Biotic interactions and diversification in southern African biodiversity hotspots

Jan Schnitzler

A thesis submitted for the degree of Doctor of Philosophy from the Division of Biology, Department of Life Sciences, Imperial College London

September 2009

2

Abstract

The south-western tip of Africa holds unique levels of species richness and , and contains two hotspots of biodiversity: the Cape Floristic Region and the Succulent Karoo. Several hypotheses have been proposed to explain the radiation of the region’s diverse flora. However, due to the lack of comprehensive, comparative studies, the major forces that drive diversification have remained unclear. My thesis combines near-complete species- level molecular phylogenies with detailed biological, ecological and biogeographical information to investigate the evolutionary processes generating 's exceptional plant diversity.

I demonstrate that in the () climatic niches retain a high degree of phylogenetic conservatism, and show that species of Babiana were only able to successfully extend their range into new biomes with the establishment of more favourable climates. Additionally, results indicate that floral characters in Babiana evolve according to a new 'reversible shift' model, which better explains the evolution of systems through multi-directional transitions in a diverse environment. These findings challenge the commonly held idea that floral specialisation is an evolutionary dead-end and offer new perspectives towards our understanding of plant-pollinator interactions.

Analyses of a comprehensive data set of four large Cape clades show that the temporal dynamics of plant radiations in southern Africa confirm that the flora represents a combination of ancient and young radiations, and that diversification rates have remained constant through time. Finally, I reveal that although several biotic and abiotic factors contribute to the diversity, soil-type shifts is the most important driver of plant diversification in southern Africa. Together with complex geomorphological conditions, this factor, rather than pollinator

3 specialisation or phenological divergence, has given rise to the exceptional diversity found in this region today. Comparisons with other biodiversity hotspots, especially those with Mediterranean climates, will reveal whether this is a global scenario for the evolution of hyper-diverse floras.

4

Declaration

I confirm that all work presented in this thesis is my own with the following acknowledgements:

Parts of the data used in chapters 4 and 5 were contributed by colleagues. Martyn Powell provided new sequences for , Peter Goldblatt compiled data on the distribution and traits of Moraea, Steven Boatwright and Tony Rebelo provided parts of the distribution data for and , respectively. The use of any materials from other sources is fully acknowledged throughout this thesis.

Jan Schnitzler London, September 2009

5

Acknowledgements

First and foremost, I would like to thank my supervisors Vincent Savolainen and Timothy Barraclough for their advice, knowledge and inspiration over the past few years.

I am very grateful to Peter Goldblatt and John Manning for their expertise and enthusiasm for this project. Furthermore, I thank Ingrid Nänni, Krytal Tolley and Koos Roux for the provision of material and help during collecting trips.

I am indebted to many colleagues at the Royal Botanic Gardens, Kew for support, expertise in the lab and helpful discussions, in particular Mark Chase, Felix Forest, Laszlo Czsiba and Edith Kapinos. Thanks also to Dion, Rhian, Jimmy, Guillaume and Jon.

A big thanks to the past and present members of the Savolainen lab: Haris, Silvana, Hanno, Andy, Rob, Guillaume, Helen, Matthieu and Richard. Think big! I am particularly grateful to Omar Fiz, Paul Rymer, and Alex Papadopulos for discussions and proofreading. Martyn Powell deserves special mentioning: thanks not only for your help in the lab, but also for introducing me to the peculiar game of cricket.

Special thanks also to Yael Kisel and Lynsey McInnes.

This work was funded by a Marie Curie fellowship of the European Commission, as part of the FP6 Early-Stage Training Network “HOTSPOTS – Understanding and Conserving Earth’s Biodiversity Hotspots” (MEST-CT-2005-020561) and I would like to thank my fellow HOTSPOTS students for an unforgettable time, whether discussing conservation strategies, ectoparasites, or life, the universe and everything.

6

Besonderer Dank gilt meiner Familie. Meiner Schwester Sonja für Unterstützung vor allem für ein spannendes Rennen um den ersten Abschluss der Disseratation. Meinen Eltern danke ich für Ihre beständige Hilfe und Unterstützung. Diese Arbeit ist Euch gewidmet.

7

"If we look to the large size and varied stations of New Zealand, extending over 780 miles of latitude, and compare its flowering , only 750 in number, with those on an equal area at the or in Australia, we must, I think, admit that something quite independently of any difference in physical conditions has caused so great a difference in number." (Darwin, 1859)

"La nature est une source inépuisable de recherches; et, à mesure que le domaine des sciences s'étend, elle présente, à ceux qui savent l'interroger, des faces sous lesquelles on ne l'avoit point encore examinée." (Alexander von Humboldt, 1815)

8

Table of Contents

ABSTRACT...... 2

DECLARATION ...... 4

ACKNOWLEDGEMENTS...... 5

TABLE OF CONTENTS ...... 8

LIST OF FIGURES ...... 12

LIST OF TABLES...... 14

CHAPTER 1: INTRODUCTION...... 15

1.1 Biodiversity Hotspots...... 15

1.2 Plant diversity in southern Africa ...... 17

1.3 Evolution of diversity in the Greater Cape Floristic Region (GFCR) ..20

1.4 Thesis aims and outline ...... 22

CHAPTER 2: PHYLOGEOGRAPHY OF THE GENUS BABIANA (:

IRIDACEAE) INFERRED FROM PLASTID AND NUCLEAR DNA SEQUENCES...... 24

Introduction...... 24

Materials and Methods...... 26 PCR Amplification, Sequencing and Alignment...... 26 Sequence data and analyses ...... 27 Species distributions ...... 30 Results...... 32 Phylogenetic analyses...... 32 Patterns of species diversity and range evolution...... 38 Discussion ...... 45

9

Sequence variability and phylogenetic incongruence...... 45 Systematics of Babiana...... 46 Biogeography...... 48 Conclusions...... 51

CHAPTER 3: THE COMPLEXITY OF PLANT-POLLINATOR COEVOLUTION IN A

BIODIVERSITY HOTSPOT ...... 52

Introduction...... 52

Materials and Methods...... 54 Taxon sampling and phylogenetic analysis ...... 54 Principal Coordinate Analysis (PCoA)...... 54 Analysis of independent contrasts ...... 55 Reconstruction of pollinator shifts and models of evolution...... 56 Results...... 58 Morphological trait analysis ...... 58 Tube length variation within and between pollination syndromes...... 58 Pollinator shifts...... 63 Discussion ...... 69

Conclusions...... 74

CHAPTER 4: TEMPORAL PATTERNS OF PLANT SPECIES DIVERSIFICATION IN

SOUTHERN AFRICAN BIODIVERSITY HOTSPOTS...... 75

Introduction...... 75

Materials and Methods...... 77 Taxon Sampling...... 77 PCR Amplification, Sequencing and Alignment...... 77 Phylogenetic Inference and Divergence Time Estimation ...... 79 Diversification Rates...... 80 Results...... 82 Phylogenetic Analysis and Timing of Divergence ...... 82 Temporal Patterns of Diversification...... 88

10

Discussion ...... 91 Radiation of the Cape flora: Timing and Dynamics...... 91 Conclusions...... 93

CHAPTER 5: REVEALING THE CAUSES OF PLANT DIVERSIFICATION IN A

BIODIVERSITY HOTSPOT: A META-ANALYSIS IN THE CAPE OF SOUTHERN

AFRICA...... 94

Introduction...... 94

Materials and Methods...... 98 Taxon Sampling...... 98 Topographic Complexity and Ancestral Range Reconstruction...... 100 Diversification Rate Shifts...... 100 Modes of Speciation ...... 102 Sister-species comparisons: Jordan Index ...... 102 Results...... 105 Reconstruction of the Centres of Origin...... 105 Analysis of Nodal Imbalances ...... 105 Geographical Patterns of Speciation...... 109 Sister-species analyses...... 113 Discussion ...... 118 Differential rates of Diversification...... 118 Spatial Patterns of Speciation ...... 118 Drivers of Plant Diversification...... 121 Conclusions...... 125

CHAPTER 6: GENERAL CONCLUSIONS ...... 126

REFERENCES...... 130

APPENDIX A: GENBANK/EMBL ACCESSION NUMBERS FOR BABIANA...... 148

11

APPENDIX B: MAXIMUM LIKELIHOOD ESTIMATES OF THE GEOGRAPHIC RANGE

EVOLUTION IN BABIANA ...... 155

APPENDIX C: FLORAL TRAITS FOR SPECIES OF BABIANA ...... 159

APPENDIX D: MINIMUM MODELS OF POLLINATOR SYNDROME EVOLUTION.....164

APPENDIX E: GENBANK/EMBL ACCESSION NUMBERS FOR MORAEA...... 168

APPENDIX F: LIST OF SPECIES TRAITS...... 172

APPENDIX G: TOPOGRAPHIC COMPLEXITY INDEX (TCI)...... 182

APPENDIX H: ANALYSIS OF THE EFFECT OF SPECIES TRAITS ON

DIVERSIFICATION ...... 184

APPENDIX J: DIVERSIFICATION OF THE AFRICAN GENUS PROTEA ()

IN THE CAPE BIODIVERSITY HOTSPOT AND BEYOND: EQUAL RATES IN DIFFERENT

BIOMES...... 186

12

List of Figures

Figure 1.1. The Earth's major terrestrial biomes and distribution of the thirty- four hotspots of biodiversity ...... 16

Figure 2.1. Distribution of Babiana...... 31 Figure 2.2. 50% majority rule consensus of 9260 most parsimonious based on the combined data set obtained after successive weighting.35 Figure 2.3. Maximum likelihood bootstrap consensus tree of the combined plastid and nuclear data...... 36 Figure 2.4. Majority rule consensus tree of the Bayesian analysis of the combined plastid and nuclear data set ...... 37 Figure 2.5. Majority rule consensus tree from the Bayesian analysis of the combined plastid data set...... 40 Figure 2.6. Majority rule consensus tree from the Bayesian analysis of the nuclear (RPB2) sequences...... 41 Figure 2.7. Frequency distribution of range-sizes in Babiana...... 42 Figure 2.8. Patterns of species richness of Babiana...... 43 Figure 2.9. Maximum likelihood estimation of the geographic range evolution in Babiana...... 44

Figure 3.1. Box plot of the variation in tube length between pollination syndromes in Babiana...... 59 Figure 3.2. Scatterplots of the first three axes of the Principal Coordinate Analysis of ten floral traits...... 61 Figure 3.3. Evolution of tube length...... 62 Figure 3.4. Phylogenetic pattern of the evolution of pollination syndromes in Babiana...... 66 Figure 3.5. Phylogenetic pattern of the of pollination syndrome evolution in Babiana according to the nuclear dataset...... 68 Figure 3.6. Evolution of floral symmetry...... 71

13

Figure 4.1. Maximum clade credibility tree of the BEAST analysis for Babiana 83 Figure 4.2. Maximum clade credibility tree of the BEAST analysis for Moraea .85 Figure 4.3. Maximum clade credibility tree of the BEAST analysis for Podalyrieae...... 86 Figure 4.4. Maximum clade credibility tree of the BEAST analysis for Protea...87 Figure 4.5. Lineage-through-time plots for Babiana, Moraea, Podalyrieae, and Protea...... 89

Figure 5.1. Map of WWF Terrestrial Ecoregions of Africa...... 106 Figure 5.2. Variability of species traits...... 107 Figure 5.3. Geographic mode of speciation...... 112 Figure 5.4. Soil-type diversity in southern Africa...... 124

14

List of Tables

Table 1.1. Species diversity of different taxonomic groups in the Cape Floristic Region and the Succulent Karoo...... 19 Table 2.1. Statistics for each of the genetic markers used in the phylogenetic analysis of Babiana...... 34 Table 3.1. Frequency of pollinator shifts...... 65 Table 4.1. Diversification rate shifts...... 90 Table 5.1. Regression between range overlap and node age...... 111

Table 5.2. Jordan Index (JSIS) of pairwise species differences...... 115

Table 5.3. Jordan Index (JSIS) of pairwise species differences for lithology and soil type data...... 116 Table 5.4. Relative variability of species traits:...... 117 Table 5.5. Effect of the spatial resolution of the distribution data on the degree of sympatry...... 120

Chapter 1 15

Chapter 1: Introduction

1.1 Biodiversity Hotspots

The concept of biodiversity hotspots was first introduced by Norman Myers in 1988, when he identified 10 tropical forest regions that combined an exceptionally high degree of plant endemism with considerable levels of loss. The aim of his assessment was to highlight areas that most urgently need attention for the preservation of biological diversity. As part of subsequent revisions (Myers 1990; Mittermeier et al. 1998; Myers et al. 2000; Mittermeier et al. 2004) strict, albeit somewhat arbitrary, criteria were established. In this framework an area must i) contain a minimum of 1,500 endemic plant species, and ii) have lost at least 70% of its original habitat to be recognised as a hotspot of biodiversity.

Currently, thirty-four regions have been identified as biodiversity hotspots (Fig. 1.1; Mittermeier, et al. 2004), covering all major terrestrial biomes (except for high latitude biomes, e.g. boreal forest, arctic tundra, Patagonia), while retaining a strong focus on tropical forests (22 regions). In total, the hotspots are believed to contain about 150,000 plant species restricted to single hotspots (about 50% of the global total) in only 2.3% of the Earth’s surface (3,379,246 km2; Mittermeier, et al. 2004). However, the degree of accuracy of the underlying plant species data varies substantially from being considered accurate within 5% to rough working estimates (Myers, et al. 2000).

Chapter 1 16

Figure 1.1. The Earth's major terrestrial biomes and distribution of the thirty-four hotspots of biodiversity (marked in red).

The identification of hotspots has been solely based on the number of endemic plant species as the measure of biodiversity, but based on the general importance of plant species in supporting other taxonomic groups, plant diversity was considered an adequate indicator of overall biodiversity (Myers, et al. 2000). Data for global species richness and endemism for four vertebrate groups (birds, mammals, reptiles and amphibians) have now been developed, yet were not used to determine the hotspots status of any particular region (Myers, et al. 2000; Mittermeier, et al. 2004). In total, 10,413 species (36% of the global total) of vertebrates are endemic to the hotspots. As the data for the five groups "are sometimes matched" by insects (Myers, et al. 2000), the hotspots concept is assumed also to apply to invertebrates.

Several hotspots have been identified as being congruent between plants and vertebrates, i.e. containing a similar proportion of the global total as endemics, most notably the Tropical Andes, Madagascar, Philippines, Indo-Burma and New- Zealand (Myers, et al. 2000). In contrast, some regions in dry to Mediterranean- type climates (e.g. the Cape Floristic Region, Southwest Australia, and the Mediterranean basin; Myers, et al. 2000) show low cross-taxon congruence, all being rich in endemic plant species but poor in endemic vertebrates. These data indicate some degree of variability in congruence between groups of organisms

Chapter 1 17 and question the validity of relying only on plant species to identify biodiversity hotspots.

The growing availability of taxon-based distribution data has allowed researchers to specifically address the problem of cross-taxon congruence. Various measures of diversity (species richness, endemism, threatened species; Orme et al. 2005; Grenyer et al. 2006) have been used to compare hotspots between plant and vertebrates (Kier et al. 2009) and among different vertebrate groups (Grenyer, et al. 2006; Lamoreux et al. 2006). These studies show that despite a high correlation in overall species richness between different groups (Grenyer, et al. 2006; Lamoreux, et al. 2006; Kier, et al. 2009), spatial congruence between species richness and endemism, and, most importantly, between rare/endemic species of different taxonomic groups is low (Orme, et al. 2005; Grenyer, et al. 2006; Kier, et al. 2009). As expected, the current hotspots of biodiversity do not perform well in capturing multi-taxon diversity (Grenyer, et al. 2006), reflecting the limited suitability of using surrogate taxa in conservation prioritisation.

1.2 Plant diversity in southern Africa

The south-western tip of Africa is characterised by unique levels of species richness and endemism, and contains two hotspots of biodiversity: the Cape Floristic Region (CFR) and the Succulent Karoo (Myers, et al. 2000; Mittermeier, et al. 2004). The Succulent Karoo represents one of only two arid regions included in the Earth's thirty-four biodiversity hotspots, while the CFR is the only hotspot that encompasses an entire floristic kingdom (Mittermeier, et al. 2004).

Containing more than 9,000 plant species within an area of only ± 90,000 km2 (Goldblatt and Manning 2000a; Goldblatt et al. 2005b), the CFR represents one of the most diverse temperate floras worldwide and is substantially richer than other regions with Mediterranean-type climates (Cowling et al. 1996). The flora comprises a unique composition of families that account for the vast majority of

Chapter 1 18 species, with Iridaceae, , , Proteaceae and among the most diverse families (Goldblatt, et al. 2005b), which has led to the recognition of this region as one of the six floristic kingdoms worldwide (Takhtajan 1986). Linder (2003) defined 33 dominant floristic elements that diversified in the CFR as ‘Cape floral clades’, the largest four of these ( s.l., Ixioideae & Nivenioideae, Restionaceae, and Crotalarieae p.p.) alone account for 20% of the species in the CFR.

The high degree of endemism (almost 70%, Goldblatt, et al. 2005b) and the exceptionally large contribution of a few diverse lineages to the flora are features usually associated with islands such as Hawaii, Madagascar or New Zealand (Goldblatt 1997; Linder 2003). Surrounded by environments that are either inhospitable or have highly contrasting climates, the location of the CFR results in a degree of isolation not unlike that of island floras (Linder 2003; Midgley et al. 2005; Mucina and Rutherford 2006). The South Atlantic and Indian Oceans bound the CFR in the west and south, respectively, while the northern limit is marked by a transition towards arid environments. Thus, the main connection of CFR lineages with surrounding regions is towards the east through the Drakensberg mountains (Galley et al. 2007; Valente et al. 2010), which differ sharply in soil composition and climate, shown in a marked shift from winter to summer rainfall regimes (Mucina and Rutherford 2006).

Bordering the CFR in the north, the Succulent Karoo extends along the South African west coast into southern . This region also harbours exceptional numbers of plant species (Driver et al. 2003; Mittermeier, et al. 2004), including 30% of the world’s 10,000 succulents. Close floristic affinities between the CFR and the Succulent Karoo led to proposals to unify them as the Greater Cape Floristic Region (GCFR; Jürgens 1997; Born et al. 2007). The high plant diversity of these two regions is in stark contrast to numbers found for other taxonomic groups, none of which feature similar levels of richness or endemism (Table 1.1). The only exceptions are amphibians and fish, which, although poor in species

Chapter 1 19 richness, have a high proportion of endemics in the CFR (Mittermeier, et al. 2004).

Table 1.1. Species diversity of different taxonomic groups in the Cape Floristic Region and the Succulent Karoo (Mittermeier, et al. 2004).

Cape Floristic Region Succulent Karoo No. of Percent No. of Percent Taxonomic Group species endemic species endemic Plants 9,000 69.0 6,356 39.9 Mammals 90 4.4 74 2.7 Birds 324 1.9 227 0.4 Reptiles 100 22.0 94 16.0 Amphibians 51 31.4 29 3.4 Freshwater Fishes 34 41.2 26 0.0

The origin of the flora has been attributed to climatic changes during the late Miocene and Pliocene, when the climate became increasingly cooler and more arid (Linder 2003), which supposedly triggered a rapid radiation of the major lineages in the GCFR (Levyns 1964; Linder et al. 1992; Sauquet et al. 2009). So far, results from molecular phylogenetic studies have been mixed. While the timing of radiation in some groups matches the establishment of the present-day climatic conditions (Richardson et al. 2001; Klak et al. 2003), much earlier origins were found for other lineages (Linder and Hardy 2004; Linder 2005; Verboom et al. 2009a; Valente, et al. 2010).

Apart from its species diversity, the most striking feature of the GCFR certainly is the remarkable degree of variation in floral characters found in some groups, including, (Oliver 1991), orchids (Johnson et al. 1998), geraniums (Bakker

Chapter 1 20 et al. 2005), and irises (Goldblatt and Manning 2006). These lineages show a high number of distinct pollination systems, often accompanied with little differentiation in vegetative morphology (Linder 2003). Furthermore, most of these pollination systems have been found to be highly specialized, with many taxa relying on a single or very few species for pollination (Goldblatt and Manning 2000b; Johnson and Steiner 2003; Goldblatt and Manning 2006), and it has been suggested that plant-pollinator interactions might play an important role in the diversification of the flora (Johnson 1996, 2006; van der Niet and Johnson 2009).

1.3 Evolution of diversity in the Greater Cape Floristic Region (GFCR)

Several hypotheses have been proposed to explain the radiation of the region’s diverse flora, including topographic and edaphic heterogeneity, pollinator specialisation, long-term climatic stability, adaptation to fire-regimes and short- distance dispersal (Linder 2003, 2005; Barraclough 2006). However, our understanding of the evolution of the Cape flora has until now been hampered by the lack of comprehensive species-level molecular phylogenies (Linder 2003; Barraclough 2006; Hawkins 2006), as many phylogenetic approaches, such as analyses of the temporal patterns of diversification or identifying correlates of speciation require complete taxon sampling.

Early work on potential drivers of plant speciation in the GCFR has focused on putative sister-species pairs based on morphological information (Kurzweil et al. 1991; Linder and Vlok 1991; Goldblatt and Manning 1996; Johnson, et al. 1998), which has been criticised for the lack of a molecular phylogenetic hypothesis to verify the sister-species status. Conclusions from these studies could therefore be misleading, if sister-species pairs are not identified correctly (Hawkins 2006). Although recent years have seen a sharp rise in the number of molecular phylogenies (Verboom et al. 2009b), good taxonomic sampling for large Cape clades (> 70% of taxa included) has only been achieved recently and for a few

Chapter 1 21 selected lineages (Verboom et al. 2003; Bakker, et al. 2005; Bytebier et al. 2007; Boatwright et al. 2008; Hardy et al. 2008; van der Niet and Linder 2008; Valente, et al. 2010).

In addition to thorough taxonomic sampling, detailed ecological, biological and biogeographical data are needed to explore the processes involved in species diversifications, which in many cases are not readily available. Thus, only a few well studied lineages have been used to investigate correlates of plant diversification. For example, Verboom et al. (2003) assessed the role of habitat shifts in the radiation of Ehrharta (Poaceae), Bakker et al. (2005) analysed the biogeographic history and shifts pollination syndromes in Pelargonium (Geraniaceae), and Valente et al. (2010; Appendix J) compared rates of diversification within and outside the CFR in Protea (Proteaceae).

Despite providing important insights into the radiation of these clades, the narrow taxonomic focus and inclusion of a small selection of traits does not allow general conclusions to be drawn about the diversification of the flora as a whole. To be able to assess the relative importance of different traits, comparative studies across a range of biotic and abiotic traits are needed, ideally with a wide taxonomic coverage. Only few studies to date have attempted to evaluate the influence of different drivers of diversification. Van der Niet et al. (2006) found evidence for reinforcement in the evolution of pollination systems, but their identification of sister-species was in part based on morphological data, potentially confounding their findings. Recently, van der Niet and Johnson (2009) used data from several molecular phylogenies to compare the relative importance of different biological and ecological factors. Their analyses however were limited by the lack of pollinator information for many lineages and methodological shortcomings, which preclude direct comparisons of the importance of different factors (see chapter 5).

Chapter 1 22

Thus, the major forces that drive plant diversification in southern Africa have remained unclear, and there is a need for a comprehensive, comparative study that evaluates the role of several biotic and abiotic drivers of plant species diversification. This study aims to close this gap.

1.4 Thesis aims and outline

The aim of this study is to understand the evolutionary processes underlying the exceptional plant diversity in southern Africa by using a combination of phylogenetic, ecological and biogeographical data. Specifically, I will address questions on i) the evolution of present-day biogeographic patterns, ii) the dynamics of plant-pollinator interactions iii) the temporal patterns of plant species radiations, and iv) the major drivers of plant diversification in the GCFR.

First, I will produce a near-complete species-level phylogeny of a species-rich genus from the iris family (Babiana: Iridaceae) using a combination of plastid and nuclear genetic marker and investigate the evolution of geographic ranges using a maximum likelihood approach that employs an explicit model of dispersal, extinction and differential range inheritance (chapter 2). The genus Babiana is near-endemic to the GCFR (species endemism 98%), and analyses of the biogeographic history will provide information on the role of broad-scale processes in the diversification of the Cape flora.

Chapter 3 will focus specifically on the co-evolution between plants and . Using the phylogenetic tree of Babiana (chapter 2), I will investigate whether the evolution of floral tube length is linked to shifts in pollinators, and test for directionality in the evolution of pollination syndromes. Maximum likelihood tests of different models of evolution of pollination syndromes will be used to gain insights into the evolution of specialised plant-pollinator interactions.

Chapter 1 23

In the following chapters (4 and 5), I will perform a meta-analysis on the evolution of plant diversity in the GCFR, with the aim of distinguishing between several key hypotheses on the diversification of the Cape flora. The data set used in this study will, besides Babiana, include several speciose Cape clades from different taxonomic groups, providing a good representation of the flora, which will allow me to apply thorough tests precluded from previous analyses. In chapter 4, I will evaluate the temporal patters of the radiations using dated molecular phylogenies to address the question whether the Cape flora is indeed the result of a rapid and recent radiation. Chapter 5 will consider five commonly discussed factors driving plant diversification. Molecular phylogenetic data will be combined with information on the distribution, predominant edaphic conditions, fire survival strategies, phenology, and pollinators to evaluate their importance in the GCFR. This will be first time that such a detailed comparative analysis of the causes of diversification in two of the major hotspots of plant biodiversity will have been accomplished.

Chapter 2 24

Chapter 2: Phylogeography of the genus Babiana (Crocoideae: Iridaceae) inferred from plastid and nuclear DNA sequences

Introduction

With over 2,030 species of predominantly herbaceous, seasonal geophytes (Goldblatt et al. 2008b), the Iridaceae represent a highly diverse plant family. Distributed almost worldwide in both tropical and temperate regions, Iridaceae favour Mediterranean-type climates and show a marked centre of diversity in southern Africa (> 50% of Iridaceae species; Goldblatt et al. 2006). One of the largest genera within the Iridaceae, Babiana (Ker Gawl.), consists of 92 species, which have radiated extensively in southern Africa (Goldblatt and Manning 2007b; Goldblatt et al. 2008a; Goldblatt and Manning in press). All species are small to medium-sized geophytes, occurring predominantly in the GCFR, with the only two species (B. hypogaea and B. bainesii) widespread throughout the summer-rainfall region of , Namibia, and . Thus, most species of the genus occur within the and Succulent Karoo biomes. The Fynbos biome comprises two main vegetation types characterized by small-leaved, evergreen and highly affected by fires: Fynbos and Renosterveld. Fynbos is dominated by restioids, ericoids, proteoids and geophytes and occurs mainly on nutrient-poor sandy soils, but can also be found on limestone and clay soils derived from shale or granite. Mean annual precipitation (MAP) is about 500mm, falling predominantly during the winter. Under more arid and/or more fertile conditions, Fynbos gives way to Renosterveld vegetation, characterized by communities dominated by (Renosterbos). Further reduction of MAP (usually below 200mm) determines the transition towards Succulent Karoo vegetation (Mucina and Rutherford 2006).

Chapter 2 25

First described by J. Ker Gawler (1802), the genus comprised 61 species after the first major revision by South African botanist G. L. Lewis (1959). Following several years of field work, Goldblatt and Manning (2007b) provided a detailed revision, including many newly described species as well as nomenclatural changes since Lewis' work. Their account listed 88 species, which were divided into three sections (Teretifolieae, Antholyzoides and Babiana).

Phylogenetic studies at the family level (Reeves et al. 2001; Goldblatt, et al. 2006; Goldblatt, et al. 2008b) have provided important insights into the relationships with other genera, including the recognition of Cyanixia as a separate genus (Goldblatt et al. 2004), which was formerly included in Babiana, but to date no comprehensive molecular phylogeny exists for this diverse genus. Here, we employ a combination of plastid and nuclear DNA sequence data to resolve the phylogenetic relationships and investigate the biogeographic history of this large genus, in order to gain insights into the origin of the southern African biodiversity hotspots.

Chapter 2 26

Materials and Methods

PCR Amplification, Sequencing and Alignment

Here, we present a near complete species-level phylogeny including 87 (out of 92 total) species of Babiana. Species were identified in the field based on a recent taxonomic revision (Goldblatt and Manning 2007b) and afterwards compared to available collections. Total genomic DNA was extracted from silica dried material using the 2X CTAB method (Doyle and Doyle 1987) and purified by caesium-chloride/ethidium-bromide density gradient (1.55 g/ml; Csiba and Powell 2006). Purified total DNA was dialyzed in 1x TE buffer and stored at - 80°C. New sequence data for Babiana were produced for nine plastid markers (four coding regions: matK, rbcL, rpoC1 and ndhF, three introns: rps16, rpl16 and trnL-F (including the trnL-trnF intergenic spacer) as well as two intergenic spacers: rpl32-trnL and 3'trnV-ndhC) and one low-copy nuclear gene (RPB2). All PCR amplifications in Babiana were carried out 20 µl reactions, containing 4 µl of Reaction Buffer (160 mM (NH4)2SO4; 670 mM Tris-HCl (pH 8.3); 0.1%

Tween-20), 2 µl of 25 mM MgCl2, 0.005% BSA, 0.2 M D-(+)-Trehalose (Sigma T-5251, Sigma-Aldrich, St. Louis, MO, USA), 0.4 µl of 10 mM dNTPs (Bioline Ltd., London, UK), 0.4 µl of 5 u/µl GoTaq DNA polymerase (Promega, Madison, WI, USA), 0.5-1.0 µl of each 100 mM primer and 1-2 µl of genomic DNA. Amplifications were performed using a Perkin-Elmer GeneAmp 9700 Thermal Cycler (Applied Biosystems, Foster City, CA, USA) with 2 min initial denaturation at 94°C followed by 30-38 cycles with 1 min denaturation at 94°C, 1 min annealing at 48-52°C (depending on the primers used), 1:30 min elongation at 72°C and a final 3-5 min elongation at 72°C. Primer pairs used were X-f (TAATTTACGATCAATTCATTC) and 5-r (GTTCTAGCACAAGAAAGTCG) for matK, 1-f and 4-r for rpoC1 (www.kew.org/barcoding), 972-f and 2110-r for ndhF (Olmstead and Sweere 1994), 1-f and 2-r for rps16 (Oxelman et al. 1997), 71-f and 1661-r for rpl16 (Jordan et al. 1996), “c” and “f” for trnL-F (Taberlet et al. 1991; Shaw et al. 2007), and rpL32-f and trnL(UAG)-r for rpl32-trnL (Shaw,

Chapter 2 27 et al. 2007). The rbcL gene was amplified using two primer pairs: 1-f and 724(m)- r as well as 636-f and 1367-r (Olmstead et al. 1992; Muasya et al. 1998). For RPB2 primers INT23-f and INT23-r (Norup et al. 2006) were used for initial amplification. To increase the quantity of the PCR-product and prevent amplification of paraloguous loci, additional primers IRID-f (GCACATATGGGGAAAGAAGG) and IRID-r (TTATCCACCTGAGATGATTGC) were designed. Prior to sequencing, amplified products were cleaned using NucleoSpin Extract II isolation kit (Macherey-Nagel GmbH, Düren, Germany) or QIAquick (Qiagen, Crawley, West Sussex, UK) and the resulting DNA concentration was measured by photospectrometry. Cycle sequencing (26 cycles; 10 s denaturation at 96°C, 5 s annealing at 50°C, 4 min extension at 60°C) with BigDye Terminators (v3.1; Applied Biosystems, Foster City, CA, USA) was performed in 10 µl volumes. Products were purified with 90% ethanol using a Biomek NX Span-8 automated workstation (Beckman Coulter, Fullerton, CA, USA) and re-suspended in water for sequencing on an automated ABI 3730 DNA Analyzer (Applied Biosystems, Foster City, CA, USA) following manufacturers protocols. Sequences were edited using Sequencher 4.8 (Gene Codes Corp. 2007, Ann Arbor, MI, USA) and aligned by eye in PAUP* v.4.0b10 (Swofford 2002). The alignment is available online at www.hotspots-e-atlas.eu/wp-content/uploads/2010/07/Babiana.nex. Alternative alignments were produced using sequence alignment algorithms implemented in MUSCLE (Edgar 2004) and MAFFT (Katoh et al. 2002; Katoh and Toh 2008). Voucher information and GenBank/EMBL accession numbers are provided in Appendix A.

Sequence data and analyses

The phylogenetic utility of the nuclear gene RPB2 has previously been explored in broad angiosperm and plant studies (Denton et al. 1998; Nickerson and Drouin 2003; Oxelman et al. 2004; Sun et al. 2009). Several studies (Larkin and Guilfoyle 1993; Warrilow and Symons 1996; Denton, et al. 1998; Oxelman, et al.

Chapter 2 28

2004) inferred the presence of a single RPB2 orthologue, but Oxelman et al. (2004) found indications for two copies in some plant grous and Norup et al. (2006) detected electropherograms which suggested the presence of multiple copies of the target sequence in four percent of their samples. While initial PCR reactions using the primers of Norup et al. (2006) amplified products of varying length for a few species, the newly designed primers allowed us to sequence all PCR products directly. In about 8% of our species we detected between one and four double-peaks in the electropherograms, which could be due to either noise or single-nucleotide polymorphisms, indicating allelic variation. All positions were coded as ambiguous. Insertions/deletions (indels) were coded as present/absent following the "simple indel coding" method of Simmons and Ochoterena (2000), implemented in SeqState (Müller 2005). Phylogenetic analyses were performed using parsimony, maximum likelihood and Bayesian approaches for the combined ten-gene data set as well as separately for the combined plastid regions and the nuclear marker (RPB2). Congruence among the plastid and nuclear datasets was assesed using the incongruence length difference (ILD) test (Farris et al. 1995) implemented in PAUP*. After the exclusion of uninformative characters (Cunningham 1997; Lee 2001), we performed 100 randomisations using a heuristic search with 100 random additions, using TBR branch swapping, holding ten trees each step, and saving no more than ten trees.

Maximum parsimony analyses were performed using the heuristic search option as implemented in PAUP* (v. 4.0b10; Swofford 2002). Initial searches were carried out using 1,000 replicates of random taxon addition with tree bisection and reconnection (TBR) branch swapping and equal character weights, retaining a maximum of 10 trees per replicate to reduce time spent on swapping large numbers of suboptimal trees. The resulting trees were used as starting trees in a second search using the same parameters as above with a limit of 10,000 trees, which were then used to reweight the characters according to the rescaled consistency index (RC). This successive weighting approach was implemented to reduce the influence of highly homoplasious characters (Farris 1969; Bakker et al.

Chapter 2 29

2000). Successive searches as described above were performed using the reweighted matrix until tree lengths reached stationarity. For each data set, we performed 1,000 bootstrap replicates using equal character weights and the TBR swapping algorithm, again keeping only 10 trees at each step.

Maximum likelihood analyses were performed in RAxML (v. 7.2.1; Stamatakis 2006) using the BINGAMMA function, with the alignment divided into partitions according to gene regions. This process implements the GTR+Γ model for each gene with individual estimation and optimisation of model parameters and a discrete morphological model as proposed by Lewis (2001) for the indels, which is comparable to the Jukes-Cantor model of nucleotide substitution. We performed 1,000 rapid bootstrap searches (Stamatakis et al. 2008) followed by a thorough ML search on the original alignment. To assess the potential impact of alternative alignments, maximum likelihood analyses were repeated for each alignment and the resulting trees compared visually.

Finally, Bayesian phylogenetic inference was performed using MrBayes (v.3.2.1; Huelsenbeck and Ronquist 2001). The best-fit models of nucleotide evolution were implemented according to the Akaike Information Criterion (AIC) scores for substitution models evaluated using MrModeltest (v.2.3; Nylander 2004). For binary traits, MrBayes implements an F81-like model, where transition rates are determined by the state frequencies. Four independent runs with six chains each were run for 20,000,000 generations, sampling the Markov chain every 5000 generations. After removal of the first 2,000,000 generations as burn-in, all runs were combined to build the consensus tree.

Some species were represented by only a subset of the markers sequenced in this study (see Appendix 1), which might affect the resolution of the phylogenetic tree. In an Adams consensus tree (Adams III 1972), unstable taxa (which occur in varying positions in different trees) are placed at the base of the smallest clade that always includes it. Thus, to determine the effect of missing data, we built an

Chapter 2 30

Adams consensus tree of the posterior distribution of trees from the combined Bayesian analysis.

Species distributions

Distribution data for all species were taken from the literature (Goldblatt and Manning 2007b; Goldblatt, et al. 2008a; Goldblatt and Manning in press) and accessions from several herbaria (BOL, K, NBG, PRE, SAM, and WIND), recoded as presence/absence data in grid cells with an edge length of a quarter degree (quarter degree square – QDS).

The evolution of geographic ranges was inferred using a maximum likelihood approach using a dispersal-extinction-cladogenesis (DEC) model implemented in the Python package 'lagrange' (v. 2.0.1; Ree et al. 2005; Ree and Smith 2008). The DEC model consists of a stochastic process of dispersal (range extension) and local extinction (range contraction) along branches of the phylogenetic tree combined with different range-inheritance scenarios at speciation events to calculate the likelihood of the observed range distribution on the tips of the phylogenetic tree. Modes of range-inheritance assume that i) one of the descendant lineages occupies a single area and ii) the other descendant inherits the remaining areas (divergence between areas) or maintains the entire ancestral range (divergence within an area). We used terrestrial biomes following Mucina and Rutherford (2006) for South Africa, and Swaziland and Olson et al. (2001) for areas outside of South Africa as biogeographic units for the range reconstructions (Fig. 2.1) with dispersal events being restricted to adjacent areas. Relative divergence times were estimated on the Bayesian consensus tree using a penalized likelihood approach (Sanderson 2002) as implemented in the ape package (Paradis et al. 2004). Following cross-validation (Paradis 2006), the smoothing parameter (λ) was set to 1.

Chapter 2 31

Figure 2.1. Distribution of Babiana. The map shows the biogeographic regions of southern Africa where species of Babiana occur. Points represent collection localities (scaled to QDS), the area bordered in red marks the extend of the Greater Cape Floristic Region (GCFR).

Chapter 2 32

Results

Phylogenetic analyses

The combined sequence matrix contains 11,350 base pairs (bp). Regions of uncertain alignment at the ends of sequences as well as extended mono- or dinucleotide repeats were excluded, resulting in an alignment of 10,669 bp that were included in the analyses (matK: 1,829 bp, ndhF: 2,016 bp, rbcL: 1,346 bp, rpl16: 1,025 bp, rpl32-trnL: 912 bp, ropC1: 585 bp, rps16: 910 bp, trnL-F: 726 bp, trnV-ndhC: 672 bp, RPB2: 648 bp). Of these, 422 sites were variable (4.0 %) with 145 being parsimony-informative (Table 2.1). Inclusion of indels increased sequence variability to 475 variable and 163 parsimony-informative sites, respectively. Alternative alignments obtained from MAFFT and MUSCLE resulted only in minor differences. Overall variation in alignment length was less than 0.5% of the total length of the manual alignment (MAFFT: 10631bp; MUSCLE: 10682bp). The partition homogeneity test suggests incongruence between the plastid and nuclear partition (p < 0.05), indicating that the datasets should be analysed separately. However, several studies have demonstrated that the ILD test is susceptible to Type-I Error (falsely rejecting the null hypothesis of congruence) (Darlu and Lecointre 2002) and is a generally poor indicator for combinability of datasets (Barker and Lutzoni 2002). This is particularly true when datasets datasets differ markedly in size (Dowton and Austin 2002). In addition, Yoder et al. (2001) showed that the combination of incongruent datasets can significantly increase the accuracy of the phylogenetic hypothesis. Given the serious concerns raised against the validity of the ILD test as a measure of phylogenetic congruence, we decided to combine the plastid and nuclear datasets, nevertheless discussing topological incongruences between the trees obtained from the separate analysis of the two datasets.

Parsimony analysis on the combined dataset with successive weighting resulted in 9,260 equally parsimonious trees of 1,102.127 steps (CI = 0.539,

Chapter 2 33 excluding uninformative characters; RI = 0.838, Fig. 2.2) with high bootstrap support (>90% BS) for several basal nodes. In addition, several sister-species pairs received moderate support (>70% BS), whereas large parts of the phylogenetic tree remained unresolved. The maximum likelihood analysis provided a similar topology and levels of nodal support (Fig. 2.3), while the Bayesian analysis resolved additional nodes with high posterior probability (>0.95 pp) in the basal clade (Fig. 2.4). Given the topological similarity found between different methods, the Bayesian trees were used for further analyses and the discussion of the phylogenetic relationships. Phylogenetic trees obtained from maximum likelihood analyses of the alternative alignments resulted in similar topologies, but number of moderately to well supported nodes (>70% BS) decreased compared to the manual alignment. The Adams consensus tree, despite being sightly less resolved, showed high similarity with the 50% majority rule consensus tree obtained in the parsimony analysis. Of the species with a high degree of missing data, only B. toximontana was placed on a basal branch within its clade, whereas other species were unaffected. For example, the position of B. avicularis as sister to B. ringens remained unchanged, although only about 30% of the total bp sequenced were available. On the other hand, even some species without missing data (e.g. B. carminea, B. cinnamomea) appear in basal positions within their clade in the Adams consensus tree, suggesting a somewhat uncertain position of these taxa.

