estimates and are difficult to conduct at night when many forest antelope are most active, although see Waltert et al. (2006). Drive counts risk injury to the and may be expensive to conduct, as they require multiple skilled people to mind the nets. Territory mapping involves radio-collaring and is therefore expensive and more applicable to research than monitoring. Extrapolation from dung counts requires accurate faecal deposition and decomposition rates amongst other assumptions (Lunt et al. 2007). Critically, dung counts rely on accurate identification of dung to species, a skill that rarely stands up to validation (van Vliet et al. 2008; Bowkett et al. 2009b; Yamashiro et al. 2010).

Camera-traps are excellent survey tools for detecting forest antelope (Bowkett et al. 2006) but do not directly estimate abundance. Photographic trap-rates can be an accurate relative abundance index but this needs to be tested with independent density estimates (Rovero and Marshall 2009). It may also be possible to estimate animal density from camera-trap data by modelling contact between the camera’s detection zone and animal movement, although this requires knowledge of daily movement patterns (Rowcliffe et al. 2008).

Few forest antelope have attracted species-specific conservation action. However, Aders’ duiker C. adersi, as the only forest antelope species currently listed as Critically Endangered (Finnie 2008), has been the subject of various strategies and plans (Masoud 2003), including translocation of small numbers of individuals to offshore islets (Mwinyi and Wiesner 2003). The rediscovery of a significant population of Aders’ duiker on the northern Kenyan coast (Andanje et al. 2011) has improved the conservation status of this species, perhaps to the extent that Abbott’s duiker is now the most seriously threatened species. This Tanzanian endemic is very poorly known but has been the subject of recent surveys in the Udzungwa Mountains (Jones and Bowkett 2012) and Southern Highlands (Machaga and Davenport 2009).

Duikers and other forest antelope have been maintained successfully at several captive facilities, e.g. Bowman and Plowman (2002), and captive breeding

33 programmes have been recommended for the most threatened species (Wilson 2001; Masoud 2003). However, very few captive populations have been able to maintain viable numbers in the long-term. In contrast, bongo antelope are intensively managed in captivity (Dresser et al. 1985; Phillips et al. 1998), partly due to the critically endangered status of the eastern or mountain subspecies T. euryceros isaaci (East 1999). This taxon is only found in a handful of forests in the Kenyan highlands and is a realistic candidate for reintroduction (Reillo 2002). Surveys and monitoring of these last few populations have been enhanced by remote sensing (Estes et al. 2011) and non-invasive genetic sampling (Faria et al. 2011).

1.3.3.3. Ecological and economic importance of forest antelope species

Given the relative paucity of research for many forest antelope taxa (Table 1.1), what is the evidence for these species being important components of African ecosystems, and therefore worthy of conservation attention beyond an ethical desire to prevent extinctions?

Firstly, the ubiquity and abundance of antelopes in forest and woodland ecosystems provides indirect evidence of their importance. Forest antelope as defined here are found throughout sub-Saharan Africa although, with the exception of the previously mentioned savannah-adapted species, they are obviously absent from large stretches of the continent without lowland, montane or coastal forests (Kingdon 1997; East 1999; Wilson and Mittermeier 2011). Within forests they often constitute a significant proportion of the vertebrate biomass (Plumptre and Harris 1995; Fa and Purvis 1997) and are amongst the most frequently photographed species in camera-trap surveys (Ahumada et al. 2011; Mugerwa et al. 2012). As antelope are so commonly found in African forests they can be inferred to play important ecological roles as seed dispersers browsers, and prey (see 1.3.3.1).

34 Table 1.2. Forest antelope found in Tanzania. Traditional species names and conservation status follow IUCN (2012); weights are from Kingdon (1997) and taxonomic notes follow Groves and Grubb (2011).

Species Body IUCN Red Notes weight List Abbott’s duiker Cephalophus spadix 50-60 kg EN Up-listed from Vulnerable in 2008.

Harvey’s red duiker Cephalophus 13-16 kg LC Distribution uncertain. See Fig. harveyi 1.3.

Natal red duiker Cephalophus 12-14 kg LC Distribution uncertain. See Fig. natalensis 1.3.

Blue duiker Philantomba monticola 3.5-9 kg LC Split into Tanzania highland blue duiker P. lugens (endemic) and coastal East African blue duiker P. aequatorialis sundevalli. Pemba Island population (C. m. pembae) not recognised as distinct. Weyn’s duiker Cephalophus weynsi 16-23 kg LC Western border forests only.

Aders’ duiker Cephalophus adersi 6.5-12 kg CR Only confirmed from Zanzibar (and mainland ). Suni Neotragus moschatus 4-6 kg LC Split into mountain suni N. kirchenpaueri and coastal suni N. moschatus. Zanzibar population (N. m. moschatus) not recognised as distinct. Bushbuck Tragelaphus scriptus 24-80 kg LC Savannah woodland species sometimes found in forest habitats. Split into three subspecies within Tanzania: T. ornatus in the west, T. fasciatus on the coast and T. sylvaticus in central and north (and most of eastern and southern Africa).

Secondly, we have evidence from studies that have investigated forest antelope ecology more directly. Analysis of duiker stomach contents indicates these species have significant seed dispersal roles in primary forest (Gautier-Hion et al. 1980), secondary forest and moist savannah (Hofmann and Roth 2003). Hard-coated seeds may either survive digestion and be deposited in faeces or be spat out during rumination at resting sites. Such processes may stimulate seed germination in some species (Feer 1995). Most soft-coated seeds probably do not survive mastication and therefore antelope may represent significant seed predators as

35 well as dispersers. The functional role of antelope in the forest understory is essentially unknown although there is evidence that small-bodied antelope regulate woody plant encroachment in savannahs (Lunt 2011). Until further research is undertaken we can only guess at the effects forest antelope have on the viability of Africa’s vast forests.

Forest antelope are also important to human livelihoods. While forest tragelaphines, and to a lesser extent duiker species, can be locally important to the trophy hunting industry (von Brandis and Reilly 2008; Lindsey et al. 2007) the main economic impact of forest antelope is as wild meat (Eaves 2000). Nasi et al. (2011) estimate that approximately 4.5 million tonnes of bushmeat are consumed each year in the Congo Basin. As forest antelope typically constitute 30-70% of hunted animals in reported studies (Fa et al. 2005; Fa et al. 2006) this represents tens of millions of individuals.

In rural areas, bushmeat may be the primary source of protein available and therefore essential to survival. Bushmeat is a significant part of the informal economy in such areas with hunting undertaken for both consumption and trade (Kümpel et al. 2010). Bushmeat is also consumed in urban areas where it may be a cheap alternative to domestic meat or a luxury product depending on the location, culture and economic context (Nasi et al. 2011). There may also be significant international trade across borders (Fa et al. 2006) and even into western countries (Chaber et al. 2010).

Given that bushmeat feeds millions of people, and represents millions of dollars in trade, it seems strange that society has invested comparatively little in understanding the species that make-up such a large component of this natural resource.

36 1.4. Discussion

1.4.1. State of scientific knowledge on forest antelope

Our crude comparison of scientific literature for different ungulate taxa illustrates the discrepancy between poorly known African forest antelope and well-studied temperate species (Table 1.1). This difference is not quite so stark for other tropical antelope groups (i.e. wildebeest and ) although these contain fewer taxa so their article to species ratio is still much higher.

The bibliometric analysis does show that, at least for duikers and tragelaphines, the number of articles on forest antelope is increasing (Fig. 1.1.A and C). For both groups there is significant exponential growth as expected under Price’s Law. Inspection of the graphs indicates that the higher r2 values for linear relationships appears to be due to scientific productivity being at an early stage of exponential growth rather than having passed a saturation point (Fig. 1.1.A and C). This pattern does not hold for the three traditionally recognised Neotragus species with a total of only 40 research articles and no trend towards increasing research activity (Fig. 1.1.B). Moreover, with a few exceptions (Feer 1979; Lawson 1989), most of these articles do not focus on the behaviour or ecology of wild populations.

While we do not provide an analysis of the literature by topic, the increasing trend is probably related to greater recognition of the impact of bushmeat hunting. There also appears to be a decline in the study of basic ecology and behaviour (as noted above in terms of radio-telemetry). These trends are counter-intuitive if you consider the need for data with which to model population viability and sustainable resource management. This dereliction of forest antelope ecology is perhaps indicative of a general denigration of natural history (Cotterill and Foissner 2010). The demographic study of a hunted blue duiker population by Mockrin (2010) requires replication for other species.

1.4.2. Non-invasive genetics and forest antelope conservation research

Frankham et al. (2002)’s description of activities encompassed by conservation genetics (see 1.1) provides a useful framework for assessing the contribution the

37 field could make to the study and management of forest antelope. Given the difficulty in humanely capturing forest antelope, and the rarity of some species, the two main sources of genetic material for these species are most often likely to be tissue from hunted animals or field-collected faecal samples. Therefore, for areas where hunting is infrequent or highly covert, non-invasive sampling is likely to be a valuable tool despite the limitations of low-quality DNA (Taberlet et al. 1999).

The genetic management of small populations was one of the first applications of conservation genetics to be recognised by the conservation community (Soulé et al. 1986). Genetic drift has a larger impact on smaller populations thereby increasing the risk of inbreeding and reducing the potential for adaptation to environmental change. This has major implications for conservation not just because threatened populations are, or will become, small, but also because they may remain so over multiple generations. This may be the case for populations on islands (or other isolated habitats), in protected areas or under captive management.

Active genetic management of threatened duikers has not been attempted for wild populations and neither has population genetic analysis been undertaken for captive or translocated duikers as far as can be determined. This will clearly need rectifying if the translocated Aders’ duikers on offshore Zanzibari islets are to play any demographic role in the species’ recovery. This is also true for the small captive populations of other duiker species (Rohr and Pfefferkorn 2003).

Less intensive monitoring and management of forest antelope in fragmented habitat may be more widely applicable. If deforested land acts as a barrier to gene flow, then forest antelope in isolated habitat patches may be affected by small population processes, although this has yet to be tested with molecular data. This situation is potentially the case in the fragmented forests of the Udzungwa Mountains, particularly for the endangered Abbott’s duiker. Translocation, or more practicably the establishment of wildlife corridors, may be needed in the long-term to maintain the genetic health of isolated populations.

38 The “molecular revolution” in taxonomy (Groves and Grubb 2011) clearly has implications for conservation as explicated above (Table 1.2). Conservationists need to be able to recognise divergent evolutionary lineages if they are to facilitate the conservation of biodiversity at the genetic as well as the species and ecosystem levels (McNeely et al. 1990). In forest antelope, conservation genetics is urgently required to validate, or otherwise, the recent revision of many taxa (Groves and Grubb 2011) The recognition of many former subspecies as species will demand re-evaluation of their conservation status. Genetic information has already led to the discovery of a new species of Philantomba (Colyn et al. 2010) and recognition of paraphyly within bushbuck (Moodley et al. 2009). In the Udzungwa Mountains, all species, except Abbott’s duiker, have been the subject of recent taxonomic discussion (Groves and Grubb 2011; Hassanin et al. 2012).

Similarly, conservation genetics has a role to play in the identification of management units within species (Palsboll et al. 2007). The separate management of demographically independent units, i.e. stocks, is fundamental to sustainable harvests of exploited species but does not appear to have been considered for forest antelope beyond the recommendation of no-take zones, e.g. Hart (2000). Meta-population dynamics (source and sink populations) has been proposed as an explanation for the persistence of duiker populations at theoretically unsustainable harvest levels (Fimbel et al. 2000). Population genetic data, potentially from non-invasive samples, could be employed to identify source populations for protection. This application is again relevant to the Udzungwa Mountains where different levels of protected area management have resulted in contrasting levels of antelope abundance (Nielsen 2006a; Rovero et al. 2010).

The forensic application of genetics to forest antelope conservation is again highly relevant. Bushmeat is often butchered and/or smoked before it reaches the village or market and so identification for monitoring, or prosecution in the case of protected species (Pitra and Lieckfeldt 1999), can be enhanced by molecular methods. Similarly, identification of forest antelope dung is often unreliable (van Vliet et al. 2008) and genetic identification can be useful to confirm distribution records or validate dung counts.

39 These applications are highly suitable to a DNA bar-coding approach (Ratnasingham and Hebert 2007) and this has been attempted for forest antelope bushmeat samples (Eaton et al. 2010). However, tests of this approach at a wider geographic scale question the suitability of standard bar-coding markers for resolving closely related antelope species (Ntie et al. 2010a; Johnston et al. 2011). Verification of Abbott’s duiker records from dung is a very useful survey tool for this rarely seen species (Jones and Bowkett 2012), especially in areas where camera-trapping is difficult (Machaga and Davenport 2009). Identification of individual animals by molecular tags (e.g. microsatellite genotypes) would allow population density estimates with capture-recapture models, e.g. Miller et al. (2005).

Finally, conservation genetics has a role in understanding species biology. This broad application could cover any aspect of a threatened population that can be investigated through genetic methods including social organisation, diet, parasites and disease (Beja-Pereira et al. 2009). The application of modern genomic methods has enormous potential for this area of conservation genetics, particularly in conserving adaptive variation (Kohn et al. 2006; Primmer 2009). In forest antelope, fine-scale sampling of genetic variation to infer social organisation and dispersal patterns may be a promising alternative to the lack of radio- telemetry studies bemoaned earlier. This approach is especially relevant to montane forests and east Africa, where there have been no long-term ecological studies as in other areas. The importance of spatial genetic structure has been identified in modelling disease transmission in deer (Cullingham et al. 2011) and may have important implications in forest antelope which can suffer Ebola infections (Leroy et al. 2004).

1.5. Structure and aims of the present study

The overall aim of the present study is to use non-invasive sampling to investigate genetic patterns in forest antelope populations in the Udzungwa Mountains, Tanzania, within the context of the conservation of these species and the wider ecosystem. In short, to apply genetic methods to the conservation of Tanzanian forest antelope species for the first time.

40 This thesis consists of seven chapters including this introduction to conservation genetics and forest antelope. The following chapters include a dedicated methods chapter, four research chapters and a concluding summary. The research chapters are intended to be self-contained documents and follow the standard research article format. However, some cross-referencing between chapters has been allowed, particularly where it allows us to avoid repetition of shared methods.

Each of the research chapters contains its own specific predictions concerning the data, and these are clearly stated in the respective introductions. Therefore, what follows are general aims for each chapter that together provide an overview of the whole study. Associated research articles, that are complimentary to the central study, are included as appendices.

1.5.1. Chapter aims

Chapter 3. Can molecular data validate morphometric identification of faecal pellets in Tanzanian forest antelope species?

Field identification of forest antelope is unreliable in the Udzungwa Mountains (Bowkett et al. 2009b) and genetic identification is not practical for routine surveys. We therefore investigate the validity of using morphometric data to statistically assign samples to species based on faecal pellet size. A method that, if reliable, could be easily replicated in the field by protected area staff to monitor the globally endangered Abbott’s duiker. This chapter has been accepted, subject to revision, by the journal Conservation Genetics Resources.

Chapter 4. Inferring evolutionary species, lineages and population subdivision from non-invasive genetic sampling of forest antelope across a tropical mountain range.

The diversity of forest antelope in the Udzungwa Mountains is poorly characterized and even knowledge of which species are present requires reassessment in light of a recent major taxonomic revision of the Bovidae (Groves and Grubb 2011). This chapter aims to demonstrate the value of non-invasive sampling across the Udzungwa landscape to assess forest antelope diversity at

41 three levels: the distribution of putative evolutionary species; significant evolutionary lineages within these species; and genetic divergence between subdivided populations.

Chapter 5. Fragmented forests, fragmented populations? Fine-scale genetic structure in Harvey’s duiker Cephalophus harveyi across a heterogeneous landscape.

The spatial distribution of individuals can have important conservation and management implications and yet fine-scale genetic variation has not previously been investigated in duikers. This chapter aims to test the prediction that forest fragmentation, and other landscape features, structure Harvey’s duiker populations, and identifies management actions to maintain or enhance genetic diversity within the Udzungwa Mountains.

Chapter 6. Distribution and genetic diversity of the endangered Abbott’s duiker Cephalophus spadix in the Udzungwa Mountains, Tanzania.

This chapter builds on Chapter 4 and aims to assess the distribution and genetic diversity of Abbott’s duiker within what is potentially a global stronghold for this species. It includes distribution records from camera-trapping, an alternative survey-tool, and all existing genetic information from other Tanzanian regions. It also provides a baseline for future genetic assessment of Tanzania’s largest endemic species and possibly Africa’s rarest duiker. This chapter will be published in part in the Proceedings of the 8th TAWIRI Scientific Conference, December 2011, Arusha, Tanzania.

Appendix A: Associated publications with author contributions.

Bowkett, A.E., Plowman, A.B., Stevens, J.R., Davenport, T.R.B. and Jansen van Vuuren, B. (2009) Genetic testing of dung identification for antelope surveys in the Udzungwa Mountains, Tanzania. Conservation Genetics 10: 251-255

DOI: 10.1007/s10592-008-9564-7

42

AEB, JRS, and ABP designed the study; AEB and TRB collected samples and field data; AEB undertook all genetic and statistical analysis under the guidance of BJvV and JRS; BJvV provided consumables and reference samples; AEB wrote the paper with comments and improvements from all other authors.

Ntie, S., Johnston, A.R., Mickala, P., Bowkett, A.E., Jansen van Vuuren, B., Colyn, M., Telfer, P., Maisels, F., Hymas, O., Rouyer, R.L., Wallace R.A., LeBlanc, K., van Vliet, N., Sonet, G., Verheyen, E., Pires, D., Wickings, E.J., Lahm, S.A. and Anthony, N.M. (2010) A molecular diagnostic for identifying Central African forest artiodactyls from faecal pellets. Animal Conservation 13: 80-93

DOI: 10.1111/j.1469-1795.2009.00303.x

SN and NMA designed and led the study. AEB provided samples and sequences and commented on the manuscript.

Jones, T. and Bowkett, A.E. (2012) New populations of an Endangered Tanzanian antelope confirmed using DNA and camera-traps. 46: 14-15

Conservation news article

TJ undertook the camera-trap work and wrote the paper with comments from AEB; TJ and AEB collected samples and field data; AEB undertook the genetic analysis.

43 CHAPTER 2.

STUDY SITE AND METHODS

44 2.1. Introduction to methods

This chapter is intended to provide an overview of the study site and methods used during the course of this study. Although specific methods are given in the relevant research chapters the concise nature of modern journal articles can prevent inclusion of background information and development details. This is an important consideration for this study as extensive development work was undertaken both in the field and the laboratory. This development work is summarized here together with details of the study site, sample collection and collaboration with other researchers.

2.2. Study site: Udzungwa Mountains, Tanzania

The Udzungwa Mountains (the “Udzungwas”) are the southernmost and largest block of the Eastern Arc Mountains, a chain of ancient mountain ranges stretching from southern Kenya to south-central Tanzania renowned for their exceptional biodiversity (Burgess et al. 2007). The Udzungwas encompass a total area of approximately 10,000 km2 including 1,600 km2 of forest (Marshall et al. 2010) representing the largest area of rainforest remaining in the Eastern Arc Mountains (Burgess et al. 2007). This makes the Udzungwas internationally important for wildlife conservation and ecosystem services (see below).

The sampling sites included in this study include almost all the major forest fragments (> 10 km2) in the central Udzungwas, an area of approximately 6,500 km2 (Fig. 2.1) excluding Image to the north (c. 106 km2) and fragmented secondary forests on the Mufindi plateau to the west. The mountains rise steeply from the Kilombero Valley on their eastern side (200-300 m above sea level) to the highest peaks (> 2,500 m above sea level). The study site crosses the administrative districts of Kilola (Iringa Region) and Kilombero (Morogoro Region).

2.2.1. Biogeography

The Eastern Arc Mountains (“Eastern Arcs”) are recognised as one of the most important global sites for biodiversity conservation due to their exceptional species richness and number of endemic species (Burgess et al. 2007). In their original designation of biodiversity hotspots Myers et al. (2000) ranked the

45 Eastern Arcs (together with the associated east African coastal forests) as the highest ranking global site for number of endemic species per area. Although since reclassified as part of Conservation International’s Eastern Afromontane hotspot (Mittermeier et al. 2005) the Eastern Arcs have established their reputation for extraordinary biodiversity with the regular discovery of new species (Lovett et al. 1988; Dinesen et al. 1994; Rovero et al. 2008; Menegon et al. 2008).

The high level of endemicity in the Eastern Arcs is likely due to a combination of exceptional factors. Firstly, in contrast to the younger volcanic mountains to the north and south, the crystalline Eastern Arcs are geologically ancient having first been uplifted 7 to 10 million years ago (Griffiths 1993). Secondly, the climate has remained stable due to the influence of the Indian Ocean circulation system (Marchant et al. 2006) allowing the probable persistence of humid forest throughout the Pleistocene (Lovett 1993) including the last glacial maximum as indicated by the paleoecological record (Mumbi et al. 2008). This is in contrast to the wider Africa region which underwent repeated climatic changes during Plio- Pleistocene. This long-term ecological stability, in addition to the ‘island’-effect of many isolated mountain ranges (Kingdon 1990), may have resulted in high speciation rates as well as the persistence of relict species (Fjeldsa and Lovett 1997).

With the largest area of forest in the Eastern Arcs the Udzungwas consistently rank amongst the highest priority sites for conservation. Burgess et al. (2007) ranked the Udzungwas first amongst Eastern Arc blocks for site-endemic vertebrate species (17 species) and for Eastern Arc endemic and near-endemic vertebrate species (96 species combined). This pattern is similar for plant species partly reflecting the altitudinal range of the forest on the eastern slopes from 300 to 2580 m (Lovett et al. 2006; Burgess et al. 2007).

2.2.2. Conservation

The importance of the Udzungwas to biodiversity conservation was first recognised internationally when a new primate species, the Sanje mangabey Cercocebus sanjei, was discovered leading to a proposal for a new national park (Homewood and Rodgers 1980; Rodgers and Homewood 1982). Much of the

46 forested land in the Udzungwas was gazetted as catchment forest reserves by the British government in the middle of the twentieth century. These early protected- areas were established primarily to protect water catchments from deforestation but served to establish generally recognised boundaries around habitat that was later discovered to be globally important for biodiversity. Four of these forest reserves were combined to form the Udzungwa Mountains National Park in 1992 thus transferring responsibility from the Forestry and Bee-keeping Division (now Tanzania Forestry Service) to Tanzania National Parks. Many important high biodiversity forests were not included in the National Park although most of the contiguous forest reserves subsequently became part of the Kilombero Nature Reserve, a higher category of protected area under the Tanzania Forestry Service (Marshall et al. 2007).

While it is possible that the Udzungwas were once covered by continuous forest this may have been disrupted by early human habitation (Rodgers 1993) and has certainly been significantly impacted by people for the last 100 years or more (Struhsaker et al. 2004). Despite retaining more forest cover than other parts of the Eastern Arcs the area had been reduced to 26 forest patches by the mid-1990s (Newmark 1998). A habitat matrix of village agriculture, savannah woodland and extensive areas of fire-maintained grassland separates these forest patches.

The causes of deforestation in the Udzungwas include clearance for subsistence and commercial agriculture (largely historical or outside of reserves) and the removal of timber species commercially (until the 1980s) and for local use (ongoing). When large trees are removed the resulting gaps in the canopy are maintained or expanded by rapid growth of light-tolerant climbing plants, increased vulnerability of neighbouring trees to wind and fire, and elephant feeding activity (Struhsaker 1997). Forest regeneration is also prevented over larger areas by dry season bushfires. Natural fires occur in the savannah areas between forests but fires are also set deliberately to maintain open areas for access and cattle grazing (Dinesen et al. 2001).

Forests are also impacted by the demand for biomass fuel. Firewood is mainly collected from forests as cut branches or deadwood. The latter was restricted but

47 legal within the Udzungwa Mountains National Park until 2011, when the activity was banned partly due to fears about the ecological impacts (Rovero, F. pers. comm.), but is largely unregulated in forest reserves. The most commonly available fuel alternative to firewood is charcoal, the most frequently used fuel by urban populations, but this increases the demand for wood from forests as charcoal production is highly inefficient (Mwampamba 2007).

Forest ecosystems are also threatened by hunting of wildlife for bushmeat. Hunting is illegal in protected areas but appears to be the primary cause of declining mammal populations in several Udzungwa forests (Rovero et al. 2012). The largest mammal species such as buffalo and elephants appear to have been hunted out of several outlying forests such as Uzungwa Scarp and Kising’a Rugaro as they have from most other Eastern Arc forests (Jones, T. unpubl. data). Other targeted by hunters, including forest antelope and primates, are also ecologically important as seed dispersal agents and as prey for top predators (Gautier-Hion et al. 1980; Lambert 2011).

Demand for forest resources such as firewood and bushmeat is directly linked to local human population growth. Migration to the Udzungwa Mountains, particularly the eastern side, is high as fertile soils and readily available water encourage people to settle. The human population in the twenty administrative wards surrounding the Udzungwas was approximately 356,000 in 2002 (GoT 2006). Harrison (2006) found that 70% of household heads in fifteen villages in the Kilombero Valley, representing nearly 70,000 people, were immigrants from elsewhere in Tanzania and annual population growth was 3.4%.

Conservation of the Udzungwas is important for socio-economic as well as biodiversity reasons (MTSN 2007). Ecosystem services are resources and processes provided by natural ecosystems that benefit human societies. The Udzungwa forests provide a crucial role in water catchment that benefits hundreds of thousands of people living in the area and many more downstream. Small rivers and streams flowing from the escarpments provide year round drinking water and irrigation to surrounding villages and commercial plantations including rice and sugar cane. Water from the Udzungwas also feeds the major rivers of the

48 Kilombero and Rufiji River Basins benefiting millions of people. At a national scale, two of Tanzania’s largest hydropower plants are found in the Udzungwas, at Kidatu and Kihansi, together providing between 30 and 50 % of Tanzania’s total energy per year (GoT 2006).

The carbon storage capacity of the Eastern Arc forests is nationally significant in mitigating global carbon emissions responsible for climate change (Munishi and Shear 2004). This means that high biodiversity forests such as those in the Udzungwas could potentially benefit from carbon payment schemes aimed at reducing deforestation and degradation (REDD+) and Tanzania has been active in preparing such policy (Burgess et al. 2010). Potentially such schemes could fund joint forest management between government and local communities as allowed for under Tanzanian law (Blomley et al. 2008).

Other economic benefits from the Udzungwa forests include protection from soil erosion and flooding, income from tourism to the National Park and other non- timber forest products such as grass thatching, medicinal plants (Ndanyalasi et al. 2007), food (Msuya et al. 2010) and honey (Njau et al. 2009).

2.2.3. The Udzungwa Mountains as a research site

International research interest in the Udzungwa Mountains did not begin until the late 1970s. The early decades of research focused on documenting the poorly known biodiversity especially plants (Lovett et al. 1988), birds (Stuart et al. 1987) and primates (Rodgers and Homewood 1982; Rodgers 1981). These surveys led to surprising discoveries of new species perhaps most notably the Sanje mangabey (Homewood and Rodgers 1980) and Udzungwa partridge (Dinesen et al. 1994). The establishment of the Udzungwa Mountains National Park in the 1990s led to a rapid expansion in research to include other taxa, e.g. Trentin and Rovero (2011), monitoring programmes for key species (Rovero and Marshall 2004; Rovero et al. 2006) and the wider conservation and socio-economic context (Harrison 2006). A significant development in facilitating scientific activity in the area has been the establishment of the Udzungwa Ecological Monitoring Centre in 2007 by the Trento Science Museum and Tanzania National Parks (Rovero 2008).

49 The modern Udzungwa landscape of forest patches more or less isolated from each other by intervening habitat provides an excellent opportunity for comparative research that addresses critical questions in conservation biology regarding habitat fragmentation, population level response to change and ecosystem resilience to anthropogenic pressure. Forest fragments in the Udzungwas differ not just in terms of their isolation but also in size, altitude, topography, plant community and human impact. All these factors may affect which species are present in any particular patch and their abundance and population structure. Careful comparison of forests allows us to identify which factors are most important, e.g. Marshall et al. (2010), and draw conclusions relevant to other areas.

2.3. Sample collection

The genetic resources employed in this study were primarily derived from forest antelope faecal samples collected in Tanzania by the author (AEB) and collaborating researchers (Fig. 2.1). Although forest antelope dung is one of the more commonly encountered mammalian field signs in many African forests, the collection of samples from areas as rugged and remote as the sites visited during this study represents a significant physical and financial effort (Table 2.1).

50

[This image has been removed by the author of this thesis for copyright reasons]

Figure 2.1. Map of the Udzungwa Mountains, Tanzania, showing sample collection sites (stars). Forest patches surveyed during this project are underlined in yellow. Modified from Marshall et al. (2010).

51 Table 2.1. Site characteristics, sampling effort and number of collected samples for nine forest blocks in the Udzungwa Mountains, Tanzania. Protected area: FR = Forest Reserve, NP = National Park, NR = Nature Reserve; Collector: AEB = Andrew Bowkett, FR = Franceco Rovero, MN = Martin Nielsen, TJ = Trevor Jones.

Forest Protected Size Elevation Collector and year visited Walked Faecal Tissue area (km2) (m) transects samples samples (km)* Matundu NP/FR 526 279-1,046 AEB (2008); TJ (2008, 40 135 0 2009) Uzungwa Scarp FR 314 290–2,144 AEB and TJ (2008) 25 56 2

Luhombero- NP/NR 231 1105–2,520 AEB, TJ and FR (2008) 76 98 1 Ndundulu

Mwanihana NP 151 351–2,263 AEB (2006, 2008, 2009); TJ 68 212 6 (2008) Kising’a-Rugaro FR 116 1,627–2,322 TJ (2007) 30 3 0

Nyumbanitu NR 57 1,074–2,322 TJ (2007) 27 11 0

Nyanganje FR 42 350–1,038 TJ (2009) 23 34 0

New Dabaga - FR 40 1,764–2,081 TJ (2009); MN (2008) 21 50 4 Ulang’ambi

Iwonde NP 5 980-1,472 TJ (2009) 9 40 0

Total 319 639 13 * Line and recce transects combined.

52 2.3.1. Personnel

There are very few roads and no permanent accommodation within the protected areas of the Udzungwa Mountains so accessing sampling sites requires hiking and staying in temporary camps. Sample collection therefore relies on a team including porters, camp attendants, research assistants and, for some areas, armed rangers for protection against dangerous wildlife. In order to maximize the number of sampling sites, especially in remote areas, collaboration was sought with other researchers working in the Udzungwas (Table 2.1). The most important arrangement involved Trevor Jones (TJ), Anglia Ruskin University, who conducted extensive surveys across the Udzungwas to assess large mammal populations during 2007-2009 (T. Jones, in prep.). Richard Laizzer worked on both projects and was responsible for antelope sample collection and associated meta-data in the absence of the principal investigators. Other researchers that helped access samples included Tim Davenport and Sophy Machaga, Wildlife Conservation Society; Martin Nielsen (MN), University of Copenhagen; and Francesco Rovero (FR) and co-workers from Trento Science Museum and Udzungwa Ecological Monitoring Centre. The latter also provided extensive logistical support.

2.3.2. Site selection and timing

Sampling sites were selected to provide extensive geographic coverage of forested areas within the central Udzungwas (Fig. 2.1) but also to sample contrasting sites in terms of forest size, altitude, habitat, protected area status and distance to human settlement (Table 2.1; T. Jones, in prep.). All surveys were conducted between November 2006 and October 2009 during the dry season (late May to November extending into the early short rains in December 2006). Dung encounter rates are lower in the wet season due to more rapid decomposition rates (Jones, T. and Bowkett, A.E. unpubl. data).

2.3.3. Recce and line transect sampling

A systematic sample collection approach was attempted by conducting transect walks following a pre-determined compass bearing and measuring distance using a hip-chain, a device that measures the precise distance traveled on foot by dispensing biodegradable cotton thread. A handheld GPS unit (GPSMAP 60Cx, Garmin, Kansas City, USA) was used to log each transect and record the location of

53 each collected dung sample (with an estimated error position of < 10 m wherever possible).

Most transects were undertaken following the reconnaissance walk or ‘recce transect’ method, modified from White and Edwards (2000). This involves following a compass bearing along a one-off route but allows for short deviations from the straight line to avoid obstacles, including dense vegetation, in contrast to formal ‘line transects’ which are normally established for repeated sampling along cleared paths, e.g. Plumptre (2000).

Recce transects allow for more rapid data collection when compared to traditional transect sampling and can be undertaken during the course of other activities such as anti-poaching patrols. However, as for any relative abundance index, encounter rates on recce walks should have a monotonic relationship with true population density (Conroy 1996). Despite potential bias due to non-random sampling of accessible habitat, several studies have found a positive relationship between field sign encounter rates on recce walks and density estimates from transect surveys (Hall et al. 1998; Walsh and White 1999). This study employed recce transects to avoid spatial sampling bias rather than to infer antelope abundance although TJ undertook both line and recce transects to examine this relationship for forest antelope and other mammals in the Udzungwas (T. Jones, in prep.).

Recce transects were generally undertaken in a triangular configuration whereby each of three transects (typically 1 km each) walked on a particular day formed one side of the triangle. The advantage of this approach is that the area sampled is independent of linear features such as valleys and ridges that might otherwise introduce bias. Moreover, a triangular circuit maximizes the potential distance travelled per day as sampling starts and finishes at a point accessible from the campsite. Single linear recce transects were also sometimes undertaken by AEB to sample particular forest areas when time was limited (mostly in 2006).

In addition to location data, the date, time, age (on an arbitrary scale of 1 – 4 with 1 being the newest) and species guess were also recorded for collected dung samples each of which was allocated a unique code based on the collector’s initials.

54 2.4. Sample storage

Faecal samples were stored in 2 mL plastic tubes containing 1 – 1.5 mL of the nucleic acid preservative RNAlater (Ambion Ltd, Huntington, UK). Samples consisted of one to five faecal pellets (depending on size) from the same dung pile and were manipulated into tubes in the field using a fresh stick or leaf for each pile. Antelope dung stored in RNAlater has been shown to have good DNA amplification success rates (> 80% for mtDNA after one month’s storage at ambient temperature) superior to some alternative media (Soto-Calderon et al. 2009) . Samples were refrigerated (c.4°C) as soon as possible after field collection and exported to the University of Exeter in the UK by courier service where they were frozen at -20°C until DNA extraction.

Tissue samples were recovered from antelope found dead in the field or from remains found in poaching camps on an opportunistic basis. Small amounts of skin or muscle (c.1-5 cm2) were stored in RNAlater or 95% Ethanol and kept at c.4°C for the duration of the study. Katharina Hecker, Nico van Rooyen Taxidermy, provided additional skin samples from trophy hunters operating in , Tanzania and . Full details for all tissue samples included in the genetic analysis are given in Chapter 4 (Supp. Table 4.1).

We tested for the effect of estimated age at collection and of the different storage conditions on genotyping success at seven combined microsatellite loci (see below) for 329 faecal samples for which we had complete sampling records. All samples were identified as C. harveyi or spadix by sequencing the mitochondrial control region (see below). There were too few failures to test for storage effects on mtDNA amplification success (Chapter 4). Whether or not a sample was successfully repeat-genotyped at all loci was used as a binary response in statistical analysis with the following explanatory variables: age category assigned in the field (field age), days stored at ambient temperature during fieldwork, days stored in the refrigerator (c.4°C) and days stored frozen before DNA extraction (- 20°C), see Fig. 2.2. A generalised linear model with a quasi-binomial error structure (to account for over-dispersion) was used to explore the combined effects of the four variables including two-way interactions. We followed the model selection protocol described by Crawley (2007) whereby interactions and main

55 effects can be dropped if there is no difference in variance explained between the model with and without that term as tested by ANOVA.

Field age Ambient temp. 5 15 4 10 3 Days 5 Age class 2 1 0

0 1 0 1

Genotype success Genotype success

c.4 °C -20 °C 200 600 Days Days 100 50 200 0

0 1 0 1

Genotype success Genotype success

Figure 2.2. Box-and-whisker plots showing median differences in field estimated age-class and days stored at different temperatures between antelope faecal samples which were successfully genotyped at 7 microsatellite loci (“1”, n = 136) or not (“0”, n = 193).

The resulting final model retained field age (t = -3.57; P < 0.001) and days stored at -20°C (t = 3.24; P = 0.001) as highly significant terms. The other two main effects were also retained in the best-fit model due to a significant interaction effect (t = - 2.16; P = 0.03). The negative effect of field age is expected and likely due to DNA deterioration before collection. In contrast, a significant positive effect of days spent frozen is unexpected as long-term storage should have a negative (or at best neutral) effect. The most likely explanation is that samples stored for longer at - 20°C were genotyped later in the life of the project when laboratory methods were 56 better optimized as opposed to a genuine effect of storage condition. Further investigation would require an experimental approach to isolate the effects of different variables.

2.5. Laboratory methods

Sample processing and genetic methods were informed by collaboration with other research groups working on duiker antelope. This study benefitted greatly from development work for west and central African duiker research by Nicola Anthony and colleagues at the University of New Orleans, USA. Training in molecular biology, access to reference tissue (van Vuuren and Robinson 2001) and optimization of methods for east African species was supervised by Bettine Jansen van Vuuren, Evolutionary Genomics Group, University of Stellenbosch, South Africa. All genetic work was undertaken at the Universities of Stellenbosch (2007; September 2010) and Exeter, UK (2007-2012).

2.5.1. DNA extraction

DNA was extracted from dung samples with the QIAamp DNA Stool Mini Kit (QIAGEN, Crawley, UK) following the manufacturer’s instructions but including an extended incubation step of 20 to 30 minutes for digestion with proteinase K. This method uses spin columns with silica-gel membranes to bind DNA but remove other material. Blanks were included in the majority of extraction series to control for contamination. For tissue samples we used a similar procedure (QIAGEN DNeasy Blood and Tissue Kit) with overnight digestion for dried skin samples.

2.5.2. Molecular marker selection

The choice of molecular markers for this study was restricted by the low quality and quantity of DNA, and the potential presence of amplification inhibitors, typical of dung samples (Taberlet et al. 1999). Mitochondrial DNA is often amplified more successfully than nuclear DNA due to the high density of mitochondria in most animal cells. Nuclear DNA in dung samples is typically degraded and so primers need to target short fragments (100-300 bp). Microsatellite markers of this size are therefore often the most appropriate nuclear markers for use in non-invasive genetic studies (Broquet et al. 2007). The background to non-invasive faecal

57 sampling is discussed in Chapter 1; this chapter describes the practical steps taken in this study to deal with faecal DNA.

2.5.3. Mitochondrial markers

Several mitochondrial genes have been used in mammals to confirm species identity (bar-coding) and infer phylogenetic structure including the cytochrome c oxidase subunit 1 - COX1 (Clare et al. 2007), cytochrome b (Telfer et al. 2003) and the control region or D-loop (Matthee and Robinson 1999b). This study initially targeted the left-hand domain of the control region because this marker had previously been used successfully with antelope dung (Pitra et al. 2006), is the most rapidly evolving region of the mitochondrial genome (Iyengar et al. 2006) and has therefore been frequently used for examining intra-specific variation in other antelope species (Matthee and Robinson 1999b; Lorenzen et al. 2007; Nersting and Arctander 2001). However, as part of an extensive collaboration, we later found that this marker distinguished duiker species more reliably than cytochrome b (Ntie et al. 2010a). Johnston et al. (2011) used the same genetic resources to show that the commonly used bar-coding marker COX1 also fails to distinguish species reliably.

Initially, we targeted a ~600 bp control region fragment using published primers in general use at the Stellenbosch lab: N777 and H16498. However, following the genetic analysis for Bowkett et al. (2009b) we switched to duiker-specific primers designed by the University of New Orleans team: CRF1 and CRR3 (Ntie et al. 2010a). For poor quality samples these primers were used in conjunction with a set of internal primers (CRR5A and CRF6A) to amplify the target sequence as two overlapping fragments of approximately 350 bp. Primer sequences and origins are given in Table 2.2. Two main sets of polymerase chain reaction (PCR) conditions were used during the course of the study depending on availability of reagents (Table 2.3). Generally, the QIAGEN kit-based method performed better and was used throughout the latter stages of laboratory work. PCR negative controls were always run to test for contamination.

PCR products were run on 2% agarose gel stained with ethidium bromide and the resulting bands cut from the gel and purified with the QIAquick PCR purification

58 kit (QIAGEN) following the gel extraction protocol with an extended second wash step of 10 minutes (‘PE buffer’). The purified products were sent to an external company for sequencing (Beckman Coulter Genomics, Takeley, UK).

Sequence electropherograms were viewed and edited in AutoAssembler (Applied Biosystems, Foster City, CA, USA) and a preliminary species assignment made using BLAST searches (NCBI, Bethesda, MD, USA). Edited sequences were aligned in SEAVIEW (Gouy et al. 2010) using MUSCLE (Edgar 2004) and checked visually.

To check for consistency in sequence reads we re-extracted 68 of the 644 dung samples that amplified antelope DNA (10.6%). We were able to sequence 63 of these re-extracted samples and all but three sequences were identical to the original extraction of the same sample (95%). The remaining three samples yielded different sequences to our original results but the cause could be traced to human error (mislabeling during the original DNA extraction) rather than amplification slips or sequencer misreads. We then re-extracted other samples from those original extraction series containing mistakes but found no further evidence for mislabeling (results included in the figures above). The re-extracted sequences were considered the true results for the three samples (all suni) following successful PCR repeats. Many PCR products were also sequenced in both directions lending further support to the accuracy of our sequence data.

Table 2.2. Primers used to amplify the mitochondrial control region from antelope faecal samples.

Primer Sequence Paired with Fragment Source length (bp) CRF1 5’-CTCCCTAAGGCTCAAGGAAGC-3’ CRR3 600 Ntie et al. (2010a) CRR3 5’-CCTGAAGRAAGAACCAGATGTC-3’ CRF1 Ntie et al. (2010a) N777* 5’-TACACTGGTCTTGTAAACC-3’ H16498 600 Hoelzel et al. (1991) H16498 5’-CCTGAAGTAGGAACCAGATG-3’ N777 Shields and Kocher (1991) CRR5A 5’-CATTAATCCTTGTTGTACTTGC-3’ CRF1 350 Ntie et al. (2010a) CRF6A 5’-GTTATACAGACATACTATGTATATAG-3’ CRR3 350 Ntie et al. (2010a) * Modified from original sequence 59

An additional source of potential error in analyzing mitochondrial DNA is the inadvertent sequencing of nuclear mitochondrial pseudogenes (Numts). Numts are mitochondrial DNA regions that have become integrated into the nuclear genome and have been documented in many species across different taxonomic groups (Bensasson et al. 2001). Numts can be detected in coding mitochondrial genes by the presence of frameshifts or stop codons but this does not apply to non-coding regions like the control region (Anthony et al. 2007). However, we find no compelling evidence that Numts are present in our dataset as we did not uncover any random or unexpected phylogenetic patterns (Chapter 4), we did not encounter many ambiguous sequence sites and faecal material is a poor source of nuclear relative to mitochondrial DNA (as demonstrated here by far higher control region amplification success compared to microsatellites).

2.5.4. Microsatellites

Thirty-four dinucleotide microsatellite markers from other bovid species were initially screened for polymorphism in duikers by the University of New Orleans team. Sixteen loci were found to be polymorphic in six central African duiker species and twelve primer pairs were combined in three multiplex panels of four loci each (Ntie et al. 2010b). Fourteen of these markers were then tested for use with forest antelope species from the Udzungwas at the University of Exeter.

Trials were carried out with unlabelled primers and DNA from Harvey’s and Abbott’s duiker tissue samples. PCR products were run on a 1% agarose gel to test for successful amplification across a gradient of annealing temperatures (50-60°C). For loci that produced clean consistent bands we selected one primer from each pair for labeling with a fluorescent tag based on a further PCR test whereby forward and reverse primers were amplified singly to check for spurious amplicons. The single-run primer with the least associated stutter was then selected for labeling. The labeled microsatellite can be accurately sized against an internal size standard on an automated sequencer. Ntie et al. (2010c) optimized their central African duiker microsatellites on an ABI 3100 sequencer.

60 Fragment analysis for this study was carried out on an eight capillary Beckman Coulter sequencer (Beckman Coulter, Fullerton, CA, USA). This platform has four different coloured dyes including the size standard. Therefore a minimum of three microsatellite loci can be scored simultaneously from a single well (multiplexed). Multiplexes with more than three loci are also possible as long as allele size ranges do not overlap between markers with the same dye. Markers can also be multiplexed at the amplification stage as in this study (Table 2.3 and 2.4). Electropherogram traces were scored visually using the CEQ 3000 software (Beckman Coulter).

Table 2.3. PCR conditions for control region (CR) and microsatellite markers (MPLX) with antelope faecal DNA.

PCR media PCR cycles CR 1 30 µl: 1x buffer, 2 mM MgCl2, Denaturation: 96°C for 5 min 0.3 units BIOTAQ Red Amplification: 96°C for 20 s, Polymerase (Bioline, London, 48°C for 20 s and 72°C for 55 s UK), 1 mM dNTP, 2 pmol of (40 cycles) each primer (forward and Extension: 72°C for 2 min reverse), 0.1 mg/ml BSA, ddH2O and 1 µl DNA extraction elute CR 2 20 µl: 10 µl QIAGEN HotStar As for CR 1 PCR kit (inc. 3 mM MgCl2), 2 pmol of each primer (forward and reverse), ddH2O and 1 µl DNA extraction elute MPLX1 11 µl: 5 µl QIAGEN Multiplex Denaturation: 95°C for 15 min PCR kit (inc. 2 mM MgCl2), 1 Amplification: 94°C for 30 s, µl of 10x primer mix*, 2 µl of 58°C for 3 min and 72°C for 60 Q-solution, 1 µl of RNAase free s (35 cycles) water, and 2 µl DNA elute Extension: 60°C for 30 min MPLX2A As for MPLX1 As for MPLX1 (40 cycles)

MPLX2B As for MPLX1 except primers As for MPLX1 (40 cycles) not pre-mixed (0.9 µM each)

* See Table 2.5  Combined for automated sequencer analysis

Despite the developmental work carried out for central African duiker species we experienced considerable difficulty optimizing microsatellite multiplexes for east African species on a different sequencing platform. Problems included small alleles being obscured by initial flare (MM12), failure to amplify or very weak amplification in single locus reactions (INRA05), failure to amplify or very weak amplification in multiplex reactions (BM1225, BRRIBO, BM143, INRA05, BM2830),

61 spurious amplification of non-target PCR products (BM143, ETH225, BM1862) and difficulty in scoring due to split peaks and forward stutter (BRRIBO in all species and BM121 in Abbott’s duiker). These problems were in addition to standard issues in non-invasive genetics such as allelic drop-out and null alleles [Taberlet et al. (1999); Chapter 4 and 5].

Some of these problems could be resolved for particular loci through a process of prolonged trial and error. Difficulty in scoring stuttered peaks in BRRIBO was overcome by -tailing the reverse primer (Brownstein et al. 1996). For INRA05 weak amplification was resolved by labeling the fluorescent primer with a different dye (blue instead of black). In some cases, false peaks of the same size were consistently amplified in multiple samples. Where these peaks did not exhibit a typical microsatellite profile and were seen in heterozygous genotypes (i.e. in the presence of two other ‘genuine’ alleles at the same locus) they were ignored and excluded from subsequent analysis. In most cases spurious peaks were only seen as a result of single reactions and were discounted after repeat PCRs (see below). Optimal amplification in multiplexed reactions required adjustment of relative primer concentrations (Table 2.5). Final PCR multiplexes are shown in Table 2.4. MM12 (Mommens et al. 1994), BM864, BM848, BM1862, BM2830 (Bishop et al. 1994) and ETH225 (Steffen et al. 1993) were discarded due to continual problems.

62 Table 2.4. Microsatellite primers and multiplex design for use with antelope faecal DNA. F = forward, R = reverse, primer labeled with fluorescent tag in bold.

Multiplex Marker Sequence Dye Size Source range* MPLX1 BM2113 F gctgccttctaccaaataccc Blue 120-150 Bishop et al. R cttcctgagagaagcaacacc (1994)

INRA40 F tcagtctccaggagagaaaac Green 135-187 Beja-Pereira R ctctgccctggggatgattg et al. (2004)

BM1225 F tttctcaacagaggtgtccac Black 221-237 Bishop et al. R acccctatcaccatgctctg (1994)

BRRIBO F cacccgtaccctcactgc Blue 236-250 Bishop et al. R tcacaaccctcttctcaccc (1994)

MPLX2A BM121 F tggcattgtgaaaagaagtaaa Black 110-120 Bishop et al. R actagcactatctggcaagca (1994)

SR12 F tgaccaggtgactaacac Blue 217-231 Kogi et al. R aatctgatttcatttcatg (1995)

MPLX2B BM143 F acctgggaagcctccatatc Green 87-109 Bishop et al. R ctgcaggcagattctttatcg (1994)

INRA05 F caatctgcatgaagtataaatat Blue 129-153 Beja-Pereira R cttcaggcataccctacacc et al. (2004)

* Twenty C. callipygus individuals Ntie et al. (2010c)  Reverse primer designed by Ntie et al. (2010c)

Errors in microsatellite genotyping from faecal DNA are common due to PCR failure, false alleles, non-amplification of one allele in heterozygotes (allelic drop- out resulting in false homozygotes) and contamination (Taberlet et al. 1999). These errors mean that scoring the correct genotype from a single PCR with low yield DNA is highly unlikely and therefore multiple repeat PCRs are necessary for reliable genotyping (Taberlet et al. 1996). In this study, we followed the multiple- tubes approach of Taberlet et al. (1996) by only accepting an individual as heterozygous at a locus if both alleles were scored in three independent PCRs (although because loci were amplified in multiplex PCRs c.95% of samples were scored a minimum of four times at each locus). However, we relaxed their threshold for accepting a homozygote (a minimum of seven PCR repeats) to four independent PCRs. While this potentially lowers the confidence level below 99% (but not below 90%) it represents a significant reduction in time and cost and remains more conservative than the comparative approach of Frantz et al. (2003) which has been shown to achieve similar levels of reliability despite requiring less

63 repeats (Frantz et al. 2003; Hansen et al. 2008). We ran negative PCR controls with the majority of PCR runs and excluded identical genotypes, including samples that differed by only one allele, which we assumed to be derived from the same individual following Pickles et al. (2011).

Table 2.5. Preparation of 10x primer mix for use with the QIAGEN Multiplex PCR Kit as recommended by the manufacturer (each primer at 2 µM) and relative primer concentrations used with antelope faecal DNA.

Recommended Multiplex 1 Multiplex 2A Primer stock 50 µM 50 µM 50 µM concentration Each primer (forward 20 µl 24 µl (BM1225, 16 µl (BM121); and reverse) (up to 12 pairs) BRRIBO); 8 µl (SR12) 16 µl (INRA40, BM2113) TE buffer or H2O Variable 340 µl 452 µl Final volume 500 µl 500 µl 500 µl

2.5.5. Other potential markers

Nuclear intron markers used previously for antelope (Willows-Munro et al. 2005) were tested with tissue samples at the University of Exeter as part of a parallel study (Johnston and Anthony 2012) but failed to amplify from faecal DNA. An additional marker, the seventh intron of the nuclear fibrinogen gene, was also tested using primers from Seddon et al. (2001). This latter marker amplified DNA from dried skin samples but only two out of thirteen dung samples. The University of New Orleans team also attempted to incorporate a sex-linked marker (Amelogenin) into their microsatellite multiplexes but without consistent success (Ntie, S. pers. comm.). For future work, redesigned primers for both these markers may allow further insight into forest antelope genetics.

2.6. Methods summary

This chapter has provided a detailed background to the study site and methods used during this study. This information is important to highlight the Udzungwas as a global priority for biodiversity conservation as well as an appropriate area in which to study forest antelope genetics. Subsequent chapters will aim to demonstrate that the latter endeavour can benefit the former.

64

Methodological detail is important both to allow future repetition by other researchers and to demonstrate the arduous development work necessary for the application of molecular techniques to wild populations of non-model organisms. Statistical analysis methods are described in the respective research chapters. While the genetic methods developed during this study are by no means exhaustive the rest of this thesis will investigate their potential to uncover significant patterns in forest antelope populations that have wider implications for the fields of conservation and evolutionary biology.

65 CHAPTER 3.

CAN MOLECULAR DATA VALIDATE MORPHOMETRIC IDENTIFICATION OF FAECAL PELLETS IN TANZANIAN FOREST ANTELOPE SPECIES?

Accepted by the journal Conservation Genetics Resources on 18th February, 2013, subject to minor revision, with the following authors:

Andrew E. Bowkett1,2, Trevor Jones3,4, Richard L. Laizzer3, Amy B. Plowman2 and Jamie R. Stevens1.

1 Molecular Ecology and Evolution Group, Biosciences, College of Life and Environmental Science, University of Exeter, Exeter EX4 4QD, United Kingdom

2 Field Conservation and Research Department, Whitley Wildlife Conservation Trust, Paignton Zoo, Totnes Road, Paignton, TQ4 7EU, United Kingdom

3 Udzungwa Elephant Project, P.O. Box 99, Mang’ula, Tanzania.

4 Animal and Environmental Research Group, Department of Life Sciences, Anglia Ruskin University, Cambridge CB1 1PT, United Kingdom

AEB, JRS, and ABP designed the study; AEB, TJ and RLL collected samples and field data; AEB undertook all genetic and statistical analysis under the guidance of JRS and ABP; AEB wrote the paper with comments and improvements from JRS, TJ and ABP.

Acknowledgements are given at the end of the chapter.

66 Abstract

Forest-dwelling antelope species are often difficult to detect during surveys due to their cryptic behaviour and densely vegetated habitats. Dung counts have traditionally been used to infer forest antelope abundance but genetic identification has shown that visual identification of ungulate dung to species is often unreliable. This study attempted to use easily obtained morphometric data from faecal pellets to statistically assign antelope dung piles to species. We measured pellets from 238 dung piles collected from the Udzungwa Mountains, south-central Tanzania, a largely forested landscape with five forest-associated antelope species including the endangered Abbott’s duiker Cephalophus spadix. The species identity of sampled dung piles was determined by amplifying a c.600 bp fragment of the mitochondrial control region and aligning DNA sequences with published references. We found no diagnostic differences in faecal pellet size between antelope species although there were significant differences in mean pellet length and width. We employed a single variable linear discriminant analysis to predict the species of dung piles based on pellet length. Despite significant differentiation between species we obtained an overall accuracy of 58.8% that did not meet our specified probability threshold (P < 0.05). Abbott’s duiker dung piles were correctly assigned in the majority of cases (74%). Overall, morphometric assignment of dung piles to species was not accurate enough to validate dung counts as a survey method for forest antelope. We recommend genetic identification of dung for species of conservation concern, in addition to alternatives such as camera-traps, despite the significant financial and technical investment required.

67 3.1. Introduction

Encounter rates and density estimates of faecal deposits are often used to infer the abundance of ungulates for research or management (Campbell et al. 2004; Lunt et al. 2007; Plumptre and Harris 1995). However genetic identification of ungulate faeces (referred to here as dung), by amplifying species-specific fragments of mitochondrial DNA, has revealed that dung piles are often mistakenly identified in the field (Faria et al. 2011; van Vliet et al. 2008; Yamashiro et al. 2010). This is a particular problem when species of conservation concern, for example the mountain bongo Tragelaphus eurycerus isaaci in Kenya (Faria et al. 2011), occupy the same areas as other ungulate species with similar dung morphology.

In common with the previously cited studies, Bowkett et al. (2009b) found that identification of forest antelope dung to species in the field was unreliable, in this case in the Udzungwa Mountains, Tanzania. This result cast doubt on the validity of using dung counts as a survey and monitoring tool for the endangered Tanzanian endemic Abbott’s duiker Cephalophus spadix and sympatric species. Abbott’s duiker is found in only a small number of highland forests and is threatened by continuing habitat disturbance and hunting for bushmeat (Moyer et al. 2008). Like other forest antelopes, this species appears to be largely solitary and nocturnal (Rovero et al. 2005) and therefore unsuitable for survey methods based on sightings.

Surveys using genetic identification of dung and automatic camera-traps have been successful in documenting the presence of Abbott’s duiker (Jones and Bowkett 2012). However these methods are relatively expensive and not easily replicated as part of routine monitoring by wildlife authorities. Hibert et al. (2008) demonstrated the potential for discriminant analysis to identify savannah antelope species based on faecal pellet morphometric data. Bowkett et al. (2009b) attempted to statistically assign dung piles to species based on the size of faecal pellets, a method that could easily be carried out in the field by local survey teams. However their preliminary morphometric analysis only sampled a small part of the Udzungwa landscape and was limited by small sample size. For this study we employ a larger data set of genetically verified antelope dung piles from across the

68 Udzungwa Mountains to assess whether dung from forest antelope can be reliably determined to species using simple size measurements.

Specifically, we aimed to test (1) whether or not diagnostic differences in faecal pellet measurements exist between target antelope species, (2) whether a discriminant analysis can assign pellets to their correct species group with a high statistical probability (P < 0.05), and (3) whether these approaches can distinguish Abbott’s duiker pellets even if other species are unresolved. In addition, we test some of the assumptions of reliability inherent in applying such a method under field conditions.

3.2. Materials and methods

Antelope faecal pellets were collected from all but one of the major forest fragments (> 10 km2) in the Udzungwa Mountains, south-central Tanzania between 2006 and 2009. General landscape and habitat details are given in Marshall et al. (2010). Each pile was visually assigned to species in order to test the accuracy of identification in the field. A subset of faecal pellets from each dung pile was stored in RNAlater (Ambion Ltd, Huntington, UK) for later DNA extraction (QIAamp DNA Stool Mini Kit, QIAGEN, Crawley, UK). Molecular identification of samples involved amplifying c.600 bp of the mitochondrial control region using the primer combinations and PCR conditions detailed by Ntie et al. (2010a). We opted not to use the cytochrome c oxidase subunit 1 (COX1) as this marker fails to delineate several duiker species under standard DNA bar-coding criteria (Johnston et al. 2011). Sequences were aligned with published references for all potential antelope species using MUSCLE (Edgar 2004) in SEAVIEW (Gouy et al. 2010). We constructed a neighbour-joining phylogeny based on Kimura 2-parameter corrected distances (Kimura 1980) in PAUP* (Swofford 2001) to show that all haplotypes formed monophyletic species clades. The tree was rooted using bushbuck sequences as an out-group. A highly variable 103 bp section was removed before tree-building as it proved difficult to align consistently.

For each dung pile, we measured up to twenty randomly selected pellets with calipers (to the nearest 0.01 mm) and subsequently treated the mean value as our sampling unit. All measured dung piles were estimated to be less than 24 hours old

69 due to their shiny moist surface. We measured length as the maximal distance between pellets ends and width as the diameter at the widest point. We also calculated the length to width ratio following Bowkett et al. (2009b). Pellets were stored in tied plastic bags and either measured during the field survey or kept refrigerated (c. 4oC) and measured at a later date (mean delay in days of 138 recorded dung piles = 6, maximum = 21, see below). Pellets were not dried before measurement as we wanted to test a method that could be easily replicated by protected area staff under humid forest conditions.

As well as testing for absolute inter-specific differences in mean pellet size we also attempted a linear discriminant analysis for statistically assigning dung piles to species. All three measured variables were normally distributed but highly inter- correlated (r = 0.35-0.73, P < 0.001). Length was identified as the variable with the highest discriminatory power following one-way ANOVA tests using species as a factor: Length (R2 = 0.67), Width (R2 = 0.63), Ratio (R2 = 0.03) where R2 measures the proportion of the observed variance explained by the independent factor species. The discriminant analysis was therefore carried out on the single variable

Length (log10 transformed to improve homogeneity of variance). Box’s M test was used to test for homogeneity in the variance-covariance matrices and the significance of discrimination between species tested with Wilks’ Λ.

We also tested two assumptions in measuring faecal pellets that could potentially be violated under field conditions. Firstly, we tested for the effect of a delay between collecting and measuring pellets by measuring individual pellets on the day of collection and again after 31 days under two different storage conditions: refrigerated (n = 135) and ambient temperature (n = 119). Secondly, we checked for inter-observer bias (different survey personnel) by two of us (AEB and RLL) measuring the same 220 individual pellets. All statistical tests were carried out in SPSS 16 (SPSS Inc., Chicago, IL, USA).

3.3. Results

We measured pellets from 238 dung piles (4654 individual pellets) from which we successfully obtained control region sequences (two additional piles did not yield readable sequences). This dataset included 48 dung piles measured for Bowkett et

70 al. (2009b). Sixty-eight dung piles (28.6 %) were incorrectly assigned to species by the survey teams.

All control region haplotypes recovered from dung samples aligned with published sequences for forest antelope species known from the Udzungwa Mountains and formed monophyletic clades with high bootstrap support (97-100%, Fig. 3.1): Harvey’s duiker Cephalophus harveyi (36 haplotypes), blue duiker Philantomba monticola (4), suni Neotragus moschatus (27), bushbuck Tragelaphus scriptus (2) and Abbott’s duiker (6). All sequence reads were at least 520 bp except for one Abbott’s duiker sample (310 bp). Two sequences aligned closely to reference species but contained too many ambiguous bases to assign to a specific haplotype and so were excluded from the phylogeny shown here.

71 Figure 3.1. Control region neighbour-joining bootstrap consensus phylogeny for faecal DNA sequences from the Udzungwa Mountains, Tanzania (c.530 bp). Bootstrap values are percentages of 1000 iterations (values below 90% not shown). GenBank accession numbers and species names are given for published sequences.

72 There was a statistically significant difference across the five species detected in untransformed mean pellet length (One way ANOVA: F4, 233 = 120.3, P < 0.001) and width (F4, 233 = 98.3, P < 0.001) but not in length:width ratio (F4, 233 = 1.67, P = 0.16). Similar P values were obtained with Welch’s ANOVA which was carried out for width (P < 0.001) and ratio (P = 0.15) which did not meet the assumption of homogeneity of variance. However the range of all variables overlapped between at least two species, thus no diagnostic differences in pellet size were apparent. Abbott’s duiker pellets overlapped with those of Harvey’s duiker and bushbuck (Fig. 3.2).

Figure 3.2. Box-and-whisker plot of faecal pellet length for five species of forest antelope from the Udzungwa Mountains, Tanzania. Species identity determined from mitochondrial control region sequences.

Discriminant analysis was carried out for faecal pellet length with equal prior probability assumed for all species. Significant discrimination was found between

2 species (Wilks’ Λ = 0.32,  = 265.28, P < 0.001) and 58.8% of samples were correctly classified. There was no significant deviation from homogeneity (Box’s M test: P = 0.131). Correct identification varied by species (Table 3.1) with 74% of Abbott’s duiker dung piles correctly assigned with the remainder predicted to be

73 either Harvey’s duiker or bushbuck. Conversely, 16% of Harvey’s duiker dung piles were incorrectly predicted to be Abbott’s duiker.

Table 3.1. Species identity of antelope dung piles from the Udzungwa Mountains, Tanzania, as established by mitochondrial control region sequencing compared to predicted species from a linear discriminant analysis of faecal pellet length.

Predicted species mtDNA Harvey’s Blue Suni Bushbuck Abbott’s duiker duiker duiker Harvey’s 136 60 (44%) 40 (29%) 14 (10%) 0 22 (16%) duiker

Blue duiker 5 2 (40%) 3 (60%) 0 0 0

Suni 57 1 (2%) 9 (16%) 47 (83%) 0 0

Bushbuck 2 0 0 0 2 (100%) 0

Abbott’s 38 5 (13%) 0 0 5 (13%) 28 (74%) duiker

We found no statistical differences in the three pellet variables following 31 days storage in plastic bags (Independent t-tests and Mann-Whitney tests depending on data distribution, all tests = P > 0.05). However there was a greater decrease in size for pellets stored at ambient temperature (e.g. mean length [log10]: day 1 = 9.07 mm, day 31 = 8.66 mm, U = 6159, P = 0.083) compared to refrigerated (mean length [log10]: day 1 = 9.15, day 31 = 9.11 mm, t268 = 0.196, P = 0.845). We therefore assume our storage protocol had a minimal effect on this study’s results (as pellets were refrigerated and usually measured within 7 days). We note that the difference in mean pellet length at ambient temperature approached statistical significance (P < 0.1).

There was a significant difference in mean length when two observers measured the same individual pellets (Paired t-test: t219 = 6.514, P < 0.001, with similar results for width and ratio). However, the mean difference was only 0.09 mm, less than the within-group variance recorded for any dung pile measured for this study (mean variance = 1.62 ± 0.15). Therefore, we included measurements taken by either observer in our final analysis but we also note that a discriminant analysis with only AEB measurements (207 dung piles) produced highly similar results

74 2 (AEB only: 58.9% correct, Wilks’ Λ = 0.326,  = 227.49, P < 0.001, results for full data-set shown above).

3.4. Discussion

Overall our results indicate that morphometric assignment of antelope dung piles to species is not a valid method for identification during dung count surveys. Our results also further emphasize the inaccuracy of visual field identification (van Vliet et al. 2008; Bowkett et al. 2009b).

We did not find diagnostic differences in pellet size between species and the discriminant analysis was unable to correctly identify species with high probability. The lack of absolute difference between species is perhaps not surprising given the likely individual variation due to age, sex and diet (Lunt and Mhlanga 2011). However, prior to this analysis it seemed likely that a statistical assignment based on mean pellet size would be valuable given the difference in adult body size between some species. For example, Harvey’s duiker weighs 13-16 kg compared to 50-60 kg for Abbott’s duiker (Kingdon 1997) and yet we found substantial overlap in faecal pellet size between the two species (Fig. 3.2).

One limitation of our discriminant approach was the small sample size included for the less frequently encountered species (bushbuck and blue duiker) even though these species clearly overlap with at least one other species. Given that blue duiker are absent from some areas of the Udzungwa Mountains (Rovero and Marshall 2009) and that bushbuck appear to be rare in our study area it could be argued that these species should be excluded from our analysis in order to improve accuracy. Indeed, the percentage of correctly assigned dung piles does improve if

2 we run the analysis with only three species (77.5% correct, Wilks’ Λ = 0.332,  = 251.18, P < 0.001). However, we do not recommend this approach because the probability of correct identification still falls far short of our specified 95%, the analysis would not be valid for large areas where blue duiker may be present, and camera-trap surveys reveal that Harvey’s duiker and bushbuck are almost always found in the same forests as Abbott’s duiker in the Udzungwa Mountains [(Rovero and Marshall (2009); TJ unpubl. data].

75

The lack of resolution for Abbott’s duiker faecal pellets invalidates dung counts as a suitable survey method for this highly endangered species (in the absence of molecular testing). The rarity and nocturnal nature of Abbott’s duiker also rules out distance sampling along walked transects for monitoring purposes (Rovero and Marshall 2004, 2009). However this study does demonstrate the value of genetic identification as a non-invasive approach to detecting rare species in remote areas, see also Jones and Bowkett (2012). Camera-trapping is another survey tool that has proven useful for Abbott’s duiker (Rovero et al. 2005; Rovero and Marshall 2009; Jones and Bowkett 2012) but similarly to genetic identification of dung it requires significant financial and scientific input. These resources are not normally available to protected area staff in Tanzania but such investment in the relatively few areas where viable Abbott’s duiker populations remain is recommended for the conservation of what may well be Africa’s rarest duiker species.

More generally we cannot recommend a morphometric approach to validating dung counts in the Udzungwa Mountains nor indeed in any area where similar- sized ungulate species overlap. For routine monitoring of forest antelope abundance, for instance as an indicator of hunting pressure, non-specific dung encounter rates may be sufficient or alternatively it may be possible to estimate diurnal species abundance from line transect encounters, e.g. Rovero and Marshall (2009). In conclusion, this study highlights the importance of genetic testing to verify species identity during surveys based on indirect field signs.

3.5. Acknowledgments

This work was funded by the Whitley Wildlife Conservation Trust, WCS Tanzania Programme, ZSL’s Erasmus Darwin Barlow Conservation Expeditions Programme, and Anglia Ruskin University. The Tanzanian Commission for Science and Technology, Tanzania Wildlife Research Institute, Tanzanian Forestry Service and

Tanzanian National Parks gave research permission to AEB and TJ. We are grateful to Francesco Rovero, Athumani Mndeme, Mwajuto Matola, Adanani Seki, Victoria Banga, Ruben Mwakisoma, Martin Mlewa, Ali Chitita and the late Amani Mahundu

76 for help in the field. The Udzungwa Mountains National Park Chief Warden and Ecologists, Paul Banga and Ponjoli Joram Kabepele, are thanked for ably assisting our research in Tanzania.

77 CHAPTER 4.

INFERRING EVOLUTIONARY SPECIES, LINEAGES AND POPULATION SUBDIVISION FROM NON-INVASIVE GENETIC SAMPLING OF FOREST ANTELOPE ACROSS A TROPICAL MOUNTAIN RANGE

Andrew E. Bowkett1, 2, Trevor Jones3, 4, Francesco Rovero5, 6, Martin Nielsen7, Amy B. Plowman2 and Jamie R. Stevens1

1 Molecular Ecology and Evolution Group, Biosciences, College of Life and Environmental Sciences, University of Exeter, Exeter EX4 4QD, United Kingdom.

2 Field Conservation and Research Department, Whitley Wildlife Conservation Trust, Paignton Zoo, Totnes Road, Paignton TQ4 7EU, United Kingdom.

3 Udzungwa Elephant Project, Box 99, Mang’ula, Morogoro, Tanzania

4 Animal and Environmental Research Group, Department of Life Sciences, Anglia Ruskin University, Cambridge CB1 1PT, United Kingdom

5 Tropical Biodiversity Section, Trento Science Museum, Via Calepina 14, 38122, Trento, Italy.

6 Udzungwa Ecological Monitoring Centre, c/o Udzungwa Mountains National Park, P.O. Box 99, Mang’ula, Tanzania.

7 Centre for Forestry and Landscape, Faculty of Life Science, University of Copenhagen, Copenhagen, Denmark.

AEB, JRS, and ABP designed the study; AEB, TJ, FR and MRN collected samples and field data, including from remote areas; AEB undertook all genetic and statistical

78 analysis under the guidance of JRS; AEB wrote the paper with comments and improvements from JRS and ABP.

79 Abstract

Forest-associated antelope are ecologically important species in African rainforest ecosystems including in the Udzungwa Mountains, south-central Tanzania. As with many other species in the region, the evolutionary distinctiveness and demographic status of antelope populations in the Udzungwa Mountains are poorly resolved despite the urgent need for evidence on which to base conservation management. The advent of non-invasive genetic sampling has provided a potential source of such evidence for cryptic species such as forest antelope. Here we employ genetic information derived from faecal samples (N = 618, collected 2006-09) to assess the status of five traditionally recognised forest antelope species in the light of a recent taxonomic revision of the Bovidae and in terms of their intra-specific diversity, distribution and population subdivision within the Udzungwa Mountains.

Forest antelope species in the Udzungwa Mountains are provisionally identified as Abbott’s duiker Cephalophus spadix (True, 1898), Tanzania highland blue duiker Philantomba lugens (Thomas, 1898), Harvey’s or East African red duiker Cephalophus harveyi (Thomas, 1893), mountain suni Neotragus kirchenpaueri (Pagenstecher, 1885) and Tragelaphus sylvaticus (Sparrman, 1780) following the taxonomy of Groves and Grubb (2011). DNA from all five species was recovered from faecal samples although T. sylvaticus was rarely detected (N= 6). Phylogenetic analyses of mtDNA control region sequences (c.600 bp) for the other species were largely consistent with the above classification with the exception of C. harveyi which was paraphyletic with respect to a putative sister species C. natalensis (Smith, 1834). There was strong support for three C. harveyi mtDNA control region clades within the Udzungwa Moutains although nuclear genetic variation (eight microsatellite loci) did not partition between these maternal lineages (FST = 0.007, P = 0.047).