Chapter 2 Table 2.1. Statistics for each of the genetic markers used in the phylogenetic analysis of Babiana. Selection of substitution models was based on AIC values provided by MrModeltest (Nylander 2004), indels were coded according to Simmons and Ochoterena (2000). Values given are for the ingroup (Babiana) only.

rpl32- rps16 trnV- matK ndhF rbcL rpl16 rpoC1 trnL-F RPB2 Total tnrL intron ndhC

Taxonomic coverage of each 88 86 85 79 83 85 83 81 89 89 - marker * (96%) (93%) (92%) (86%) (90%) (92%) (90%) (88%) (97%) (97%)

Model of nucleotide substitution GTR+ Γ GTR+I GTR+I GTR+ Γ GTR+ Γ GTR+ Γ GTR+ Γ GTR+ Γ GTR+ Γ HKY+ Γ -

Number of characters

Total included (excl. indels) 1829 2016 1346 1025 912 585 910 726 672 648 10669

No. of Indels 8 7 2 11 19 0 25 12 6 18 108 Variable characters (excl. indels) 69 66 16 42 36 11 26 25 27 104 422 Percent variable characters 3.8% 3.3% 1.2% 4.1% 3.9% 1.9% 2.9% 3.4% 4.0% 16.0% 4.0% Variable characters (total) 71 73 16 51 42 12 36 31 31 113 475 Parsimony informative sites (excl. 17 24 6 14 16 1 8 4 11 44 145 indels) Parsimony informative sites 17 24 6 17 18 1 11 6 13 50 163 (total)

* Numbers include two of B. nana and B. mucronata, and two populations of B. melanops. 34

Chapter 2 35

Figure 2.2. 50% majority rule consensus tree of 9260 most parsimonious trees based on the combined data set obtained after successive weighting. Values above nodes indicate group frequencies, numbers in bold italic below nodes are bootstrap support values (>50%).

Chapter 2 36

Figure 2.3. Maximum likelihood bootstrap consensus tree of the combined plastid and nuclear data. Bootstrap values >50% are indicated at nodes.

Chapter 2 37

Figure 2.4. Majority rule consensus tree of the Bayesian analysis of the combined plastid and nuclear data set. Bayesian posterior probabilities >0.5 are shown at nodes. Brackets indicate strongly supported monophyletic clades identified in this study. The infrageneric classification as identified by Goldblatt and Manning is indicated next to species names (T: Teretifolieae, A: Antholyzoides, B: Babiana).

Chapter 2 38

The combined data set confirms the monophyly of Babiana, which includes three strongly supported clades (A – C, Fig. 2.4) with B. lobata as sister to the rest of the genus. Comparisons of the tree topologies from separate analyses of the plastid and nuclear datasets (Fig. 2.5 & 2.6) revealed two hard incongruences (>0.9 pp). Firstly, in the plastid trees, B. spathacea is related to B. virginea, B. praemorsa and B. cuneata (clade B), but associated with B. patersoniae and B. nana (clade C) according to the nuclear data set. Secondly, the sister-species relationship of B. lobata and B. tritonioides found in the analysis of the nuclear marker is not matched in the plastid data set, where B. lobata falls as sister to the rest of the genus.

Patterns of species diversity and range evolution

Species of Babiana have predominantly narrow ranges with a median range size of five QDS grid cells (Fig. 2.7), with 85% being endemic to a single biome. Species diversity is generally highest in the south-western part of the GCFR and peaks in two marked centres of species richness (Fig. 2.8): On the Bokkeveld Plateau around Nieuwoudtville (15 spp), and in the Cape lowlands north-east of Cape Town (13 spp). The Bokkeveld centre includes a mixture of Succulent Karoo and Fynbos vegetation, while the dominant vegetation types in the Cape centre are lowland Fynbos and Renosterveld (Mucina and Rutherford 2006).

The reconstruction of range evolution patterns revealed a strong ecological phylogenetic structure (Fig. 2.9). The clade A is dominated by lineages from the Succulent Karoo biome, while species of the most derived clade (C) are found mainly in the Fynbos. Species ranges of lineages in clade B include the Succulent Karoo and, in some cases adjacent biomes (Fynbos, Nama-Karoo; Fig. 2.9). Dispersal events (range extensions) were found to be predominantly from the Succulent Karoo into the Fynbos (8), the Nama-Karoo (1) or into the biomes outside the GCFR (2), while dispersals from the Fynbos into the Succulent Karoo occurred only three times. The majority of these events were reconstructed on

Chapter 2 39 terminal branches, with only three shifts from the Succulent Karoo into the Fynbos reconstructed at internal nodes (Fig. 2.9): One dispersal occurred along the branch leading to the B. tubulosa – B. teretifolia clade, followed by local extinction, which resulted in the Succulent Karoo endemics B. torta and B. tritonioides being nested within a Fynbos clade. The second dispersal from the Succulent Karoo into the Fynbos was reconstructed along the branch subtending clade C, followed immediately by a range-split and re-dispersal into the Fynbos after the next speciation event (Fig. 2.9). After several divergences within the Succulent Karoo and Fynbos (i.e. one descendant inherits the entire range), two between-area divergences lead to splits of the ancestral range at basal nodes of the B. tanquana – B. fourcadei and B. blanda – B. scabrifolia clade, respectively, which resulted in single area occupancies including a Fynbos endemic ancestor of the B. secunda – B. melanops clade (Fig. 2.9).

Chapter 2 40

Figure 2.5. Majority rule consensus tree from the Bayesian analysis of the combined plastid data set. Values at nodes indicate Bayesian posterior probabilities.

Chapter 2 41

Figure 2.6. Majority rule consensus tree from the Bayesian analysis of the nuclear (RPB2) sequences. Values at nodes indicate Bayesian posterior probabilities.

Chapter 2 42

Figure 2.7. Frequency distribution of range-sizes in Babiana; measured as number of QDS grid cells occupied (median range size = 5 QDS grid cells).

Chapter 2 43

Figure 2.8. Patterns of species richness of Babiana. Number of species per QDS grid cell.

Chapter 2 44

Figure 2.9. Maximum likelihood estimation of the geographic range evolution in Babiana. Labels at nodes indicate changes in the ancestral range. For clarity, labels were omitted if ranges remain unchanged and for dispersal events that were inferred along terminal branches. Labels correspond to biogeographic regions as shown in Figure 2.1: Succulent Karoo (S), Fynbos (F), Nama-Karoo (N), Desert (D), Montane (M) , and Savanna (SV).

Chapter 2 45

Discussion

Sequence variability and phylogenetic incongruence

Similar to other studies of major Cape clades (Klak, et al. 2003; Valente, et al. 2010; Valente pers. comm.), sequence divergence in Babiana was found to be low. This pattern is commonly associated with rapid radiations where molecular evolution does not match the rapid divergence of morphological characters (Hawkins 2006). However, a comparison with another large genus of Iridaceae (Moraea) from the GCFR (Goldblatt et al. 2002) shows that despite being of similar age (chapter 4), molecular evolution of plastid regions in Moraea is nearly an order of magnitude higher than in Babiana. Thus, the low level of molecular evolution seems to be more pronounced in Babiana and cannot be explained solely by a recent origin of the group. Furthermore, both genera consist of perennial geophytes with similar life histories, thus we do not consider the different rates of molecular evolution to be a direct result of differences in life history. Even though the inclusion of a low-copy nuclear marker (RPB2) significantly increased the number of parsimony-informative sites (providing 44 out of 145 in total), overall resolution of the phylogenetic tree remained low.

In concordance with previous empirical (Sanderson et al. 2003; de la Torre- Bárcena et al. 2009) and simulation studies (Wiens 2003; Philippe et al. 2004), the amount of missing data seems to have little effect on the overall resolution of the phylogenetic inference. Most importantly, for species of Babiana with a high degree of missing data, sequences from at least three of the four most informative markers (which together provide 69% of the parsimony informative characters) could be obtained, thus still containing a high proportion of informative characters.Together with the finding that even some represented species were found to be unstable, we assume that the low overall amount of informative sites is responsible for the low resolution and inconsistent position of some species.

Chapter 2 46

We found few strongly-supported incongruences between the plastid and nuclear markers. Tree discordance can be caused by various processes, which are often hard to distinguish. For example, lineage sorting can be problematic in cases of rapid diversifications (Wendel and Doyle 1998), where the time for alleles within a lineage to coalesce might be greater than the interval between speciation events (Page and Holmes 1998). Thus, short branch length can lead to an increased persistence of different gene genealogies and potentially might result in coalescences between lineages that are not from the most closely related species, causing discordance between gene trees and the species tree (Degnan and Rosenberg 2009). While lineage sorting is likely to have an impact in the case of Babiana, where low sequence variation results in 'shallow' trees, other processes like hybridisation and introgression need to be taken into consideration as potential causes of the hard incongruences. Both cases (B. spathacea and B. lobata) will be discussed in the following section.

Systematics of Babiana

All analyses strongly support the monophyly of Babiana, but the current infrageneric classification proposed by Goldblatt and Manning (2007b), who defined three sections mainly based on floral characters (, ovary), is not supported by the molecular data. None of the sections defined based on morphological characters forms a monophyletic group (Fig. 2.4). It is noteworthy however, that, with the exception of B. sinuata, species of sections Antholyzoides and Babiana fall into separate clades (clade A, and clades B and C, respectively). Clade A consists of 30 species from sections Teretifolieae and Antholyzoides that are found predominantly in the Succulent Karoo, but also includes both widespread species (B. hypogaea and B. bainesii; Fig. 2.9). The small clade B contains only four species, three from section Teretifolieae (B. cuneata, B. praemorsa, and B. virginea) and B. spathacea, which was assigned to section Babiana. Range overlap between these species would suggest introgression as a potential cause of this association. One scenario could involve chloroplast capture

Chapter 2 47 in B. spathacea, the only species of this clade belonging to section Babiana, being more closely related to B. patersoniae and B. nana according to the nuclear dataset. The distribution of this clade extends southwards and eastwards from the Bokkeveld Plateau and most species can be found in more than one biome (Fig. 2.9). Lastly, clade C comprises 52 species mainly from sections Babiana and Teretifolieae, but also B. sinuata, which belongs to section Antholyzoides. Species within this clade occur predominantly in the Fynbos biome.

The position of B. lobata as sister to the rest of the genus is somewhat surprising. Due to high morphological similarities, the species was suggested to be closely related to B. tritonioides (Goldblatt and Manning 2007b), an association that is well supported by the nuclear data set. The basally divergent position of B. lobata in the plastid tree (Fig. 2.6) would suggest potential intergeneric hybridisation or a hybridisation event with one of the species of Babiana not included in this study, two of which (B. gariepensis and B. lapeirousioides) co-occur with B. lobata in the Richtersveld region of northern Namaqualand (Goldblatt and Manning 2007b). However, too little is known about these species from the limited herbarium material to make inferences about their potential phylogenetic position, and the morphology does not seem in any way unusual and potentially indicative of an isolated phylogenetic position (Goldblatt and Manning 2007b). Interestingly, the two subspecies of B. mucronata as well as the two forms of B. melanops were not recognized as sister-taxa in this analysis. In the case of B. mucronata, the phylogenetic position of both subspecies is not strongly supported, and further data would be needed to clarify the relationships of these two taxa. B. melanops, which co-occurs with B. villosa in the Tulbagh valley, on the other hand is one of the few cases where potential hybrids have been found in the wild (Goldblatt and Manning 2007b), which could explain the association of these two species (Fig. 2.4). Further examples of topological incongruence between the nuclear and plastid dataset include B. virescens, B. minuta, and B. carminea. Low support for the position of these species in either the plastid or nuclear tree make inferences about the causes of the different positions difficult. Thus, it would

Chapter 2 48 clearly be desirable to include additional data as well as further populations from species with contradicting positions to obtain more robust phylogenetic hypotheses for both the plastid and nuclear dataset and to resolve the relationships within this genus.

Biogeography

The distribution of Babiana in southern Africa follows a common pattern of many narrowly distributed species with only very wide-ranging species found in many different groups of organisms and across various spatial scales (Gaston 1996). Possible causes of these patterns include ecological as well as evolutionary factors (habitat availability, dispersal, competition, historical constraints). Additionally, species ranges could in part also reflect a sampling artefact, but the good overall knowledge of the Cape flora, a result of long history of botanical research (Linder 2003), suggests that sampling intensity will have a negligible effect.

The Cape lowland centre of species richness is part of the CFR, which represents a global centre of plant diversity (Küper et al. 2004; Mutke and Barthlott 2005; Kreft and Jetz 2007). Species diversity in this region is driven by a combination of high topographic and edaphic heterogeneity (Mucina and Rutherford 2006), providing a small-scale mosaic of different . This pattern reflects the restriction of many plant species in the GCFR (including Babiana) to specific soil types (Chapter 5; Linder 2003). The second centre lies near the edge of the Bokkeveld escarpment, which features a rapid transition from Fynbos to Succulent Karoo. As a result of an altitudinal difference of about 600 m between the coastal lowlands and the top of the escarpment, a steep climatic gradient combined with a small-scale succession of geological formations drives high species turnover over small spatial scales. This area forms part of the Roggeveld- Hantam Centre of Endemism (van Wyk and Smith 2001), which contains the richest geophytic flora of the Cape (Mucina and Rutherford 2006). Thus, both

Chapter 2 49 centres of species richness of Babiana are likely to be the result of a combination of climatic and geological factors.

The inference of range evolution patterns in Babiana revealed a striking ecological phylogenetic structure with the two major clades (A and C) being dominated by species from different biomes (Succulent Karoo and Fynbos, respectively). This pattern suggests an initial diversification within the Succulent Karoo with a subsequent dispersal into the Fynbos followed by a second radiation. Bayesian dating of the phylogeny (chapter 4) suggests that the first establishment of Babiana in the Fynbos occurred about 4.8 mya (2.3 – 6 mya 95% HPD), which coincides with the onset of aridification and increased seasonality in the Cape (Linder 2003). The climatic deterioration in the region started in the mid-Miocene (8 – 10 mya) and by the end of the Pliocene (2.6 mya) had led to the establishment of the current climatic conditions (Cowling et al. 2009). Thus, it seems likely that the establishment of Babiana in the Fynbos reflects novel opportunities for dispersal into an environment becoming increasingly favourable for geophytes.

It should be noted that the assumptions underlying the range inheritance scenarios of the DEC model could under some circumstances have a strong effect on the range reconstructions, especially if species ranges are widespread. Assuming that at speciation events one of the descendant lineages inherits a single geographic area leads to several alternative reconstructions within two log-likelihood units in cases where species ranges include two or more biomes (Nodes N107, N97, N85 and N130 in Appendix B). In one case (N97), this leads to the reconstruction of a local extinction in the Fynbos in one lineage, followed by an immediate re- dispersal. An alternative scenario where both descendants inherit widespread ranges is not possible under the DEC model. The DEC model was developed primarily on the basis of cross-continental disjunctions and island systems (Ree, et al. 2005; Ree and Smith 2008), where different regions are well separated and, in these cases, has been shown to provide reconstructions which were in accordance with independent evidence (Clark et al. 2008). However, if boundaries between

Chapter 2 50 areas are transitional and species ranges can occupy the interface between biogeographic regions, the performance of the DEC model might be limited. These limitations are however unlikely to have an impact on the results of this study, as the majority of species (85%) in Babiana are single biome endemics.

Chapter 2 51

Conclusions

We presented the first comprehensive analysis of the phylogeny and biogeography of the genus Babiana. Given the low level of sequence divergence among species, the addition of more variable markers and/or population genetics techniques (e.g. AFLP, microsatellites; see Valente, et al. 2010) could further improve our understanding of the phylogenetic relationships within Babiana. In the absence of such additional information, it would be preferable to be able to reconstruct the evolution of geographic ranges using samples from the posterior distribution of trees from a Bayesian analysis to account for phylogenetic uncertainty, which currently however is not implemented in 'lagrange'.

The strong ecological phylogenetic structure shows that species' climatic niches retain a high degree of phylogenetic conservatism. This is reflected by a low frequency of shifts between biomes, which are distinguished largely based on climatic parameters (Mucina and Rutherford 2006). This is in concordance with recent findings of large-scale biome conservatism in vascular plants (Schrire et al. 2005; Crisp et al. 2009), and suggests that species of Babiana were probably only able to successfully extend their range into the Fynbos with the establishment of drier and cooler climates. The diversification within each biome on the other hand is driven mainly through adaptations to different soil types and/or pollinators (chapters 4 & 5).

Chapter 3 52

Chapter 3: The complexity of plant-pollinator coevolution in a biodiversity hotspot

Adapted from a manuscript submitted to Evolution ("The complexity of plant-pollinator coevolution in a biodiversity hotspot", authors J. Schnitzler, R. J. Dyer, T. G. Barraclough and V. Savolainen).

Introduction

Hypotheses on the underlying processes behind plant-pollinator coevolution date back to Charles Darwin (1862), who suggested that the extremely long spur of the Malagasy star orchid Angraecum sesquipedale was the result of reciprocal selection between the plant and its proposed pollinator. According to his hypothesis, natural selection favors pollinators with longer probosces, as it would allow them to obtain a larger amount of nectar, while at the same time longer spurs in plants are favored as these will ensure optimal contact of the pollinator with the plant's reproductive organs. Wasserthal (1997), working on the same species, on the other hand proposed the 'pollinator shift' hypothesis. He suggested that pollinator proboscis lengths had evolved for predator avoidance (Wasserthal 1998), and thus represent predefined optima for the evolution of nectar spurs. In cases where the tongue of floral visitors exceeds the length of the nectar spur, nectar rewards might be exploited without providing effective pollen transfer. Selective pressure imposed by the new visitor would then lead to a lengthening of the nectar spur, eventually resulting in a pollinator shift (Wasserthal 1997). Despite slight differences between Darwin's view and the pollinator shift model, both processes would lead to a continued elongation of nectar spurs, constrained by structural limitations and presence of adequate pollinators, respectively.

Strong support for directional elaboration of nectar spurs was recently provided by Whittall and Hodges (2007) in a study on North American columbines

Chapter 3 53

(Aquilegia: Ranunculaceae). They demonstrated that the evolution of nectar spur length follows a predictable pathway in a series of shifts between pollinators with increasing tongue length, as predicted by the pollinator shift model. Reversals in pollination syndromes (i.e. a shortening of the nectar spur to adapt to shorter- tongued pollinators) on the other hand are regarded as highly unlikely, as pollinators would not visit where the nectar reward provided is out of reach (Grant and Temeles 1992). Indeed, only few studies to date have found reversals in pollination syndromes (Tripp and Manos 2008, and references therein), and, apart from few exceptions (e.g. Armbruster and Baldwin 1998), these received little attention or were considered "unorthodox" (Goldblatt and Manning 1996; Perret et al. 2003; Tripp and Manos 2008). Owing to the functional similarity, the mechanisms described for nectar spurs in orchids (Darwin 1862; Wasserthal 1997) and columbines (Whittall and Hodges 2007) also apply to floral tubes, where tube lengths should match the pollinator's proboscis length, thereby restricting access to nectar which accumulates at the base of the floral tube to selected pollinators.

Here, we use a species-rich genus of irises (Babiana: Iridaceae), one of the largest plant families in the Cape flora (Goldblatt, et al. 2005b), to test predictions of Darwin's coevolutionary hypothesis and the pollinator shift model. The genus displays a wide range of specialized pollination syndromes associated with a 10- fold variation in tube length (Goldblatt and Manning 2007a), and thus provides an ideal model system to study the evolution of floral diversity. Specifically, we use a near-complete species-level phylogeny to i) reconstruct the evolution of pollination syndromes, ii) investigate whether tube length evolution is linked to pollinator shifts, and iii) test for directionality in the evolution of pollination syndromes.

Chapter 3 54

Materials and Methods

Taxon sampling and phylogenetic analysis

The genus Babiana (Iridaceae: Crocoideae) consists of 92 species (Lewis 1959; Goldblatt and Manning 2007b; Goldblatt, et al. 2008a; Goldblatt and Manning in press) of small to medium-sized geophytes, which occur throughout the Greater Cape Floristic Region. Pollination studies involving about 60% of the species have led to the description of six largely non-overlapping pollination syndromes (Goldblatt and Manning 2007a). Documented pollinators include large anthophorine bees foraging for nectar (Apidae s.l.), native honey-bees actively collecting pollen (Apidae), hopliine beetles (Scarabidae: Hopliini), moths (Noctuidae), (Nectariniidae) and long-proboscid flies (Nemestrinidae). Here, we present a near complete species-level phylogeny including 87 species (chapter 2). Bayesian phylogenetic inference was performed using MrBayes (v.3.2.1; Huelsenbeck and Ronquist 2001). The alignment was divided into partitions according to the gene regions, and the best-fit models of sequence evolution were implemented according to the Akaike Information Criterion (AIC) scores for substitution models evaluated using MrModeltest (v.2.3; Nylander 2004). Indels were coded as present/absent according to Simmons and Ochoterena (2000). Three independent runs with six chains each were run for 20,000,000 generations, sampling the Markov chain every 5000 generations. After removal of the first 2,000,000 generations as burn-in, all runs were combined to build the consensus tree.

Principal Coordinate Analysis (PCoA)

We performed multivariate analyses to delineate the different pollination syndromes in Babiana. A set of eight floral variables for all species were taken from the literature (Goldblatt and Manning 2007b; Goldblatt, et al. 2008a; Goldblatt and Manning in press), comprising of five linear measurements and

Chapter 3 55 three binary characters (floral tube structure, circadian rhythm and color). In addition, measurements of nectar volumes and sugar concentrations were available for 45 species (Goldblatt and Manning 2007a). Finally, the ratio between the dorsal and lower was calculated as a measure of floral symmetry (a complete list of traits is provided in Appendix C). All floral characters were combined in a Principal Coordinate Analysis (Goslee and Urban 2007) using a pair-wise dissimilarity matrix (Maechler et al. 2005) based on Gower's (1971) coefficient, which allows continuous and binary data to be combined into a single dissimilarity matrix. The first three axes of the PCoA were plotted to identify clusters of the different pollination syndromes. Observational data for 53 species (58%) (Goldblatt and Manning 2007a) were then used to infer the pollination system of the remaining species.

Analysis of independent contrasts

The pollinator shift hypothesis predicts that changes in tube length should occur in a punctuated fashion when pollinators change rather than gradually together with pollinator tongue length as predicted in coevolutionary models (Whittall and Hodges 2007). To test this, we coded each pollination syndrome as a binary character (e.g. sunbirds vs. non-avian pollinators) and used phylogenetically independent contrasts (Purvis and Rambaut 1995) to compare the magnitude of tube length variation when pollinators change compared to when they remain the same. To account for phylogenetic uncertainty, the analysis was performed using 1,000 trees selected at random from the posterior distribution of trees from the Bayesian analysis. The significance of differences in tube length contrasts was assessed by determining the proportion of samples within pollination syndromes that have contrasts more extreme than the mean tube length contrast for pollinator shifts.

Chapter 3 56

Reconstruction of pollinator shifts and models of evolution

Pollinator shifts were reconstructed using a maximum likelihood approach (Mk1 model) implemented in Mesquite (Maddison and Maddison 2008). To account for phylogenetic uncertainty only shifts at nodes with a posterior probability of 0.5 or higher were included. Given the topological incongruences between the plastid and nuclear phylogenies (Chapter 2) and the fact that the combined analysis mainly reflects the tree topology of the plastid data, we also reconstructed the evolution of pollination sydromes on the nuclear tree (Fig. 2.6). Furthermore, to obtain a conservative estimate of the frequency of pollinator shifts, we selected pollination syndromes that minimize the number of shifts in cases where species were assigned a bimodal pollination system based either on observations (i.e. flowers are visited and successfully pollinated by more than one pollinator guild), or overlap of pollination syndromes in the PCoA.

To test for directionality of tube length evolution as predicted by the pollinator shift hypothesis, we evaluated 75 different models of pollination syndrome evolution organized in three nested sets using BayesTraits (Pagel et al. 2004). BayesTraits employs a continuous-time Markov process for discrete data, which allows transitions between states at any given time and estimates the rates and likelihoods of different states for each node. Pollination syndromes were ordered based on mean tube length (anthophorine bees, hopliine beetles, pollen collecting bees, moth, sunbirds, long-proboscid flies; Fig. 3.1), species with a bimodal pollination syndrome were coded as polymorphic. Minimum models of evolution were restricted to linear increases in tube length (setting all other transitions equal to zero), and additional transitions were included successively to test if more complex models provide a better fit to our data. The first two sets followed Whittall and Hodges' model of stepwise increase in tube length, successively extending models to incorporate larger changes and reversals. A third set was based on direct transitions from bees to all other syndromes as the minimum model. As models were non-nested, we used the Akaike Information Criterion (AIC; Akaike 1973) to compare model performance. A second-order correction

Chapter 3 57 for small sample sizes for the Akaike Information Criterion was implemented (AICc; Hurvich and Tsai 1989) due to a low sample size/parameter ratio

(Burnham and Anderson 2002). The models with the lowest AICC values and highest Akaike weights (ωi) were considered to be the best-fit models.

Chapter 3 58

Results

Morphological trait analysis

The analysis of floral morphology revealed considerable differences between species associated with different pollination syndromes. Plots of the first three axes of the PCoA (Fig. 3.2) confirmed the observed differentiation between pollination syndromes and allowed inference of the remaining species, despite some degree of overlap between some syndromes. For example, beetles and pollen collecting bees occupy a similar morphospace, and therefore are more difficult to separate. Based on observational data, three species were assigned a bimodal pollination syndrome: B. fragrans (hopliine beetles and anthophorine bees), B. melanops (Mamre form; hopliine beetles and pollen collecting bees) and B. vanzijliae (anthophorine bees and long-proboscid flies). In addition, B. radiata was coded as pollinated by either pollen collecting bees or hopliine beetles due to high similarity of both pollination syndromes.

Tube length variation within and between pollination syndromes

The analysis of tube length contrasts revealed that in anthophorine bee, and long-proboscid fly pollinated species, the average tube length difference is significantly larger if pollinators shift compared to when they remain the same (Fig. 3.3). In the moth and hopliine beetle pollination syndromes, tube length contrasts do not differ significantly within and between syndromes, while tube length change to a significantly smaller extent at shifts involving the pollen collecting bee syndrome (Fig. 3.3).

Chapter 3 59

Figure 3.1. Box plot of the variation in tube length between pollination syndromes in Babiana: Anthophorine bee (0), hopliine beetle (1), pollen collecting bee (2), moth (3), sunbird (4), and long-proboscid fly (5).

Chapter 3 60

Figure 3.2. Caption on following page.

Chapter 3 61

Figure 3.2. Scatterplots of the first three axes of the Principal Coordinate Analysis of ten floral traits. Symbols represent the different pollination syndromes (observed syndromes are displayed in colour, inferred syndromes in grey): Anthophorine bee (open triangles facing upwards, red), hopliine beetle (open squares, cyan), pollen collecting bee (filled squares, blue), moth (open triangles facing downwards, black), sunbird (stars, yellow), and long-proboscid fly (filled circles, green). Labels represent species identities as provided in Appendix C.

Chapter 3 62

Figure 3.3. Evolution of tube length. Comparison of the mean tube length contrasts between (black bars) and within (white bars) pollination syndromes using 1000 random trees from the posterior distribution of the Bayesian analysis. n(0) and n(1) indicate the average number of contrasts if pollinators remain the same or if they change, respectively. P-values represent the proportion of samples within pollination syndromes that have contrasts more extreme than the mean tube length contrast for pollinator shifts.

Chapter 3 63

Pollinator shifts

Reconstruction of the history of pollination syndrome evolution revealed anthophorine bee pollination to be the ancestral state for the genus (Fig. 3.4). Table 3.1 gives an overview of the transitions between pollination syndromes. In total, we found 22 pollinator shifts, with the majority (72%) switching from anthophorine bees to the other syndromes. Most of these shifts were to long- proboscid flies (8), followed by moths (3), hopliine beetles and pollen collecting bees (2 shifts each) and one shift to sunbirds. Shifts between the other pollination syndromes are observed less frequently (6 in total), with two shifts from hopliine beetles to pollen collecting bees and moths respectively and one shift from sunbirds to long-proboscid flies. Shifts to pollination syndromes with a shorter mean tube length were found in three cases, from either long-proboscid flies (twice) or hopliine beetles (one time) to anthophorine bees. The former include two pairs of sister-species (B. attenuata and B. spiralis, and B. flabellifolia and B. sambucina), which are nested within clades of species pollinated by long- proboscid flies. In the latter case, the bee pollinated B. odorata represents a shift from hopliine beetle pollination. Reconstruction of pollination syndromes on the majority rule consensus tree of the nuclear dataset (Fig. 3.5) revealed an overall similar pattern of pollination syndrome shifts. A total of 21 shifts were found, mostly from anthophorine bees to long-proboscid flies (8), moth (3), and hopliine beetles, pollen collecting bees and sunbirds (2 shifts each). In addition, we found shifts from beetles to anthophorine bees (1), pollen collecting bees (1) and moth (1). However, the low resolution of the phylogenetic hypothesis prevented more detailed alayses on the directionality of pollination syndrome evolution.

Comparisons of the AICC scores revealed that models of evolution, which include reversals of pollination syndromes, provided a significantly better fit than more restricted models, which only include shifts towards increasing in tube length (

Appendix D). The overall best minimum model of evolution (AICc = 218.67, ωi =

Chapter 3 64

0.606) included reciprocal transitions between the anthophorine bee syndrome and all other pollination syndromes.

Chapter 3 65

Table 3.1. Frequency of pollinator shifts. Overview of the transitions between pollination syndromes based on maximum likelihood reconstructions of ancestral characters.

Pollen Long- Anthophorine Hopliine collecting Moths Sunbirds proboscid bees beetles bees flies Anthophorine - 2 2 3 1 8 bees

Hopliine beetles 1 - 1 1 0 0 Pollen collecting 0 0 - 0 0 0 bees Moths 0 0 0 - 0 0

Sunbirds 0 0 0 0 - 1 Long-proboscid 2 0 0 0 0 - flies

Chapter 3 66

Figure 3.4. Phylogenetic pattern of pollination syndrome evolution in Babiana based on the combined plastid and nuclear datasets. Consensus tree (displaying all compatible groups) of the Bayesian analysis with posterior probability values at nodes. Branches are colored according to maximum (continued)

Chapter 3 67 likelihood reconstructions of pollination syndromes. The insert shows results of the maximum likelihood test of 75 models of pollination syndrome evolution with

AICC values and Akaike weights (ωi) of the overall best-fit model (red, solid arrows) and the best scoring model without reverse transitions (black, dashed arrows). Frequencies of reconstructed transitions are provided next to the arrows. The first number indicates shifts from anthophorine bee pollination, the second number indicates reverse transitions. Photographs show representatives of each pollination syndrome: a) anthophorine bee – red, B. ambigua (Roem. & Schult.) G.J.Lewis; b) hopliine beetle – cyan, B. rubrocyanea (Jacq.) Ker Gawl.; c) pollen collecting bee – blue, B. leipoldtii G.J.Lewis; d) moth – black, B. patersoniae L.Bolus, e) sunbird – yellow, B. ringens (L.) Ker Gawl.; and f) long-proboscid fly – green, B. framesii L.Bouls. Photographs by J. Manning (c) and J. Schnitzler (a, b, d, e, f).

Chapter 3 68

Figure 3.5. Phylogenetic pattern of the of pollination syndrome evolution in Babiana according to the nuclear dataset. 50% majority rule consensus tree of the Bayesian analysis with posterior probablilities given at nodes. Branch colours correspond to Fig. 3.4.

Chapter 3 69

Discussion

The floral traits included in this study allowed us to identify largely non- overlapping clusters of species associated with different pollinator guilds that are consistent with observational data (Goldblatt and Manning 2007a). Only the hopliine beetle and pollen collecting bee syndromes show considerable overlap in 'trait space', which highlights the importance of additional traits (e.g. scent, floral reflectance measurements) to further distinguish these syndromes, as exemplified in two distinct populations of B. melanops (Mamre and Tulbagh forms). Despite being morphologically similar, one population, which is exclusively visited by hopliine beetles, has flowers that are odorless and produce only traces of nectar, a second population has scented flowers and provides nectar as a reward that also attracts apid bees (Goldblatt and Manning 2007a). This suggests that either nectar production is a labile trait showing high variability or that the two populations represent ongoing differential pollinator adaptations. In any case, the similarities between syndromes are unlikely to impact subsequent analyses, as assignments in case of ambiguous states always minimized the number of shifts in pollination syndromes.

Our data show that most pollination syndromes in Babiana have evolved several times (Fig. 3.4). Unlike in Aquilegia, pollinator shifts in Babiana do not follow a strict pattern of stepwise tube length increases (Whittall and Hodges 2007), but involve frequent transitions between anthophorine bees and the other pollination syndromes. This pattern results from a considerable tube length overlap between syndromes (Fig. 3.1), which in part is due to high variability in pollinator tongue length, especially in long-proboscis flies, where tongue length varies between 20 and 80 mm (Goldblatt and Manning 2000b).

Furthermore, our analyses of independent contrasts reveal that in species pollinated by anthophorine bees, sunbirds and long-proboscid flies more tube length evolution occurs during pollinator shifts (Fig. 3.3), which highlights the importance of tube length as an adaptive trait in these syndromes (Pauw et al.

Chapter 3 70

2009). We did not detect this pattern in hopliine beetle, pollen collecting bee and moth syndromes, where tube length is not likely to be the sole determining factor in adaptation to pollinators. Instead, changes in floral orientation, symmetry and rewards in hopliine beetle and pollen collecting bee syndromes, and scent and circadian rhythm in moth pollinated species are probably important adaptive traits (Goldblatt and Manning 2006). To further explore this, we used the same approach to assess the changes in floral symmetry (ratio between dorsal and lower tepals) for hopliine beetle and pollen collecting bee syndromes, which showed that changes in floral symmetry is significantly higher (mean difference in symmetry: 0.845, p < 0.001, Fig. 3.6) at pollinator shifts. These findings are consistent with the pollinator shift model, in which tube length (and floral symmetry in case of hopliine beetles and pollen collecting bees) mainly - and most likely rapidly - evolves when pollinators change, with significantly less change within pollination syndromes (Whittall and Hodges 2007).

However, our results show that the evolution of pollination syndromes in Babiana is not uni-directional. In contrast to Whittall and Hodges (2007) and earlier studies (Kay et al. 2005; Wilson et al. 2007), we identified repeated reverse transitions, which show two markedly different adaptations: First, B. spiralis has a significantly shorter floral tube (8-10 mm) than it's close relatives, enabling anthophorine bees to access the nectar at the base of the tube. Its sister-species B. attenuata however has a floral tube that is substantially longer (25-35 mm) than the proboscis of bees, but has thickened tube walls, which tightly enclose the style and thus block the lower part of the tube, forcing nectar upwards where it can be reached by short-tongued pollinators. Whether this indicates a stepwise process, whereby first species with thickened tube walls would become attractive to short- tongued pollinators, which subsequently would relax selective pressure on tube length, remains to be determined.

Chapter 3 71

Figure 3.6. Evolution of floral symmetry. Comparison of mean difference in floral symmetry (ratio between dorsal and lower tepals) between (black bars) and within (white bars) pollination syndromes using 1000 trees random trees from the posterior distribution of the Bayesian analysis. n(0) and n(1) indicate the average number of contrasts if pollinators remain the same or if they change, respectively. The p-value represents the proportion of samples within pollination syndromes that have contrasts more extreme than the mean contrast for pollinator shifts.

Chapter 3 72

The same mechanism is also found in the second species pair that shifts back to bee pollination, B. flabellifolia and B. sambucina subsp. sambucina, which both have long floral tubes with thick walls. Thus, even without a significant reduction in tube length, adaptations to shorter-tongued pollinators can be achieved. Despite these morphological differences, we consider both flowers functionally equivalent, especially as selective pressures exerted by long-tongued pollinators should act against both evolutionary pathways equally. Interestingly, a similar pattern was described by Tripp and Manos (2008) for a neotropical clade of Ruellia (Acanthaceae), where bee pollinated taxa which derived from hummingbird pollinated ancestors were found to be morphologically more similar to hummingbird flowers than to other bee pollinated taxa that have ancestrally bee pollinated flowers. Studies of floral development have shown that two closely related genes (CYCLOIDEA and DICHOTOMA) control floral symmetry and how differential expression of these genes could explain shifts in floral symmetry between closely related species (Hileman et al. 2003; Citerne et al. 2006). Thus, changes in the pattern of expression in these genes could provide an explanation for the observed shifts between the anthophorine bee pollination syndrome and pollination by pollen collecting bees or hopliine beetles. Studies of the expression of floral genes like CYCLOIDEA in Babiana could provide a useful framework to better understand the observed patterns of pollinator shifts.

Moreover, the minimum model of evolution that best fits our data includes pollinator shifts both towards increasing and decreasing tube length, predominantly reciprocal transitions between the anthophorine bee and all other syndromes. Even though the 75 models of evolution tested here do not include all theoretically possible models, we consider it unlikely that the addition of alternative models would change the general pattern that models including reversals provide a significantly better fit than models based only on increasing tube length. Thus, we are confident that the models considered here are sufficient to assess the fit of the pollinator shift hypothesis and conclude that, while the pollinator shift model might be appropriate in cases like Aquilegia, it seems too

Chapter 3 73 simplistic to account for the complex history of pollination syndrome evolution in Babiana. Clearly, reverse transitions occur less frequently than shifts towards longer-tubed (and likely more specialized) syndromes (Tripp and Manos 2008; Harder and Johnson 2009), but the increasing number of cases suggest that the evolutionary significance might have previously been overlooked, and we agree with Tripp and Manos (2008) that some highly specialized syndromes (in our case long-proboscid flies) might not represent evolutionary dead-ends. More importantly, these transitions provide novel evolutionary pathways that could positively affect species' adaptive potential, which is particularly important under changing environmental conditions, which are likely to alter plant-pollinator interactions (Memmott et al. 2007; Hegland et al. 2009). Tripp and Manos (2008) further suggest that the occurrence of multiple reverse transitions might be caused by the higher number of biotic interactions in tropical environments compared to temperate zones, but our data shows that this phenomenon is not restricted to the tropics. We suggest that a 'reversible shift' model, which includes multi- directional transitions as an extension of the pollinator shift model could lead towards a better understanding of the origin of floral and species diversity in biodiversity hotspots.

Chapter 3 74

Conclusions

Studies of floral adaptation have in the past found support for both Darwin's coevolutionary hypothesis (Anderson and Johnson 2008) and the pollinator model (Johnson and Steiner 1997; Alexandersson and Johnson 2002; Whittall and Hodges 2007). More recently, researchers have outlined how both processes work together to shape mutualistic networks of plant-pollinator interactions (Anderson and Johnson 2009; Pauw, et al. 2009). Regardless of the nature of interactions, a common feature of these models is the proposed directional increase in the evolution of tube length, while our data reveal multiple reverse transitions, indicating multi-directional evolution of pollination systems. Thus, the historical constraint on the course of diversification (Harder and Johnson 2009) – albeit unquestionably present (e.g. Tripp and Manos 2008) – might be less stringent than previously thought.

Chapter 4 75

Chapter 4: Temporal patterns of plant species diversification in southern African biodiversity hotspots

Adapted from a manuscript accepted for publication in Systematic Biology ("Revealing the Causes of Plant Diversification in a Biodiversity Hotspot: A Meta-Analysis in the Cape of southern Africa ", authors J. Schnitzler, T. G. Barraclough, J. S. Boatwright, P. Goldblatt, J. C. Manning, M. P. Powell, T. Rebelo and V. Savolainen).*

Introduction

Much of the diversity in the Cape Floristic Region (CFR) is due to the exceptionally large representation of a few diverse clades that originated and radiated within the Cape (Linder 2003), with some estimates of speciation rates in the Cape being higher than for tropical rainforests (Latimer et al. 2005; but see Etienne et al. 2006). Indeed, recent and rapid radiations have been demonstrated for some plant groups in the Greater Cape Floristic Region (GCFR) (Richardson, et al. 2001; Klak, et al. 2003) and it has been suggested that the whole flora might reflect such a recent burst of speciation (Levyns 1964; Linder, et al. 1992; Sauquet, et al. 2009). This “orgy of speciation” (Linder 2003) has been supposed to have been triggered by the climatic changes near the Miocene/Pliocene boundary, with the establishment of the Benguela current leading to a substantially cooler and more arid climate (Goldblatt and Manning 2000a). However, recent analyses – particularly with the sharp increase in the availability of dated molecular phylogenies – indicate that this might not be a general feature of the Cape flora, but that the radiation of several plant lineages had started well before these climatic changes took place (Linder and Hardy 2004; Linder 2005; Verboom, et al. 2009a).