Most species were detected in the majority of forest fragments, except for P. ‘lugens’, which was absent from major central and eastern forests despite seemingly suitable habitat. mtDNA haplotype and nucleotide diversities were high and there was significant partitioning of variation amongst sampling areas (FST = 0.08 – 0.37, P < 0.001) indicating genetic differentiation within all species except

80 the endangered C. spadix which had much lower genetic diversity and no evidence for subdivision (FST = 0.04, P = 0.124). Partitioning of microsatellite variation in the most frequently encountered species, C. harveyi, was weaker than for mtDNA but still highly significant (FST = 0.04, P < 0.001). These patterns suggest that habitat fragmentation may impact genetic structure in these forest antelope populations although we also detected a significant effect of isolation by distance. Overall, our results demonstrate the value of non-invasive genetic sampling to conservation managers by documenting species presence, identifying significant evolutionary lineages and prioritising populations for conservation.

81 4.1. Introduction

Antelope constitute a major trophic guild in African forest and woodland ecosystems (Wilson 2001) and play important roles as seed dispersal agents (Gautier-Hion et al. 1980), understory browsers (Lunt 2011) and as a food base for top predators (Boshoff et al. 1994; Hart et al. 1996) and human communities (Nasi et al. 2011). Forest antelope populations are, therefore, likely to be key components of some of Africa’s major biomes but these roles are not well studied, particularly in comparison to savannah antelope (Du Toit and Cumming 1999; Lorenzen et al. 2012). The ecological effects of well-documented population declines in response to habitat loss and hunting are particularly difficult to determine.

Identifying the products of evolutionary and demographic processes in forest antelope is important for selecting appropriate conservation units, accurately documenting their role in ecosystems and predicting the ecological consequences of anthropogenic change. And yet forest antelope diversity is poorly defined at all levels from populations through to species and higher taxonomic ranks. This may seem surprising for relatively large mammal species but it could be argued that antelope taxa were poorly characterised by taxonomic revisions in the mid- twentieth century, e.g. Ellerman et al. (1953). Under the influence of the Biological Species Concept (Mayr 1963) taxonomists at that time tended to lump superficially similar antelope from geographically separated populations as subspecies (Cotterill 2003a, 2005; Groves and Grubb 2011). At a local scale, forest antelope distributions and population dynamics are also poorly known primarily due to the difficulty of studying cryptic species in dense habitats leading to stark differences in how studies estimate demographic rates (van Vliet and Nasi 2008b).

Non-invasive genetic sampling from low-quantity DNA sources such as shed hair or faeces is potentially a highly informative tool for characterising populations in terms of taxonomy or conservation management (Kohn and Wayne 1997; Beja- Pereira et al. 2009). While ecological or behavioural data are often also required for robust conclusions regarding evolutionary or demographic status (Crandall et al. 2000; Fraser and Bernatchez 2001), non-invasive genetics provides information that would otherwise be very difficult or expensive to obtain.

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This study investigates genetic variation within forest antelope by non-invasive faceal DNA sampling across an Afrotropical mountain range, the Udzungwa Mountains in Tanzania. The Udzungwa Mountains are a high priority conservation landscape of global significance due to their remarkable levels of species diversity and endemism (Burgess et al. 2007). This biodiversity, and associated ecosystem services, is under increasing threat from habitat fragmentation and extraction of natural resources, including hunting for bushmeat (Rovero et al. 2010; Rovero et al. 2012). Despite recent efforts to document the mammalian fauna (Rovero and De Luca 2007; Rovero et al. 2009), including development of survey methods for forest antelope (Bowkett et al. 2009a; Rovero and Marshall 2009), the patterns of diversity within traditionally recognised species across the Udzungwas remain essentially unknown. We present the first genetic assessment and test predictions at three levels of antelope diversity.

4.1.1. Antelope taxonomy and evolutionary species distribution

The term antelope is not a taxonomic grouping as it encompasses some, but not all, members of the two bovid subfamilies Bovinae and Antilopinae. Nevertheless, the term is commonly used to refer to horned ungulates from Africa and Asia and we employ it here to differentiate our species of interest from sympatric artiodactyls, i.e. wild cattle and , which have very different ecological niches. A recent taxonomic treatment of hoofed mammals recognises 204 antelope species (Groves and Grubb 2011), a substantial increase on the 92 recognised by the World Conservation Union (IUCN 2012). We employ this new taxonomy to re-examine those forest antelopes found in the Udzungwas and discuss their status as ‘evolutionary species’ (Simpson 1961), a term used here to differentiate these taxa from traditionally recognised species. We test the prediction that forest antelope species are distributed uniformly throughout the major Udzungwa forested areas using genetic identification of faecal samples (Bowkett et al. 2009b). Regarding our genetic data we predict that mitochondrial haplotypes will be monophyletic with respect to other species named by Groves and Grubb (2011) within those species groups (i.e. the traditionally recognised species).

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4.1.2. Intra-specific lineage diversity in antelope

Recognition of evolutionary diversity below the species level in antelopes has most often been attempted through the designation of subspecies. In some, but not all, cases this has led to separate management of subspecies in zoos, game ranches and protected areas, e.g. Pitra et al. (2006). Phylogeographic study of savannah antelope species has revealed regionally distinct evolutionary histories that in many cases are consistent with named subspecies (Lorenzen et al. 2012). In contrast, very little is known about variation within forest antelope species and the apparent recent Pleistocene radiation of duiker species (Johnston and Anthony 2012) brings into question at what point divergent lineages should be considered as species. We assess the genetic evidence for intra-specific diversity within the Udzungwas in the context of identifying evolutionary significant units for conservation (Ryder 1986; Fraser and Bernatchez 2001). We predict that mitochondrial haplotypes will be monophyletic with respect to available conspecific sequences from other regions and we test strongly supported clades for significant genetic differentiation at nuclear loci sensu Moritz (1994).

4.1.3. Conservation management units and population subdivision

It is often desirable from a management perspective to identify demographically independent populations within species, e.g. fish stocks, and genetic data can be critical to such decisions (Moritz 1994; Palsboll et al. 2007). Management of such units conserves overall evolutionary potential by maintaining local adaptations but also has practical implications for achieving sustainable harvesting of populations – a situation highly relevant to forest antelope many of which are hunted for bushmeat (van Vliet and Nasi 2008b). Conversely, managers may wish to identify populations that are not demographically or genetically viable without intervention in order to prioritise units for recovery. This may be the case where habitat fragmentation has resulted in population subdivision and reduced gene flow, e.g. Goossens et al. (2005). We predict significant partitioning of genetic variation across the Udzungwas and divergence in allele frequencies between discrete forest fragments.

84

4.1.4. Study aims and limitations

Our intention is to demonstrate the value of non-invasive genetic data in providing useful information to conservation managers at a population or landscape scale. By “useful” we mean information that either conflicts with or supports our specific predictions concerning local populations and thereby identifies areas for further research or improves the evidence upon which management decisions are currently based. We do not seek to definitively resolve the taxonomy or phylogeography of those antelope species found in the Udzungwas as this would require range-wide information constituting multiple lines of evidence pertinent to the broader evolution of these taxa. Similarly, robust conclusions concerning meta- population dynamics would require greater sampling effort over a longer time period than feasible within this study. Nevertheless, the genetic patterns revealed by our non-invasive sampling, interpreted with the appropriate caveats, have important implications for forest antelope conservation in the Udzungwa Mountains.

4.2. Methods

4.2.1. Study site and subjects

The Udzungwa Mountains have been described in detail elsewhere (Chapter 2 and references therein). The salient facts to consider with regards to forest antelope diversity are that forest and woodland habitats are patchily distributed across the landscape and so is the level of protection received under the different protected area categories (Fig. 4.1; Chapter 2).

Rovero and De Luca (2007) list 12 species of antelope in the Udzungwa Mountains based on the taxonomy of Wilson and Reeder (2005). Their list includes three species only found in forest habitats: Abbott’s duiker Cephalophus spadix, blue duiker Philantomba monticola and suni Neotragus moschatus and two species that also occur in dry woodland: Harvey’s duiker Cephalophus harveyi and bushbuck Tragelaphus scriptus. The remaining species, including the grey or bush duiker Sylvicapra grimmia, are almost entirely restricted to grassland and savannah woodland and are not considered further.

85

We re-assessed the specific status of forest antelope by examining the descriptions and distributions given by Groves and Grubb (2011) and adopted by the Handbook of the Mammals of the World (Wilson and Mittermeier 2011). For recent literature, particularly molecular studies, we were able to cross-check Groves and Grubb (2011)‘s interpretation of primary sources. We also checked for additional references, including subsequent publications, with a systematic search of electronic databases (1970-2012; Chapter 1).

4.2.2. Sample collection and processing

We undertook transect-based surveys in all forest fragments in the central Udzungwas over 10 km2, except for Iyondo, during the dry seasons between late 2006 and 2009 (Fig. 4.1). Transects were walked in a triangular configuration (typically 1 km per side) and all antelope faecal samples visible from the transect line were recorded (Chapter 2). A subsample of recorded samples were collected in RNAlater (Ambion Ltd, Huntington, UK) and stored for genetic analysis (Chapter 2). DNA was extracted using the QIAamp DNA Stool Mini Kit (QIAGEN, Crawley, UK) following the manufacturer’s instructions but including an extended proteinase K digestion step of 20 to 30 minutes. Blank extractions were undertaken to control for contamination.

4.2.3. Genetic analysis

Antelope genetic diversity was characterised in the first instance using the mitochondrial control region (or d-loop), a highly variable non-coding marker that has been recommended for species identification in forest antelope (Ntie et al. 2010a; Johnston et al. 2011) and is often used in ungulate phylogeography (Lorenzen et al. 2012). Primers and PCR conditions followed Ntie et al. (2010a), see Chapter 2 for details. Sequences were edited in AutoAssembler (Applied Biosystems, Foster City, CA, USA) and aligned by MUSCLE (Edgar 2004) in SEAVIEW (Gouy et al. 2010).

For the two Cephalophus species we developed a panel of nuclear microsatellite markers from those optimized for central African species by Ntie et al. (2010c). Loci that amplified consistently for our target species were combined in three pre-

86 PCR multiplexes: MPLX1 = INRA40 (Beja-Pereira et al. 2004), BM1225, BM2113 and BRRIBO (Bishop et al. 1994), MPLX2A = BM121 (Bishop et al. 1994), and SR12 (Ntie et al. 2010b; modified from Kogi et al. 1995) and MPLX2B = BM143(Bishop et al. 1994) and INRA05 (Vaiman et al. 1994). MPLX2A and B were then combined for automated sequencer analysis. The majority of these loci failed to amplify consistently in the other antelope species tested and so microsatellite analysis was restricted to Cephalophus species. We used the QIAGEN PCR Multiplex Kit (QIAGEN) with PCR conditions modified for degraded DNA (Chapter 2). Fragment analysis was undertaken on a Beckman Coulter capillary sequencer and allele sizes scored using CEQ 8000 software (Beckman Coulter, Fullerton, CA, USA).

4.2.3.1. Data quality

To check for consistency in sequencing from degraded DNA we re-extracted and re-sequenced just over 10% of all identified faecal samples. We also took a conservative approach to scoring microsatellite alleles, modified from Taberlet et al. (1996), whereby we only accepted heterozygous or homozygous genotypes at each locus if scored from three or four separate PCR reactions respectively (See Chapter 2). Negative PCR controls for both types of marker were used throughout.

Deviations from Hardy–Weinberg (HW) and linkage equilibria in the microsatellite dataset were tested for in GENEPOP 4.0.10 (Raymond and Rousett 1995) with significance thresholds corrected for multiple tests using the False Discovery Rate (FDR) (Verhoeven et al. 2005). The presence of null alleles, stutter and large allelic drop-out were tested for in MICROCHECKER (Van Oosterhout et al. 2004)

4.2.4. Data analysis

4.2.4.1. Phylogenetics

Phylogenetic analysis of the control region haplotypes was employed to confirm species identification and explore intra-specific diversity. Separate analyses were carried out for each species. Trees were built using a maximum-likelihood approach in PhyML (Guindon and Gascuel 2003) and a Bayesian Monte-Carlo Markov Chain (MCMC) approach in MrBayes 3.2 (Huelsenbeck and Ronquist 2001). Trees included all GenBank/EMBL entries for target taxa, plus representative sequences for other genus members, with the exception of

87 Philantomba where some published sequences contain a c.100 bp insertion, as reported by Ntie et al. (2010a), and so were excluded. We were also able to sequence a number of tissue or faecal samples from outside of the study area including C. natalensis from other African countries (see Supp. Table 4.1). Out- group taxa from the sister genus to each of the species concerned, as presented in Hassanin et al. (2012), were used to root phylogenies, e.g. Philantomba for Cephalophus species.

Maximum-likelihood analysis (ML) was undertaken starting with an initial neighbour-joining tree and with default PhyML settings except for user-defined substitution models and parameters which were selected in jModelTest 0.1.1 (Posada 2008), see Table 4.1. The final choice of models was based on AIC values (all models were also ranked in the top three for AICc and BIC). The best tree topology was estimated from both NNI and SPR search options and branch- support was calculated with 1000 bootstrap repetitions.

Table 4.1. Best-fit evolutionary substitution models selected for Maximum- Likelihood analysis of forest antelope mitochondrial control region haplotypes from the Udzungwa Mountains, selected with jModelTest (Posada 2008).

Species group Model Transition/ Proportion of Gamma shape transversion invariable sites ratio C. harvey HKY+I+G 16.38 0.33 0.64

C. spadix TPM3uf+I+G * 0.30 0.50

N. moschatus HKY+I+G 11.55 0.41 0.44

P. monticola TIM2+I+G * 0.39 0.88

* Unequal base frequencies entered separately.

The Bayesian analysis implemented two Metropolis coupled MCMC searches each consisting of one cold and three heated chains. Multiple chains are recommended to avoid false convergence on a local optimum. Model parameters were left at default settings except that instead of pre-selecting a substitution model we sampled across the entire general time reversible (GTR) model space (Huelsenbeck et al. 2004) by averaging different models according to their posterior probability (lset nst=mixed rates=gamma) and checking for convergence

88 across different runs with the sump command (Ronquist et al. 2011). Convergence of MCMC runs was achieved by allowing the analysis to continue until the standard deviation of split frequencies reached the recommended threshold of 0.01 (between 1 and 1.5 million iterations in our case). The default burn-in fraction of 25% was applied to the convergence diagnostic and the subsequent parameter and tree summaries. Effective sample sizes, log-likelihood values and stationarity plots were checked using the sump command.

Phylogenetic relationships within species were also explored using the median- joining algorithm in Network 4.6.1.0 (Fluxus Technology, Clare, UK). For the C. harveyi control region phylogeny we also calculated uncorrected genetic distances between major clades and other species in MEGA5 (Tamura et al. 2011) including additional GenBank sequences (Supp. Table 4.1). We also investigated microsatellite variation between samples grouped by their position in the mtDNA phylogeny (i.e. control region clades) with principal coordinates analysis in GenAlEx 6 (Peakall and Smouse 2006).

4.2.4.2. Genetic diversity and differentiation

Standard genetic diversity values were calculated in GenAlEx and Arlequin 3.5.1.2 (Excoffier et al. 2005). To account for differences in number of samples per location we recalculated control region haplotype richness, microsatellite allelic richness and private allelic richness by rarefaction to the smallest sample size in Contrib (Petit et al. 1998) and HP-Rare (Kalinowski 2005).

Partitioning of genetic variation within and amongst sampling locations was tested for using AMOVA, excluding sample sizes less than 5. Pair -wise FST values between sampling locations were estimated with FDR correction applied to probability values. To test for an effect of isolation by distance we carried out Mantel tests for the larger datasets (> 5 sampling locations) in Arlequin.

4.3. Results

We extracted DNA from 639 antelope faecal samples from the Udzungwa Mountains of which all but 10 yielded a sequence that could be assigned to one of the five traditionally recognised species. Samples that contained many ambiguous

89 bases were excluded leaving a total of 618 Udzungwa sequence reads for genetic analysis plus sequences from other regions (Supp. Table 4.1).

4.3.1. Revised species designations

Three of the five traditionally recognised species found in the Udzungwas have been split by Groves and Grubb (2011): P. monticola (into 10 species), N. moschatus (3 spp.) and T. scriptus (8 spp.). In all cases the new taxa were assigned names previously treated as subspecies or synonyms.

P. monticola has been split into two groups: red-legged (6 spp.) and grey-legged blue duikers (4 spp.); Udzungwa populations fall within the latter and are assigned to Tanzanian highland blue duiker, P. lugens (Thomas, 1898), together with those in the rest of the Eastern Arcs, the Southern Highlands and Kigoma district. N. moschatus has been split into two species north of the Zambezi River, the nominate moschatus (Von Dueben, 1846) and kirchenpaueri (Pagenstecher, 1885), with a third species, livingstonianus (Kirk, 1865), to the south. N. kirchenpaueri is “not positively recorded” from the Udzungwas according to Groves and Grubb (2011), although given that kirchenpaueri is described from highland areas to the north and south we follow Wilson and Mittermeier (2011) in provisionally assigning Udzungwa populations to this species rather than moschatus. T. scriptus is split on morphological and genetic (Moodley et al. 2009) evidence into two groups: “scriptus” (3 spp.) and keeled bushbuck “sylvaticus” (5 spp.). Bushbuck from central Tanzania, including the Udzungwas, are assigned to T. sylvaticus (Sparrman, 1780). As these species designations are provisional we employ inverted commas when referring specifically to populations in the Udzungwas.

The two remaining species, C. spadix and C. harveyi, retain their previous mono- specific status under Groves and Grubb (2011). Strangely, Groves and Grubb (2011) describe the southern limit of harveyi as north of the Udzungwas although the area is included in the range map of Wilson and Mittermeier (2011) along with highlands in Malawi.

90 4.3.2. Distribution

T. ‘sylvaticus’ was only detected on six occasions during our study, three times in Ruipa and once in Iwonde, Lumemo, and New Dabaga. C. harveyi was recorded from all sampling locations and C. spadix and N. ‘kirchenpaueri’ from all but one each. P. ‘lugens’ had the most disparate distribution with no faecal DNA records from the central or eastern forests despite sampling effort being greatest in these areas (Fig. 4.1a, Table 4.2; see Chapter 2).

91

Figure 4.1a. Forest antelope non-invasive sampling locations in the Udzungwa Mountains, Tanzania. Charts represent the proportion of samples assigned to each species (see Table 4.2). See text for taxonomic notes.

92

Figure 4.1b. Forest antelope non-invasive sampling locations in the Udzungwa Mountains, Tanzania. Charts represent the proportion of major mtDNA control region phylogenetic clades in C. harveyi samples (Fig. 4.2a; N = 334).

93

Table 4.2. Sample size (N), number of mitochondrial control region haplotypes, haplotypic richness and genetic diversity values for four species of antelope sampled in twelve locations in the Udzungwa Mountains, Tanzania. Haplotypic richness was rarefied to the smallest sample size (min. 5).

Iw Ki Luh Lum Ma Mw Nd Ne Nya Nyu Ru Uz Total C. harveyi N 28 2 6 30 12 137 1 21 16 4 47 29 334 Haplotypes 11 2 4 15 7 29 1 10 9 3 19 7 59 Haplotypic richness 3.740 - 3.000 3.959 2.999 4.009 - 3.811 3.857 - 4.257 2.827 23.922 Gene diversity 0.905 1 0.800 0.917 0.773 0.923 0 0.910 0.917 0.833 0.946 0.808 0.953 Nucleotide diversity 0.038 0.003 0.031 0.039 0.035 0.034 0 0.030 0.041 0.004 0.036 0.030 0.037

C. spadix N 8 1 8 3 1 35 9 2 0 1 2 1 73 Haplotypes 2 1 3 2 1 4 1 2 - 1 2 1 6 Haplotypic richness 1 - 2 - - 1.049 0 - - - - NA 4.266 Gene diversity 0.429 1 0.464 0.667 1 0.266 0 1 - 1 1 1.000 0.381 Nucleotide diversity 0.010 0 0.010 0.001 0 0.003 0 0.017 - 0 0.017 0.000 0.006

N. ‘kirchenpaueri’ N 2 0 26 12 2 30 12 26 17 1 12 17 157 Haplotypes 2 - 7 6 2 8 5 5 5 1 6 7 28 Haplotypic richness NA - 3.639 5 - 4.82 4 2.63 3.676 - 5 4.794 16.366 Gene diversity 1 - 0.649 0.818 1 0.837 0.788 0.582 0.809 1 0.849 0.582 0.922 Nucleotide diversity 0.044 - 0.025 0.036 0.0178 0.033 0.029 0.010 0.043 0 0.038 0.010 0.039

P. ‘lugens’ N 0 0 34 0 0 0 3 2 0 0 0 9 48 Haplotypes - - 9 - - - 3 2 - - - 3 13 Haplotypic richness - - 1.594 - - - 2 - - - - 1.226 12 Gene diversity - - 0.857 - - - 1 1 - - - 0.722 0.905 Nucleotide diversity - - 0.019 - - - 0.021 0.032 - - - 0.033 0.026

Key to forests: Iw = Iwonde, Ki = Kising’a-Rugaro, Luh = Luhomero, Lum = Lumemo, Ma =Mat. west, Mw = Mwanihana, Nd = Ndundulu, Ne = New Dabaga, Nya = Nyanganje, Nyu = Nyumbanitu, Ru = Ruipa, Uz = Uzungwa Scarp

94 Table 4.3. Sample size, polymorphism, unbiased expected heterozygosity (HE) and allelic richness for two species of antelope sampled in nine locations in the Udzungwa Mountains, Tanzania. C. spadix and C. harveyi were genotyped at seven and eight microsatellite loci respectively.

Iwonde Luhomero Lumemo Mat.west Mwanihana New Nyanganje Ruipa Uzungwa Total# Dabaga Scarp C. harveyi N 8 2 20 6 49 10 8 34 12 149 Polymorphic loci 8 8 8 7 8 8 7 8 8 8 Unbiased HE 0.627 0.729 0.570 0.663 0.609 0.613 0.614 0.611 0.653 0.635 Allelic richness 4.875 2.625 6.500 4.375 7.250 5.250 4.500 6.875 4.500 8.375 Rarefied Allelic 4.05 - 3.82 4.07 3.86 3.97 3.77 3.83 3.6 6.79 richness* Private allelic richness 0 0 0 0 0.13 0 0 0.25 0.13 7.29 Rarefied Private allelic 0.21 - 0.12 0.1 0.12 0.27 0.19 0.09 0.19 5.2 richness*

C. spadix N 5 1 0 0 10 1 0 1 1 19 Polymorphic loci 4 0 - 4 2 - 2 2 5 Unbiased HE 0.411 - - - 0.503 1 - 1 1 0.431 Allelic richness 2.750 - - - 2.750 2 - 2 2 4 Rarefied Allelic 2 - - - 1.87 - - - - 2.81 richness* Private allelic richness 0.429 0 - - 0 0.143 - 0.143 0 1.14 Rarefied Private allelic 0.57 - - - 0.44 - - - - 1.22 richness*

* Allelic richness values calculated with rarefaction to the smallest sample size for locations where N > 5. # Total private allele values were calculated with respect to species across seven loci, private and standard allelic richness were rarefied to the smallest sample size (17).

95

1 FJ823297 S. grimmia 100 FJ823296 S. grimmia 1 1 AM903084 C. spadix 100 1 82 FJ823354 C. sylvicultor 91 FJ823376 C. dorsalis 0.98 FJ823342 C. callipygus 0.74 1 81 FJ823360 C. ogilbiyi 1 99 FJ823385 C. weynsi 83 FJ823346 C. niger 1 FJ823337 C. leucogaster 0.77 100 FJ823333 C. leucogaster 1 FJ823328 C. nigrifrons 2 99 FJ823322 C. rufilatus 2 0.93 1 FJ823325 C. rufilatus 1 100 FJ823326 C. rufilatus 1 FJ823327 C. nigrifrons 1 AM903088+ AM903090+ 1 AB008+ 69 AB141 AB152 RL182+ 0.61 TJ017 M1+ SA13+ AB162+ MIZ02+ 1 CS08 0.57 90 AB024 MZ03+ B 1 CS05+ 82 AB153+ FR006 1 RL109 79 AB154 AB110+ RL113+ AB002 SA16+ AB062+ RL168 SA12+ AB054 1 AB099+ MN08 1 RL131 A 79 1 AB138+ 98 RL129 0.51 AB101+ SELOUS FJ823314+FJ823315 C. natalensis 0.92 AJ235318 C. natalensis NAT01 NAT09 NAT10 1 NAT02 96 NAT03 1 NAT04+NAT05 NAT 0.93 87 NAT08 NAT12 1 NAT07 NAT13 NAT11 NAT06 1 AB059+ 99 AB124+ AB183 RL157 MIZ04+ 1 AB123 1 95 AB294 AM903089 90 AB222 0.99 AB025+ 0.53 74 AB028+ AB039+ MW02+ MIZ01+ AB061+ AB073 C 0.77 AB071+ 50 AB126+ RL120+ 0.9 SA09 52 FJ823317 CS02+ AB119+ AB093+ MZ06+ FJ823318+FJ823316 AM903087 CS01+ SA17+ 1 YS008 100 FJ823311 P. maxwelli FJ823301 P. monticola

0.2

Figure 4.2a. Bayesian consensus phylogeny for Cephalophus harveyi CR haplotypes from the Udzungwa Mountains, with representative sequences from other regions and species. Node support values are posterior probabilities (black) and bootstrap percentages (1000 iterations) from Maximum-Likelihood analysis (red). C. harveyi clades are labelled A-C with non-Tanzanian natalensis labelled NAT. Red clades represent haplotypes from outside of the Udzungwas. GenBank accession numbers and species names are given for published sequences.

96 FJ823297 S. grimmia 1 100 FJ823296 S. grimmia

0.64 FJ823342 C. callipygus 57 1 FJ823360 C. ogilbiyi 0.98 98 87 FJ823385 C. weynsi 0.74 FJ823346 C. niger

1 FJ823337 C. leucogaster 1 100 FJ823333 C. leucogaster 100 0.73 FJ823328 C. nigrifrons 1 100 FJ823322 C. rufilatus 0.77 FJ823327 C. nigrifrons

0.97 FJ823325 C. rufilatus 62 1 97 FJ823326 C. rufilatus

0.68 1 FJ823317 C. harveyi 100 FJ823314 C. natalensis

FJ823366 C. dorsalis 1 100 FJ823376 C. dorsalis

FJ823356+FJ823354 C. sylvicultor 1 FJ823357+FJ823353 C. sylvicultor 100 1 93 1 1 96 FJ823358 C. sylvicultor 95 FJ823359 C. sylvicultor 0.98 85 FJ823348 0.85 61 AM903085+ 0.98 82 TJ050+

0.91 AB004+ 1 87 AM903086+

WCSB465+ 0.93 63 SA18+ 0.98 C. spadix 87 AB035+

0.84 TJ005

WCS07+

1 AM903084+ 83 0.86 AM080 66 AM903083+FJ823350

FJ823351+FJ823349

FJ823311 P. maxwelli 1 100 FJ823301 P. monticola

0.1

Figure 4.2b. Bayesian consensus phylogeny for Cephalophus spadix haplotypes from the Udzungwa Mountains, with representative sequences from other regions and species. Node support values are posterior probabilities (black) and bootstrap percentages (1000 iterations) from Maximum-Likelihood analysis (red). Red clades represent spadix haplotypes from outside of the Udzungwas. GenBank accession numbers and species names are given for published sequences.

97 JN632668 N. batesi

CAM01+

MZ02+ 0.55 1 RL073 92 AB143+

AB115+ 1 1 100 AB171 75 0.85 AB063+ 72 AB076 1 99 AB075 RL183+ 0.97 74 MW01+ 0.83 1 52 MW05+

AB060+

MIZ05+ 1 100 AB045+

AB067+ 0.87 66 AB072 1 86 0.99 RL202 88 0.83 RL020 58 RL175+

RL167 0.83 69 AB114+

AB121+

AB204 0.93 AB287+

AB288

1 TJ080+ 89 TJ095

1 FJ985773 100 JN632669

0.72 AJ235323

FJ985772

JN632592 Aepyceros melampus

0.1

Figure 4.2c. Bayesian consensus phylogeny for putative Neotragus kirchenpauri CR haplotypes from the Udzungwa Mountains, with representative sequences from other regions and species. Node support values are posterior probabilities (black) and bootstrap percentages (1000 iterations) from Maximum-Likelihood analysis (red). Red clades represent suni haplotypes from outside of the Udzungwas. GenBank accession numbers and species names are given for published sequences.

98 0.9 53 FJ823311 P. maxwelli 0.9 JN632685 P. maxwellii 62 1 FJ823310 P. maxwellii 100 FJ823309 P. maxwellii

0.6 FJ823308 51 1 0.86 FJ823306 100 0.93 64 FJ823302 67 0.96 78 JN632687

FJ823307 1 83 FJ823301

0.91 RL008+ 76 AB228 1 0.86 100 56 1 RL071+ 82 RL014+

AB210+ 0.7 1 98 MN05 1 89 RL012+ P. lugens? 0.53 0.67 RL065

AB211+ 1 95 0.79 FR013+ 62 AB213+ 0.97 80 AB203+

0.94 SR007 71 FJ823314 C. natalensis

FJ823328 C. nigrifrons 1 0.97 100 70 FJ823322 C. rufilatus

FJ823327 C. nigrifrons

1 FJ823325 C. rufilatus 98 FJ823326 C. rufilatus

1 FJ823297 S. grimmia 100 FJ823296 S. grimmia 0.97 63 0.92 AM903084 C. spadix 80 1 1 FJ823354 C. sylvicultor 91 FJ823376 C. dorsalis

0.75 FJ823342 C. callipygus 68 1 FJ823360 C. ogilbiyi 98 0.98 FJ823385 C. weynsi 75 FJ823346 C. niger

1 FJ823337 C. leucogaster 100 FJ823333 C. leucogaster

0.07

Figure 4.2d. Bayesian consensus phylogeny for putative Philantomba lugens CR haplotypes from the Udzungwa Mountains, with representative sequences from other regions and species. Node support values are posterior probabilities (black) and bootstrap percentages (1000 iterations) from Maximum-Likelihood analysis (red). Red clades represent ‘P. monticola’ haplotypes from outside of the Udzungwas. GenBank accession numbers and species names are given for published sequences.

99 4.3.3. Phylogenetic analysis

A total of 111 control region haplotypes were recovered from Udzungwa faecal samples. Sixty of 68 re-extracted samples yielded identical sequences while five failed to amplify target DNA on repeat and three samples had different sequences due to a mislabelling error that was subsequently corrected (Chapter 2). We did not conduct phylogenetic analysis for T. ‘sylvaticus’ as we only recovered 5 haplotypes and an extensive CR phylogeny for this species group has been published previously (Moodley et al. 2009). However, we note that our sequences were highly similar to haplotypes from the sylvaticus cluster, as opposed to scriptus, in BLAST (NCBI, Bethesda, MD, USA).

In comparison to published multi-locus phylogenies (Johnston and Anthony 2012) Bayesian analysis recovered inter-species relationships more accurately than Maximum-Likelihood. Therefore, Bayesian trees are presented with bootstrap values (BS) for nodes supported by ML. Posterior probability values (PP) below 0.5 (or bootstraps below 50%) are not shown and some node values near terminal ends were omitted for clarity.

Three of the four evolutionary species were potentially monophyletic with respect to other sequences (Fig. 4.2b-d). For P. ‘lugens’ and N. ‘kirchenpaueri’ this pattern is uncertain as the more strongly supported clades also included sequences from elsewhere in Tanzania but because precise localities are unknown these sequences could potentially originate from other evolutionary species, i.e. coastal taxa also elevated to species by Groves and Grubb (2011). Reciprocal monophyly was strongly supported for C. spadix, as was a sister relationship to C. sylvicultor (Fig. 4.2b).

In contrast, C. harveyi was paraphyletic with respect to C. natalensis. C. harveyi haplotypes from the Udzungwas fell into three strongly supported clades (Fig. 4.2a clades A-C; PP = 1, BS = 82-98%; Fig. 4.1b), two of which appeared closer to C. natalensis than the third although this deeper node was not supported by the ML tree. All three clades were recovered from the majority of sampling locations with the only exceptions being locations with minimal sampling (Fig. 4.1b; Table 4.2). Clade B was monophyletic with respect to sequences outside of the Udzungwas as

100 were Udzungwa P. ‘lugens’ haplotypes although with less support (PP = 0.86, BS = 56%). Uncorrected genetic distances between clades within C. harveyi were equivalent to those between C. harveyi clades and C. natalensis (4.5 – 6.3%) but less than inter-species values for other red duiker.

The median-joining haplotype networks were largely congruent with tree-based analyses although this approach highlights the long branch-lengths typically connecting haplotypes from other regions to those in the Udzungwas (Fig. 4.4). The one sample from the Selous Game Reserve, to the east of the Udzungwas and mapped as part of the range of C. natalensis in East (1999), clustered closely with Clade A in both tree and network analyses (Fig. 4.2a and 4.4a).

Microsatellite variation did not partition strongly between mitochondrial clades in the Udzungwas for C. harveyi (AMOVA: Among clade variation 0.67%, within clade variation 99.33%, global FST = 0.007, P = 0.047) as illustrated by a plot of principal components (Fig. 4.3). Therefore, we conclude that these lineages do not fulfil Moritz (1994)’s criteria for evolutionary significant units whereby mitochondrial monophyletic groups should also exhibit corresponding significant divergence at nuclear loci.

101 A B C NAT Coordinate 2, 2, 17% Coordinate

Coordinate 1, 27%

Figure 4.3. Two dimensional PCA plot of C. harveyi / natalensis microsatellite genotypes labelled by phylogenetic clade (Fig. 2a).

4.3.4. Genetic diversity and differentiation

All eight microsatellite loci were polymorphic in C. harveyi but only four loci showed any variation in C. spadix (BM2113, INRA40, BRRIBO and BM143). There were no significant deviations from Hardy-Weinberg or Linkage expectations in C. spadix following FDR correction but three loci deviated from HW equilibrium for C. harveyi (BRRIBO, BM143 and INRA05). Furthermore, three loci exhibited homozygote excess indicating the potential presence of null alleles in C. harveyi (BM2113, BRRIBO, BM143) and one locus in C. spadix (BM2113)

Genetic diversity differed markedly between species with the most notable result being the low values for C. spadix. mtDNA results for C. spadix were typically less than half than those of other species (Table 4.2) and microsatellite heterozygosity and allelic richness were much lower than for C. harveyi (Table 4.3). Analysis of genetic diversity within sampling locations was confounded by small sample size particularly for the more rarely encountered species. However, most locations had similar results within species with no obvious outliers (Table 4.2 and 4.3).

102 Figure 4.4a + b. Control region median-joining network for C. harveyi and natalensis (a) and C. spadix (b). Yellow nodes indicate haplotypes recovered from the Udzungwa Mountains, Tanzania, red nodes indicate haplotypes from elsewhere and grey nodes are inferred mutational steps. Long branches are labelled with the number of mutational steps. Major clades are labelled as in Fig. 4.2a. Nodes are proportional to frequency for C. spadix to illustrate the dominance of one haplotype in the Udzungwas (SA18).

103

Figure 4.4c + d. Control region median-joining network for N. moschatus including kirchenpaueri (c) and P. monticola including lugens (d). Yellow nodes indicate haplotypes recovered from the Udzungwa Mountains, Tanzania, red nodes indicate haplotypes from elsewhere and grey nodes are inferred mutational steps. Long branches are labelled with the number of mutational steps.

104 Table 4.4. Pair-wise mitochondrial control region uncorrected genetic distances between red duiker clades and species. Values along the diagonal (in bold) are mean within-clade distances. Within-species clades (A-C; 1-2) are labelled on Fig. 4.2a. Number of haplotypes is given in parentheses.

1 2 3 4 5 6 7 8 9 10 1 C. harveyi A(9) 0.012

2 C. harveyi B (25) 0.051 0.017

3 C. harveyi C (30) 0.063 0.048 0.024

4 C. natalensis (14) 0.055 0.045 0.056 0.021

5 C. nigrifrons 1 (2) 0.097 0.088 0.095 0.093 0.000

6 C. nigrifrons 2 (2) 0.104 0.086 0.100 0.105 0.100 0.000

7 C. rufilatus 1 (5) 0.080 0.083 0.093 0.079 0.079 0.087 0.011

8 C. rufilatus 2 (2) 0.127 0.108 0.116 0.118 0.102 0.050 0.099 0.000

9 C. leucogaster (4) 0.133 0.131 0.150 0.136 0.144 0.127 0.110 0.144 0.022

10 C. callipygus (6) 0.146 0.154 0.148 0.148 0.145 0.157 0.140 0.173 0.137 0.058

105 Table 4.5. Pair-wise FST values between sampling locations for two species of antelope in the Udzungwa Mountains, Tanzania. Values below the diagonal are for mtDNA control region and above for eight microsatellite loci (the latter not used for N. ‘kirchenpaueri’).

C. harveyi Iwonde Luhomero Lumemo Mat.west Mwani- Ndundulu New Nyanganje Ruipa Uzungwa hana Dabaga Scarp Iwonde - -0.002 0.019 0.024 - 0.099* 0.025 0.003 0.095* Luhomero 0.200* ------Lumemo 0.162* -0.007 0.024 0.019* - 0.118* 0.021 0.006 0.095* Mat.west 0.126* 0.132 0.099* 0.019 - 0.066* 0.026 0.007 0.099 Mwanihana 0.160* -0.008 0.000 0.117* - 0.104* 0.001 0.028* 0.094* Ndundulu ------New Dabaga 0.204* 0.079 0.078* -0.034 0.090* - 0.086* 0.081* 0.065* Nyanganje 0.050 0.032 0.007 0.076 0.010 - 0.103* 0.025 0.070* Ruipa 0.155* -0.010 0.003 0.121* 0.009 - 0.100* 0.015 0.069* Uzungwa Scarp 0.223* 0.125 0.119* -0.012 0.126* - -0.009 0.129* 0.146*

N. 'kirchenpaueri' Iwonde Luhomero - Lumemo - 0.250* Mat.west - - - Mwanihana - 0.149* 0.253* - Ndundulu - 0.274* 0.309* - 0.153* New Dabaga - 0.558* 0.576* - 0.459* 0.515* Nyanganje - 0.230* 0.049 - 0.073* 0.190* 0.466* Ruipa - 0.166* -0.010 - 0.203* 0.249* 0.492* 0.058* Uzungwa Scarp - 0.342* 0.291* - 0.238* 0.328* 0.454* 0.206* 0.245*

* Significant at α = 0.05 level after correction for False Discovery Rate.

106 Small sample sizes reduced the number of pair-wise FST estimates for C. spadix to six (FST = 0 - 0.16 ; P > 0.05) and one for P. ‘lugens’ (Luhomero vs. Uzungwa Scarp,

FST = 0.37; P < 0.001). Most comparisons were statistically significant for N. ‘kirchenpaueri’ as were half for C. harveyi (Table 4.5). For microsatellite loci, all but two significant comparisons for C. harveyi involved New Dabaga or Uzungwa Scarp (Table 4.5), while only one estimate was made for C. spadix (Iwonde and

Mwanihana; FST = 0.04; P~1). AMOVA results were highly significant for all species except C. spadix indicating potential population subdivision (Table 4.6).

There was moderate support for isolation by distance across sampling locations for the control region: N. ‘kirchenpaueri’ (r = 0.47; P = 0.04) and C. harveyi (r = 0.25; P = 0.08) but a strong signal for microsatellite variation in C. harveyi (r = 0.54; P < 0.001).

Table 4.6. Analyses of molecular variance among and within sampling locations for four species of forest antelope in the Udzungwa Mountains, Tanzania.

Control Region Microsatellites C. harveyi C. spadix N. ‘kirchenpaueri’ P. ‘lugens’ C. harveyi C. spadix

Sampling locations 9 4 8 2 8 2

Among population 8.02 4.92 29.36 36.62 4.26 3.83 variation Within population 91.98 95.08 70.64 63.38 95.74 96.17 variation Global FST 0.08 0.05 0.29 0.37 0.04 0.04

P * 0.00 0.12 0.00 0.00 0.00 0.17

* Probability values calculated with 1023 permutations

4.4. Discussion

4.4.5. Antelope taxonomy and evolutionary species distribution

As noted by Ntie et al. (2010a) the highly variable control region is potentially not well suited to investigating evolutionary relationships above the species level. Nevertheless our results are largely consistent with other published phylogenies (van Vuuren and Robinson 2001; Hassanin et al. 2012; Johnston and Anthony 2012) in recovering major monophyletic species groups including the giant duikers (C. sylvicultor, spadix and dorsalis), dwarf duikers (P. maxwelli and

107 monticola including lugens), west African red duikers (C. callipygus, ogilbiyi, weynsi and niger) and east African red duikers (C. natalensis, harveyi, nigrifrons and rufilatus). Placement of C. leucogaster, normally allied to the east African red duikers (Johnston and Anthony 2012), was inconsistent in our trees. There was also only weak support for S. grimmia being sister to all other duiker species or just to Cephalophus (Fig. 4.2a, b, d). In fact, both Hassanin et al. (2012) and Johnston and Anthony (2012) place Sylvicapra as sister to the giant duiker clade making Cephalophus paraphyletic (Fig. 4.2d). Systematic resolution of this issue requires including grimmia in Cephalophus or splitting the genus into at least three groups with the name Cephalophura suggested for the red duiker clade by Hassanin et al. (2012).

At the species level we have provisionally classified forest antelope in the Udzungwas according to the taxonomic revision of Groves and Grubb (2011). The name changes for three species reflect the tendency for these authors to split geographically widespread morphologically variable taxa. In many cases the authors admit that a robust classification still requires more research and this may be the case for P. monticola and N. moschatus where populations have been separated based largely on pelage characters. Our genetic results are clearly not suitable for establishing the phylogenetic status of these putative species although they did not contradict our prediction that such taxa would be monophyletic at the locus tested. Much more extensive sampling is needed to document morphological and genetic variation across the ranges of these new taxa.

The most interesting phylogenetic result was the mitochondrial paraphyly of C. harveyi with respect to C. natalensis (Fig. 4.2a). Although this has been reported before, based on a small number of sequences including nuclear loci (Ntie et al. 2010a; Johnston and Anthony 2012), the present study (despite limitations) represents the most extensive genetic assessment of these species to date. Several authors have treated C. harveyi as a subspecies of C. natalensis (Ellerman et al. 1953) or interpreted narrow genetic distance as support for this proposition (van Vuuren and Robinson 2001; Hassanin et al. 2012). However, differences between the two taxa in body size and coat pattern have led other authors and the IUCN Antelope Specialist Group to continue to recognise them as separate species

108 (Kingdon 1997; East 1999; Groves and Grubb 2011). Kingdon (1982) suggested that the two forms hybridise in southern Tanzania but, crucially, we lack samples from south and east of the Udzungwas except for the single Selous sample that grouped with harveyi clade A (Fig. 4.2a and 4.4a). Given the geographical and genetic evidence the situation may be more complicated than a simple choice between one or two species or subspecies (see below).

Groves and Grubb (2011) employed a phylogenetic species concept that recognises a population, or aggregation of populations, with “fixed heritable differences” from all other such groups as a species. This approach is likely to prove controversial, particularly with taxonomists who favour the more traditionally applied Biological Species Concept. While the debate over species concepts has been discussed extensively elsewhere (Wheeler and Meier 2000; Hey 2006; De Queiroz 2007) we note here that failure to recognise genuine evolutionary divergence may have serious applied consequences in the conservation of species (Groves et al. 2010; Manceau et al. 1999) and landscapes (Cotterill 2006).

Our prediction that these evolutionary species of forest antelope would be distributed uniformly across the forested Udzungwas was fulfilled for C. harveyi and nearly so for N. ‘kirchenpaueri’ and C. spadix which were detected in all but one forest each. In the case of N. ‘kirchenpaueri’ this is almost certainly a false absence due to under-sampling in Nyumbanitu whereas the genuine absence of C. spadix from Nyanganje is possible (Chapter 6). The exception to this pattern is P. ‘lugens’ which is almost certainly absent from Mwanihana, contra to reports in Dinesen et al. (2001), and probably also from the central Matundu, Iwonde and Nyanganje forests. This species not only went unrecorded during this study but also during intensive camera-trap surveys of the same areas (Rovero and Marshall 2009). There is no obvious explanation for the patchy distribution of this duiker in the Udzungwas as the habitat and anthropogenic threats appear similar and blue duiker coexist with suni in the western Udzungwas as well as in east African coastal forests (Fitzgibbon et al. 1995). Interestingly, there are other forest mammals that have highly localised distributions within the Udzungwas e.g. the grey-faced elephant shrew Rhyncocyon udzungwensis (Rovero et al. 2008) and kipunji Rungwecebus kipunji (Davenport et al. 2008).

109

4.4.6. Intra-specific lineage diversity in antelope

There was little evidence for forest antelope populations in the Udzungwas constituting potential evolutionary significant units. Udzungwa haplotypes did not form exclusive monophyletic clades, except for P. ‘lugens’ (weak support, Fig. 2d) and Clade B in C. harveyi (see below). We interpret this to mean that for the majority of species gene flow with other regions is ongoing or if populations are now isolated they have yet to undergo complete lineage sorting.

The lineage diversity within C. harveyi is more puzzling. On the one hand, the three strongly supported clades within the Udzungwas are as different from one another as they are to C. natalensis samples from a wide geographic area and exhibit equivalent genetic variation (Table 4.5). On the other, the geographic and nuclear genetic data do not indicate any separation of individuals from different clades into different populations (or ESUs).

Unexpected diversity in mitochondrial DNA can be caused by a variety of processes including inadvertent amplification of mitochondrial pseudo-genes from the nuclear genome (Numts), hybridisation with other species or incomplete sorting of ancestral lineages. Given the limited nature of our dataset we cannot rule out any of these explanations. However, in light of the climatic history of Africa (DeMenocal 2004) and the radiation of Cephalophus during the Pleistocene (Johnston and Anthony 2012) we speculate that an ancestral red duiker lineage may have diverged whilst populations were isolated during arid inter-pluvial periods and repeatedly undergone secondary contact in the Udzungwas (and elsewhere) when a wetter climate allowed forest refugia to reconnect. This is the opposite pattern to that shown by savannah antelope species (Lorenzen et al. 2012). This secondary contact resulted in inter-breeding populations that show no segregation at microsatellite loci but retain the mitochondrial (and perhaps nuclear intron) signature of past isolation. This hypothesis requires testing with multi-locus data from throughout the range of C. harveyi and related species together with coalescent dating of lineage divergence as in Bowie et al. (2006).

110 4.4.7. Genetic diversity and differentiation

Pair-wise comparisons and significant partitioning of molecular variation among sampling locations indicates substantial population subdivision in the Udzungwas at least for the two most sampled species (Table 4.5 and 4.6). As the majority of sampling locations represent discrete forest patches this potential structure could be interpreted as reduced gene flow across unsuitable habitat and implicate deforestation as a threat to population persistence. Indeed, the majority of significant FST values for the C. harveyi microsatellite dataset involved the relatively isolated forests of Uzungwa Scarp and New Dabaga, which therefore stand out as priorities for conservation attention.

However, we also found a moderate-to-strong effect of isolation by distance which may complicate interpretation of spatial genetic structure (Safner et al. 2011). In our case, we note that many pair-wise FST values were highly significant over relatively short distances and that most comparisons between sampling locations within continuous forest were not significant (e.g. Lumemo and Ruipa). Therefore, we conclude that there is a likely effect of habitat fragmentation, in addition to isolation by distance, operating on forest antelope population structure.

Despite the potential impact of population subdivision we found little evidence for negative effects on genetic diversity. Diversity values were generally high and microsatellite heterozygosity in C. harveyi was very similar to the mean for healthy bovid populations (Garner et al. 2005). The stark exception to this pattern was the endangered Tanzanian endemic C. spadix for which we consistently estimated the lowest diversity values regardless of sample size or genetic marker (Table 4.2 and 4.3). Being one of the largest duiker species, C. spadix exists at much lower population densities than other species and so may suffer disproportionately from threats such as hunting (Chapter 6).

4.4.8. Conservation and management implications

We suggest that the data presented here may be typical of non-invasive genetic surveys undertaken over a relatively short period, although few such multi-species studies have been published to date. Problems such as small sample size, DNA quality and failure of loci to cross-amplify in related species are likely to feature in

111 similar attempts to non-invasively sample ecological guilds at a landscape scale. Nevertheless, we have attempted to demonstrate that such surveys can yield valuable information that can guide further research and informs conservation management.

The recognition of highland populations of suni and blue duiker as separate species, the latter endemic to Tanzania, attaches a far greater significance to the Udzungwas as a potential stronghold for what could now be considered range- restricted taxa largely confined to protected areas. The Udzungwas are one of the largest remaining areas of forest within either species’ range. This designation did not depend on our genetic sampling but is informed by the knowledge that N. ‘kirchenpaueri’ is found throughout the Udzungwas, whereas P. ‘lugens’ is restricted to the western and southern escarpments. Whether these taxa are referred to as evolutionary species, subspecies or Evolutionary Significant Units may not matter as long as they prove to be individually diagnosable lineages and are recognised as such in conservation priority-setting exercises.

In contrast, C. spadix is recognised by all recent authorities as a unique species restricted to Tanzanian highlands and highly endangered (Moyer 2003). This study provides the first genetic assessment of this species in the Udzungwas (or anywhere else) and provides evidence for its continued existence in the majority of surveyed forests (Jones and Bowkett 2012). Conservationists are challenged to maintain this rare species across remote areas and facilitate maintenance or improvement of its genetic variation (Chapter 6).

Given the potentially negative effect of habitat fragmentation on forest antelope gene flow, conservation management should aim to maintain connectivity between forest patches where it persists and potentially establish wildlife corridors to connect outlying areas as has been recommended for Uzungwa Scarp (MTSN 2007). While this would be very difficult for forests surrounded by agricultural land, many central forests are now within the Udzungwa Mountains National Park and contiguous Kilombero Nature Reserve. Corridors within these protected areas could be established principally by the control of fire and enforcing bans on cutting trees and hunting. For forests outside of the core protected area such as New Dabaga and Uzungwa Scarp the priority for antelope conservation is to control

112 illegal hunting and halt current population declines (Nielsen 2006b; Rovero et al. 2010).

4.4.9. Conclusion

Non-invasive genetic sampling is an increasingly important tool for assessing the evolutionary and conservation status of rare or elusive species (Zhan et al. 2006; Gebremedhin et al. 2009). This study has used these methods to assess forest antelope diversity across the Udzungwa landscape at three levels: the distribution of putative evolutionary species, evolutionary lineages within these species and genetic divergence between subdivided populations. While expensive and technology-intensive, non-invasive genetic sampling provides a method to monitor the response of forest antelope populations to conservation efforts in terms of abundance, distribution and genetic health.

113 CHAPTER 4: Supplementary table 1. Details of mitochondrial control sequences included in this study excluding those derived from faecal samples.

Sample name Species Geographic origin Provider GenBank

PhC20 A. melampus JN632592 VV14* C. callipygus Republic of the Congo B.J. van Vuuren FJ823338 VV17* C. callipygus Republic of the Congo B.J. van Vuuren FJ823339 VV18* C. callipygus Republic of the Congo B.J. van Vuuren FJ823340 OK23* C. callipygus Okondja, Gabon S. Touladjan FJ823341 OK27 C. callipygus Okondja, Gabon S. Touladjan FJ823342 OK18* C. callipygus Okondja, Gabon S. Touladjan FJ823345 N2274 C. dorsalis Republic of the Congo D. Pires FJ823366 861ou198 C. dorsalis Dja, Cameroon M. Colyn FJ823376 AB5 / HD01 C. harveyi Uzungwa Scarp, Udz., F. Rovero AM903088 Tanzania AB36 / TIS03 C. harveyi Rubeho Mts., Tanzania F. Rovero AM903089 AB105 /TIS02 C. harveyi Mwanihana, Udz., Tanzania A.E. Bowkett AM903090 SUN125 / C. harveyi Mt. Meru, Tanzania B.J. van Vuuren FJ823316 VV125 SUN117 / C. harveyi Usambara Mts., Tanzania B.J. van Vuuren FJ823317 VV117 SUN115 / VV15 C. harveyi Mt. Meru, Tanzania B.J. van Vuuren FJ823318 SUN130 / C. harveyi Usambara Mts., Tanzania B.J. van Vuuren FJ823319 / VV130 AM903087 TIS01 C. harveyi Mwanihana, Udz., Tanzania A.E. Bowkett M1 C. harveyi New Dabaga, Udz., Tanzania M.R. Nielsen M2 C. harveyi New Dabaga, Udz., Tanzania M.R. Nielsen M3 C. harveyi New Dabaga, Udz., Tanzania M.R. Nielsen M4 C. harveyi New Dabaga, Udz., Tanzania M.R. Nielsen NAT14 C. harveyi? Selous, Tanzania K. Hecker VV16* C. leucogaster Republic of the Congo B.J. van Vuuren FJ823334 N22157* C. leucogaster Republic of the Congo D. Pires FJ823335 N22151* C. leucogaster Republic of the Congo D. Pires FJ823336 OK17 C. leucogaster Okondja, Gabon S. Touladjan FJ823337 VV11 C. leucogaster Republic of the Congo B.J. van Vuuren FJ823333 105483 C. maxwelli AMNH FJ823309 OR587013 C. maxwelli San Diego Zoo FJ823310 OR837 C. maxwelli San Diego Zoo FJ823311 E11-9 C. maxwelli JN632685 VV124 C. monticola Tanzania B.J. van Vuuren FJ823301 KB15149 C. monticola Cape Province, S. Africa San Diego Zoo FJ823302 86307M28 C. monticola Kinsangani, DRC M. Colyn FJ823306 DIV009 C. monticola Bamenda, Cameroon M. Colyn FJ823307 R16520 C. monticola Lefini, Republic of the Congo M. Colyn FJ823308 Cameroon C. monticola Cameroon JN632686 AJ235318 C. natalensis E.J.P. Douzery AJ235318 VV1470 C. natalensis KwaZulu-Natal, S. Africa B.J. van Vuuren FJ823314 VV1467 C. natalensis KwaZulu-Natal, S. Africa B.J. van Vuuren FJ823315 NAT01 C. natalensis Zambezi Delta, Mozambique K. Hecker NAT02 C. natalensis Zambezi Delta, Mozambique K. Hecker NAT03 C. natalensis Zambezi Delta, Mozambique K. Hecker NAT04 C. natalensis Lorursberg, South Africa K. Hecker NAT05 C. natalensis KwaZulu-Natal, South Africa K. Hecker NAT06 C. natalensis KwaZulu-Natal? South Africa K. Hecker NAT07 C. natalensis Hluhluwe, South Africa K. Hecker

114 NAT08 C. natalensis KwaZulu-Natal, South Africa K. Hecker NAT09 C. natalensis KwaZulu-Natal, South Africa K. Hecker NAT10 C. natalensis KwaZulu-Natal, South Africa K. Hecker NAT11 C. natalensis KwaZulu-Natal, South Africa K. Hecker NAT12 C. natalensis KwaZulu-Natal, South Africa K. Hecker NAT13 C. natalensis Zambezi Delta, Mozambique K. Hecker OR2758 C. niger Liberia San Diego Zoo FJ823346 N221004 C. nigrifrons Republic of the Congo D. Pires FJ823327 VV12 C. nigrifrons Republic of the Congo B.J. van Vuuren FJ823328 N2293* C. nigrifrons Republic of the Congo D. Pires FJ823329 VV24* C. nigrifrons Republic of the Congo B.J. van Vuuren FJ823331 AJuin1995 C. ogilbyi Brazzaville, Republic of the M. Colyn FJ823360 Congo GA172 C. ogilbyi Malounga, Gabon M. Colyn FJ823363 OR2115* C. rufilatus San Diego Zoo FJ823320 VV19* C. rufilatus Central African Republic B.J. van Vuuren FJ823321 VV22 C. rufilatus Central African Republic B.J. van Vuuren FJ823322 KB11228* C. rufilatus Guinea San Diego Zoo FJ823323 KB13889* C. rufilatus San Diego Zoo FJ823324 KB14034 C. rufilatus San Diego Zoo FJ823325 OR3182 C. rufilatus Guinea San Diego Zoo FJ823326 N22224 C. silvicultor Republic of the Congo D. Pires FJ823353 OR356 C. silvicultor Liberia San Diego Zoo FJ823354 VV25 C. silvicultor Republic of the Congo B.J. van Vuuren FJ823355 OR409 C. silvicultor Liberia San Diego Zoo FJ823356 N220853 C. silvicultor Republic of the Congo D. Pires FJ823357 DIE2 C. silvicultor Diecke, Guinea M. Colyn FJ823358 NIM2 C. silvicultor Mt. Nimba, Guinea M. Colyn FJ823359 AB6 /TIS08 C. spadix W. Usambaras, Tanzania J. Beraducci AM903084 AB37 /TIS07 C. spadix S. Highlands, Tanzania T.R.B. Davenport AM903085 SUN118 / C. spadix Kilimanjaro, Tanzania B.J. van Vuuren FJ823348 VV118 SUN122 / C. spadix Usambara Mts., Tanzania B.J. van Vuuren FJ823349 VV122 SUN121 / C. spadix Usambara Mts., Tanzania B.J. van Vuuren FJ823350 VV121 SUN120 C. spadix Usambara Mts., Tanzania B.J. van Vuuren FJ823351 /VV120 SUN126 / C. spadix Usambara Mts., Tanzania B.J. van Vuuren FJ823352 / VV126 AM903083 ROV01 C. spadix Mwanihana, Udz., Tanzania F. Rovero ROV02 C. spadix Mwanihana, Udz., Tanzania F. Rovero ROV03 C. spadix Mwanihana, Udz., Tanzania F. Rovero ROV04 C. spadix Mwanihana, Udz., Tanzania F. Rovero TRD01 C. spadix S. Highlands, Tanzania T.R.B. Davenport TJ050 C. spadix Luhomero, Udz., Tanzania T. Jones AB107 / TIS05 C. spadix S. Highlands, Tanzania T.R.B. Davenport AM903086 D456 C. weynsi Rwanda B.J. van Vuuren FJ823385 CAR86 N. batesi JN632668 AJ235323 N. moschatus E.J.P. Douzery AJ235323 68 N. moschatus Mozambique FJ985772 108 N. moschatus Tanzania A.E. Bowkett FJ985773 SUN N. moschatus JN632669 OR1502 S. grimmia San Diego Zoo FJ823296 VV26 S. grimmia Central African Republic B.J. van Vuuren FJ823297

* Included in genetic distance matrix (Table 4.4) but not in phylogenetic analysis.

115 CHAPTER 5.

FRAGMENTED FORESTS, FRAGMENTED POPULATIONS? FINE- SCALE GENETIC STRUCTURE IN HARVEY’S DUIKER CEPHALOPHUS HARVEYI ACROSS A HETEROGENEOUS LANDSCAPE.

Andrew E. Bowkett1,2, Trevor Jones3,4, Amy B. Plowman2 and Jamie R. Stevens1

1 Molecular Ecology and Evolution Group, Biosciences, College of Life and Environmental Sciences, University of Exeter, Exeter EX4 4QD, United Kingdom.

2 Field Conservation and Research Department, Whitley Wildlife Conservation Trust, Paignton Zoo, Totnes Road, Paignton TQ4 7EU, United Kingdom.

3 Udzungwa Elephant Project, Box 99, Mang’ula, Morogoro, Tanzania

4 Animal and Environmental Research Group, Department of Life Sciences, Anglia Ruskin University, Cambridge CB1 1PT, United Kingdom

AEB, JRS, and ABP designed the study; AEB and TJ collected samples and field data, including from remote areas; AEB undertook all genetic and statistical analysis under the guidance of JRS; AEB wrote the paper with comments and improvements from JRS and ABP. 116 Abstract

Fine-scale genetic structure has important implications for population management and conservation but has not previously been studied in a duiker species. We investigated spatial genetic structure in Harvey’s duiker Cephalophus harveyi, a small-bodied antelope dependent on vegetation cover, in the high- biodiversity fragmented forests of the Udzungwa Mountains, Tanzania. We genotyped 141 geo-referenced faecal samples collected over 35 months from across the central Udzungwa range at eight microsatellite loci. We found a highly significant pattern of isolation by distance across the study area (rxy = 0.189; P < 0.001) and positive global spatial autocorrelation at distances below 10 km (r = 0.033; P = 0.001). However, within sampling areas of continuous forest there was little evidence for genetic structure, indicating possible local panmixia. There was no evidence for temporal structure across the sampling period. Bayesian individual-based methods indicated a genetic structure of between two and four clusters with a well-supported division between the central forest blocks and two outlying forests. Although designated as Forest Reserves, these latter forests are outside of the Udzungwa Mountains National Park and Kilombero Nature Reserve, and subject to illegal hunting of forest antelope and other species. Although limited by sample size, our results indicate that different landscape features, in addition to isolation by distance, influence genetic structure in Harvey’s duiker. Quantifying the effects of landscape and habitat on gene flow represents a promising area for future research. This study suggests that Harvey’s duiker are able to disperse through non-forest habitats and therefore will likely benefit from attempts to establish wildlife corridors between protected areas in the Udzungwa Mountains.

117 5.1. Introduction

The spatial organisation of related individuals has the potential to reveal much about dispersal and gene flow in wild populations. Such fine-scale genetic structure has more commonly been studied in plants than animals (Vekemans and Hardy 2004) but has important implications for managing wildlife populations. Several studies have addressed spatial genetic structure in commercially or ecologically important ungulate species in northern temperate regions (Coltman et al. 2003; Coulon et al. 2006; Perez-Espona et al. 2008; Robinson et al. 2012). Such studies have revealed non-random spatial distribution of individuals with respect to genotype and highlight the effect of social organisation and landscape in shaping population structure and therefore management strategies (Cullingham et al. 2011; Vander Wal et al. 2012).

Spatial genetic studies in tropical ungulates have been largely confined to social structure in elephant and rhinoceros species (Fernando and Lande 2000; Garnier et al. 2001; Archie et al. 2006) despite the equally urgent conservation need for information on other species on which to base management decisions (Chapter 1). One study has used genetic markers to investigate social organisation in a savannah population of bushbuck Tragelaphus scriptus (Apio et al. 2010). Most forest-associated antelope species are particularly poorly known in terms of their ecology and population dynamics (Plowman 2003). Understanding the fine-scale genetic structure of forest antelope populations may allow us to quantify and predict the impact of common threats such as hunting and deforestation. The fragmentation of formerly continuous habitat has had deleterious population effects on a variety of forest species, e.g. Goossens et al. (2006), but has yet to be investigated in forest antelope.

The Udzungwa Mountains of south-central Tanzania constitute a heterogeneous landscape of discrete forest patches surrounded by a matrix of more arid and open habitats. These forests are internationally recognised for their extraordinary biodiversity and highly localised endemic species (Burgess et al. 2007; Seki et al. 2011; Rovero et al. 2008). Harvey’s duiker Cephalophus harveyi, the most frequently encountered antelope species in the Udzungwa forests (Rovero and Marshall 2009), represents a potential model species for investigating the effects

118 of habitat fragmentation as it is found throughout the study area (Chapter 4). This small antelope, 13-16 kg (Kingdon 1997), is dependent on vegetative cover for food and refuge (Bowkett et al. 2008) but is not restricted to montane forest, being also found in adjacent woodland and coastal forests (Rovero and De Luca 2007). Therefore, spatial genetic structure in this species is likely to reflect the effects of behaviour and landscape rather than specialist habitat requirements.

Traditional equilibrium-based population genetics may reflect evolutionary history rather than contemporary dispersal patterns. More recent methods based on individual genotypes may have more value to conservation as they can measure dispersal directly through the identification of migrants (Rannala and Mountain 1997) and indirectly through fine-scale genetic structure (Peakall et al. 2003). Knowledge of the spatial distribution of individuals increases our statistical power to detect genetic structure (Hubisz et al. 2009) and non-invasive sampling of faeces or shed hair extends the use of these methods to species that would be difficult to sample directly (Kohn and Wayne 1997).

This study employs two individual-based approaches to investigate fine-scale genetic structure in Harvey’s duiker across the fragmented forests of the Udzungwa Mountains. Firstly, we relate spatial and genetic distances between individuals at different scales with spatial autocorrelation and related analyses. Secondly, we use Bayesian algorithms, and an alternative based on maximum- likelihood, to identify clusters of genotypes indicating population subdivision. The results of these analyses will be interpreted in the context of habitat fragmentation and conservation management of the Udzungwa Mountains.

5.2. Methods

5.2.1. Study site and sample collection

Antelope faecal samples were collected from nine locations across the Udzungwa Mountains between November 2006 and October 2009 (Chapter 4). Within sampling locations fresh samples were collected along transect walks and stored in RNAlater (Ambion Ltd, Huntington, UK) until DNA extraction (Chapter 2). Spatial coordinates were recorded with a handheld GPS unit (GPSMAP 60Cx, Garmin,

119 Kansas City, USA) with an average estimated position error of less than 10 m. Samples were confirmed as C. harveyi by sequencing c.600 bp of the mitochondrial control region (Chapter 2, 3 and 4). In order to avoid repeatedly sampling the same individual we only collected one sample from clusters of faecal deposits believed to have originated from C. harveyi. We also attempted to only collect samples once every 100 m along transects, however, this proved difficult as samples were often misidentified to species in the field (Bowkett et al. 2009b)(Chapter 3).

5.2.2. Microsatellite genotyping

Dinucleotide microsatellite markers were optimised for C. harveyi from those developed for central African duiker species by Ntie et al. (2010c). Markers were combined in three pre-PCR multiplexes, one with four loci (MPLX1) and two with two loci each (MPLX2A and B; Table 5.2). MPLX2A and B were then combined for automated sequencer analysis. PCR conditions and fragment analysis details are given in Chapter 2.

To take account of potential genotyping errors when working with faecal DNA we took a multiple-tubes approach to scoring microsatellite alleles whereby we only accepted heterozygous or homozygous genotypes if scored from three or four separate PCR reactions respectively (See Chapter 2). This is a compromise between the method of Taberlet et al. (1996) and the less stringent method of Frantz et al. (2003). We excluded identical genotypes, including samples that differed by only one allele, which we assumed to be derived from the same individual. Negative PCR controls were used to detect contamination.

Deviations from Hardy–Weinberg (HW) and linkage equilibria were tested for in GENEPOP 4.0.10 (Raymond and Rousett 1995), using the probability test and log likelihood ratio options respectively, with 10,000 dememorisation steps and 500 batches of 10,000 MCMC iterations (Raymond and Rousett 1995). Probability thresholds were corrected for multiple tests using the False Discovery Rate (FDR) (Verhoeven et al. 2005). The presence of null alleles, stutter and large allelic drop- out were tested for in MICROCHECKER (Van Oosterhout et al. 2004). Based on population-level genetic patterns (Chapter 4) we expected some loci to deviate from global equilibria due to Wahlund effects.

120

To determine the power of our markers to discriminate between individuals we calculated the unbiased probability of identity (PID) and for siblings (PID-sibs) with the global dataset in GIMLET v1.3.3 (Valiere 2002).

Temporal variation in genetic structure was tested for in GenAlEx 6.2 (Peakall and Smouse 2006) with an analysis of molecular variation (AMOVA) across years for samples collected in Mwanihana (the only location with more than three genotypes recovered in more than one particular year). Standard diversity values and pair-wise sampling location FST values are given in Chapter 4 (sample sizes for some locations are different to those presented here as spatial coordinates were not available for all samples). Global FST and FIS values for the whole data-set were calculated in SPAGeDi 1.3 with 10,000 permutations of genotypes between and within sampling locations respectively (Hardy and Vekemans 2002).

5.2.3. Spatial autocorrelation and isolation by distance

Spatial autocorrelation analysis was carried out in GenAlEx following Peakall et al. (2003) and Griffiths et al. (2009). This approach calculates the autocorrelation coefficient (r) between matrices of pairwise individual genetic distance (Peakall and Smouse 2006) and linear Euclidean distance. Correlograms were then plotted for various Euclidean distance classes to determine at what spatial scale individuals are distributed non-randomly with regard to their genetic relationships. We used variable distance intervals of 0-1 km, 1-5 km, 5-10 km, 10- 20 km, 20-30 km, 30-40 km, 40-50 km, 50-60 km, 60-80 km, 80-100 km and 100- 123 km but also tested various other configurations. We also undertook a multi- distance class analysis whereby increasing distance classes are cumulatively combined to detect the extent to which autocorrelation remains significant across the study area. Ninety-five percent confidence intervals were estimated for r using 999 bootstrap replicates and for the null hypothesis of no spatial genetic structure with 999 permutations (Peakall et al. 2003).

For comparative purposes we also undertook Mantel tests of matrix correspondence, the most commonly used test for isolation by distance (Mantel 1967). Mantel tests were executed in GenAlEx using the same pair-wise individual

121 genetic distances as above and the natural log of the geographic distance as recommended for two dimensional environments (Slatkin and Maddison 1990)

As an alternative approach to detecting spatial genetic structure we also estimated the regression slopes of the relationship between individual relatedness and geographic distance in SPAGeDi as determined for social ungulates by Coltman et al. (2003) and Frantz et al. (2012). Relatedness was calculated using the kinship coefficient of Loiselle et al. (1995). This measure does not require loci to be at HW or linkage equilibrium and is robust to the effects of rare alleles (Vekemans and Hardy 2004). Pairwise kinship coefficients were calculated relative to the whole dataset. Under this scenario, negative coefficient values are possible when individuals are less related than random. Kinship and its relationship with the natural log of Euclidean distance were estimated both within and among sampling locations to test the prediction that duikers would be more related within areas of continuous forest. The one-tailed probability of slopes being less than zero was calculated using 10,000 permutations of individual locations. This procedure is equivalent to a Mantel test (Hardy and Vekemans 2002).

All spatial genetic tests were carried out for the global dataset and also within local sampling areas where more than 10 genotypes were recovered. For some analyses we combined Ruipa and Lumemo as these locations are within the continuous Matundu forest (Fig. 5.7).

5.2.4. Genetic clusters

We used two individual-based Bayesian clustering methods to test for population subdivision. These methods use MCMC algorithms to find clusters of genotypes that maximize Hardy–Weinberg and linkage equilibrium and display different allele frequencies. The two methods were STRUCTURE (Pritchard et al. 2000), a widely used programme that often serves as a benchmark in algorithm comparisons, and Geneland (Guillot et al. 2005), a programme that uses individual spatial coordinates to inform clustering of genotypes and has been shown to outperform STRUCTURE in detecting subtle differentiation (Frantz et al. 2012; Safner et al. 2011).

122 We ran STRUCTURE with no prior knowledge of sampling location and also with each genotype assigned to a sampling location (LOCPRIOR model). The latter approach is designed to help infer weak genetic structure without producing spurious subdivision results (Hubisz et al. 2009). For both analyses all other parameters were identical and consisted of 5 independent runs of K = 1–10 (the number of potential subpopulations) with 1 million iterations, a burn-in of 100,000 iterations, correlated allele frequencies and the admixture model. We assessed the value of K based on log-likelihood values and the method of Evanno et al. (2005). In cases where we inferred K = 2, we followed a hierarchical approach and reanalysed the two data subsets separately using the above parameters (Hobbs et al. 2011).

In Geneland we ran the spatial model allowing for correlated allele frequencies, i.e. under conditions more sensitive to differentiation (Guillot 2008), and putative null alleles (Guillot et al. 2008). We initially ran 10 independent runs of K = 1–10 with 1 million iterations (thinning every 1000 runs) discarding 10% of iterations as burn- in. However, the models failed to converge on a single value of K so we repeated the analysis with 10 runs of 5 million iterations. We also repeated this longer analysis under the uncorrelated model that may be less sensitive to departure from model assumptions (GDG 2012). We set uncertainty around the spatial coordinates to 0.5 km based on home range sizes in the related C. natalensis (Bowland and Perrin 1995).

We also ran a recently published iterative algorithm implemented in FLOCK (Duchesne and Turgeon 2012) that allocates genotypes to clusters based on the multi-locus maximum-likelihood method of Paetkau et al. (1995). We ran FLOCK with default settings, concentrated reference statistics and following the recommended ‘stopping’ conditions for when to accept K (Duchesne and Turgeon 2012).

Significant differences in allele frequencies between identified clusters were tested for by calculating FST in Arlequin 3.5.1.2 (Excoffier et al. 2005) and the alternative Jost’s D (Jost 2008) in SMOGD (Crawford 2010). The validity of identified clusters

123 was also tested by calculating HW and linkage equilibria independently in GENEPOP, as described for the global dataset.