*At time of submission, parts of the sequences for Babiana were not yet available, thus the analyses are based on a slightliy reduced dat set comapred to chapters 2 and 3. Details can be found in Appendix A.

Chapter 4 76

Here, we present results of one of the most comprehensive meta-analysis of plant diversification in the Cape, using data for more than 470 species from four major Cape clades for which we were able to generate near complete, multi-gene species-level phylogenetic trees: the genus Protea (Proteaceae), the tribe Podalyrieae () and the genera Babiana and Moraea (Iridaceae), representing three of the seven largest plant families in the CFR (Goldblatt, et al. 2005b). We use molecular dated phylogenies to examine temporal patterns of the evolution of the Cape flora and assess whether the start of the radiations coincide with the onset of the current climatic conditions. Given the fact that these clades could have been elements of the precursor flora, we specifically test whether rates of diversification might have been influenced by climatic changes.

Chapter 4 77

Materials and Methods

Taxon Sampling

The genus Babiana (Iridaceae: Crocoideae) consists of 92 species (Goldblatt and Manning 2007b; Goldblatt, et al. 2008a; Goldblatt and Manning in press), which have radiated extensively in southern Africa, with the vast majority (97%) being endemic to the GCFR. Here, we present a near complete species-level phylogeny representing 86 species based on one nuclear and several plastid markers. Comprising some 200 species of herbaceous geophytes, the genus Moraea (Iridaceae: Iridoideae) is widely distributed in sub-saharan Africa, the Mediterranean Basin and the Middle East (Goldblatt, et al. 2008b), but has the majority of its species (>75%) in the Cape of South Africa. The dataset from a previous phylogenetic analysis of 73 species by Goldblatt et al. (2002) was extended substantially here to 161 species of the genus. The tribe Podalyrieae consists of eight genera of papilionoid legumes (Schutte and van Wyk 1998) which – except for six species – are endemic to the CFR, with growth forms ranging from shrublets to tall, upright trees. Here, we use data from a phylogenetic study for 107 out of a total of about 128 species in the tribe (Boatwright, et al. 2008). Finally, the genus Protea is the largest and most widely distributed genus of Proteaceae in Africa, comprising about 115 species (Goldblatt and Manning 2000a), with about 60% of these being endemic to the CFR. We use a species-level molecular phylogenetic data set of 90 taxa (Valente, et al. 2010) including all 70 Cape species.

PCR Amplification, Sequencing and Alignment

New sequence data are reported here for Babiana and Moraea. Total genomic DNA was extracted from 0.2 – 1.0 g of silica dried leaf material using the 2X CTAB method (Doyle and Doyle 1987) and purified by caesium- chloride/ethidium-bromide density gradient (1.55 g/ml; Csiba and Powell 2006).

Chapter 4 78

Purified total DNA was dialyzed in 1x TE buffer and stored at -80°C. For Moraea, three plastid regions were amplified (the trnL-trnF region, i.e. the trnL intron and trnL-trnF intergenic spacer; hereafter trnL-F, the rps16 intron and the rbcL gene), while for Babiana DNA sequences were produced for nine plastid markers (four coding regions: matK, rbcL, rpoC1 and ndhF, three introns: rps16, rpl16 and trnL-F (including the trnL-trnF intergenic spacer) as well as two intergenic spacers: rpl32-trnL and 3'trnV-ndhC) and one low-copy nuclear gene (RPB2).

PCR amplifications in Moraea were carried out in 50 µl PCR reactions, composed of the ReddyMix PCR Master Mix with 2.5 mM MgCl2 for trnL-F, rps16 and rbcL (ABgene, Epsom, Surrey, UK), with the addition of 1 µl of bovine serum albumin (BSA, 0.4%), 50 ng of each primer and 20–50 ng DNA template; the total volume was made up to 50 µl with the addition of sterile distilled water. All markers for Babiana were amplified in 20 µl reactions, containing 4 µl of

Reaction Buffer (160 mM (NH4)2SO4; 670 mM Tris-HCl (pH 8.3); 0.1% Tween-

20), 2 µl of 25 mM MgCl2, 0.005% BSA, 0.2 M D-(+)-Trehalose (Sigma T-5251, Sigma-Aldrich, St. Louis, MO, USA), 0.4 µl of 10 mM dNTPs (Bioline Ltd., London, UK), 0.4 µl of 5 u/µl GoTaq DNA polymerase (Promega, Madison, WI, USA), 0.5-1.0 µl of each 100 mM primer and 1-2 µl of genomic DNA. All amplifications were performed using a Perkin-Elmer GeneAmp 9700 Thermal Cycler (Applied Biosystems, Foster City, CA, USA) with 2 min initial denaturation at 94°C followed by 30-38 cycles with 1 min denaturation at 94°C, 1 min annealing at 48-52°C (depending on the primers used), 1:30 min elongation at 72°C and a final 3-5 min elongation at 72°C.

Primer pairs used were X-f (TAATTTACGATCAATTCATTC) and 5-r (GTTCTAGCACAAGAAAGTCG) for matK, 1-f and 4-r for rpoC1 (www.kew.org/barcoding), 972-f and 2110-r for ndhF (Olmstead and Sweere 1994), 1-f and 2-r for rps16 (Oxelman, et al. 1997), 71-f and 1661-r for rpl16 (Jordan, et al. 1996), “c” and “f” for trnL-F (Taberlet, et al. 1991; Shaw, et al.

Chapter 4 79

2007), rpL32-f and trnL(UAG)-r for rpl32-trnL (Shaw, et al. 2007) and trnV(UAC)x2- f and ndhC-r for trnV-ndhC (Shaw, et al. 2007). rbcL was amplified using two primer pairs: 1-f and 724(m)-r as well as 636-f and 1367-r (Olmstead, et al. 1992; Muasya, et al. 1998). For RPB2 primers INT23-f and INT23-r (Norup, et al. 2006) were used for initial amplification. To increase the quantity of the PCR-product and prevent amplification of paraloguous loci, additional primers IRID-f (GCACATATGGGGAAAGAAGG) and IRID-r (TTATCCACCTGAGATGATTGC) were designed.

Prior to sequencing, amplified products were cleaned using NucleoSpin Extract II isolation kit (Macherey-Nagel GmbH, Düren, Germany) or QIAquick (Qiagen, Crawley, West Sussex, UK) and the resulting DNA concentration was measured by photospectrometry. Cycle sequencing (26 cycles; 10 s denaturation at 96°C, 5 s annealing at 50°C, 4 min extension at 60°C) with BigDye Terminators (v3.1; Applied Biosystems, Foster City, CA, USA) was performed in 10 µl volumes. Products were purified with 90% ethanol using a Biomek NX Span-8 automated workstation (Beckman Coulter, Fullerton, CA, USA) and re-suspended in water for sequencing on an automated ABI 3730 DNA Analyzer (Applied Biosystems, Foster City, CA, USA) following manufacturers protocols. Sequences were edited using Sequencer 4.5 (Gene Codes Corp. 2004, Ann Arbor, MI, USA) and aligned by eye in PAUP* v.4.0b10 (Swofford 2002). Voucher information and GenBank/EMBL accession numbers are provided in Appendix A (Babiana) and Appendix E (Moraea).

Phylogenetic Inference and Divergence Time Estimation

Phylogenetic trees and divergence times for all four groups were reconstructed using a Bayesian Markov Chain Monte Carlo (MCMC) approach as implemented in BEAST (v.1.4.7; Drummond and Rambaut 2007), which allows topology, substitution rates and node ages to be estimated simultaneously (Drummond and Rambaut 2007). The datasets were divided into partitions according to the gene

Chapter 4 80 regions used, and the best-fit models of sequence evolution were implemented according to the Akaike Information Criterion (AIC) scores for substitution models evaluated using MrModeltest (v.2.3; Nylander 2004). A speciation model following a Yule process was selected as the tree prior, with an uncorrelated lognormal (UCLN) model for the rate variation among branches.

The following calibration points were used for the analysis, constraining nodes to a normal distribution: For Podalyrieae, the split between Podalyrieae s.s. and was set to a mean 33.58 mya (central 95% range 29.6 - 37.5; Boatwright, et al. 2008), for Protea the split between Protea and was constrained with a mean of 28.4 mya (central 95% range 24.4 - 32.3; Sauquet, et al. 2009). As no fossils have been found to date for Moraea and Babiana, the calibration points for the phylogenetic trees were inferred from a recalibration of the Iridaceae family tree (Goldblatt, et al. 2008b) using 82 mya as the root node age from the study of Wikström et al. (2001). This provided ages of 17.53 mya (central 95% range 13.6 - 21.4) for the root node of Moraea (the split between Moraea and Ferraria) and 16.42 mya (central 95% range 12.5 - 20.3) for the split between Babiana and Chasmanthe. Between 10 and 25 independent runs of 3,000,000 to 10,000,000 generations, sampling every 2,000-5,000 generations were performed.

The adequacy of sampling was assessed using the Effective Sample Size (ESS) diagnostic with Tracer (v.1.4; Rambaut and Drummond 2007) and after removing the first 10-25% of the samples as burn-in, all runs were combined to build the maximum clade credibility tree using TreeAnnotator (v.1.4.7; Drummond and Rambaut 2007).

Diversification Rates

Net diversification rates of all four groups were calculated following equation 7 of Magallón and Sanderson (2001) for crown groups under the assumption of either

Chapter 4 81 no extinction (ε = 0) and a high relative extinction rate (ε = 0.9) to evaluate the tempo of species diversification.

To assess the temporal dynamics of diversification, a maximum likelihood approach (Rabosky 2006b) was used to test whether diversification rates have changed over time, contrasting the likelihoods of the data under models with constant diversification rates against models where rates have varied through time. The test statistic for a change in diversification rates is the difference in the AIC score between best-fit rate-constant and rate-variable models. Models included in the analysis were a pure birth (Yule) and a birth-death model with constant rates as well as a two-rate Yule model and two density-dependent (logistic and exponential) models as rate-variable options. To estimate the null distribution of the test statistic, we generated 1,000 trees for each study group using PhyloGen (Rambaut 2002). This simulation accommodates incomplete taxon sampling by first generating phylogenetic trees consisting of the number of species described for each group following a Yule process, and subsequently sampling these trees to reconstruct phylogenies containing the same number of taxa as included in our datasets.

Chapter 4 82

Results

Phylogenetic Analysis and Timing of Divergence

Post-run analysis of the MCMC log files from BEAST indicated adequate sampling (Effective Sample Size (ESS) values were all above 200). The topologies of the chronograms (Fig. 4.1 – 4.4) were in accordance with trees obtained using other reconstruction methods (parsimony, maximum likelihood – data not shown); taxonomic implications for the study groups will be discussed in detail elsewhere. Examination of the standard deviation of the uncorrelated lognormal relaxed clock (UCLD) revealed substantial rate variation among lineages, confirming the implementation of a relaxed molecular clock (Babiana: σ = 0.685, 95% Highest Posterior Density (HPD) interval 0.52-0.861; Moraea: σ = 0.665, 95% HPD 0.566-0.763; Podalyrieae: σ = 0.959, 95% HPD 0.81-1.125; Protea: σ = 0.669, 95% HPD 0.533-0.809), while the measure of covariance indicates no autocorrelation between branches. Divergence time estimations revealed stem node ages of the four groups are spread from the early Oligocene to the mid-Miocene, ranging from 33.5 mya (95% HPD 37.6-29.7) for Podalyrieae to 15.6 mya (95% HPD 19.6-11.5) for Babiana (Fig. 4.1 – 4.4).

Chapter 4 83

Figure 4.1. Maximum clade credibility tree of the BEAST analysis for Babiana. Values at nodes represent Bayesian posterior probabilities, the time scale represents Myr before present.

Chapter 4 84

Figure 4.2. Caption on following page.

Chapter 4 85

Figure 4.2. Maximum clade credibility tree of the BEAST analysis for Moraea. Values at nodes represent Bayesian posterior probabilities, the time scale represents Myr before present.

Chapter 4 86

Figure 4.3. Maximum clade credibility tree of the BEAST analysis for Podalyrieae. Values at nodes represent Bayesian posterior probabilities, the time scale represents Myr before present.

Chapter 4 87

Figure 4.4. Maximum clade credibility tree of the BEAST analysis for Protea. Values at nodes represent Bayesian posterior probabilities, the time scale represents Myr before present.

Chapter 4 88

Temporal Patterns of Diversification

Considering no extinction, net diversification rates calculated for crown group ages obtained by divergence dating using a relaxed molecular clock approach varied between 0.15 Myr-1 (95% HPD 0.12-0.20) for Podalyrieae and 0.40 Myr-1 (95% HPD 0.29-0.64) for Babiana, with Moraea and Protea having intermediate rates of 0.29 Myr-1 (95% HPD 0.24-0.39) and 0.22 Myr-1 (95% HPD 0.15-0.39), respectively. Alternatively, considering a high relative rate of extinction (ε = 0.9), diversification rates are significantly lower (Podalyrieae: 0.09 Myr-1, 95% HPD 0.07-0.12; Protea: 0.13 Myr-1, 95% HPD 0.09-0.23; Moraea: 0.19 Myr-1, 95% HPD 0.15-0.25; Babiana: 0.23 Myr-1, 95% HPD 0.17-0.37).

The lineage-through-time plot (Fig. 4.5) shows the substantial spread of the onset of the radiations, starting in the early Oligocene. Although visual inspection suggests that rates of diversification have remained largely constant over time, the graphs show a slight decrease of diversification rates towards the present. Using a maximum likelihood approach to test for differential diversification rates through time (Rabosky 2006a), we found that all groups show significantly decreased diversification rates towards the present (Table 4.1). Such a decrease could however also be an artefact caused by incomplete taxon sampling (missing or cryptic species; Pybus and Harvey 2000). Thus, to assess whether these decreases in diversification rates are likely to be attributable to incomplete sampling, we excluded branching events younger than one million years from the analysis (Fig. 4.5). This also enabled us to potentially detect further rate shifts in periods unaffected by sampling biases. Comparisons show that for the constrained dataset, rate-variable models do not provide a significantly better fit than rate-constant models (Babiana: ΔAIC = 3.301, p = 0.09; Moraea: ΔAIC = -1.061, p = 0.83; Protea: ΔAIC = 2.815, p = 0.24) apart from Podalyrieae, in which case a Yule model with two different rates performed significantly better (ΔAIC = 8.126, p = 0.03), indicating a decrease in diversification rates about 1.7 million years before present.

Chapter 4 89

Figure 4.5. Lineage-through-time plots for Babiana, Moraea, Podalyrieae, and Protea. Plots are based on the mean node ages of the maximum clade credibility trees using an uncorrelated lognormal relaxed clock in BEAST. The dashed vertical line represents the cut-off (at one million years before present), which was employed to exclude samples from the analysis of diversification rate shifts.

Chapter 4 90

Table 4.1. Diversification rate shifts. Results of the ML test for differential diversification rates through time. The best-fit model in all cases was a Yule model with two rates. ΔAIC = difference in AIC scores between the Yule-2-rate model and the best-fit rate-constant model; r1, r2 = initial and final net diversification rate respectively; st = shift-time; all p-values < 0.001.

ΔAIC r1 r2 st

Babiana 26.13 0.559 0.045 0.496 Moraea 16.93 0.283 0.042 0.441 Podalyrieae 17.68 0.17 0.039 1.672 Protea 25.29 0.208 0.017 1.358

Chapter 4 91

Discussion

Radiation of the Cape flora: Timing and Dynamics

The radiation of the plant lineages analysed here started between 34 and 15 mya. These dates lie well within the range of starting dates identified for several other lineages in southern Africa (Linder 2005; Verboom, et al. 2009a), indicating that the radiation of the Cape flora was not a singular event, which corroborates findings that it represents a mixture of older and more recent radiations (Linder 2005, 2008; Verboom, et al. 2009a). Although the clades analysed here diversified at higher rates than the average of their respective families and orders (see Magallón and Castillo 2009), diversification rates are in general intermediate and not among the highest rates reported for other major radiations in southern Africa (Richardson, et al. 2001, 0.56-0.65 spp per Myr; Klak, et al. 2003, 0.76-1.75 spp per Myr), on oceanic islands (Baldwin and Sanderson 1998, 0.43-0.57 spp per Myr), or in the tropics (Hughes and Eastwood 2006, 2.1-3.1 spp per Myr). Thus, diversification rates of the Cape clades cannot be considered extraordinary on a global scale, suggesting that the diversity of the Cape flora might in large parts not be the result of a recent and rapid burst in speciation; a radiation triggered by the climatic deterioration at the end of the Miocene seems therefore unlikely.

Here, we additionally demonstrate that net rates of diversification remained constant through time, lacking marked changes during periods of changing environmental conditions. These results contradict those of Crisp and Cook (2009), who analysed the diversification of Podalyrieae and concluded that a significant increase in diversification rates reveals the signature of a mass extinction event at the end of the Eocene. However, their study includes only a subset of the markers used in this study and their root node age of about 50 mya for the tribe is somewhat puzzling given that Boatwright et al. (2008) provide a much younger age for the root of this clade (33.58 mya). Although an actual decrease in speciation rates cannot be ruled out, we attribute the observed slowdown in diversification rates towards the present to a sampling artefact. Both

Chapter 4 92 the exclusion of branching events within the last one million years as well as the simulation of incompletely sampled phylogenies resulted in the absence of significant diversification rate shifts. The only exception is the tribe Podalyrieae, for which we still found evidence for a decrease in net diversification rates, which could reflect limitations for speciation in an increasingly diverse flora.

Chapter 4 93

Conclusions

In spite of prominent examples of high rates of diversification in the GCFR, this study shows that the remarkable plant diversity is not the result of a recent and rapid radiation triggered by climatic changes, but that diversification took place over an extended period of time, probably with low extinction rates due to the relative climatic stability in the Cape throughout the Cenozoic (Linder 2003; Cowling, et al. 2009). Furthermore, global analyses of species richness (Kreft and Jetz 2007) and of the plant family Iridaceae (Davies et al. 2005) have shown that plant diversity in the Cape is significantly higher than expected given its contemporary environmental conditions. This suggests that, despite the strong influence of climatic conditions on plant diversity, it is unlikely that climate alone is responsible for the high levels of plant diversity in the GCFR (chapter 2; Goldblatt and Manning 2000a).

Chapter 5 94

Chapter 5: Revealing the Causes of Plant Diversification in a Biodiversity Hotspot: A Meta-Analysis in the Cape of southern Africa

Adapted from a manuscript accepted for publication in Systematic Biology ("Revealing the Causes of Plant Diversification in a Biodiversity Hotspot: A Meta-Analysis in the Cape of southern Africa", authors J. Schnitzler, T. G. Barraclough, J. S. Boatwright, P. Goldblatt, J. C. Manning, M. P. Powell, T. Rebelo and V. Savolainen).*

Introduction

Available data on the timing of the radiation of the flora in the Greater Cape Floristic Region (GCFR; chapter 4) and global analyses of plant species richness (Davies, et al. 2005; Kreft and Jetz 2007) have shown that plant diversity in this region is significantly higher than expected given its contemporary climate. This suggests that climatic conditions are unlikely to be the main determinant of the high levels of plant diversity in the GCFR (Goldblatt and Manning 2000a). Indeed, the major forces that drive plant diversification in southern Africa have remained unclear. Several hypotheses have been proposed to explain the Cape’s exceptional diversity based on various biotic and abiotic factors that potentially create reproductive barriers and/or divergent selection pressures and thus promote speciation (Linder 2003; Barraclough 2006).

*At time of submission, parts of the sequences for Babiana were not yet available, thus the analyses are based on a slightliy reduced dat set comapred to chapters 2 and 3. Details can be found in Appendix A.

Chapter 5 95

We consider five commonly discussed factors. First, the Cape is topographically complex and, therefore, geographical isolation by physical barriers, or divergent selection caused by sharp altitudinal gradients, might cause speciation (Cowling, et al. 2009). This hypothesis predicts that sister-species should tend to be isolated geographically. In addition, lineages inhabiting more topographically complex regions should tend to diversify into more species than those in less complex regions. Furthermore, it has been suggested that mountain regions have experienced relatively stable climatic conditions and promoted the long-term persistence of lineages (Cowling and Lombard 2002; Linder 2008). This hypothesis predicts that mountain regions should harbour a mixture of old and recently derived lineages, whereas lowland regions should contain more derived lineages Linder (2008).

The second factor, edaphic heterogeneity, might be important by providing a mosaic of divergent selection pressures promoting divergence and speciation (Rourke 1972; Linder 2003). This hypothesis predicts that recently diverged sister-species should tend to occur in different edaphic environments. Third and fourth, pollinator specialisation and phenological shifts might promote speciation by causing reproductive isolation among populations (Johnson 1996; Linder 2003). If these are frequent causes of speciation, we predict that recently diverged sister-species should tend to have different pollinators or timing of flowering.

Finally, in fire-prone environments, such as the Cape, two different fire-survival strategies have evolved – species either sprout from underground roots and stems (resprouters) or regenerate only from (reseeders; Schutte et al. 1995). These strategies are coupled with distinct life histories, which could result in a reduction of gene flow between populations and the avoidance of competition for resources (Linder 2003). According to this hypothesis, sister-species should frequently have contrasting fire-survival strategies. A second hypothesis concerning fire-survival is the suggestion that reseeding lineages have diversified more than species that resprout. The mechanism is unclear but could involve shorter generation times

Chapter 5 96 resulting in higher rates of molecular evolution in reseeders than resprouters (Cowling 1987; but see Verdú et al. 2007). This hypothesis predicts that clades of reseeders should tend to have more species than clades of resprouters.

In a recent meta-analysis, van der Niet and Johnson (2009) found that sister- species in the Cape frequently differ in general habitat, pollinators and fire- survival strategy. Their analyses however include phylogenies with a high proportion of missing taxa, which introduces uncertainty regarding the correct assignment of sister-species and lacks detailed information on biological traits for many of the species included. Furthermore, the study did not control for the number of realized states in each trait, which eventually precludes a direct comparison of shift frequencies. The present study on the other hand is not only based on a more thorough taxonomic sampling, but also accounts for phylogenetic uncertainty and includes statistical approaches to incorporate the expected null distributions of the hypotheses.

Here, we present results of one of the most comprehensive meta-analysis of plant diversification in the Cape, using data for more than 470 species from four major Cape clades for which we were able to generate near complete, multi-gene species-level phylogenetic trees: the genus Protea (Proteaceae), the tribe Podalyrieae (Fabaceae) and the genera Babiana and Moraea (Iridaceae), representing three of the seven largest plant families in the CFR (Goldblatt, et al. 2005b). Combining phylogenetic, ecological and biogeographical information, we evaluate the competing hypotheses of plant diversification. Specifically, we use two approaches to test biotic and abiotic correlates of plant diversification in the Cape. Firstly, using a whole-tree approach, character states are optimised on the phylogenetic trees and links between diversification rate shifts and shifts in biological and ecological traits are evaluated. Shifts in diversification rates are expected to coincide with shifts in traits related to proposed drivers of diversification (e.g. fire-survival strategy). Secondly, differences in traits between sister-species are compared, based on the assumption that sister-species can be

Chapter 5 97 expected to differ in traits that tend to diverge during speciation (Kurzweil, et al. 1991; Linder 2003; Barraclough 2006). In this case, considering the potential bias of underestimating the number of shifts in labile characters on interior nodes, we test whether sister-species differ more often than expected under a null model according to which traits are distributed randomly.

Chapter 5 98

Materials and Methods

Taxon Sampling

The genus Babiana (Iridaceae: Crocoideae) consists of 92 species (Goldblatt and Manning 2007b; Goldblatt, et al. 2008a; Goldblatt and Manning in press), which have radiated extensively in southern Africa, with the vast majority (97%) being endemic to the GCFR. All species are small to medium-sized geophytes and display a considerable amount of floral variation. Pollination studies have recently led to the description of six largely non-overlapping pollination systems (Goldblatt and Manning 2007a). Here, we present a near complete species-level phylogeny representing 86 species based one nuclear and several plastid markers.

Comprising some 200 species of herbaceous geophytes, the genus Moraea (Iridaceae: Iridoideae) is widely distributed in sub-saharan Africa, the Mediterranean Basin and the Middle East (Goldblatt, et al. 2008b), but has the majority of its species (>75%) in the Cape of South Africa. Similar to Babiana, floral diversity in Moraea is remarkable with five distinct pollination systems (Goldblatt et al. 2005a). The dataset from a previous phylogenetic analysis of 73 species by Goldblatt et al. (2002) was extended substantially here to 161 species of the genus.

The tribe Podalyrieae consists of eight genera of papilionoid legumes (Schutte and van Wyk 1998) which – except for six species – are endemic to the CFR, with growth forms ranging from shrublets to tall, upright trees. Species display two different life histories in response to fire – either sprouting from underground roots and stems (resprouters), or regenerating only from seeds (reseeders). With very few exceptions, species in this tribe are adapted to pollination by carpenter bees (Schutte and van Wyk 1998). Here, we use data from a phylogenetic study for 107 out of a total of about 128 species in the tribe (Boatwright, et al. 2008).

Chapter 5 99

Finally, the genus Protea is the largest and most widely distributed genus of Proteaceae in Africa, comprising about 115 species (Goldblatt and Manning 2000a), with about 60% of these being endemic to the CFR. All species are woody shrubs or trees and, like in Podalyrieae, two different adaptations to fire regimes (resprouters and reseeders) can be found. Again, this genus shows a variety of pollination syndromes, including bird, arthropod, and pollination (Collins and Rebelo 1987). We use a species-level molecular phylogenetic data set of 90 taxa (Valente, et al. 2010) including all 70 Cape species.

Phylogenetic trees and divergence times for all four groups were reconstructed using a Bayesian Markov Chain Monte Carlo (MCMC) approach as implemented in BEAST (v.1.4.7; Drummond and Rambaut 2007; chapter 4).

All species distributions were recorded as presence/absence data in grid cells with an edge length of a quarter degree (quarter degree square – QDS). Data for Babiana were based on collection localities of herbarium accessions from various herbaria (PRE, NBG, SAM, BOL, WIND, and K). For Podalyrieae, data were taken from Schutte (1995) and Beaumont et al. (1999) and cross-referenced with the PRECIS (National Herbarium Pretoria Computerised Information System) database. Distribution data for Moraea follow Goldblatt (1986, 1992, 1998) and Goldblatt and Manning (2000c, 2002, 2004). For Protea, fine scale distribution data (1km2) from the Protea-Atlas Project were rescaled to QDS to match the resolution of the other groups and where unavailable were taken from PRECIS. Information on fire-survival strategy, lithology, soil type, pollinators, flowering time and altitudinal ranges for each group were taken from the literature (Goldblatt 1986, 1992; Schutte 1995; Goldblatt 1998; Beaumont, et al. 1999; Goldblatt and Manning 2000c; Rebelo 2001; Goldblatt and Manning 2002, 2004; Goldblatt, et al. 2005a; Goldblatt and Manning 2007a; 2007b; additional data on soil types and pollinators were contributed by P. Goldblatt, J.S. Boatwright and T. Rebelo). Lithology and soil type data were combined in an index for the edaphic conditions. All ecological data are available in Appendix F.

Chapter 5 100

Topographic Complexity and Ancestral Range Reconstruction

Topographic complexity was calculated as the standard deviation of all grid altitude values at 1 x 1 km within a QDS grid following Thullier et al. (2006) using a digital elevation model obtained from Worldclim (v.1.4; Hijmans et al. 2005). To assess the importance of topographic complexity for plant diversification, we tested whether clades originated and diversified predominantly in topographically complex regions. Ancestral range reconstructions were performed using a MCMC approach, as implemented in BayesTraits (Pagel, et al. 2004) with the WWF Terrestrial Ecoregions (Olson, et al. 2001) as discrete geographic units to identify the ecoregion that most likely constitutes the origin of each clade. BayesTraits employs a continuous-time Markov model that allows traits to change at any given time to derive the posterior probability distribution of the likelihood parameters of the model. Each chain was run for 2,000,000 generations, sampling parameters every 1,000 generations while discarding the first 150,000 generations as burn-in. Transition rates between states (i.e. rate of shifts between geographic areas) were set equal using a uniform prior and an estimate of the probability of each state at the root node was derived by combining the particular posterior probabilities.

The mean topographic complexity was calculated for all terrestrial ecoregions in sub-saharan Africa occupied by at least one of the species in our study. In addition, to test if topographically complex areas are more species-rich, phylogenetically independent contrasts as implemented in MacroCAIC (Agapow and Isaac 2002) were calculated using the relative rate difference (RRD) measure to evaluate the correlation between per QDS species richness and topographic complexity.

Diversification Rate Shifts

A topological approach was used to assesses whether at any given node an imbalance similar or greater than that observed can be expected under a pure birth

Chapter 5 101

(Yule) model of cladogenesis (Chan and Moore 2005), thus identifying clades that potentially have undergone differential diversification rates. The statistic to locate a potential shift in diversification rates is based on the probability of a rate shift along the lone internal branch of a three-taxon tree by comparing the likelihood ratios of i) a homogeneous (both groups evolve at the same rate), and ii) a heterogeneous (both clades evolve at different rates) model for both the nested and the more inclusive node of the three taxon clade (Moore et al. 2004). To avoid false detection of a rate shift at the inclusive node (the so-called "trickle-down" problem), the likelihood of a shift along the internal branch is conditioned with the likelihood of a shift occurring within the ingroup. Rate shifts were analysed using the Δ1 statistic as implemented in SymmeTREE (Chan and Moore 2005). The null distribution of the shift statistic was estimated by a Monte Carlo simulation, generating 1,000,000 trees of the same size as the study groups under the equal-rates Markov model. To avoid a potential bias caused by incomplete sampling, missing taxa were added to the phylogeny using PERL (script by James Cotton, unpublished). For each group, 100 trees were created, adding missing species to the phylogeny at random points along branches within clades according to current , subsequently calculating the Δ1 statistic for each tree. Only diversification rate shifts at nodes with a posterior probability of 0.5 or higher were taken into consideration for further analysis.

To identify synchronised shifts in diversification rates and species traits (fire- survival, edaphic conditions and pollinators), ancestral character states were reconstructed using a maximum likelihood approach (Mk1 model) implemented in Mesquite (Maddison and Maddison 2008). In cases where the maximum likelihood (ML) reconstruction was equivocal, the most parsimonious character state was estimated.

Chapter 5 102

Modes of Speciation

To test whether speciation tends to involve geographical isolation, we used the age-range correlation (ARC) approach proposed by Barraclough and Vogler (2000), which considers the degree of geographical range overlap between sister clades in relation to node age. The degree of range overlap was calculated by dividing the area of overlap between sister clades by the range size of the clade with the smaller range. Hence, values may vary between 0, indicating no range overlap (allopatry) and 1, in which case the range of one clade is encompassed entirely by the range of its sister clade (sympatry). Fitting a regression line to the plot between range overlap and node ages can reveal the predominant mode of speciation, while the slope of the regression line contains information on the degree of range movements subsequent to speciation, which might constrain our ability to correctly identify the predominant mode of speciation. As the measure is bound between 0 and 1, values were arcsine transformed before fitting the regression line (Sokal and Rohlf 1995). To assess whether present-day ranges still contain a phylogenetic signal of the mode of speciation, ranges were randomly shuffled among tips 1,000 times, each time re-calculating the intercept. The p- value represents the proportion of tests with an intercept more extreme than the one observed (Perret et al. 2007). The geographical mode of speciation was also investigated using Jordan Indices (next section).

Sister-species comparisons: Jordan Index

Differences in traits between sister-species were analysed using the Jordan Index

(JSIS) as proposed by Fitzpatrick and Turelli (2006). The index was calculated for the following traits: fire-survival strategy (for Podalyrieae and Protea only), edaphic conditions, and pollinators with the index taking the value of either 0 (indicating sister-species do not differ) or 1 (indicating that sister-species differ in a given trait) for each pairwise species comparison. Averaged over all sister- species pairs, the index provides a measure of the proportion of species pairs that differ in the trait in question. Accordingly, a high frequency of sister-species

Chapter 5 103 differences would be expected for traits that drive speciation. Initial calculations of the Jordan Index were based on the predominant states of each trait (e.g. the soil type that species were most commonly found on). However, to incorporate the variability in species traits (in particular with regard to the edaphic conditions, Appendix F), we re-calculated the Jordan Index, this time coding polymorphic species so as to minimize the number of sister-species differences. This gave a conservative estimate of the variability (i.e. the minimum number of shifts) for each trait.

In contrast, phylogenetic clustering would be expected in cases where traits represent a key innovation. Therefore, we tested the effect of each trait on diversification rates using the BiSSE Ln Likelihood test (Maddison et al. 2007) implemented in Mesquite (Maddison and Maddison 2008). The test calculates the likelihood and parameter estimates of a six-parameter model consisting of speciation, extinction, and character shift rates for both states of a binary trait. For the calculations, each trait was scored as a binary character (e.g. bird pollinated plants vs. plants pollinated by other vectors) and tested individually for its influence on rates of speciation and extinction. Significant differences in speciation and extinction rates in relation to trait changes were assessed using likelihood ratio tests between unconstrained (six parameters) and constrained (five parameters, with either speciation or extinction rates set equal) models.

Geographical range overlap for all sister-species pairs was calculated as described in the previous section. Finally, we calculated the degree of temporal overlap in flowering times between sister-species by dividing the number of months of phenological overlap by the flowering time of the species with the shorter flowering period. The average phenological overlap indicates whether sister- species show a pattern of co-flowering (index near or equal to 1) or at different times (close or equal to 0). To assess the significance of the observed differences between sister-species, we performed 1,000 random associations of sister-species pairs. The significance of sister-species differences was determined

Chapter 5 104 by calculating whether the observed values fall outside the 95% confidence interval of the randomisation tests. To be conservative, if data for one species of a sister-species pair was missing or if the main state could not be assigned with confidence, the pair was scored as not differing in the trait under consideration, thus avoiding an artificial inflation of sister-species differences. In addition, only sister-species pairs with a posterior probability of 0.5 or higher were included in the analysis.

Chapter 5 105

Results

Reconstruction of the Centres of Origin

Reconstruction of ancestral areas (Fig. 5.1) following a MCMC approach identified the 'Montane Fynbos and Renosterveld' ecoregion as the most likely ancestral area for Moraea, Podalyrieae and Protea, while the 'Succulent Karoo' ecoregion constitutes the most likely ancestral area of the genus Babiana. Median probablilities for these reconstructions across the MCMC analyses were 0.979 (Moraea), 0.987 (Podalyrieae), 0.998 (Protea) and 0.999 (Babiana). The 'Montane Fynbos and Renosterveld' is among the ecoregions with the highest topographic complexity (Appendix G), while the 'Succulent Karoo' is characterized by a significantly less diverse topography (t = 13.469, dF = 91, p < 0.001). Analyses of phylogenetically independent contrasts showed no significant correlation between species richness and topographic complexity (Babiana t = 0.786, dF = 57, p = 0.446; Moraea t = -0.153, dF = 111, p = 0.879; Podalyrieae t = -0.93, dF = 70, p = 0.355; Protea t = -0.225, dF = 57, p = 0.823).

Analysis of Nodal Imbalances

The topological approach to identify potential shifts in diversification rates identified four significant and four additional marginally significant diversification rate shifts (Fig. 5.2). Comparisons with the reconstructed trait states revealed the absence of direct links between diversification rate shifts and shifts in the traits included in our analysis (Fig. 5.2, data for fire-survival not shown).

Chapter 5 106

Figure 5.1. Map of WWF Terrestrial Ecoregions of Africa. Areas included in the reconstruction of ancestral ranges are highlighted in green. The insert shows the southern African ecoregions identified as ancestral ranges: 'Montane Fynbos and Renosterveld' (blue) for Moraea, Podalyrieae and Protea and the 'Succulent Karoo' (yellow) for Babiana.

Chapter 5 107

Figure 5.2. Variability of species traits. Maximum clade credibility trees of the BEAST analysis for a) Babiana, b) Moraea, c) Podalyrieae, and d) Protea. Branches are coloured according to maximum likelihood reconstructions of soil (continued)

Chapter 5 108 types (blue – Rocky outcrops; red – Gravel, yellow – Sand; green – Loam; cyan – Clay; black – Marshy soil). Unknown states and/or equivocal reconstructions are coloured in grey. Shifts in pollination system are marked with an asterisk. Arrows indicate branches along which a significant increase in diversification rates (Δ1, Chan and Moore 2005) has been detected, the time scales represents Myr before present. Photos show some of the species included in this study: a) Babiana patersoniae L.Bolus; b) Moraea villosa Ker Gawl. ex Rchb.; c) spledens (Burm.f.) Bos & de Wit; d) (L.) L. Photographs by J. Manning (b,c) and J. Schnitzler (a,d).

Chapter 5 109

Geographical Patterns of Speciation

The observed intercept of the regression between the degree of sympatry and node ages for Babiana, Moraea and Protea is significantly higher (p < 0.001, Table 5.1) than those obtained from the randomisations, indicating that the degree of range overlap in these groups is not random with respect to the phylogeny. Only in Podalyrieae is the observed intercept not significantly different from the null distribution, suggesting that present-day ranges might not carry a phylogenetic signal.

In Babiana 26% (23 of 88) of all nodes show no range overlap, while 16% (14 nodes) are sympatric, including some of the most recent speciation events (Fig. 5.3). These include the split between B. vanzijliae and B. papyracea, the latter being a narrow endemic known only from two populations on the Bokkeveld Plateau. Further sister-species pairs with sympatric ranges include B. teretifolia and B. hirsuta, both occurring along the west coast of South Africa, as well as B. karooica and B. radiata which are known only from a narrow region in the Little Karoo. Finally, the range of B. tubulosa is embedded within that of its sister- species B. tubiflora, which is widespread in coastal regions of western South Africa. The slope of the regression line is slightly negative (-0.014) indicating a low amount of range movements leading to a decrease in range overlap with time.

The low observed intercept together with the positive slope of the regression (0.052) in Moraea suggests predominantly allopatric speciation with range movements occurring subsequent to speciation. No range overlap is shown in 36% (58 of 159) of the nodes, while only 6% (10 nodes) are sympatric, among which are some of the most recent splits (Fig. 5.3). Sister-species pairs with overlapping ranges include M. kamiesensis and M. fenestralis, with the range of the former being restricted to the Kamiesberg Mountains in the Northern Cape and entirely enclosed in the range of M. fenestralis. The second pair includes M. verecunda and M. pseudospicata, two local endemics from the Bokkeveld Plateau around Nieuwoudtville.

Chapter 5 110

In Podalyrieae, the low intercept and positive slope of the regression (0.041) again suggests predominantly allopatric speciation in this group. This pattern was, however, not found to be significant (Table 5.1). In addition, 50% of all nodes (54 of 108) are allopatric, while only 6% (7 nodes) show complete range overlap, none of which represent recent speciation events (Fig. 5.3).

In Protea, the observed intercept, despite being significantly higher than expected at random, together with the positive slope of the regression (0.039), suggests once more that speciation is predominantly allopatric. However, while 16% (14 out of 84) of the nodes have no range overlap, 19% (16 nodes) are fully sympatric, including several recent splits (Fig. 5.3). In most cases, these sisters- species pairs consist of one range-restricted species (P. pudens, P. stokoei and P. scabriusucula) whose range is encompassed entirely by their sister-species (P. longifolia, P. speciosa and P. scolopendriifolia respectively). In another case, both P. susannae and P. obtusifolia are common on the coastal flats of the south- western Cape.

Chapter 5 111

Table 5.1. Regression between range overlap and node age. Y-intercepts of the linear regression between the degree of sympatry (arcsine transformed) and node age. Ranges were randomly shuffled among tips (n = 1,000) to test whether intercepts were significantly different from those obtained under random distribution of species.

Range randomisation test Number Observed Mean Min Max p-value of nodes intercept intercept intercept intercept

Babiana 88 0.406 < 0.001 0.005 0.018 x 10-6 0.062 Moraea 159 0.086 < 0.001 0.016 0.014 x 10-4 0.075 Podalyrieae 108 0.018 ns 0.014 0.014 x 10-4 0.092 Protea 84 0.333 < 0.001 0.051 0.063 x 10-5 0.289

Chapter 5 112

Figure 5.3. Geographic mode of speciation. Plots of the degree of sympatry (y- axis) against node ages (x-axis) in a) Babiana, b) Moraea, c) Podalyrieae, and d) Protea. Results of the regression analysis and significance tests are provided in Table 5.1.

Chapter 5 113

Sister-species analyses

Geographical range overlap between sister-species was found to be relatively low (Babiana: 0.299, Moraea: 0.19, Podalyrieae: 0.217, Protea: 0.369), consistent with the predominantly allopatric mode of speciation found in the ARC analysis. The proportion of sister-species differing in traits was found to vary substantially between groups and for different traits. All ecological traits were found to be phylogenetically clustered in a least some of the groups studied (Table 5.2 and Fig. 5.2).

Within Podalyrieae, about half of the sister-species pairs show differences in their fire-survival strategy (JSIS 0.55; Table 5.2); whereas in Protea different fire- survival strategies were found in less than 20% of sister-species pairs (Table 5.2). Differences in edaphic conditions were very high, especially in Babiana, Moraea and Podalyrieae, where between 68% and 92% of sister-species pairs occur in different edaphic environments (Table 5.2 and Fig. 5.2). Differences in Protea on the other hand are much lower (JSIS 0.389) and both Podalyrieae and Protea have significantly lower values than expected at random. To further scrutinise the effect of edaphic conditions, we also calculated the Jordan Index separately for lithology and soil types (Table 5.3), which despite yielding a similar pattern to the combined factor, did not show a significant clustering. With the exception of

Babiana (JSIS 0.5; Table 5.2), pollinator shifts between sister-species occur significantly less frequently than based on our randomisations, revealing again a pattern of phylogenetic clustering, with a low frequency of shifts between sister- species (Moraea 0.285, p < 0.001; Podalyrieae 0, p < 0.01; Protea 0.235, p < 0.001; Table 5.2). The BiSSE Ln Likelihood test yielded no significant differences in speciation or extinction rates for any of the traits analysed (Appendix H).

As direct comparisons of the proportion of sister-species differences (JSIS) between lineages and between traits is restricted by the variable number of states, the shift frequency (JSIS) was conditioned by the number of realized states, thus

Chapter 5 114 obtaining a measure of the relative variability of each trait. The results show that for Babiana, Moraea, and Protea, soil types exhibit the highest variability between sister-species (Table 5.4), while changes in fire-survival strategy show the highest degree of variability in Podalyrieae (Table 5.4). As expected, the re- calculations of the Jordan Index incorporating trait variability resulted in slightly lower estimates of sister-species differences, but had little impact on the overall patterns. Most importantly, after controlling for the number of states, soil types still displayed the highest variability in Babiana (0.111), Moraea (0.12) and Protea (0.097). Thus, even assuming the most conservative pattern of trait shifts between sister-species, soil types remains the most variable factor. Results are here reported only for the re-analyses of the edaphic conditions, as the other traits (fire-survival and pollinators) showed significantly less variability (Appendix F) and therefore are less likely to provide biased results. The analysis of flowering times showed that sister-species exhibit a high degree of phenological overlap (Babiana 0.651, p = 0.14; Moraea 0.695, p < 0.001; Podalyrieae 0.583, p = 0.18; Protea 0.703, p < 0.001), which in the case of Moraea and Protea is significantly higher than expected based on the randomisation of flowering times.

Chapter 5 115

Table 5.2. Jordan Index (JSIS) of pairwise species differences. Numbers indicate the proportion of observed pairwise species differences (obs.) in Babiana (n = 19), Moraea (n = 29), Podalyrieae (n = 21) and Protea (n = 19). Significance was assessed by creating 1,000 random associations of the respective number of sister-species pairs (* p = 0.05, ** p < 0.01, *** p < 0.001).

Fire survival Edaphic conditions Pollinators

obs. 95% CI obs. 95% CI Obs. 95% CI

Babiana - - 0.833 (0.77 - 1) 0.5 (0.39 - 0.83) Moraea - - 0.923 (0.79 - 1) 0.285*** (0.44 - 0.79)

Podalyrieae 0.55 (0.25 - 0.7) 0.687* (0.68 - 1) 0** (0 - 0.142)

Protea 0.167** (0.26 - 0.74) 0.389* (0.39 - 0.83) 0.235*** (0.5 - 0.89)

Chapter 5 116

Table 5.3. Jordan Index (JSIS) of pairwise species differences for lithology and soil type data. Numbers indicate the proportion of observed pairwise species differences in edaphic conditions, analysed separately for lithology and soil type data.

Lithology Soil type 95% conf. 95% conf. observed observed interval interval Babiana 0.67 (0.53 - 0.94) 0.61 (0.56 – 0.94) Moraea 0.6 (0.53 - 0.89) 0.76 (0.48 – 0.86) Podalyrieae 0.5 (0 - 1) 0.54 (0.44 – 0.92) Protea 0.22 (0.11 - 0.5) 0.39 (0.26 - 0.7)

Chapter 5 117

Table 5.4. Relative variability of species traits. Proportion of observed pairwise species differences conditioned by the number of states in each category. Traits with the highest variability for each lineage are in bold.

Fire Edaphic Pollinators Lithology Soil type survival conditions

Babiana - 0.037 0.083 0.083 0.122

Moraea - 0.038 0.057 0.076 0.152

Podalyrieae 0.275 0.052 0 0.1 0.089

Protea 0.083 0.038 0.078 0.044 0.097

Chapter 5 118

Discussion

Differential rates of Diversification

It is important to note though that while the maximum likelihood method to detect temporal shifts in diversification rates (chapter 4) assumes constant rates across clades (Rabosky 2006b), and therefore is suitable to identify shifts that affect the entire lineage (e.g. large-scale environmental changes that act throughout the geographic range of a lineage), a node-by-node examination of tree imbalances can identify diversification rate shifts that occur in only one or a few clades ('concealed' shifts).

The whole-tree test for diversification rate shifts identified eight potential shifts, none of which were directly linked to shifts in species traits, suggesting that trait shifts and diversification rate shifts seem to be largely decoupled, indicating that other factors not included in this study might be driving these rate shifts. The test, however, cannot distinguish between increases and decreases in diversification rates, as decreases (which can be caused by either a significant increase in extinction or decrease in speciation rates) will eventually lead to the loss of the corresponding phylogenetic information throughout the evolutionary history of the lineage (Moore, et al. 2004), and thus, rate shifts are interpreted as increases along the branch leading to the more diverse clade. Nevertheless, we remain cautious about the interpretation of the diversification rate shifts, especially in cases where species-poor lineages are nested within more diverse clades, but currently available methods are unable to further distinguish the effects of speciation and extinction rates.

Spatial Patterns of Speciation

Our analysis of the geographical mode of speciation showed that all groups apart from Podalyrieae have intercepts significantly different from those obtained under

Chapter 5 119 the range randomisation test. Thus, present-day distributions might still contain information on the mode of speciation. Using the correlation between node ages and degree of sympatry, we infer that the geographic mode of speciation is predominantly allopatric, although extensive range movements have occurred as apparent by the wide scatter of overlap values. However, in a few cases we found recently diverged sister-species occurring in sympatry, a pattern that might reflect sympatric speciation occurring at least at low frequencies.

Alternatively, the high degree of range overlap observed might be due to the spatial resolution of the distribution data used in this study. The quarter degree square (QDS) grid cells have an area of approximately 625 km2 and thus omit variations at finer scales. The availability of fine-scale distribution data for Protea allowed us to evaluate the true extent of range overlap of putative sympatric species, which revealed that the actual degree of range overlap is substantially lower than at the QDS scale (mean difference in range overlap: 59%), with no sister-species pair still being fully sympatric (Table 5.5). Thus, the relatively coarse scale of this study clearly inflates the degree of range overlap and although fine scale distribution data are currently not available for the other groups, we can assume that at least some but possibly all of the cases identified as being sympatric might in fact also rather be parapatric or allopatric.

Chapter 5 120

Table 5.5. Effect of the spatial resolution of the distribution data on the degree of sympatry. Range overlap was calculated using the same dataset on two different spatial scales, a 1km2 grid and quarter degree square (QDS) gridcells.

Range overlap Range overlap Species pair (QDS) (1km2 grid)

P.pudens/P.longflora 1 0.071 P.scbriusucula/P.scolopendriifolia 1 0.25 P.stokoei/P.speciosa 1 0.656 P.susannae/P.obtusifolia 1 0.669

Chapter 5 121

Drivers of Plant Diversification

Ancestral range reconstructions identified the Montane Fynbos and Renosterveld ecoregion as the most likely ancestral area for the three of the clades studied here, with only Babiana having its ancestral range in the Succulent Karoo. These findings suggest that high topographical complexity of the Cape Mountains could be promoting species diversity by i) providing opportunities for habitat differentiation, ii) restricting gene flow between geographically isolated populations, or iii) increasing the persistence of species. A test for habitat differentiation reveals only a weak negative correlation between geographical and altitudinal range overlap (b = -0.15, r = -0.09; n=17, sister-species with geographical range overlap > 0.5). The predominantly allopatric mode of speciation (Table 5.1) on the other hand, together with evidence that dispersal distances in the Cape are generally short (Goldblatt and Manning 2000a; Latimer, et al. 2005), suggests that the second scenario provides a more plausible explanation. Finally, our results show that species diversity is not significantly different between areas of high and low topographic complexity. Together with the finding that ancestral ranges are predominantly located in topographically complex areas, this provides evidence for a higher persistence of lineages these regions, suggesting that, in part, low levels of extinction in mountainous regions have contributed to present-day patterns of diversity.

Although sister-species analyses avoid uncertainties associated with the reconstruction of character changes on the phylogeny, this approach relies on a number of assumptions. First, the identification of traits driving speciation is based on the concept of competition for limiting factors, and second, species ranges and ecologies are assumed to have remained constant since the speciation event. Despite a high probability that some ecological changes and range movements have occurred after sister-species have become separated, the large sample size and wide taxonomic range allows us to identify patterns of plausible drivers of speciation even in the presence of these potentially confounding effects.

Chapter 5 122

The factor with the highest variability between sister-species in three out of four groups (Babiana, Moraea and Protea) is soil type, consistent with the idea that adaptation to different soil types could be a major driver of plant diversification in southern Africa. This is supported by the high diversity of soil types found in the south-western Cape (Fig. 5.4), which has gradually increased throughout the Cenozoic (Cowling, et al. 2009). Furthermore, these results are consistent with studies of Neotropical plant species (Fine et al. 2004; Fine et al. 2005), which highlighted the potential importance of edaphic heterogeneity in plant speciation. In Podalyrieae, fire-survival strategy is more variable than other traits and thus it is considered a potential driver of diversification in this group. This is highlighted by the fact that fire-survival strategy is often the most important distinguishing character between closely related – and morphologically almost identical – species (Schutte, et al. 1995). These findings are robust with regard to sampling effort, which could potentially affect the correct identification of sister-species. Considering only clades where taxon sampling is above 90% (the genera Liparia, , Stirtonanthus, , and ), we find the same level of variation and thus conclude that the observed differences are unlikely to be a sampling artefact. Shifts in fire-survival strategy have previously received little attention and the frequent shifts between sister-species is somewhat surprising given the potentially complex adaptations involved in a switch between different fire-survival strategies, especially as species with mixed fire biology seem to be rare (Linder 2003). Finally, the high phenological overlap between sister-species indicates that shifts in flowering times do not act as a driving force in the creation and/or maintenance of reproductive barriers in these clades, although evidence in other clades suggest a potential role of phenology in the reduction of gene-flow (Linder 2001).

One constraint of the Jordan Index used here for sister-species comparisons is the limited power to distinguish traits with a causal role in speciation from those that show a high degree of variability (as in the null model). However, the index can be used to reject hypotheses of potential drivers of plant diversification in cases

Chapter 5 123 where traits i) are found to rarely differ between sister-species, and ii) lack the signature of a key innovation. For example, apart from the genus Babiana, pollination systems show a high degree of phylogenetic conservatism and the absence of a significant effect on speciation and/or extinction rates indicates that shifts in pollination systems are unlikely to be a main driver of diversification in these clades. These conclusions contradict the results of van der Niet and Johnson (2009), who found shifts in pollinators to be more frequent than shifts in soil types in a study including legumes, orchids and grasses. However, as their analysis did not control for the number of realized states in each trait, no definitive conclusions can be drawn regarding the causes of speciation. Potential explanations for the high frequency of pollinator shifts observed in Babiana could be that pollinator shifts are the result of a direct selection for reproductive isolation in secondary contact zones between sister-species adapted to different soil types (Goldblatt and Manning 1996; van der Niet, et al. 2006), or that the diversity of pollination systems might in fact be the result, not the cause, of high species richness (Armbruster and Muchhala 2009).

Chapter 5 124

Figure 5.4. Soil-type diversity in southern Africa. Number of soil types per quarter degree square (QDS) based on the SOTER-based soil parameter estimates for Southern Africa (Version 1.a) (Batjes 2004).

Chapter 5 125

Conclusions

Sister-species comparisons of eighty-eight sister-species pairs from several lineages show that soil-type shifts are probably the single most important driver of diversification in Babiana, Moraea and Protea, while shifts in fire-survival strategy is the most important factor for Podalyrieae. Together with complex geomorphologic conditions – in particular the increase in topo-edaphic complexity and the establishment of frequent fires – we argue that this, rather than pollinator specialisation or phenological divergence has generated the exceptional plant diversity found in the Cape biodiversity hotspot today. Contrary to previous findings in other groups, such as orchids, pollination syndromes show a high degree of phylogenetic conservatism, including groups with a large number of specialised pollination syndromes like Moraea. Thus, pollinator specialisation seems to important only in a few specific groups (e.g. Babiana, chapter 3). Comparisons with other hotspots, especially those with a Mediterranean climate will reveal whether this is a global scenario for the evolution of hyper-diverse floras.

Conclusions 126

Chapter 6: General conclusions

In this thesis, I have investigated biotic and abiotic drivers of plant diversification in order to understand the evolutionary processes underlying the exceptional plant diversity in southern African biodiversity hotspots.

The strong ecological phylogenetic structure in Babiana (chapter 2) shows that species' climatic niches retain a high degree of phylogenetic conservatism, which is reflected by a low frequency of shifts between biomes. This is in concordance with recent findings of large-scale biome conservatism in vascular plants (Crisp, et al. 2009), and suggests that species of Babiana were probably only able to successfully extend their range into the Fynbos with the establishment of drier and cooler climates.

The results are robust despite a strikingly low level of sequence variation, a result that in itself would merit further investigation. The growing availability of near- complete species-level phylogenies, in particular in the iris family, provides the opportunity to explore potential taxonomic and/or geographic correlates of this pattern. The two genera analysed here show contrasting patterns, but preliminary results from other studies indicate similarly low substitution rates in CFR-endemic lineages (e.g. Gladiolus, L. Valente pers. comm.).

My analyses of the evolution of pollination syndromes (chapter 3) reveal that current models on plant-pollinator coevolution are too restrictive. I demonstrate that floral characters (tube length and floral symmetry) evolve according to the pollinator shift model, but these adaptations do not proceed in a unidirectional fashion. A new 'reversible shift' model better explains the evolution of pollination systems through multi-directional transitions in a pollinator environment, which, despite being relatively species-poor, is rich in functional types (Johnson and

Conclusions 127

Steiner 2003). These findings challenge the commonly held idea that floral specialisation is an evolutionary dead-end and offer new perspectives towards our understanding of plant-pollinator interactions and the origin of floral and species diversity in biodiversity hotspots.

The results are of particular importance in light of global change, which is likely to disrupt plant-pollinator interactions through changes in composition and abundance of pollinator, leaving plant species with highly specialised pollination systems at high risk of extinction. The ability to adapt to pollinators with shorter proboscis lengths on the other hand could increase their resilience in this system. Nevertheless, reverse transitions were rare, and should not be viewed as a general safeguard mechanism under changing environmental conditions.

As discussed in chapter 4, my analyses of the temporal dynamics of plant radiations in the GCFR confirm recent findings that the flora represents a combination of ancient and young radiations (Linder 2008; Verboom, et al. 2009a). My results show that Cape clades in general do not have outstandingly high diversification rates and that the few well documented cases of recent and rapid radiations (Richardson, et al. 2001; Klak, et al. 2003) should be seen as exceptional rather than representative for the Cape flora. Furthermore, Valente et al. (2010; Appendix J) demonstrate that diversification rates of Cape and non- Cape clades in Protea are similar, rejecting the hypothesis that differences in species numbers is the result of different diversification rates between the Cape and the rest of Africa.

In addition, I show that diversification rates remain constant through time and do not increase or decrease in conjunction with climatic changes. Moreover, the lineage-through-time plots do not show a pattern of either an "explosive-early" diversification, the initial rapid diversification in the early history of a radiation (Rabosky and Lovette 2008), or a signal of a mass extinction event (Crisp and Cook 2009), which indicates that lineages have accumulated at a relatively

Conclusions 128 constant pace over extended periods. Hence, the climatic changes that occurred throughout the evolutionary history or these lineages (Linder 2003; Cowling, et al. 2009) did not affect their net diversification rates, providing further evidence against the hypothesized effect of the climatic deterioration at the end of the Miocene. Instead, I suggest that the relatively stable climatic conditions during the late Cenozoic (Cowling, et al. 2009) facilitated long-term species persistence, and combined with a constant rate of species diversification result in a more probable scenario to produce the diverse flora found in the Cape today.

Finally, using several large Cape clades, I have evaluated the importance of a range of biotic and abiotic factors in driving the diversification of the flora (chapter 5). Although most factors contribute to the diversity, my results strongly suggest that soil-type shifts are the single most important cause of speciation and thus the main driver of plant diversification in the GCFR. Other factors like differential adaptation to fire or pollinator shifts are important in some groups, but these do not contribute equally to shape present patterns of diversity.

It has been argued that none of the ecological factors diving plant diversification in southern Africa are unique to this region (Cowling, et al. 1996; Barraclough 2006). For example, the diversity of soil types in the GCFR is matched by areas in eastern South Africa (chapter 5, Fig. 5.4), a region significantly less diverse than the Cape. This indicates that other factors – presumably in combination with soil types – contribute to the region's diversity. Future studies considering the interactions between soil types and other factors will help to further understand the unique evolutionary history of the GCFR. For example, as the distribution of pollinators is often also linked to specific soil types, local plant-pollinator interactions are likely to be driven by interactions of both factors.

In conclusion, I have compiled the most comprehensive data set to study the evolution of two major hotspots of biodiversity: the Cape Floristic Region and the Succulent Karoo. The clades included in this study represent major lineages of the

Conclusions 129 flora in the GCFR and provide a broad taxonomic coverage (two monocot and two eudicot lineages). My study highlight how the combination of near-complete species-level molecular phylogenies together with detailed biological, ecological and biogeographical information allows light to be shed on the origin of present- day patterns of biodiversity.

My thesis offers new insights into the evolutionary processes underlying the exceptional plant diversity in southern Africa and provides a basis for comparisons with other lineages in southern Africa and other hotspot regions, like Southwest Australia or the Mediterranean basin to test whether these processes represent a global scenario for the evolution of hyper-diverse floras.

References 130

References

Adams III, E. N. 1972. Consensus techniques and the comparison of taxonomic trees. Syst Zool 21:390-397. Agapow, P.-M. and N. J. B. Isaac. 2002. MacroCAIC: revealing correlates of species richness by comparative analysis. Divers. Distrib. 8:41-43. Akaike, H. 1973. Information theory as an extension of the maximum likelihood principle. In: Petrov BN, Csaki F editors. Second International Symposium on Information Theory. Akademiai Kiado, Budapest. Alexandersson, R. and S. D. Johnson. 2002. Pollinator-mediated selection on flower-tube length in hawkmoth-pollinated Gladiolus (Iridaceae). Proc. R. Soc. Lond. B. 269:631-636. Anderson, B. and S. D. Johnson. 2008. The geographical mosaic of coevolution in plant-pollinator mutualism. Evolution 62:220-225. Anderson, B. and S. D. Johnson. 2009. Geographical covariation and local convergence of flower depth in a guild of fly-pollinated plants. New Phytol. 182:533-540. Armbruster, W. S. and B. B. Baldwin. 1998. Switch from specialized to generalized pollination. Nature 394:632. Armbruster, W. S. and N. Muchhala. 2009. Associations between floral specialisation and species diversity: cause, effect, or correlation? Evol. Ecol. 23:159-179. Bakker, F. T., A. Culham, E. M. Marais and M. Gibby. 2005. Nested radiation in Cape Pelargonium. In: Bakker FT, Chatrou LW, Gravendeel B, Pelser PB editors. Plant species-level systematics: new perspectives on pattern & process, A.R.G. Gantner, Ruggell, Liechtenstein, p. 75-100. Bakker, F. T., A. Culham, C. E. Pankhurst and M. Gibby. 2000. Mitochondrial and chloroplast DNA-based phylogeny of Pelargonium (Geraniaceae). American Journal of Botany 87:727-734.

References 131

Baldwin, B. G. and M. J. Sanderson. 1998. Age and rate of diversification of the Hawaiian silversword alliance (Compositae). Proc. Natl. Acad. Sci. USA 95:9402-9406. Barker, F. K. and F. M. Lutzoni. 2002. The utility of the incongruence length difference test. Syst Biol 51:625-637. Barraclough, T. G. 2006. What can phylogenetics tell us about speciation in the Cape flora? Divers. Distrib. 12:21-26. Barraclough, T. G. and A. P. Vogler. 2000. Detecting the geographical pattern of speciation from species-level phylogenies. Am. Nat. 155:419-434. Batjes, N. H. 2004. SOTER-based soil parameter estimates for Southern Africa. Report 2004/04. Wageningen, ISRIC - World Soil Information. Beaumont, A. J., R. P. Beckett, T. J. Edwards and C. H. Stirton. 1999. Revision of the genus (: Leguminosae). Bothalia 29:5-23. Boatwright, J. S., V. Savolainen, B.-E. van Wyk, A. L. Schutte-Vlok, F. Forest and M. van der Bank. 2008. Systematic position of the anomalous Genus Cadia and the phylogeny of the tribe Podalyrieae (Fabaceae). Systematic Botany 33:133-147. Born, J., H. P. Linder and P. Desmet. 2007. The Greater Cape Floristic Region. J. Biogeogr. 34:147-162. Burnham, K. P. and D. A. Anderson. 2002. Model selection and multimodel inference: a practical information-theoretic approach. 2nd ed. ed. New York, Springer. Bytebier, B., D. U. Bellstedt and H. P. Linder. 2007. A molecular phylogeny for the large African orchid genus Disa. Mol. Phylogenet. Evol. 43:75-90. Chan, K. M. A. and B. R. Moore. 2005. SymmeTREE: whole-tree analysis of differential diversification rates. Bioinformatics 21:1709-1710. Citerne, H. L., R. T. Pennington and Q. C. B. Cronk. 2006. An apparent reversal in floral symmetry in the legume Cadia is a homeotic transformation. Proceedings of the National Academy of Sciences of the United States of America 103:12017-12020.

References 132

Clark, J. R., R. H. Ree, M. E. Alfaro, M. G. King, W. L. Wagner and E. H. Roalson. 2008. A Comparative Study in Ancestral Range Reconstruction Methods: Retracing the Uncertain Histories of Insular Lineages. Syst. Biol. 57:693 - 707. Collins, B. and T. Rebelo. 1987. Pollination biology of the Proteaceae in Australia and southern Africa. Aust. J. Ecol. 12:387-421. Cowling, R. M. 1987. Fire and its role in coexisistence and speciation in Gondwanan shrublands. S. Afr. J. Bot. 83:106-112. Cowling, R. M. and A. T. Lombard. 2002. Heterogeneity, speciation/extinction history and climate: explaining regional plant diversity patterns in the Cape Floristic Region. Divers. Distrib. 8:163-179. Cowling, R. M., Ş. Procheş and T. C. Partridge. 2009. Explaining the uniqueness of the Cape flora: Incorporating geomorphic evolution as a factor for explaining its diversification. Mol. Phylogenet. Evol. 51:64-74. Cowling, R. M., P. W. Rundel, B. B. Lamont, M. K. Arroyo and M. Arianoutsou. 1996. Plant diversity in Mediterranean-climate regions. Trends Ecol. Evol. 11:362-366. Crisp, M., M. T. K. Arroyo, L. G. Cook, M. A. Gandolfo, G. J. Jordan, M. S. McGlone, P. H. Weston, M. Westoby, P. Wilf and H. P. Linder. 2009. Phylogenetic biome conservatism on a global scale. Nature 458:754-756. Crisp, M. D. and L. G. Cook. 2009. Explosive radiation or cryptic mass extinction? Interpreting signatures in molecular phylogenies. Evolution 63:2257-2265. Csiba, L. and M. P. Powell. 2006. DNA extraction protocols In: Savolainen V, Powell MP, Davis K editors. DNA and tissue banking for biodiversity conservation: Theory, Practice and Uses Richmond, Royal Botanic Gardens, Kew. Cunningham, C. W. 1997. Is congruence between data partitions a reliable predictor of phylogenetic accuracy? Empirically testing an iterative procedure for choosing among phylogenetic methods. Syst Biol 46:464-478.

References 133

Darlu, P. and G. Lecointre. 2002. When does the incongruence length difference test fail? Mol Biol Evol 19:432-437. Darwin, C. 1859. On the origin of species by means of natural selection, or the preservation of favoured races in the struggle for life. London, Murray. Darwin, C. 1862. On the various contrivances by which British and foreign orchids are fertilized by insects. London, Murray. Davies, T. J., V. Savolainen, M. W. Chase, P. Goldblatt and T. G. Barraclough. 2005. Environment, area, and diversification in the species-rich family Iridaceae. Am. Nat. 166:418-425. de la Torre-Bárcena, J. E., S.-O. Kolokotronis, E. K. Lee, D. W. Stevenson, E. D. Brenner, M. S. Katari, G. M. Coruzzi and R. DeSalle. 2009. The impact of outgroup choice and missing data on major seed plant phylogenetics using genome-wide EST data. PLoS ONE 4. Degnan, J. H. and N. A. Rosenberg. 2009. Gene tree discordance, phylogenetic inference and the multispecies coalescent. Trends Ecol. Evol. 24:332-340. Denton, A. L., B. L. McConaughy and B. D. Hall. 1998. Usefulness of RNA polymerase II coding sequences for estimation of green plant phylogeny. Mol Biol Evol 15:1082-1085. Dowton, M. and A. D. Austin. 2002. Increased congruence does not necessarily indicate increased phylogenetic accuracy--The behavior of the incongruence length difference test in mixed-model analyses. Syst Biol 51:19-31. Doyle, J. J. and J. L. Doyle. 1987. A rapid DNA isolation procedure for small quantities of fresh leaf tissue. Phytochemistry Bulletin 19:11-15. Driver, A., P. G. Desmet, M. Rouget, R. M. Cowling and K. E. Maze. 2003. Succulent Karoo Ecosystem Plan. Biodiversity Component, Technical Report. Cape Town, Cape Conservation Unit, Botanical Society of South Africa. Drummond, A. J. and A. Rambaut. 2007. BEAST: Bayesian evolutionary analysis by sampling trees. BMC Evol. Biol. 7:214. Edgar, R. C. 2004. MUSCLE: a multiple sequence alignment method with reduced time and space complexity. BMC Bioinformatics 5:113.

References 134

Etienne, R. S., A. M. Latimer, J. A. Silander Jr. and R. M. Cowling. 2006. Comment on "Neutral Ecological Theory Reveals Isolation and Rapid Speciation in a Biodiversity Hot Spot". Science 311:610b. Farris, J. S. 1969. A successive approximations approach to character weighting. Syst Zool 18:374-&. Farris, J. S., M. Kallersjo, A. G. Kluge and C. Bult. 1995. Testing significance of incongruence. Cladistics 10:315-319. Fine, P. V. A., D. C. Daly, G. V. Munoz, I. Mesones and K. M. Cameron. 2005. The contribution of edaphic heterogeneity to the evolution and diversity of Burseraceae trees in the western Amazon. Evolution 59:1464-1478. Fine, P. V. A., I. Mesones and P. D. Coley. 2004. Herbivores promote habitat specialization by trees in amazonian forests. Science 305:663-665. Fitzpatrick, B. M. and M. Turelli. 2006. The geography of mammalian speciation: mixed signals from phylogenies and range maps. Evolution 60 601-615. Galley, C., B. Bytebier, D. U. Bellstedt and H. P. Linder. 2007. The Cape element in the Afrotemperate flora: from Cape to Cairo? Proc. R. Soc. Lond. B. 274:535-543. Gaston, K. J. 1996. Species-range-size distributions: patterns, mechanisms and implications. Trends Ecol. Evol. 11:197-201. Goldblatt, P. 1986. The of Southern Africa. National Botanic Gardens. Goldblatt, P. 1992. New species, chromosome cytology and notes on the southern African Iridaceae- - Moraea, Roggeveldia and Homeria. S. Afr. J. Bot. 58:209-214. Goldblatt, P. 1997. Floristic diversity in the Cape Flora of South Africa. Biodivers. Conserv. 6:359-377. Goldblatt, P. 1998. Reduction of Barnardiella, Galaxia, Gynandriris, Hexaglottis, Homeria, and Roggeveldia in Moraea (Iridaceae: Irideae). Novon 8:371-377. Goldblatt, P., P. Bernhardt and J. C. Manning. 2005a. Pollination mechanisms in the African genus Moraea (Iridaceae: Irioideae): floral divergence and adaptation for pollinators. Adansonia 27:21-46.

References 135

Goldblatt, P., T. J. Davies, J. C. Manning, M. van der Bank and V. Savolainen. 2006. Phylogeny of Iridaceae subfamily Crocoideae based on a combined multigene plastid DNA analysis. Aliso 22:399-411. Goldblatt, P. and J. Manning. 2000a. Cape Plants - A conspectus of the Cape flora of South Africa. Pretoria, National Botanical Institute. Goldblatt, P. and J. C. Manning. 1996. Phylogeny and speciation in Lapeirousia subgenus Lapeirousia (Iridaceae: Ixioideae). Ann. Mo. Bot. Gard. 83:346- 361. Goldblatt, P. and J. C. Manning. 2000b. The long-proboscid fly pollination system in southern Africa. Ann. Mo. Bot. Gard. 87:146-170. Goldblatt, P. and J. C. Manning. 2000c. New species of Moraea (Iridaceae- Iridoideae) from southern Africa. Novon 10:14-21. Goldblatt, P. and J. C. Manning. 2002. Notes and new species of Moraea (Iridaceae : Iridoideae) from the southern African winter-rainfall zone. Novon 12:352-359. Goldblatt, P. and J. C. Manning. 2004. New species of Ixia (Crocoideae) and Moraea (Iridoideae), and taxonomic notes on some other African Iridaceae. Novon 14:288-298. Goldblatt, P. and J. C. Manning. 2006. Radiation of pollination systems in the Iridaceae of sub-Saharan Africa. Ann Bot-London 97:317-344. Goldblatt, P. and J. C. Manning. 2007a. Floral biology of Babiana (Iridaceae: Crocoideae): Adaptive floral radiation and pollination. Ann. Mo. Bot. Gard. 94:709-733. Goldblatt, P. and J. C. Manning. 2007b. A revision of the southern African genus Babiana, Iridaceae: Crocoideae. South African National Biodiversity Institute, Pretoria. Goldblatt, P. and J. C. Manning. in press. New taxa of Babiana (Iridaceae: Crocoideae) from coastal Western Cape, South Africa. Bothalia. Goldblatt, P., J. C. Manning, J. Davies, V. Savolainen and S. Rezai. 2004. Cyanixia, a new genus for the Socotran endemic Babiana socotrana (Iridaceae-Crocoideae). Edinb. J. Bot. 60:517-532.

References 136

Goldblatt, P., J. C. Manning and R. Gereau. 2008a. Two new species of Babiana (Iridaceae: Crocoideae) from western South Africa, new names for B. longiflora and B. thunbergii, and comments on the original publication of the genus. Bothalia 38:49-55. Goldblatt, P., J. C. Manning and D. Snijman. 2005b. Cape plants: corrections and additions to the flora. 1. Bothalia 35:35-46. Goldblatt, P., A. Rodriguez, M. P. Powell, T. J. Davies, J. C. Manning, M. Van der Bank and V. Savolainen. 2008b. Iridaceae 'Out of Australasia'? Phylogeny, biogeography, and divergence time based on plastid DNA sequences. Syst. Bot. 33:495-508. Goldblatt, P., V. Savolainen, O. Porteous, I. Sostaric, M. Powell, G. Reeves, J. C. Manning, T. G. Barraclough and M. W. Chase. 2002. Radiation in the Cape flora and the phylogeny of peacock irises Moraea (Iridaceae) based on four plastid DNA regions. Mol. Phylogenet. Evol. 25:314-360. Goslee, S. C. and D. L. Urban. 2007. The ecodist package for dissimilarity-based analysis of ecological data. Journal of Statistical Software 22:1-19. Gower, J. C. 1971. A general coefficient of similarity and some of its properties. Bionformatics 27:623-637. Grant, V. and E. J. Temeles. 1992. Foraging ability of rufous hummingbirds on hummingbird flowers and hawkmoth flowers. Proc. Natl. Acad. Sci. USA 89:9400-9404. Grenyer, R., C. D. L. Orme, S. F. Jackson, G. H. Thomas, R. G. Davies, T. J. Davies, K. E. Jones, V. A. Olson, R. S. Ridgley, P. C. Rasmussen, T.-S. Ding, P. M. Bennett, T. M. Blackburn, K. J. Gaston, J. L. Gittleman and I. P. F. Owens. 2006. Global distribution and conservation of rare and threatened vertebrates. Nature 444:93-96. Harder, L. D. and S. D. Johnson. 2009. Darwin's beautiful contrivances: evolutionary and functional evidence for floral adaptation. New Phytol. 183:530-545.

References 137

Hardy, C. R., P. Moline and H. P. Linder. 2008. A phylogeny for the African Restionaceae and new perspectives on morphology’s role in generating complete species phylogenies for large clades. Int. J. Plant Sci. 169:377-390. Hawkins, J. A. 2006. Using phylogeny to investigate the origins of the Cape flora: the importance of taxonomic, gene and genome sampling strategies. Diversity and Distributions 12:27-33. Hegland, S. J., A. Nielsen, A. Lázaro, A.-L. Bjerknes and Ø. Totland. 2009. How does climate warming affect plant-pollinator interactions? Ecol. Lett. 12:184- 195. Hijmans, R. J., S. E. Cameron, J. L. Parra, P. G. Jones and A. Jarvis. 2005. Very high resolution interpolated climate surfaces for global land areas. International Journal of Climatology 25:1965-1978. Hileman, L. C., E. M. Kramer and D. A. Baum. 2003. Differential regulation of symmetry genes and the evolution of floral morphologies. Proceedings of the National Academy of Sciences of the United States of America 100:12814- 12819. Huelsenbeck, J. P. and F. Ronquist. 2001. MrBayes: Bayesian inference of phylogenetic trees. Bioinformatics 17:754-755. Hughes, C. and R. Eastwood. 2006. Island radiation on a continental scale: Exceptional rates of plant diversification after uplift of the Andes. Proc. Natl. Acad. Sci. USA 103:10334-10339. Hurvich, C. M. and C.-L. Tsai. 1989. Regression and time series model selection in small samples. Biometrika 76:297-307. Johnson, S. D. 1996. Pollination, adaptation and speciation models in the Cape flora of South Africa. Taxon 45:59-66. Johnson, S. D. 2006. Pollinator-driven speciation in plants. In: Harder Lawrence D, Barrett Spencer CH editors. Ecology and Evolution of Flowers. New York, Oxford University Press, p. 295-310. Johnson, S. D., H. P. Linder and K. E. Steiner. 1998. Phylogeny and radiation of pollination systems in Disa (Orchidaceae). Am. J. Bot. 85:402-411.

References 138

Johnson, S. D. and K. E. Steiner. 1997. Long-tongued fly pollination and evolution of floral spur length in the Disa draconis complex (Orchidaceae). Evolution 51:45-53. Johnson, S. D. and K. E. Steiner. 2003. Specialized pollination systems in southern Africa. S. Afr. J. Sci. 99:345-348. Jordan, W. C., M. W. Courtney and J. E. Neigel. 1996. Low levels of intraspecific genetic variation at a rapidly evolving chloroplast DNA locus in North American duckweeds (Lemnaceae). Am. J. Bot. 83:430-439. Jürgens, N. 1997. Floristic biodiversity and history of African arid regions. Biodivers. Conserv. 6:495-514. Katoh, K., K. Misawa, K. Kuma and T. Miyata. 2002. MAFFT: a novel method for rapid multiple sequence alignment based on fast Fourier transform. Nucleic Acids Res 30:3059-3066. Katoh, K. and H. Toh. 2008. Recent developments in the MAFFT multiple sequence alignment program. Brief Bioinform 9:286-298. Kay, K. M., P. A. Reeves, R. G. Olmstead and D. W. Schemske. 2005. Rapid speciation and the evolution of hummingbird pollination in neotropical Costus subgenus Costus (Costaceae): evidence from nrDNA ITS and ETS sequences. Am. J. Bot. 92:1899-1910. Ker Gawler, J. 1802. Babiana plicata. Sweet-scented babiana. Curtis's Bot. Mag. 16:t. 576. Kier, G., H. Kreft, T. M. Lee, W. Jetz, P. L. Ibisch, C. Nowicki, J. Mutke and W. Barthlott. 2009. A global assessment of endemism and species richness across island and mainland regions. Proc. Natl. Acad. Sci. USA 106:9322-9327. Klak, C., G. Reeves and T. Hedderson. 2003. Unmatched tempo of evolution in Southern African semi-desert ice plants. Nature 427:63-65. Kreft, H. and W. Jetz. 2007. Global patterns and determinants of vascular plant diversity. Proc. Natl. Acad. Sci. USA 104:5925-5930. Küper, W., J. H. Sommer, J. C. Lovett, J. Mutke, H. P. Linder, H. J. Beentje, R. S. A. R. Van Rompaey, C. Chatelain, M. Sosef and W. Barthlott. 2004. Africa's Hotspots of biodiversity redefined. Ann. Mo. Bot. Gard. 91:525-535.

References 139

Kurzweil, H., H. P. Linder and P. Chesselet. 1991. The phylogeny and evolution of the Pterygodium-Corycium complex (Coryciinae, Orchidaceae). Plant Syst. Evol. 175:161-223. Lamoreux, J. F., J. C. Morrison, T. H. Ricketts, D. M. Olson, E. Dinerstein, M. W. McKnight and H. H. Shugart. 2006. Global tests of biodiversity concordance and the importance of endemism. Nature 440:212-214. Larkin, R. and T. Guilfoyle. 1993. The second largest subunit of RNA polymerase II from Arabidopsis thaliana. Nucleic Acids Res 21:1038. Latimer, A. M., J. A. Silander Jr. and R. M. Cowling. 2005. Neutral ecological theory reveals isolation and rapid speciation in a biodiversity hot spot. Science 309:1722-1725. Lee, M. S. Y. 2001. Uninformative characters and apparent conflict between molecules and morphology. Mol Biol Evol 18:676-680. Levyns, M. R. 1964. Migrations and the origins of the Cape flora. Trans. R. Soc. S. Afr. 37:85-107. Lewis, G. J. 1959. The genus Babiana. Journal of South African Botany Suppl. Vol. III. Lewis, P. O. 2001. A likelihood approach to estimating phylogeny from discrete morphological character data. Syst. Biol. 50:913-925. Linder, H. P. 2001. The African Restionaceae The African Restionaceae. Contrib. Bolus Herb. 20. Linder, H. P. 2003. The radiation of the Cape flora, southern Africa. Biol. Rev. Camb. Philos. Soc. 78:597-638. Linder, H. P. 2005. Evolution of diversity: the Cape flora. Trends Plant Sci. 10:536-541. Linder, H. P. 2008. Plant species radiations: where, when, why? Philos. Trans. R. Soc. Lond. B. 363:3097-3105. Linder, H. P. and C. R. Hardy. 2004. Evolution of the species-rich Cape flora. Philos. Trans. R. Soc. Lond. B. 359:1623-1632.