5.3. Results

A total of 161 faecal samples yielded microsatellite genotypes with at least seven loci meeting our repeat PCR criteria. This represents a 46.7% amplification success rate from 345 samples identified as C. harveyi. Sixteen genotypes were excluded as they were identical to at least one other sample and were assumed to be from the same individual. An additional four genotypes were excluded as they did not have spatial coordinates. Therefore, the final dataset included 141 genotypes (Table 5.1) six of which had data missing at one locus (Table 5.2).

Table 5.1. Number of C. harveyi microsatellite genotypes (N) from nine sampling locations in the Udzungwa Mountains together with mean genetic distance (Peakall and Smouse 2006), kinship coefficient (Loiselle et al. 1995), mean and maximum Euclidean distance (km).

Sampling N Genetic distance Kinship Distance Max. distance location (km) (km)

Iwonde 8 10.93 0.0142 1.12 2.48

Luhomero 2 13.00 0.0017 9.95 9.95

Lumemo 20 9.34 0.0162 1.64 5.17

Mat. west 6 10.87 -0.0007 2.06 4.22

Mwanihana 48 11.21 0.0156 3.97 10.96

New Dabaga 6 12.13 0.1291 2.84 7.19

Nyanganje 6 11.53 -0.0128 1.00 2.07

Rupia 34 9.76 0.0127 1.54 4.60

Uzungwa Scarp 11 11.60 0.1216 2.50 5.25

ALL 141 11.20 -0.0004 43.7 122.33

Four loci did not meet HW expectations at α = 0.05 for the global dataset but two of these results (BM143, P = 0.023; and BM1225, P = 0.041) were not statistically significant following FDR correction for multiple tests (Critical P value = 0.019; Table 5.2). Two pairs of loci were out of linkage equilibrium (INRA40 with 124 BRRIBO, P = 0.028; BM143 with BM1225, P = 0.050) but neither result was significant following FDR correction (Pcrit = 0.004). Two loci showed evidence of homozygote excess indicating the presence of null alleles (Table 5.2) and for a further two loci more than 50% of alleles were in one size class so the probability of excess could not be calculated (BM121 and INRA05). There was no evidence for scoring errors due to stutter or large allelic drop-out.

The overall PID was < 0.0001 and PID-sibs was 0.002 indicating high statistical power to discriminate between individuals. There was no partitioning of genetic variation between years (AMOVA, 0% variance among populations, PhiPT = -0.007, P =

0.68). Global FST was significantly greater than zero (0.035 ± 0.009, P < 0.001) as was FIS (0.082 ± 0.022, P < 0.001).

Table 5.2. Microsatellite locus details for 141 C. harveyi genotypes from the Udzungwa Mountains, Tanzania. MPLX = pre-PCR multiplex, N = number of samples, Na = number of alleles, Ho = observed heterozygosity and uHe = unbiased heterozygosity.

MPLX and N Na Ho uHe Source locus 1. BM2113 141 8 0.723 0.785 Bishop et al. (1994) 1. INRA40 141 12 0.830 0.864 Beja-Pereira et al. (2004) 1. BM1225 141 3 0.255 0.279 Bishop et al. (1994) 1. BRRIBO*# 140 13 0.750 0.863 Bishop et al. (1994) 2A. BM143# 141 13 0.688 0.782 Bishop et al. (1994) 2B. BM121 141 4 0.390 0.480 Bishop et al. (1994) 2B. INRA05* 136 4 0.279 0.377 Bishop et al. (1994) 2A. SR12 141 10 0.603 0.628 Kogi et al. (1995) Mean 140.25 8.38 0.565 0.632 (± SD) (±0.620) (±1.499) (±0.080) (±0.081)

* Deviated from Hardy-Weinberg equilibrium at α = 0.05 level after correction for False Discovery Rate. # Homozygosity excess indicates null alleles (P < 0.05)

5.3.1. Spatial autocorrelation and isolation by distance

There was significant positive autocorrelation at the shorter distance classes (< 5 km) with r intercepting the x-axis at 14 km. In larger distance classes there was 125 some oscillation with r positive again between 20 and 40 km (although still within the 95% confidence intervals of the null hypothesis) before declining to significantly negative by 80 km (Fig. 5.1A). The multi-distance class analysis indicated that positive autocorrelation is detectable even when including samples up to 80 km apart (Fig. 5.1B).

Within sampling locations the spatial genetic patterns were harder to ascertain. As for the global dataset, r was significantly more positive than zero at short distance intervals in Mwanihana and Ruipa (< 1 km) but there was large overlap between confidence intervals (Fig. 5.2A and B). For Lumemo and Uzungwa Scarp r was non- significant at all distance intervals indicating no spatial genetic structure (Fig. 5.2C and D).

As indicated by the spatial autocorrelation results, there was a significant effect of isolation by distance for the global dataset (Table 5.3). The regression slope analysis in SPAGeDi also showed a highly significant relationship between distance and kinship. Again, the evidence for isolation by distance within sampling locations was much weaker than for the global dataset. The Mantel tests and equivalent regression slopes were broadly in agreement except for Mwanihana and Ruipa (Table 5.3). Mean kinship was higher within than among sampling locations (0.022 vs. -0.005) and the regression slope less steep (-0.011 vs. -0.015) although significantly less than zero for both (P < 0.002).

Table 5.3. Isolation by distance in C. harveyi in the Udzungwa Mountains, Tanzania. P values are from one-tailed tests of whether observed values are greater or less than random (Mantel and regression tests respectively), P < 0.05 in bold.

Dataset Pairs Mantel Regression Rxy P Slope P Global 9870 0.189 0 -0.0084 0

Mwanihana 1128 0.139 0.022 -0.0061 0.0967

Ruipa 561 0.095 0.104 -0.0163 0.0038

Lumemo 190 -0.123 0.142 0.00427 0.6685

Uzungwa Scarp 55 0.153 0.120 -0.0482 0.0352

Matundu* 1431 0.007 0.421 -0.0023 0.0699

* Ruipa and Lumemo combined. 126 A

B

Figure 5.1. Global relationship between genetic and geographic distance for C. harveyi in the Udzungwa Mountains, Tanzania. A. Correlogram showing the autocorrelation coefficient (r) for different distance class sizes. B. The autocorrelation coefficient at increasing cumulative distance size classes. 95% confidence error bars shown for r. Red dashes are 95% CI about the null hypothesis of a random distribution of genotypes.

127

Figure 5.2. Local relationships between genetic and geographic distance for C. harveyi within sampling locations in the Udzungwa Mountains, Tanzania. Correlogram showing the autocorrelation coefficient (r) for different distance class sizes in A. Mwanihana, B. Ruipa, C. Lumemo, and D. Uzungwa Scarp. 95% confidence error bars shown for r. Red dashes are 95% CI about the null hypothesis of a random distribution of genotypes.

128 5.3.2. Genetic clusters

There was no support for subdivision in the standard STRUCTURE model (Fig. 5.3). However, spatial clustering methods indicated between two and four genetic clusters with both the location prior model in STRUCTURE and the uncorrelated- alleles model in Geneland identifying two populations (Fig. 5.4 and 5.5). The first cluster included all individuals from New Dabaga and Uzungwa Scarp and the second consisted of individuals from the rest of the sampling locations in the main Udzungwa range. Re-analysis of these subsets in separate STRUCTURE runs, following a hierarchical approach, inferred one cluster for New Dabaga / Uzungwa Scarp and two for central Udzungwa. However, in the latter case very few individuals assigned to a second cluster and none strongly (8 genotypes had a membership percentage of 0.51-0.58 in the highest likelihood run). This inferred subdivision appeared to be due to admixture in individuals from Mwanihana.

The correlated model in Geneland suggested further division by separating New Dabaga and Uzungwa Scarp and splitting the eastern forest blocks (Mwanihana and Nyanganje) from the rest of the central Udzungwas (Fig. 5.6 and 5.7). All New Dabaga and Uzungwa Scarp individuals were allocated to their respective clusters across every independent run. The majority of other genotypes were also assigned consistently to the central and eastern forest clusters with the exception of those from Mat. west. Individuals from this location formed a fifth cluster in one run but in all other cases were allocated between multiple clusters (Fig. 5.7).

Both the initial analyses (1 million iterations) and the longer runs (5 million) were equally divided between K = 4 and K = 5. However, the composition of the fifth cluster was not consistent between runs often consisting of very few individuals or no individuals in two cases, including the run with the highest mean posterior probability (Fig. 5.6). Geneland can sometimes output an overall estimate of K that is higher than the number of clusters to which individuals have been allocated (GDG 2012). These “ghost” clusters (Guillot et al. 2008) can indicate violation of modelling assumptions (see below). Given these caveats we took K = 4 to be the most robust inference.

129 The maximum-likelihood programme FLOCK ran for the four initial consecutive values of K with no plateau and therefore met the stopping rule for ‘undecided’ i.e. K = 1 or the number of samples or markers is insufficient to detect structure (Duchesne and Turgeon 2012)

To check the validity of inferred genetic clusters it is recommended to test for HW and LD equilibria and estimate FST values between clusters (GDG 2012). Therefore, we analysed these parameters for K = 2, based on individual allocation by the uncorrelated model in Geneland (identical over 10 runs), and K = 4, based on the correlated alleles model in Geneland (all individuals were allocated to the same clusters in 80% of runs) (Table 5.4 and 5.5). We excluded the Mat. west genotypes from both analyses as these six individuals appeared to be admixed and/or migrants from other locations.

130

Figure 5.3. STRUCTURE analysis with standard model for C. harveyi genotypes from the Udzungwa Mountains (N = 148). 5.3A: Proportion of each individual’s genotype allocated to each cluster at K = 4 for comparison between models. 5.3B: Mean log likelihood of the data for 5 runs showing that K = 1. Likelihood plot generated in Structure Harvester (Earl and vonHoldt 2011).

131

Figure 5.4. STRUCTURE analysis with location prior model for C. harveyi genotypes from the Udzungwa Mountains (N = 148). A: Proportion of each individual’s genotype allocated to each cluster at K = 4 for comparison between models. B: Mean log likelihood of the data for 5 runs showing that K = 1. C: Delta K-values for 5 runs showing K = 2. Plots generated in Structure Harvester (Earl and vonHoldt 2011).

132 5. Uncorrelated spatial model Number of clusters along the chain after burnin 0.6 -860 0.5 -880 0.4 0.3 Density -900 0.2 -920 0.1 -940 0.0 160 180 200 220 240 260 2 4 6 8 10 Nb. of clusters along the chain Estimated cluster membership (after a burnin of 100x5000i t.)

Figure 5.5. Geneland analysis with uncorrelated alleles spatial model (K = 2) for C. harveyi microsatellite genotypes from the Udzungwa Mountains (N = 141). Map shows the posterior mode of population membership for the highest probability model run (black dots are sample spatial coordinates).

133 6. Correlated spatial model Number of clusters along the chain after burnin 0.35 -860 0.30 0.25 -880 0.20 Density -900 0.15 0.10 -920 0.05 -940 0.00 160 180 200 220 240 260 2 4 6 8 10 Nb. of clusters along the chain Estimated cluster membership (after a burnin of 200x5000i t.)

Figure 5.6. Geneland analysis with uncorrelated correlated alleles spatial model (K = 5) for C. harveyi microsatellite genotypes from the Udzungwa Mountains (N = 141). Map shows the posterior mode of population membership for the highest probability model run (black dots are sample spatial coordinates). Note that the fifth cluster (dark green) has no individuals allocated to it (see main text).

134

Figure 5.7. Map of sampling locations in the Udzungwa Mountains, south-central Tanzania. Colour-coded locations represent C. harveyi microsatellite genotypes allocated to genetic clusters by the Geneland correlated alleles model run with the highest mean posterior probability (see text and Fig. 5.6).

135 Table 5.4. Genetic diversity and equilibrium values for C. harveyi inferred spatial genetic clusters in the Udzungwa Mountains, Tanzania. N = number of samples, Ho = observed heterozygosity and uHe = unbiased heterozygosity, HW = loci outside of HW equilibrium at P < 0.05, LD = pairs of loci out of linkage equilibrium at P < 0.05.

Cluster N Ho uHe HW LD K = 2: Main 118 0.5614 0.6156 INRA05 INRA40/BRRIBO* BRRIBO BM1225/INRA05* INRA40* BM2113/BM143* New Dabaga / 17 0.5588 0.6615 BM143 - Uzungwa Scarp BM2113* K =4: Central 64 0.5925 0.6069 INRA05* BM1225/INRA05* (Luhomero, Iwonde, Lumemo and Ruipa) Eastern 54 0.5240 0.6110 INRA05 BM1225/INRA05* (Mwanihana SR12 BM2113/INRA05* and BRRIBO* Nyanganje)

New Dabaga 6 0.5476 0.6926 BM143* -

Uzungwa Scarp 11 0.6023 0.6607 BM143* -

* Not significant after FDR correction.

All inferred genetic clusters had loci that did not meet HW expections, although at K = 4 only the Eastern cluster retained loci out of equilibrium following FDR correction for multiple tests. None of the locus pairs were significantly out of linkage equilibrium following FDR correction (Table 5.4). There was significant genetic differentiation between all clusters inferred at K = 2 (Main vs. New dabaga

/ Uzungwa Scarp; FST = 0.0668; P < 0.001) and K = 4 (Table 5.5).

Table 5.5. Pair-wise FST (below diagonal) and Jost’s D values (above diagonal) between inferred spatial genetic clusters (K = 4) for C. harveyi in the Udzungwa Mountains, Tanzania.

Central Eastern New Dabaga Uzungwa Scarp Central - 0.0275 0.0378 0.1160

Eastern 0.0218 - 0.1327 0.1122 P < 0.001 New Dabaga 0.0878 0.0965 - 0.0603 P < 0.001 P < 0.001 Uzungwa Scarp 0.0783 0.0894 0.0610 - P < 0.001 P < 0.001 P = 0.025

136

5.4. Discussion

5.4.1. Patterns and processes in Harvey’s duiker genetic structure

Our results strongly indicate that genetic structure is not random in C. harveyi across the Udzungwa Mountains. At the scale of the entire study area there was a strong pattern of isolation by distance associated with positive spatial autocorrelation at short distance intervals (< 10 km) and indicative of restricted dispersal in this species (Peakall et al. 2003). At the local scale, within sampling areas, there was little evidence for genetic structure. This may be because sampling locations were discrete forest patches within which dispersal is not limited. However, this explanation contradicts the positive autocorrelation in the global dataset, which was most significant at the shortest distance intervals (< 1 km), and therefore within forest patches. It is possible that genetic structure could not be detected within sampling locations due to limited statistical power as indicated by the significant Mantel test for Mwanihana (the largest sample size; Table 5.3) and positive autocorrelation at very short distances in Mwanihana and Ruipa (Fig. 5.2A and B).

Social organisation in small antelope is often assumed to take the form of monogamous pairs, or territorial males and transient females, that defend temporarily stable home-ranges (Jarman 1974). However, Bowland and Perrin (1995) found that radio-collared Natal red duiker C. natalensis showed no sign of territoriality with extensive overlap in home range even between males. A more flexible social structure may allow individuals to tolerate mature offspring in their home-ranges or adjacent areas. For example, bushbuck females share home-ranges with relatives (Apio et al. 2010) but males maintain exclusive zones within larger areas (Wronski 2005). Similarly, white-tailed deer virginianus form matrilineal social groups whereby daughters establish home-ranges that overlap those of their mothers, leading to highly positive spatial genetic autocorrelation (Cullingham et al. 2011), but see Comer et al. (2005). Although we did not set out to investigate social organisation in duikers specifically, one limitation of the current study is that the gender of genotyped individuals is unknown and so we cannot infer what role sex-biased dispersal has on the observed spatial genetic patterns.

137

We also found evidence for at least two genetically differentiated spatial clusters of individuals (Fig. 5.4 - 5.7). The biological significance of these clusters is difficult to interpret given the potential confounding effects of isolation by distance and the characteristics of our microsatellite data (e.g. putative null alleles and small sample sizes). Various studies have found that clustering algorithms can falsely detect spurious clusters when isolation by distance is strong (Frantz et al. 2009; Safner et al. 2011). Moreover, the presence of null alleles in a dataset (Table 5.1 and 5.4) can also lead to over-estimation of K because excess homozygosity disrupts the HW and linkage equilibria which clustering algorithms use to differentiate populations (Guillot et al. 2008). Null alleles are primarily caused by mutations in the microsatellite flanking regions (Chapuis and Estoup 2007), a distinct possibility in this study as our primers were originally designed in other species (Ntie et al. 2010c). Null alleles may be responsible for the occasional inference of spurious populations (that contained no or very few individuals) by the correlated alleles model in Geneland despite use of the algorithm to reduce their effect (Guillot et al. 2008).

Despite these caveats, the genetic clusters identified by this study are biologically plausible as they relate to the landscape of the study area (i.e. are not randomly distributed, Fig. 5.7), are broadly consistent across different statistical approaches and independent model runs (particularly the division between New Dabaga / Uzungwa Scarp and the rest of the Udzungwas) and show significant pairwise genetic differentiation (Table 5.5). The latter argument is open to interpretation as null alleles can also artificially inflate FST estimates (Chapuis and Estoup 2007), however, we also found significant differentiation between sampling locations with an independent mitochondrial marker (Chapter 4).

5.4.2. Habitat fragmentation and conservation

If we accept that the spatial genetic patterns observed here are not due to statistical artefact (despite some potential bias), what implications are there for the conservation of forest antelope species in the Udzungwas?

138 Diniz-Filho and De Campos Telles (2002) suggested that spatial autocorrelation analysis could be valuable in the conservation of continuous populations. The global spatial correlogram in this study most closely resembles the profile of a long-distance cline [Fig. 1 in Diniz-Filho and De Campos Telles (2002)]. This would indicate that conserving populations at geographical extremes would conserve most of the species’ genetic diversity. However, the slight oscillation at middle distance intervals, and the genetic clustering results, may reflect a stabilising profile (genetic patches) whereby conserving populations at the extremes of the range does not necessarily ensure that the most divergent populations are preserved. In either case, the intercept of the x-axis is proposed as a useful measurement for defining the minimum distance at which genetic diversity should be conserved or sampled with maximum efficiency (Diniz-Filho and De Campos Telles 2002). For C. harveyi in the Udzungwas that intercept is approximately 14 km. While this measure is potentially confounded as a conservation tool in a complex two dimensional landscape like the Udzungwas it may represent a useful guide to sampling for genetic monitoring or future research (see below).

The genetic clusters identified here are not entirely unexpected given prior knowledge of the landscape. The western forests of New Dabaga and Uzungwa Scarp are not only geographically distant from the central Udzungwa range but are also to some extent isolated. New Dabaga is surrounded by farmland and open grassland (Marshall et al. 2010), whereas Uzungwa Scarp is only connected to the western Matundu forest, and the rest of the Kilombero Nature Reserve, by a partially wooded corridor of 2.1 – 6.8 km width (MTSN 2007). Forest antelope in both these forests are threatened by illegal hunting (Nielsen 2006b; Rovero et al. 2010). In contrast, genotypes from distant sampling locations within continuous forest, such as Ruipa and Lumemo, are consistently allocated to the same cluster (Fig. 5.7).

These patterns of genetic clustering are not necessarily consistent with forest fragmentation. For example, individuals from Mwanihana and Nyanganje form one cluster despite the gap in forest canopy between the two areas (Fig. 5.7) and the similar Euclidian distance between Nyanganje and Iwonde which does not form part of the same cluster (when K = 4). C. harveyi are found in miombo woodland

139 and dry coastal thickets as well as true forest (Rovero and De Luca 2007) and may be resident in small patches of woodland or riverine vegetation that connect the areas sampled in this study. Alternatively, duikers may be capable of occasional long distance dispersal through natural habitat despite our evidence for spatial autocorrelation at short distances.

Other environmental features that can constitute less permeable barriers to gene flow include roads and rivers (Balkenhol and Waits 2009; Frantz et al. 2012; Robinson et al. 2012; Jalil et al. 2008). While there are no major roads crossing the Udzungwa Mountains, there are numerous rivers that could potentially affect forest antelope genetic structure (Fig. 5.7). The Mngeta River separates Uzungwa Scarp from Matundu forest while the Lumemo River separates Matundu from Nyangange and Mwanihana. This observation presents a promising hypothesis that awaits rigorous testing with paired sampling sites and more detailed mapping of river features. Indeed, quantitative mapping of various environmental data including topography, human activity and habitat, as well as potential barriers, would enable us to parameterise resistance surfaces (Spear et al. 2010; Garroway et al. 2011) and test multiple hypotheses within the framework of landscape genetics (Cushman et al. 2006; Zhu et al. 2011).

Wildlife corridors have been recommended to restore connectivity between protected areas within the Udzungwa Mountains and with adjacent ecosystems (MTSN 2007; Jones et al. 2007). Recently, an initiative to establish the Mngeta corridor between Uzungwa Scarp Forest Reserve and Kilombero Nature Reserve has received international funding (Sumbi and Rusengula 2012). Our results suggest that these corridors do not need to be closed-canopy forest to allow gene flow in C. harveyi, as more open habitats do not necessarily constitute absolute barriers. However, other factors not measured during this study, principally hunting (Nielsen 2011), are also likely to impact on the efficacy of corridors in facilitating duiker dispersal.

This study is the first to investigate spatial genetic structure in a forest antelope species. The significant structure in C. harveyi across the Udzungwas appears to be linked to social organisation at the local scale and potentially also to landscape

140 features such as habitat and rivers. Our results also highlight the need to conserve the threatened populations in the outlying Uzungwa Scarp and New Dabaga forests as they represent the most genetically divergent subgroups within the Udzungwas Mountains and therefore potential conservation management units (Palsboll et al. 2007). Landscape-scale genetic studies of other forest antelopes are highly recommended to provide valuable information, as demonstrated for ungulate species elsewhere (Cullingham et al. 2011; Vander Wal et al. 2012), for managing some of the most heavily harvested and ecologically important wildlife in Africa.

141 nd S1. Correlated spatial model. 2 ranked model. Number of clusters along the chain after burnin 0.35 0.30 -860 0.25 -880 0.20 Density -900 0.15 0.10 -920 0.05 -940 0.00 160 180 200 220 240 260 2 4 6 8 10 Nb. of clusters along the chain Estimated cluster membership (after a burnin of 100x5000i t.)

rd S2. Correlated spatial model. 3 ranked model. Number of clusters along the chain after burnin -860 0.3 -880 0.2 Density -900 0.1 -920 -940 0.0 160 180 200 220 240 260 2 4 6 8 10 Nb. of clusters along the chain Estimated cluster membership (after a burnin of 100x5000i t.)

Supplementary Figures 5.1 and 5.2. Geneland analysis with correlated spatial model for C. harveyi microsatellite genotypes from the Udzungwa Mountains (N = 141). Maps show the posterior mode of population membership for the model run indicated (black dots are sample spatial coordinates). The highest probability model is shown in Fig 5.6.

142 th S3. Correlated spatial model. 4 ranked model. Number of clusters along the chain after burnin -860 0.3 -880 0.2 Density -900 0.1 -920 -940 0.0 160 180 200 220 240 260 2 4 6 8 10 Nb. of clusters along the chain Estimated cluster membership (after a burnin of 100x5000i t.)

S4. Correlated spatial model. 5th ranked model. Number of clusters along the chain after burnin 0.35 -860 0.30 0.25 -880 0.20 Density -900 0.15 0.10 -920 0.05 -940 0.00 160 180 200 220 240 260 2 4 6 8 10 Nb. of clusters along the chain Estimated cluster membership (after a burnin of 100x5000i t.)

Supplementary Figure 5.3 and 5.4. Geneland analysis with correlated spatial model for C. harveyi microsatellite genotypes from the Udzungwa Mountains (N = 141). Maps show the posterior mode of population membership for the model run indicated (black dots are sample spatial coordinates). The highest probability model is shown in Fig. 5.6.

143 CHAPTER 6.

DISTRIBUTION AND GENETIC DIVERSITY OF THE ENDANGERED ABBOTT’S DUIKER CEPHALOPHUS SPADIX IN THE UDZUNGWA MOUNTAINS, TANZANIA

An earlier version of this chapter has been accepted for publication in the Proceedings of the 8th Tanzania Wildlife Research Institute Scientific Conference, 6- 8th December 2011, Arusha, Tanzania.

Andrew E. Bowkett1, 2, Trevor Jones3, 4, Francesco Rovero5, 6, Amy B. Plowman2 and Jamie R. Stevens1

1 Molecular Ecology and Evolution Group, Biosciences, College of Life and Environmental Sciences, University of Exeter, Exeter EX4 4QD, United Kingdom.

2 Field Conservation and Research Department, Whitley Wildlife Conservation Trust, Paignton Zoo, Totnes Road, Paignton TQ4 7EU, United Kingdom.

3 Udzungwa Elephant Project, Box 99, Mang’ula, Morogoro, Tanzania

4 Animal and Environmental Research Group, Department of Life Sciences, Anglia Ruskin University, Cambridge CB1 1PT, United Kingdom

5 Tropical Biodiversity Section, Trento Science Museum, Via Calepina 14, 38122, Trento, Italy.

6 Udzungwa Ecological Monitoring Centre, c/o Udzungwa Mountains National Park, P.O. Box 99, Mang’ula, Tanzania.

144 AEB, TJ and FR designed the study; AEB, TJ and FR collected genetic samples and field data, including from remote areas; TJ undertook camera-trap surveys with additional records from AEB and FR. AEB undertook all genetic and statistical analysis under the guidance of JRS; AEB wrote the paper with comments and improvements from all other authors. AEB presented this study at the 9th TAWIRI conference on behalf of all the authors (see above).

Acknowledgements are given at the end of the chapter.

145 Abstract

Abbott’s duiker, Cephalophus spadix, is a forest antelope endemic to a very few highland forests in Tanzania. Apparently extinct over much of its historical range, the species is listed as Endangered by the IUCN based on its rarity and its likely current distribution in only five isolated upland areas: Kilimanjaro, Southern Highlands, West Usambara, Rubeho and Udzungwa Mountains. In contrast to the situation in the rest of its range, Abbott’s duiker is relatively well documented and locally abundant in parts of the Udzungwa Mountains, which may therefore be the only stronghold for the species. We review the distribution of Abbott’s duiker within the Udzungwa Mountains and present new information based on the non- invasive genetic identification of dung piles collected from the majority of forest blocks between 2006 and 2010 (73 confirmed dung samples). Our results include new records from outlying forest blocks where the presence of Abbott’s duiker was previously unknown. Moreover, we present the first population-level analysis of genetic structure and diversity in this endangered species based on nuclear microsatellites and mitochondrial sequence data. While these genetic results should be considered preliminary due to small sample sizes, they indicate differentiation from other Abbott’s duiker populations as well as low genetic diversity relative to sympatric antelope species. Finally, we discuss threats to Abbott’s duiker, and other antelope populations, in the context of our results and identify broad trends within the differently managed Udzungwa Mountain forests that suggest potentially successful conservation strategies for this neglected species.

146 6.1. Introduction

The highland forests of Tanzania are amongst the most important areas in the world for biodiversity conservation due to the exceptional density of threatened and endemic species found there (Burgess et al. 2007). These forests are also of great value to the people of Tanzania through their provision of ecosystem services such as watershed protection and carbon sequestration (Burgess et al. 2009). The endemic species of Tanzania’s highlands are valuable indicators of the health of these important ecosystems.

One of the most threatened highland endemic species of Tanzania is Abbott’s duiker, Cephalophus spadix, a forest antelope found in only a few upland areas. This duiker species is notable for its head crest of pink or red hair and its large body size (Kingdon 1997). Despite these characteristics it is very rarely seen due to its secretive behaviour (often crepuscular or nocturnal), densely vegetated habitats and naturally low population density. The species is threatened by habitat loss, due to agricultural encroachment and selective logging, and hunting, particularly with snares, ongoing in many areas (Moyer et al. 2008).

Not much is known about the historical distribution of Abbott’s duiker, but the species has long gone unrecorded in many sites where it was formerly known including the Uluguru and East Usambara Mountains, the Gregory Rift forests, and the Poroto Mountains and Njombe escarpment in southern Tanzania (Moyer 2003). This apparent decline resulted in the species’ IUCN Red List status being changed from Vulnerable to Endangered in 2008 (Moyer et al. 2008). This assessment considered Abbott’s duiker to survive in just four isolated mountain ranges (Fig. 6.1): Kilimanjaro, Udzungwa, West Usambara and Southern Highlands (Mount Rungwe and Livingstone forest). A small isolated population had also been discovered in the southern Rubeho Mountains in 2006. No information on abundance was available from Kilimanjaro or West Usambara, and the species was considered very rare in the Southern Highlands (Machaga and Davenport 2009), leaving the Udzungwa Mountains as the only known stronghold for the species.

The Udzungwa Mountains in south-central Tanzania are the southernmost and largest block of the Eastern Arc Mountains (Fig. 6.2). Many of the area’s forests are

147 protected by the Udzungwa Mountains National Park, and the more recently gazetted Kilombero Nature Reserve, although other forests are less well protected and threatened by illegal activities, e.g. Rovero et al. (2010). This variation in protected status was reflected in the Red List’s assessment of Abbott’s duiker within the Udzungwas with the species listed as “locally common” in Mwanihana, Luhomero and Ukami (the latter only 7 km2) and “rare” or “scarce” in Matundu, Nyumbanitu and Uzungwa Scarp (Moyer et al. 2008). The status of Abbott’s duiker in several other forests was unknown.

Knowledge of the status of Abbott’s duiker in the Udzungwas has increased greatly since the last Red List assessment due to extensive survey work and the use of remotely triggered camera-traps (Rovero et al. 2005) and non-invasive genetics (Bowkett et al. 2009a; Bowkett et al. 2009b). These techniques not only provide more reliable survey methods than traditionally available but also a wealth of further information on abundance (Rovero and Marshall 2009), habitat-use (Bowkett et al. 2008), and, in the case of genetic analysis, population structure and genetic health (Beja-Pereira et al. 2009). Here we present results from recent surveys for Abbott’s duiker, including all major forest blocks in the Udzungwa Mountains, and an exploratory analysis of genetic information recovered from dung samples collected during this work.

6.2. Materials and methods

We surveyed 24 sites within 10 forests throughout the Udzungwas between 2006 and 2010 (Fig. 6.2; Table 6.1). Within each forest we walked reconnaissance transects in a triangular configuration (typically 3 km per day) using hip-chains to record distance. Suspected Abbott’s duiker dung piles, encountered along transects or elsewhere, were recorded and collected for genetic analysis (unless desiccated). We also employed camera-traps in many forests both specifically to detect Abbott’s duiker and as part of other research programmes, e.g. Bowkett et al. (2008) and Rovero and Marshall (2009).

148

[This image has been removed by the author of this thesis for copyright reasons]

Figure 6.1. Global distribution of the endangered Abbott’s duiker (red hatching). Highland areas referred to in the text are italicized. Modified from IUCN (2012).

149 Ndundulu / Luhomero

*# Mwanihana Kising'a-Rugaro #* Nyumbanitu *# *# *# *#

#* Iwonde Ukami

Nyanganje Lumemo R. New Dabaga-Ulang'ambi Ruipa Lumemo

R. Ruipa

Mat. west R. Kilombero 4 Abbott's duiker Rivers Closed forest

R. Mngeta Open forest Elevation (m asl) Value Uzungwa Scarp High : 2636 Kilometers 0 3.5 7 14 Low : -24

Figure 6.2. Map of the Udzungwa Mountains, Tanzania, showing recorded presence of Abbott’s duiker (red circles are precise, and red triangles approximate, dung sample locations) See Table 6.1 for more details. Outlined shapes represent closed- and open-canopy forest were identified from satellite imagery by Marshall et al. (2010).

150 Dung pellets were processed, and genetic markers analysed, as described in Chapter 2. In brief, to verify species identity and sample mitochondrial DNA (mtDNA) variation, we targeted a ~600 bp fragment of the left-hand domain of the mtDNA control region using a combination of various primers and PCR conditions, see Ntie et al. (2010a) for details. To sample nuclear DNA variation we used seven microsatellite markers in two pre-PCR multiplexes: MPLX1 = INRA40 (Beja-Pereira et al. 2004), BM1225, BM2113 and BRRIBO (Bishop et al. 1994), and MPLX2 = BM143 (Bishop et al. 1994), INRA05 (Vaiman et al. 1994) and SR12 [Ntie et al. (2010c) modified from Kogi et al. (1995)]. PCR products were processed on a Beckman Coulter capillary sequencer and scored using CEQ 8800 software (Beckman Coulter, Fullerton, CA, USA).

Sequence data were aligned using MUSCLE (Edgar 2004) and checked in SEAVIEW (Gouy et al. 2010). Species identity was established by visual inspection of aligned sequences and confirmed using the BLAST programme (NCBI, Bethesda, MD, USA). We undertook a Bayesian phylogenetic analysis of recovered haplotypes in MrBayes 3.2 (Huelsenbeck and Ronquist 2001) as detailed in Chapter 4. The tree was rooted for display with two sequences for , C. dorsalis, a monophyletic sister group to Abbott’s duiker, reflecting published duiker phylogenies (van Vuuren and Robinson 2001; Ntie et al. 2010a). The Bayesian analysis included all haplotypes recovered from dung and tissue samples in the Udzungwas (Table 6.1) and the Southern Highlands (S. Machaga & T. Davenport, Wildlife Conservation Society), plus all published control region sequences for Abbott’s duiker and its sister species, the yellow-backed duiker, C. sylvicultor. We conducted an AMOVA to test for partitioning of genetic variation between the Udzungwas and Southern Highlands in Arlequin 3.5.1.2 (Excoffier et al. 2005).

For microsatellite loci, we scored each allele at least four times from separate PCRs to avoid the problems associated with reproducing consistent profiles from faecal DNA; this multiple-tubes approach is generally regarded as standard when working with faecal DNA, see Taberlet et al. (1999). We constructed a neighbour- joining tree using DAS shared allele distance (Chakraborty and Jin 1993) in the program POPULATIONS (Langella 1999) including all available genotypes from the Udzungwas and Southern Highlands. We were also able to genotype five Northern

151 Highlands samples (Kilimanjaro SUN118 and Usambara SUN115 – 122) from the tissue collection at the University of Stellenbosch, South Africa (van Vuuren and Robinson 2001). Microsatellite data variation was also explored using a principal components analysis in GenAlEx 6 (Peakall and Smouse 2006). Standard genetic diversity values for both data sets, and deviations from Hardy–Weinberg and linkage equilibria in the microsatellite data, were tested for using Arlequin and in GENEPOP 4.0.10 (Raymond and Rousett 1995) with False Discovery Rate correction (Verhoeven et al. 2005). Null alleles were tested for with the program MICROCHECKER (Van Oosterhout et al. 2004).

Comparative data for sympatric populations of Harvey’s duiker C. harveyi, blue duiker P. monticola and suni N. moschatus were extracted from Chapter 4. To control for differences in sample size, we used rarefaction for microsatellite allelic richness in the program HP-Rare (Kalinowski 2005) and for control region haplotypic richness in CONTRIB (Petit et al. 1998).

6.3. Results

We confirmed a total of 73 antelope dung samples from eight different forests as Abbott’s duiker (Table 6.1). Many samples identified as Abbott’s duiker in the field were in fact Harvey’s duiker or bushbuck, Tragelaphus scriptus (species complex), and so were excluded from this study. In addition, we obtained camera-trap records from six forests in the Udzungwa Mountains (Supp. Figure 6.1 and 6.2), including Ukami for which we were unable to collect dung samples.

While we included 14 control region haplotypes in our phylogenetic analysis, only six were recovered from the Udzungwas and the vast majority of samples shared one particular haplotype (Table 6.2). Almost all Udzungwa haplotypes were unique to the region (Table 6.2). As anticipated, there was strong bootstrap support for the monophyly of Abbott’s duiker with respect to its sister species (Fig. 6.3). The microsatellite phylogenetic tree showed differentiation between regions, with a distinct Southern Highlands clade (Fig. 6.4).