References 140

Linder, H. P., M. E. Meadows and R. M. Cowling. 1992. History of the Cape flora. In: Cowling RM editor. The Ecology of Fynbos: Nutrients, Fire and Diversity, Oxford University Press, p. 113-134. Linder, H. P. and J. H. Vlok. 1991. The morphology, taxonomy and evolution of Rhodocoma (Restionaceae). Plant Syst. Evol. 175:139-160. Maddison, W. P. and D. R. Maddison. 2008. Mesquite: a modular system for evolutionary analysis. Version 2.5. Maddison, W. P., P. E. Midforrd and S. P. Otto. 2007. Estimating a binary character's effect on speciation and extinction. Syst. Biol. 56:701-710. Maechler, M., P. Rousseeuw, A. Stuyf and M. Hubert. 2005. Cluster analysis basics and extensions. Magallón, S. and A. Castillo. 2009. Angiosperm diversification through time. Am. J. Bot. 96:000-000. Magallón, S. and M. J. Sanderson. 2001. Absolute diversification rates in angiosperm clades. Evolution 55:1762–1780. Memmott, J., P. G. Craze, N. M. Waser and M. V. Price. 2007. Global warming and the disruption of plant-pollinator interactions. Ecol. Lett. 10:710-717. Midgley, G. F., G. Reeves and C. Klak. 2005. Late Tertiary and Quaternary climate change and centres of endemism in the southern African flora. In: Purvis A, Gittleman JL, Brooks TM editors. Phylogeny and Conservation. Cambridge, UK, Cambridge University Press, p. 230-242. Mittermeier, R. A., P. R. Gil, M. Hoffmann, J. D. Pilgrim, T. M. Brooks, C. G. Mittermeier, J. F. Lamoreux and G. A. B. da Fonseca. 2004. Hotspots revisited: Earth's biologically richest and most endangered terrestrial ecoregions. Sierra Madre, University of Virginia. Mittermeier, R. A., N. Myers, J. B. Thomsen, G. A. B. da Fonseca and S. Olivieri. 1998. Biodiversity hotspots and major tropical wilderness areas: Approaches to setting conservation priorities. Conserv. Biol. 12:516-520. Moore, B. R., K. M. A. Chan and M. J. Donoghue. 2004. Detecting diversification rate variation in supertrees. In: Bininda-Emonds ORP editor. Phylogenetic

References 141

Supertrees: Combining Information to Reveal the Tree of Life. Dordrecht, the Netherlands, Kluwer Academic, p. 487-533. Muasya, A. M., D. A. Simpson, M. W. Chase and A. Culham. 1998. An assessment of suprageneric phylogeny in Cyperaceae using rbcL DNA sequences. Plant Syst. Evol. 211:257-271. Mucina, L. and M. C. Rutherford. 2006. The Vegetation of South Africa, Lesotho and Swaziland. Strelitzia 19. Pretoria, SANBI. Müller, K. 2005. SeqState - Primer Design and Sequence Statistics for Phylogenetic DNA Datasets. Applied Bioinformatics 4:65-69. Mutke, J. and W. Barthlott. 2005. Patterns of vascular plant diversity at continental to global scales. In: Friis I, Balslev H editors. Biologiske Skrifter. Copenhagen, The Royal Danish Academy of Sciences and Letters, p. 521- 537. Myers, N. 1988. Threatened Biotas: "Hot Spots" in Tropical Forests. The Environmentalist 8:187-208. Myers, N. 1990. The Biodiversity Challenge: Expanded Hot-Spots Analysis. The Environmentalist 10:243-256. Myers, N., R. A. Mittermeier, C. G. Mittermeier, G. A. B. da Fonseca and J. Kent. 2000. Biodiversity hotspots for conservation priorities. Nature 403:853-858. Nickerson, J. and G. Drouin. 2003. The sequence of the largest subunit of RNA polymerase II is a useful marker for inferring seed plant phylogeny. Molecular Phylogenetics and Evolution 31:403-415. Norup, M. V., J. Dransfield, M. W. Chase, A. S. Barford, E. S. Fernando and W. J. Baker. 2006. Homoplasious character combinations and generic delimitation: A case study from the Indo-Pacific Arecoid palms (Arecaceae: Areceae). Am. J. Bot. 97:1065-1080. Nylander, J. A. A. 2004. MrModeltest v.2. Evolutionary Biology Centre, Uppsala University, Program distributed by the author. Oliver, E. G. H. 1991. The Ericoideae (Ericaceae) - a review., Contrib. Bol. Herb. 19.

References 142

Olmstead, R. G., H. J. Michaels, K. M. Scott and J. D. Palmer. 1992. Monophyly of the Asteridae and identification of their major lineages inferred from DNA sequences of rbcL. Ann. Mo. Bot. Gard. 79:249-265. Olmstead, R. G. and J. A. Sweere. 1994. Combining data in phylogenetic systematics: An empirical approach using three molecular data sets in the Solanaceae. Syst. Biol. 43:467-481. Olson, D. M., E. Dinerstein, E. D. Wikramanayake, N. D. Burgess, G. V. N. Powell, E. C. Underwood, J. A. D’Amico, I. Itoua, H. E. Strand, J. C. Morrison, C. J. Loucks, T. F. Allnutt, T. H. Ricketts, Y. Kura, J. F. Lamoreux, W. W. Wettengel, P. Hedao and K. R. Kassem. 2001. Terrestrial Ecoregions of the World: A New Map of Live on Earth. Bioscience 51:933-937. Orme, C. D. L., R. G. Davies, M. Burgess, F. Eigenbrod, N. Pickup, V. A. Olson, A. J. Webster, T.-S. Ding, P. C. Rasmussen, R. S. Ridgely, A. J. Stattersfield, P. M. Bennett, T. M. Blackburn, K. J. Gaston and I. P. F. Owens. 2005. Global hotspots of species richness are not congruent with endemism or threat. Nature 436:1016-1019. Oxelman, B., M. Lidén and D. Berglund. 1997. Chloroplast rps16 intron phylogeny of the tribe Sileneae (Caryophyllaceae). Plant Syst. Evol. 206:393- 410. Oxelman, B., N. Yoshikawa, B. L. McConaughy, J. Luo, A. L. Denton and B. D. Hall. 2004. RPB2 gene phylogeny in flowering plants, with particular emphasis on . Mol Phylogenet Evol 32:462-479. Page, R. D. M. and E. C. Holmes. 1998. Molecular evolution: a phylogenetic approach. Oxford, Blackwell Publishing. Pagel, M., A. Meade and D. Barker. 2004. Bayesian Estimation of Ancestral Character States on Phylogenies. Syst. Biol. 53:673-684. Paradis, E. 2006. Analysis of phylogenetics and evolution with R. New York, Springer. Paradis, E., J. Claude and K. Strimmer. 2004. APE: analyses of phylogenetics and evolution in R language. Bioinformatics 20:289-190.

References 143

Pauw, A., J. Stofberg and R. J. Waterman. 2009. Flies and Flowers in Darwin's Race. Evolution 63:268-279. Perret, M., A. Chautems, R. Spichiger, T. G. Barraclough and V. Savolainen. 2007. The geographical pattern of speciation and floral diversification in the neotropics: The tribe Sinningieae (Gesneriaceae) as a case study. Evolution 61:1641-1660. Perret, M., A. Chautems, R. Spichiger, G. Kite and V. Savolainen. 2003. Systematics and evolution of tribe Sinningieae (Gesneriaceae): Evidence from phylogenetic analyses of six plastid DNA regions and nuclear ncpGS. Am. J. Bot. 90:445-460. Philippe, H., E. A. Snell, E. Bapteste, P. Lopez, P. W. H. Holland and D. Casane. 2004. Phylogenomics of eukaryotes: impact of missing data on large alignments. Mol Biol Evol 21:1740-1752. Purvis, A. and A. Rambaut. 1995. Comparative analysis by independent contrasts (CAIC): an Apple Macintosh application for analysing comparative data. CABIOS 11:247-251. Pybus, O. G. and P. H. Harvey. 2000. Testing macro-evolutionary models using incomplete molecular phylogenies. Proc. R. Soc. Lond. B. 267:2267-2272. Rabosky, D. L. 2006a. LASER: A maximum likelihood toolkit for detecting temporal shifts in diversification rates from molecular phylogenies. Evolutionary Bioinformatics Online 2:257-260. Rabosky, D. L. 2006b. Likelihood methods for detecting temporal shifts in diversification rates. Evolution 60:1152-1164. Rabosky, D. L. and I. J. Lovette. 2008. Explosive evolutionary radiations: Decreasing speciation or increasing extinction through time? Evolution 62:1866-1875. Rambaut, A. 2002. Phyl-O-Gen. Phylogenetic Tree Simulator Package v.1.1. Oxford, University of Oxford. Rambaut, A. and A. J. Drummond. 2007. Tracer v.1.4. Available from http://beast.bio.ed.ac.uk/Tracer.

References 144

Rebelo, T. 2001. : A field guide to the Proteas of South Africa. 2nd ed., Fernwood Press. Ree, R. H., B. R. Moore, C. O. Webb and M. J. Donoghue. 2005. A likelihood framework for inferring the evolution of geographic range on phylogenetic trees. Evolution 59:2299-2311. Ree, R. H. and S. A. Smith. 2008. Maximum likelihood inference of geographic range evolution by dispersal, local extinction, and cladogenesis. Syst. Biol. 57:4-14. Reeves, G., M. W. Chase, P. Goldblatt, P. Rudall, M. F. Fay, A. V. Cox, B. Lejeune and T. Souza-Chies. 2001. Molecular Systematics of Iridaceae: Evidence from four plastid DNA regions. Am. J. Bot. 88:2074-2087. Richardson, J. E., F. M. Weitz, M. F. Fay, Q. C. B. Cronk, H. P. Linder, G. Reeves and M. W. Chase. 2001. Rapid and recent origin of species richness in the Cape flora of South Africa. Nature 412:181-183. Rourke, J. P. 1972. Taxonomic studies on R.Br. Journal of South African Botany 8 (Suppl.):1-194. Sanderson, M. J. 2002. Estimating absolute rates of molecular evolution and diversgence times: A penalized likehood approach. Mol. Biol. Evol. 19:101- 109. Sanderson, M. J., A. C. Driskell, R. H. Ree, O. Eulenstein and S. Langley. 2003. Obtaining maximal concatenated phylogenetic data sets from large sequence databases. Mol Biol Evol 20:1036-1042. Sauquet, H., P. H. Weston, C. L. Anderson, N. P. Barker, D. J. Cantrill, A. R. Mast and V. Savolainen. 2009. Contrasted patterns of hyperdiversification in Mediterranean hotspots. Proc. Natl. Acad. Sci. USA 106:221-225. Schrire, B. D., M. Lavin and G. P. Lewis. 2005. Global distribution patterns of the Leguminosae: insights from recent phylogenies. Biol. Skr. 55:375-422. Schutte, A. L. 1995. A taxonomic study of the tribes Podalyrieae and Liparieae (Fabaceae). . University of Johannesburg.

References 145

Schutte, A. L. and B. E. van Wyk. 1998. Evolutionary relationships in the Podalyrieae and Liparieae (Fabaceae) based on morphological, cytological and chemical evidence. Plant Syst. Evol. 209:1-31. Schutte, A. L., J. H. J. Vlok and B. E. van Wyk. 1995. Fire-survival strategy - a character of taxonomic, ecological and evolutionary importance in fynbos legumes. Plant Syst. Evol. 195:243-259. Shaw, J., E. B. Lickey, E. E. Schilling and R. L. Small. 2007. Comparison of whole chloroplast genome sequences to choose noncoding regions for phylogenetic studies in angiosperms: The Tortoise and the Hare III. Am. J. Bot. 94:275-288. Simmons, M. P. and H. Ochoterena. 2000. Gap characters in sequence-based phylogenetic analyses. Syst. Biol. 49:369-381. Sokal, R. R. and F. J. Rohlf. 1995. Biometry: The principles and practice of statistics in biological research. 3rd ed. New York, W.H. Freeman. Stamatakis, A. 2006. RAxML-VI-HPC: Maximum likelihood-based phylogenetic analyses with thousands of taxa and mixed models. Bioinformatics 22:2688- 2690. Stamatakis, A., P. Hoover and J. Rougemont. 2008. A rapid bootstrap algorithm for the RAxML web servers. Syst. Biol. 57:758-771. Sun, G., M. Pourkheirandish and T. Komatsuda. 2009. Molecular evolution and phylogeny of the RPB2 gene in the genus Hordeum. Ann Bot:mcp020. Swofford, D. L. 2002. PAUP* Phylogenetic Analysis Using Parsimony (*and other methods) v. 4.0b10. Sunderland, Massachusetts, Sinauer Associates. Taberlet, P., L. Gielly, G. Pautou and J. Bouvet. 1991. Universal primers for amplification of three non-coding regions of the chloroplast DNA. Plant Mol. Biol. 17:1105-1109. Takhtajan, A. 1986. Floristic regions of the world. Berkeley, CA, University of California Press. Thuiller, W., G. F. Midgley, M. Rouget and R. M. Cowling. 2006. Predicting patterns of plant species richness in megadiverse South Africa. Ecography 29:733-744.

References 146

Tripp, E. A. and P. S. Manos. 2008. Is floral specialisation an evolutionary dead- end? Pollination system transitions in Ruella (Acanthaceae). Evolution 62:1712-1737. v. Humboldt, A. and A. Bonpland. 1815. Voyage aux Régions équinoxiales du Nouveau Continent, fait en 1799, 1800, 1801, 1802, 1803 et 1804. Vol. II. Paris. Valente, L., G. Reeves, J. Schnitzler, I. Pizer Mazon, M. Fay, T. Rebelo, M. Chase and T. Barraclough. 2010. Diversification of the African genus Protea in the Cape biodiversity hotspot and beyond: equal rates but different spatial scales. Evolution 64:745-760. van der Niet, T. and S. D. Johnson. 2009. Patterns of plant speciation in the Cape floristic region. Mol. Phylogenet. Evol. 51:85-93. van der Niet, T., S. D. Johnson and H. P. Linder. 2006. Macroevolutionary data suggest a role for reinforcement in pollination system shifts. Evolution 60:1596-1601. van der Niet, T. and H. P. Linder. 2008. Dealing with incongruence in the quest for the species tree: A case study from the orchid genus Satyrium. Mol. Phylogenet. Evol. 47:154-174. van Wyk, A. E. and G. F. Smith. 2001. Regions of floristic endemism in southern Africa. Hatfield, Umdaus Press. Verboom, G. A., J. K. Archibald, F. T. Bakker, D. U. Bellstedt, F. Conrad, L. L. Dreyer, F. Forest, C. Galley, P. Goldblatt, J. F. Henning, K. Mummenhoff, H. P. Linder, A. M. Muasya, K. C. Oberlander, V. Savolainen, D. A. Snijman, T. Van der Niet and T. L. Nowell. 2009a. Origin and diversification of the Greater Cape flora: Ancient species repository, hot-bed of recent radiation, or both? Mol. Phylogenet. Evol. 51:44-53. Verboom, G. A., L. L. Dreyer and V. Savolainen. 2009b. Understanding the origins and evolution of the world's biodiversity hotspots: The biota of the African `Cape Floristic Region' as a case study. Mol. Phylogenet. Evol. 51:1- 4.

References 147

Verboom, G. A., H. P. Linder and W. D. Stock. 2003. Phylogenetics of the grass genus Ehrharta: Evidence for radiation in the summer-arid zone of the South African Cape. Evolution 57:1008-1021. Verdú, M., J. G. Pausas, J. G. Segarra-Moragues and F. Ojeda. 2007. Burning phylogenies: Fire, molecular evolutionary rates, and diversification. Evolution 61:2195-2204. Warrilow, D. and R. H. Symons. 1996. Sequence analysis of the second largest subunit of tomato RNA polymerase II. Plant Mol Biol 30:337-342. Wasserthal, L. T. 1997. The pollinators of the Malagasy star orchids Angraecum sesquipedale, A. sororium and A. compactum and the evolution of extremely long spurs by pollinator shift. Botanica Acta 110:343-359. Wasserthal, L. T. 1998. Deep flowers for long tubes. Trends Ecol. Evol. 13:459- 460. Wendel, J. F. and J. J. Doyle. 1998. Phylogenetic incongruence: Window into genome history and molecular evolution. In: Soltis DE, Soltis PS, Doyle JJ editors. Molecular systematics of plants II: DNA sequencing. Boston, Kluwer Academic Press, p. 265-296. Whittall, J. B. and S. A. Hodges. 2007. Pollinator shifts drive increasingly long nectar spurs in columbine flowers. Nature 447:706-710. Wiens, J. J. 2003. Missing data, incomplete taxa, and phylogenetic accuracy. Systematic Biology 52:528-538. Wikström, N., V. Savolainen and M. W. Chase. 2001. Evolution of the angiosperms: calibrating the family tree. Proc. R. Soc. Lond. B. 268:2211- 2220. Wilson, P., A. D. Wolfe, W. S. Armbruster and J. D. Thomson. 2007. Constrained lability in floral evolution: counting convergent origins of hummingbird pollination in Penstemon and Keckiella New Phytol. 176:883-890. Yoder, A. D., J. A. Irwin and B. A. Payseur. 2001. Failure of the ILD to determine data combinability for slow loris phylogeny. Syst Biol 50:408-424.

Appendix A List of Babiana species included in this study with voucher information and GenBank/EMBL accession numbers for each gene region. Accession numbers in bold represent sequences only available for analyses in chapters 1 and 2.

rpl32- trnV- Species Voucher matK ndhF rbcL rpl-16 rpoC1 rps-16 trnL-F RPB2 trnL ndhC Goldblatt 11464 (MO), B. ambigua (Roemer & Schnitzler & Manning 24 GQ381324 GQ381390 GQ381548 GQ381629 GQ381709 GQ381793 GQ381879 GQ381959 GQ382032 GQ381470 Schultes) G.J.Lewis (NBG)

B. angustifolia Sweet Helme 2137 (NBG) GQ381325 GQ381391 GQ381549 GQ381630 GQ381710 GQ381794 GQ381880 GQ381960 GQ382033 GQ381471 B. arenicola Goldblatt & Goldblatt & Porter 12172 (MO) GQ381326 GQ381392 GQ381550 GQ381631 GQ381711 GQ381795 GQ381881 GQ381961 GQ382034 GQ381472 J.C.Manning Goldblatt & Manning 11323 B. attenuata G.J.Lewis GQ381327 GQ381393 GQ381551 N/A GQ381712 GQ381796 GQ381882 GQ381962 GQ382035 GQ381473 (MO), Schnitzler 34 (K) B. auriculata G.J.Lewis Goldblatt & Porter 12728 (MO) GQ925455 GQ381394 GQ381552 GQ381632 GQ381713 GQ381797 GQ381883 GQ381963 GQ382036 GQ381474

B. avicularis Goldblatt & Goldblatt & Porter 13109 GQ925456 N/A N/A N/A GQ925502 N/A N/A N/A N/A GQ925484 J.C.Manning (NBG) Goldblatt & Porter 11954A B. bainesii Baker GQ925457 GQ381395 GQ381553 N/A GQ381714 N/A GQ381884 GQ381964 GQ382037 GQ381475 (NBG) B. blanda (L.Bolus) Schnitzler 54 (K) GQ381328 GQ381396 GQ381554 GQ381633 GQ381715 GQ381798 GQ381885 N/A GQ382038 GQ381476 G.J.Lewis B. carminea J.C.Manning & Goldblatt & Porter 12131 (MO) GQ381329 GQ381397 GQ381555 GQ381635 GQ925503 GQ381799 GQ381886 GQ381965 GQ382039 GQ381477 Goldblatt

B. cedarbergensis Goldblatt & Porter 12165 GQ381330 GQ381398 GQ381556 GQ381636 GQ381716 GQ381800 GQ381887 GQ381966 GQ382040 GQ381478 G.J.Lewis (NBG) B. cinnamomea Manning 2228 (NBG) GQ381331 GQ381399 GQ381557 GQ381637 GQ381717 GQ381801 GQ381888 GQ381967 GQ382041 GQ381479 J.C.Manning & Goldblatt B. confusa (G.J.Lewis) Manning 3034 (NBG) GQ381332 GQ381400 GQ381558 GQ381638 GQ381718 GQ381802 GQ381889 GQ381968 GQ382042 GQ381480 Goldblatt & J.C.Manning Goldblatt & Manning 11077 148 B. crispa G.J.Lewis GQ381333 GQ381401 GQ381559 N/A GQ381719 GQ381803 GQ381890 N/A GQ382043 GQ381481 (MO)

Appendix B. cuneata J.C.Manning & Chase 36645 (K) GQ381334 GQ381402 GQ381560 GQ381639 GQ381720 GQ381804 GQ381891 GQ381969 GQ382044 GQ381482 Goldblatt Goldblatt & Manning 11326 B. curviscapa G.J.Lewis GQ381335 GQ381403 GQ381561 GQ381640 GQ381721 GQ381805 GQ381892 GQ381970 GQ382045 GQ925485 (NBG), Schnitzler 39 (K) A B. dregei Baker Ramsey 667 (NBG) GQ381336 GQ381404 GQ381562 GQ381641 GQ381722 GQ381806 GQ381893 GQ381971 GQ382046 GQ381483 Schnitzler & Manning 07 B. ecklonii Klatt GQ925458 GQ381405 GQ381563 GQ381642 GQ381723 GQ381807 GQ381894 GQ381972 GQ382047 GQ381484 (NBG) B. engysiphon J.C.Manning Schnitzler 47 (K) GQ925459 GQ381406 GQ381564 GQ381643 GQ381724 GQ381808 GQ925505 N/A GQ382048 GQ381485 & Goldblatt B. fimbriata (Klatt) Baker Goldblatt 11452 (NBG) GQ925460 GQ381407 GQ381565 GQ381644 GQ381725 N/A GQ381895 GQ381973 GQ382049 GQ925486

B. flabellifolia Harv. ex Goldblatt & Manning 11367 GQ381337 GQ925477 GQ381566 GQ381645 GQ381726 GQ381809 GQ381896 GQ381974 GQ382050 GQ381486 Klatt (NBG), Schnitzler 46 (K) Goldblatt & Porter 12204 B. fourcadei G.J.Lewis GQ381338 GQ381408 GQ381567 GQ381646 GQ381727 GQ381810 GQ381897 GQ381975 GQ382051 GQ381487 (NBG)

B. fragrans (Jacq.) Schnitzler & Manning 26 GQ381339 GQ381409 GQ381568 GQ381647 GQ381728 GQ381811 GQ381898 GQ381976 GQ382052 GQ381488 Goldblatt & J.C.Manning (NBG), Steiner 3245 (NBG) Goldblatt & Porter 12416 B. framesii L.Bouls GQ381340 GQ381410 GQ381569 GQ381648 N/A GQ381812 GQ381899 GQ381977 GQ382053 GQ381489 (NBG) Schnitzler & Manning 02 B. geniculata G.J.Lewis GQ381341 GQ381411 GQ381570 GQ381649 GQ381729 GQ381813 GQ381900 GQ381978 GQ382054 GQ381490 (NBG) Goldblatt & Porter 11780 B. grandiflora Goldblatt & (NBG), Goldblatt & Porter GQ925461 GQ925478 GQ381571 GQ381650 GQ381730 N/A GQ381901 GQ381979 GQ382055 GQ381491 J.C.Manning 12864 (MO) Schnitzler & Manning 04 B. hirsuta (Lam.) Goldblatt (NBG), Goldblatt & Porter GQ381342 GQ381412 GQ381572 GQ381651 GQ381731 GQ381814 GQ381902 GQ381980 GQ382056 GQ381492 & J.C.Manning 11737 (MO) B. horizontalis G.J.Lewis Manning 3000 (NBG) GQ381343 GQ381413 GQ381573 GQ381652 GQ381732 GQ381815 GQ381903 GQ381981 GQ382057 GQ381493

B. hypogaea Burch. Mannheimer 1960 (NBG) GQ925462 GQ381414 N/A N/A GQ381733 GQ381816 N/A N/A GQ382058 GQ925487 Goldblatt & Nänni 11946 B. inclinata J.C.Manning & GQ381344 GQ381415 GQ381574 GQ381653 GQ381734 GQ381817 GQ381904 GQ381982 GQ382059 GQ381494 (MO), Goldblatt & Porter 149 Goldblatt 12381 (NBG)

Appendix

B. karooica Goldblatt & Goldblatt & Porter 12051 GQ381345 GQ381416 GQ381575 GQ381654 GQ925504 GQ381818 GQ381905 GQ381983 GQ382060 GQ381495 J.C.Manning (NBG)

B. lanata Goldblatt & van Jaarsveld & Krüger 19207 GQ381346 GQ381417 GQ381576 GQ381655 GQ381735 GQ381819 GQ381906 GQ381984 GQ382061 GQ381496 J.C.Manning (NBG) A B. latifolia L.Bolus Goldblatt & Porter 12127 (MO) GQ381347 GQ381418 GQ381577 GQ381656 GQ381736 N/A GQ381907 N/A GQ382062 GQ381497 Goldblatt & Manning 11416 (NBG), Goldblatt & Nänni B. leipoldtii G.J.Lewis GQ381348 GQ381419 GQ381578 GQ381657 GQ381737 GQ381820 GQ381908 N/A GQ382063 GQ381498 11089 (NBG), Low 7425 (NBG) Goldblatt & Porter 11898 B. lewisiana B.Nord. GQ925463 GQ381420 N/A N/A GQ381738 GQ381821 N/A N/A GQ382064 GQ381499 (NBG) Goldblatt 11631 (NBG), B. lineolata Klatt GQ925464 GQ381421 GQ381579 GQ381658 GQ381739 GQ381822 GQ381909 GQ381985 GQ382065 GQ381500 Schnitzler 68 (K) Goldblatt & Manning 9901 B. lobata G.J.Lewis GQ925465 GQ381422 GQ381580 GQ381659 GQ381740 GQ381823 GQ381910 GQ381986 GQ382066 GQ381501 (MO) B. longicollis Dinter van Berkel 561 (NBG) N/A N/A N/A N/A N/A GQ381824 N/A N/A GQ382067 GQ381502

B. melanops (Mamre form) Goldblatt 10239 (NBG), GQ381349 N/A GQ381581 GQ925500 GQ381741 N/A GQ925506 N/A GQ382068 GQ381503 Goldblatt & J.C.Manning Schnitzler 80 (K) B. melanops (Tulbagh form) Schnitzler 62 (K) GQ925466 N/A N/A N/A GQ381742 N/A N/A N/A GQ382069 GQ925488 Goldblatt & J.C.Manning B. minuta G.J.Lewis Lewis 2322 (SAM) GQ381350 GQ381423 GQ381582 GQ381660 GQ381743 GQ381825 GQ381911 GQ381987 GQ382070 GQ381504 Schnitzler & Manning 18 B. montana G.J.Lewis GQ381351 GQ381424 GQ381583 GQ381661 GQ381744 GQ381826 GQ381912 GQ381988 GQ382071 GQ381505 (NBG) B. mucronata ssp. minor Schnitzler & Manning 21 (G.J.Lewis) Goldblatt & (NBG), Goldblatt & Porter GQ381352 GQ381425 GQ381584 GQ381662 GQ381745 GQ381827 GQ381913 GQ381989 GQ382072 GQ925489 J.C.Manning 11798 (MO) B. mucronata ssp. Goldblatt & Manning 9919 GQ381353 GQ381426 GQ381585 GQ381663 GQ381746 GQ381828 GQ925507 GQ381990 GQ382073 GQ925490 mucronata G.J.Lewis (NBG) B. namaquensis Baker Helme 2748A (NBG) GQ925467 GQ381427 N/A N/A GQ381747 GQ381829 N/A N/A GQ382074 GQ925491

B. nana ssp. nana Schnitzler & Manning 12 GQ381354 GQ381428 GQ381586 GQ381664 GQ381748 GQ381830 GQ381914 N/A GQ382075 GQ381506 150 (Andrews) Sprengel (NBG)

Appendix B. nana ssp. maculata (Klatt) Goldblatt & Schnitzler 28 (K) GQ925468 GQ381429 GQ381587 GQ381665 GQ381749 GQ381831 GQ381915 GQ381991 GQ382076 GQ381507 J.C.Manning

B. noctiflora J.C.Manning Schnitzler & Manning 11 GQ925469 GQ381430 GQ381588 GQ381666 GQ381750 GQ381832 GQ381916 GQ381992 GQ382077 GQ381508

& Goldblatt (NBG) A Goldblatt & Manning 11418 B. odorata L.Bolus GQ381355 GQ381431 GQ381589 GQ381667 GQ381751 GQ381833 N/A GQ381993 GQ382078 GQ381509 (NBG)

B. papyracea Goldblatt & Goldblatt 11611 (NBG), Nänni GQ381356 GQ381432 GQ381590 GQ381668 GQ381752 GQ381834 GQ381917 GQ381994 GQ382079 GQ381510 J.C.Manning 342 (K) Schnitzler 83 (K), Paterson- B. patersoniae L.Bolus GQ381357 GQ381433 GQ381591 GQ381669 GQ381753 GQ381835 GQ381918 GQ381995 GQ382080 GQ925492 Jones 566 (NBG) Goldblatt & Porter 12030 B. patula N.E.Brown (MO), Goldblatt & Porter GQ381358 GQ381434 GQ381592 GQ381670 N/A GQ381836 GQ381919 GQ381996 GQ382081 GQ381511 12171 (MO) B. pauciflora G.J.Lewis Goldblatt & Porter 12881 (MO) GQ925470 GQ925479 GQ381593 GQ381671 GQ381754 GQ381837 GQ381920 N/A GQ382082 GQ381512

B. petiolata Goldblatt & Goldblatt & Porter 12302 GQ925471 GQ381435 GQ381594 GQ381672 GQ381755 GQ381838 GQ381921 N/A GQ382083 GQ381513 J.C.Manning (MO), Schnitzler 71 (K) B. pilosa G.J.Lewis Schnitzler 43 (K) GQ925472 GQ381436 GQ381595 GQ381673 GQ381756 GQ381839 GQ381922 N/A GQ382084 GQ381514 B. planifolia (G.J.Lewis) Manning 3018A (NBG) GQ381359 GQ381437 GQ381596 GQ381674 GQ381757 GQ381840 GQ381923 GQ381997 GQ382085 GQ381515 Goldblatt & J.C.Manning B. praemorsa Goldblatt & Goldblatt & Porter 12733 (MO) GQ381360 GQ381438 GQ381597 GQ381675 GQ381758 GQ381841 GQ381924 GQ381998 GQ382086 GQ381516 J.C.Manning Goldblatt & Manning 9907 B. pubescens (Lam.) (NBG), Goldblatt & Porter GQ381361 GQ381439 GQ925481 GQ381676 N/A GQ381842 GQ381925 GQ381999 GQ382087 GQ381517 G.J.Lewis 12094 (NBG)

B. purpurea (Jacq.) Ker Goldblatt 11533 (NBG), GQ381362 GQ381440 GQ381598 GQ381677 GQ381759 GQ381843 GQ381926 GQ382000 GQ382088 GQ925493 Gawl. Schnitzler 29 (K) B. pygmaea (Burm.f.) Baker Schnitzler 53 (K) GQ381363 GQ381441 GQ381599 GQ381678 GQ381760 GQ381844 GQ381927 N/A GQ382089 GQ925494

B. radiata Goldblatt & Goldblatt & Porter 12187 GQ381364 GQ381442 GQ381600 GQ381679 GQ381761 GQ381845 GQ381928 GQ382001 GQ382090 GQ381518 J.C.Manning (NBG)

B. regia (G.J.Lewis) Goldblatt & Manning 11558 GQ381365 GQ381443 GQ925482 GQ381680 GQ381762 GQ381846 GQ381929 GQ382002 GQ382091 GQ381519 151 Goldblatt & J.C.Manning (NBG), Schnitzler 49 (K)

Appendix

B. rigidifolia Goldblatt & Schnitzler & Manning 20 GQ381366 GQ381444 GQ381601 GQ381681 GQ381763 GQ381847 GQ381930 GQ382003 GQ382092 GQ381520 J.C.Manning (NBG) Schnitzler & Manning 13 B. ringens (L.) Ker Gawl. GQ381367 GQ381445 GQ381602 GQ381682 GQ381764 GQ381848 GQ381931 GQ382004 GQ382093 GQ381521 (NBG) A B. rubella Goldblatt & Goldblatt & Porter 11825 GQ925473 GQ381446 GQ381603 GQ925501 GQ381765 GQ381849 GQ381932 GQ382005 GQ382094 GQ381522 J.C.Manning (NBG) Schnitzler & Manning 15 B. rubrocyanea (Jacq.) Ker (NBG), Goldblatt & Manning GQ381368 GQ381447 GQ381604 GQ381683 GQ381766 GQ381850 GQ381933 GQ382006 GQ382095 GQ381523 Gawl. 11555 (MO) B. salteri G.J.Lewis Manning 2227 (NBG) GQ381369 GQ381448 GQ381605 GQ381684 GQ381767 GQ381851 GQ381934 GQ382007 GQ382096 GQ381524 B. sambucina ssp. Goldblatt & Porter 11838 sambucina (Klatt) GQ381370 GQ381449 GQ381606 GQ381685 N/A GQ381852 GQ381935 GQ382008 N/A GQ381525 (MO), Goldblatt 11465 (NBG) G.J.Lewis B. scabrifolia Brehm. ex Goldblatt 11445 (MO) GQ381371 GQ925480 GQ381607 GQ381686 GQ381768 GQ381853 GQ381936 GQ382009 GQ382097 GQ381526 Klatt B. scariosa G.J.Lewis Goldblatt 11614 (MO) GQ381372 GQ381450 GQ381608 GQ381687 GQ381769 GQ381854 GQ381937 GQ382010 GQ382098 GQ381527 B. secunda (Thunb.) Ker Walters 1537 (NBG) GQ925474 GQ381451 GQ381609 GQ381688 GQ381770 GQ381855 GQ381938 GQ382011 GQ382099 GQ925495 Gawl. Goldblatt & Manning 9610 B. sinuata G.J.Lewis (MO), Goldblatt 11443 (MO), GQ381373 GQ381452 GQ381610 GQ381689 GQ381771 GQ381856 GQ381939 GQ382012 GQ382100 GQ381528 Snijman 1988 (NBG) Goldblatt & Porter 12742 B. spathacea (L.f.) Ker (MO), Goldblatt, Manning & GQ381374 GQ381453 GQ381611 GQ381690 GQ381772 GQ381857 GQ381940 GQ382013 GQ382101 GQ381529 Gawl. Savolainen 11521 (MO) Goldblatt & Nänni 11453 B. spiralis Baker GQ381375 GQ381454 GQ381612 GQ381691 GQ381773 GQ925508 GQ381941 GQ382014 GQ382102 GQ381530 (NBG) Goldblatt & Porter 11823 B. striata (Jacq.) G.J.Lewis GQ925475 GQ381455 GQ381613 GQ381692 GQ381774 GQ381858 GQ381942 GQ382015 GQ382103 GQ381531 (NBG), Harrow 2059 (NBG) B. stricta (Aiton) Ker Gawl. Goldblatt 11434 (NBG) GQ381376 N/A GQ381614 GQ381693 N/A GQ381859 GQ381943 N/A GQ382104 GQ381532 B. symmetrantha Goldblatt Goldblatt & Porter 12772 (MO) GQ381377 GQ381456 GQ381615 GQ381694 GQ381775 GQ381860 GQ381944 GQ382016 GQ382105 GQ381533 & J.C.Manning 152 B. tanquana J.C.Manning & Manning 2748 (NBG) GQ381378 GQ381457 GQ381616 GQ381695 GQ381776 GQ381861 GQ381945 GQ382017 GQ382106 GQ381534 Goldblatt

Appendix Babiana teretifolia Goldblatt & Porter 12865 (MO) GQ381323 GQ381389 GQ381547 N/A GQ381708 GQ381792 GQ381878 N/A GQ382031 GQ925496 Goldblatt & J.C.Manning Goldblatt & Manning 11334 B. torta G.J.Lewis GQ381379 GQ381458 GQ381617 GQ381696 GQ381777 GQ381862 GQ381946 GQ382018 GQ382107 GQ925497

(NBG) A B. toximontana Forest & Manning 540 (NBG) N/A GQ381459 N/A N/A GQ381778 GQ381863 N/A N/A GQ382108 GQ381535 J.C.Manning & Goldblatt Goldblatt & Porter 12095 B. tritonioides G.J.Lewis GQ381380 GQ381460 GQ381618 GQ381697 GQ381779 GQ381864 GQ381947 GQ382019 GQ382109 GQ381536 (NBG)

B. tubaeformis Goldblatt & Goldblatt & Porter 12129 GQ381381 GQ381461 GQ381619 GQ381698 GQ381780 GQ381865 GQ381948 GQ382020 GQ382110 GQ381537 J.C.Manning (NBG) Schnitzler & Manning 17 B. tubiflora (L.f.) Ker Gawl. GQ381382 GQ381462 GQ381620 GQ381699 GQ381781 GQ381866 GQ381949 GQ382021 GQ382111 GQ381538 (NBG)

B. tubulosa (Burm.f.) Ker Goldblatt & Porter 11879 GQ925476 GQ381463 GQ381621 GQ381700 GQ381782 GQ381867 GQ381950 GQ382022 GQ382112 GQ925498 Gawl. (MO), Schnitzler 101 (K) B. unguiculata G.J.Lewis IBSA 1 (NBG) GQ381383 GQ381464 GQ381622 GQ381701 GQ381783 GQ381868 GQ381951 GQ382023 GQ382113 GQ381539

B. vanzijliae L.Bolus Goldblatt 11401 (NBG) GQ381384 GQ381465 GQ381623 N/A N/A GQ381869 GQ381952 GQ382024 GQ382114 N/A Goldblatt 11426A (NBG), B. villosa (Aiton) Ker Gawl. GQ381385 GQ381466 GQ381624 GQ381702 GQ381784 GQ381870 GQ381953 GQ382025 GQ382115 GQ381540 Goldblatt 11420 (MO) B. villosula (Gmelin) Ker Goldblatt 11312 (MO) GQ381386 N/A GQ925483 GQ381703 GQ381785 GQ381871 GQ381954 GQ382026 GQ382116 GQ381541 Gawl. ex Steud.

B. virescens Goldblatt & Goldblatt & Porter 12145 GQ381387 GQ381467 GQ381625 GQ381704 GQ381786 GQ381872 GQ381955 GQ382027 GQ382117 GQ381542 J.C.Manning (NBG) B. virginea Goldblatt Goldblatt & Porter 12790 (MO) GQ381388 GQ381468 GQ381626 GQ381705 GQ381787 GQ381873 GQ381956 GQ382028 GQ382118 GQ381543 Chasmanthe aethiopica (L.) Chase I-3 (K) AJ579938 N/A AJ309660 N/A GQ381788 GQ381874 AJ578771 AJ409572 GQ382119 GQ381544 N.E.Br. Cyanixia socotrana (Hook.f.) Goldblatt & Lavaranos s.n. (MO) N/A GQ381469 GQ381627 GQ381706 GQ381789 GQ381875 GQ381957 GQ382029 GQ382120 GQ925499 J.C.Manning

Sparaxis variegata (Sweet) Rymer 32 (NBG), Goldblatt AJ579984 N/A AJ309669 N/A GQ381790 GQ381876 AJ578817 AJ409582 GQ382121 GQ381545

Goldblatt 2460 (MO) 153

Appendix Goldblatt & Manning 9545 Tritonia disticha Baker N/A N/A GQ381628 GQ381707 GQ381791 GQ381877 GQ381958 GQ382030 N/A GQ381546 (MO)

A

154

Appendix B 155

Appendix B: Maximum likelihood estimates of the geographic range evolution in Babiana

Maximum likelihood estimation of the geographic range evolution in Babiana. Numbers indicate node numbers corresponding to range reconstructions in Table in Appendix B (below).

Appendix B 156

Range division/inheritance scenarios at internal nodes. Split format: [left|right], where 'left' and 'right' are the ranges inherited by each descendant branch (Figure in Appendix B, 'left' is the upper branch, and 'right' the lower branch). Only splits within 2 log-likelihood units of the maximum for each node are shown. 'Rel.Prob' is the relative probability (fraction of the global likelihood) of a split. Ranges correspond to biogeographic regions in Fig. 2.1: SKAR – Succulent Karoo; FYN – Fynbos; NKAR – Nama-Karoo; DES – Desert; MONT – Montane Grassland; SAV – Savanna.

Node Split lnL Rel.Prob N178 [SKAR|SKAR] -119.8 0.967 N176 [SKAR|SKAR] -119.8 0.960 N116 [SKAR|SKAR] -119.9 0.923 N108 [SKAR|SKAR] -120.1 0.704 [SKAR|SKAR+FYN] -121.4 0.206 N2 [SKAR|SKAR] -119.9 0.881 N107 [SKAR+FYN|FYN] -120.6 0.438 [SKAR|SKAR] -120.8 0.349 [FYN|FYN] -122.6 0.061 N97 [SKAR|SKAR] -121.0 0.308 [SKAR|SKAR+FYN] -121.2 0.250 [FYN|SKAR+FYN] -121.3 0.214 [FYN|FYN] -122.4 0.075 [SKAR+FYN|FYN] -122.4 0.074 [SKAR+FYN|SKAR] -122.4 0.0734 N85 [SKAR|SKAR] -120.8 0.368 [SKAR+FYN|SKAR] -120.8 0.353 [FYN|FYN] -121.5 0.174 [FYN|SKAR] -122.5 0.066 N77 [FYN|SKAR+FYN] -120.2 0.691 [FYN|FYN] -121.1 0.266 N69 [FYN|FYN] -119.8 0.962 N57 [FYN|FYN] -119.9 0.934 N55 [FYN|FYN] -120.1 0.762 [SKAR+FYN|FYN] -121.2 0.233 N37 [FYN|SKAR] -119.8 1 N35 [FYN|FYN] -119.8 1 N31 [FYN|FYN] -119.8 1 N29 [FYN|FYN] -119.8 1 N21 [FYN|FYN] -119.8 1 N9 [FYN|FYN] -119.8 1 N5 [FYN|FYN] -119.8 1 N8 [FYN|FYN] -119.8 1 N20 [FYN|FYN] -119.8 1 N14 [FYN|FYN] -119.8 1 N12 [FYN|FYN] -119.8 1 N19 [FYN|FYN] -119.8 1 N17 [FYN|FYN] -119.8 1 N28 [FYN|FYN] -119.8 1

Appendix B 157

N26 [FYN|FYN] -119.8 1 N24 [FYN|FYN] -119.8 1 N34 [FYN|FYN] -119.8 1 N54 [FYN|FYN] -119.8 0.989 N52 [FYN|FYN] -119.8 0.951 N44 [FYN|FYN] -120.0 0.799 [FYN|SKAR+FYN] -121.4 0.199 N40 [FYN|FYN] -119.8 1 N43 [FYN|SKAR] -119.8 1 N51 [FYN|FYN] -119.9 0.896 N50 [FYN|FYN] -120.2 0.670 [SKAR+FYN|FYN] -121.0 0.285 N48 [SKAR|SKAR+FYN] -119.9 0.935 N68 [FYN|FYN] -119.8 0.988 N66 [FYN|FYN] -119.8 0.945 N64 [FYN|FYN] -120.1 0.764 [FYN|SKAR+FYN] -121.2 0.234 N60 [FYN|FYN] -119.8 1 N63 [FYN|SKAR] -119.8 1 N76 [FYN|SKAR] -120.3 0.597 [FYN|SKAR+FYN] -121.4 0.206 [FYN|FYN] -121.4 0.197 N75 [SKAR|SKAR] -120.3 0.594 [SKAR|SKAR+FYN] -120.7 0.396 N74 [SKAR|SKAR] -120.4 0.563 [SKAR+FYN|SKAR] -120.7 0.407 N84 [SKAR|SKAR] -120.0 0.774 [SKAR+FYN|SKAR] -121.3 0.219 N82 [SKAR|SKAR] -120.1 0.748 [SKAR|SKAR+FYN] -121.7 0.153 N80 [SKAR|SKAR] -119.9 0.853 [SKAR|SKAR+FYN] -121.8 0.139 N96 [FYN|SKAR] -119.8 1 N92 [FYN|FYN] -119.8 1 N90 [FYN|FYN] -119.8 1 N88 [FYN|FYN] -119.8 1 N95 [SKAR|SKAR] -119.8 1 N106 [FYN|FYN] -120.6 0.456 [SKAR+FYN|FYN] -120.8 0.372 [SKAR|FYN] -121.6 0.172 N100 [SKAR+FYN|SKAR] -120.0 0.799 [SKAR|SKAR] -121.7 0.151 N105 [FYN|FYN] -119.8 1 N104 [FYN|FYN] -119.8 1 N115 [SKAR|SKAR] -119.9 0.899 N113 [SKAR|SKAR] -120.0 0.837 N111 [SKAR|SKAR] -120.0 0.796 [SKAR+FYN|SKAR] -121.4 0.197 N175 [SKAR|SKAR] -119.8 0.995 N135 [SKAR|SKAR] -119.8 0.994 N133 [SKAR|SKAR] -119.8 0.959 N131 [SKAR|SKAR] -120.0 0.833 N127 [SKAR|SKAR] -119.8 0.998 N125 [SKAR|SKAR] -119.8 0.992 N123 [SKAR|SKAR] -119.8 0.973 N121 [SKAR|SKAR] -119.8 1 N119 [SKAR|SKAR] -119.8 1

Appendix B 158

N130 [DES|NKAR+SKAR+MONT+DES] -121.6 0.159 [MONT|NKAR+SKAR+MONT+DES] -121.6 0.159 [NKAR|NKAR+SKAR+MONT+DES] -122.0 0.105 [DES|NKAR+SKAR+DES] -122.2 0.089 [DES|SKAR+DES] -122.5 0.067 [NKAR|NKAR+SKAR+DES] -122.6 0.059 [MONT|NKAR+SKAR+MONT] -122.7 0.055 [MONT|SKAR+MONT+DES] -122.7 0.052 [DES|SKAR+MONT+DES] -122.7 0.052 [NKAR|NKAR+SKAR] -122.8 0.049 [NKAR|NKAR+SKAR+MONT] -123.1 0.036 N174 [SKAR|SKAR] -119.8 0.998 N164 [SKAR|SKAR] -119.8 0.995 N160 [SKAR|SKAR] -119.8 0.973 N154 [SKAR|SKAR] -120.0 0.841 [SKAR+FYN|SKAR] -121.7 0.149 N150 [FYN|SKAR+FYN] -120.0 0.851 N142 [FYN|FYN] -120.0 0.849 [SKAR+FYN|FYN] -121.7 0.144 N138 [FYN|FYN] -120.1 0.698 [FYN|SKAR+FYN] -121.0 0.294 N141 [FYN|FYN] -119.8 1 N149 [SKAR|FYN] -119.8 1 N145 [SKAR|SKAR] -119.8 1 N148 [FYN|FYN] -119.8 1 N153 [SKAR|SKAR] -119.8 1 N159 [SKAR|SKAR] -119.8 1 N157 [SKAR|SKAR] -119.8 1 N163 [SKAR|SKAR] -119.8 1 N173 [SKAR|SKAR] -119.8 1 N172 [SKAR|SKAR] -119.8 1 N170 [SKAR|SKAR] -119.8 1 N169 [SKAR|SKAR] -119.8 1

Appendix C Floral traits for all 92 species of Babiana. Abbreviations are as follows: TBL – length of floral tube; DTPL – length of dorsal ; LTPL – length of lower tepals; ANTHL – anther length; FIL – filament length; RTDL – size ratio between dorsal and lower tepals; FTS – structure of floral tube; CR – circadian rhythm; COL – floral colour; NVOL – nectar volume; SUG – sugar concentration. 'Status' indicates whether the pollination syndrome was observed (Goldblatt and Manning 2007) or inferred by PCoA. All measurements are in millimetre unless stated otherwise.

NVOL SUG ID Species Status TBL DTPL LTPL ANTHL FIL RTDL FTS CR COL [µl] [%] 1 B. ambigua observed 14.5 36.5 25 7 14.5 0.685 0 0 0 1.5 22 2 B. angustifolia observed 14 21 21 5.25 9 1.000 0 0 0 n/a n/a 3 B. arenicola inferred 30 32 29 6.5 15 0.906 0 0 0 n/a n/a 4 B. attenuata observed 30 30.5 27.5 6 15 0.902 1 0 0 2.05 28.1 5 B. auriculata inferred 17.5 24 15 3 16.5 0.625 0 0 0 n/a n/a 6 B. avicularis inferred 18.5 16.5 8 4.5 28 0.485 0 0 1 4.4 25 7 B. bainesii inferred 50 45 32.5 9 12.5 0.722 0 0 0 n/a n/a 8 B. blanda inferred 23.5 28 28 8 6.5 1.000 0 0 0 n/a n/a 9 B. brachystachys inferred 72.5 22 16.5 5 4.5 0.750 0 0 0 6.75 23.4 10 B. carminea inferred 58 44 37.5 9 40 0.852 0 0 1 23.5 21 11 B. cedarbergensis observed 17 31.5 24 8 13 0.762 0 0 0 1.2 28.4 12 B. cinnamomea inferred 23.5 30 20 7.5 15 0.667 0 1 0 n/a n/a 13 B. confusa inferred 26 36.5 25 7.5 13.5 0.685 0 0 0 n/a n/a 14 B. crispa observed 17 27.5 17 6.5 15 0.618 0 0 0 2.8 22

15 B. cuneata observed 50 33 29 6 16.5 0.879 0 0 0 2.5 28.7 159 16 B. curviscapa observed 39.5 21.5 18 5 13 0.837 0 0 0 4.025 27.775

Appendix C 17 B. dregei observed 57.5 23 20 8 13 0.870 1 0 0 4.85 22.65 18 B. ecklonii observed 41 25 14.5 6 15 0.580 0 0 0 4.85 29.7 19 B. engysiphon observed 35 24.5 15.5 5.5 11 0.633 0 0 0 2.95 29.3 20 B. fimbriata observed 11 21.5 11 6 14.5 0.512 0 0 0 2.25 40.1 21 B. flabellifolia observed 24 31 26 7 16.5 0.839 1 0 0 n/a n/a 22 B. fourcadei inferred 25 27.5 21 6.5 16.5 0.764 0 0 0 n/a n/a 23 B. fragrans inferred 19 20 18 6.5 14 0.900 0 0 0 2 35.2 24 B. framesii observed 65 31 28 8 11 0.903 0 0 0 3.725 26.9 25 B. geniculata observed 40 26 18.5 7 14 0.712 0 0 0 4 29.3 26 B. grandiflora inferred 19 50 35 8.5 10 0.700 1 0 0 n/a n/a 27 B. hirsuta observed 35 18 14 6 38.5 0.778 0 0 1 27 25.5 28 B. horizontalis inferred 25 27 21.5 6 16.5 0.796 0 0 0 n/a n/a 29 B. hypogaea inferred 35 38.5 32.5 9.5 16.5 0.844 0 0 0 n/a n/a 30 B. inclinata observed 10 23 19 5.5 13.5 0.826 0 0 0 1.8 36.6 31 B. karooica inferred 27.5 26 21 6.5 10.5 0.808 0 0 0 n/a n/a 32 B. lanata inferred 24 17 20 5 14 1.176 0 0 0 n/a n/a 33 B. latifolia observed 35 24 19 6 12 0.792 0 0 0 5.3 29.9 34 B. leipoldtii observed 17.5 27.5 27.5 7 8 1.000 1 0 0 1 24.2 35 B. lewisiana inferred 10 25 20 5.5 15 0.800 0 0 0 n/a n/a 36 B. lineolata observed 13 21 17 5.25 14.5 0.810 0 0 0 0.95 39 37 B. lobata inferred 7.5 20.5 13 4 15 0.634 0 0 0 n/a n/a 38 B. longicollis inferred 42.5 25 15 5.5 10 0.600 0 0 0 n/a n/a B. melanops (Mamre 39 observed 16.5 23.5 23.5 7.5 8.5 1.000 1 0 0 1.3 24.2 Form) B. melanops (Tulbagh

40 observed 23.5 23.5 23.5 8.5 9.5 1.000 1 0 0 n/a n/a 160 Form)

Appendix C 41 B. minuta inferred 21 32.5 22.5 5.5 16 0.692 0 0 0 n/a n/a 42 B. montana observed 18.5 30 26 7 8.5 0.867 0 0 0 n/a n/a B. mucronata subsp. 43 observed 12 27.5 22.5 6 13 0.818 0 0 0 1.775 31.9 minor B. mucronata subsp. 44 observed 20 30 16.5 6 14.5 0.550 0 0 0 n/a n/a mucronata 45 B. namaquensis inferred 26.5 35 30 5.5 12 0.857 0 0 0 n/a n/a B. nana subsp. 46 observed 13.5 22.5 23.5 5.75 12 1.044 0 0 0 1.95 36.3 maculata 47 B. nana subsp. nana observed 15.5 27.5 22.5 5.75 12 0.818 0 0 0 n/a n/a 48 B. noctiflora observed 42.5 29.5 23.5 6.5 29 0.797 0 1 0 5.5 30.6 49 B. odorata observed 12 30 21 5.5 15.5 0.700 0 0 0 1.85 28.5 50 B. papyracea observed 40 29 29 8 7 1.000 1 0 0 n/a n/a 51 B. patersoniae inferred 25 20.5 18.5 4.5 10 0.902 0 1 0 2.25 31.7 52 B. patula observed 12 22.5 20 6 13 0.889 0 0 0 1.7 42.3 53 B. pauciflora inferred 40 40 32.5 8 15 0.813 0 0 0 n/a n/a 54 B. petiolata inferred 11 30 19 6 12 0.633 0 0 0 n/a n/a 55 B. pilosa inferred 16.5 32 24 6 15 0.750 0 0 0 n/a n/a 56 B. planifolia inferred 12 25.5 17.5 3 15 0.686 0 0 0 n/a n/a 57 B. praemorsa observed 50 20 20 5.5 8.5 1.000 0 0 0 6.75 27 58 B. pubescens observed 50 20 16 6.5 15 0.800 0 0 0 4 28 59 B. purpurea observed 23 21.5 21.5 7 12 1.000 0 0 0 1.35 32.3 60 B. pygmaea observed 20 28 28 9 4.5 1.000 0 0 0 n/a n/a 61 B. radiata inferred 33.5 32.5 32.5 9 9 1.000 1 0 0 n/a n/a 62 B. regia observed 11 19 19 4.5 8 1.000 0 0 0 n/a n/a

63 B. rigidifolia observed 50 39 33.5 8 19 0.859 0 0 0 n/a n/a 161

Appendix C 64 B. ringens observed 37.5 40 20 5.5 47.5 0.500 0 0 1 23.975 23.25 65 B. rubella inferred 13.5 29 24 4.5 14 0.828 0 0 0 n/a n/a 66 B. rubrocyanea observed 17.5 22 22 6.5 11.5 1.000 0 0 0 1 33.9 67 B. salteri inferred 9 23 14 7 1.3 0.609 0 0 0 n/a n/a B. sambucina subsp. 68 observed 45 32.5 27.5 8.5 17.5 0.846 1 0 0 4.7 27.7 sambucina 69 B. scabrifolia observed 15 37.5 25 7 15.5 0.667 0 0 0 1.675 27.95 70 B. scariosa observed 15.5 25 18.5 6 13 0.740 0 0 0 0.9 38.2 71 B. secunda inferred 7 19 15.5 6 13 0.816 0 0 0 n/a n/a 72 B. sinuata observed 8.5 28 28 7 20 1.000 0 0 0 1.95 27.8 73 B. spathacea inferred 37.5 21.5 15 5 10 0.698 0 0 0 4.175 30.75 74 B. spiralis observed 9 19.5 19.5 4 15 1.000 0 0 0 3 33.3 75 B. striata inferred 7 22.5 13 5.25 15 0.578 0 0 0 n/a n/a 76 B. stricta observed 13 20 20 5.5 10.5 1.000 0 0 0 n/a n/a 77 B. symmetrantha inferred 60 31.5 28.5 6 10 0.905 0 0 0 n/a n/a 78 B. tanquana inferred 14 27.5 19 6 12 0.691 0 0 0 n/a n/a 79 B. teretifolia inferred 67.5 30 26 6 12 0.867 0 0 0 n/a n/a 80 B. torta inferred 21 32 23.5 6 15 0.734 0 0 0 n/a n/a 81 B. toximontana inferred 13 30 20 7 15 0.667 0 0 0 n/a n/a 82 B. tritonioides observed 7 25 13 5 16 0.520 0 0 0 0.5 50 83 B. tubaeformis observed 27.5 22 20 6 15 0.909 0 0 0 n/a n/a 84 B. tubiflora observed 72.5 20.5 20.5 4.5 14.5 1.000 0 0 0 4.5 25.25 85 B. tubulosa observed 85 32 20.5 6 21 0.641 0 0 0 n/a n/a 86 B. unguiculata observed 11 25 17.5 5.5 13.5 0.700 0 0 0 1.1 31.3 87 B. vanzijliae observed 34 34 27.5 7.5 16 0.809 0 0 0 1.95 28.7 88 B. villosa observed 26 30.5 30.5 8 11.5 1.000 0 0 0 n/a n/a 162

Appendix C 89 B. villosula observed 24 25 25 5 4.75 1.000 0 0 0 n/a n/a 90 B. virescens inferred 20.5 35 27.5 7.5 14 0.786 0 0 0 n/a n/a 91 B. virginea inferred 55 37.5 32.5 10 15 0.867 0 1 0 19.5 26 92 B. foliosa inferred 20 22 22 5.5 10 1.000 0 0 0 n/a n/a 93 B. gariepensis inferred 22 30 21 5 16 0.700 0 0 0 n/a n/a 94 B. lapeirousioides inferred 22 10 10 4.5 7 1.000 0 0 0 n/a n/a B. sambucina subsp. 95 observed 37.5 32.5 27.5 8.5 15 0.846 0 0 0 5.25 30 longibracteata 96 B. stenomera inferred 13 23 15 6 15 0.652 0 0 0 n/a n/a

164

Appendix D Minimum models of pollinator syndrome evolution. Differences between models were calculated using the Akaike Information

Criterion with a second-order correction for small sample sizes (AICC). Likelihood estimates were calculated for models using Rate restrictions were imposed by setting transition rates (q) equal to zero, numbers indicate transitions from ancestral to descendant state: anthophorine bee (0), hopliine beetle (1), pollen collecting bee (2), moth (3) sunbird (4), and long-proboscid fly (5). K denotes the number of parameters in each model. The overall best-fit model (A21) is marked in bold.

AICc id Restriction lnL K AICc weights A01 NONE -92.921 30 277.368 9.77 x 10-14 A02 q54 = 0 -93.023 29 273.045 8.48 x 10-13 A03 q54 q53 = 0 -93.720 28 270.062 3.77 x 10-12 A04 q54 q53 q45 = 0 -94.652 27 267.692 1.23 x 10-11 A05 q54 q53 q45 q43 = 0 -94.560 26 263.406 1.05 x 10-10 A06 q54 q53 q45 q43 q35 = 0 -94.479 25 259.271 8.31 x 10-10 A07 q54 q53 q45 q43 q35 q34 = 0 -94.771 24 256.003 4.26 x 10-09 A08 q54 q53 q45 q43 q35 q34 q52 = 0 -94.700 23 252.126 2.96 x 10-08 A09 q54 q53 q45 q43 q35 q34 q52 q42 = 0 -96.249 22 251.602 3.84 x 10-08 A10 q54 q53 q45 q43 q35 q34 q52 q42 q32 = 0 -96.734 21 249.055 1.37 x 10-07 A11 q54 q53 q45 q43 q35 q34 q52 q42 q32 q25 = 0 -97.146 20 246.465 5.01 x 10-07 A12 q54 q53 q45 q43 q35 q34 q52 q42 q32 q25 q24 = 0 -97.127 19 243.110 2.68 x 10-06 A13 q54 q53 q45 q43 q35 q34 q52 q42 q32 q25 q24 q23 = 0 -97.138 18 239.909 1.33 x 10-05 A14 q54 q53 q45 q43 q35 q34 q52 q42 q32 q25 q24 q23 q51 = 0 -97.174 17 236.848 6.15 x 10-05

A15 q54 q53 q45 q43 q35 q34 q52 q42 q32 q25 q24 q23 q51 q41 = 0 -97.103 16 233.658 0.0003 164 A16 q54 q53 q45 q43 q35 q34 q52 q42 q32 q25 q24 q23 q51 q41 q31 = 0 -97.160 15 230.807 0.0012

Appendix D A17 q54 q53 q45 q43 q35 q34 q52 q42 q32 q25 q24 q23 q51 q41 q31 q21 = 0 -97.153 14 227.906 0.0053 A18 q54 q53 q45 q43 q35 q34 q52 q42 q32 q25 q24 q23 q51 q41 q31 q21 q15 = 0 -97.131 13 225.052 0.0224 A19 q54 q53 q45 q43 q35 q34 q52 q42 q32 q25 q24 q23 q51 q41 q31 q21 q15 q14 = 0 -97.125 12 222.302 0.0886 A20 q54 q53 q45 q43 q35 q34 q52 q42 q32 q25 q24 q23 q51 q41 q31 q21 q15 q14 q13 = 0 -97.835 11 221.055 0.1653 A21 q54 q53 q45 q43 q35 q34 q52 q42 q32 q25 q24 q23 q51 q41 q31 q21 q15 q14 q13 q12 = 0 -97.836 10 218.457 0.6061 A22 q54 q53 q45 q43 q35 q34 q52 q42 q32 q25 q24 q23 q51 q41 q31 q21 q15 q14 q13 q12 q50 = 0 -101.223 9 222.696 0.0728 A23 q54 q53 q45 q43 q35 q34 q52 q42 q32 q25 q24 q23 q51 q41 q31 q21 q15 q14 q13 q12 q50 q40 = 0 -104.409 8 226.595 0.0103 A24 q54 q53 q45 q43 q35 q34 q52 q42 q32 q25 q24 q23 q51 q41 q31 q21 q15 q14 q13 q12 q50 q40 q30 = 0 -106.863 7 229.091 0.0029 A25 q54 q53 q45 q43 q35 q34 q52 q42 q32 q25 q24 q23 q51 q41 q31 q21 q15 q14 q13 q12 q50 q40 q30 q20 = 0 -108.558 6 230.128 0.0017 A26 q54 q53 q45 q43 q35 q34 q52 q42 q32 q25 q24 q23 q51 q41 q31 q21 q15 q14 q13 q12 q50 q40 q30 q20 q10 = 0 -119.910 5 250.534 6.56 x 10-08

B02 q50 = 0 -92.883 29 272.767 9.75 x 10-13 B03 q50 q51 = 0 -92.936 28 268.495 8.25 x 10-12 B04 q50 q51 q40 = 0 -92.912 27 264.211 7.03 x 10-11 B05 q50 q51 q40 q52 = 0 -92.920 26 260.126 5.42 x 10-10 B06 q50 q51 q40 q52 q41 = 0 -92.919 25 256.150 3.95 x 10-09 B07 q50 q51 q40 q52 q41 q30 = 0 -93.589 24 253.639 1.38 x 10-08 B08 q50 q51 q40 q52 q41 q30 q53 = 0 -95.416 23 253.560 1.44 x 10-08 B09 q50 q51 q40 q52 q41 q30 q53 q42 = 0 -95.545 22 250.195 7.77 x 10-08 B10 q50 q51 q40 q52 q41 q30 q53 q42 q31 = 0 -95.259 21 246.107 6.00 x 10-07 B11 q50 q51 q40 q52 q41 q30 q53 q42 q31 q20 = 0 -98.674 20 249.522 1.08 x 10-07 B12 q50 q51 q40 q52 q41 q30 q53 q42 q31 q20 q54 = 0 -98.674 19 246.205 5.71 x 10-07 B13 q50 q51 q40 q52 q41 q30 q53 q42 q31 q20 q54 q43 = 0 -98.674 18 242.982 2.86 x 10-06 B14 q50 q51 q40 q52 q41 q30 q53 q42 q31 q20 q54 q43 q32 = 0 -99.913 17 242.325 3.97 x 10-06 B15 q50 q51 q40 q52 q41 q30 q53 q42 q31 q20 q54 q43 q32 q21 = 0 -99.915 16 239.282 1.82 x 10-05 165 B16 q50 q51 q40 q52 q41 q30 q53 q42 q31 q20 q54 q43 q32 q21 q10 = 0 -110.640 15 257.766 1.76 x 10-09

Appendix D B17 q50 q51 q40 q52 q41 q30 q53 q42 q31 q20 q54 q43 q32 q21 q10 q05 = 0 -110.881 14 255.363 5.86 x 10-09 B18 q50 q51 q40 q52 q41 q30 q53 q42 q31 q20 q54 q43 q32 q21 q10 q05 q15 = 0 -110.862 13 252.514 2.43 x 10-08 B19 q50 q51 q40 q52 q41 q30 q53 q42 q31 q20 q54 q43 q32 q21 q10 q05 q15 q04 = 0 -110.910 12 249.872 9.13 x 10-08 B20 q50 q51 q40 q52 q41 q30 q53 q42 q31 q20 q54 q43 q32 q21 q10 q05 q15 q04 q25 = 0 -112.337 11 250.059 8.32 x 10-08 B21 q50 q51 q40 q52 q41 q30 q53 q42 q31 q20 q54 q43 q32 q21 q10 q05 q15 q04 q25 q14 = 0 -112.336 10 247.456 3.05 x 10-07 B22 q50 q51 q40 q52 q41 q30 q53 q42 q31 q20 q54 q43 q32 q21 q10 q05 q15 q04 q25 q14 q03 = 0 -112.718 9 245.687 7.40 x 10-07 B23 q50 q51 q40 q52 q41 q30 q53 q42 q31 q20 q54 q43 q32 q21 q10 q05 q15 q04 q25 q14 q03 q35 = 0 -116.055 8 249.887 9.07 x 10-08 B24 q50 q51 q40 q52 q41 q30 q53 q42 q31 q20 q54 q43 q32 q21 q10 q05 q15 q04 q25 q14 q03 q35 q24 = 0 -116.340 7 248.045 2.27 x 10-07 B25 q50 q51 q40 q52 q41 q30 q53 q42 q31 q20 q54 q43 q32 q21 q10 q05 q15 q04 q25 q14 q03 q35 q24 q13 = 0 -116.254 6 245.519 8.05 x 10-07 B26 q50 q51 q40 q52 q41 q30 q53 q42 q31 q20 q54 q43 q32 q21 q10 q05 q15 q04 q25 q14 q03 q35 q24 q13 q02 = 0 -116.250 5 243.215 2.54 x 10-06

C03 q50 q05 = 0 -93.606 28 269.836 4.22 x 10-12 C04 q50 q05 q51 = 0 -93.582 27 265.551 3.59 x 10-11 C05 q50 q05 q51 q40 = 0 -93.614 26 261.514 2.70 x 10-10 C06 q50 q05 q51 q40 q15 = 0 -93.586 25 257.484 2.03 x 10-09 C07 q50 q05 q51 q40 q15 q04 = 0 -93.586 24 253.633 1.39 x 10-08 C08 q50 q05 q51 q40 q15 q04 q52 = 0 -93.607 23 249.942 8.82 x 10-08 C09 q50 q05 q51 q40 q15 q04 q52 q41 = 0 -93.596 22 246.296 5.46 x 10-07 C10 q50 q05 q51 q40 q15 q04 q52 q41 q30 = 0 -94.212 21 244.013 1.71 x 10-06 C11 q50 q05 q51 q40 q15 q04 q52 q41 q30 q25 = 0 -94.254 20 240.683 9.04 x 10-06 C12 q50 q05 q51 q40 q15 q04 q52 q41 q30 q25 q14 = 0 -94.205 19 237.267 4.98 x 10-05 C13 q50 q05 q51 q40 q15 q04 q52 q41 q30 q25 q14 q03 = 0 -94.423 18 234.481 0.0002 C14 q50 q05 q51 q40 q15 q04 q52 q41 q30 q25 q14 q03 q53 = 0 -96.100 17 234.700 0.0001 C15 q50 q05 q51 q40 q15 q04 q52 q41 q30 q25 q14 q03 q53 q42 = 0 -96.132 16 231.716 0.0008 C16 q50 q05 q51 q40 q15 q04 q52 q41 q30 q25 q14 q03 q53 q42 q31 = 0 -101.549 15 239.585 1.56 x 10-05 166 C17 q50 q05 q51 q40 q15 q04 q52 q41 q30 q25 q14 q03 q53 q42 q31 q20 = 0 -101.127 14 235.855 0.0001

Appendix D C18 q50 q05 q51 q40 q15 q04 q52 q41 q30 q25 q14 q03 q53 q42 q31 q20 q35 = 0 -101.542 13 233.874 0.0002 C19 q50 q05 q51 q40 q15 q04 q52 q41 q30 q25 q14 q03 q53 q42 q31 q20 q35 q24 = 0 -101.526 12 231.105 0.0010 C20 q50 q05 q51 q40 q15 q04 q52 q41 q30 q25 q14 q03 q53 q42 q31 q20 q35 q24 q13 = 0 -101.517 11 228.418 0.0041 C21 q50 q05 q51 q40 q15 q04 q52 q41 q30 q25 q14 q03 q53 q42 q31 q20 q35 q24 q13 q02 = 0 -101.518 10 225.821 0.0152 C22 q50 q05 q51 q40 q15 q04 q52 q41 q30 q25 q14 q03 q53 q42 q31 q20 q35 q24 q13 q02 q54 = 0 -108.196 9 236.641 6.82 x 10-05 C23 q50 q05 q51 q40 q15 q04 q52 q41 q30 q25 q14 q03 q53 q42 q31 q20 q35 q24 q13 q02 q54 q43 = 0 -108.894 8 235.566 0.0001 C24 q50 q05 q51 q40 q15 q04 q52 q41 q30 q25 q14 q03 q53 q42 q31 q20 q35 q24 q13 q02 q54 q43 q32 = 0 -110.517 7 236.399 7.69 x 10-05 C25 q50 q05 q51 q40 q15 q04 q52 q41 q30 q25 q14 q03 q53 q42 q31 q20 q35 q24 q13 q02 q54 q43 q32 q21 = 0 -111.497 6 236.006 9.37 x 10-05 C26 q50 q05 q51 q40 q15 q04 q52 q41 q30 q25 q14 q03 q53 q42 q31 q20 q35 q24 q13 q02 q54 q43 q32 q21 q10 = 0 -116.591 5 243.897 1.81 x 10-06

167

Appendix E List of Moraea species with voucher information and GenBank/EMBL accession number for each gene region. All accession numbers for taxa not listed here can be found in Goldblatt et al. (2002).

Species Voucher rbcL rps16 trnL-F

Moraea albiflora (G.J.Lewis) Goldblatt Goldblatt & Manning 11557 (NBG) GQ285129 GQ294121 GQ294030 Moraea algoensis Goldblatt Goldblatt 11589 (NBG) GQ285130 GQ294122 GQ294031 Moraea angulata Goldblatt Goldblatt 2176 (MO) GQ285131 GQ294123 GQ294032 Moraea anomala G.J.Lewis Goldblatt & Nänni 11947 (NBG) GQ285132 GQ294124 GQ294033 Moraea ardesiaca Goldblatt Goldblatt & Manning 10142 (NBG) GQ285133 GQ294125 GQ294034 Moraea aristata Lam. Compton 16041 (NBG) GQ285134 GQ294126 GQ294035 Moraea aspera Goldblatt Goldblatt 11455 (NBG) GQ285135 GQ294127 GQ294036 Moraea atropunctata Goldblatt Goldblatt 5635 (NBG) GQ285136 GQ294128 GQ294037 Moraea australis (Goldblatt) Goldblatt Goldblatt & Porter 12351 (MO) GQ285137 GQ294129 GQ294038 Moraea barkerae Goldblatt Manning 2904 (NBG) GQ285138 GQ294130 GQ294039 Moraea barnardii L.Bolus Goldblatt 11475 (NBG) GQ285139 GQ294131 GQ294040 Moraea bellendenii N.E.Br. Goldblatt & Manning 11552 (NBG) GQ285140 GQ294132 GQ294041 Moraea bipartita L.Bolus Goldblatt & Porter 11830 (NBG) GQ285141 GQ294133 GQ294042 Moraea bolusii Baker Goldblatt & Manning 11504 (NBG) GQ285142 GQ294134 GQ294043 Moraea brachygyne (Schltr.) Goldblatt Goldblatt 11444 (NBG) GQ285143 GQ294135 GQ294044 Moraea brevituba (Goldblatt) Goldblatt Goldblatt & Manning 11497 (NBG) GQ285144 GQ294136 GQ294045 Moraea bubalina Goldblatt Goldblatt & Manning 11481 (NBG) GQ285145 GQ294137 GQ294046 Moraea bulbillifera (G.J.Lewis) Goldblatt Goldblatt 11429 (NBG) GQ285146 GQ294138 GQ294047

Moraea caeca Barnard ex Goldblatt Steiner 3026 (NBG) GQ285147 GQ294139 GQ294048 168 Moraea calcicola Goldblatt Goldblatt 4118 (NBG) GQ285148 GQ294140 GQ294049

Appendix E Moraea callista Goldblatt Spurier s.n. (MO) GQ285149 GQ294141 GQ294050 Moraea cantharophila Goldblatt & J.C.Manning Goldblatt & Manning 11542 (MO) GQ285150 GQ294142 GQ294051 Moraea cedarmontana (Goldblatt) Goldblatt Goldblatt 11606 (MO) GQ285151 GQ294143 GQ294052 Moraea citrina (G.J.Lewis) Goldblatt Goldblatt 2470 (NBG) GQ285152 GQ294144 GQ294053 Moraea comptonii (L.Bolus) Goldblatt Goldblatt 11474 (NBG) GQ285153 GQ294145 GQ294054 Moraea contorta Goldblatt Goldblatt & Manning s.n. (NBG) GQ285154 GQ294146 GQ294055 Moraea cooperi Baker Goldblatt 10795A (NBG) GQ285155 GQ294147 GQ294056 Moraea crispa Ker-Gawl. Goldblatt 12451 (NBG) GQ285156 GQ294148 GQ294057 Moraea deltoidea Goldblatt & J.C.Manning Drewe 1073 (NBG) GQ285157 GQ294149 GQ294058 Moraea demissa Goldblatt Goldblatt & Porter 12383 (MO) GQ285158 GQ294150 GQ294059 Moraea deserticola Goldblatt & J.C.Manning Hall 5089 (NBG) GQ285159 GQ294151 GQ294060 Moraea elegans Jacq. Goldblatt 11424 (NBG) GQ285160 GQ294152 GQ294061 Moraea elsiae Goldblatt Paine s.n. (MO) GQ285161 GQ294153 GQ294062 Moraea exiliflora Goldblatt Goldblatt 12556 A (NBG) GQ285162 N/A N/A Moraea falcifolia Klatt Goldblatt & Manning 11519 (NBG) GQ285163 GQ294154 GQ294063 Moraea fenestralis (Goldblatt & E.G.H.Oliv.) Goldblatt Oliver 9844 (NBG) GQ285164 GQ294155 GQ294064 Moraea fenestrata (Goldblatt) Goldblatt Bruyns 9199 (NBG) GQ285165 GQ294156 GQ294065 Moraea fergusoniae L.Bolus Goldblatt 11432 (NBG) GQ285166 GQ294157 GQ294066 Moraea flavescens (Goldblatt) Goldblatt Goldblatt & Porter 12168 (MO) GQ285167 GQ294158 GQ294067 Moraea flexicaulis Goldblatt Goldblatt & Manning 11494 (NBG) GQ285168 GQ294159 GQ294068 Moraea fragrans Goldblatt Goldblatt & Nänni 11404 (NBG) GQ285169 GQ294160 GQ294069 Moraea fugacissima (L.f.) Goldblatt Goldblatt s.n. (NBG) GQ285170 GQ294161 GQ294070 Moraea fuscomontana (Goldblatt) Goldblatt Goldblatt & Manning 11403A (NBG) GQ285171 GQ294162 GQ294071 Moraea galaxia (L.f.) Goldblatt & J.C.Manning Goldblatt s.n (NBG) GQ285172 GQ294163 GQ294072 Moraea gigandra L.Bolus Helme 2582 (NBG) GQ285173 GQ294164 GQ294073 Moraea gracilenta Goldblatt Goldblatt 3279 (MO) GQ285174 GQ294165 GQ294074

Moraea graminicola Oberm. Nänni 332 (NBG) GQ285175 GQ294166 GQ294075 169

Appendix E Moraea insolens Goldblatt Goldblatt 570 (NBG) GQ285176 GQ294167 GQ294076 Moraea kamiesensis Goldblatt Oliver 5967 (NBG) GQ285177 GQ294168 GQ294077 Moraea kamiesmontana (Goldblatt) Goldblatt Goldblatt & Porter 11910 (NBG) GQ285178 GQ294169 GQ294078 Moraea karooica Goldblatt Goldblatt & Nänni 11409 (NBG) GQ285179 GQ294170 GQ294079 Moraea knersvlaktensis Goldblatt Goldblatt & Manning 11482 (NBG) GQ285180 GQ294171 GQ294080 Moraea lilacina Goldblatt & J.C.Manning Goldblatt 11587 (NBG) GQ285181 GQ294172 GQ294081 Moraea longiaristata Goldblatt Goldblatt 11591 (NBG) GQ285182 GQ294173 GQ294082 Moraea longiflora Ker-Gawl. Goldblatt s.n. (NBG) GQ285183 GQ294174 GQ294083 Moraea longistyla (Goldblatt) Goldblatt Goldblatt 11422 (NBG) GQ285184 GQ294175 GQ294084 Moraea loubseri Goldblatt Goldblatt 2076 (NBG) GQ285185 GQ294176 GQ294085 Moraea louisabolusiae Goldblatt Goldblatt & Porter 12120 (MO) GQ285186 GQ294177 GQ294086 Moraea lugubris (Salisb.) Goldblatt Goldblatt 11568 (NBG) GQ285187 GQ294178 GQ294087 Moraea macrocarpa Goldblatt Duncan 45 (NBG) GQ285188 GQ294179 GQ294088 Moraea macronyx G.J.Lewis Goldblatt 11630 A (MO) GQ285189 GQ294180 GQ294089 Moraea margaretae Goldblatt Goldblatt & Manning 11501 (NBG) GQ285190 GQ294181 GQ294090 Moraea marlothii (L.Bolus) Goldblatt Goldblatt & Manning 11523 (NBG) GQ285191 GQ294182 GQ294091 Moraea maximiliani (Schltr.) Goldblatt & J.C.Manning Goldblatt 11608 (NBG) GQ285192 GQ294183 GQ294092 Moraea modesta Killick Manning 2800 (NBG) GQ285193 GQ294184 GQ294093 Moraea moggii N.E.Br. Compton (Bill) 7 (NBG) GQ285194 GQ294185 GQ294094 Moraea namaquana (Goldblatt) Goldblatt Goldblatt & Manning 11506 (NBG) GQ285195 GQ294186 GQ294095 Moraea nana (L.Bolus) Goldblatt & J.C.Manning Goldblatt & Manning 11487 (NBG) GQ285196 GQ294187 GQ294096 Moraea pallida (Baker) Goldblatt Manning 2931 (NBG) GQ285197 GQ294188 GQ294097 Moraea pendula (Goldblatt) Goldblatt Goldblatt & Porter 12139 (MO) GQ285198 GQ294189 GQ294098 Moraea pseudospicata Goldblatt Goldblatt & Nänni 11305 (NBG) GQ285199 GQ294190 GQ294099 Moraea pyrophila Goldblatt Goldblatt 11600 (NBG) GQ285200 GQ294191 GQ294100 Moraea reflexa Goldblatt Goldblatt & Manning 10042 (NBG) GQ285201 GQ294192 GQ294101

Moraea reticulata Goldblatt Mc Master s.n. (NBG) GQ285202 GQ294193 GQ294102 170

Appendix E Moraea robusta (Goldblatt) Goldblatt Lotter s.n. (NBG) GQ285203 GQ294194 GQ294103 Moraea saxicola Goldblatt Goldblatt & Porter 12082 (MO) GQ285204 GQ294195 GQ294104 Moraea schlechteri (L.Bolus) Goldblatt Goldblatt & Manning 11368 (NBG) GQ285205 GQ294196 GQ294105 Moraea serpentina Baker Goldblatt & Manning 11485 (NBG) GQ285206 GQ294197 GQ294106 Moraea setifolia Eckl. Goldblatt 11570 (NBG) GQ285207 GQ294198 GQ294107 Moraea simplex Goldblatt & J.C.Manning Goldblatt & Porter 12231 (MO) GQ285208 GQ294199 GQ294108 Moraea speciosa (L.Bolus) Goldblatt Goldblatt & Porter 12192 (MO) GQ285209 GQ294200 GQ294109 Moraea stagnalis (Goldblatt) Goldblatt Goldblatt 3883 (NBG) GQ285210 GQ294201 GQ294110 Moraea tanzanica Goldblatt Wiland et al. 98 (MO) GQ285211 GQ294202 GQ294111 Moraea tortilis Goldblatt Goldblatt 2771 (NBG) GQ285212 GQ294203 GQ294112 Moraea tricolor Andrews Hiemstra s.n. (NBG) GQ285213 GQ294204 GQ294113 Moraea vallisbelli (Goldblatt) Goldblatt Goldblatt s.n. (NBG) GQ285214 GQ294205 GQ294114 Moraea variabilis (G.J.Lewis) Goldblatt Goldblatt 11471 (NBG) GQ285215 GQ294206 GQ294115 Moraea versicolor (Salisb.ex Klatt) Goldblatt Goldblatt 11456 (NBG) GQ285216 GQ294207 GQ294116 Moraea vespertina Goldblatt & J.C.Manning Goldblatt & Manning 10580 (NBG) GQ285217 GQ294208 GQ294117 Moraea virgata Jacq. Goldblatt 11603 (NBG) GQ285218 GQ294209 GQ294118 Moraea vlokii Goldblatt Goldblatt & Manning 10764A (NBG) GQ285219 GQ294210 GQ294119 Moraea worcesterensis Goldblatt Goldblatt 6977A (NBG) GQ285220 GQ294211 GQ294120