Nineteen microsatellite genotypes from Udzungwa samples were included in our analysis of genetic diversity for this region (Table 6.3). Two samples had missing

152 values at one locus. Identical genotypes were excluded to avoid including multiple samples from the same individual (one case in the Udzungwas, one in the Southern Highlands). Data for locus BM2113 failed to meet Hardy-Weinberg expectations (P = 0.016), although this was not significant after FDR correction, and also showed evidence for potential null alleles (P < 0.025). There was no evidence for significant linkage disequilibrium within the data set. Loci BM1225 and SR12 were monomorphic within the Udzungwas, although not in other regions. Overall heterozygosity values were much lower than for sympatric Harvey’s duiker genotyped with the same loci (Table 6.3).

Control region variation was heavily partitioned between regions (AMOVA: Among region variation 56.4%, within region variation 43.6%, global FST = 0.56, P < 0.001).

Table 6.1. Forest characteristics and Abbott’s duiker survey results for the ten forest blocks studied in the Udzungwa Mountains, Tanzania. CT = camera-trap; S = sightings.

Forest Size Elevation Walked Faecal Other (km2) (m) transects (km) DNA records records Matundu 526 279-1,046 40 6 CT,

Uzungwa Scarp 314 290–2,144 25 1 S*

Luhombero- 231 1105–2,520 76 17 CT, S Ndundulu

Mwanihana 151 351–2,263 68 33 CT, S

Kising’a-Rugaro 116 1,627–2,322 30 1 -

Nyumbanitu 57 1,074–2,322 27 2 CT

Nyanganje 42 350–1,038 23 0 -

New Dabaga - 40 1,764–2,081 21 4 - Ulang’ambi

Ukami 7 902–1,651 10 NA** CT

Iwonde 5 980-1,472 9 9 CT

* Sighting by Arafat Mtui, 25th March 2005. ** Dung samples were not collected from Ukami.

153 Figure 6.3. mtDNA control region Bayesian consensus phylogeny for C. spadix from the Udzungwa Mountains (~600 bp). Node values are posterior probability (values below 0.5 not shown, results shown as polytomy). Haplotypes from the Udzungwas are labelled in blue, Southern Highlands in green and Northern Highlands in red.

154 Figure 6.4. Neighbour-joining dendrogram based on DAS distances (Chakraborty and Jin 1993) using genotype data from seven microsatellite loci for Abbott’s duiker. Bootstrap values are the result of 1000 pseudo-replicates (values < 10% not shown). Genotypes from the Udzungwas are in blue, Southern Highlands (TIS04 – 7, TRD01, WCS07) in green and Northern Highlands in red (TIS08, SUN115 – 122).

155 SUN115 TIS08

SUN118 SUN120 SUN122

SUN121 NORTH SH AB056AB088 AB274 Udz RL198

Coordinate2, 27% RL093 ROV01 AB035 TIS06TIS05 WCS02 AB018 RL127 TIS04 TRD01 ROV03AB259 TIS07 RL140 AB295 RL126 AB254ROV04 RL136AM080 RL145

Coordinate 1, 31%

Figure 6.5. Two-dimensional PCA plot of Abbott’s duiker microsatellite genotypes labelled by geographical regions in Tanzania. NORTH = Northern Highlands (Usambaras and Kilimanjaro), SH = Southern Highlands, Udz = Udzungwa Mountains.

Table 6.2. Frequency of Abbott’s duiker mtDNA control region haplotypes recovered from the Udzungwa Mountains, together with available data from other regions. MA = Matundu, LU = Luhomero-Ndundulu, UZ = Uzungwa Scarp, MW = Mwanihana, KR = Kising’a-Rugaro, NY = Nyumbanitu, ND = New Dabaga- Ulang’ambi, I = Iwonde.

Haplotype Udzungwa Southern Northern Udzungwa forests Highlands Highlands AM903084 0 0 2* SA18 57 0 0 MA, LU, MW, NY, ND, I AB004 7 0 0 MA, UZ, MW, ND AB035 3 0 0 MA, MW AM903086 0 6 0 WCS07 0 2 0 WCSB465 0 2 0 AM903085 0 2 0 TJ005 1 0 0 KR TJ050 4 0 0 LU, MW, I AM080 1 0 0 LU AM903083 0 0 2 FJ823349 0 0 2 FJ823348 0 0 1 * Also Ilole forest, Rubeho Mountains (x 1).

156 Table 6.3. Number of alleles (Na), observed (HO) and unbiased expected (uHE) heterozygosities for seven microsatellite loci in Abbott’s duiker (n = 19) and Harvey’s duiker (n = 149) within the Udzungwa Mountains.

Locus Na Ho uHe C. spadix C. harveyi C. spadix C. harveyi C. spadix C. harveyi BM2113 5 8 0.47 0.71 0.72 0.78 INRA40 4 12 0.37 0.83 0.47 0.86 BM1225 1 3 - 0.27 - 0.30 BRRIBO 4 13 0.58 0.75 0.63 0.87 BM143 3 13 0.32 0.69 0.28 0.78 INRA05 2 4 0.06 0.27 0.06 0.36 SR12 1 10 - 0.61 - 0.64 Mean 2.86 9 0.26 0.59 0.31 0.66 (Rarefied*) (2.81) (6.79)

* Mean number of alleles (allelic richness) rarefied to the smallest number of complete genotypes (17).

Table 6.4. Mitochondrial control region diversity in four species of forest antelope in the Udzungwa Mountains, Tanzania. See Chapter 4 for sampling and analysis details.

Suni Blue duiker Harvey’s Abbott’s duiker duiker Sequences 157 48 334 73

Haplotypes 28 13 59 6

Haplotypic 16.37 12 23.92 4.27 richness* Polymorphic sites 81 61 131 27

Gene diversity 0.922 0.905 0.953 0.381

Nucleotide 0.039 0.026 0.037 0.006 diversity * Haplotype number rarefied to the smallest sample size (48).

6.4. Discussion

As an endangered species found only in Tanzania our results for Abbott’s duiker in the Udzungwa Mountains have global conservation significance. These surveys have confirmed the presence of Abbott’s duiker in nine forests in the Udzungwa Mountains, including three areas lacking molecular or camera-trap records prior to this study (Jones and Bowkett 2012). While the species was already known from New Dabaga-Ulang’ambi (Nielsen 2006b), we provide the first records from Iwonde, a small forest patch within the National Park, and Kising’a-Rugaro, a much larger outlying forest that has been heavily hunted and logged [Marshall et al. (2010); TJ unpubl. data].

157

Almost all the recovered control region haplotypes were unique to individual regions (Table 6.2) but several were too similar to be resolved by our phylogenetic analysis and many clades with strong bootstrap support contained sequences from more than one region (Fig. 6.3). This preliminary analysis therefore provides little evidence for geographic structuring of mitochondrial lineages. One possible explanation for this is incomplete lineage sorting, whereby haplotypes may have undergone small sequence changes but there has not been sufficient time for groups of related haplotypes to become fixed in particular areas (Maddison and Knowles 2006).

In contrast, our microsatellite analysis appears to differentiate genotypes from the three sampled regions (Fig. 6.3). This result may reflect the more rapid evolution of microsatellite markers compared to mitochondrial DNA but caution should be taken in interpreting this preliminary analysis given the restricted sample sizes and marker limitations (see Results). Furthermore, bootstrap support was generally low for the main clades (Fig. 6.3), although this may reflect the limited information available for reconstructing evolutionary relationships from such a small number of microsatellite loci.

Overall, genetic diversity values were very low for Abbott’s duiker compared to Harvey’s duiker in the Udzungwa Mountains [Table 6.3 and 6.4; Bowkett et al. (2009a)] or published values for other mammal species, see Garner et al. (2005) and Appendix 1 of Gebremedhin et al. (2009). In addition, two of the microsatellite loci examined in this study appear to have undergone fixation within the Udzungwa Mountains, although they were polymorphic in other regions. This lack of diversity may be the result of long-term isolation from other regions (as indicated by AMOVA; FST = 0.56), but has likely been exacerbated by more recent habitat loss and hunting.

While historical reduction in the size of forest fragments has likely affected Abbott’s duiker in the Udzungwas, the most serious current threat is almost certainly illegal hunting. Hunting occurs throughout the Udzungwas, including within the National Park, but is far more prevalent in the outlying forest reserves,

158 including Kisin’ga-Rugaro and Uzungwa-Scarp where our surveys suggest Abbott’s duiker is much less abundant (Table 6.1). The threat to the large mammal communities of Uzungwa Scarp is particularly severe, as outlined in a recent report documenting population declines in antelope and primates (Rovero et al. 2010).

However, in those forests where conservation action has been taken there is some evidence that Abbott’s duiker populations may be able to recover. Martin Nielsen reports, in Rovero et al. (2010), that hunted species have increased in abundance in New Dabaga-Ulang’ambi following the successful introduction of domestic livestock schemes to local villages. Anecdotally there are also reports that Abbott’s duiker has benefitted from anti-poaching patrols by National Park staff although (Nielsen 2011) found that duiker dung densities remained approximately stable, rather than increased, in Nyambanitu and Ndundulu forests between 2001 and 2008. Nevertheless, these situations are in stark contrast to the example of the unprotected Uzungwa Scarp cited above.

While our survey results provide renewed hope for the survival of Abbott’s duiker it is clear that the species remains threatened and potentially vulnerable to the negative impacts of small population size. We strongly recommend further survey work and non-invasive genetic sampling of Abbott’s duiker in the Udzungwas and throughout the species’ historical range. Prevention of further habitat loss and poaching is essential for the survival of the small isolated populations reported in this study and for the long-term viability of the species as a whole.

6.5. Acknowledgements

The Tanzania Wildlife Research Institute, Tanzanian Commission for Science and Technology, Forestry and Beekeeping Division and Tanzanian National Parks gave permission to conduct the study. The staff of the UMNP are thanked for their support especially Paul Banga and Ponjoli Joram Kabepele, Ecologists. S Ricci, K Ally, V Banga, A Muhundu, R Mwakisoma, Y Sama, M Matola, A Mtui, A Mndeme, M Mlewa, A Chitita and Saidi are thanked for invaluable assistance in Tanzania. Martin Nielsen, Tim Davenport and Sophy Machaga helped access additional

159 samples from important areas. Particular thanks to Richard Laizzer for collaboration with sample collection and assistance with sample export. The Wildlife Conservation Society and Zoological Society of London supported T. Jones. Bettine Jansen van Vuuren, Nicola Anthony and Stephan Ntie are thanked for their technical support on duiker genetics. The director and staff of the TAWIRI are thanked for their support during this study and for the scientific conferences.

160

Supplementary Figure 6.1. Abbott’s duiker photographed in Mwanihana, Udzungwa Mountains National Park, Tanzania. Credit: Andrew Bowkett.

Supplementary Figure 6.2. Abbott’s duiker photographed in Nyumbanitu, Kilombero Nature Reserve, Tanzania. Photo credit: Trevor Jones.

161 CHAPTER 7.

GENERAL DISCUSSION

162 7.1. Preamble

The aim of the present study was to use non-invasive sampling to investigate genetic patterns in forest antelope populations in the Udzungwa Mountains, Tanzania, within the context of the conservation of these species and the wider ecosystem. Within this we identified specific aims, as detailed in the introduction chapter, the fulfilment of which we discuss below. In some cases, the robustness of our conclusions must be qualified by limitations to the data as discussed in the research chapters. Nevertheless, our efforts, including the appended research papers, constitute a number of significant milestones. These include, a significant contribution to the development of a widely applicable molecular diagnostic for African forest antelope samples (Bowkett et al. 2009b; Ntie et al. 2010b); the most comprehensive assessment of phylogenetic relationships within east African red duiker to date (Chapter 4); the first investigation of fine-scale genetic variation in a tropical antelope species in a forest habitat (Chapter 5); and the first assessment of genetic diversity in the endangered Abbott’s duiker Cephalophus spadix including new records from previously unknown populations [Jones and Bowkett (2012); Chapter 6)].

7.2. Main findings

7.2.1. Morphological identification of forest antelope dung

Field identification of forest antelope dung to species is unreliable in the Udzungwa Mountains (Bowkett et al. 2009b), as elsewhere (van Vliet et al. 2008; Yamashiro et al. 2010; Faria et al. 2011), thus casting doubt on the use of species- specific dung counts as a monitoring tool. A discriminant analysis of faecal pellet measurements has been successfully used to discriminate between savannah antelope species (Hibert et al. 2008) and demonstrated potential in a pilot study (Bowkett et al. 2009b). However, with access to a larger data-set from across the Udzungwas, this study found no diagnostic differences between target antelope species, poor resolution by discriminant analysis and only moderate accuracy when restricting results to Abbott’s duiker (74% correct identification). While it is possible that even greater sample size, and/or more sophisticated morphometric

163 analysis, could improve these results we conclude that rapid field measurements are unlikely to validate dung surveys in areas where even approximately similar- sized forest antelope co-exist.

7.2.2. Genetic patterns as revealed by non-invasive sampling

In order to investigate genetic patterns across five different forest antelope species in the Udzungwas we characterized 618 faecal samples in terms of species, mitochondrial control region haplotype and, for a subset of samples (160), multi- locus microsatellite genotype. This data provides new distribution records and reveals the likely absence of blue duiker Philantomba monticola / lugens (see 7.3.1) from central and eastern forest blocks, despite seemingly suitable habitat.

These samples also provide the first assessment of genetic diversity for these populations (and indeed these species) including unexpected mitochondrial lineage divergence within Harvey’s duiker C. harveyi. In fact, Harvey’s duiker sequences from within the Udzungwas were paraphyletic with respect to southern African Natal red duiker C. natalensis. However, nuclear variation did not correspond to these mtDNA control region harveyi clades, indicating that they are not reproductively isolated. Although this finding requires verification with additional genetic loci, and more widespread geographical sampling, we tentatively invoke isolation of forest refugia during the Pleistocene as a possible explanation.

Significant differences in mtDNA haplotype and microsatellite allele frequency between sampling sites were broadly consistent with population subdivision due to habitat fragmentation in all species except Abbott’s duiker. This chapter also re- assessed the taxonomy of antelope species present in the Udzungwas following the classification of Groves and Grubb (2011).

7.2.3. Spatial genetic structure in Harvey’s duiker

The results presented in Chapter 5 strongly indicate that genetic structure is not random in C. harveyi across the Udzungwa Mountains. We found a strong pattern

164 of isolation by distance across the study area linked to positive spatial autocorrelation at short distances i.e. individuals that were sampled close together were highly likely to be related, indicating restricted dispersal. This social structure may mean that this species is less likely to colonise new areas or exchange migrants with distant populations although we cannot rule out occasional long-distance dispersal not detected by our sampling.

We also found evidence for at least two distinct genetic clusters. The spatial clustering methods consistently identified genotypes from western outlying forests as forming either one or two clusters separate from the rest of the sampling area. This pattern is not entirely consistent with that expected if strongly biased by isolation by distance. The outlying clusters are at one extreme of the sampled area but similar distances exist within the main Udzungwas. Neither is the pattern best explained by forest fragmentation, as similar distances between forests do not necessarily relate to the identified clusters. Therefore, it seems that landscape permeability to duiker gene flow is variable between forest blocks due to factors not directly measured during this study. Waterways are a possible factor as rivers flowing from the Udzungwas into the Kilombero valley divide some of the main genetic clusters. This landscape approach is a promising area of future work (See 7.4.3.).

7.2.4. Abbott’s duiker in the Udzungwa Mountains

Abbott’s duiker is more widely distributed in the Udzungwa Mountains than previously feared, e.g. Moyer et al. (2008). Our molecular and camera-trap records confirm the species from three forests not considered in the species’ Red List assessment, New-Dabaga Ulang’ambi Forest Reserve, Kising’a-Rugaro Forest Reserve, and Iwonde forest (Jones and Bowkett 2012). However, genetic diversity within the Udzungwas is very low for Abbott’s duiker in comparison with other species and similar to other highly endangered bovid species, e.g. Gebremedhin et al. (2009).

165

7.3. Implications for conservation

7.3.1. Conservation status of newly recognised evolutionary species

We provisionally assigned forest antelope taxa occurring in the Udzungwas to evolutionary species (as opposed to traditionally recognised species) under the taxonomic revision of Groves and Grubb (2011). This re-classification, if accepted, has conservation implications for at least two of these species.

Firstly, the elevation of the Tanzanian highland blue duiker P. lugens from a subspecies of the widespread P. monticola necessitates the recognition of a second forest antelope species endemic to Tanzania (the other being Abbott’s duiker). Moreover, this species would be restricted to the country’s relatively small area of highland forest, even within which it is not uniformly distributed (Chapter 4). Despite its puzzling distribution within the Udzungwas, the area may constitute a significant proportion of this species’ range. Although C. monticola appears to be one of the most resilient species to overhunting in central Africa (Mockrin 2010), this may not be the case for highland populations where source-sink dynamics, as inferred elsewhere (Lawes et al. 2000), may not apply. Careful evaluation of the evidence is required to assess this taxon under IUCN Red List criteria (IUCN 2012).

Secondly, the recognition of mountain suni Neotragus kirchenpaueri as distinct from coastal populations has similar implications, although this new taxon would have a wider range than the Tanzanian highland blue duiker in east Africa, extending into Kenya and possibly Malawi (Groves and Grubb 2011).

7.3.2. Uzungwa Scarp Forest Reserve and New Dabaga-Ulang’ambi Forest Reserve

All five forest antelope species were recorded from New Dabaga and all except bushbuck from Uzungwa Scarp. These populations are important for the conservation of Abbott’s duiker, which is not confined to the main protected areas. These forests are similarly significant to the conservation of genetic diversity within Harvey’s duiker as they form distinct genetic clusters. We also detected the

166 relatively rare mtDNA control region haplogroup (clade A) in both forests and a private microsatellite allele in Uzungwa Scarp (Chapter 4).

Both reserves suffer from forest degradation and illegal hunting and clearly require improved protection (Zilihona et al. 1998; Nielsen 2006b; Topp-Jorgensen et al. 2009; Rovero et al. 2010). However, there is recent evidence to suggest that wildlife populations, including Abbott’s duiker, have recovered to some extent in New Dabaga under joint forest management with the local communities (Nielsen 2011). This conclusion is based on increasing dung densities along transects undertaken between 2001 and 2008. Although the species-specific trends reported by the authors should be treated with caution, given the likelihood of correct dung identification (Bowkett et al. 2009b), the overall recovery of wildlife suggests that similar community-based patrol efforts should be established elsewhere in the Udzungwas.

In contrast, the situation in Uzungwa Scarp is far less positive and has been described as a crisis in a recent report (Rovero et al. 2010). The authors documented lower antelope abundance in Uzungwa Scarp than in any other area surveyed in the Udzungwas, with similar results for primates, and conclude that this is primarily due to hunting. Several recent initiatives provide an opportunity to address these problems including the proposed upgrading of the area to the status of Nature Reserve (Marshall et al. 2007) and the establishment of a corridor linking the reserve to Matundu forest in the existing Kilombero Nature Reserve (Sumbi and Rusengula 2012). Both these initiatives will involve the engagement of local communities and potentially lead to management agreements like those that appear to have been successful for New Dabaga.

7.3.3. Connectivity and wildlife corridors

Wildlife corridors have been proposed to connect protected areas both within the Udzungwas (MTSN 2007) and with neighbouring ecosystems (Jones et al. 2007; Jones et al. 2009). Many of these proposals advocate the protection of existing natural habitat from conversion to agriculture, rather than active forest regeneration, as they aim to maintain large mammal migrations. Nevertheless, our

167 genetic results indicate that partially wooded corridors may allow residency or dispersal of Harvey’s duiker. This is because genotypes from several sampling locations separated by gaps between forests clustered together, indicating gene flow. In the case of the proposed corridor between Uzungwa Scarp and Matundu forest the presence of the Mngeta River may be a more significant barrier, although this remains to be tested.

7.3.4. Feasibility of genetic monitoring of forest antelope populations

Given our assertion that non-invasive genetics can provide valuable information for conservation management it is worth exploring the feasibility of on-going genetic monitoring of forest antelope populations (Schwartz et al. 2007). Regular collection and genotyping of antelope dung from the Udzungwas would allow management authorities to evaluate the success of conservation measures in terms of antelope abundance, genetic diversity and connectivity. Moreover, it would supply researchers with much greater sample sizes for investigating the genetic structure of forest antelope populations at multiple spatial scales.

In practical terms, it would not be difficult to equip patrol teams to collect samples at set temporal and spatial intervals as part of their routine duties. There are also modern molecular laboratories available in Tanzania with the technical capacity to perform the genetic analysis. The main limitation is therefore financial. DNA extraction and mitochondrial sequencing for this project cost approximately £10 (~US $15) per sample and microsatellite fragment analysis cost at least double that amount due to the multiple-PCR repeats needed. These estimates do not include labour. Protected area management in Tanzania is vastly under-resourced, particularly in forest reserves, and often lacks the capacity to patrol key areas effectively. Therefore, while we strongly endorse further externally funded genetic research to build on the results presented here (see 7.4), regular genetic monitoring does not appear feasible in the short-term even for critically important areas like the Udzungwas.

168 7.4 Future research directions

All of the investigations undertaken during this study could be extended, with further sampling and loci, to obtain a more comprehensive assessment of genetic patterns in forest antelope populations. Rather than repeat our discussion of potential limitations for each analysis (see Chapters 3-6), we instead propose three directions for future research that would greatly improve scientific knowledge of forest antelope to better reflect the enormous ecological and economic value of these taxa.

7.4.1. Novel genomic resources for forest antelope conservation

Although the microsatellite panel used in this study was able to distinguish individuals with high statistical probability and detected population clusters at a relatively fine scale (Chapter 5), we still identified some limitations likely due to null alleles and failed to amplify loci consistently in two of the species tested (Chapter 4). Microsatellite libraries developed for specific forest antelope taxa e.g. Cephalophus species, would likely be a significant improvement. More specific sex- linked markers would also aid our interpretation of spatial genetic structure in these species.

Modern genomic methods could vastly enhance our ability to detect genetic patterns in forest antelope. While the genomes of these important species are yet to be sequenced, the technology to do so is rapidly becoming cheaper and more efficient (Hudson 2008). In addition, it may be possible to identify regions homologous to related model organism genomes (Kohn et al. 2006). One application of such an approach is to identify large numbers of candidate markers, e.g. microsatellites or single nucleotide polymorphisms, SNPs (Morin et al. 2004). Large-scale application of neutral markers (i.e. > 100 loci) allow for improved characterisation of genetic variation within populations and finer resolution of phylogenetic and phylogeographic patterns (Brito and Edwards 2009).

A genomic approach also facilitates the discovery of loci under selection, allowing the measurement, and potentially conservation, of adaptive variation (Kohn et al.

169 2006). Identification of markers linked to health, growth and fecundity in species heavily exploited for human consumption could have significant economic potential. SNPs for rapidly evolving reproductive loci have been identified in wild goats (Jordan et al. 2006) and could potentially be tested in forest antelope taxa. Lastly, genomic scans for detrimental genetic variation or deleterious mutations may also have implications for managing harvested or captive forest antelope populations sustainably, e.g. monitoring disease or inbreeding depression.

7.4.2. Landscape genetics in the Udzungwa Mountains and east African region

Landscape genetics aims to understand the processes of gene flow and local adaptation by studying the interactions between genetic and spatial or environmental variation (Manel et al. 2003; Manel and Segelbacher 2009). While our spatial analysis of genetic structure in Harvey’s duiker (Chapter 5) certainly falls within this aim, we were unable to include direct measures of environmental variation, aside from Euclidean distance. Some relevant data is potentially available for incorporation into a Geographic Information System (GIS) although the most pertinent variables, such as hunting intensity, habitat quality and hydrology, would require extensive ground-truthing. While such efforts were beyond the scope of the current study, our results suggest that analyzing the spatial distribution of duiker genotypes with respect to potential barriers, and differential permeability of habitats, could prove highly relevant to the ecology and conservation of these species.

At a regional scale, Nicola Anthony and colleagues are investigating speciation in central African rainforests using duiker species as a model. Their research tests multiple competing hypotheses concerning the historical effect of landscape on evolution, including putative forest refugia and major river systems. Given our preliminary results for lineage diversity in Harvey’s duiker (Chapter 4), a similar approach is strongly recommended for east Africa where the diverse geomorphology and climatic history has resulted in a rich, but often overlooked, biodiversity (Faulkes et al. 2011; Goodier et al. 2011; Lorenzen et al. 2012).

170 7.4.3. Charting the ‘duiker-sphere’ – towards an evolutionary map of the Cephalophini

The previous research directions are complimentary to a more ambitious and far- reaching proposal to document the evolutionary diversity of duikers across Africa. A comprehensive inventory of duiker antelope is long overdue if for no other reason than their importance as a human food source in some of the world’s least developed countries. Initiatives to conserve genetic diversity in crops and livestock, and their wild relatives, have been on-going for over a century (Tanksley 1997). A continent-wide effort to collect and store genetic material from as many duiker populations as possible, including all the putative species named by Groves and Grubb (2011), would likely yield major benefits to this enigmatic group.

Firstly, multi-locus sequencing of multiple populations from across the range of all species would shed much needed light on duiker taxonomy. Recent phylogenetic studies of artiodactyls in general (Hassanin et al. 2012), and duikers in particular (Johnston and Anthony 2012), greatly improve our knowledge but are limited by poor coverage within traditionally recognised taxa. There is simply no available genetic information to support or reject the elevation of many species, such as the Itombwe duiker C. hypoxanthus, by Groves and Grubb (2011). The proposed accumulation of idiographic discoveries concerning the taxonomy and distribution of duiker species would likely include many revisions to currently accepted knowledge, just as recent more narrowly focussed research has done (Colyn et al. 2010; Andanje et al. 2011).

Secondly, baseline knowledge of the evolutionary history of duiker species would help us target populations for more detailed research into population structure, demography, adaptive variation and ecology. Such research would also improve our understanding of duiker natural history but more practically would inform management decisions concerning conservation of threatened species, identification of management units, artificial enhancement of hunted population densities and, potentially, selection of populations better adapted to changing environmental conditions. Detailed study of evolution may seem unnecessary for such applications, and yet there have been expensive consequences for livestock

171 and wildlife management as a result of failing to take evolutionary processes into account (Fraser 2008; Taberlet et al. 2008).

A comprehensive evolutionary map of the Cephalophini would clearly require significant financial and logistical resources. However, an informal network of duiker genetic researchers is already in place, as exemplified by the wide geographic sampling required to develop a molecular diagnostic (Ntie et al. 2010b). Building on this network to include additional sampling sites (principally in ) and research institutes could achieve widespread geographic coverage, with targeted expeditions to fill any gaps. A combination of bushmeat samples, museum specimens, hunting trophies and non-invasive faecal samples would provide a flexible approach to obtaining genetic material at least at the baseline stage. One potential strategy could be to ensure representative coverage of suitable eco-regions (Olson and Dinerstein 1998), an approach already employed to assess evolutionary diversity within bushbuck (Moodley and Bruford 2007). Groves and Grubb (2011) list a total of 56 duiker species and subspecies, even with multiple loci and additional novel taxa this is surely not an insurmountable sequencing challenge.

7.5 Conclusion

The theoretical role of genetics in population declines was instrumental in the emergence of conservation biology as a scientific discipline (Soulé and Wilcox 1980). Despite this early role, convincing evidence for genetic factors impacting wild populations has only been documented relatively recently (Saccheri et al. 1998; Madsen et al. 1999). This research was facilitated by the widespread adoption of polymerase chain reaction methods in molecular ecology (Arnheim et al. 1990) and the adaptation of such methods for use with low-quantity sources of DNA such as hair and faeces (Kohn and Wayne 1997).

The advent of non-invasive genetic sampling has enabled conservation biologists and wildlife managers to investigate the evolutionary history, genetic status and distribution of rare and cryptic species for which direct sampling would be

172 extremely difficult or prohibitively expensive. Early examples include identifying faecal deposits of highly threatened foxes in the presence of sympatric canid species (Paxinos et al. 1997) and the identification of different individual seals and their gender from scats (Reed et al. 1997). Since then such methods have become widely used in the genetic study of charismatic endangered species such as orang- utans Pongo pygmaeus (Goossens et al. 2005) and giant pandas Ailuropoda melanoleuca (Zhu et al. 2011).

Non-invasive genetic sampling has yet to be widely employed in the high- biodiversity forests of east Africa, despite the threatened status of many species in these habitats (Chapter 2). Many of the endemic species in areas such as the Eastern Arc Mountains have highly restricted distributions qualifying them as threatened with extinction under IUCN Red List criteria (Burgess et al. 2007). Conventional sampling of montane birds and reptiles in east Africa (i.e. collection of scientific specimens) has demonstrated the valuable biogeographical and evolutionary insights derived from genetic research and some of the implications for conservation (Bowie et al. 2006; Kahindo et al. 2007; Measey and Tolley 2011; Tolley et al. 2011).

In one of the few examples of genetic sampling of a threatened mammal species in the Eastern Arcs Mountains, Roberts et al. (2010) obtained mitochondrial DNA sequences from the faeces of critically endangered kipunji monkeys Rungwecebus kipunji in the Udzungwa Mountains. These sequences indicated that a separate population of kipunji in the Southern Highlands had acquired different mitochondrial genes via hybridisation with baboons (Papio spp.) thus obscuring the evolutionary distinctness of the kipunji and causing taxonomic controversy (Singleton 2009; Zinner et al. 2009).

The present study has demonstrated the value of non-invasive genetic sampling in multiple applications to forest antelope research in the Udzungwa Mountains. All of these applications have involved the interpretation of genetic patterns at different evolutionary and geographical scales in order to inform the conservation management of these species. Although some patterns require further evidence before firm conclusions can be drawn, others were derived from multiple

173 independent analyses and are consistent with previous knowledge of the species and landscape.

The main findings of this study, including the importance of outlying forests and low genetic diversity in Abbott’s duiker, have significant conservation implications and will be disseminated to the Tanzanian wildlife authorities and the wider conservation community.

174 BIBLIOGRAPHY

Ahumada JA, Silva CE, Gajapersad K, Hallam C, Hurtado J, Martin E, McWilliam A, Mugerwa B, O'Brien T, Rovero F, Sheil D, Spironello WR, Winarni N, Andelman SJ (2011) Community structure and diversity of tropical forest mammals: data from a global camera trap network. Philosophical transactions of the Royal Society of London Series B, Biological sciences 366 (1578):2703-2711. doi:10.1098/rstb.2011.0115 Allsopp R (1978) Social biology of bushbuck (Tragelaphus scriptus Pallas 1776) in the Nairobi National Park, Kenya. East African Wildlife Journal 16 (3):153- 166 Andanje SA, Bowkett AE, Agwanda BR, Ngaruiya GW, Plowman AB, Wacher T, Amin R (2011) A new population of the Critically Endangered Aders' duiker Cephalophus adersi confirmed from northern coastal Kenya. Oryx 45 (3):444-447. doi:10.1017/s003060531000181x Anthony NM, Clifford SL, Bawe-Johnson M, Abernethy KA, Bruford MW, Wickings EJ (2007) Distinguishing gorilla mitochondrial sequences from nuclear integrations and PCR recombinants: guidelines for their diagnosis in complex sequence databases. Mol Phylogenet Evol 43:553–566 Apio A, Kabasa JD, Ketmaier V, Schroder C, Plath M, Tiedemann R (2010) Female philopatry and male dispersal in a cryptic, bush-dwelling antelope: a combined molecular and behavioural approach. Journal of Zoology 280 (2):213-220. doi:10.1111/j.1469-7998.2009.00654.x Apio A, Wronski T (2005) Foraging behaviour and diet composition of bushbuck (Tragelaphus scriptus Pallas, 1766) in Queen Elizabeth National Park, western Uganda. African Journal of Ecology 43 (3):225-232. doi:10.1111/j.1365-2028.2005.00576.x Archie EA, Moss CJ, Alberts SC (2006) The ties that bind: genetic relatedness predicts the fission and fusion of social groups in wild African elephants. Proc Biol Sci 273 (1586):513-522. doi:10.1098/rspb.2005.3361 Arnheim N, White T, Rainey WE (1990) Application of PCR: organismal and population biology. BioScience 40 (3):174-182 Asquith NM (2001) Misdirections in conservation biology. Conserv Biol 15 (2):345-352 Balkenhol N, Waits LP (2009) Molecular road ecology: exploring the potential of genetics for investigating transportation impacts on wildlife. Mol Ecol 18 (20):4151-4164. doi::10.1111/j.1365-294X.2009.04322.x Beja-Pereira A, Oliveira R, Alves PC, Schwartz MK, Luikart G (2009) Advancing ecological understandings through technological transformations in noninvasive genetics. Mol Ecol Resour 9 (5):1279-1301. doi:10.1111/j.1755-0998.2009.02699.x Beja-Pereira A, Zeyl E, Ouragh L, Nagash H, Ferrand N, Taberlet P, Luikart G (2004) Twenty polymorphic microsatellites in two of North Africa's most threatened ungulates: Gazella dorcas and Ammotragus lervia (Bovidae; Artiodactyla). Molecular Ecology Notes 4 (3):452-455 Bensasson D, Zhang DX, Hartl DL, Hewitt GM (2001) Mitochondrial pseudogenes: evolution’s misplaced witnesses. Trends Ecol Evol 16:314-321. doi:S0169- 5347(01)02151-6

175 Bishop MD, Kappes SM, Keele JW, Stone RT, Sunden SLF, Hawkins GA, Toldo SS, Fries R, Grosz MD, Yoo JY, Beattie CW (1994) A Genetic-Linkage Map for Cattle. Genetics 136 (2):619-639 Blomley T, Pfliegner K, Isango J, Zahabu E, Ahrends A, Burgess N (2008) Seeing the wood for the trees: an assessment of the impact of participatory forest management on forest condition in Tanzania. Oryx 42 (3):380-391. doi:10.1017/s0030605308071433 Boshoff AF, Palmer NG, Vernon CJ, Avery G (1994) Comparison of the Diet of Crowned Eagles in the Savanna and Forest Biomes of South-Eastern South- Africa. South Afr J Wildl Res 24 (1-2):26-31 Bowie RCK, Fjeldsa J, Hackett SJ, Bates JM, Crowe TM (2006) Coalescent models reveal the relative roles of ancestral polymorphism, vicariance, and dispersal in shaping phylogeographical structure of an African montane forest robin. Mol Phylogenet Evol 38 (1):171-188 Bowkett AE (2003) The conservation status of small antelope: a review based on the African Antelope Database 1998. In: Plowman A (ed) Ecology and Conservation of Small Antelope: Proceedings of an International Symposium on Duiker and Dwarf Antelope in Africa. Filander Verlag, Fürth, pp 171-178 Bowkett AE, Lunt N, Rovero F, Plowman AB (2006) How do you monitor rare and elusive mammals? Counting duikers in Kenya, Tanzania and . In: Zgrabczyñska E, Ćwiertnia P, Ziomek J (eds) Animals, Zoos and Conservation. Zoological Garden in Poznan, Poland, pp 21-28 Bowkett AE, Plowman A, Jones T, Stevens JR (2009a) The use of non-invasive genetic sampling to assess conservation status: a case study of forest antelope in the Udzungwa Mountains, Tanzania. In: Proceedings of the 7th Tanzania Wildlife Research Institute Scientific Conference. Arusha, Tanzania, Bowkett AE, Plowman AB, Stevens JR, Davenport TRB, van Vuuren BJ (2009b) Genetic testing of dung identification for antelope surveys in the Udzungwa Mountains, Tanzania. Conservation Genetics 10 (1):251-255. doi:10.1007/s10592-008-9564-7 Bowkett AE, Rovero F, Marshall AR (2008) The use of camera-trap data to model habitat use by antelope species in the Udzungwa Mountain forests, Tanzania. African Journal of Ecology 46 (4):479-487. doi:10.1111/j.1365- 2028.2007.00881.x Bowland AC (1990) The response of red duikers Cephalophus natalensis to drive counts. Koedoe 33 (1):47-53 Bowland AE, Perrin MR (1994) Density estimate methods for blue duikers Philantomba monticola and red duikers Caphalophus natalensis in Natal, South Africa. Journal of African Zoology 108 (6):505-519 Bowland AE, Perrin MR (1995) Temporal and spatial patterns in blue duikers Philantomba monticola and red duikers Cephalophus natalensis. J Zool 237:487-498 Bowland AE, Perrin MR (1998) Food habits of blue duikers and red duikers in Kwazulu-Natal, South Africa. Lammergeyer 45:1-16 Bowman V, Plowman A (2002) Captive duiker management at the duiker and mini- antelope breeding and research institute (Dambari), Bulawayo, Zimbabwe. Zoo Biology 21 (2):161-170. doi:10.1002/zoo.10032