171

Appendix F 172

Appendix F: List of species traits

Fire-survival: 1 – resprouters; 2 – reseeders; Pollinators: 1 – Apidae s.l. (foraging for nectar); 2 – Apidae s.l. (actively collecting pollen); 3 – Long-proboscid flies (Nemestrinidae & Tabanidae); 4 – Moths (Noctuidae); 5 – Short-proboscid flies (Calliphoridae, Muscidae & Sarcophagidae); 6 – Sunbirds (Nectariniidae); 7 – Insects (general); 8 – Sugarbirds & Sunbirds (Promeropidae & Nectariniidae); 9 – ; 10 – Hopliine beetles (Scarabaeidae: Hopliini) Soil type: 1 – rock crevices; 2 – gravel; 3 – sand; 4 – loam; 5 – clay; 6 - marshy soil Lithology: 1 – sandstone; 2 – granite; 3 – shale; 4 – limestone; 5 – quartzite; 6 – dolerite; 7 – tillite; 8 – conglomerate; 9 – basalt N/A indicates missing information

Fire Pollinator Soil type Lithology Species survival Babiana ambigua - 1 3 2,1 - 10 3,4 2,3 Babiana arenicola - 1 3 1 Babiana attenuata - 1 2 2 Babiana auriculata - 1 1 1 Babiana bainesii - 1 3,2 N/A Babiana blanda - 2 2,3 3 Babiana carminea - 6 1 4 Babiana cedarbergensis - 1 3,2 1 Babiana cinnamomea - 4 2 5,2 Babiana confusa - 1 3 1,2 Babiana crispa - 1 5,2 3 Babiana cuneata - 1 3,2 1,6 Babiana curviscapa - 3 3,1 2 Babiana dregei - 3 1 2 Babiana ecklonii - 3 1,2 1 Babiana engysiphon - 3 3,2 1 Babiana fimbriata - 1 3,2 2,5 Babiana flabellifolia - 1 5,2 2 Babiana fourcadei - 1 4 3

Appendix F 173

Babiana fragrans - 1,10 4,3 3,2,1 Babiana framesii - 3 1 6 Babiana geniculata - 3 3 1 Babiana grandiflora - 1 3,4 1 Babiana hirsuta - 6 3 1 Babiana horizontalis - 1 1 2 Babiana hypogaea - 1 4,3 N/A Babiana inclinata - 1 5,4 3 Babiana karooica - 1 1,2 8 Babiana lanata - 1 3,2 N/A Babiana latifolia - 3 4 1 Babiana leipoldtii - 2 3 1 Babiana lewisiana - 1 2 2,5 Babiana lineolata - 1 3,4 1 Babiana lobata - 1 2,3 5 Babiana longicollis - 1 1 2 Babiana melanops Mamre - 1,10 2 2 Babiana melanops Tulbagh - 10 3,2 1,2 Babiana minuta - 1 1,2,3 3,1 Babiana montana - 1 3,4 1,4 Babiana mucronata ssp minor - 1 1,2 1 Babiana mucronata ssp mucronata - 1 1,2 1,3 Babiana namaquensis - 1 1,2 4 ssp maculata - 1 3 4 Babiana nana ssp nana - 1 3 1,4 Babiana noctiflora - 4 1,4 2 Babiana odorata - 1 2 2,1 Babiana papyracea - 10 5 7 Babiana patersoniae - 4 5 3 Babiana patula - 1 4,5 1 Babiana pauciflora - 1 5,3 3 Babiana petiolata - 1 3 1 Babiana pilosa - 1 3,2 5 Babiana planifolia - 1 4,3 5 Babiana praemorsa - 3 1,3 6 Babiana pubescens - 3 2 2 Babiana purpurea - 1 5,4 3 Babiana pygmaea - 10 3,2 2 Babiana radiata - 2,10 3 8 Babiana regia - 10 3,2 3,2 Babiana rigidifolia - 3 1,3 1 - 6 3 1 Babiana rubella - 1 3 1 Babiana rubrocyanea - 10 2,3 2 Babiana salteri - 1 5 3,5 Babiana sambucina - 1 1,2 1 Babiana scabrifolia - 1 3,2 1 Babiana scariosa - 1 5,3 3,1 Babiana secunda - 1 4,5 1,3

Appendix F 174

Babiana sinuata - 1 5 3 Babiana spathacea - 3 5 6 Babiana spiralis - 1 2 2 Babiana striata - 1 2 2 - 10 5,3 3,1 Babiana symmetrantha - 3 5 6 Babiana tanquana - 1 1 6 Babiana teretifolia - 3 3 1 Babiana torta - 1 1,2 2 Babiana toximontana - 1 3 1 Babiana tritonioides - 1 2 2 Babiana tubaeformis - 1 3 1 Babiana tubiflora - 3 3 1 Babiana tubulosa - 3 3 2,4 Babiana unguiculata - 1 3 1,4 Babiana vanzijliae - 3,1 3 1,7 Babiana villosa - 10 2,5 2 Babiana villosula - 2 3,2 1,2 Babiana virescens - 1 2 2 Babiana virginea - 4 1 6

Moraea albicuspa - 1 2 9 Moraea albiflora - 2 3 1,2 Moraea algoensis - 1 4,5 1 Moraea alpina - 1 2 9 Moraea alticola - 1 2 9 Moraea angulata - 2 4,3 1 Moraea angusta - 1 2 2,1 Moraea anomala - 1 2,5 1 Moraea ardesiaca - 1 3 9 Moraea aristata - 10 5 3 Moraea aspera - 2,10 5 7 Moraea atropunctata - 10 5 3 Moraea australis - 1 3,2 1 Moraea autumnalis - 1 4,5 1 Moraea barkerae - 1 3,2 1 Moraea barnardiella - 2 5 3,1 Moraea barnardii - 1 3,2 1 Moraea bella - 1 N/A N/A Moraea bellendenii - 1,10 5 2 Moraea bifida - 2 5 7,6 Moraea bipartita - 1 5 1 Moraea bituminosa - 1 2 1,2 Moraea bolusii - 1 2,3 2 Moraea brachygyne - 2 1,5 1 Moraea brevistyla - 1 4 9,1 Moraea brevituba - 2 3 2 Moraea brittenniae - 2 4 1 Moraea bubalina - 1 2 1

Appendix F 175

Moraea bulbillifera - 2 3 1,4 Moraea caeca - 1 2 1 Moraea calcicola - 10 4 4 Moraea callista - 1 N/A N/A Moraea cantharophila - 10 4 1 Moraea carsonii - 1 N/A N/A Moraea cedarmontana - 1 3,2 1 Moraea cedarmonticola - 2 2 1 Moraea ciliata - 1 3,5 1,2,6 Moraea citrina - N/A 1,3 1 Moraea collina - 2,10 4,3 2 Moraea comptonii - 2,10 5 3,1 Moraea contorta - 1 5 6 Moraea cookii - 2 2 1,6 Moraea cooperi - 1 4 1 Moraea crispa - 2 5 6 Moraea deltoidea - 1 2 1 Moraea demissa - 2 1,2 1 Moraea deserticola - 2 5 4 Moraea dracomontana - 1 1 9 Moraea elegans - 2 5 3,1 Moraea elliotii - 1 3 1 Moraea elsiae - 2 3 1 Moraea exiliflora - 1 1 1 Moraea falcifolia - 1 5 3,1 Moraea fenestralis - 2 1 2 Moraea fenestrata - 2 5 3 Moraea fergusoniae - 1 5 3,4 Moraea flaccida - 2,5 3 2 Moraea flavescens - 2 1,2 1 Moraea flexicaulis - 1 5 N/A Moraea fragrans - 2 5 6 Moraea fugacissima - 2 3 1 Moraea fugax - 1 2,1 1 Moraea fuscomontana - 2 2 1 Moraea galaxia - 1,2 1 1 Moraea galpinii - 1 N/A N/A Moraea garipensis - 1 1 2 Moraea gawleri - 1 4 2 Moraea gigandra - 10 5,4 3 Moraea gracilenta - 1 3 1 Moraea graminicola - 1 5 3 Moraea graniticola - 2 2,3 2 Moraea herrei - 2 2 2 Moraea hesperantha - 1 5 6 Moraea huttonii - 1 N/A N/A Moraea inclinata - 2 4 9,1 Moraea inconspicua - 1,4 3,5 2,1 Moraea incurva - 1 5 3,1

Appendix F 176

Moraea insolens - 10 4,5 3 Moraea kamiesensis - 2 2 2 Moraea kamiesmontana - 2 1 2 Moraea karooica - 2 5 3 Moraea knersvlaktensis - 2 5 N/A Moraea lewisiae - 1 4,5 1 Moraea lilacina - 1 2,5 1 Moraea longiaristata - 2 2 1 Moraea longiflora - 1 2 2,1 Moraea longistyla - 2 4,3 1,3 Moraea loubseri - 10 4,3 4 Moraea louisabolusiae - 2 5,2 2 Moraea lugubris - 1 2 1 Moraea lurida - 5 2 1,2 Moraea luteoalba - 2 1 1 Moraea macgregorii - 1 5 3 Moraea macrocarpa - 1 3 1,3 Moraea macronyx - 1 5 N/A Moraea margaretae - 1 2 2 Moraea marlothii - 2 5,2 6,1 Moraea maximiliani - 2 2 1 Moraea melanops - 10 5 3 Moraea miniata - 2 5 N/A Moraea minor - 2 2 2,1 Moraea modesta - 1 4,5 9 Moraea moggii - 1 4 9 Moraea muddii - 1 5 5 Moraea namaquamontana - 1 3 5 Moraea namaquana - 2 5 3,1 Moraea nana - 2 2 2 Moraea natalensis - 1 N/A N/A Moraea neglecta - 1 3 1,3 Moraea ochroleuca - 2,5 3,2 1 Moraea pallida - 2 5 N/A Moraea papilionacea - 1 5,4 1 Moraea patens - 2 3 1 Moraea pendula - 2 4 2 Moraea pilifolia - 2 5 3 Moraea polyanthos - 2 5 3 Moraea polystachya - 1 5 N/A Moraea pritzeliana - 1 5 6,7 Moraea pseudospicataa - 2 5 7 Moraea pubiflora - 1 2 1,2 Moraea pyrophila - 2 2 1 Moraea radians - 2 5 3 Moraea ramosissima - 1 2 1 Moraea reflexa - 2 2 6 Moraea regalis - 1 2 1 Moraea reticulata - 1 N/A N/A

Appendix F 177

Moraea rigidifolia - 2 4 2 Moraea rivulicola - 1 2 2 Moraea robusta - 1 N/A 1 Moraea saxicola - 1 2,5 2 Moraea schimperi - 1 N/A N/A Moraea schlechteri - 2 2 2 Moraea serpentina - 1 2,5 2 Moraea setifolia - 1 3 2 Moraea simplex - N/A 3,4 1 Moraea sisyrinchium - 1 N/A N/A Moraea spathulata - 1 4 1,9 Moraea speciosa - 2 3 3,1 Moraea stagnalis - 2 1 1 Moraea tanzanica - 1 N/A N/A Moraea thomasiae - 1 5 3,1 Moraea tortilis - 1 2 2 Moraea tricolor - 1 3 N/A Moraea trifida - 1 2 9 Moraea tripetala - 1 3,2 1,2 Moraea tulbaghensis - 10 5 3 Moraea umbellata - 2 5 1 Moraea unguiculata - 1 5 2 Moraea vallisbelli - 2 2 1 Moraea variabilis - 2 5 1 Moraea vegeta - 1 5 2 Moraea ventricosa - 1 N/A N/A Moraea verdickii - 1 N/A N/A Moraea verecunda - 2 2 1 Moraea versicolor - 2 5 2 Moraea vespertina - 4 5 6 Moraea vigilans - 1 1 9 Moraea villosa - 10 5 2 Moraea virgata - 2 2 1,2 Moraea vlokii - 2 2 1 Moraea worcesterensis - 2 5 1

Amphithalea alba 2 1 3 4 axillaris 2 1 2 N/A Amphithalea biovulata 1 1 3 N/A Amphithalea ciliaris 1 1 4,5 N/A Amphithalea cuneifolia 2 1 5 N/A Amphithalea dahlgrenii 1 1 N/A N/A Amphithalea ericifolia 1 1 3 N/A Amphithalea flava 1 1 3 N/A Amphithalea fourcadei 2 1 2 N/A Amphithalea imbricata 2 1 3 N/A Amphithalea intermedia 1 1 2 N/A Amphithalea micrantha 1 1 3 N/A Amphithalea monticola 1 1 2 3

Appendix F 178

Amphithalea muirii 1 1 3 N/A Amphithalea muraltioides 2 1 4 N/A Amphithalea obtusiloba 2 1 N/A N/A Amphithalea oppositifolia 2 1 3 N/A Amphithalea pageae 2 1 3 N/A Amphithalea parvifolia 2 1 3 N/A Amphithalea phylicoides 1 1 4,3 N/A Amphithalea rostrata 1 1 3 N/A Amphithalea speciosa 1 1 3 N/A Amphithalea spinosa 2 1 5 N/A Amphithalea stokoei 2 1 3 1 Amphithalea tomentosa 1 1 3 N/A Amphithalea tortilis 1 1 N/A 3 Amphithalea villosa 1 1 2 N/A Amphithalea violacea 1 1 3 N/A Amphithalea virgata 1 1 3 N/A Amphithalea vlokii 1 1 5 N/A Amphithalea williamsonii 1 1 N/A N/A alopecuroides 2 1 N/A 3 Cyclopia alpina 1 1 3 1 Cyclopia aurescens 1 1 3 N/A Cyclopia bolusii 1 1 3,1 N/A Cyclopia burtonii 2 1 3 1 Cyclopia falcata 1 1 5 1 Cyclopia galioides 1 1 3 N/A Cyclopia genistoides 1 1 3 N/A Cyclopia glabra 1 1 1 1 Cyclopia intermedia 1 1 4,3 1 Cyclopia longifolia 2 1 3 N/A Cyclopia maculata 2 1 6 N/A Cyclopia meyeriana 2 1 N/A N/A Cyclopia plicata 2 1 4 3 Cyclopia pubescens 2 1 6 N/A Cyclopia sessiliflora 1 1 4,3 1 Cyclopia subternata 2 1 4 1 Calpurnia aurea 1 1 N/A N/A Calpurnia glabrata N/A 1 N/A N/A Calpurnia intrusa 2 1 3 1 Calpurnia ser x wood N/A 1 N/A N/A Calpurnia sericea 2 1 N/A N/A Calpurnia woodii 1 1 N/A 3 Liparia angustifolia 2 1 6 N/A Liparia bonaespei 2 1 N/A N/A Liparia boucheri 2 1 2 1 Liparia calycina 2 1 2 N/A Liparia capitata 1 1 2 1 Liparia confusa 1 1 4,3 N/A Liparia congesta 2 1 3 N/A Liparia genistoides 2 1 3 N/A

Appendix F 179

Liparia hirsuta 2 1 6 N/A Liparia latifolia 1 1 2 N/A Liparia myrtifolia 2 1 3,4 N/A Liparia parva 1 9 2 N/A Liparia racemosa 2 1 3 N/A Liparia rafnioides 2 1 3 N/A Liparia splendens ssp comantha 1 6 1 1 Liparia splendens ssp splendens 1 6 1 1 Liparia striata 1 1 4,5 N/A Liparia umbellifera 2 1 6 1 Liparia vestita 1 1 1 N/A Podalyria argentea 1 1 3 1 Podalyria biflora 1 1 3 1 Podalyria burchellii 1 1 4 1 Podalyria buxifolia 1 1 4 N/A 2 1 4,3 1 Podalyria canescens 1 1 N/A N/A Podalyria cordata 1 1 3 1 Podalyria cuneifolia 2 1 4,3 4,3 Podalyria hirsuta 1 1 N/A 1 Podalyria intermedia 2 1 3,4 N/A Podalyria lanceolata 2 1 3 1 Podalyria leipoldtii 1 1 4,3 1 Podalyria microphylla 2 1 1 5 Podalyria myrtillifolia 1 1 5,3 5,1 Podalyria oleaefolia 1 1 3 1 Podalyria orbicularis 1 1 3 1 Podalyria pearsonii 1 1 3 1 Podalyria rotundifolia 1 1 3 1 Podalyria sericea 2 1 4 2,1 Podalyria speciosa 1 1 N/A N/A Podalyria variabilis 1 1 N/A 1 Podalyria velutina N/A 1 3 N/A Stirtonanthus chrysanthus 2 1 3 N/A Stirtonanthus insignis 1 1 3 N/A Stirtonanthus taylorianus 2 1 4 N/A Virgila divaricata 2 1 4,3 N/A Virgila oroboides ssp ferruginea 2 1 4,3 N/A Virgila oroboides ssp oroboides 2 1 4,3 N/A Xiphotheca canescens 2 1 2 1 Xiphotheca cordifolia 2 1 2 N/A Xiphotheca elliptica 1 1 N/A 2,1 Xiphotheca fruticosa 2 1 3,4 1 Xiphotheca guthriei 2 1 4,5 N/A Xiphotheca lanceolata 2 1 N/A 2 Xiphotheca phylicoides 1 1 4 N/A Xiphotheca reflexa 1 1 3 N/A Xiphotheca tecta 1 1 N/A 3,1

Appendix F 180

Protea acaulos 1 9,8 3,4 1 Protea acuminata 1 7 3 1,3 Protea amplexicaulis 1 9 3,4 1,3 Protea angolensis 1 N/A 3 2 Protea angustata 1 8 3 1 Protea aristata 1 8 3 1 Protea aspera 1 9,8 3 1 1 8 4 1 1 7,8 3,4 2,1 Protea caespitosa 1 9 4,2 3,1 1 7,8 4,3 1 Protea canaliculata 1 7 3,4 1 1 8 3 1 1 7 4 1,8 Protea convexa 1 9,7 2,3 1 1 9 3 1 1 8 3,4 1,3 Protea cryophila 1 9,7 2 1 1 7 4 1 Protea cynaroides 1 8 3 1 Protea decurrens 1 9 4 3 Protea denticulata 1 8 3,4 1 Protea dracomontana 1 7 4 1,9 Protea effusa 1 9 3,2 1 Protea enervis 1 N/A 3,4 1,3 Protea ericifolia N/A 9 4,5 3 1 8 3,4 1 Protea foliosa 1 9,8 4 1,2 1 7 4 2 Protea glabra 1 7 3,2 1 1 8 3,4 1 Protea heckmanniana N/A N/A N/A N/A Protea holosericea 1 9 3 1 Protea humiflora 1 9 3,2 1 Protea inopina 1 7 3,4 1 Protea intonsa 1 7,8 4 1 1 8 3,4 1,3 Protea laetans 1 7,8 3 1 Protea laevis 1 7 3 1 Protea lanceolata 1 8 3,4 4 1 8 3,4 1 Protea lepidocarpodendron 1 8 3 1 1 8 3 1 Protea lorea 1 8 4 1,3 Protea lorifolia 1 8 3,4 1 1 8 3 1 Protea montana 1 9,8 3,4 1 Protea mucronifolia 1 7 3 3 1 8 4 1

Appendix F 181

Protea mundiieast 1 8 4 1 Protea namaquana 1 7 4 2 Protea nana 1 7 3,2 1 1 8 3,4 1 1 8 3,4 1 Protea nubigena 1 7 4,2 9 1 8 3 4 1 7 5 3 Protea parvula 1 7 4,3 1,2 Protea pendula 1 8 3 1,3 Protea piscina 1 9,8 3 1 Protea pityphylla 1 8 3,2 1 Protea pruinosa 1 9 3 1 Protea pudens 1 7,8 4 3,1 1 8 3,4 1 Protea recondita 1 9 3,2 1 1 8 3 1 Protea restionifolia 1 9,8 4 3,1 Protea revoluta 1 9,8 4,3 1,3 1 8 4 1 Protea rubropilosa 1 7,8 4 1 Protea rupicola 1 8 3,2 1 Protea scabra 1 9,7 3 1,3 Protea scabriuscula 1 9 3,2 1 Protea scolopendrifolia 1 9,8 3 1,3 1 7 3 1 Protea scorzonerifolia 1 9,8 3,4 1,3 Protea simplex 1 7 4 1 1 8 3 1 Protea stokoei 1 8 3 1 Protea subulifolia 1 9 3 1 Protea subvestita 1 8 4 1,9 Protea sulphurea 1 9,8 4,3,2 1 1 8 3 4 Protea tenax 1 7 3,4 1 Protea venusta 1 8 3,4 1 Protea vogtsiae 1 9,8 4 1 1 7 4,3 1,2 Protea wentzeliana 1 7 4 1,3 Protea witzenbergiana 1 8 3,2 1 Protea woeskaensis 1 N/A 3,2 1

Appendix G 182

Appendix G: Topographic Complexity Index (TCI)

WWF Terrestrial Ecoregions with at least one occurence of one of the study groups ordered according to the Topographic Complexity Index (TCI), calcutalted as the standard deviation of all grid altitude values at 1 x 1 km within a quarter degree square (QDS).

Rank WWF Ecoregion TCI

1 Drakensberg alti-montane and woodlands 316.506 2 Ethiopian montane grasslands and woodlands 282.684 3 Cameroonian Highlands forests 280.673 4 Montane fynbos and renosterveld 252.581 5 Eastern Arc forests 248.214 6 Albertine Rift montane forests 242.287 7 Southern Rift montane forest-grassland mosaic 242.157 8 Ethiopian montane forests 239.968 9 Eastern Zimbabwe montane forest-grassland mosaic 218.544 10 Maputaland-Pondoland bushland and thickets 177.085 11 Drakensberg montane grasslands, woodlands and forests 171.286 12 Albany thickets 120.173 13 Succulent Karoo 104.857 14 Namibian savanna woodlands 99.722 15 Southern Acacia-Commiphora bushlands and thickets 95.212 16 KwaZulu-Cape coastal forest mosaics 86.861 17 Namib desert 86.097 18 Lowland fynbos and renosterveld 80.206 19 Southern woodlands 80.119 20 Eastern Miombo woodlands 78.176 21 Victoria Basin forest-savanna mosaic 73.574

Appendix G 183

22 Central Zambezian Miombo woodlands 69.221 23 Nama Karoo 68.021 24 Southern Africa 66.870 25 Zambezian and Mopane woodlands 58.499 26 Guinean forest-savanna mosaic 57.251 27 Knysna-Amatole montane forests 56.523 28 Angolan Miombo woodlands 53.570 29 Highveld grasslands 53.370 30 Southern Congolian forest-savanna mosaic 49.980 31 Maputaland coastal-forest mosaic 46.659 32 Northern Congolian forest-savanna mosaic 43.621 33 East Sudanian savanna 43.489 34 Southern Zanzibar-Inhambane coastal forest mosaic 42.465 35 Angolan Mopane woodlands 31.664 36 Zambezian flooded grasslands 24.318 37 Kalahari xeric savanna 23.946 38 Sahelian Acacia savanna 22.517 39 Kalahari Acacia-Baikiaea woodlands 17.160 40 Zambezian Baikiaea woodlands 12.310 41 North Saharan steppe and woodlands 9.859

Appendix H Analysis of the effect of species traits on diversification. BiSSE Ln Likelihood differences were calculated for all traits between unconstrained and constrained models (with either speciation rates ( ) or extinction rates (µ) set equal). With a single degree of freedom, twice the likelihood difference follows a 2 distribution, which was used to test for significance.

Babiana Moraea Podalyrieae Protea

0= 1 µ0= µ1 0= 1 µ0= µ1 0= 1 µ0= µ1 0= 1 µ0= µ1

Fire-survival strategy reseed - - - - 0.0022 -0.01242 3.90E-05 -3.78E-05

Pollination anthophorine bees (nectar 2.32E-02 0.001365 0 7.05E-04 - - - - foraging) pollen-collecting bees 6.04E-05 0 2.08E-03 7.19E-06 - - - - short-tongued flies - - 1.91E-02 3.75E-02 - - - - long-tongued flies 6.91E-08 0 ------moths 0 0 -1.6961 -0.00215 - - - - hopliine beetles 0 0 3.24E-05 8.52E-08 - - - - sunbirds 0 0 ------insects - - - - -4.31E-02 -2.23E-03 0 -2.94E-03 birds - - - - 1.16E-02 2.38E-02 -1.76E-03 7.70E-04 rodents - - - - 1.80E-02 1.00E-02 -7.22E-04 -1.20E-03 184

Appendix H Soil type rock crevices 5.16E-02 2.76E-04 1.71E-01 -0.82703 -1.21E-05 7.61E-04 - - gravel 2.09E-03 2.71E-04 1.54E-03 -5.22E-04 8.38E-05 -0.02015 -1.53E-08 -0.14097 sand -9.39E-01 0.03556 0.5834 -0.58545 -1.13E-03 -2.47E-04 8.95E-05 -7.00E-04 loam 4.55E-01 7.02E-04 3.24E-05 -1.60E-02 -0.0028 -0.0037 -5.77E-04 -1.88E-01 clay -4.68E-04 5.27E-04 8.99E-04 2.66E-03 0.16580 2.11E-02 -0.04425 -0.02656 marshy soil - - - - -4.97E-05 -0.0077 - -

Lithology sandstone -1.06E-04 -7.33E-4 -5.19E-04 -7.40E-04 - - -1.15921 -0.00763 granite -2.80E-02 -7.10E-7 4.69E-04 6.72E-04 - - 0.42301 0.05456 shale -2.76E-04 1.24E-01 -1.43E-04 7.37E-03 - - 7.83E-06 -2.29E-04 limestone 0.33626 -0.05050 0.06326 0.00953 - - 4.54E-02 -0.00384 quartzite 0.00123 1.01E-03 -0.02118 0.01862 - - - - dolerite -0.00206 -6.70E-06 0.06102 2.38E-05 - - - - tilite -2.34E-02 8.15E-04 -0.11693 8.49E-04 - - - - conglomerate 0.04078 -0.0044 ------basalt - - 2.05E-02 0.00133 - - -5.93E-04 0.0129

185

Appendix J 186

Appendix A: Diversification of the African genus Protea (Proteaceae) in the Cape biodiversity hotspot and beyond: equal rates in different biomes

Luis M. Valente, Gail Reeves, Jan Schnitzler, Ilana Pizer Mason, Michael F. Fay, Tony G. Rebelo, Mark W. Chase & Timothy G. Barraclough. 2010. Evolution. 64(3): 745-760. doi:10.1111/j.1558-5646.2009.00856.x

ABSTRACT

The Cape region of South Africa is an undisputed hotspot of flowering plant biodiversity. However, the reasons why levels of diversity and endemism are so high remain obscure. Here, we reconstructed phylogenetic relationships among species in the genus Protea, which has its center of species richness and endemism in the Cape, but also extends through tropical Africa as far as Eritrea and . Contrary to previous views, the Cape is identified as the ancestral area for the radiation of the extant lineages: most species in subtropical and tropical Africa are derived from a single invasion of that region. Moreover, diversification rates have been similar within and outside the Cape region. Migration out of the Cape has opened up vast areas, but those lineages have not diversified as extensively at fine spatial scales as lineages in the Cape. Therefore, higher net rates of diversification do not explain the high diversity and endemism of Protea in the Cape. Instead, understanding why the Cape is so diverse requires an explanation for how Cape species are able to diverge and persist at such small spatial scales.

Appendix J 187

INTRODUCTION

Explaining why some taxa and geographical regions contain more species than others is an important goal of evolutionary biology. Phylogenetic approaches have been increasingly used to explore the timing and rates of diversification, in terms of the net accumulation of species through time (Barraclough and Vogler 2002; Rabosky 2006; Ricklefs 2007). Incorporating earlier ideas from island biogeography and ecological studies of diversity patterns (MacArthur and Wilson 1967), it is now clear that the potential for clades to diversify depends on the area of the geographical region they inhabit (Losos and Schluter 2000; Ricklefs 2003; Davies et al. 2004; Phillimore et al. 2006). These findings shift the focus of evolutionary diversity studies to understanding why some geographic regions and taxa contain more species than expected based on their area, while still taking the time available for diversification into account. One of the best studied biodiversity hotspots is the Cape region of southern Africa (Linder 2003). Southern Africa as a whole contains 20,400 indigenous flowering plant species, of which 80% are endemic (Goldblatt and Manning 2000). Even more remarkable is that over 40% of these species are concentrated within the Cape region of South Africa, which covers less than four percent of the surface area. The Cape is characterized by a Mediterranean-type climate of winter rainfall, contrasting with the summer-rainfall climate of subtropical and tropical southern Africa. Recent work has confirmed that the Cape represents an outlier from general trends between species richness and environmental parameters (Davies et al. 2005; Kreft and Jetz 2007). Such high levels of diversity and endemism have provoked intense interest in the origins of the Cape flora (Levyns, 1952, 1964; Linder et al., 1992; Rourke, 1998; Galley & Linder 2006). One hypothesis has been that Cape diversity resulted from recent and rapid diversification (Levyns, 1952, 1964; Linder, 2003; Latimer et al. 2005 but see Etienne et al. 2006). Recently, several studies have reconstructed phylogenetic relationships within endemic clades from the Cape to test the hypothesis of recent diversification (Bakker et al. 1999; Richardson et al.

Appendix J 188

2001b; Goldblatt et al. 2002; Forest et al. 2007); reviewed in (Linder and Hardy 2004; Linder 2005; Hawkins 2006). Contrary to the hypothesis of recent origin and rapid speciation, Cape clades display a wide range of ages: some clades did originate recently, but many others result from prolonged diversification that began before the onset of winter-rainfall conditions (Linder 2008). Therefore, high diversity in the region might reflect high levels of species persistence and sustained diversification rather than recent rapid diversification (Linder 2003; Barraclough 2006), perhaps explained by the relative climatic stability combined with continuing geomorphological dynamism (Dynesius & Janson 2000, Cowling et al. 2009). One limitation of this work has been the lack of formal comparisons between related clades within and outside the Cape region (Barraclough 2006). A recent study by Sauquet et al. (2009a) found a strong positive correlation between diversification rates and the proportion of Cape species in genera of Proteaceae. However, the study lacked information on the phylogenetic positioning of non- Cape representatives within each genus, and, therefore, it could not confidently conclude that higher-than-average diversification rates in some clades were due to elevated rates in the Cape region. In particular, for understanding the concentration of species in the Cape, comparisons of related lineages between the Cape and neighboring regions of southern Africa would be especially informative (van der Niet & Johnson 2009). In an investigation of the biogeography of four Cape clades that extend into the region, Galley et al. (Galley et al. 2007) discussed levels of in situ speciation outside the Cape, which were judged to be low. However, diversification rates in Cape and non-Cape lineages were not compared, due to uncertainty over the number of immigration events versus in situ speciation for these clades. Here, we reconstruct the phylogeny of the genus Protea (Proteaceae). The family Proteaceae, including Protea, is an important component of the fynbos vegetation of the Cape region, and its ecology and distribution have been studied extensively (Cowling et al., 1992; Rebelo, 2001). For example, the ‘Protea Atlas Project’ (http://protea.worldonline.co.za/) has recorded the distribution of all

Appendix J 189 members of South African Proteaceae over a twelve-year period, including ecological data on habitats and pollination syndromes (Rebelo, 2001). However, evolutionary relationships within genera remain obscure: there are no pollen or macro-fossil data in southern Africa for the extant genera, and only has been subjected to a comprehensive, DNA sequence-based study (Barker et al. 2004). Critically for our aims, only two Leucadendron species are found outside the Cape region (Rebelo, 2001). Protea is an excellent clade for exploring the causes of high diversity in the Cape. Although its center of diversity and endemism is the Cape region (69 of the 112 species recognized by Rourke 1980 are Cape endemics), the genus also extends through tropical Africa north to Eritrea and west to Angola (fig 1.). This distribution allows comparisons of diversification between the Cape region and the rest of Africa. For conciseness, we refer to the species from outside the Cape region as the non-Cape species. Previous work has shown the overall diversification rate of Protea to be significantly higher than the background diversification rate for the rest of Proteaceae (Sauquet et al 2009a). Here, we investigate whether Protea has indeed diversified more rapidly in the Cape than in the rest of Africa. Our first aim was to reconstruct changes in distribution onto the phylogenetic tree to evaluate the history of migration between the Cape region and the rest of Africa. Earlier authors speculated that Protea originated in tropical or subtropical regions based on its sister relationship with the tropical genus Faurea and the presumed simpler and more uniform morphology of tropical species (Levyns 1964; Rourke 1980, 1998), which would fit with the idea of rapid and recent radiation within the Cape. However, our analyses demonstrate the opposite pattern: the non-Cape species are nested within a wider radiation of Cape lineages and all but two of them belong to a single clade. Therefore, most extant lineages outside the Cape originated by in situ diversification from a single ancestor that arrived there from the Cape. Second, we tested whether Cape lineages have differed in their diversification rates from non-Cape lineages, using recent methods that allow

Appendix J 190 simultaneous estimation of speciation and extinction rates and migration rates between the two regions (Maddison et al. 2007). The analyses show that the non- Cape clade has, if anything, diversified at a faster rate than the Cape lineages. For Protea, migration out of the Cape region triggered diversification by opening up a larger area in which to diversify, despite the loss of whatever traits or environmental features allowed sustained diversification at fine spatial scales in the Cape.

Appendix J 191

MATERIALS AND METHODS

Systematic background. No previous phylogenetic study of Protea has been attempted and its classification is complicated by separate treatments of South African and tropical species. The most recent treatment of South African species was that by Rourke (1980). Of the 82 recognized species included in his revision, 69 are endemic to the Cape Region, ten are found only in summer rainfall regions of South Africa, and three (P. gaguedi, P. caffra, and P. welwitschii) extend into tropical Africa north of the . Protea subvestita was the only species believed to occur both in the Cape and summer-rainfall regions of South Africa. However, Cape populations previously assigned to P. subvestita are now known to constitute a hybrid between P. punctata and P. mundii (Rachel Prunier, unpublished data) and so we treat P. subvestita as a non-Cape species. Tropical species were classified by Beard (1963, 1993; Table 1) into five sections. Conflict between treatments by Rourke (1980) and Beard (1963, 1993) is largely due to morphological variability within the tropical species, which makes their circumscription difficult, in contrast with the distinct species of the western Cape (Rourke, 1998). The effects of alpha-taxonomy on comparisons of diversification rates in the two regions will be returned to in the Discussion. In the absence of a recent subgeneric taxonomic scheme for Protea, we use the informal groupings defined by Rebelo (2001; based on Rourke 1980 and unpublished data) as a basis for evaluating the phylogenetic trees presented here (table S1). Two of the species of Rourke (1980) are actually named as subspecies, and we have treated them as such, i.e. our species list contains 110 species of which 70 are restricted to the Cape (including P. namaquana, found just to the north of the Cape, which was not described at the time of Rourke, 1980). Several putative species have been excluded. A list of Protea species, their historical taxonomic treatments, geographical distribution, and nomenclature is provided in supplementary table S1.

Appendix J 192

Sampling and DNA extraction. We sampled 87 species, which includes all 70 Cape species and 17 of the 40 species from outside the Cape. Voucher information and GenBank accession records are provided in supplementary table S2. We were unable to collect more species outside the Cape due to logistical constraints but, for reasons explained below, we believe that our sample is sufficient for the comparisons made here when combined with methods to compensate for under-sampling. Taxa from three genera belonging to subfamily Proteoideae (Johnson & Briggs 1975; Weston & Barker 2006) were chosen as outgroups: five species of Faurea, one species of Leucadendron and three of . Faurea is the sister clade of Protea, a relationship that is well supported on both morphological and molecular grounds (Rourke 1998;(Sauqueta et al. 2009). Total genomic DNA was extracted from 0.2–1.0g of silica dried leaf material using a modified 2Χ CTAB method (Doyle & Doyle, 1987) with purification by cesium-chloride/ethidium-bromide density gradient (1.55g/ml). Ethidium bromide was removed with butanol, and the purified total DNAs were dialyzed in 1X TE buffer and stored at -80° C. All DNA extracts were further purified and concentrated using QIAquick silica columns (Qiagen Inc.) according to the manufacturer’s protocol for cleaning PCR products.

DNA markers and sequencing. We generated two kinds of DNA markers. First, we sequenced DNA from six non-coding regions in the plastid and nuclear genomes. Plastid regions were trnL intron, trnL-trnF intergenic spacer (Taberlet et al. 1991), rps16 intron (Oxelman et al., 1997) and atpB-rbcL intergenic spacer (Savolainen et al. 1994). Nuclear regions were the ITS ribosomal region (Sun et al. 1994; Álvarez & Wendel 2003; Chase et al. 2003) and a portion of the region encoding the plastid-expressed isozyme of the glutamine synthetase gene (ncpGS, Emshwiller & Doyle 1999). Second, anticipating low levels of sequence variation among species, we generated 138 amplified fragment length polymorphism markers (AFLP, (Vos et al. 1995). AFLP markers sample widely distributed restriction endonuclease sites across the nuclear genome. Polymorphism is apparent as the presence or absence of bands. AFLPs are used mostly for

Appendix J 193 population and species-boundary studies (Mueller and Wolfenbarger 1999), but also have been employed to resolve species-level relationships in recent, rapid radiations (Beardsley et al. 2003; Sullivan et al. 2004). Details of primers and PCR amplification conditions are provided in the supplementary methods. Sequencing was performed on an Applied Biosystems 3730 DNA Analyser (Applied Biosystems, Warrington, Cheshire, UK). Sequences were aligned manually in Maclade 4.08 (Maddison & Maddison, 2000). For all plastid regions and the ncpGS region, sequence length variation among species was low. Gaps were coded as missing data. For ITS, a region of 66 base pairs was excluded from all analyses due to extreme length variation. Interpretation of AFLP fragments was carried out using Genescan (version 2.02) and Genotyper (version 1.1) analysis software (Applied Biosystems) and fragments were scored as present/absent.

Phylogenetic analyses. The four non-coding plastid regions were combined into a single data set. Because of their uniparental mode of inheritance and non-coding nature, these regions are expected to produce congruent results (confirmed by inspection of the individual gene trees, fig S1 to S3 and supplementary results). Therefore, our data matrices comprised: i) the plastid dataset for 85 Protea and 9 outgroup species, ii) the ncpGS dataset for 77 Protea and 5 outgroup species, iii) the ITS dataset for 75 Protea and 5 outgroup species; iv) the plastid and nuclear datasets combined for 87 Protea and 9 outgroup species; v) the AFLP dataset for 72 Protea species, and vi) the AFLP, plastid and nuclear data sets combined for 87 Protea and 9 outgroup species species. Data for taxa absent from any of the separate partitions were coded as missing.

Bayesian analyses were performed in MrBayes 3.12 (Ronquist and Huelsenbeck, 2003). Analyses were repeated in turn for each of the six datasets listed above. Model selection for each partition (ITS, plastid and ncpGS) was based on the Akaike Information Criterion (AIC) scores for substitution models evaluated using MrModeltest (v. 2.3; Nylander 2004). The general time reversible (GTR) model with gamma-distributed rate variation among sites and a proportion

Appendix J 194 of invariant sites was chosen for the plastid and ITS regions, whereas a simpler two-parameter substitution model with gamma-distributed rate variation and no invariant sites was chosen for ncpGS. The AFLP data were analyzed using the restriction site (binary) model. The combined analyses v) and vi) fitted a separate substitution model to each partition but assumed a single tree topology and branch lengths. The Markov chain Monte Carlo (MCMC) algorithm was run with two independent runs, each with four chains, and initiated with a random tree. Analyses were run for 10 million generations, sampling the Markov chains every 100 generations. Samples from the first 5 million (ITS, ncpGS, AFLP) or 6 million (plastid, all DNA sequences, all data) generations were discarded based on stabilization of the standard deviation of split frequencies between the two independent runs. Bayesian posterior probabilities were estimated as the proportion of trees sampled after burn-in that contained each of the observed clades. For comparison, conservative estimates of the internal support under parsimony analysis were obtained from 1000 bootstrap replicates using an heuristic search with 10 replicates of random taxon addition, with TBR (tree bisection reconnection) swapping and one tree held at each step. Parsimony analyses were run in PAUP* version 4.10b (Swofford 2001).