176 Brito PH, Edwards SV (2009) Multilocus phylogeography and phylogenetics using sequence-based markers. Genetica 135 (3):439-455. doi:10.1007/s10709- 008-9293-3 Broquet T, Menard N, Petit E (2007) Noninvasive population genetics: a review of sample source, diet, fragment length and microsatellite motif effects on amplification success and genotyping error rates. Conservation Genetics 8 (1):249-260. doi:10.1007/s10592-006-9146-5 Brownstein MJ, Carpten JD, Smith JR (1996) Modulation of non-templated nucleotide addition by tag DNA polymerase: Primer modifications that facilitate genotyping. Biotechniques 20 (6):1004-& Burgess ND, Bahane B, Clairs T, Danielsen F, Dalsgaard S, Funder M, Hagelberg N, Harrison P, Haule C, Kabalimu K, Kilahama F, Kilawe E, Lewis SL, Lovett JC, Lyatuu G, Marshall AR, Meshack C, Miles L, Milledge SAH, Munishi PKT, Nashanda E, Shirima D, Swetnam RD, Willcock S, Williams A, Zahabu E (2010) Getting ready for REDD plus in Tanzania: a case study of progress and challenges. Oryx 44 (3):339-351. doi:10.1017/s0030605310000554 Burgess ND, Butynski TM, Cordeiro NJ, Doggart NH, Fjeldsa J, Howell KM, Kilahama FB, Loader SP, Lovett JC, Mbilinyi B, Menegon M, Moyer DC, Nashanda E, Perkin A, Rovero F, Stanley WT, Stuart SN (2007) The biological importance of the Eastern Arc Mountains of Tanzania and Kenya. Biol Conserv 134 (2):209-231 Burgess ND, Mwakalila S, Madoffe S, Ricketts T, Olwero N, Swetnam RD, Mbilinyi B, Marchant R, Mtalo F, White S, Munishi PK, Marshall AR, Malimbwi RE, Jambiya G, Fisher B, Kajembe G, Morse-Jones S, Kulindwa K, Green J, Balmford A (2009) Valuing the Arc: a programme to map and value ecosystem services in Tanzania. Mountain Research Initiative Newsletter 3:18-21 Campbell D, Swanson GM, Sales J (2004) Comparing the precision and cost- effectiveness of faecal pellet group count methods. J Appl Ecol 41 (6):1185- 1196 Caro TM, Laurenson MK (1994) Ecological and genetic factors in conservation - a cautionary tale. Science 263 (5146):485-486 Caughley G (1994) Directions in Conservation Biology. J Anim Ecol 63 (2):215-244 Centre for Evidence-Based Conservation (2010) Guidelines for systematic review in environmental management. Version 4.0. Chaber A-L, Allebone-Webb S, Lignereux Y, Cunningham AA, Marcus Rowcliffe J (2010) The scale of illegal meat importation from Africa to Europe via Paris. Conservation Letters 3 (5):317-321. doi:10.1111/j.1755- 263X.2010.00121.x Chakraborty R, Jin L (1993) Determination of relatedness between individuals using DNA fingerprinting. Human Biology 65:875-895 Chapuis MP, Estoup A (2007) Microsatellite null alleles and estimation of population differentiation. Mol Biol Evol 24 (3):621-631. doi:10.1093/molbev/msl191 Clare EL, Lim BK, Engstrom MD, Eger JL, Hebert PDN (2007) DNA barcoding of Neotropical bats: species identification and discovery within Guyana. Molecular Ecology Notes 7 (2):184-190. doi:10.1111/j.1471- 8286.2006.01657.x Coates GD, Downs CT (2005) Survey of the status and management of sympatric bushbuck and in KwaZulu-Natal, South Africa. South African Journal of Wildlife Research 35 (2):179-190

177 Coates GD, Downs CT (2006) A preliminary study of valley thicket and coastal -grassland habitat use during summer by bushbuck (Tragelaphus scriptus): a telemetry based study. South African Journal of Wildlife Research 36 (2):167-172 Coltman DW, Pilkington JG, Pemberton JM (2003) Fine-scale genetic structure in a free-living ungulate population. Mol Ecol 12:733–742 Colyn M, Hulselmans J, Sonet G, Oude P, De Winter J, Natta A, Nagy ZT, Verheyen E (2010) Discovery of a new duiker species (Bovidae: Cephalophinae) from the Dahomey Gap, West Africa. Zootaxa (2637):1-30 Comer CE, Kilgo JC, D'Angelo GJ, Glenn TC, Miller KV (2005) Fine-scale genetic structure and social organization in female white-tailed deer. The Journal of Wildlife Management 69 (1):332-344 Conroy MJ (1996) Abundance indices. In: Wilson DE, Cole FR, Nichols JD, Rudran R, Foster MS (eds) Measuring and monitoring biological diversity: standard methods for mammals. Smithsonian Institution Press, Washington, D.C., Cotterill FP, Foissner W (2010) A pervasive denigration of natural history misconstrues how biodiversity inventories and taxonomy underpin scientific knowledge. Biodivers Conserv 19 (1):291-303. doi:10.1007/s10531-009-9721-4 Cotterill FPD (2003a) Insights into the taxonomy of tssesebe antelopes lunatus (Bovidae: Alcelaphini) with the description of a new evolutionary species in south-central Africa. Durban Museum Novitates 28:11-30 Cotterill FPD (2003b) Species concepts and the real diversity of antelopes. In: Plowman A (ed) Ecology and Conservation of Small Antelope: Proceedings of an International Symposium on Duiker and Dwarf Antelope in Africa. Filander-Verlag, Fürth, pp 59-118 Cotterill FPD (2005) The Upemba , anselli: an antelope new to science emphasizes the conservation importance of Katange, Democratic Republic of Congo. J Zool 265:113-132 Cotterill FPD (2006) Taxonomic status and conservation importance of the avifauna of Katanga (south-east Congo Basin) and its environs. Ostrich 77 (1-2):1-21 Coulon A, Guillot G, Cosson JF, Angibault JM, Aulagnier S, Cargnelutti B, Galan M, Hewison AJ (2006) Genetic structure is influenced by landscape features: empirical evidence from a population. Mol Ecol 15 (6):1669-1679. doi:10.1111/j.1365-294X.2006.02861.x Crandall KA, Bininda-Emonds ORP, Mace GM, Wayne RK (2000) Considering evolutionary processes in conservation biology. Trends Ecol Evol 15 (7):290-295 Crawford NG (2010) SMOGD: software for the measurement of genetic diversity. Mol Ecol Resour 10 (3):556-557 Crawley MJ (2007) The R Book. John Wiley & Sons, Ltd., Chichester, UK Cullingham CI, Merrill EH, Pybus MJ, Bollinger TK, Wilson GA, Coltman DW (2011) Broad and fine-scale genetic analysis of white-tailed deer populations: estimating the relative risk of chronic wasting disease spread. Evolutionary Applications 4 (1):116-131. doi:10.1111/j.1752-4571.2010.00142.x Cushman SA, McKelvey KS, Hayden J, Schwartz MK (2006) Gene flow in complex landscapes: Testing multiple hypotheses with causal modeling. The American Naturalist 1684 (486-499) Davenport TRB, De Luca DW, Jones T, Mpunga NE, Machaga SJ, Kitegile A, Phillipps GP (2008) The Critically Endangered kipunji Rungwecebus kipunji of

178 southern Tanzania: first census and conservation status assessment. Oryx 42 (03). doi:10.1017/s0030605308000422 De Queiroz K (2007) Species concepts and species delimitation. Syst Biol 56 (6):879-886. doi:10.1080/10635150701701083 DeMenocal PB (2004) African climate change and faunal evolution during the Pliocene-Pleistocene. Earth and Planetary Science Letters 220 (1-2):3-24. doi:10.1016/s0012-821x(04)00003-2 Dinesen L, Lehmberg T, Rahner MC, Fjeldsa J (2001) Conservation priorities for the forests of the Udzungwa Mountains, Tanzania, based on primates, duikers and birds. Biological Conservation 99 (2):223-236. doi:10.1016/s0006- 3207(00)00218-4 Dinesen L, Lehmberg T, Svendsen JO, Hansen LA, Fjeldsa J (1994) A new genus and species of perdicine bird (phasianidae, perdicini) from Tanzania - a relict form with Indo-Malayan affinities. . Ibis 136 (1):3-11. doi:10.1111/j.1474- 919X.1994.tb08125.x Diniz-Filho JAF, De Campos Telles MP (2002) Spatial Autocorrelation Analysis and the Identification of Operational Units for Conservation in Continuous Populations. Conserv Biol 16 (4):924-935. doi:10.1046/j.1523- 1739.2002.00295.x Dresser BL, Pope CE, Kramer L, Kuehn G, Dahlhausen RD, Maruska EJ, Reece B, Thomas WD (1985) Birth of bongo antelope (Tragelaphus euryceros) to eland antelope (Tragelaphus oryx) and cryopreservation of bongo embryos. Theriogenology 23 (1):190-190. doi:10.1016/0093-691x(85)90096-2 Du Toit JT, Cumming DHM (1999) Functional significance of ungulate diversity in African savannas and the ecological implications of the spread of pastoralism. Biodivers Conserv 8 (12):1643-1661 Dubost G (1980) Ecology and social behavior of the blue duiker - small African forest . Zeitschrift Fur Tierpsychologie-Journal of Comparative Ethology 54 (3):205-266 Duchesne P, Turgeon J (2012) FLOCK Provides Reliable Solutions to the "Number of Populations" Problem. The Journal of heredity. doi:10.1093/jhered/ess038 Earl DA, vonHoldt BM (2011) STRUCTURE HARVESTER: a website and program for visualizing STRUCTURE output and implementing the Evanno method. Conservation Genetics Resources 4 (2):359-361. doi:10.1007/s12686-011- 9548-7 East R (1999) African Antelope Database 1998. Occasional Paper of the IUCN Species Survival Commission, No. 21. IUCN, Gland, Switzerland and Cambridge, UK Eaton MJ, Meyers GL, Kolokotronis SO, Leslie MS, Martin AP, Amato G (2010) Barcoding bushmeat: molecular identification of Central African and South American harvested vertebrates. Conservation Genetics 11 (4):1389-1404. doi:10.1007/s10592-009-9967-0 Eaves HE (2000) Duikers: A primary target for Africa’s bushmeat trade. Animal Keepers' Forum 27:496-505 Edgar RC (2004) MUSCLE: a multiple sequence alignment method with reduced time and space complexity. BMC Bioinformatics 5 (August 19):1-19 Ellerman JR, Morrison-Scott TCS, Hayman FW (1953) Southern African Mammals, 1758 to 1951: A Reclassification. British Museum (Natural History). London Estes LD, Mwangi AG, Reillo PR, Shugart HH (2011) Predictive distribution modeling with enhanced remote sensing and multiple validation techniques

179 to support mountain bongo antelope recovery. Animal Conservation 14 (5):521-532. doi:10.1111/j.1469-1795.2011.00457.x Evanno G, Regnaut S, Goudet J (2005) Detecting the number of clusters of individuals using the software STRUCTURE: a simulation study. Mol Ecol 14 (8):2611-2620. doi:10.1111/j.1365-294X.2005.02553.x Excoffier L, Laval G, Schneider S (2005) Arlequin, version 3.0: an integrated software package for population genetics data analysis. Evol Bioinf Online 1:47-50 Fa JE, Purvis A (1997) Body size, diet and population density in Afrotropical forest mammals: A comparison with neotropical species J Anim Ecol 66:98-112 Fa JE, Ryan SF, Bell DJ (2005) Hunting vulnerability, ecological characteristics and harvest rates of bushmeat species in afrotropical forests. Biol Conserv 121 (2):167-176. doi:10.1016/j.biocon.2004.04.016 Fa JE, Seymour S, Dupain J, Amin R, Albrechtsen L, Macdonald D (2006) Getting to grips with the magnitude of exploitation: Bushmeat in the Cross–Sanaga rivers region, Nigeria and Cameroon. Biol Conserv 129 (4):497-510. doi:10.1016/j.biocon.2005.11.031 Fa JE, Yuste JEG, Castelo R (2000) Bushmeat markets on Bioko Island as a measure of hunting pressure. Conservation Biology 14 (6):1602-1613. doi:10.1046/j.1523-1739.2000.99067.x Faria PJ, Kavembe GD, Jung'a JO, Kimwele CN, Estes LD, Reillo PR, Mwangi AG, Bruford MW (2011) The use of non-invasive molecular techniques to confirm the presence of mountain bongo Tragelaphus eurycerus isaaci populations in Kenya and preliminary inference of their mitochondrial genetic variation. Conservation Genetics 12 (3):745-751. doi:10.1007/s10592-011-0181-5 Faulkes CG, Bennett NC, Cotterill FPD, Stanley W, Mgode GF, Verheyen E, Kitchener A (2011) Phylogeography and cryptic diversity of the solitary-dwelling silvery mole-rat, genus Heliophobius (family: Bathyergidae). J Zool 285 (4):324-338. doi:10.1111/j.1469-7998.2011.00863.x Feer F (1979) Ecological studies on Bates's pygmy antelope Neotragus batesi in north-east Gabon. Terre Et La Vie-Revue D Ecologie Appliquee 33 (2):159- 239 Feer F (1989a) Comparative diet of Cephalophus callypygus and C. dorsalis, sympatric bovids of the African sempervirent forest. Mammalia 53 (4):563- 604. doi:10.1515/mamm.1989.53.4.563 Feer F (1989b) The use of space by 2 sympatric duikers (Cephalophus callipygus and Cephalophus dorsalis) in an African rainforest - the role of activity rhythms. Revue D Ecologie-La Terre Et La Vie 44 (3):225-248 Feer F (1995) Seed dispersal in African forest ruminants. Journal of Tropical Ecology 11:683-689 Fernando P, Lande R (2000) Molecular genetic and behavioral analysis of social organization in the Asian elephant ( Elephas maximus). Behav Ecol Sociobiol 48 (1):84-91. doi:10.1007/s002650000218 Fimbel C (1994) The relative use of abandoned farm clearings and old forest habitats by primates and a forest antelope at Tiwai, Sierra Leone, west Africa. Biological Conservation 70 (3):277-286. doi:10.1016/0006- 3207(94)90173-2 Fimbel C, Curran B, Usongo L (2000) Enhancing the sustainability of duiker harvesting through community participation and controlled access in the Lobe ́ke ́ region of southeastern Cameroon. In: Robinson JG, Bennett EL

180 (eds) Hunting for sustainability in tropical forests. Columbia University Press, New York, NY, USA, Finnie D (2008) Cephalophus adersi. In: IUCN 2012. IUCN Red List of Threatened Species. Version 2012.1. Fitzgibbon CD, Mogaka H, Fanshawe JH (1995) Subsistence Hunting in Arabuko- Sokoke Forest, Kenya, and Its Effects on Mammal Populations. Conserv Biol 9 (5):1116-1126 Fjeldsa J, Lovett JC (1997) Geographical patterns of old and young species in African forest biota: The significance of specific montane areas as evolutionary centres. Biodivers Conserv 6 (3):325-346 Frankel OH (1970) Variation, the essence of life. Proc Linn Soc NSW 95:158-169 Frankel OH (1974) Genetic conservation: our evolutionary responsibility Genetics 78:53-65 Frankham R, Ballou JD, Briscoe DA (2002) Introduction to Conservation Genetics. Cambridge University Press, Cambridge, UK Frantz AC, Bertouille S, Eloy MC, Licoppe A, Chaumont F, Flamand MC (2012) Comparative landscape genetic analyses show a Belgian motorway to be a gene flow barrier for ( elaphus), but not wild boars (Sus scrofa). Mol Ecol 21 (14):3445-3457. doi:10.1111/j.1365- 294X.2012.05623.x Frantz AC, Cellina S, Krier A, Schley L, Burke T (2009) Using spatial Bayesian methods to determine the genetic structure of a continuously distributed population: clusters or isolation by distance? J Appl Ecol 46 (2):493-505. doi:10.1111/j.1365-2664.2008.01606.x Frantz AC, Pope LC, Carpenter PJ, Roper TJ, Wilson GJ, Delahay RJ, Burke T (2003) Reliable microsatellite genotyping of the Eurasian badger (Meles meles) using faecal DNA. Mol Ecol 12 (6):1649-1661. doi:10.1046/j.1365- 294X.2003.01848.x Fraser DJ (2008) How well can captive breeding programs conserve biodiversity? A review of salmonids. Evolutionary Applications 0 (0):080602014503553- ??? doi:10.1111/j.1752-4571.2008.00036.x Fraser DJ, Bernatchez L (2001) Adaptive evolutionary conservation: towards a unified concept for defining conservation units. Mol Ecol 10:2741-2752 Garner A, Rachlow JL, Hicks JF (2005) Patterns of Genetic Diversity and Its Loss in Mammalian Populations. Conserv Biol 19 (4):1215-1221. doi:10.1111/j.1523-1739.2005.00105.x Garnier JN, Bruford MW, Goossens B (2001) Mating system and reproductive skew in the black rhinoceros. Mol Ecol 10:2031–2041 Garroway CJ, Bowman J, Wilson PJ (2011) Using a genetic network to parameterize a landscape resistance surface for fishers, Martes pennanti. Mol Ecol 20 (19):3978-3988. doi:10.1111/j.1365-294X.2011.05243.x Gautier-Hion A, Emmons LH, Dubost G (1980) A comparison of the diets of three Major Groups of Primary Consumers of Gabon (Primates, Squirrels and Ruminants). Oecologia 45:182-189 GDG GDG (2012) Population genetic and morphometric data analysis using R and the Geneland program. Gebremedhin B, Ficetola GF, Naderi S, Rezaei HR, Maudet C, Rioux D, Luikart G, Flagstad Ø, Thuiller W, Taberlet P (2009) Combining genetic and ecological data to assess the conservation status of the endangered Ethiopian . Anim Conserv 12 (2):89-100. doi:10.1111/j.1469-1795.2009.00238.x

181 Gentry AW (1992) The subfamilies and tribes of the family Bovidae. Mammal Review 22 (1):1-32. doi:10.1111/j.1365-2907.1992.tb00116.x Goodier SAM, Cotterill FPD, O'Ryan C, Skelton PH, de Wit MJ (2011) Cryptic diversity of African tigerfish (Genus Hydrocynus) reveals palaeogeographic signatures of linked Neogene geotectonic events. PLoS One 6 (12):e28775. doi:10.1371/journal.pone.0028775.g001 Goossens B, Chikhi L, Ancrenaz M, Lackman-Ancrenaz I, Andau P, Bruford MW (2006) Genetic signature of anthropogenic population collapse in orang- utans. Plos Biology 4 (2):285-291 Goossens B, Chikhi L, Jalil MF, Ancrenaz M, Lackman-Ancrenaz I, Mohamed M, Andau P, Bruford MW (2005) Patterns of genetic diversity and migration in increasingly fragmented and declining orang-utan (Pongo pygmaeus) populations from Sabah, Malaysia. Mol Ecol 14 (2):441-456. doi:10.1111/j.1365-294X.02421.x GoT (2006) A study to establish a mechanism for payments for water environmental services for the Rufiji River Basin in Tanzania. Economic Research Bureau, University of Dar es Salaam., Dar es Salaam Gouy M, Guindon S, Gascuel O (2010) SeaView Version 4: A Multiplatform Graphical User Interface for Sequence Alignment and Phylogenetic Tree Building. Mol Biol Evol 27 (2):221-224. doi:10.1093/molbev/msp259 Griffiths AM, Koizumi I, Bright D, Stevens JR (2009) A case of isolation by distance and short-term temporal stability of population structure in brown trout (Salmo trutta) within the River Dart, southwest England. Evolutionary Applications 2 (4):537-554. doi:10.1111/j.1752-4571.2009.00092.x Griffiths CJ (1993) The geological evolution of East Africa. In: Lovett JC, Wasser Sk (eds) Biogeography and Ecology of the Rainforests of Eastern Africa. Cambridge University Press, Cambridge, pp 9-21 Groves CP, Fernando P, Robovsky J (2010) The sixth rhino: a taxonomic re- assessment of the critically endangered northern white rhinoceros. PLoS One 5 (4):e9703. doi:10.1371/journal.pone.0009703 Groves CP, Grubb P (2011) Ungulate taxonomy. The Johns Hopkins University Press, Baltimore Grubb P, Groves CP, Powell B (2003) Duikers and dwarf antelopes: new or uncertain records. In: Plowman A (ed) Ecology and Conservation of Small Antelope: Proceedings of an International Symposium on Duiker and Dwarf Antelope in Africa. Filander Verlag, Fürth, pp 127-140 Guillot G (2008) Inference of structure in subdivided populations at low levels of genetic differentiation--the correlated allele frequencies model revisited. Bioinformatics 24 (19):2222-2228. doi:10.1093/bioinformatics/btn419 Guillot G, Estoup A, Mortier F, Cosson JF (2005) A spatial statistical model for landscape genetics. Genetics 170 (3):1261-1280. doi:10.1534/genetics.104.033803 Guillot G, Santos F, Estoup A (2008) Analysing georeferenced population genetics data with Geneland: a new algorithm to deal with null alleles and a friendly graphical user interface. Bioinformatics 24 (11):1406–1407 Guindon S, Gascuel O (2003) A Simple, Fast, and Accurate Algorithm to Estimate Large Phylogenies by Maximum Likelihood. Systematic Biology 52 (5):696- 704. doi:10.1080/10635150390235520 Hall JS, White LJT, Inogwabini BI, Omari I, Morland HS, Williamson EA, Saltonstall K, Walsh P, Sikubwabo C, Bonny D, Kiswele KP, Vedder A, Freeman K (1998) Survey of Grauer's gorillas (Gorilla gorilla graueri) and eastern

182 (Pan troglodytes schweinfurthi) in the Kahuzi-Biega National Park lowland sector and adjacent forest in eastern Democratic Republic of Congo. Int J Primatol 19 (2):207-235. doi:10.1023/a:1020375430965 Hansen H, Ben-David M, McDonald DB (2008) Effects of genotyping protocols on success and errors in identifying individual river otters (Lontra canadensis) from their faeces. Mol Ecol Resour 8 (2):282-289. doi:10.1111/j.1471- 8286.2007.01992.x Hardy OJ, Vekemans X (2002) SPAGeDi: a versatile computer program to analyse spatial genetic structure at the individual or population levels. Molecular Ecology Notes 2:618–620. doi:10.1046/j.1471-8278 Harrison P (2006) Socio-Economic study of the Udzungwa Scarp area: a potential wildlife corridor. Incorporating livelihood assessments and options for future management of Udzungwa Forests. Critical Ecosystem Partnership Fund and WWF-Tanzania, Dar es Salaam, Tanzania Hart JA (2000) Impact and sustainability of indigenous hunting in the Ituri Forest, Congo-Zaire: a comparison of unhunted and hunted duiker populations. In: Robinson JG, Bennett EL (eds) Hunting for sustainability in tropical forests. Columbia University Press, New York, NY, USA, Hart JA, Katembo M, Punga K (1996) Diet, prey selection and ecological relations of and golden cat in the Ituri Forest, Zaire. African Journal of Ecology 34 (4):364-379 Hassanin A, Delsuc F, Ropiquet A, Hammer C, Jansen van Vuuren B, Matthee C, Ruiz-Garcia M, Catzeflis F, Areskoug V, Nguyen TT, Couloux A (2012) Pattern and timing of diversification of Cetartiodactyla (Mammalia, ), as revealed by a comprehensive analysis of mitochondrial genomes. Comptes rendus biologies 335 (1):32-50. doi:10.1016/j.crvi.2011.11.002 Hedrick PW, Lacy RC, Allendorf FW, Soulé ME (1996) Directions in conservation biology: Comments on Caughley. Conserv Biol 10 (5):1312-1320 Henschel P, Abernethy KA, White LJT (2005) Leopard food habits in the Lope National Park, Gabon, Central Africa. African Journal of Ecology 43 (1):21- 28 Hey J (2006) On the failure of modern species concepts. Trends Ecol Evol 21 (8):447-450. doi:10.1016/j.tree.2006.05.011 Hibert F, Fritz H, Poilecot P, Abdou HN, Dulieu D (2008) Morphological criteria to identify faecal pellets of sympatric ungulates in West African savanna and estimates of associated error. African Journal of Ecology 46 (4):523-532. doi:10.1111/j.1365-2028.2007.00889.x Higgins JPT, Green S (2011) Cochrane Handbook for Systematic Reviews of Interventions Version 5.1.0. The Cochrane Collaboration, Hillman JC (1986) Aspects of the biology of the bongo antelope Tragelaphus erycerus Ogilby 1837 in south west Sudan. Biological Conservation 38 (3):255-272. doi:10.1016/0006-3207(86)90125-4 Hillman JC, Gwynne MD (1987) Feeding of the bongo antelope, Tragelaphus eurycerus (Ogilby, 1837), in south west Sudan. . Mammalia 51 (1):53-63. doi:10.1515/mamm.1987.51.1.53 Hobbs GI, Chadwick EA, Bruford MW, Slater FM (2011) Bayesian clustering techniques and progressive partitioning to identify population structuring within a recovering otter population in the UK. J Appl Ecol 48 (5):1206- 1217. doi:10.1111/j.1365-2664.2011.02028.x

183 Hoelzel AR, Hancock JM, Dover GA (1991) Evolution of the cetacean mitochondrial d-loop region. Mol Biol Evol 8 (4):475-493 Hofmann T, Roth H (2003) Feeding preferences of duiker (Cephalophus maxwelli, C-rufilatus, and C-niger) in Ivory Coast and Ghana. Mammalian Biology 68 (2):65-77. doi:10.1078/1616-5047-00065 Homewood KM, Rodgers WA (1980) A previously undescribed Mangabey from southern Tanzania. Int J Primatol 2:47-55 Hubisz MJ, Falush D, Stephens M, Pritchard JK (2009) Inferring weak population structure with the assistance of sample group information. Mol Ecol Resour 9 (5):1322-1332. doi:10.1111/j.1755-0998.2009.02591.x Hudson ME (2008) Sequencing breakthroughs for genomic ecology and evolutionary biology. Mol Ecol Resour 8:3-17 Huelsenbeck JP, Larget B, Alfaro ME (2004) Bayesian phylogenetic model selection using reversible jump Markov chain Monte Carlo. Mol Biol Evol 21 (6):1123-1133. doi:10.1093/molbev/msh123 Huelsenbeck JP, Ronquist F (2001) MRBAYES: Bayesian inference of phylogenetic trees. Bioinformatics 17:754–755 IUCN (2012) IUCN Red List of Threatened Species. Version 2012.1. Iyengar A, Diniz FM, Gilbert T, Woodfine T, Knowles J, Maclean N (2006) Structure and evolution of the mitochondrial control region in oryx. Mol Phylogenet Evol 40 (1):305-314. doi:10.1016/ympev.2006.02.015 Jalil MF, Cable J, Inyor JS, Lackman-Ancrenaz I, Ancrenaz M, Bruford MW, Goossens B (2008) Riverine effects on mitochondrial structure of Bornean orang- utans (Pongo pygmaeus) at two spatial scales. Mol Ecol 17 (12):2898-2909. doi:10.1111/j.1365-294X.2008.03793.x Jarman PJ (1974) The social organisation of antelope in relation to their ecology. Behaviour 48:215-267 Johnston AR, Anthony NM (2012) A multi-locus species phylogeny of African forest duikers in the subfamily Cephalophinae: evidence for a recent radiation in the Pleistocene. BMC Evolutionary Biology 12. doi:doi:10.1186/1471-2148- 12-120 Johnston AR, Morikawa MK, Ntie S, Anthony NM (2011) Evaluating DNA barcoding criteria using African duiker antelope (Cephalophinae) as a test case. Conservation Genetics 12 (5):1173-1182. doi:10.1007/s10592-011-0220-2 Jones T, Bowkett AE (2012) New populations of an Endangered Tanzanian antelope confirmed using DNA and camera traps. Oryx 46 (1):14-15 Jones T, Caro T, Davenport TRB (2009) Wildlife Corridors in Tanzania. Tanzania Wildlife Research Institute (TAWIRI), Arusha, Tanzania Jones T, Rovero F, Msirikale J (2007) Vanishing corridors: a last chance to preserve ecological connectivity between the Udzungwa and Selous-Mikumi ecosystems of Southern Tanzania. Conservation International, Washington DC Jordan S, Miquel C, Taberlet P, Luikart G (2006) Sequencing primers and SNPs for rapidly evolving reproductive loci in endangered ibex and their kin (Bovidae, spp.). Molecular Ecology Notes 6 (3):776-779. doi:10.1111/j.1471-8286.2006.01340.x Jost LOU (2008) GSTand its relatives do not measure differentiation. Mol Ecol 17 (18):4015-4026. doi:10.1111/j.1365-294X.2008.03887.x Kahindo C, Bowie R, Bates J (2007) The relevance of data on genetic diversity for the conservation of Afro-montane regions. Biol Conserv 134 (2):262-270. doi:10.1016/j.biocon.2006.08.019

184 Kalinowski ST (2005) hp-rare 1.0: a computer program for performing rarefaction on measures of allelic richness. Molecular Ecology Notes 5 (1):187-189. doi:10.1111/j.1471-8286.2004.00845.x Kimura M (1980) A simple method for estimating evolutionary rates of base substitutions through comparative studies of nucleotide sequences. Journal of Molecular Evolution 16 (2):111-120. doi:10.1007/bf01731581 Kingdon J (1982) East African mammals: an atlas of evolution in Africa, vol Volume IIIC. Academic Press, London Kingdon J (1990) Island Africa: the evolution of Africa's rare animals and plants. London, Collins Kingdon J (1997) The Kingdon field guide to African mammals. Academic Press, London Klaus-Hugi C, Klaus G, Schmid B (2000) Movement patterns and home range of the bongo (Tragelaphus eurycerus) in the rain forest of the Dzanga National Park, Central African Republic. African Journal of Ecology 38 (1):53-61. doi:10.1046/j.1365-2028.2000.00211.x Klaus-Hugi C, Klaus G, Schmid B, Konig B (1999) Feeding ecology of a large social antelope in the rainforest. Oecologia 119 (1):81-90. doi:10.1007/s004420050763 Kogi J, Yeh CC, Bhebhe E, Burns BM, Ruvuna F, Davis SK, Taylor JF (1995) Caprine microsatellite dinucleotide repeat polymorphisms at the SR-CRSP-11, SR- CRSP-12, SR-CRSP-13, SR-CRSP-14 and SR-CRSP-15 loci. Animal Genetics 26 (6):449-449 Kohn MH, Murphy WJ, Ostrander EA, Wayne RK (2006) Genomics and conservation genetics. Trends Ecol Evol 21 (11):629-637. doi:10.1016/j.tree.2006.08.001 Kohn MH, Wayne RK (1997) Facts from faeces revisited. Trends Ecol Evol 12 (6):223-227 Kümpel NF, Milner-Gulland EJ, Cowlishaw G, Rowcliffe JM (2010) Incentives for hunting: the role of bushmeat in the household economy in rural Equatorial Guinea. Human Ecology 38:251-264 Lambert JE (2011) Primate Seed Dispersers as Umbrella Species: A Case Study From Kibale National Park, Uganda, With Implications for Afrotropical Forest Conservation. American Journal of Primatology 73 (1):9-24. doi:10.1002/ajp.20879 Lande R (1988) Genetics and demography in biological conservation. Science 241:1455-1460 Langella O (1999) Populations, 1.2.30. http://www.bioinformatics.org/~tryphon/populations/ (accessed 18/11/11). Lawes MJ, Mealin PE, Piper SE (2000) Patch occupancy and potential metapopulation dynamics of three forest mammals in fragmented afromontane forest in South Africa. Conservation Biology 14 (4):1088-1098. doi:10.1046/j.1523-1739.2000.99120.x Lawson D (1989) The food habits of suni antelopes (Neotragus moschatus) (Mammalia, Artiodactyla). Journal of Zoology 217:441-448 Leroy EM, Rouquet P, Formenty P, Souquiere S, Kilbourne A, Froment JM, Bermejo M, Smit S, Karesh W, Swanepoel R, Zaki SR, Rollin PE (2004) Multiple Ebola virus transmission events and rapid decline of central African wildlife. Science 303 (5656):387-390. doi:10.1126/science.1092528

185 Lindsey PA, Roulet PA, Romanach SS (2007) Economic and conservation significance of the trophyhunting industry in sub-Saharan Africa. Biol Conserv 134:455–469 Loiselle BA, Sork VL, Nason J, Graham C (1995) Spatial genetic structure of a tropical understory shrub, Psychotria officinalis (Rubiaceae). American Journal of Botany 82 (11):1420-1425 Lorenzen ED, Arctander P, Siegismund HR (2007) Three reciprocally monophyletic mtDNA lineages elucidate the taxonomic status of Grant’s gazelles. Conservation Genetics 9 (3):593-601. doi:10.1007/s10592-007-9375-2 Lorenzen ED, Heller R, Siegismund HR (2012) Comparative phylogeography of African savannah ungulates(1). Mol Ecol 21 (15):3656-3670. doi:10.1111/j.1365-294X.2012.05650.x Lovett JC (1993) Climatic history and forest distribution in eastern Africa. In: Lovett JC, Wasser SK (eds) Biogeography and Ecology of the Rainforests of Eastern Africa. Cambridge University Press, Cambridge, pp 23-29 Lovett JC, Bridson DM, Thomas DW (1988) A Preliminary list of the moist forest angiosperm flora of Mwanihana Forest Reserve, Tanzania. Annals of the Missouri Botanical Garden 75 (3):874-885. doi:10.2307/2399376 Lovett JC, Marshall AR, Carr J (2006) Changes in tropical forest vegetation along an altitudinal gradient in the Udzungwa Mountains National Park, Tanzania. African Journal of Ecology 44 (4):478-490 Lunt N (2011) The role of small antelope in ecosystem functioning in the Matobo Hills, Zimbabwe. Rhodes University, Lunt N, Bowkett AE, Plowman AB (2007) Implications of assumption violation in density estimates of antelope from dung-heap counts: a case study on grey duiker (Sylvicapra grimmia) in Zimbabwe. African Journal of Ecology 45 (3):382-389. doi:10.1111/j.1365-2028.2006.00724.x Lunt N, Mhlanga MR (2011) Defecation rate variability in the : importance of food quality, season, sex and age. South African Journal of Wildlife Research 41 (1):29-35 Machaga SJ, Davenport TRB (2009) Distribution and conservation of Abbott’s duiker (Cephalophus spadix) in the Southern Highlands. Paper presented at the 7th Tanzania Wildlife Research Institute Scientific Conference, Arusha, Tanzania, MacLeod SB, Kerley GIH, Gaylard A (1996) Habitat and diet of bushbuck Tragelaphus scriptus in the Woody Cape Nature Reserve: Observations from faecal analysis. South African Journal of Wildlife Research 26 (1):19- 25 Maddison WP, Knowles LL (2006) Inferring phylogeny despite incomplete lineage sorting. Syst Biol 55 (1):21-30. doi:10.1080/10635150500354928 Madsen T, Shine R, Olsson M, Wittzell H (1999) Restoration of an inbred adder population. Nature 402 (6757):34-35. doi:10.1038/46941 Manceau V, Crampe JP, Boursot P, Taberlet P (1999) Identification of evolutionary significant units in the Spanish wild , Capra pyrenaica (Mammalia, Artiodactyla). Anim Conserv 2 (1):33-39. doi:10.1111/j.1469- 1795.1999.tb00046.x Manel S, Schwartz MK, Luikart G, Taberlet P (2003) Landscape genetics: combining landscape ecology and population genetics. Trends Ecol Evol 18 (4):189- 197. doi:10.1016/s0169-5347(03)00008-9 Manel S, Segelbacher G (2009) Perspectives and challenges in landscape genetics. Mol Ecol 18 (9):1821-1822. doi:10.1111/j.1365-294X.2009.04151.x