Dating. Divergence times of Protea were estimated using a relaxed-clock Bayesian MCMC approach as implemented in BEAST (v. 1.4.8, (Drummond et al. 2006; Drummond and Rambaut 2007). For this analysis, only dataset iv), the combined DNA sequence data without AFLPs, was used. The consensus tree from the Bayesian analysis of dataset vi) was used as the starting tree. A speciation model following a Yule process was selected as the tree prior, with an uncorrelated lognormal (UCLN) model for the rate variation among branches. The root node age was constrained to a normal distribution with a mean of 28.4 My (SD=2) based on the comprehensive fossil calibration of Proteaceae by (Sauqueta et al. 2009). Twenty-five independent runs of 5 million generations, sampling every 2000 generations were performed. The adequacy of sampling was assessed using the effective sample size (ESS) diagnostic with Tracer (v.1.4, Rambaut and

Appendix J 195

Drummond, 2007, http://beast.bio.ed.ac.uk/Tracer). Since the starting tree was near optimal, each of the 25 runs converged immediately. After removing the first 50,000 generations as burn-in, the maximum clade credibility tree was built using TreeAnnotator (ver. 1.4.8, Drummond and Rambaut, 2007). Analyses were repeated with an alternative tree prior, a birth-death process, to check for the influence of the prior on the estimated dates.

Biogeography. We coded species distributions as a single binary character, 0 for the Cape and 1 for outside the Cape. We did not classify into more subdivided regions (c.f. Galley et al. 2007) because our analyses require a large sample of species in each region to compare diversification rates. Markov models implemented in BayesTraits (Pagel et al. 2004) were used to reconstruct ancestral distributions onto the dated trees to determine whether the Cape or the subtropical and tropical region represents a more likely ancestral area for the genus and to infer the relative rate of migration events in each direction. We first performed maximum likelihood optimization of a two-parameter model (q01 = emigration from the Cape, q10 = immigration into the Cape) on 50000 sampled trees from BEAST. Finding that parameter estimates were roughly exponentially distributed, we then used an exponential prior with mean equal to the observed mean from the maximum likelihood results to implement a reverse jump MCMC Bayesian analysis with 5 million iterations. Reverse jump allows all simpler nested models to be visited during MCMC, and the proportion of time they are visited is proportional to their posterior probability, thus incorporating model selection into the MCMC procedure. As demonstrated by Maddison (Maddison 2006), interpretation of the relative rate of change between two trait values (in this case region) might be confounded by an effect of the same trait on speciation and/or extinction rates. This possibility is explored in the next section.

Diversfication rates. Log-lineage-through-time plots were used to visualize the temporal dynamics of diversification in the whole genus and for subtrees of the species restricted to each region in turn. The dynamics of diversification were

Appendix J 196 compared using BiSSE likelihood methods implemented in the program Mesquite (Maddison and Maddison 2009). The model estimates six parameters: rates of speciation in each region (λ0 and λ1), rates of extinction in each region (µ0 and µ1), and rates of change between the two states, here equivalent to migration rates (q01 and q10 = migration rates from the Cape to the rest of Africa and from the rest of Africa to the Cape, respectively). To estimate the significance of observed differences in λ and µ between the two character states and perform model simplification, we used log likelihood ratio tests to compare constrained and unconstrained models. Under the null model, twice the log likelihood ratio should be distributed as chi-square with degrees of freedom equal to the difference in the number of parameters of the models. The comparison of diversification rates assumes that all extant species have been sampled. Therefore, missing species were added using an unpublished Perl script by James Cotton (Day et al. 2008). The script adds species at random points along branch lengths descended from the ancestral node of the narrowest clade in which the species are believed to belong based on current taxonomy. This conforms to a constant speciation rate model, i.e. branching events are equally likely at any point along lineages. Random taxon addition was performed separately on each of a random subsample of 1000 trees from the BEAST output to account for uncertainty in the topology and branching times. The BiSSE model was optimized onto each of the resulting trees. The subsample of 1000 trees was shown to represent adequately the entire sample from the BEAST output by comparing posterior probabilities and confidence limits of node ages with the full sample (figs S9, S10). Note that the present version of BiSSE model in Mesquite does not output information on the likelihood of inferred ancestral states: this is why we also performed the BayesTraits analyses.

Appendix J 197

RESULTS

Phylogenetic relationships.

Phylogenetic reconstruction recovered several clades in agreement with the current informal subgeneric taxonomy of Protea (Fig 2, table 1). Moreover, all species belonging to the subgeneric groupings that lack Cape representatives, namely the grassland, mountain, savanna and red proteas, were resolved as a single clade (which we call the non-Cape clade) in the ncpGS, AFLP and combined analysis. Only two species that are not found in the Cape, P. roupelliae and P. subvestita, fell outside the non-Cape clade. These two species are the only non-Cape species that were previously classified in sections with Cape species (Rebelo, 2001).

Most of the differences between trees recovered for different partitions reflected low variation (i.e. sampling error) rather than strongly supported conflict (figs. S4 to S8). Combining the datasets improved resolution and the recovery of previously described and morphologically based subgeneric groupings (table 1). For example, adding the AFLP data to the combined sequence data increased the percentage of nodes with posterior probabilities >0.5 from 57% to 80% and led to recovery of two additional informal subgroupings: the white and western ground proteas. Four cases of hard conflict between partitions were apparent (judged by topological differences associated with posterior probability > 0.8, table 1). However, in each case, at least two partitions confirmed the monophyly of the morphologically recognized grouping recovered in the combined analysis and so we feel confident that the combined analysis is converging on reliable groupings, rather than being drawn towards the results of a single partition.

Two conflicts are potentially relevant for the biogeographic comparison. First, P. subvestita (a white protea) and P. roupelliae were recovered as sister species in the plastid analysis, whereas P. roupelliae falls just outside the white proteas in other partitions. Because these two species are found outside the Cape, but closely related to Cape species, this potentially affects inferences of number of dispersal and speciation events. Second, in the ITS analysis, P. sulphurea groups

Appendix J 198 with the non-Cape clade rather than with the penduline proteas as expected, and P. enervis groups with the rodent proteas rather than with the non-Cape clade as expected. Because these involve switching between distantly related groups found in distant geographical regions, the most likely explanation is an undiagnosed database or phylogenetic error, rather than hybridization or incomplete lineage sorting. However, no obvious mistake in data assembly was found, and the deletion of these sequences did not affect relationships in the combined analysis, so we have retained the sequences in the matrix. Matrices and trees in nexus format are available from TreeBase.

Biogeography

The ancestral region for Protea is reconstructed as the Cape with high probability: the median probability that the crown node of Protea has state 0 is 1.00 with a minimum of 0.979 across the MCMC results from the reverse jump analysis. We refer here to the statistical reconstruction of trait values on the tree; whether these can be taken to indicate likely ancestral areas will be returned to in the Discussion. On average, migration rates are reconstructed to have been greater from the Cape to the rest of Africa (q01, fig 3A) than in the opposite direction (q10, fig 3B). However, although the best-supported model is of migration only from the Cape to the rest of Africa (q01>0, q10=0), this has only marginally higher support than a model of equal migration rates (posterior probabilities = 0.55 and 0.45, respectively). The alternative model, that q01 and q10 are both positive but differ in value, requires optimization of an additional parameter and was not supported (posterior probability = 0.0057). Maximum likelihood optimization across the BEAST trees yielded similar results to the Bayesian approach but with stronger evidence for unidirectional migration. Using the Akaike Information Criterion (AIC = twice the log likelihood minus twice the number of parameters), the model with q10 = 0 was preferred in 84.9% of trees, the model constraining q10 = q01 in 14.9% of trees and the unconstrained model in 0.2% of trees.

Appendix J 199

Timing of diversification

The median age of the crown node of Protea across trees sampled by the BEAST analysis was 17.7 Mya with 95% confidence limits of 11.2 - 27.2 Mya (the Early to Mid-Miocene, consensus tree shown in fig S9). The average age of the crown node of the non-Cape clade was 14.9 Mya (C.I. 9.3-24.0 Mya). The only possible internal calibration relates to endemic species on coastal flats that were probably submerged before 2.6 Mya (Linder 2003; Cowling et al. 2009). The best candidates are P. obtusifolia (found on limestone) and P. susannae (found on calcareous and neutral sands), which have long been viewed as a case of ecological speciation on two quaternary soil formations (Mustart et al. 1993). We did not use this in our calibration because of uncertainty over the date of emergence in relation to sea-level changes and uplift of southern Africa (Cowling et al. 2009). However, 60.7% of our dates for the split between P. susannae and P. obtusifolia are <2.6Mya (median 2.31, 0.78- 5.2), which provides broad corroboration except that our confidence limits extend to older dates than predicted by the coastal flat scenario. The apparent rate of diversification slows down towards the present (fig 4A, solid lines): all sampled trees yielded negative (Pybus and Harvey 2000) gamma values, and 99.8% of them were significant (two-tailed test). One possible cause of an apparent decrease in rate is incomplete sampling of species. The 23 recognized species that were missing from our sample all belong to the subgeneric groupings within the non-Cape clade, but the groupings themselves were not monophyletic. Therefore, we added 23 tips within the non-Cape clade at random points along branches descended from its crown node for each BEAST tree in turn. There remains a trend for a slow-down towards the present (Fig. 4A, dashed lines: all sampled trees yielded negative gamma, 85.4% of them below the 95% critical value). Comparison of plots between the Cape and the non-Cape lineages reveals that the radiation of the non-Cape clade began after the Cape radiation and occurred at a slightly faster rate (fig 4B, Cape = solid lines, non-Cape = dashed

Appendix J 200 lines). Note that the shape of the curve for the non-Cape clade will be affected by how we added missing species; our approach was conservative in that it assumes a constant branching rate model rather than introducing major departures in shape from constant exponential growth. Using a birth-death prior in BEAST instead of the Yule prior had little effect on conclusions; there remained a slow-down in rate towards the present, and 81.2% of trees displayed a significantly negative gamma.

Reconstructing rates of speciation, extinction and migration

Optimization of the BiSSE model onto BEAST trees yielded results that were consistent whether missing species were included or not; we describe the results including missing species. The absolute scale of estimated parameters varied according to the dating of the root node of the genus, i.e. values tend to be positively correlated across trees (fig. 5). We focus on comparing estimates between the two regions rather than on absolute numbers. Per lineage speciation rates, λ, and extinction rates, µ, were higher in the non-Cape lineages than in the Cape (fig. 6A & 6B). The median speciation rate in -1 the Cape, λ0, was 0.17 species Myr (95% C.I. 0.12 – 0.27) and the median value of λ1/λ0 was 2.0 (95% C.I. 1.3 – 3.3). The median extinction rate in the Cape, µ0, was zero to within three decimal places (95% C.I. 1.4 x 10-7 - 1.0 x 10-4). Outside the Cape, two distinct solutions were obtained: either extinction rate was estimated as zero, or it took a wide range of values up to an upper 95% confidence limit of being 73.8% of the non-Cape speciation rate. Estimates for the migration rate from the rest of Africa into the Cape (q10) covary with those for extinction rates outside the Cape: either extinction rate is high and emigration rate is low, or extinction rate is low and emigration rate is high, indicated by the two distinct lines of points in figure 5B. The per lineage net diversification rate also falls into two distinct solutions (fig. 6D). In 65.8% of trees, it is higher outside the Cape, taking a median value of twice the net diversification rate in the Cape. In 34.2% of trees, namely those in which the non-Cape extinction rate is notably greater

Appendix J 201 than zero, the net diversification rate in the non-Cape lineages is between 79% and 93% of the value in the Cape (95% C.I.). We performed further analyses to identify the basis of the two alternative solutions described above. We checked whether our sample of trees contains distinct islands of trees by constructing separate majority rule consensus trees for those trees associated with the two solutions. No major differences were apparent (fig. S11). In particular, there was no tendency for alternative placements of P. roupelliae and P. subvestita, reflecting differences among the separate partitions described under Phylogenetic Relationships, to be associated with the two solutions. Instead, exploration of the likelihood surface with respect to q10 and µ1 for trees representing both solutions shows that a broad range of values are almost equally likely, and the two alternative solutions represent small peaks in this surface (fig. S12). Therefore, the BiSSE analysis is jumping between two solutions with near equal ability to explain the data. Despite apparently large differences between the regions when fitting the full BiSSE model, there is further uncertainty in parameter estimates due to the stochastic nature of the underlying processes. The fully parameterized BiSSE model was only preferred in one out of 1000 trees compared to a minimum feasible model of equal speciation rates in both regions, no extinction in either region and equal migration rates between regions (AIC maximum model minus AIC minimum model = -8.0; C.I. -10.2 to -6.0). Under the minimum model, the net diversification rate was estimated at 0.19 – 0.58 species Myr-1 (95% C.I.).

Appendix J 202

DISCUSSION

Our results provide the first phylogenetic analysis of relationships among Protea species. Levels of variation were low, especially in plastid regions, but combined analysis greatly improved resolution, yielding a phylogenetic hypothesis in broad agreement with recent ideas about their taxonomy. For example, the snow proteas and shale proteas were recovered as monophyletic in every partition; the rodent proteas were recovered in the plastid and ncpGS analyses; and the white and the western ground proteas were recovered in the ITS and AFLP analyses. Morphological characters distinguishing these groupings are described in Rebelo (2001). Several of the exceptions correspond to stated uncertainty in taxonomy. For example, the rose and penduline proteas were already thought to share a close relationship, and P. pendula is believed to hybridize with members of the rose proteas (Rebelo, 2001 and personal observation). Similarly, apart from bearded involucral bracts and non-opening flowerheads, the bearded proteas share all the morphological features of the spoon- proteas (Rebelo, 2001). The implications of the tree for Protea taxonomy will be discussed elsewhere. Our main finding is that all sections that contain only species from outside the Cape comprise a single clade nested within the wider radiation of Cape lineages. This finding coincides with morphological characteristics shared by these sections of long lifespan, unadorned involucral bracts, nearly actinomorphic flowers, undifferentiated pollen presenters, fruits that are shed, and a lignotuberous, epicormically resprouting tree-habit (Rebelo, 2006). The Cape shaving-brush proteas were believed to be Cape representatives of grassland proteas (section Leiocephalae, Rourke 1980) based on shared morphological features – P. nitida is the only Cape species to resprout from epicormic buds - but these species group more closely with other Cape taxa in all our analyses. Although it is possible that the non-Cape species we were unable to sample could belong elsewhere in the tree, we believe this is unlikely; our sample included around 50% of recognized species from outside the Cape and representatives of all recognized non-Cape subgeneric-groupings. Also, our sample of non-Cape

Appendix J 203 species was biased towards those in southern Africa, and we would expect any close relatives of Cape species to be found in neighboring regions (c.f. Galley et al. 2007). The two other species with distributions outside the Cape, P. roupelliae and P. subvestita, were confirmed to be related to Cape clades. Contrary to previous taxonomy, however, P. roupelliae was recovered as closely related to the white proteas, which include P. subvestita. The Bayesian analysis of species distributions confirmed the Cape to be the most likely ancestral area. Dispersal between the two regions has been rare: at most three separate events. This stands in contrast to other Cape clades, in which exchange between the Cape and especially the Drakensberg mountains of eastern South Africa is relatively common (Galley et al. 2007). The rarity of dispersal in Protea cannot just be explained by assuming lower dispersal capabilities, as both within the Cape and outside some lineages have colonized broad geographic areas. Instead, it implies that species adapted to one environment are unlikely to colonize the other. The non-Cape clade comprises long-lived trees that resprout after fire and have ‘simple’ flowers that are nearly actinomorphic with undifferentiated pollen presenters, structures used for placement of pollen onto pollinators (Rebelo, 2001). The Cape region includes a few species with similar traits, for example P. nitida, but most species are serotinous (plants are killed by fire but recruit by seed released from fire-proof cones) and have more differentiated flower morphology associated with a range of pollination syndromes (birds, rodents and insects, Collins and Rebelo, 1987). Therefore, although there are some exceptions, broad differences in ecological and reproductive strategies might limit migrations between biomes. Although migration in both directions cannot be ruled out, presumably because of the low number of distributional shifts in either direction, there is marginally greater support for migration only from the Cape to the rest of Africa rather than in both directions. Together, these results contradict the traditional view that the genus originated in tropical regions and has only more recently invaded the Cape. An alternative explanation for our findings would be if there were repeated invasion of lineages from the rest of Africa into the Cape, but that a

Appendix J 204 subsequent extinction event killed off all but one of the non-Cape lineages present at that time. In the absence of any alternative evidence for this, we favor the simpler interpretation. It remains possible that the original ancestor invaded the Cape from elsewhere (for example the sister genus Faurea has a distribution centered in tropical Africa and Madagascar), but the extant lineages outside the Cape appear to result from invasion from the Cape rather than vice versa. Several other Cape taxa display similar patterns (Richardson et al. 2001a, b; Goldblatt et al. 2002; Galley et al. 2007), whereas in others, such as Pelargonium, the Cape taxa comprise a derived clade within a more widely distributed set of species (Bakker et al., 1999). Dating the radiation of Protea is limited by the lack of internal calibration points. We used the date of the split between Protea and Faurea from Sauquet et al. (2009b), which in turn is derived from fossil dates applied to the wider Proteaceae tree. The 95% confidence intervals of the age of the crown node of Protea are broad, but 11.2 to 27.2 Mya encompasses the assumed date for the formation of the circum-Antarctic Benguela current and the start of the shift to cooler and drier climates (Linder, 2003; Cowling et al. 2009). The results establish the radiation of Protea as being of medium age compared to other Cape clades (Linder 2005; Hawkins 2006). In addition, the estimated net diversification rate for the genus (0.19 – 0.58 species Myr-1) is moderate when compared to other groups. It is higher than the average rate in angiosperms (Magallon & Sanderson 2001) but considerably lower than that of recent Cape radiations such as Aizoaceae (Klak et al 2004). The log-lineage-through time plots provide no indication that diversification rates in the Cape accelerated at any period, contrary to the hypothesized effects of the steep increase in aridity and seasonality, accompanied by the onset of modern fire regimes since 3-5 Mya (Linder 2003). Instead, our analyses agree with recent studies that propose that the fynbos biome has experienced a relatively stable environment during the Late Tertiary and Quaternary (Verboom et al 2008), allowing the accumulation of lineages at a relatively constant pace (Linder 2008). The remarkably even tempo at which extant lineages have accumulated matches findings in Restionaceae, although

Appendix J 205 restios began diversifying much earlier than Protea (Linder and Hardy 2004). The timing of migration events into the rest of southern Africa also falls within the broad range of dates obtained by Galley et al. (2007) for four monocot clades (dates fell within 26 Mya: Disa, Irideae, the Pentaschistis clade and Restionaceae). Comparison of diversification rates between the Cape and non-Cape lineages using the maximum BiSSE model was affected by switching between two alternative solutions depending on minor alterations in the tree topology and branch lengths among Bayesian samples of dated trees. Either the model inferred a high rate of extinction in non-Cape lineages and low emigration rate or a low extinction rate and high emigration rate. In line with discussion by Maddison (2006), the appearance of two non-Cape species isolated from the main non-Cape clade is consistent with either high extinction rate of non-Cape species or an excess of migration events into the Cape, an interpretation confirmed by exploration of the likelihood surface. However, the number of shifts between the two regions was too few to permit strong inferences from the maximum model; simplifying to the minimum model, all we can say with confidence is that diversification rates outside the Cape have been similar to those in the Cape; or, if anything, slightly higher. Despite uncertainty in the details, our results show that diversification rates of Protea were no higher in the Cape than elsewhere. Previous studies of other clades have inferred that diversification rates in the Cape were not especially rapid, but without statistically comparing diversification rates of the same clade in the Cape and neighboring regions. One caveat is that our analyses placed missing species at random into the non-Cape clade, assuming a constant speciation rate model. If divergence times for missing species were concentrated more towards the present than in our simulations, for example, this might increase estimates of extinction rates outside the Cape (Nee et al. 1994). However, the estimate of net diversification rate is likely to be fairly robust to this uncertainty, because the same number of species for approximately similar total branching times will be present no matter where missing species are added. Nonetheless, uncertainty in the location of missing

Appendix J 206 species may well explain the wide confidence limits in extinction rate estimates outside the Cape. Although the timing of diversification has been similar in both regions, the spatial extent and pattern of diversification is strikingly different. The area of occupancy of Protea outside the Cape is seventeen times greater than within the Cape region (fig 1), but the density of species is far lower. Many species outside the Cape have exceptionally broad ranges (fig. 7). There are some possible cases of speciation on a narrow scale, such as the five species restricted to the Barberton-Transvaal escarpment (the mountain proteas P. comptonii, P. curvata, P. laetans, P. rubropilosa, and the grassland protea P. parvula), which comprise a clade with weak support in our tree together with the more widespread grassland protea P. simplex. However, the extent of diversification within equivalent-sized areas is greatly reduced, despite the existence of areas of contiguous habitat suitable for Protea species of equivalent or greater size than the Cape. For example, based on the quarter degree scale Protea Atlas data, P. subvestita has an occurrence of 113,500 km2 in the Natal Drakensberg region, which is greater than the total area of the Cape floristic region (90,000 km2, Linder, 2005). Without a more complete sample of species outside the Cape in our phylogenetic tree, we cannot compare the spatial pattern of diversification quantitatively between the two regions, but it is clear that controlling for geographical area would yield a far greater net rate of diversification in the Cape than in the rest of Africa. The key to understanding the Cape biodiversity hotspot, therefore, lies in understanding how so many species can have originated and persisted in such a small area, not in the speed of their diversification. Our comparisons assume that we have an accurate representation of species present in both regions. Monophyly of non-Cape sections facilitated addition of missing species to our analyses. Clearly, however, it would be desirable to sample the missing species and determine their relationships, which might narrow down the range of estimates obtained by the random placement of species. A more fundamental limitation, in common with most other broad-scale diversity studies, is that we relied on the existing alpha-taxonomy of the genus for

Appendix J 207 our list of species. One possible explanation for the recent slowdown in lineage branching is that young species have not been recognized as such by traditional taxonomy. As mentioned above, no recent revision has considered the genus across its entire range. The Cape representatives have been better studied than the non-Cape ones in recent times, but there are putative new Cape species awaiting investigation, and several species, such as P. cynaroides, exhibit morphological variation among populations (Rebelo, 2001). The non-Cape species are less morphologically distinct than Cape species and exhibit morphological variation within populations over large regions, rather than between geographically isolated populations (Chisumpa & Brummit, 1987). However, whether this reflects further divergence in those species or simply phenotypic plasticity is not known: DNA studies at the population level would be needed to confirm or refute the current taxonomy. Another complication is that hybridization is known to occur between some species. We found no strong evidence consistent with hybrid speciation (i.e. contradictory results between unlinked markers for species living in close enough proximity to interbreed), but more variable markers such as microsatellites or further AFLPs and population samples would be needed to confirm this. Overall, we do not believe that changes in alpha-taxonomy could be of sufficient magnitude to change our conclusions and, in any case, the well-known differences in species density between the Cape and the rest of Africa are based on traditional taxonomic species; our results show that this difference is not due to the rate of accumulation of those units. To conclude, our results provide strong evidence that the radiation of extant Protea lineages began in the Cape region and that the present-day diversity in this region arose through the prolonged accumulation of species at a moderate rate, rather than by recent rapid radiation. The radiation of Protea outside the Cape resulted from a single colonization event and subsequent expansion. Diversification has occurred at similar net rates in both regions. However, the spatial scale over which species are distributed is vastly different. Some feature of the Cape permits many unrelated plant taxa to speciate and especially to persist at fine spatial scales. Persistence is hard to study in the Cape flora because of the

Appendix J 208 lack of detailed paleoecological data, but analyses of species abundance distributions combined with direct estimates of speciation rates, population history and dispersal from genetic data could be used to evaluate alternative explanations (Latimer et al. 2005; Barraclough 2006; Etienne et al. 2006). Outside of the Cape, species have not diversified to such an extent within small areas, but the geographical area over which suitable habitat is present is much larger. Future work sampling in the rest of Africa could establish how they have expanded through that region. In the Cape, studies at the population and species-level are needed to uncover the causes of fine-scale divergence and persistence in the Cape.

ACKNOWLEDGEMENTS This research was supported by a NERC studentship to GR, a Royal Society University Research Fellowship to TGB, a European Comission Marie Curie EST Fellowship (“HOTSPOTS”) to JS and LMV, the Royal Botanic Gardens, Kew, and the NERC Centre for Population Biology at Silwood Park. The authors would like to thank Jeff Joseph, Martyn Powell, Robyn Cowan, Félix Forest and Laszlo Csiba at RBG Kew for technical help, three anonymous reviewers for comments and John Lawton and Richard Cowling for their enthusiastic support of this project.

Appendix J 209

REFERENCES

Álvarez I. and Wendel J. F. 2003. Ribosomal ITS sequences and plant phylogenetic inference. Mol. Phylogen. Evol. 29:417-434. Bakker F. T. A. Culham, and M. Gibby. 1999 Phylogenetics and diversification in Pelargonium. Pp. 353-374 in P. M. Hollingsworth, R. M. Bateman, and R. J. Gornall (eds.), Molecular systematics and plant evolution. Taylor and Francis, London, UK Bakker, F. T., A. Culham, and M. Gibby. 1999. Phylogenetics and diversification in Pelargonium. Molecular Systematics and Plant Evolution 57:353-374. Barker, N. P., A. Vanderpoorten, C. M. Morton, and J. P. Rourke. 2004. Phylogeny, biogeography, and the evolution of life-history traits in Leucadendron (Proteaceae). Mol. Phylogen. Evol. 33:845-860. Barraclough, T. G. 2006. What can phylogenetics tell us about speciation in the Cape flora? Divers. Distrib. 12:21-26. Barraclough, T. G., and A. P. Vogler. 2002. Recent diversification rates in North American tiger beetles estimated from a dated mtDNA phylogenetic tree. Mol. Biol. Evol. 19:1706-1716. Beardsley, P. M., A. Yen, and R. G. Olmstead. 2003. AFLP phylogeny of Mimulus section Erythranthe and the evolution of hummingbird pollination. Evolution 57:1397-1410. Davies, T. J., V. Savolainen, M. W. Chase, P. Goldblatt, and T. G. Barraclough. 2005. Environment, area, and diversification in the species-rich flowering plant family Iridaceae. Am. Nat. 166:418-425. Davies, T. J., V. Savolainen, M. W. Chase, J. Moat, and T. G. Barraclough. 2004. Environmental energy and evolutionary rates in flowering plants. Proceedings of the Royal Society of London Series B-Biological Sciences 271:2195-2200. Day, J. J., J. A. Cotton, and T. G. Barraclough. 2008. Tempo and mode of diversification of lake Tanganyika cichlid fishes. PLoS ONE 3:e1730.

Appendix J 210

Drummond, A. J., S. Y. W. Ho, M. J. Phillips, and A. Rambaut. 2006. Relaxed phylogenetics and dating with confidence. PLoS Biol. 4:699-710. Drummond, A. J., and A. Rambaut. 2007. BEAST: Bayesian evolutionary analysis by sampling trees. BMC Evol. Biol. 7. Forest, F., I. Nanni, M. W. Chase, P. R. Crane, and J. A. Hawkins. 2007. Diversification of a large genus in a continental biodiversity hotspot: Temporal and spatial origin of Muraltia (Polygalaceae) in the Cape of South Africa. Mol. Phylogen. Evol. 43:60-74. Galley, C., B. Bytebier, D. U. Bellstedt, and H. P. Linder. 2007. The cape element in the Afrotemperate flora: from Cape to Cairo? Proceedings of the Royal Society B-Biological Sciences 274:535-543. Goldblatt, P., and J. C. Manning. 2000. Cape Plants: a conspectus of the Cape Flora. National Botanical Institute, RSA & Missouri Botanical Garden, St. Louis. USA. Goldblatt, P., V. Savolainen, O. Porteous, I. Sostaric, M. Powell, G. Reeves, J. C. Manning, T. G. Barraclough, and M. W. Chase. 2002. Radiation in the Cape flora and the phylogeny of peacock irises Moraea (Iridaceae) based on four plastid DNA regions. Mol. Phylogen. Evol. 25:341-360. Hawkins, J. A. 2006. Using phylogeny to investigate the origins of the Cape flora: the importance of taxonomic, gene and genome sampling strategies. Divers. Distrib. 12:27-33. Kreft, H., and W. Jetz. 2007. Global patterns and determinants of vascular plant diversity. Proceedings of the National Academy of Sciences of the United States of America 104:5925-5930. Linder, H. P. 2003. The radiation of the Cape flora, southern Africa. Biological Reviews 78:597-638. Linder, H. P. 2005. Evolution of diversity: the Cape flora. Trends Plant Sci. 10:536-541. Linder, H. P. 2008. Plant species radiations: where, when, why? Philosophical Transactions of the Royal Society B-Biological Sciences 363:3097-3105.

Appendix J 211

Linder, H. P., and C. R. Hardy. 2004. Evolution of the species-rich Cape flora. Philosophical Transactions of the Royal Society of London Series B- Biological Sciences 359:1623-1632. Losos, J. B., and D. Schluter. 2000. Analysis of an evolutionary species-area relationship. Nature 408:847-850. MacArthur, R. H., and E. O. Wilson. 1967. The equilibrium theory of island biogeography. Princeton University Press, Princeton, N.J. Maddison, W. P. 2006. Confounding asymmetries in evolutionary diversification and character change. Evolution 60:1743-1746. Maddison, W. P., and D. R. Maddison. 2009. Mesquite: a modular system for evolutionary analysis. . Mueller, U. G., and L. L. Wolfenbarger. 1999. AFLP genotyping and fingerprinting. Trends Ecol. Evol. 14:389-394. Pagel, M., A. Meade, and D. Barker. 2004. Bayesian estimation of ancestral character states on phylogenies. Syst. Biol. 53:673-684. Phillimore, A. B., R. P. Freckleton, C. D. L. Orme, and I. P. F. Owens. 2006. Ecology predicts large-scale patterns of phylogenetic diversification in birds. Am. Nat. 168:220-229. Pybus, O. G., and P. H. Harvey. 2000. Testing macroevolutionary models using incomplete molecular phylogenies. Proceedings of the Royal Society of London Series B-Biological Sciences 267:2267-2272. Rabosky, D. L. 2006. Likelihood methods for detecting temporal shifts in diversification rates. Evolution 60:1152-1164. Richardson, J. E., F. M. Weitz, M. F. Fay, Q. C. B. Cronk, H. P. Linder, G. Reeves, and M. W. Chase. 2001a. Phylogenetic analysis of Phylica L. (Rhamnaceae) with an emphasis on island species: evidence from plastid trnL-F and nuclear internal transcribed spacer (ribosomal) DNA sequences. Taxon 50:405-427. Richardson, J. E., F. M. Weitz, M. F. Fay, Q. C. B. Cronk, H. P. Linder, G. Reeves, and M. W. Chase. 2001b. Rapid and recent origin of species richness in the Cape flora of South Africa. Nature 412:181-183.

Appendix J 212

Ricklefs, R. E. 2003. Global diversification rates of passerine birds. Proceedings of the Royal Society of London Series B-Biological Sciences 270:2285- 2291. Ricklefs, R. E. 2007. Estimating diversification rates from phylogenetic information. Trends Ecol. Evol. 22:601-610. Sauqueta, H., P. H. Weston, C. L. Anderson, N. P. Barker, D. J. Cantrill, A. R. Mast, and V. Savolainen. 2009. Contrasted patterns of hyperdiversification in Mediterranean hotspots. Proceedings of the National Academy of Sciences of the United States of America 106:221-225. Sullivan, J. P., S. Lavoue, M. E. Arnegard, and C. D. Hopkins. 2004. AFLPs resolve phylogeny and reveal mitochondrial introgression within a species flock of African electric fish (Mormyroidea : Teleostei). Evolution 58:825- 841. Swofford, D. L. 2001. PAUP*. Phylogenetic Analysis Using Parsimony (*and Other Methods). Sinauer Associates, Sunderland, Massachusetts. Vos, P., R. Hogers, M. Bleeker, M. Reijans, T. Vandelee, M. Hornes, A. Frijters, J. Pot, J. Peleman, M. Kuiper, and M. Zabeau. 1995. Aflp - a New Technique for DNA-Fingerprinting. Nucleic Acids Res. 23:4407-4414. Weston, P.H. and N. P. Barker. 2006. A new suprageneric classification of the Proteaceae, with an annotated checklist of genera. Telopea. 11: 314-344.

Appendix J 213

TABLE 1. Summary of the performance of different partitions. PP = posterior probability from Bayesian analysis. BS = bootstrap support from parsimony analysis. Dataset Plastid NcpGS ITS AFLP Sequence AFLP+sequence Number of sites 2529 841 829 138 4199 4337 Number of variable sites 329 245 316 136 890 1026 Number of parsimony informative sites 211 79 235 113 525 638 % nodes PP>0.5 32.1 33.8 56.8 36.6 57.0 80.2 % nodes BS>50 23.8 28.6 32.4 16.9 31.4 41.9

PP support for groupings (letters indicate grouping is contradicted with PP > 0.8, see footnotes) Snow 1 1 0.63 1 1 1 White A - 0.99 0.97 - 0.95 Rodent 0.99 1 - - 1 1 Non-Cape - 0.94 C 0.52 0.54 1 Shale 1 0.93 1 0.99 1 1 Western-ground - B 1 0.53 D 0.79

Footnotes to Table 1

A) P. subvestita groups with P. roupelliae (spoonbract, PP 0.94)

B) P. angustata groups with P. pendula (penduline) and P. canaliculata (rose, PP 1.0)

C) P. sulphurea (penduline) falls within non-Cape clade and P. enervis falls outside (several nodes distant with pp between 0.54 and 0.68)

D) Paraphyletic, P. nana and P. scolymocephala (rose) fall within the clade (PP 0.9)

- indicates that the grouping is not contradicted by any nodes with PP >0.8

Appendix J 214

FIGURE LEGENDS

Figure 1. Map of the number of Protea species within 1 degree by 1 degree grid cells across Africa. The Cape floristic region is indicated by hashed shading. Data are from the Protea Atlas (http://protea.worldonline.co.za/).

Figure 2. Consensus of 80,000 sampled Bayesian trees for the combined analysis of all DNA sequence and AFLP data, showing all groupings with posterior probability above 0.5. Bayesian posterior probabilities are shown above the branches leading to each node; parsimony bootstrap percentages >50% are shown below the equivalent branches. No nodes with bootstrap >50% in the parsimony analysis contradicted those shown here. Bars indicate informal sections or combinations thereof according to the classification of Rebelo (2001), based on the treatment of Rourke (1980). Bars are provided as a guide for navigating the taxonomy rather than implying monophyletic or strongly supported clades: solid lines around the bars indicate a section that is monophyletic in our tree; dashed lines indicate sections that are not monophyletic. For polyphyletic sections, letters after species names show which species belong to which section as defined in the label for the broader grouping within which they fall. The black circle indicates the ancestral branch of the non-Cape clade. Consensus branch lengths and a scale bar are shown in units of changes per site.

Figure 3. Frequency histogram of estimated migration rates between A) the Cape and the rest of Africa, q01, and B) the rest of Africa and the Cape, q10. The units are migration events per lineage per million years. Means of the distributions are indicated by dashed lines.

Figure 4. Log lineage through time plots of A) the entire genus with (dashed lines) and without (solid lines) missing species added to the non-Cape clade and B) for just Cape lineages (solid lines) and for just non-Cape lineages (dashed lines).

Appendix J 215

Black lines indicate the curve of median ages for each diversification event and grey lines indicate the 95% confidence limits. B) shows two curves for the non- Cape lineages: the higher black dashed line is the plot including missing species, the lower black dashed line is the plot without missing species added. Confidence limits are shown for both as grey dashed lines.

Figure 5. Plot of parameter estimates from the BiSSE model for A) the Cape and

B) the non-Cape lineages. Parameters are the rates of speciation (λ0 and λ1) and rates of extinction (µ0 and µ1) in the Cape and non-Cape lineages respectively, and the migration rates from the Cape to the rest of Africa, q01, and from the rest of Africa to the Cape, q10. Units are in events per lineage per million years.

Figure 6. Histograms of relative parameter estimates between the Cape (0) and non-Cape (1) lineages from optimization of the BiSSE model onto a random subsample of 1000 output trees from BEAST. A) The difference in speciation rates, B) the difference in extinction rates, C) the difference in the logarithm of migration rates and D) the difference in per lineage net diversification rates. Units are events per lineage per million years. Differences are shown rather than absolute estimates in each region because of covariation of estimates resulting from variation in the absolute scale of branch lengths across the trees (fig. 5).

Figure 7. Frequency distribution of geographic range sizes of Protea species from the Cape region (white) and the rest of Africa (black). Range sizes were estimated conservatively as the number of one-degree grid squares occupied (data at a finer spatial scale are only available inside South Africa). The range size distribution differs significantly between the two regions (Kolmogorov-Smirnov test, D=0.26, p<0.05). The total area occupied by Protea in each region is 24 cells within the Cape and 380 cells in the rest of Africa.

Appendix J 216

Species richness 29 - 45 25 - 28 16 - 24 12 - 15 8 - 11 5 - 7 3 - 4 0 - 2

Appendix J 217

0.94 P_acaulos 1 P_convexa 68 P_revoluta western ground 0.79 P_laevis 82 P_angustata 0.96 P_glabra 0.98 P_nitida shaving brush proteas (- rupicola) 0.92 56 P_inopina P_lanceolata True 1 P_mucronifolia shale 0.68 100 P_odorata 1 P_nana R 90 P_scolymocephala R P_namaquana P P_acuminata R 1 1 P_canaliculata R rose (R) + 1 98 P_pendula P 0.89 P_effusa P penduline (P) 0.76 P_recondita P (- sulphurea) 0.76 P_pityphylla R 52 P_witzenbergiana R P_sulphurea P 0.8 P_angolensis M 1 0.59 52 P_welwitschii S 70 P_gaguedi S 0.65 P_heckmanniana S 0.52 P_caffra G Non-Cape clade 0.89 0.79 P_nubigena G 0.78 P_dracomontana G 1 P_wentzeliana M grassland (G) + mountain (M) 0.81 P_comptonii M + savanna (S) + red (R) 57 0.55 70 P_rubropilosa M P_curvata M 0.74 0.71 P_laetans M P_simplex G P_parvula P_enervis R P_rupicola Shaving brush 0.83 P_amplexicaulis 1 73 P_humiflora rodent 0.99 P_decurrens 0.69 56 1 86 P_subulifolia 0.6 56 78 P_cordata 0.96 P_coronata B P_caespitosa Bishop 76 1 P_longifolia S 0.98 P_pudens S 0.91 60 0.99 P_neriifolia B P_compacta S 1 P_obtusifolia S 0.6 65 P_susannae S 0.67 0.86 P_restionifolia D spoon-bract (S) 58 P_piscina D + bearded (B) + 0.53 P_scorzonerifolia D P_scabra D dwarf-tufted (D) 0.79 1 P_speciosa B (- lorea) + P_stokoei B P_denticulata D bishop (Bishop) P_aspera D 1 P_burchellii S 1 P_laurifolia B 0.99 64 1 84 P_lepidocarpodendron B P_holosericea B 74 P_lorifolia B 0.93 P_magnifica B 100 P_eximia S 0.8 P_grandiceps B P_roupelliae S 0.72 P_subvestita 0.99 0.54 P_mundii 1 white 69 0.84 P_lacticolor 0.9 0.95 P_aurea 0.51 P_punctata P_venusta 0.52 1 P_intonsa 0.86 P_vogtsiae eastern ground P_montana P_tenax P_foliosa 0.98 P_aristata true (- lanceolata) 77 P_repens 1 P_cryophila 1 84 P_pruinosa snow 0.82 100 0.71 P_scabriusucula 0.9 70 P_scolopendrifolia P_cynaroides King P_lorea Dwarf-tufted

0.05

Appendix J 218

A) 6000 Frequency 200 0 0

0.000 0.005 0.010 0.015 0.020 0.025

q01 B) 25000 Frequency 1000 0 0

0.000 0.005 0.010 0.015 0.020 0.025

q10

Appendix J 219

A) 100 5 0 2 0 1 0 5 Number of species 2

-25 -20 -15 -10 -5 0

Age (millions of years)

B) 50 2 0 1 0 5 Number of species 2

-25 -20 -15 -10 -5 0

Age (millions of years)

Appendix J 220

A) B)

0.4 0.8

0.3 0.6

0.2 0.4

0.1 0.2 0.020 0.0015 0.15 0.6 0.0010 0.10 0.010 0.4 q01 0.0005 q10 0.05 0.2

Appendix J 221

A) B) Frequency Frequency

C) D) Frequency Frequency

Appendix J 222

60

50

40 y

30 equen c r F 20

10

0 1 2-10 11-99 >100 Range size (number of 1 degree cells occupied)