186 Mantel NA (1967) The detection of disease clustering and a generalized regression approach. Cancer Research 27:209-220 Marchant R, Mumbi C, Behera S, Yamagata T (2006) The Indian Ocean dipole ? the unsung driver of climatic variability in East Africa. African Journal of Ecology 45 (1):4-16. doi:10.1111/j.1365-2028.2006.00707.x Marshall AR, Aloyce Z, Mariki S, Jones T, Burgess N, Kilahama F, Massao J, Nashanda E, Sawe C, Rovero F, Watkin J (2007) Tanzania's second Nature Reserve: improving the conservation status of the Udzungwa Mountains? Oryx 41 (4):429-430 Marshall AR, Jorgensbye HI, Rovero F, Platts PJ, White PC, Lovett JC (2010) The species-area relationship and confounding variables in a threatened monkey community. Am J Primatol 72 (4):325-336. doi:10.1002/ajp.20787 Masoud TS (2003) Towards conserving the Aders' duiker: what is working? In: Plowman A (ed) Ecology and Conservation of Small Antelope: Proceedings of an International Symposium on Duiker and Dwarf Antelope in Africa, vol 217-228. Filander-Verlag, Fürth, Matthee CA, Robinson TJ (1999a) Cytochrome b phylogeny of the family bovidae: Resolution within the Alcelaphini, Antilopini, Neotragini, and Tragelaphini. Molecular Phylogenetics and Evolution 12 (1):31-46. doi:10.1006/mpev.1998.0573 Matthee CA, Robinson TJ (1999b) Mitochondrial DNA population structure of roan and : implications for the translocation and conservation of the species. Mol Ecol 8 (2):227-238 Mayr E (1963) Animal Species and Evolution. Belknap Press, Harvard McNeely JA, Miller KR, Reid WV, Mittermeier RA, Werner TB (1990) Conserving the World's Biological Diversity. IUCN, World Resources Institute, Conservation International, WWF-US and the World Bank, Washington, DC Measey GJ, Tolley KA (2011) Sequential Fragmentation of Pleistocene Forests in an East Africa Biodiversity Hotspot: Chameleons as a Model to Track Forest History. Plos One 6 (10). doi:e26606 10.1371/journal.pone.0026606 Menegon M, Doggart N, Owen N (2008) The Nguru mountains of Tanzania, an outstanding hotspot of herpetofaunal diversity. Acta Herpetologica 3 (2):107-127 Miller CR, Joyce P, Waits LP (2005) A new method for estimating the size of small populations from genetic mark-recapture data. Mol Ecol 14 (7):1991-2005 Mittermeier RA, Gil PR, Hoffman M, Pilgrim J, Brooks T, Mittermeier CG, Lamoreux J, da Fonseca GAB (2005) Hotspots revisited: earth's biologically richest and most threatened terrestrial ecoregions. Conservation International, Washington, D.C. Mockrin MH (2010) Duiker demography and dispersal under hunting in Northern Congo. African Journal of Ecology 48 (1):239-247. doi:10.1111/j.1365- 2028.2009.01107.x Mommens G, Coppieters W, Vandeweghe A, Vanzeveren A, Bouquet Y (1994) Dinucleotide repeat polymorphism at the bovine MM12E6 and MM8D3 loci. Animal Genetics 25 (5):368-368 Moodley Y, Bruford MW (2007) Molecular Biogeography: Towards an Integrated Framework for Conserving Pan-African Biodiversity. Plos One 2 (5). doi:10.1371/journal.pone.0000454 Moodley Y, Bruford MW, Bleidorn C, Wronski T, Apio A, Plath M (2009) Analysis of mitochondrial DNA data reveals non-monophyly in the bushbuck

187 (Tragelaphus scriptus) complex. Mammalian Biology - Zeitschrift fur Saugetierkunde 74 (5):418-422. doi:10.1016/j.mambio.2008.05.003 Morin PA, Luikart G, Wayne RK, Grp SNPW (2004) SNPs in ecology, evolution and conservation. Trends Ecol Evol 19 (4):208-216. doi:10.1016/j.tree.2004.01.009 Moritz C (1994) Defining Evolutionarily Significant Units for conservation. Trends Ecol Evol 9 (10):373-375 Moyer DC (2003) Conservation status of Abbott’s duiker, Cephalophus spadix, in the United Republic of Tanzania. In: Plowman AB (ed) Ecology and Conservation of Small Antelope: Proceedings of an International Symposium on Duiker and Dwarf Antelope in Africa. Filander Verlag, Fürth, pp 201-210 Moyer DC, Jones T, Rovero F (2008) Cephalophus spadix In: IUCN 2012. IUCN Red List of Threatened Species. Version 2012.1. Msuya TS, Kideghesho JR, Mosha TCE (2010) Availability, Preference, and Consumption of Indigenous Forest Foods in the Eastern Arc Mountains, Tanzania. Ecology of Food and Nutrition 49 (3):208-227. doi:10.1080/03670241003766048 MTSN (2007) Conservation status, connectivity, and options for improved management of southern Forest Reserves in the Udzungwa Mountains, Tanzania: urgent need for intervention. Critical Ecosystem Partnership Fund, Muchaal PK, Ngandjui G (1999) Impact of village hunting on wildlife populations in the western Dia Reserve, Cameroon. Conservation Biology 13 (2):385-396. doi:10.1046/j.1523-1739.1999.013002385.x Mugerwa B, Sheil D, Ssekiranda P, Heist M, Ezuma P (2012) A camera trap assessment of terrestrial vertebrates in Bwindi Impenetrable National Park, Uganda. African Journal of Ecology:n/a-n/a. doi:10.1111/aje.12004 Mumbi CT, Marchant R, Hooghiemstra H, Wooller MJ (2008) Late Quaternary vegetation reconstruction from the Eastern Arc Mountains, Tanzania. Quaternary Research 69 (2):326-341. doi:10.1016/j.yqres.2007.10.012 Munishi PKT, Shear TH (2004) Carbon storage in afromontane rain forests of the Eastern Arc Mountains of Tanzania: Their net contribution to atmospheric carbon. Journal of Tropical Forest Science 16 (1):78-93 Mwampamba TH (2007) Has the woodfuel crisis returned? Urban charcoal consumption in Tanzania and its implications to present and future forest availability. Energy Policy 35 (8):4221-4234. doi:10.1016/j.enpol.2007.02.010 Mwinyi AA, Wiesner H (2003) Aders' duiker (Cephalophus adersi): hunt, capture and translocation in Zanzibar. In: Ecology and Conservation of Small Antelope: Proceedings of an International Symposium on Duiker and Dwarf Antelope in Africa. Filander-Verlag, pp 229-238 Myers N, Mittermeier RA, Mittermeier CG, da Fonseca GAB, Kent J (2000) Biodiversity hotspots for conservation priorities. Nature 403 (6772):853- 858. doi:10.1038/35002501 Nasi R, Taber A, Van Vliet N (2011) Empty forests, empty stomachs? Bushmeat and livelihoods in the Congo and Amazon Basins. International Forestry Review 13 (3):355-368 Ndanyalasi HJ, Bitariho R, Dovie DBK (2007) Harvesting of non-timber forest products and implications for conservation in two montane forests of East Africa. Biol Conserv 134 (2):242-250. doi:10.1016/j.biocon.2006.06.020

188 Nersting LG, Arctander P (2001) Phylogeography and conservation of and greater . Mol Ecol 10 (3):711-719. doi:10.1046/j.1365- 294x.2001.01205.x Newmark WD (1998) Forest area, fragmentation and loss in the Eastern Arc Mountains: implications for the conservation of biological diversity. Journal of East African Natural History 87:29–36 Nielsen M (2011) Improving the conservation status of the Udzungwa Mountains, Tanzania? The effect of joint forest management on bushmeat hunting in the Kilombero nature reserve. Conservation and Society 9 (2):106. doi:10.4103/0972-4923.83721 Nielsen MR (2006a) Importance, cause and effect of bushmeat hunting in the Udzungwa Mountains, Tanzania: Implications for community based wildlife management. Biological Conservation 128 (4):509-516. doi:10.1016/j.biocon.2005.10.017 Nielsen MR (2006b) Importance, cause and effect of bushmeat hunting in the Udzungwa Mountains, Tanzania: Implications for community based wildlife management. Biol Conserv 128:509–516. doi:10.1016/j.biocon.2005.10.017 Njau MA, Mpuya PM, Mturi FA (2009) Apiculture potential in protected areas: the case of Udzungwa Mountains National Park, Tanzania. International Journal of Biodiversity Science & Management 5 (2):95-101. doi:10.1080/17451590903087821 Noss AJ (1999) Censusing rainforest game species with communal net hunts. African Journal of Ecology 37 (1):1-11. doi:10.1046/j.1365- 2028.1999.00154.x Ntie S, Johnston AR, Mickala P, Bowkett AE, Jansen van Vuuren B, Colyn M, Telfer P, Maisels F, Hymas O, Rouyer RL, Wallace RA, LeBlanc K, van Vliet N, Sonet G, Verheyen E, Pires D, Wickings EJ, Lahm SA, Anthony NM (2010a) A molecular diagnostic for identifying central African forest artiodactyls from faecal pellets. Anim Conserv 13 (1):80-93. doi:10.1111/j.1469- 1795.2009.00303.x Ntie S, Johnston AR, Mickala P, Bowkett AE, van Vuuren BJ, Colyn M, Telfer P, Maisels F, Hymas O, Rouyer RL, Wallace RA, LeBlanc K, van Vliet N, Sonet G, Verheyen E, Pires D, Wickings EJ, Lahm SA, Anthony NM (2010b) A molecular diagnostic for identifying central African forest artiodactyls from faecal pellets. Animal Conservation 13 (1):80-93. doi:10.1111/j.1469- 1795.2009.00303.x Ntie S, Soto-CalderÓN ID, Eaton MJ, Anthony NM (2010c) Cross-species amplification of bovid microsatellites in central African duikers (genus Cephalophus) and other sympatric artiodactyls. Mol Ecol Resour 10 (6):1059-1065. doi:10.1111/j.1755-0998.2010.02860.x Odendaal PB, Bigalke RC (1979) Home range and groupings of bushbuck in the Southern Cape. South African Journal of Wildlife Research 9 (3-4):96-101 Okiria R (1980) Habitat exploitation by the bushbuck in Rwenzori National Park. African Journal of Ecology 18 (1):11-17. doi:10.1111/j.1365- 2028.1980.tb00266.x Olson DM, Dinerstein E (1998) The global 200: A representation approach to conserving the Earth’s most biologically valuable ecoregions. Conserv Biol 12:502-515 Owen-Smith N (1988) Megaherbivores: the influence of very large body size on ecology. Cambridge University Press, Cambridge, UK

189 Paetkau D, Calvert W, Sterling I, Strobeck C (1995) Microsatellite analysis of population structure in Canadian polar bears. Mol Ecol 4 (3):347–354. Palsboll PJ, Berube M, Allendorf FW (2007) Identification of management units using population genetic data. Trends Ecol Evol 22 (1):11-16. doi:10.1016/j.tree.2006.09.003 Paxinos E, McIntosh C, Ralls K, Fleischer R (1997) A noninvasive method for distinguishing among canid species: Amplification and enzyme restriction of DNA from dung. Mol Ecol 6 (5):483-486 Peakall R, Ruibal M, Lindenmayer DB (2003) Spatial Autocorrelation Analysis Offers New Insights into Gene Flow in the Australian Bush Rat, Rattus Fuscipes. Evolution 57 (5):1182-1195. doi:10.1111/j.0014- 3820.2003.tb00327.x Peakall ROD, Smouse PE (2006) GenAlEx 6: genetic analysis in Excel. Population genetic software for teaching and research. Molecular Ecology Notes 6 (1):288-295. doi:10.1111/j.1471-8286.2005.01155.x Perez-Espona S, Perez-Barberia FJ, McLeod JE, Jiggins CD, Gordon IJ, Pemberton JM (2008) Landscape features affect gene flow of Scottish Highland red deer (Cervus elaphus). Mol Ecol 17 (4):981-996. doi:10.1111/j.1365- 294X.2007.03629.x Perrin MR, Bowland AE, Faurie AS (2003) Ecology and conservation biology of blue duiker and red duiker in KwaZulu-Natal, South Africa. In: Plowman A (ed) Ecology and Conservation of Small Antelope: Proceedings of an International Symposium on Duiker and Dwarf Antelope in Africa. Filander Verlag, Fürth, pp 141-155 Petit RJ, El Mousadik A, Pons O (1998) Identifying populations for conservation on the basis of genetic markers. . Conserv Biol 12:844-855 Phillips M, Grandin T, Graffam W, Irlbeck NA, Cambre RC (1998) Crate conditioning of bongo (Tragelaphus eurycerus) for veterinary and husbandry procedures at the Denver Zoological Gardens. Zoo Biology 17 (1):25-32. doi:10.1002/(sici)1098-2361(1998)17:1<25::aid-zoo3>3.0.co;2-c Pickles RSA, Groombridge JJ, Rojas VDZ, Damme P, Gottelli D, Ariani CV, Jordan WC (2011) Genetic diversity and population structure in the endangered giant otter, Pteronura brasiliensis. Conservation Genetics 13 (1):235-245. doi:10.1007/s10592-011-0279-9 Pitra C, Lieckfeldt D (1999) Molecular-forensic contribution to the conviction of an alleged poacher: A case report. Zeitschrift Fur Jagdwissenschaft 45 (4):270- 275. doi:10.1007/bf02241543 Pitra C, VazPinto P, O'Keeffe BWJ, Willows-Munro S, van Vuuren BJ, Robinson TJ (2006) DNA-led rediscovery of the giant sable antelope in Angola. European Journal of Wildlife Research 52 (3):145-152. doi:10.1007/s10344-005- 0026-y Plowman A (2003) Ecology and Conservation of Small Antelope: Proceedings of an International Symposium on Duiker and Dwarf Antelope in Africa. Filander Verlag, Fürth Plumptre AJ (2000) Monitoring mammal populations with line transect techniques in African forests. J Appl Ecol 37 (2):356-368 Plumptre AJ, Harris S (1995) Estimating the Biomass of Large Mammalian Herbivores in a Tropical Montane Forest - a Method of Fecal Counting That Avoids Assuming a Steady-State System. J Appl Ecol 32 (1):111-120 Posada D (2008) jModelTest: phylogenetic model averaging. Mol Biol Evol 25 (7):1253-1256. doi:10.1093/molbev/msn083

190 Price D (1951) Quantitative measures of the development of science. . Archives Internationales d’Histoire des Sciences 14:85-93 Prieto R, Janiger D, Silva MA, Waring GT, GonÇAlves JM (2011) The forgotten whale: a bibliometric analysis and literature review of the North Atlantic sei whale Balaenoptera borealis. Mammal Rev:no-no. doi:10.1111/j.1365- 2907.2011.00195.x Primmer CR (2009) From conservation genetics to conservation genomics. Annals of the New York Academy of Sciences 1162:357-368. doi:10.1111/j.1749- 6632.2009.04444.x Pritchard JK, Stephens M, Donelly P (2000) Inference of population structure using multilocus genotype data. Genetics 155:945-959 Pullin AS, Stewart GB (2006) Guidelines for systematic review in conservation and environmental management. Conserv Biol 20 (6):1647-1656. doi:10.1111/j.1523-1739.2006.00485.x Rannala B, Mountain JL (1997) Detecting immigration by using multilocus genotypes. Proceedings of the National Academy of Sciences of the United States of America 94 (17):9197-9201 Ratnasingham S, Hebert PDN (2007) BOLD: The Barcode of Life Data System (www.barcodinglife.org). Molecular Ecology Notes 7 (3):355-364. doi:10.1111/j.1471-8286.2006.01678.x Raymond M, Rousett F (1995) GENEPOP (version 1.2): population genetics software for exact tests and ecumenicism. J Hered 86:248–249 Reed JZ, Tollit DJ, Thompson PM, Amos W (1997) Molecular scatology: the use of molecular genetic analysis to assign species, sex and individual identity to seal faeces. Mol Ecol 6 (3):225-234. doi:10.1046/j.1365-294X.1997.00175.x Reillo P (2002) Reillo P (2002) Repatriation of mountain bongo to Kenya. Gnusletter 21:11-15 Roberts P, Stewart G, Pullin A (2006) Are review articles a reliable source of evidence to support conservation and environmental management? A comparison with medicine. Biol Conserv 132 (4):409-423. doi:10.1016/j.biocon.2006.04.034 Roberts TE, Davenport TR, Hildebrandt KB, Jones T, Stanley WT, Sargis EJ, Olson LE (2010) The biogeography of introgression in the critically endangered African monkey Rungwecebus kipunji. Biol Lett 6 (2):233-237. doi:10.1098/rsbl.2009.0741 Robinson SJ, Samuel MD, Lopez DL, Shelton P (2012) The walk is never random: subtle landscape effects shape gene flow in a continuous white-tailed deer population in the Midwestern United States. Mol Ecol 21 (17):4190-4205. doi:10.1111/j.1365-294X.2012.05681.x Rodgers WA (1981) The distribution and conservation status of colobus monkeys in Tanzania. . Primates 22:33-45 Rodgers WA (1993) The conservation of the forest resources of eastern Africa: past influences , present practices and future needs. In: Lovett JC, Wasser SK (eds) Biogeography and Ecology of the Rainforests of Eastern Africa. Cambridge University Press, Cambridge, pp 283-331 Rodgers WA, Homewood KM (1982) Biological values and conservation prospects for the forests and primate popuations of the Uzungwa mountains, Tanzania. Biol Conserv 24 (4):285-304. doi:10.1016/0006-3207(82)90016- 7 Rohr L, Pfefferkorn C (2003) Captive management of dukers and how it can affect in situ conservation efforts. In: Plowman A (ed) Ecology and Conservation

191 of Small Antelope: Proceedings of an International Symposium on Duiker and Dwarf Antelope in Africa. Filander-Verlag, Fürth, pp 247-253 Ronquist F, Huelsenbeck JP, Teslenko M (2011) Draft MrBayes version 3.2 Manual: Tutorials and Model Summaries. Rovero F (2008) A newly-established centre for monitoring primates in the Udzungwa Mountains of Tanzania. Folia Primatologica 79 (3):157-157 Rovero F, De Luca DW (2007) Checklist of mammals of the Udzungwa mountains of Tanzania. Mammalia 71 (1-2):47-55 Rovero F, Jones T, Sanderson J (2005) Notes on Abbott's duiker (Cephalophus spadix True 1890) and other forest antelopes of Mwanihana Forest, Udzungwa Mountains, Tanzania, as revealed by camera-trapping and direct observations. Tropical Zoology 18 (1):13-23 Rovero F, Marshall AR (2004) Estimating the abundance of forest antelopes by line transect techniques: a case from the Udzungwa Mountains of Tanzania. Tropical Zoology 17 (2):267-277 Rovero F, Marshall AR (2009) Camera trapping photographic rate as an index of density in forest ungulates. Journal of Applied Ecology 46 (5):1011-1017. doi:10.1111/j.1365-2664.2009.01705.x Rovero F, Marshall AR, Jones T, Perkin A (2009) The primates of the Udzungwa Mountains: diversity, ecology and conservation. Journal of Anthropological Sciences 87:93-126 Rovero F, Mtui A, Kitegile A, Nielsen MR, Jones T (2010) Uzungwa Scarp Forest Reserve in crisis. An urgent call to protect one of Tanzania’s most important forests. . Dar es Salaam, Tanzania Rovero F, Mtui AS, Kitegile AS, Nielsen MR (2012) Hunting or habitat degradation? Decline of primate populations in Udzungwa Mountains, Tanzania: An analysis of threats. Biol Conserv 146 (1):89-96. doi:10.1016/j.biocon.2011.09.017 Rovero F, Rathbun GB, Perkin A, Jones T, Ribble DO, Leonard C, Mwakisoma RR, Doggart N (2008) A new species of giant sengi or elephant-shrew (genus Rhynchocyon) highlights the exceptional biodiversity of the Udzungwa Mountains of Tanzania. J Zool 274 (2):126-133. doi:10.1111/j.1469- 7998.2007.00363.x Rovero F, Struhsaker TT, Marshall AR, Rinne TA, Pedersen UB, Butynski TM, Ehardt CL, Mtui AS (2006) Abundance of diurnal primates in Mwanihana Forest, Udzungwa Mountains, Tanzania. Int J Primatol 27 (3):675-697 Rowcliffe JM, Field J, Turvey ST, Carbone C (2008) Estimating animal density using camera traps without the need for individual recognition. J Appl Ecol 45 (4):1228-1236. doi:10.1111/j.1365-2664.2008.01473.x Rudnick JA, Katzner TE, Bragin EA, Rhodes OE, Jr., DeWoody JA (2005) Using naturally shed feathers for individual identification, genetic parentage analyses, and population monitoring in an endangered Eastern imperial eagle (Aquila heliaca) population from Kazakhstan. Mol Ecol 14 (10):2959- 2967. doi:10.1111/j.1365-294X.2005.02641.x Ryder OA (1986) Species conservation and systematics: the dilemma of subspecies. Trends Ecol Evol 1:9-10 Saccheri I, Kuussaari M, Kankare M, Vikman P, Fortelius W, Hanski I (1998) Inbreeding and extinction in a butterfly metapopulation. Nature 392 (6675):491-494 Safner T, Miller MP, McRae BH, Fortin MJ, Manel S (2011) Comparison of bayesian clustering and edge detection methods for inferring boundaries in

192 landscape genetics. International journal of molecular sciences 12 (2):865- 889. doi:10.3390/ijms12020865 Schmidt JL (1983) A comparison of census techniques of common duiker and bushbuck in timber plantations. South African ForestryJournal 126:15-19 Schwartz MK, Luikart G, Waples RS (2007) Genetic monitoring as a promising tool for conservation and management. Trends Ecol Evol 22 (1):25-33. doi:10.1016/j.tree.2006.08.009 Seddon JM, Santucci F, Reeve NJ, Hewitt GM (2001) DNA footprints of European hedgehogs, Erinaceus europaeus and E-concolor. Pleistocene refugia, postglacial expansion and colonization routes. Mol Ecol 10 (9):2187-2198. doi:10.1046/j.0962-1083.2001.01357.x Seki H, Bowkett AE, Bungard M, Doggart N, Menegon M (2011) A survey for three threatened, narrow endemic amphibian species in the Uzungwa Scarp Forest Reserve, Tanzania. Oryx 45 (4):474-474 Senut B, Pickford M, Ségalen L (2009) Neogene desertification of Africa. Comptes Rendus Geoscience 341 (8-9):591-602. doi:10.1016/j.crte.2009.03.008 Shields GF, Kocher TD (1991) Phylogenetic relationships of North American ursids based on analysis of mitochondrial DNA Evolution 45 (1):218-221. doi:10.2307/2409495 Simbotwe MP, Sichone PW (1989) Aspects of behavior in Kafue National Park, Zambia. South African Journal of Wildlife Research 19 (1):38-41 Simpson GG (1961) Principles of Animal Taxonomy. Columbia University Press, New York Singleton M (2009) The phenetic affinities of Rungwecebus kipunji. Journal of Human Evolution 56 (1):25-42. doi:10.1016/j.jhevol.2008.07.012 Slatkin M, Maddison WP (1990) Detecting isolationby distance using phylogenies of genes. Genetics 126:249-260 Sloane MA, Sunnucks P, Alpers D, Beheregaray LB, Taylor AC (2000) Highly reliable genetic identification of individual northern hairy-nosed wombats from single remotely collected hairs: a feasible censusing method. Mol Ecol 9 (9):1233-1240 Snively E, Theodor JM (2011) Common functional correlates of head-strike behavior in the Pachycephalosaur Stegoceras validum (Ornithischia, Dinosauria) and combative Artiodactyls. Plos One 6 (6). doi:10.1371/journal.pone.0021422 Soto-Calderon ID, Ntie S, Mickala P, Maisels F, Wickings EJ, Anthony NM (2009) Effects of storage type and time on DNA amplification success in tropical ungulate faeces. Mol Ecol Resour 9 (2):471-479. doi:10.1111/j.1755- 0998.2008.02462.x Soulé ME (1985) What is conservation biology? Bioscience 35:727-734 Soulé ME, Gilpin M, Conway W, Foose T (1986) The millenium ark: how long a voyage, how many staterooms, how many passengers? Zoo Biol 5:101-113 Soulé ME, Mills LS (1998) No need to isolate genetics. Science 282 (5394):1658- 1659. doi:10.1126/science.282.5394.1658 Soulé ME, Simberloff D (1986) What do genetics and ecology tell us about the design of nature reserves? Biol Conserv 35:19-40 Soulé ME, Wilcox BA (1980) Conservation biology: an evolutionary-ecological perpective. Sinauer, Sunderland, MA Spear SF, Balkenhol N, Fortin MJ, McRae BH, Scribner K (2010) Use of resistance surfaces for landscape genetic studies: considerations for parameterization

193 and analysis. Mol Ecol 19 (17):3576-3591. doi:10.1111/j.1365- 294X.2010.04657.x Steffen P, Eggen A, Dietz AB, Womack JE, Stranzinger G, Fries R (1993) Isolation and mapping of polymorphic microsatellites in cattle Animal Genetics 24 (2):121-124 Struhsaker TT (1997) Ecology of an African Rain Forest. Logging in Kibale and the conflict between conservation and exploitation. University Press of Florida, Gainesville Struhsaker TT, Marshall AR, Detwiler K, Siex K, Ehardt C, Lisbjerg DD, Butynski TM (2004) Demographic variation among Udzungwa red colobus in relation to gross ecological and sociological parameters. Int J Primatol 25 (3):615-658 Stuart SN, Jensen FP, Brogger-Jensen S (1987) Altitudinal zonation of the avifauna in Mwanihana and Magombera forests eastern Tanzania Gerfaut 77 (2):165-186 Sumbi P, Rusengula F (2012) WWF's initiative to increase forest connectivity in four critical habitat corridors with support from CEPF. The Arc Journal 27:29-31 Swofford D (2001) PAUP*: Phylogenetic Analysis Using Parsimony (*and Other Methods), Version 4.0b2. Sinauer Associates, Sunderland, Massachusetts Taberlet P, Griffin S, Goossens B, Questiau S, Manceau V, Escaravage N, Waits LP, Bouvet J (1996) Reliable genotyping of samples with very low DNA quantities using PCR. Nucleic Acids Research 24 (16):3189-3194 Taberlet P, Valentini A, Rezaei HR, Naderi S, Pompanon F, Negrini R, Ajmone- Marsan P (2008) Are cattle, sheep, and goats endangered species? Mol Ecol 17 (1):275-284. doi:10.1111/j.1365-294X.2007.03475.x Taberlet P, Waits LP, Luikart G (1999) Noninvasive genetic sampling: look before you leap. Trends Ecol Evol 14 (8):323-327 Tamura K, Peterson D, Peterson N, Stecher G, Nei M, Kumar S (2011) MEGA5: molecular evolutionary genetics analysis using maximum likelihood, evolutionary distance, and maximum parsimony methods. Mol Biol Evol 28 (10):2731-2739. doi:10.1093/molbev/msr121 Tanksley SD (1997) Seed banks and molecular maps: unlocking genetic potential from the wild. Science 277 (5329):1063-1066. doi:10.1126/science.277.5329.1063 Telfer PT, Souquiere S, Clifford SL, Abernethy KA, Bruford MW, Disotell TR, Sterner KN, Roques P, Marx PA, Wickings EJ (2003) Molecular evidence for deep phylogenetic divergence in Mandrillus sphinx. Mol Ecol 12 (7):2019-2024 Tolley KA, Tilbury CR, Measey GJ, Menegon M, Branch WR, Matthee CA (2011) Ancient forest fragmentation or recent radiation? Testing refugial speciation models in chameleons within an African biodiversity hotspot. Journal of Biogeography 38 (9):1748-1760. doi:10.1111/j.1365- 2699.2011.02529.x Topp-Jorgensen E, Nielsen MR, Marshall AR, Pedersen U (2009) Relative densities of mammals in response to different levels of bushmeat hunting in the Udzungwa Mountains, Tanzania. Tropical Conservation Science 2 (1):70-87 Trentin L, Rovero F (2011) Results of an inventory of bats in the Uzungwa Scarp Forest Reserve, Tanzania Journal of East African Natural History 100 (Part 1-2):45-57 Vaiman D, Mercier D, Moazami-Goudarzi K, Eggen A, Ciampolini R, Lepingle A, Velmala R, Kaukinen J, Varvio SL, Martin P, Leveziel H, Guerin G (1994) A set of 99 cattle microsatellites: characterization, synteny mapping, and

194 polymorphism. Mammalian Genome 5 (5):288-297. doi:10.1007/bf00389543 Van Oosterhout C, Hutchinson WF, Wills DPM, Shipley P (2004) micro-checker: software for identifying and correcting genotyping errors in microsatellite data. Molecular Ecology Notes 4 (3):535-538. doi:10.1111/j.1471- 8286.2004.00684.x van Vliet N, Kaniowska E, Bourgarel M, Fargeot C, Nasi R (2009) Answering the call! Adapting a traditional hunting practice to monitor duiker populations. African Journal of Ecology 47 (3):393-399. doi:10.1111/j.1365- 2028.2008.00999.x Van Vliet N, Nasi R (2008a) Mammal distribution in a Central African logging concession area. Biodiversity and Conservation 17 (5):1241-1249. doi:10.1007/s10531-007-9300-5 van Vliet N, Nasi R (2008b) Why do models fail to assess properly the sustainability of duiker (Cephalophus spp.) hunting in Central Africa? Oryx 42 (3):392-399. doi:10.1017/s0030605308000288 van Vliet N, Nasi R, Emmons L, Feer F, Mbazza P, Bourgarel M (2007) Evidence for the local depletion of bay duiker Cephalophus dorsalis, within the Ipassa Man and Biosphere Reserve, north-east Gabon. African Journal of Ecology 45 (3):440-443. doi:10.1111/j.1365-2028.2007.00783.x van Vliet N, Zundel S, Miquel C, Taberlet P, Nasi R (2008) Distinguishing dung from blue, red and yellow-backed duikers through noninvasive genetic techniques. African Journal of Ecology 46 (3):411-417. doi:10.1111/j.1365- 2028.2007.00879.x van Vuuren BJ, Robinson TJ (2001) Retrieval of four adaptive lineages in duiker antelope: Evidence from mitochondrial DNA sequences and fluorescence in situ hybridization. Molecular Phylogenetics and Evolution 20 (3):409-425. doi:10.1006/mpev.2001.0962 Vander Wal E, Paquet PC, Andres JA (2012) Influence of landscape and social interactions on transmission of disease in a social cervid. Mol Ecol 21 (5):1271-1282. doi:10.1111/j.1365-294X.2011.05431.x Vekemans X, Hardy OJ (2004) New insights from fine-scale spatial genetic structure analyses in plant populations. Mol Ecol 13 (4):921-935. doi:10.1046/j.1365-294X.2004.02076.x Verhoeven KJF, Simonsen KL, McIntyre LM (2005) Implementing false discovery rate control: increasing your power. Oikos 108: 643-647 von Brandis RG, Reilly BK (2008) Spatial variation in trophy quality of popular hunted ungulate species in South Africa. South African Journal of Wildlife Research 38 (1):17-21. doi:10.3957/0379-4369-38.1.17 Walsh PD, White LJT (1999) What it will take to monitor forest elephant populations. Conserv Biol 13 (5):1194-1202 Waltert M, Heber S, Riedelbauch S, Lien JL, Muhlenberg M (2006) Estimates of blue duiker (Cephalophus monticola) densities from diurnal and nocturnal line transects in the Korup region, south-western Cameroon. African Journal of Ecology 44 (2):290-292. doi:10.1111/j.1365-2028.2006.00631.x Wheeler QD, Meier R (2000) Species concepts and phylogenetic theory. Columbia University Press, New York White LJT, Edwards A (2000) Conservation Research in the African Rain Forests: a Technical Handbook. Wildlife Conservation Society, New York

195 Wilkie DS, Carpenter JF (1999) Bushmeat hunting in the Congo Basin: An assessment of impacts and options for mitigation. . Biodivers Conserv 8:927-955 Willows-Munro S, Robinson TJ, Matthee CA (2005) Utility of nuclear DNA intron markers at lower taxonomic levels: Phylogenetic resolution among nine Tragelaphus spp. Mol Phylogenet Evol 35 (3):624-636. doi:10.1016/j.ympev.2005.01.018 Wilson DE, Mittermeier RA (2011) Hoofed Mammals., vol 2. Handbook of the Mammals of the World. Lynx Edicions, Barcelona Wilson DE, Reeder DM (2005) Mammal species of the World. A taxonomic and geographic reference. Johns Hopkins University Press, Baltimore Wilson VJ (1966) Notes on the food and feeding habits of the common duiker, Sylvicapra grimmia in eastern Zambia. Arnoldia 2 (14):1-19 Wilson Vj (2001) Duikers of Africa: masters of the African forest floor. Chipangali Wildlife Trust, Bulawayo Wronski T (2005) Home-range overlap and spatial organization as indicators for territoriality among male bushbuck (Tragelaphus scriptus). Journal of Zoology 266:227-235. doi:10.1017/s0952636905006825 Wronski T, Apio A (2006) Home-range overlap, social vicinity and agonistic interactions denoting matrilineal organisation in bushbuck, Tragelaphus scriptus. Behavioral Ecology and Sociobiology 59 (6):819-828. doi:10.1007/s00265-005-0128-2 Wronski T, Apio A, Plath M (2006a) Activity patterns of bushbuck (Tragelaphus scriptus) in Queen Elizabeth National Park. Behavioural Processes 73 (3):333-341. doi:10.1016/j.beproc.2006.08.003 Wronski T, Apio A, Plath M, Averbeck C (2009) Do ecotypes of bushbuck differ in grouping patterns? Acta Ethologica 12 (2):71-78. doi:10.1007/s10211-009- 0058-5 Wronski T, Tiedemann R, Apio A, Plath M (2006b) Cover, food, competitors and individual densities within bushbuck Tragelaphus scriptus female clan home ranges. Acta Theriologica 51 (3):319-326. doi:10.1007/bf03192684 Yamashiro A, Yamashiro T, Baba M, Endo A, Kamada M (2010) Species identification based on the faecal DNA samples of the Japanese (Capricornis crispus). Conservation Genetics Resources 2 (S1):409-414. doi:10.1007/s12686-010-9281-7 Zhan XJ, Li M, Zhang ZJ, Goossens B, Chen YP, Wang HJ, Bruford MW, Wei FW (2006) Molecular censusing doubles giant panda population estimate in a key nature reserve. Current Biology 16 (12):R451-R452 Zhu L, Zhang S, Gu X, Wei F (2011) Significant genetic boundaries and spatial dynamics of giant pandas occupying fragmented habitat across southwest China. Mol Ecol 20 (6):1122-1132. doi:10.1111/j.1365-294X.2011.04999.x Zilihona I, Shangali C, Mabula CK, Hamisy C (1998) Human activities threatening the biodiversity of the Uzungwa Scarp Forest Reserve, Tanzania. East African Journal of Natural History 87:319-326 Zinner D, Arnold ML, Roos C (2009) Is the new primate genus rungwecebus a baboon? PLoS One 4 (3):e4859. doi:10.1371/journal.pone.0004859

196 Appendix A: Associated publications with author contributions.

The appended articles have been removed from the public version of this thesis by the author for copyright reasons.

Bowkett, A.E., Plowman, A.B., Stevens, J.R., Davenport, T.R.B. and Jansen van Vuuren, B. (2009) Genetic testing of dung identification for antelope surveys in the Udzungwa Mountains, Tanzania. Conservation Genetics 10: 251‐255 DOI: 10.1007/s10592‐008‐9564‐7

The final publication is available at link.springer.com

Ntie, S., Johnston, A.R., Mickala, P., Bowkett, A.E., Jansen van Vuuren, B., Colyn, M., Telfer, P., Maisels, F., Hymas, O., Rouyer, R.L., Wallace R.A., LeBlanc, K., van Vliet, N., Sonet, G., Verheyen, E., Pires, D., Wickings, E.J., Lahm, S.A. and Anthony, N.M. (2010) A molecular diagnostic for identifying Central African forest artiodactyls from faecal pellets. Animal Conservation 13: 80‐93 DOI: 10.1111/j.1469‐1795.2009.00303.x

The final publication is available at link.springer.com

Jones, T. and Bowkett, A.E. (2012) New populations of an Endangered Tanzanian antelope confirmed using DNA and camera‐traps. Oryx 46: 14‐15 DOI: 10.1017/S003060531100216X Conservation news article

The final publication is available at Cambridge Journals Online journals.cambridge.org